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A video and framework to help you explore your why and sense of purpose before embarking on KE and Commercialisation activities

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Start with why -- how great leaders inspire action | Simon Sinek | TEDxPugetSound

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2. The fact and figures of Knowledge Exchange and commercialisation 

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2. The fact and figures of Knowledge Exchange and commercialisation 

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Praxis Auril 2016 survey of the UK KEC market - including spread and breakdowns of activity

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Submitted by amy.firth@praxi... on Tue, 04/19/2016 - 13:54

PraxisUnico commissioned this national survey of KEC practitioners and Higher Education institutions in late 2015, to explore the roles and experiences of KEC professionals and the institutional environments in which they work.
View the full report here
To view full-size infographics, click images below:

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Knowledge Exchange & Commercialisation: The State of the Profession in UK Higher Education (2016)

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PraxisUnico commissioned this national survey of KEC practitioners and Higher Education institutions in late 2015, to explore the roles and experiences of KEC professionals and the institutional environments in which they work. View the full report here To view full-size infographics, click images below:

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PDF of a canvas to explore KEC opportunities with your research

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Explore the big challenges of our time. Problems worth solving

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Sustainable Development Goals | unfoundation.org

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The SDGs are the world's shared plan to end extreme poverty, reduce inequality, and protect the planet by 2030.

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Strategyzer's Value Proposition Canvas Explained

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6. Tools for Policy Impact - A handbook for researchers 

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6. Tools for Policy Impact - A handbook for researchers 

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7. Ten Simple Rules for a Successful Cross-Disciplinary Collaboration

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7. Ten Simple Rules for a Successful Cross-Disciplinary Collaboration

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Bernhard Knapp,

Rémi Bardenet,

Miguel O. Bernabeu,

Rafel Bordas,

Maria Bruna,

Ben Calderhead,

Jonathan Cooper,

Alexander G. Fletcher,

Derek Groen,

Bram Kuijper

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Citation: Knapp B, Bardenet R, Bernabeu MO, Bordas R, Bruna M, Calderhead B, et al. (2015) Ten Simple Rules for a Successful Cross-Disciplinary Collaboration. PLoS Comput Biol 11(4): e1004214. https://doi.org/10.1371/journal.pcbi.1004214

Published: April 30, 2015

Copyright: © 2015 Knapp et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Funding: The 2020 Science programme is funded through the EPSRC Cross-Disciplinary Interface Programme (grant number EP/I017909/1). The authors received no specific funding for this article.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Cross-disciplinary collaborations have become an increasingly important part of science. They are seen as key if we are to find solutions to pressing, global-scale societal challenges, including green technologies, sustainable food production, and drug development. Regulators and policy-makers have realized the power of such collaborations, for example, in the 80 billion Euro "Horizon 2020" EU Framework Programme for Research and Innovation. This programme puts special emphasis on "breaking down barriers to create a genuine single market for knowledge, research and innovation" ( http://ec.europa.eu/programmes/horizon2020/en/what-horizon-2020).

Cross-disciplinary collaborations are key to all partners in computational biology. On the one hand, for scientists working in theoretical fields such as computer science, mathematics, or statistics, validation of predictions against experimental data is of the utmost importance. On the other hand, experimentalists, such as molecular biologists, geneticists, or clinicians, often want to reduce the number of experiments needed to achieve a certain scientific aim, to obtain insight into processes that are inaccessible using current experimental techniques, or to handle large volumes of data, which are far beyond any human analysis skills.

The synergistic and skilfulcombining ofdifferent disciplines can achieve insight beyond current borders and thereby generate novel solutions to complex problems. The combination of methods and data from different fields can achieve more than the sum of the individual parts could do alone. This applies not only to computational biology but also tomany other academic disciplines.

Initiating and successfully maintaining cross-disciplinary collaborations can be challenging but highly rewarding. In a previous publication in this series, ten simple rules for a successful collaboration were proposed [ 1]. In the present guide, we go one step further and focus on the specific challenges associated with cross-disciplinary research, from the perspective of the theoretician in particular. As research fellows of the 2020 Science project ( http://www.2020science.net) and collaboration partners, we bring broad experience of developing interdisciplinary collaborations. We intend this guide to be for early career computational researchers as well as more senior scientists who are entering a cross-disciplinary setting for the first time. We describe the key benefits, as well as some possible pitfalls, arising from collaborations between scientists with very different backgrounds.

Rule 1: Enjoy Entering a Completely New Field of Research

Collaborating with scientists from other disciplines is an opportunity to learn about cutting-edge science directly from experts. Make the most of being the novice. No one expects you to know everything about the new field. In particular, there is no pressure to understand everything immediately, so ask the "stupid" questions. Demonstrating your interest and enthusiasm is of much higher value than pretending to know everything already. An interested audience makes information sharing much easier for all partners in a collaboration.

You should prepare for a deluge of new ideas and approaches. It is a good practice to read relevant textbooks and review papers, which your collaborators should be able to recommend, in order to quickly grasp the vocabulary (see Rule 3) and key ideas of the new field. This will make it easier for you to establish a common parlance between you and your collaborators, and allow you to build from there.

You should try to discuss your work with a range of scientists from complementary fields. As well as getting feedback, this can help you identify new collaborative opportunities. Remember that contacts that do not lead directly to collaborations can still prove useful later in your career.

Rule 2: Go to the Wet Lab

It is vitally important to understand where specific data sets come from. Just like mathematical and computational models, experiments have their own in-built assumptions, strengths, and weaknesses that you need to understand. What was the exact process of data collection? How many experiments can be performed in a given timeframe and how much do they cost? What were the constraints that led to the design of the experiments-how will you include this in your interpretation? If you plan to use the resulting data for model calibration or parameter fitting then try to obtain sufficient information to reproduce the experiment in silico. Papers in different domains have different perspectives and might not contain the data you are looking for in sufficient detail. Visiting the lab in person is often the most efficient way to get the information you need. A good understanding of the experimental setup might also suggest appropriate testcases for the computational studies. Try to talk to both the junior and senior scientists in the lab as they may give you different perspectives.

There are social, as well as scientific, reasons for understanding life in the wet lab. As a computational scientist, it is easy to underestimate the commitment and resources necessary to acquire experimental data (see rule 4). Visiting a lab, and taking an interest in data collection, is a way of acknowledging your colleagues' effort and the value of their data and expertise.

Rule 3: Different Fields Have Different Terminologies: Learn the Language

Science is full of subcultures using diverse and evolving jargon. Forming a successful cross-disciplinary relationship requires that you fully understand your collaboration partner. From classification schemes and methods to journals and research philosophy, it can be hard enough keeping up with developments in your own field, let alone others. For instance, neologisms can be ubiquitous in computational and biological sciences, where new terminology continually emerges from new methods, tools, and knowledge. Learn the other field's jargon early on in the collaboration and ask basic questions about the meanings of words.

For example:

Ambiguity: "Model" is probably the most ambiguous word in science. Mathematical, statistical, experimental, observational, theoretical, computational, analytical, verbal, legal, mental, graphical, geometrical, structural, and workflow models all have different meanings. Almost every field will have its own interpretation of "model" and the semantics differ significantly.

Synonyms: For example, removing entities above and below certain thresholds is termed "positive and negative selection" in immunology, while it is called "band-pass filter" in signal transduction.

Context often matters, so try to understand nuances in the use of terms. It can be beneficial to build up a technical glossary. Evaluate your understanding by presenting it back to new colleagues and observe where your rudimentary understanding needs more work.

Finally, agree on a joint nomenclature with your collaborators early in the project. Write equations and code in a consistent manner, standardise data formats, and use consistent style schemes in figures. Then talk through your outputs to discuss your collaborators' understanding and involvement. A good relationship is based on mutually understandable communication.

Rule 4: Different Fields Move at Different Speeds: Do not Become Impatient

A huge variety of cultures and expectations regarding research and subsequent publication exist in different scientific disciplines. However, these differences can lead to stress when embarking upon multidisciplinary collaborations, unless they are acknowledged and effectively communicated at an early stage. It is important to accept the different pace of different fields, communicate well, and be patient.

Research in experimental biology, for example, often involves long and arduous experiments, taking perhaps months or even years to complete. Animals or tissues may need to be grown, and weekends or nights spent in the lab tending to cell cultures and repeating experiments may be necessary. Some projects generate publications and co-authorships several years after a theoretician may have actually performed their in silico contribution to the work. Vice versa, computational aspects often involve more than simply pressing a button and computational resources may be limited.

Do not make assumptions about how hard fellow collaborators are working based on how long they take to get back to you with results. Here, communication is of particular importance.

Similarly, journals in different disciplines might have different periods of time from submission to publication. This can have knock-on effects when demonstrating your research output (see rule 5).

Early communication of how long your part of the work is likely to take and why this amount of time is needed will help your collaboration to run more smoothly.

Rule 5: Different Fields Have Different Reward Models: Know What You Can Expect

It is important to recognise that the publication culture in the life sciences, and in experimental biology particularly, differs from that of the theoretical sciences. Such differences can include:

Publication speed varies greatly. In experimental biology, publishing often takes several years, while certain theoretical papers can be published in a much shorter timescale (see also rule 4).

Metrics, such as the impact factor (IF), are used by many organisations to evaluate your research [2,3]. Be aware that different fields have different impact factor scales. The journal impact factors mainly depend on the average length of reference lists in the field. For example, a journal with an impact factor of 3 in mathematics (295 journals, median IF 0.57, maximum IF 3.57) might be more prestigious than a journal with an impact factor of 30 in cell biology (184 journals; median IF 3.2; maximum IF 37.16) (based on Journal Citation Reports of Thomson Reuters, version 2012).

In some fields, such as information technology, it can be the norm to publish new research in peer-reviewed conference proceedings instead of journals.

The preferred ordering of authors on a manuscript may also depend strongly on the academic environment. The first author might be the scientist who contributed most or whose surname comes first alphabetically. The last author may be the principal investigator, the author who contributed least, or the author with the last surname alphabetically. The corresponding author can be seen as "in charge of the paper," the principal investigator, or the person who volunteered for dealing with the correspondence.

In some areas of biology, large consortia of authors are needed to conduct research. In some theoretical fields, people tend to publish with fewer authors. Thus, the definition of a "significant" contribution to a manuscript might differ markedly.

It is important to be aware of these differences. Make sure that you discuss all these issues early with your collaborators to avoid misunderstandings and frustration. A well-planned publication strategy is fundamental in order to fulfil everyone's expectations and accommodate the potential mismatch of timescales of theoretical/experimental work (see rule 4).

Does your field and that of your partner value less frequent, higher impact publications or a series of smaller publications? One option is to start with methodological papers (both theoretical and experimental) while final publications describing the major breakthrough and how all the components are brought together could follow. Early methodological papers should already highlight the benefits of collaborating, e.g., theoretical work with experimentally sound assumptions and parameters, experimental work with solid data analytics. It is advisable to design initial publications without forgetting the greater scope of the collaboration. However, be aware that preceding papers might weaken your main publication if they anticipate parts of the results.

Rule 6: What Different Fields Mean by "Data"

Be prepared that scientists with experimental backgrounds might not have the same structured view on data and terminologies (see also rule 3) as you have. For scientists with a background in computer science, the lowest level of data organisation might be a spreadsheet where each column and row is well defined. For scientists with non-technical backgrounds, such a spreadsheet might represent the highest form of data organisation.

Whenever possible, ask your collaboration partner for a standardized data format. Good guides on how to share data can be found in [ 4, 5]. Always favour electronic forms of data and always keep a copy of the original file. You might also consider writing minutes about meetings and data specifications to avoid later misunderstandings.

Do not blindly trust experimental data. Always perform "sanity checks" on the data you receive (graphs, frequency tables, mutually exclusive data, unnatural distributions, etc.). This will help you to see if you have interpreted the data correctly and allow you to ask questions if you have any doubts.

Rule 7: Assess the Advantages and Disadvantages of Service Work

Theoretical scientists can be a huge asset to multidisciplinary projects by providing experimentalists with computational tools to gather data, predictive methods, and advanced statistical modelling. Theoretical scientists often do substantial amounts of "service work" in a collaborative project, for example, by maintaining computer infrastructure and databases, keeping their in-house code base up-to-date, and statistically analysing data.

Service work is often an excellent way to establish a collaboration, get the partners to trust in your ability and expertise, and learn enough about other disciplines to start making direct contributions, whilst, at the same time, co-authoring high-quality publications. Service work will show that you take the collaboration seriously and help you to establish a reputation as a reliable and analytically keen scientist who delivers fast, structured, and correct results.

Nonetheless, service work is also risky, as it may take more time than you anticipated. Therefore, make sure to evaluate the amount of service work on a regular basis and be clear with your collaborators about what you expect in return before engaging in service work. To gain more insight into the "cost" of service work, keep a record of the amount of time spent on service tasks. This not only prevents your collaborators from treating your contributions lightly but also gives you a clear idea as to whether it is worthwhile to engage in such tasks and/or take on new ones.

Crucial to minimizing the service "'load" is to make it easy to delegate tasks to others. When starting to develop analytical tools for others (whether software or mathematics), always take the perspective that these tools won't primarily be used by you, but by other collaborators. Hence, making your tools user-friendly, for example, by providing illustrative examples and documenting your code extensively [ 6], is essential.

Rule 8: Create and Manage Structural Bonds

Cross-disciplinary collaborations require structural bonds between the collaborators. It is only possible to break the silos of scientific disciplines and to become truly cross-disciplinary if a proper framework for scientific exchange is established. This can include regular meetings, workshops, symposia, attendance of each other's group meetings, and co-teaching of courses. However, to keep your collaboration efficient, be careful about imposing too many obligations. While it is often necessary to leave the "comfort zone"of scientific disciplines, it is equally important not to frustrate your collaboration partners with too many details not relevant to their endeavours. Therefore, keep the number of meetings at a reasonable level and set clear agendas.

Moreover, establishing these bonds often requires financial support, which can be achieved in different ways. For the initial setup phase, seed funding schemes maybe a useful resource. On the basis of these initial bonds, applications for larger grants can be submitted collaboratively. Many funding bodies offer special calls for cross-disciplinary research or favour cross-disciplinary proposals for both national and international settings. Examples include the "Horizon 2020" EU Framework ( http://ec.europa.eu/programmes/horizon2020/en/what-horizon-2020), the Human Frontiers in Science Program ( http://www.hfsp.org/), the US NSF/BIO-UK BBSRC Lead Agency Pilot ( http://www.bbsrc.ac.uk/funding/internationalfunding/nsfbio-lead-agency-pilot.aspx), or internal projects created to achieve inter-faculty cooperation within a university.

Once funding is secured, shared PhD students and postdocs can further strengthen bonds between collaboration partners. Shared supervision rewards with constant knowledge exchange, shared publications, and an interdisciplinary training for all. However, it is also important to protect junior scientists from "getting lost in cross-disciplinary collaboration."In particular, there is a risk that they fall between two stools in their attempts to comply with the expectations and advice of two or more supervisors from completely different fields.

Rule 9: Recognise When Things Are Not Working Well

Unlike a marriage, collaborations are not necessarily intended to be continuous and permanent. A pragmatic approach can favour both parties. If major problems arise that cannot be solved after a couple of attempts, there are several possible next steps:

1. Ostrich approach: Pretend that nothing has happened and hope that normality will soon be restored. We strongly advise against this tactic as it might lead to more frustration and potential damage to the relationship.

2. Pause: Sometimes, one of the collaborators may find it harder than expected to deliver the agreed results or is overwhelmed with other work-related duties. If your collaborator has trouble with their side of the workload, it is often better to introduce a pause in the collaboration than to exert pressure on them. Deliberate pauses take pressure off your collaborations, and can save potential frustration on your side as well.

3. Search for alternatives: Collaborations do not need to be exclusive. If you prioritise the collective work more highly than your collaborator, you might consider establishing a fresh collaboration with someone else who also values the work more. But be aware of your actions and the consequences they might have on your current collaboration.

4. End a collaboration: If a collaboration has become unworkable or reached a natural terminus, it may be best to make a clean cut and end the collaboration once and for all.

Many failures in collaborations could have been avoided by an early, proactive approach on arising problems. In cross-disciplinary settings, it could just be a problem of understanding (rule 3), impatience (rule 4), or lack of reward (rule 5). If you decide to end a collaboration, make sure to keep a working relationship with your former collaborator. This will allow you to properly handle existing structural bonds (rule 8) and allow you to potentially initiate other collaborations with the same partner.

Rule 10: Be Synergistic

Probably the most important quality of collaborations is the mutual gain that emerges. This usually works best when scientists with different, but complementary, skills decide to work together. One example might be the application of a novel, high-throughput computer algorithm to a vast quantity of experimental data. While the algorithm by itself might be brilliant, and publishable by benchmarking it on a publicly available dataset, it will shine even more when applied to a huge, unpublished dataset. At the same time, the huge dataset could be analysed by standard, semi-manual methods. This might also be publishable, but would take a long time and essential insights may be missed, which the novel algorithm would have delivered. Only by combining contributions from both sides does the work become more than the sum of its parts, achieving useful things for each partner and enabling insights that would not have been possible for either alone.

Initiating and nurturing successfully synergistic relationships is an important and valuable skill. The success of an interdisciplinary collaboration depends on any number of factors, including which partner approached which and over what timescale each party envisages collaboration. One very important property for a long-term, effective, and mutually beneficial relationship is that both sides should feel they are winners during and, especially, after a project (see also rule 5). An outstanding book on how such win/win situations can be achieved is Fisher, Ury, and Patton's Getting to Yes: Negotiating Agreement Without Giving In [ 7]: their advice includes inventing options for mutual gain, making sure to always give enough credit to partners, and caring for their interests as you would for your own. Then you will establish a truly successful and synergistic collaboration.

References

1. 5. White EP, Baldridge E, Brym ZT, Locey KJ, McGlinn DJ, Supp SR (2014) Nine simple ways to make it easier to (re)use your data. PeerJ PrePrints 1:e7v2.

2. 7. Fisher R., Ury W. L., and Patton B. (2011) Getting to Yes: Negotiating Agreement Without Giving In. Penguin Books.

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Ten Simple Rules for a Successful Cross-Disciplinary Collaboration

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Bernhard Knapp, Rémi Bardenet, Miguel O. Bernabeu, Rafel Bordas, Maria Bruna, Ben Calderhead, Jonathan Cooper, Alexander G. Fletcher, Derek Groen, Bram Kuijper Subject Areas For more information about PLOS Subject Areas, click here. We want your feedback. Do these Subject Areas make sense for this article?

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8. Ten Simple Rules for a Successful Collaboration

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8. Ten Simple Rules for a Successful Collaboration

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Citation: Vicens Q, Bourne PE (2007) Ten Simple Rules for a Successful Collaboration. PLoS Comput Biol 3(3): e44. https://doi.org/10.1371/journal.pcbi.0030044

Published: March 30, 2007

Copyright: © 2007 Vicens and Bourne. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this article.

Competing interests: The authors have declared that no competing interests exist.

Scientific research has always been a collaborative undertaking, and this is particularly true today. For example, between 1981 and 2001, the average number of coauthors on a paper for the Proceedings of the National Academy of Sciences U S A rose from 3.9 to 8.4 [ 1]. Why the increase? Biology has always been considered the study of living systems; many of us now think of it as the study of complex systems. Understanding this complexity requires experts in many different domains. In short, these days success in being a biologist depends more on one's ability to collaborate than ever before. The Medical Research Centers in the United Kingdom figured this out long ago, and the new Janelia Farm research campus of the Howard Hughes Medical Institute in the United States has got the idea, as it strongly promotes intra- and inter-institutional collaborations [ 2].

Given that collaboration is crucial, how do you go about picking the right collaborators, and how can you best make the collaboration work? Here are ten simple rules based on our experience that we hope will help. Additional suggestions can be found in the references [ 3, 4]. Above all, keep in mind that these rules are for both you and your collaborators. Always remember to treat your collaborators as you would want to be treated yourself-empathy is key.

Rule 1: Do Not Be Lured into Just Any Collaboration

Learn to say no, even if it is to an attractive grant that would involve significant amounts of money and/or if it is a collaboration with someone more established and well-known. It is easier to say no at the beginning-the longer an ill-fated collaboration drags on, the harder it is to sever, and the worse it will be in the end. Enter a collaboration because of a shared passion for the science, not just because you think getting that grant or working with this person would look good on your curriculum vitae. Attending meetings is a perfect opportunity to interact with people who have shared interests [ 5]. Take time to consider all aspects of the potential collaboration. Ask yourself, will this collaboration really make a difference in my research? Does this grant constitute a valid motivation to seek out that collaboration? Do I have the expertise required to tackle the proposed tasks? What priority will this teamwork have for me? Will I be able to deliver on time? If the answer is no for even one of these questions, the collaboration could be ill-fated.

Enter a collaboration because of a shared passion for the science . . .

Rule 10: If Your Collaboration Satisfies You, Keep It Going

Ever wondered why a pair of authors has published so many papers together? Well, it is like any good recipe: when you find one that works, you cook it again and again. Successful teamwork will tend to keep flourishing-the first paper will stimulate deeper and/or broader studies that will in turn lead to more papers. As you get to know your collaborators, you begin to understand work habits, strengths but also weaknesses, as well as respective areas of knowledge. Accepting these things and working together can make the work advance rapidly, but do not hurry: it takes time and effort from both sides to get to this point.

Collaborations often come unexpectedly, just like this one. One of us (PEB) as Editor-in-Chief was approached not just with the idea for these Ten Rules, but with a draft set of rules that needed only minor reworking. As you can see, we have obeyed Rule 8.

Acknowledgments

We wish to thank Tom Cech for insightful discussions, and Chrysa Latrick, David Zappulla, Barbara Cohen, Emma Veitch, Catherine Nancarrow, and Hemai Parthasarathy for helpful suggestions on the manuscript.

Author Contributions

QV and PEB wrote the paper.

References

4. Burroughs Wellcome Fund, Howard Hughes Medical Institute (2006) Making the right move. A practical guide to scientific management for postdocs and new faculty. Chevy Chase: Available: http://www.hhmi.org/labmanagement. Accessed 21 February 2007.

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Ten Simple Rules for a Successful Collaboration

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Citation: Vicens Q, Bourne PE (2007) Ten Simple Rules for a Successful Collaboration. PLoS Comput Biol 3(3): e44. https://doi.org/10.1371/journal.pcbi.0030044 Published: March 30, 2007 Copyright: © 2007 Vicens and Bourne. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Testing Business Ideas: A Field Guide for Rapid Experimentation (The Strategyzer Series)

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Buy Testing Business Ideas: A Field Guide for Rapid Experimentation (The Strategyzer Series) 1 by Bland, David J., Osterwalder, Alexander (ISBN: 9781119551447) from Amazon's Book Store. Everyday low prices and free delivery on eligible orders.

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How do you support someone when there are no good solutions and everything just, well... sucks?

When I open up to someone, sometimes I end up feeling unheard. I feel much safer and more competent in " holding space " for others, and have much less trust in their ability to hold space for me - or even in their desire to do so.

This is not the case with everyone, mid you. Some of the people I'm closest to are great at the kind of mutual support I seek. And oftentimes, a person sharing their struggles with me and allowing me to support them is exactly what I need, as it gets me out of my head and focused on someone else's challenges other than my own. Several of my friends and acquaintances want to provide support and want me to open up to them, but when I do, their response is advice and problem-solving.

It sucks, because I have a huge advantage in my skillfulness. Not everyone has access to the kind of education that a graduate counseling program and post-graduate therapist trainings get you. Active listening and good communication are all skills, and it's really unfortunate that people don't usually get taught them. I teach this stuff to my clients all the time, and I might as well do my part by writing about it.

Want to be better at supporting people and listening well? Here are some basic strategies.

Listening well can be healing in and of itself.

Your first instinct when someone comes to you with a challenge, struggle, or hurt may be the "righting reflex", the urge to fix or problem-solve, to make it better. That's a compassionate response! There's nothing wrong with it. At the same time, it's not actually helpful.

See, when you give advice, you place yourself above the other person in power and competence. You are speaking from a place of authority and judgment rather than a place of connection. Advice, unskillfully given, disconnects. What helps the most when someone is hurting is connection. Look for ways to connect rather than ways to fix. When you have the impulse to "fix it", remind yourself: listening and supporting the person in their autonomy is the most helpful thing I can do. Fixing it for them or telling them what to do is actually unhelpful and can even be harmful.

When someone defaults immediately to advice or problem solving when I've just opened myself up to them and shared a struggle I'm having, some part of me shrivels and deflates. I can practically feel the connection shutting down, the walls coming up. I feel sad, missed, judged, and a little hurt.

Empathy is much harder than problem-solving. Problem-solving and giving advice is safe; it is cerebral, distant, and then you can pat yourself on the back for "helping", or get upset at the other person for not following your advice or not wanting help if they shut down or object.

If you absolutely must give advice, there are some better options.

1. Listen first. Try to make sure you have really heard the person's story. Try to help them feel heard and seen. We'll cover this in the next section.

2. Speak from your own experience. Rather than saying "you should do this, I know how to fix your problem, take my advice", go for connection and vulnerability. (Are you seeing a pattern yet?) Say, "When I was in a similar situation, here's what happened and what helped me." Tell your story. It's amazing how much more effective this is. When you give advice, people often get defensive; when you speak from your experience, they often stay receptive and can consider and hear it.
When I told a client this recently, they considered it and reflected on how they'd done this inadvertently in the past, and how it did indeed go much better than when they gave advice. They said, then, "but it's really vulnerable to do it that way". YES! Yes, it so is, and that's the point. That's why it's connective rather than disconnective; sharing from your experience means going to the place where you can relate to what the other person is going through, where you've experienced something similar, the painful memories you hold, and opening back up to them in return. It means joining them in their vulnerability.

3. Ask permission. If, after steps 1 and 2, you still feel the need to share advice, or have advice you think might be valuable, ask their permission to give advice. "Would you be open to some suggestions?" or "Are you interested in advice?" or "Do you want me to listen or do you help in finding solutions?"
DO NOT LEAD WITH THIS QUESTION. Do steps 1 and 2 first. If you lead with this in immediate response to their sharing of their challenge, they may say yes just because they are feeling vulnerable and want to seem stronger or open to the kind of help that is valued in our society, or because they think of themselves as intellectual, reasonable people and deny their own vulnerability. (I've seen this happen multiple times.) If they say "no," "not right now", "thanks but no thanks", then respect that. Don't give advice.
If they say "yes", that doesn't necessarily mean you should charge straight in. Start with one piece of advice. (Again, it's still always best if you frame it by speaking from your own experience. Also, avoid the words "should" or "shouldn't"; those are red-flag judgment words. Be as nonjudgmental and compassionate as possible in how you speak.)
If they respond with consideration, you're doing well! They actually wanted advice! If they respond with rejection or explain how it won't work, and continue to do that with your next suggestions or pieces of advice, abort immediately: they don't actually want problem solving, they probably don't feel heard or listened to, and they are not in a place where they can receive suggestions.

I'm going to link you to one of the most useful fundamental skillsets in active listening. It's from an intervention style called Motivational Interviewing, and if you ever get a chance to take a training on it, I highly recommend it. It was developed for addictions counseling, but can be applied to basically everything ever.

The core skill I'm talking about is called OARS:

Open-ended questions

Affirmations

Reflections

Summaries

And you want to do this while in the spirit of Motivational Interviewing, summarized by the acronym PACE:

Collaboration (Partnership): "We are going to work together"

Autonomy (Acceptance): "I value you and am delighted to talk with you", and supporting the person's autonomy or independence

Compassion: "I want to understand and respect you and your experience"

Evocation: "I am going to create a space for you to share yourself and your story with me"

Listen to what the person is saying. Try to hear what they're not saying, too. Be really curious, interested, and with a spirit of compassion. Don't get judgmental or defensive, don't make it about you; even if they're talking about something that has to do with you, try really hard to detach yourself from the situation and focus on the other person and their feelings with empathy and understanding. Give yourself time before you speak. You don't have to respond right away, you don't have to say anything right away. You can take a silence or a breath to think it through and notice your own internal responses. Slow down, respond rather than reacting.

Open-ended questions: Ask questions. This is part of being interested and curious, of evoking, drawing out their ideas and wisdom rather than imposing your own ideas. Seek to hear the story deeper and more fully, more completely. AVOID any question starting with "why"; for whatever reason, "why" brings in judgment and shame as often an automatic response, it moves someone from their heart to their head. Ask who/what/when/where/how. Ask them to tell you more. DON'T ask questions that can be answered with yes or no (close-ended questions); ask questions that are open ended.

Affirmations: Validate their experience. Affirm their strengths and successes. Build empathy, find that place in yourself that resonates with what they're going through and voice it. "That sounds really hard", "It takes a lot to share something like that," "You really want to make this work," "You're putting a lot of effort into this," etc. Note: you do not have to validate their story of a situation (which you may very well disagree with) in order to validate their feelings and experiences. Oftentimes, having feelings heard and validated reduces the intensity. People will often get more intense and loud about their hurt, story, or feelings if they feel unheard or invalidated, which can quickly escalate into a full-blown argument. Defuse it from the start by looking to the emotion and validating the emotion. There's a great Dialectic Behavioral Therapy lesson on validation here - you only validate the valid parts, but feelings are always valid.

Reflections: This is also part of validation, with a focus on reflecting back to them what's going on. A lot of people struggle with reflections, thinking it's just parroting back what the person said. That isn't it at all: you are finding the very heart and gem of the person's story and giving it back to them. Skillful reflection finds the unsaid intent of the person's story, the emotion of it, and states it aloud. You'll know you gave a good reflection when they light up and say "YES, EXACTLY", which may seem odd when you've just told them something close to what they said to you. Sometimes you get it wrong, and that's okay! Don't be attached to your reflection. Reflection is also a great way to make sure you understand what's going on. If you reflect gently and have built rapport through open-ended questions and been non-judgmental, then they should hopefully feel comfortable correcting you if you got the wrong emotion. For example, I've reflected back to clients, "And that feels... sad...?" and they've responded with "...No, not quite sad, more like... disappointed." But that leads to better understanding anyway! So it still works out well. You can also reflect what's happening in their body, not just what they're saying. "I'm noticing your voice is shaky," "I'm seeing tears in your eyes," "you smiled when you said that". This can get really intense really quickly; people aren't used to being seen on a somatic level, to having all the little things noticed, and some people can get really self-conscious about this. Sometimes it feels too vulnerable to have someone notice that much. So use this carefully, and observe how they respond; do they close off and tense up, or do they relax and open up more? One thing that can aid reflection is mirroring their body language. Do it subtly, not blatantly, or else it looks like you're mocking or copying. But if you are in a similar position to them, not only does it create a sense of connection, but it can also help you understand what they're feeling. Mood follows body; if you shift to their position and you notice a different emotion in yourself, or sensation, then you can try out that as a reflection and see if that's what is going on for them too.

Summaries: A kind of advanced reflection. You are taking everything you heard and tying it all together in a neat bow and giving it back to the other person. A great way for this is, "Let me make sure I understand. I heard you say..." and then summarize the conversation or situation. This gives them a chance to fill in anything you may have missed, it allows you to demonstrate understanding and get a correction if you misunderstood something, and it allows them to see their story from a different perspective, which can sometimes provide insight. "Did I miss anything?" is a great closing question.

At that point, you can move to sharing from your experience. That allows you to relate to them, to be vulnerable in exchange, to connect further to their story, and to share what worked for you in a way they might be able to use.

This is an advanced skill.

Realizations, decisions, and ideas are most powerful when the person in question speaks them. If you tell them what to do, they are less likely to be committed to it than if they figure it out for themselves. All the reflection and summarizing done above? That helps people come to powerful conclusions on their own, because it allows them to see their situation from a little distance or perspective. The open ended questions? Sometimes that can lead a person to figuring out what they could do.

When you ask permission to give advice and they say "yes", sometimes it works really well to still not give advice, but rather to ask some questions first. What have they considered doing? What worked for them in similar or tangentially related situations in the past? What ideas do they have about how they can approach this situation? What do they plan to do next?

Tap into the other person's own wisdom. This means you are supporting them in their own autonomy and growth, rather than trying to do it for them. Only offer your own advice after they've shared their own thoughts. Sometimes, their own thoughts contain what you were going to advise them on. And believe me, it sucks to have someone tell you to do something you've already tried or already knew to do; it feels patronizing, even though there's no way they could have known you already tried it. So investigate first!

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How do you support someone when there are no good solutions and everything just, well... sucks? When I open up to someone, sometimes I end up feeling unheard. I feel much safer and more competent in " holding space " for others, and have much less trust in their ability to hold space for me - or even in their desire to do so.

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11. Customer Discovery - What do you ask? 

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11. Customer Discovery - What do you ask? 

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https://www.youtube.com/watch?v=OTkP2JDeGWM

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Customer Discovery: What Do You Ask, with Justin Wilcox

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12. Validating Product Market Fit - Part 1 ( blog article) 

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12. Validating Product Market Fit - Part 1 ( blog article) 

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Product market fit: what is it and what next if you don't have it?

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What do you do next when you realise your established startup does not have product market fit?

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13. Validating Product Market Fit - Part 2 ( video) 

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13. Validating Product Market Fit - Part 2 ( video) 

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Validation: Be Sure Your Startup Vision Isn't a Hallucination. 2 Minutes to See Why

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14. How smart people ask great questions (and get better answers)

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14. How smart people ask great questions (and get better answers)

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I spent weeks sifting through the data, modeling different scenarios, and perfecting my plan. I knew it was awesome -- but just to be sure, I asked a key employee for input. (OK, maybe I wasn't trying to be sure. In retrospect, I see I was hoping he would be impressed by how smart I was.)

"I'm thinking of moving two crews to a different shift rotation to get a better process flow," I told him. "I've run the numbers and overall productivity should go up by at least 10 percent. What do you think?"

He stood quietly for a moment. "I suppose it could work," he finally said.

Bingo! Just what I wanted to hear.

So I moved the crews.

My new shift rotation worked on paper. It even worked in practice. But it screwed up the personal lives of a bunch of great employees. (Luckily, I pulled my head out of my rear and moved everyone back to their old rotations.)

What happened? Sure, I asked the right question.

But I asked the wrong way.

You probably have, too. Maybe you asked a leading question. Or a limiting question. Or a question that presumed a certain answer. You asked the right question, but you asked it the wrong way.

Here are some ways to ask much better questions and as a result, get much better answers -- which is the point of asking questions in the first place.

Start asking one sentence questions.

Feel free to state the problem or issue in detail, but limit your question to one sentence. "How can we increase productivity?" "How can we improve quality?" "What would you do if you were me?"

Sticking to one sentence helps ensure your questions will be open-ended.

Stop asking either/or questions.

Say you have a quality problem and have come up with two possible solutions. Each has positives and negatives. So you seek input from an employee. "Should we just scrap everything and rework the whole job," you ask, "or should we ship everything and hope the customer doesn't notice?" Most of the time, she'll pick one of the two answers you proposed.

But what if there's a better option you haven't considered?

Here's a better way to ask that question:
"There are defects throughout the whole order. What do you think we should do?"
Maybe she'll say scrap it. Maybe she'll say ship and hope.

Or maybe she'll say, "What if we tell the customer up front there is a problem, ship everything to them, and take a crew to their warehouse to sort product? That reduces the impact on the customer. They can use whatever is good and won't have to wait for the entire job to be rerun."

Either/or questions assume an answer and therefore limit the possibility of different ideas.

Instead of sharing options, just state the problem. Then ask, "What do you think?" Or, "What would you do?" Or, "How should we handle this?"

Then be quiet. Let people think.

They'll respond much more thoughtfully -- and often will come up with ideas you never considered.

Start asking humble clarifying questions.

As a leader, you're supposed to have all the answers, which means asking certain questions might make you feel vulnerable.

But it shouldn't. When you ask questions, you show respect. You show trust. You show you're willing to listen and learn. Win, win, win.

Plus, asking for clarification is easy. Just say:

"I'm impressed. Now pretend I don't know anything about how that works. How would you explain it to me?"

"That sounds really good. Let me make sure I didn't miss anything, though. Can you walk me through it one more time?"

Or, best of all: "I have to be honest: I'm not sure I understand what you're saying, but I really want to." (A little humility goes a long way.)

Above all, don't pretend you understand when you don't.

If only because that's a behavior you never want to model for your employees.

Stop asking leading questions.

Asking a question that assumes a particular answer is easy to do when you already think you're right and just want people to say you're right. (Which is exactly what I did when I asked an employee about my fancy shift rotation.)

"Do you think we should wait any longer than we already have for an answer?"

"Don't you think we should go ahead and release that order?"

"Do you think it's time we turned that project over to someone else?"

Each question assumes an answer: You clearly think you should stop waiting for feedback, release the order, and replace a project leader. Though a few people may disagree, most won't, because the answer you want to hear is obvious.

Here are better ways to ask those questions:

"The customer hasn't signed off on the prototype. What do you think we should do?"

"What do you think we should do about that order?"

"What do you think is the best way to handle that struggling project team?"

None of those questions imply a particular answer. And each also leaves room for a variety of options.

Start talking as little as possible.

You already know what you know. Great questions are designed to find out what the other person knows.

So stay quiet and listen.

You never know what you might learn.

And if you don't stay quiet and listen, you'll never know what you could have learned.

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How Smart People Ask Great Questions (and Get Better Answers)

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I spent weeks sifting through the data, modeling different scenarios, and perfecting my plan. I knew it was awesome -- but just to be sure, I asked a key employee for input. (OK, maybe I wasn't trying to be sure. In retrospect, I see I was hoping he would be impressed by how smart I was.)

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15. Talking to Humans Assumptions Exercise 

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15. Talking to Humans Assumptions Exercise 

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A great framework for speaking to potential stakeholders and testing ideas

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https://www.bloomberg.com/news/articles/2007-06-24/fail-fast-fail-cheap

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Rapid prototyping Google Glass - Tom Chi

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18 Types of MVPs that won't break the bank

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18 Types of MVPs that won't break the bank

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Another crucial technique in good estimates is to ignore everything but what you deem to be the most important factor(s) in the problem. In the baseball problem, for example, we assumed the major sources of revenue to be tickets, TV, and the merchandise that people buy at the stadium or elsewhere, and we took all other sources of income to be negligible. Likewise, if we estimate the daily energy budget of a person, we can assume that the energy used by their electric toothbrush makes a negligible contribution when compared with the energy that goes into making their food, driving their vehicle, heating their home, etc. If we consider the time required to get in a car and drive 100 miles, we can ignore the time required to open the door, et cetera.

This takes some bravado to get started. Getting a sense for the dominant contribution(s) is not always obvious and may take some numerical comparisons to get a good sense for it. However, reducing the number of variables we need to keep track of always reduces the complexity of our mathematical problems, and can lead us toward more accurate solutions. For example, if we solve a problem first in one extreme case, then in another, we may be able to identify the short and long time behavior, or low and high energy behavior, and therefore know what we should be looking for when we undertake the full-blown analytical solution. Treating the dominant parts leads us to answers that are substantially correct, i.e. only differ from the true answer by a factor of twenty or thirty percent, and therefore give us a good idea of what's going on.

How long will it take to melt a frozen water bottle by shaking it?

To make this estimate, we have to identify a mechanism for heating the ice. For simplicity, we assume that the system is at zero degrees Celsius. Under this assumption, we can't melt the ice if there's none melted to begin with, i.e. if we shake a solid block of ice back and forth, no heat is delivered to the ice.

When we accelerate the bottle in one direction, all the material in the bottle is given kinetic energy. When we decelerate to force the bottle in the other direction, the ice is decelerates by traveling through the liquid water and the liquid water essentially moves out of the way to the back of the bottle. This is the manner in which heat enters the system. In decelerating through the liquid water, the ice experiences a frictional force that's converted into heat. Since the ice and water are at zero degrees Celsius, the heat goes toward melting the ice.

At time the liquid volume is given by , and the volume of frozen ice is given by (in this step, we explicitly ignore the density difference of ice and water). If we approximate the water bottle as a cylinder, the distance that the ice will decelerate through is given by the liquid volume divided by the cross-sectional area: . Let us make the crude approximation that the ice doesn't lose any velocity as it pushes the water out of the way, i.e. the accelerating force from our arm is equal to the force of water friction on the ice. Thus the ice travels at the speed until it hits the end of the bottle. Let's assume a vigorous shake. The human arm is capable of throwing a ball (baseball, cricket) at ; let's take 10% of that value as the sustained steady state velocity of the ice, .

We can turn this into an average power by estimating the time taken to decelerate through the water. Here we have several choices. We can make a simplistic model in which each shake travels through the same distance and is shaken back and forth at the speed . We can also make a slightly more sophisticated model in which the shaker changes directions as soon as they feel the ice hit the bottle. This is more realistic. First we'll calculate the simple model.
Constant shake frequency
which yields the solution

Our choice for was poor because shakes at the beginning should have a higher frequency (short distance for ice to travel through liquid) while shakes at the end should have a lower frequency (greater distance to travel). Thus, initially, this model should underestimate the average power delivered, and as time goes on, it may overestimate it.
Scaled shake frequency
Using this new estimate for , we find the average power to be constant in time

Are these reasonable estimates for the number of shakes to melt a frozen water bottle? When should we expect these models to work? Are there any cases where this model should fail terribly, i.e. should it be accurate for small initial unmelted volume?
Challenge yourself
How long would it take for you to shake the melted ice from zero degrees Celsius up to boiling?

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Fermi Estimate | Brilliant Math & Science Wiki

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A Fermi estimate is one done using back-of-the-envelope calculations and rough generalizations to estimate values which would require extensive analysis or experimentation to determine exactly. Physics is celebrated for its ability to make extremely accurate predictions about tough problems such as the magnetic moment of electrons, the deflection of light by the Sun's gravity, or the orbit of the planets around the Sun.

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20. Fermi Estimation in Action - how many piano tuners are there in Chicago 

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20. Fermi Estimation in Action - how many piano tuners are there in Chicago 

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How many Piano tuners are there in Chicago?

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is a handbook for visionaries, game changers, and challengers striving to defy outmoded business models and design tomorrow's enterprises. If your organization needs to adapt to harsh new realities, but you don't yet have a strategy that will get you out in front of your competitors, you need Business Model Generation.

Co-created by 470 "Business Model Canvas" practitioners from 45 countries, the book features a beautiful, highly visual, 4-color design that takes powerful strategic ideas and tools, and makes them easy to implement in your organization. It explains the most common Business Model patterns, based on concepts from leading business thinkers, and helps you reinterpret them for your own context. You will learn how to systematically understand, design, and implement a game-changing business model--or analyze and renovate an old one. Along the way, you'll understand at a much deeper level your customers, distribution channels, partners, revenue streams, costs, and your core value proposition.

Business Model Generation features practical innovation techniques used today by leading consultants and companies worldwide, including 3M, Ericsson, Capgemini, Deloitte, and others. Designed for doers, it is for those ready to abandon outmoded thinking and embrace new models of value creation: for executives, consultants, entrepreneurs, and leaders of all organizations. If you're ready to change the rules, you belong to "the business model generation!"

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Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers (The Strategyzer Series)

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Buy Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers (The Strategyzer Series) 1 by Osterwalder, Alexander, Pigneur, Yves (ISBN: 9780470876411) from Amazon's Book Store. Everyday low prices and free delivery on eligible orders.

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Explore various business and revenue models for your company

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Business Model Pattern Network | Business Model Navigator

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Use our network visualization tool to explore how business model patterns by iconic firms connect, and generate new insights for your own business model innovation initiatives.

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23.  How to conduct a quick patent search

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23.  How to conduct a quick patent search

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Table of contents

What Is a Patent Search?

A patent search or patentability search is the process of evaluating the patentability of your invention. Patent applications will be rejected by any authoritative patent offices you apply to (e.g., USPTO, JPO, CNIPA, etc.) if a granted patent for the same invention already exists. Therefore, it's certainly worth verifying the originality of your invention before you invest too much time and money into it.
Conducting a patent search on your own is not that difficult; in fact, many inventors and entrepreneurs conduct their own patent search to save money. However, if you have the budget for it, seeking professional assistance or using patent search software is always an excellent option for a more detailed search result.

Why Do You Need to Do a Patent Search?

A patent is legal protection to ensure that your inventions aren't used or monetized without your permission. Conducting a patent search is one of the best ways to protect yourself from patent infringement. Other than the obvious advantages, there are plenty of benefits to performing a patent search.

Avoid costly investment decisions

Once you understand the patentability of your invention, you"ll be in a better position to make further decisions to avoid unnecessary investments.

Familiarize yourself with prior art

Familiarizing yourself with prior art allows you understand the patentability of your invention and makes it easier for you to draft unique claims and descriptions for it.

Improve your patent application or design around

By becoming more knowledgeable and familiar with the literature available for the field of the invention, inventors can benefit from using this knowledge to strengthen their patent application or develop a design around strategy.

How to Do a Patent Search Yourself

Here are the steps you should take to do a patent search through the USPTO (US patent search) or other online patent search tools (global patent search):

1. Keyword brainstorm
You can begin your patent search by choosing a relevant keyword if the patent search tool you are using offers more advanced search features such as semantic search. Such features may help to identify your search intent and can provide you with the most accurate search results.

2. Find the relevant CPC classification using your keyword
Once you find out the CPC classification that is most relevant to your invention, you can dig deeper and search for the patent documents that are similar to your invention.

3. Search through the patent documents of both PatFT (issued patents) and AppFT (published patent applications) with the most relevant CPC classification that you previously identified.

If you conduct a US patent search using the USPTO, you will need to go through all of the patent documents in two databases: The Published Patent Applications Database and The Complete Patent Documents Database. This will help you to identify the patent documents that are most similar to your invention.
When utilizing other online patent search tools to conduct a global patent search, you will find that many of them will provide you with both the related issued patents and the published patent applications within one search. With advanced filters, you can quickly narrow down your search scope for more specific results.

4. Dig deeper for more related patents by using the citations of the prior art of the most relevant patents from your search.

Once you have found the most similar patent applications and issued patents, you can then expand your search scope by looking into their forward and backward citations. Looking through the citations and the prior art layer by layer can help you to get the most comprehensive results.
Some online search tools offer search features that can eliminate the effort required to go through the citations layer by layer. Utilizing such tools and algorithms may prove to be a more efficient and beginner-friendly way to do a patent search.

5. Repeat the process to dig deeper and broaden your search scope.

To broaden your search scope, looking into the non-US patents and non-patent literature in other authoritative patent offices is important. Looking into a patent's family is also a great way to gain more insights or related information from other countries.

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How To Do a Patent Search Yourself In 5 Steps: Why Do You Need it

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Table of contents A patent search or patentability search is the process of evaluating the patentability of your invention. Patent applications will be rejected by any authoritative patent offices you apply to (e.g., USPTO, JPO, CNIPA, etc.) if a granted patent for the same invention already exists.

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24. Instant Genius - Science Focus Magazine Podcast 

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24. Instant Genius - Science Focus Magazine Podcast 

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Listen to a range of researchers share their insights ( and details on how they are undertaking knowledge exchange activities)

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The team behind the Science Focus Podcast have launched two brand new series: and Instant Genius Extra.

Whether you're curious about getting healthy, the Big Bang or the science of cooking, find out everything you need to know in 30 minutes with Instant Genius. We talk to world-leading scientists and experts to bring you a bite-sized masterclass on a new subject each week.

If that's not enough, you'll hear even more from our experts on Instant Genius Extra. On this subscription-only show, available on Apple Podcasts, we take a deeper dive into the topics covered on Instant Genius.

Discover how the Universe began with Prof Jim Al-Khalili, or look forwards to its final moments with Dr Katie Mack. Let Prof Steve Brusatte tell you about the discoveries changing everything we thought we knew about dinosaurs, or hear Ginny Smith explain how brain chemicals control every aspect of your life. Improve your cooking skills with food science, thanks to Dr Stuart Farrimond.

Make sure you don't miss an episode by subscribing to Instant Genius on Acast, Apple Podcasts, Spotify, or wherever you get your podcasts.

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Instant Genius: The brand new podcast from BBC Science Focus

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The team behind the Science Focus Podcast have launched two brand new series: and Instant Genius Extra. Whether you're curious about getting healthy, the Big Bang or the science of cooking, find out everything you need to know in 30 minutes with Instant Genius.

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25. Ten Simple Rules for Getting Started on Twitter as a Scientist

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25. Ten Simple Rules for Getting Started on Twitter as a Scientist

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Citation: Cheplygina V, Hermans F, Albers C, Bielczyk N, Smeets I (2020) Ten simple rules for getting started on Twitter as a scientist. PLoS Comput Biol 16(2): e1007513. https://doi.org/10.1371/journal.pcbi.1007513

Editor: Scott Markel, Dassault Systemes BIOVIA, UNITED STATES

Published: February 10, 2020

Copyright: © 2020 Cheplygina et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this article.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Twitter is one of the most popular social media platforms, with over 320 million active users as of February 2019. Twitter users can enjoy free content delivered by other users whom they actively decide to follow. However, unlike in other areas where Twitter is used passively (e.g., to follow influential figures and/or information agencies), in science it can be used in a much more active, collaborative way: to ask for advice, to form new bonds and scientific collaborations, to announce jobs and find employees, to find new mentors and jobs. This is particularly important in the early stages of a scientific career, during which lack of collaboration or delayed access to information can have the most impact.

For these reasons, using Twitter appropriately [ 1] can be more than just a social media activity; it can be a real career incubator in which researchers can develop their professional circles, launch new research projects and get helped by the community at various stages of the projects. Twitter is a tool that facilitates decentralization in science; you are able to present yourself to the community, to develop your personal brand, to set up a dialogue with people inside and outside your research field and to create or join professional environment in your field without mediators such as your direct boss.

This article is written by a group of researchers who have a strong feeling that they have personally benefited from using Twitter, both research-wise and network-wise. We (@DrVeronikaCH, @Felienne, @CaAl, @nbielczyk_neuro, @ionicasmeets) share our personal experience and advice in the form of ten simple rules, and we hope that this material will help a number of researchers who are planning to start their journey on Twitter to take their first steps and advance their careers using Twitter.

Twitter terms

Before we start with the rules, inspired by [ 2] we briefly introduce a number of relevant Twitter terms.

Timeline-The tweets from the people you follow.

Retweet (RT)-sharing a tweet that was originally made by someone else.

Quote-tweet-sharing a tweet by someone else in a quote, while adding your own comments.

Like (♡), used for showing you like a tweet-a fast way to give feedback without replying. There is no similar function for disliking a tweet.

Notifications-Tweets that mention you and replies, retweets and likes for your tweets.

Mentioning (@)-If you mention someone with their handle ("This paper by @CaAl is great"), your tweet will show up in their notifications.

Direct message (DM)-A private message that is only visible to the sender and the specifically identified recipients. By default, regular Twitter messages are visible to the whole world, including (via search engines such as Google) people who do not have a Twitter account.

Hashtag (#)-used to make it easier to find tweets with a common theme by defining ad hoc keywords, for instance tweets about a conference (#ICA19) or career talks (#PhDChat).

List-a list of Twitter users that can be public (followed by anyone) or private. Lists can be used to follow accounts that tweet about specific topics, but which you don't want to follow yourself.

Hat Tip or Heard Through (HT), used for thanking the source of a tweet.

Subtweeting-Tweeting about somebody without explicitly mentioning their handle, so that they are not informed of your comment (see 'Mentioning').

Live-tweeting-Tweeting short summaries of an event, for example of a conference talk, as it is happening.

Thread-A series of tweets on one subject, for instance ten tweets about a new research paper.

Rule 1: Start somewhere, but show up

To get into the habit of using Twitter, you need to do just that: start. You can do this even before you finish reading this article! You don't need to know everything about Twitter before posting a tweet, just like you don't need to know everything about running a marathon before going for a jog. Create an account (you can change your username later) and tweet something-for example, a comment about a paper you are writing or a conference you are going to. This way you can use ideas that you already have-but put them into the form of a tweet, which aligns with the advice of not treating research and outreach as separate entities [ 1].

A bigger challenge is to continue tweeting regularly once you have started, which is essential to habit change [ 3], similar to going running every few days. There is no one-size-fits-all set of rules for this, but we provide a few suggestions here. Once that paper is completed or the conference is over, perhaps you can follow other people in your field and respond to their tweets first, or check hashtags where everybody is invited to contribute, such as #PhDChat or #ECRChat. Such general hashtags are also great ways to discover and follow people who you would not run into at your department or conferences. Hashtags are good places to start posting your own questions or content-for an easy start, try #AcademicsWithCats, where you can show off your cat helping you with reviewing duties ( Fig 1).

Pictures that someone might post on Twitter to convey they are reading papers AND like cats. Possible relevant hashtags include #AcademicsWithCats, #Caturday, #ECRchat (chat as in conversation, not as in cat in French).

Rule 2: Discover opportunities in academia

To Early Career Researchers (ECRs), Twitter has become an invaluable source of information [ 4, 5]. One can follow granting agencies, particular labs and dedicated career columns in popular research journals, which allows for tracking the first-hand information on recently opened positions, grant calls, and new trends on the academic job market.

But even more importantly, Twitter culture has exposed a part of academia that has traditionally always been hidden from view, namely the inception of new research activities. Now, ECRs can observe and even join the process of creating national or international research projects (for instance, [ 6] stems from a discussion at Twitter and Bik et al [ 1] write their work resulted from online interactions). Senior researchers openly share ideas through Twitter and this can lead to the development of new concepts which often move on to become fully-fledged research projects.

Many researchers active on Twitter are also open about their everyday struggles, share their frustrations from rejections and bureaucracy, and support each other emotionally [ 7]. This is especially valuable for ECRs who have experienced the black side of academic life: patronizing behaviors, inequality and lack of inclusivity in research projects. For these researchers, Twitter gives an opportunity to follow senior role models who have managed to develop successful research careers and are willing to openly share their difficult experiences from the past. Twitter can play the role of a distributed mentoring system which can have a profound impact on a researcher's self-image and can help to mitigate imposter syndrome. Similar reasons are listed by [ 8], who crowdsourced over 400 responses from academics on why they use Twitter.

For ECRs, starting Twitter activity may be hard. Thus, we recommend joining a peer group, together with members of your local research group, together with other collaborators or friends in the research community. In this way, you can make sure that you have a few followers right at the start, and that your peer group helps you in distributing your content.

Rule 3: Tweet stuff

Twitter is useful for absorbing and sending information, but the potential for interaction with fellow scientists is where its real power lies [ 9]. Especially for new Twitter users, it is good practice to retweet other users' content. Retweeting means sharing someone else's tweet with your followers [ 10]. This way, your timeline demonstrates the scientific topics of your interest. You can either retweet directly or quote-tweet, which means adding your own commentary to the tweet. Quote-tweeting adds a personal flavor to your tweets.

Interact with others by asking them their opinion on your (re)tweet. Interactivity can be increased by using (simple) polls. Do not hesitate to ask scientists questions about their work (e.g., "can you share the code for that paper?"): they signed up to Twitter because they intend to interact. Also, feel free to ask questions, both content-related ("Which R package can we use for this?") and logistic ("How do you handle requests for letters of recommendation?").

Many scientists are bilingual on Twitter, tweeting both in their mother tongue and in English. Do not worry that this might be confusing: Twitter users can easily skip over tweets in languages they are not proficient in, or make use of the automatic translation facility that is offered on every tweet in a foreign language.

Rule 4: Learn the rules

It should go without saying, but as with any situation where you communicate with others, you should treat them with respect. If you wouldn't do something in real life, you probably should not do it on Twitter. Next to conventional politeness, there is also some basic Twitter-etiquette that you should follow: credit ideas from others by mentioning them with HT (there is some discussion whether HT means "hat tip" or "heard through"-but there is absolutely no discussion about whether to use it) and their username. Be careful about subtweeting people (i.e., talking about them or their work without mentioning them explicitly in your tweet). It is like gossiping, but then clearly visible to everyone. This is especially problematic if you are in a position of privilege with respect to the person you are quoting. Repeatedly tweeting at somebody who is not interested is another behavior to avoid. This is called "sealioning", and is one of the negative sides of Twitter experienced at a higher rate by underrepresented groups on Twitter.

Use hashtags when appropriate. You can give tips for people who are interesting to follow on Friday with: #FollowFriday or share stories of colleagues who are helping others with #AcademicKindness. Next to hashtags, there are many memes that are like little inside jokes; use those only when you really understand them. One of our favorite examples is "Asking for a friend" used in tweets where you are asking something for yourself that is a bit embarrassing. One example is: "Why is Reviewer 2 always the one who says you should have written on a different topic using a different method? Asking for a friend" [ 11].

Also, do not respond impulsively if someone is critical towards your research on Twitter. Diplomacy is one of the key components to building a scientific reputation [ 12].

It might be good to check whether your institute has any social media guidelines (and stick to those). For example, Stanford University tells researchers not to endorse commercial partners ( https://ucomm.stanford.edu/policies/social-media-guidelines/) and the University of Oxford warns employees to be mindful of what pictures might reveal in the background ( https://www.admin.ox.ac.uk/personnel/during/socialmedia/). Some employers, for example Leiden University ( https://www.medewerkers.universiteitleiden.nl/binaries/content/assets/ul2staff/reglementen/communicatie-en-marketing/gedragscode-social-media-2012.pdf) may ask you to add a disclaimer that opinions are your own.

Rule 5: Take care of yourself

Unfortunately, online conversation can easily go off the rails and you might need to protect yourself from trolls and nasty discussions. Curate who you choose to follow and be prepared to mute or block people. Muting people means that you will generally not see their tweets anymore, but they will still be able to read your tweets and reply to them. You can also mute specific words for which you do not want to see tweets: names of politicians, TV shows, and many other topics. This can be useful if you want to follow a colleague who is both tweeting about interesting research and ranting about politics. When muting somebody, the person will not be aware that you muting them. Blocking is a more drastic measure than muting and makes the blocked user unable to read your tweets or react to them. The blocked user will be aware that they are blocked, if they attempt to read your tweets. There is no single guideline on when to block somebody-some may only support blocking trolls, and refrain from blocking other scientists who may be critical, while others believe it's perfectly fine to mute or block any account [ 13]. A good rule of thumb might be to block somebody if they have a repeated negative effect on your Twitter experience. For example a critic with many followers who frequently quote-tweets your posts might want to start a discussion, but could end up unleashing many trolls, in which case blocking would be the only sure way to prevent the same from happening again.

Never feel pressured to read everything on Twitter. It is perfectly okay to not look at Twitter for hours or even days. You definitely don't need to read up your entire timeline when you have been away, just join the conversation at the point where you fall in again. See it as dipping into the stream. It might be good to read up on your mentions (rather than all notifications), since these are personal remarks and questions just for you.

In fact, using Twitter irresponsibly can yield negative impact on mental health. One should remember that tweeting can be addictive and should therefore be self-managed with care. Getting notifications on social media activates the same dopamine loop as gambling [ 14]. When you become addicted to Twitter and you are not able to check your phone, adrenaline and cortisol are released to your body [ 15]. Therefore, it is beneficial to dose the amount of Twitter in your daily life: set fixed times during the day or during the week when you remain logged into your Twitter account, and switch it off otherwise. One strategy is to limit Twitter use to your time on public transport, or while in the queue to order a coffee.

Rule 6: Build your own community

A nice feature of Twitter is its asymmetry: you do not have to follow everyone who follows you and vice versa. You can follow big names you have never met and should not be afraid to join a conversation with more senior researchers or people you do not know. Following diverse voices will broaden your horizon.

To a certain extent, you can also determine the kind of community your followers form. Twitter offers the possibility for communicating directly with people who would otherwise not find out about your work and field. This is a very relevant form of science communication, since it is a transaction of ideas and not just a transmission [ 16]. If this appeals to you, you could share and explain papers that might be interesting to people outside your field, comment on news items from your expertise and answer questions from people you do not know. And vice versa: posing questions is also a great way to build a community, since people who share common interests will find each other on your timeline.

Rule 7: Interface with real life

Twitter is a great way to make networking more easy and fun and less scary. For example, it can change your experience in attending a conference [ 2]. Firstly, you can find interesting people to follow by browsing through the list of users who include the conference hashtag in their tweets. If a conference does not have a well-defined hashtag, try a few options to see what most people are using (for example #sigcse19 versus #sigcse2019; [ 2]). One way to use the conference hashtag is to announce your presence or your research ahead of time. Another possibility is to live-tweet-briefly summarize talks you are attending-which is helpful both for other people at the conference, as well as people who could not travel, thus potentially communicating the content to a more diverse audience. It also helps if your Twitter avatar includes your face, so that people who also follow the conference hashtag can spot you in the coffee breaks at the conference site.

Following people from conferences has value after the conference too. Twitter is a good way to stay in touch after a conference, aa it provides a low-cost way of tracking what people are working on or interested in. Knowing a bit about their interests in research and in life will make it a lot easier and less scary to say "hi" to people the year after!

This advice also holds outside of conferences: if you are approaching people you do not know, for example, to ask for a committee membership or a collaboration-you can check out their Twitter account and get an idea, not just of what they work on, but also their style of communication in general. Finally, when you are visiting a conference or university, you can advertise your visit in advance and perhaps plan extra meetings or talks.

There are also possibilities to make your Twitter network more extensive and more interesting by interfacing with real life. Put your Twitter handle on slides (see Fig 2) and other material, so people that you meet at conferences or talks can follow you, thus further extending your Twitter network.

Example of adding your Twitter handle to your slides. Source: [ 17].

Rule 8: Spread your message

Whenever you have some scientific accomplishment, you can share this information by, for instance, sharing a link to a preprint or vacancy notice. In order to get the best exposure, summarize the content and include an image and an appropriate hashtag (e.g., #phdposition). You can "tag" (i.e., mention the usernames of) scientists if you think your tweet is of special interest to them, but do not overdo this. Retweeting can also be a way to put scientists whom you feel deserve more attention in the spotlight.

Adding humor to your tweets (e.g., [ 18, 19]) can help to get you noticed by more users who might then follow you and also receive updates of your more serious contributions. Twitter is not only useful to communicate with fellow scientists, but also with people outside of science [ 20]. This does, however, require a different style of communication [ 21].

Although Twitter is accessible worldwide, it is useful to consider the time of tweeting things, especially when your tweet has more of a local audience. Many people do not read the whole of their timeline and so may never see tweets that were sent while they were asleep. Also keep in mind that not all scientists work at weekends, so science Twitter is a bit quieter then than on weekdays. Tweets for global audiences can be retweeted at a later moment in order to reach different time zones.

Rule 9: Be a real person

Even if you use Twitter only for professional purposes, consider opening up a little bit to show your followers you are a real person. People outside your field are not likely to follow you if your tweets are only about sharing events, articles, and positions in your own field. You need to add an extra ingredient-your opinions, or something personal-to what you share. One way to do this is through sharing failures: a rejected paper or job application, or even a spilt coffee. This is a great way to give and receive moral support from other academics.

Beyond academia, sharing something personal shows non-academics that scientists are just people too. You don't need to go into details of your life to do this-think about a nice book you have read recently, a concert you visited, or your cat, as long as you keep the vibe positive. Similar to [ 22], we think that humor and individuality should not impact the perceived value of science.

Rule 10: Great power & great responsibility

Once you reach a substantial number of followers, let's say around 1,000, Twitter changes a bit from being a place where you go to share to a place where other people go to learn from you! This change can be a bit similar to going from PhD student to staff: suddenly you are (part of) defining what is and what isn't appropriate, interesting or cool.

This responsibility means that you will need to spend a bit more time to think about what you tweet. There are cases of people getting in trouble for things they have shared through Twitter-going as far as being taken to court for retweeting somebody else's opinion [ 23]. [ 8] also report an instance of having to seek help from the university's legal service. One challenge as you acquire more followers is to strike a balance between remaining open and personal, while making sure you do not upset too many people in power, whether inside or outside your own institution or field.

On the other hand, a large Twitter network also helps you to spread important ideas, and help people learn about opportunities you know about. To a certain extent, Twitter is an equalizer, whether you are a full professor or an undergraduate student.

You can also use Twitter to lift deserving people up. Ensure that you properly credit collaborators, mentors, and especially students to increase their visibility. A great hashtag to promote researchers is #ScholarSunday, an academic version of #FollowFriday created by Dr. Raul Pacheco-Vega.

Conclusion

In this article, we have introduced the way in which researchers can benefit from using Twitter, and we listed a number of tips for effective tweeting, both for people who are considering joining Twitter and for those who are already ardent Twitter users.

As with any other aspect of human life, Twitter has trade-offs between quantity and quality. It is, unfortunately, a frequent practice to retweet any content spread by one's collaborators or friends without even reading it. This allows users with high numbers of followers to gain further attention with minimal effort in a kind of "snowball" effect. But chasing after numbers in this or other ways (e.g. tweeting with lots of hashtags in order to maximize your number of followers) is typically a short-sighted strategy, as Twitter is also inhabited by a large number of bots (automated accounts) which will tend to follow you and instead of contributing to your networking efforts, distract you by flooding you with a lot of unwanted content.

Twitter is also a public platform and one should be careful about spreading any content that is judgmental about other people, including their personality, their beliefs and their work. Some people have an inborn sense of diplomacy while others need to learn it the hard way. You might find it prudent to observe other researchers' interactions on Twitter for a while before you indulge in heated conversations on any topic.

Furthermore, one should remember that Twitter's business model is based on advertising. Therefore, the users need to be aware that some of the content is sponsored and aims solely at selling products to the user. As mentioned earlier, Twitter is also populated by bots which are designed to enable certain accounts to increase their numbers of followers. This often takes the form of a certain users following you and then unfollowing you immediately after you follow them back.

Regardless of these potential downsides, Twitter is still strongly recommended for anyone who needs to develop themselves in academia, to learn and to teach, to develop and tighten bonds with researchers overseas, and to join the #AcademicTwitter community.

Acknowledgments

We would like to acknowledge all the inspiring people we have met on Twitter during our careers. We would also like to personally acknowledge Melanie Stefan (@MelanieIStefan) for encouraging us to write this piece, and for brainstorming with us and Stefanie Brackenhoff for assisting with the editing. Finally, we thank Nick Brown (@sTeamTraen) for correcting the English in a tweet from one of the authors and then volunteering to proofread more or less our entire manuscript.

References

1. 3. Duhigg C. (2013). The Power of Habit: Why we do what we do and how to change. London, United Kingdom: Random House.

2. 4. Baker M. (2015). Social media: A network boost. Nature 518: 263-265. pmid:25679032

3. 5. Lee J.-S. (2019). How to use Twitter to further your research career. Nature.

4. 8. Britton B., Jackson C., & Wade J. (2019). The reward and risk of social media for academics. Nature Reviews Chemistry, 3:459-461.

5. 11. Daddow O. (2018) Why is Reviewer 2 always the one who says you should have written on a different topic using a different method? Asking for a friend [Twitter Post] https://twitter.com/oliver_daddow/status/1070305080711241729

6. 16. Horst M., Davies S., & Irwin A. (2016). Reframing science communication. In Handbook of Science and Technology Studies (pp. 881-908). Cambridge, United States: MIT Press.

7. 20. Priem J. and Costello K. (2010), How and why scholars cite on Twitter. Proceedings of the American Society for Information Science and Technology, 47: 1-4.

8. 21. Smeets I. (2014). Het exacte verhaal. Wetenschapscommunicatie voor bèta's (Science communication for scientists). Amsterdam, Netherlands: Nieuwezijds

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Ten simple rules for getting started on Twitter as a scientist

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Citation: Cheplygina V, Hermans F, Albers C, Bielczyk N, Smeets I (2020) Ten simple rules for getting started on Twitter as a scientist. PLoS Comput Biol 16(2): e1007513. https://doi.org/10.1371/journal.pcbi.1007513 Editor: Scott Markel, Dassault Systemes BIOVIA, UNITED STATES Published: February 10, 2020 Copyright: © 2020 Cheplygina et al.

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27. Guy Kawasaki: The Only 10 Slides Your Need in Your Pitch

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27. Guy Kawasaki: The Only 10 Slides Your Need in Your Pitch

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27

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The Only 10 Slides You Need in Your Pitch - Guy Kawasaki

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A pitch and presentation should have ten slides, last no more than twenty minutes, and contain no font smaller than thirty points.

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28. Ten Simple Rules for Hitting. A home Run with your Elevator Pitch

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28. Ten Simple Rules for Hitting. A home Run with your Elevator Pitch

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Whitney Rachel Morgan,

Erik Scott Wright

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Citation: Morgan WR, Wright ES (2021) Ten simple rules for hitting a home run with your elevator pitch. PLoS Comput Biol 17(3): e1008756. https://doi.org/10.1371/journal.pcbi.1008756

Editor: Scott Markel, Dassault Systemes BIOVIA, UNITED STATES

Published: March 18, 2021

Copyright: © 2021 Morgan, Wright. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Many academics feel ill-prepared for a world where selling yourself is part of the game. Maybe you did a sacrificial bunt with your social skills way back in childhood to chase more brainy pursuits. Or perhaps you never bothered studying the intricacies of pitching because it is outside your field of expertise. Now you have found yourself in the major league: meeting a visiting professor, drafting a personal statement, giving a poster presentation, interviewing for a job, or advertising your work on Twitter. A bit longer than the half second a baseball travels from mound to mitt, pitching yourself effectively can spread the word about your work and open doors for your future academic success in only a few minutes. Beyond the elevator, you never know what opportunities life will throw your way, so it is best to be prepared with these 10 simple rules.

Rule 1: Define success

Before you can succeed, you must identify what it is you want to achieve. Pitches fall into 1 of 3 categories, depending on your goal: (i) creating connection with your audience in order to establish shared values or build communion, (ii) encouraging collaboration by getting the audience to take action or bring them to your side, or (iii) generating awareness through education and being informative [ 1]. Like any good pitcher, you must decide on the type of pitch you will throw before your windup. Ask yourself, "What would success look like in this situation?" Knowing your goal also helps to establish the best mode of persuasion. Aristotle defined 3 types of appeals as ethos (to credibility), pathos (to emotion), and logos (to logic) [ 2]. Academics often construct logical appeals, purposefully devoid of emotion, and take their credibility for granted. However, people tend to buy into appeals based on emotion or credibility and later justify their decision based on logic [ 3]. Good pitches will make all 3 types of appeals, albeit in varying proportions, but focus on a single type of pitch from the outset.

Rule 2: Make the introduction

The windup can be the most important and intimidating part of the pitch. People form their initial opinion about a person within only a few seconds, and this opinion determines much of what they think about later appeals. But there is no need to stress. Remember that you are a natural pitcher and have been making pitches through your emails and conversations for years [ 4]. Moreover, your audience is secretly cheering for you to succeed, as it may benefit them too [ 5]. Most people have many competing interests for their time, so a good way to begin is by asking for the amount of commitment you need upfront. For example, at a poster session, you could ask someone glancing at your poster, "Could I take three minutes to explain the discoveries I made in this project?" This relieves your audience's anxiety about time commitment and breaks the ice for you to introduce yourself. Be sure to provide personal context and establish credibility with a quick sentence about yourself. Including specific imagery can make it easier for your audience to connect ("I am from Pittsburgh, which is home to one of the best ballparks in America because of its panoramic views of the city skyline."). You might also consider sparking your audience's curiosity with a rhetorical question. For example, you could say, "Did you know the terminal velocity of a baseball is 100 miles per hour? Imagine if every pitcher could learn to throw faster than terminal velocity." Asking a rhetorical question followed by a sentence starting with "Imagine" is a great way to avoid appearing out of left field [ 6].

Rule 4: Tailor to your audience

The most critical, and perhaps most challenging, component of the elevator pitch is to appeal to your audience's motivations. In this way, you must be a servant of your audience and not of yourself, because you will miss the strike zone if you only serve your own ends. The most successful pitches are the ones where the catcher feels engaged to the point where they are collaborating with the pitch [ 6]. To achieve this sort of win-win situation, you have to know a thing or two about your catcher. Ideally, you would do your homework about their interests, position, and accomplishments ahead of meeting. This is particularly true for written communication such as personal statements, cover letters, and emails where there is an opportunity to research your audience in advance. If you do not get a head start, you can look around for signs that will help you read your catcher, such as paying attention to what's on their nametag (at a conference) or bookshelves (in an office) [ 8]. Here especially is where asking questions and being a good listener can pay dividends by helping you establish favorability and tune your pitch to your audience.

Rule 6: Focus on "why"

As you finish your story, it is time to keep up your winning streak by explicitly stating the "why": Why what you are pitching is important to you, to your audience (see Rule 4: Tailor to your audience), and to the world in general. The "why" is your money pitch, what makes your audience feel moved to the point of taking action [ 9]. This is the point in the pitch where you establish how you want your audience to feel and impart the perspective that they will take with them after your pitch is over. One way to accomplish this is to teach people how to think about the story (see Rule 5: Tell a story) by starting a sentence with, "The way I think about this is..." [ 10]. Do not assume that the "why" is obvious to everyone else. Clearly conveying why what you are pitching is compelling or changed your thinking will help you reach a more diverse audience with a wide variety of experiences and backgrounds. You may even find yourself needing to rediscover the "why" that motivated you in the first place. Bringing the "why" to the forefront of your pitch will make it more influential and may very well make the difference between striking out and the hall of fame.

Rule 8: Be memorable

Now it is the perfect time to bring in the closer. Here your goal should be for the person walking away to want to share what you said with others, which is the best way to spread the word about what you are pitching [ 12]. The challenge is to ensure that your audience understands and appreciates your pitch well enough to retell it [ 3]. To achieve this high aim, you must distill what you want your audience to remember down to just a few memorable words. After all, communication is what the person takes away, not what you said. Try to recall previous presentations that you have attended in the past year. Likely many of them are difficult to remember. But you can probably remember the best ones because of specific moments that stuck. One way to generate these moments is by developing sticky phrases with rhymes or alliteration that summarize your pitch, such as "good pitches yield riches" or "perfect pitches possess positivity." Advertisers employ these short phrases regularly to embed their product in your memory. Repeating your own memorable phrase will help convert your audience into a fan base.

Conclusions

The short format of the elevator pitch makes it challenging to accurately convey a message while avoiding generalities and being transparent about limitations. The process of preparing in advance goes a long way toward making a concise pitch as complete and effective as possible. With enough thought, it is possible to construct a pitch that covers all the bases, engages your audience, and hits on the main points. There are many examples available to those hoping to master the art of pitching, including short TED talks, lightning talks on YouTube, and even the television show Shark Tank. Pitching is ubiquitous, so keep an eye out for opportunities to practice your pitch, and remember to ask for feedback when the opportunity arises. While great pitches are challenging to deliver, being well-prepared can help you knock it out of the park.

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Ten simple rules for hitting a home run with your elevator pitch

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The power of networks

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In this RSA Animate, Manuel Lima explores the power of network visualisation to navigate our complex modern world.

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Casey Brown: Know your worth, and then ask for it

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Your customers probably aren't paying you what you're worth -- instead, they're paying you what they think you're worth. Take the time to learn how to shape their thinking. Pricing consultant Casey Brown shares helpful stories and learnings that can help you better communicate your value and get paid for your excellence.

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Good morning everybody, this is Russell Brunson. I want to welcome you to a special three part series of the Marketing Secrets podcast. Over the next three episodes I'm going to be sharing with you guys clips from a presentation I did at Dan Derricks Dream 100 con.

I told you guys before that I've been working on the Traffic Secrets book and testing my material, so the first time I ever taught parts of this publicly was at Dana's event. So I wanted to share it with you. It's not the whole process, just a piece of it, but hopefully it will get the wheels in your head spinning about how Dream 100 works, how you buy your way in, and work your way in and how customer life line would work, and a whole bunch of other amazing things.

I'm doing this as a tease, to get you excited for the Traffic Secrets book that's coming out next year. So that's kind of the game plan. Hopefully it gives you guys some ideas. This is part one of three, when we get back from the intro, we will start right into that presentation. So thanks so much you guys, I hope you enjoy behind the scenes from my presentation from Dream 100 Con.

To begin with, I just want to thank Dana. It's been so fun, I've been talking about Dream 100 for the last decade or so, and almost nobody ever listens to me, except for Dana. And then Dana took it and has taken it to such bigger, such a big mass level. It's so funny, yesterday I was watching as he talked about Chet Holmes, who is kind of the originator of the Dream 100, and Chet was a personal friend of mine who passed away a few years ago, and his daughter has actually taken over his company since. I messaged her yesterday to let her know that you guys are here in this room, talking about a concept that was pioneered by her father, which is really special for her and for Chet and for everything.

I'm just so grateful for everything Dana's put together, and doing this. Isn't he just one of the coolest humans you've ever met on earth? He's always giving and always serving.

Alright, to kick today off, I actually wanted to talk about something and get it on film so that when I pass away someday you guys can watch this and then fulfill what I need to be done. Does that sound good?

So I have a friend in our community, his name is Mark Holverson. Does anyone know Mark Holverson? A couple of you guys. Mark Holverson's an amazing human being in our community, one of my peers and he passed away last weekend of cancer, which was a really tragic thing. But what's interesting is, right after he found out he had cancer, about five years ago, he was onstage speaking at an event like this, and Vince Reed actually published it on his Facebook page and I had a chance to watch his whole presentation. And it was right after Mark found out he was going to pass away.

And during his presentation he talked about legacy and where he was going and all sorts of stuff. And it just got me really inspired. And I'm not going to pass away, that I know of, so don't think that that's what I'm saying here. But I was thinking about, if I was to pass away how do I leave this whole thing? What would I want my legacy, at the end of this?

I've heard other people say, "My goal is to get a million people at my funeral, that's my biggest thing." I don't really want. In fact, I don't any of you guys to be at my funeral. I want my wife and kids and family to do that. But the day my funeral is over I need all of your guy's help. So everyone's got to remember this, someone's going to be here and remember this part of it, and I need you to take this part out. So the day my funeral is over, I want to build out the biggest Dream 100 campaign of all time.

Every single person I've ever had a chance to influence or impact I want to invite them to the last ever Funnel Hacking Live. I want to get, Dana, if you're still alive at this time, I want you to be in charge of this whole thing. But I want Dream 100 voxes going out to all the biggest influencers, all the people, and I want to throw the biggest party ever.

This actually happened a little while ago, there was a guy who was a legend in our industry, his name was Marty Ellison, he owned a company called Board Room. Anyone ever heard of Board Room? And he owned that company and when he passed away, his partner Brian Kurts did something similar. They threw a huge marketing party, it was called the Titans of Marketing Summit. They had all the best speakers come in and speak. And I remember listening to that event, that course and I thought, when I die this is how I want my legacy to end.

So we're going to do a big, huge Dream 100 campaign, we're going to get everyone in the world to come to the last Funnel Hacking Live ever, and then I'm going to have all my friends and colleagues come and speak about what we've all learned on this journey together. So that's my game plan, I've got it on video now, so that someday you guys will know my wishes. So that's the game plan, does that sound good?

There's a little twang that keeps happening, is that from....anyway, I don't know if it's from I'm talking too loud.

Okay, one other story and then we'll get to the good stuff. So this is going to hopefully help you guys understand the importance of what you're learning here at the Dream 100.

So I had a chance, I actually flew here from Jackson Hole, Wyoming. I was in Jackson Hole at a secret Illuminati, maybe it was the Bilderberg, I don't know a secret group of marketers who all got together for like three days and we did a whole bunch of manly stuff, which anyone who knows me, I'm not very good at manly stuff. We were shooting guns, I took last place. We were fly fishing, I caught the least amount of fish. We went hiking, all these things that I don't normally do, but it was fun because I hung out with all these guys who were all amazing in their own spirit, in their own businesses.

One of the guys who was there is a guy named Tom Bilyeu. How many of you guys know who Tom is? Oh good job, a bunch of you guys do. So Tom, he started a company called Quest. Who has ever had a Quest bar before, Quest nutrition? So he started this company and it blew up to a value of over a billion dollars before he sold his portion of the company and retired. Now he's doing Impact theory and a bunch of other cool things.

But he built his company up to over a billion dollars, and as I'm asking him, "How did you start this thing up?" And he explained how he started the entire company. And what's cool, after he told me I talked to Dave about last night, and Dave's like, "let me see if he said it anywhere else." And Dave found an interview, an actual quote of this. So I want you guys to hear this. This is a quote from the podcast where he explained how he launched Quest. But what's interesting is he did the same thing that you guys are all doing here in this room today. So this is what Tom said about launching Quest.

He said, "We had a very different approach that got a lot of people excited, not just about the product, but they felt good about the way we treated them. We went old school (old school dream 100) researching several hundred health and fitness influencers, then sending them hand written letters and free samples. This was all about showing an understanding of what others were trying to achieve, that Quest was interested in helping them connect with their audiences. When people are building community they have real sense of service to that community. We would send them a free product and say, 'if you like it, tell people about it. And if you hate it, tell them about it too.'

"Not trying to steer people's comments, gave us a pretty good reputation. Some didn't like it and said so. But the vast majority loved it and were grateful that we showed an understanding of who they were and what they were trying to do so they spread the word."

The foundation of his billion dollar supplement company all started here with the dream 100. Is that awesome? Okay so this is the foundation, you guys, for all these things.

Alright, so what Dana's talking about is true. Okay, so some of the, what I'm going to be talking about for the next couple of hours, so you guys can context. I'm in the middle, well not the middle, the beginning of writing my third book, which is called Traffic Secrets. So there's Dotcom Secrets, there's Expert Secrets, and this is the last and final book in the trilogy, and then I'll never write a secrets book again, because I'm out of them. But the third one is called Traffic Secrets. So I've been working on the framework for the whole thing.

And I messaged Dana about two or three months ago, "Just so you know dude, this is so good for you." Because the entire framework for the Traffic Secrets book is based on the Dream 100. So a lot of the stuff you guys have been talking about is the foundation of everything that is going to be inside that book. But I spent all day, the last month or so, working on the outline and getting things done. And then yesterday I was sitting in the back of the room doodling out all the key frameworks for the book. And I got them back this morning at 4:30 in the morning. So I'm really excited they got done in time.

So I'm going to be going through seven different images that are kind of the framework for the book, but it's also the framework for how we do the Dream 100. There are, well you'll see. It's taken it to a whole other level. And hopefully this will give you guys, those who are beginning will give you guys a really good foundation. And those who are advanced, it's going to give you the next five or six steps in the process. Does that sound like fun?

Alright cool. So I'm going to start off, this is the very first core thing that you have to understand to really understand Dream 100, understand traffic, understand everything. There are three types of traffic, this is the framework for the entire book, the entire concept, if you can understand this everything else becomes really easy.

So there are three types of traffic. The first type of traffic here on the top left hand, is traffic you control. This is traffic where you're going to buy your way in. So if you think about that, how many of you guys run Facebook ads in here? So you don't own that traffic, right. Zuckerberg owns all that traffic. He built the platform, he owns the thing, but he gave us the ability to come and to buy some of that traffic. So we can buy some of that traffic and we can control it, we can send it wherever we want to. We send it to buy a product, buy a service, listen to a podcast. We can control that traffic, but we don't own it.

How many of you guys have ever gotten a Facebook account shut down before? There's proof you don't own it right there. We've lost hundreds of them, so don't feel bad. It's just part of the game.

So traffic control is the first thing. So whenever I got any kind of thing I'm buying, it's traffic that I control. The second type of traffic is traffic that you earn. This is where you're working your way in. The first one you're buying your way in, the second one you're working your way. So for this it means, you're getting traffic, you're not paying for it, but you're earning it. You're getting on a podcast interview, you're doing a Facebook live with somebody, you put out really good content and people share it. You're earning that traffic.

How many of you guys, the majority of your traffic comes from traffic that you're earning? And how many of you guys, the majority of your traffic you're buying? About 50/50 split.

So it's important to understand, there's traffic you control and traffic that you earn. The third type of traffic, and this is the best and most important is traffic that you own. My entire goal of our business, if you look at everything we're doing from a traffic generation standpoint, is to convert traffic I can control, traffic that I earn, into traffic that I actually own. Because if you own the traffic, that's the secret sauce. When I own it, I can do whatever I want with it.

You can be like zuckerberg and you can rent it to people, you can do a product launch and sell your own products. You can do affiliate stuff, you can do whatever you want. But the goal is to own the traffic, that's the big thing. You own the traffic, we own the platform, that's how you win.

How many of you guys have your own platform, your own list, your own following, your own whatever? How many of you guys don't yet and you're like, "That's the thingy I think I need really bad." Okay, cool. This is the goal, this is the big secret about internet marketing. I remember when I first started learning this game I was struggling because I couldn't figure it out. There were so many things that were happening. And this is pre all the stuff that you guys do every day. It was pre-facebook, pre-myspace, it was actually pre-friendster. How many of you guys remember Friendster? Melanie was on Friendster, she's still buying ads on that one. Just kidding.

That was like the original social network and it was like, this is amazing, and then Myspace came out and crushed Friendster, and then Facebook came out and crushed Myspace, and soon the next thing is going to come out and crush Facebook someday, hopefully. Actually, I love Zuckerberg, I hope he keeps doing what he's doing.

But l was out there trying to figure out this whole game, and there's so many things and I could not figure out how this whole game was played. And one day I was at the good old Google searching for probably how to get rich on the internet or some key word like that, like a lot of us start with. And some guy wrote an article and in this article he was talking about, he's like, "how many of you guys have seen those stories online where people say I made $30,000 in an hour, or 100,000 in a day? You probably thought those things were lies, right?"

I was like, "yeah, I'm sure they're inflating their numbers." He's like, "No, actually those things are true." And I was like, "Wait, what?" And he started explaining, he said, "This is the way it works. Imagine this, what these marketing people do is they build up a list of a thousand or ten thousand or a hundred thousand people and then they send an email to that list, and they just get a percentage, like 2 or 3% of that email to buy, and then do the math. They have 100,000 people, 2% buy a $50 product, how much is that?" It's whatever the math is, $15,000, and if they send an email out every day, they literally can be doing what they're talking about.

And me, as a 20 year kid, sitting in my college dorm room reading this article, I'm like, "Oh my gosh, that's the secret. I just need my own platform. If I had my own list of people, that's the magic." Now I, unfortunately, went about it the wrong way. So I had that idea and I was like, "This is the key, I'm going to do it. It's going to be amazing." So the first thing I did, of course, I went to Google and I typed in, because I wanted my own email, so I typed in "buy an email list" and then I was reading about spam, I want to spam for email address.

So I went to a website and I think it was, spam-free email addresses dot com. I was like, this is awesome. And they were selling DVD's of email addresses. And you could buy like a hundred thousand, a million, 5 million. I was like, "Oh my gosh. I'm going to buy 5 million, I'm going to start with the biggest platform known to man."

So I bought, I think it was $67 or $69 for a DVD with like 5 million email addresses. I was like, "Oh my gosh." And I ordered this thing, it's coming to me, I'm like telling my wife the math. I'm like, "Collette, okay so 5million people, let's just say we get 1% to buy the thing we sell, 1% of 5 million," I'm doing the math, "That's like 100 grand every time I click send." And then I'm like, "wait, but let's just say I can serve only 1% of 1%.." I'm doing the math, I'm like, "Even if we screw this up it's like $30 grand every time I send an email out."

And I'm so excited because at the time my wife's working, making $9.50 an hour, while I'm at school wrestling and trying to not fail. So this thing comes and I get this disc, and I remember I plugged it in and there was this software that would send the emails out for you, and I put it in and I remember queuing it up and I saw the little software of all these like 5 million emails out there. I was like, "Oh my gosh, this is amazing."

Now, I didn't have a product at the time, so I was like making up a product. I'm like, "What would people like to buy." So I made up an idea for a product, I wrote an email that was probably, I don't know, 60 words, with a link to my paypal account. Like, "Click here to send me $10 for this product." And then that was it. I was like, "If people buy it, then I'll go make the thing."

So I queued up the email, back then it was pre-high speed internet, so I had to crawl under the desk, unplug our phone line, plug in the internet, bleep, bleep, bleep, all that kind of stuff pops up. I'm like alright, click send. I'm like, "Collette, let's go to bed. By morning we are going to be rich." I see like, "Email 1 sent, number 2, 3, 4, 5...." And I'm like, "Oh my gosh, this is it. This is going to be the greatest day of my life."

So I go to bed, all night long I'm dreaming of how many sells are going to come in the night, what's going to happen, how amazing it's going to be. And I wake up in the morning, and Collette's getting ready for work because she's still working at the time, and I'm just telling her, "I haven't looked at the stats yet, but my guess is you can probably quit after today. If a just a fraction of a fraction of a fraction buy, that's more than you make in an entire month. This is going to be amazing."

So I'm all excited, telling her the whole story. And then she's like, "Okay, I gotta use the phone." I'm like, "You can't use the phone." This is pre-cell phones too. "You can't use the phone, it's mailing.." I look at the thing, $65,000 emails have been sent. "you can't." She's like, "I have to call, I have to use the phone." I'm like, "Ugh, you're going to ruin everything." So I crawl under the, pause the mailing thing, I crawl under the desk, unplug the modem, plug back in our phone line, and while I'm still under the desk the phone rings. I stood up and hit my head and I'm like, "Oh, dangit."

So come out under the thing, answer the phone, and on the other end is my internet service provider cursing me out on the phone. "What are you doing? Why are you sending these?" I'm like, "No, no, no, sir, I didn't send spam." He's like, "No, you sent out tons of spam." I'm like, "No, you don't understand, I bought this DVD, it was called spam free email addresses dot com. You can go check out the site, they're completely spam free." I explained the whole thing to him.

I still remember he told me, he's like, "Son, that is the definition of spam." And I was like, "what? Their spam free." And he's like, "How do you think that, that's not a thing. You can't buy spam free email addresses." And I'm like, "But the domain name...." and we were going back and forth. And he's telling me he was going to sue me and a bunch of stuff, but the last thing was, "I'm shutting down your internet." And then it was gone.

I hung up the phone and then Collette's like, "Who was that on the phone?" I'm like, "Oh just some guy who had some questions about the thing. You shouldn't quit yet though. Let's just go to work, enjoy it, we'll recap in the morning or tonight when you get back." So she leaves out the door to go to work and I'm sitting there, and I can't even check to see what happened because our internet is now gone. I have no access to even get back on.

So I'm all depressed, I get ready for school, I go to the school and I get to school and I get to the computer lab, and I'm like, "I'm going to check to see what happened." And I log in, still in kind of a depressed state, and I open my email, and then my email though, I see order notification from paypal, order notification paypall. Boom, boom, boom, boom, probably 20-25 orders had come through for like $10 apiece through paypal. I was like, "Oh my gosh, it worked. I gotta go create a product."

So I had to go back and figure out what I sold and make something to send out to these people. And I realized at the time, what I did was wrong, I didn't understand the ethics or how to do it, I did it wrong. But what happened was right. The goal was to get a platform, an email list, to get something. And that was the whole secret to the game.

So after that was done, I went and found a new internet service provider and I spent the next year of my life trying to figure out how I build a list the right way, where it's not going to end me up in jail. There's got to be a way to do it.

What I learned is basically this process. I can buy ads, I gotta send them somewhere to join my list, or I can go work my, do things, and get people to come to my website and give me the email, but that was the goal. So I started focusing from that point forward on building my email list. That was my core focus, building a list, building a list, building a list. And I started growing it.

It's been interesting, that concept of list building became my whole life. I started looking, over the last 15 years I've been doing this, I've see a lot of people who have built huge companies, are no longer in our industry. Now back at their day jobs trying to survive. And I've seen things over and over and over again.

How many of you guys remember back in the day when Adsense first started and it was like Christmas every day? A couple of you guys. So let me paint this picture. So this is in the middle of me trying to build my list, my first mentor, his name is Mark Joyner. He's like, "Focus on a list, focus on a list." I'm like, focusing on a list. And all the sudden this shiny object pops out of nowhere called adsense, and what adsense was back in the day, you could buy this software for $97, there was a couple of them, Traffic Equalizer, there were probably a dozen or so that ended up coming out.

You load up software on the computer, you click a button, it would go out there and it would pick a keyword, so let's say whatever, it would find a keyword that people were paying a lot of money on, go to the internet, scrape all the sites that have written about that, pull them in, and then you publish a site and have like 8,000 pages in like a minute.

And then it would put ads in all the site, and because of the way it structured the pages, Google would instantly index it and you started making money. It was like the easiest, most stupid way to make money. We had teams of people in the Philippians, like 30-40 people, that all day sat there and build another site, build another site, build another site. Just cranking out sites like crazy. Not me, a lot of my friends, they built these assembly lines of people doing that.

I had friends making a half a million bucks a month or more just cranking out crappy little sites. And it was amazing for about a year, year and a half. And so I'm watching all these people make so much money, and I'm over here building a list, building a list, and stressing out because I'm seeing all these people make all this money. I'm like, "I'm just going to do that, that's easy. It takes no effort at all." And my mentor, Mark, was luckily like, "No dude, focus on building a list, this will go away." I'm like, "No, you don't understand. This is the greatest thing in the world. It's going to change....$500,000 a month.." How many of you guys would that radically shift your life right now?

Yeah, I was just like, "And they're doing nothing, providing zero value to the world. They're just clicking a button over and over and over again. I could get a million button clickers. I'm going to be the first adsense billionaire." And he's like, "No, build a list, build a list." And I'm like torn. And luckily for me, I'm really coachable. So I'm like, "Fine, I'll build the stupid list." So I'm doing this, and sure enough about a year later, boom, the algorithm changed, Google found all the software that was building the sites, deindexed every single page, and I had friends go from half a million dollars a month to zero over night. I was like, "Oh my gosh." But guess what I had? I had been building my list and it didn't affect me.

Now anyone who knows my story, who here thinks that I've always done everything right in my business? Only Dave. I haven't. So what happened is I built my company up really, really big, and then I bankrupted it almost. Luckily both times I didn't. The second time I built it even bigger and then I almost bankrupted, I've told those stories before, at Funnel Hacking Live.

But what's interesting, the thing that got me through the ups and the downs every single time was this platform I'd built, this list. I got to the point where everything is collapsing around me, I could still send emails to my list and people would buy because we have a relationship, and it kept us through the down times. So when you do this and build a platform, it's the key.

I still see today, it's unfortunate, I still see today people in our industry who get the next object where they're like, "Oh, I can do this thing." And they start buying Facebook ads to sell this, and doing this, and doing things like that, but they're not focusing on the fundamentals. The fundamentals are what will protect you over the long haul. The reason why I'm still in business 15 years later, and a lot of the people I know that have made a crap ton of money along the way aren't, is because this has been my focal point, building the platform, building the platform.

So why do you need a platform? The platform is the key for so many reasons. How many of you guys watch the Apprentice, back when it was still on? How many of you saw the season where Arsenio Hall was on the show? How many of you guys have no idea who Arsenio hall is? Okay, for the younger generation, Arsenio Hall was a dude that had his own late night show. He'd come out and get everyone excited and be like, "woo, woo, woo." And get everyone pumped up, and he was the man on late night for, I don't know, a decade or so.

So then his show got cut, and then he's like normal Arsenio Hall, no one heard from him, and then 20 years later he pops up on Celebrity Apprentice because at one time he was a celebrity apparently. It was fascinating, they're on celebrity apprentice, competing with all these other celebrities. And there was this one task that was like a fund raising task, where they had to go and raise money.

So they're all going through and trying to raise money for their charities and Arsenio is back there, he's got his rolodex of people and he's calling the first person, the second person, the third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, and what's interesting, while all the other celebrities were doing deals, Arsenio couldn't get anyone to return his call, couldn't get anyone to pick up the call or anything. And it shows him that night, sitting on the couch and he's all depressed and frustrated and upset, and they're talking about it. And they're like, "dude, you're Arsenio Hall, how come you can't get any money?" He's like, "I don't know man. When I had my own show, everybody returned my call and now nobody will."

And when I heard that, I was like, "Oh my gosh, what a lesson that is." Some of you guys are starting the dream 100 right now at the very beginning, you don't have this platform, and it's tough. It's tough initially to get people to return your calls, to say yes, to look at your vox, whatever those things are. At first it's hard right.

But when you have a platform, everyone returns your call. When I first met Tony Robbins back in the day, my platform was little tiny. I even became friends with Tony, we hung out for 10 years, and Tony never promoted me, we were just kind of, I got to know him, a couple of times we hung out and helped consult him, that was while my platform got bigger, and bigger, and bigger. Last year we launched Expert Secrets book, I was like, "Hey Tony, will you help me promote this book?" And Tony's like, "Russell's platform is huge. Yes, Russell. I would love to."

There are few people in our world right now that I couldn't pick up the phone and be like, "Hey can you help me do this deal?" Because they know my platform is huge. And because of that, they will say yes. So the dream 100 gets better and better and better as you build your platform. At first it's going to be more difficult, but as you focus on this building your platform, it becomes easier and easier and easier, because the bigger your platform is, the more likely people are to return your call.

So this should be the focus, again, if I come back to this. Everything you focus on is this, traffic you control, buying ads, traffic I earn, earning ads and we're going to go deeper into this in a minute, but the goal of both of those is to turn it into your own platform.

Now platform can mean a lot of things. When I got started, all a platform was, was your email list. I still think focusing on email is one of the most important things because if an email service provider shuts you down you can move your email list to another. So that's an asset that can follow you.

But your platform is also different things like, how many of you guys are on Instagram right now? Your Instagram following is a platform. How many of you guys have a podcast right now? Your podcast listeners are a following. How many of you guys have a Facebook following? YouTube subscribers? All those things are platforms. So what I'm going to be talking about, and I'm going to break this down, the platform doesn't always have to be one thing. In fact, usually there's a platform on each of these different channels that we're going to talk about. And you should be building more than one.

But the biggest problem I have with other ones is like, the reason I like email so much is because, again, if you get shut down you can move it. If Facebook shuts you down, you're done. If YouTube shuts down your channel because you did something aggressive, you can't do much about it. So always focusing on email is one, but then a secondary and more as we go through is a big piece of it. So that's the key, building your platform.

Alright, so that's the first step here. Step number two, so now that you have got this stuff figured out, and I'm going to go deeper into this. All these things will kind of layer upon each other and the end will be this beautiful picture like, oh crap, that's what it is. This is so much easier than I thought.

Okay, so this is the first part. What I want to do, I want to kind of, before we go deep back into this part right here, I want to go deeper on this right here. So you have your own platform, what most people don't think through, all they're thinking about is themselves initially. Like, I'm going to build a platform, have a big email list, a big podcast, a big whatever... That's our focus, because that's who we are as entrepreneurs. We like significance, we want a big, huge following.

But what I wish I would have done, from day number one when I first got started in this business is spend more time thinking about the person. Who do I actually want on my list? If you read the Dotcom Secrets book, the very introduction in here I talk about you finding your dream customer. I talked about for me, I had 5 or 6 years in my business and one day I woke up and I was depressed. I was so upset, I wished, I remember sitting there in bed, laying there thinking, "I wish I had a boss so they could fire me, so I could separate myself from my customers." But unfortunately I had built a company, I had built a platform, but the people who were in my platform were not people who I wanted to serve every single day, and it can be depressing. I promise you, if you build the wrong platform, it can be really, really frustrating when you show up one day and you're like, "I hate my customers."

How many of you guys have ever hated your customers before? So many of you. Good, I'm glad I'm the only one. It's tough. If you don't think through who is my dream customer ahead of time, you're going to wake up one day with a customer that you hate. So I want you thinking like, who is my dream customer? Who is that person I'm trying to serve?

So what I want to do, I want to talk about your dream customer's journey. So who is my dream customer? I want to understand their journey. So the first thing I'm going to do, I'm gonna draw a picture here. So this is your dream customer. How many of you guys know who your dream customer is in your mind? You're like, "I know exactly who that person is." If you don't, I encourage you. People are like, "Uh, I want to serve awesome people." Spend time actually thinking through this. Who do I actually want to serve? Who is that person? What do they look like? What are their fears? What are they excited about? What is the pain that they are trying to get away from, and what is the pleasure they're trying to move towards?

If you ever studied NLP you learn a really cool thing about the human mind. All humans are doing one of two things, number one they're trying to move away from pain, and number two they try to move towards pleasure. And what's interesting, most people are more dominant on one or the other.

I'm curious, how many of you guys make most of your core decision because you're trying to move away from pain in your life? You're trying to lose weight so you're moving away from pain in that. You want to make money here because you want to get rid of your boss. You're in pain and you want to move away from pain. How many of you guys make most of your decisions based on moving away from pain?

Now the other side, you make choices like, I'm moving towards pleasure. I want that nice car. I want to have six pack abs for the girls. I want to have all these things like that. How many of you guys make your choices moving towards pleasure? Interesting, it's like 50/50 in this room.

Typically, what I find in an entrepreneurial room, most entrepreneurs make their choices towards pleasure, not all but most of them do. Whereas in the real world, most humans make their decisions moving away from pain. But regardless, you as someone who is going to be serving this dream customer, you have to know what those things are. What is the pain they're trying to get out of? You have to think about that and figure it out. What is that pain they're trying to get out of?

If you don't know what that is, it's going to be really hard for you to serve them. The second thing is like, what's the pleasure they're trying to move towards? What is it that they actually want? What's that thing that, the biggest desires they have? You have to know that, you have to know everything about that with your dream customer.

The next thing I want you guys to think about and this is going to come into a lot of the dream 100 stuff here in a minute. If you draw a timeline of your dream customer's life, this is all the stuff that they're dealing with before you come to them, and this is all the stuff they're dealing with after.

So as I start building out my dream 100 list, I look at my customer and I'm like, let's say for me, I sell funnels. Do you guys know that's what I sell? Okay, so that's what I sell, so my dream customer, they need a funnel. But what do they need before they need a funnel? Most people more and more are like, "I need a funnel." But what do they need before they need a funnel. Before they need a funnel they need, so I'm thinking about what's the thing they need before they get my funnel? And what do they need before that, and before that, and before that? I'm taking them backwards on this journey.

So if they're going to get a funnel, before they get a funnel they probably need a business maybe. Somebody here got that they needed a business. Maybe they needed a logo, they need branding, they need.....there's probably things that happened before they come to me and they're ready for me.

And then after they've got a funnel, what are things they need after they have a funnel? They might need traffic, they might need graphic design, they have other things afterwards. So I look at this customer journey, where's my customer at? So this is the first phase. How many of you guys have ever thought about this before?

Jay Abraham taught me this initially, he said, "Look, if you're trying to find joint venture partners, or dream 100, the first thing you got to think about is will your product or your service fit in the timeline of your dream customer? And you gotta figure out what are all the services they need before they come to you? And what are all the services they need after they come to you?"

When we identify that, we know exactly who your partners are, right? So if I go to somebody else who is selling funnel software, which is like right here, which is where most people go. If I'm selling weight loss product, why would they promote my weight loss product if they have one too? They probably won't because they're selling the same thing. You've got to go on this customer journey prior to that, before they wanted weight loss, what else did they get? What did they need? Maybe they had high blood pressure, oh my gosh, if they have high blood pressure, maybe I could dream 100 all the people who have a list with high blood pressure, because that's something they need before they lose weight.

So I'm looking at this customer journey, like what are the things that are happening before they come to me? And the things that happen after? Because that's where you're going to fit in. Your dream 100 partners are going to be better here and here probably, than the other place away.

Alright guys, thank you for listening to the first of the three parts of my presentation at Dream 100 Con. I hope you enjoyed it. Hopefully it's getting the wheels in your head spinning about traffic and kind of how traffic works. Tomorrow I will give you guys part two. Thanks so much and we'll see you guys tomorrow.

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The Dream 100 (Part 1 of 3) - Marketing Secrets with Russell Brunson

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The Dream 100 (Part 1 of 3) On today's very special 3 episode series Russell speaks at Dream 100 Con about traffic and how to build your list. Here are some of the awesome things in this portion: Find out how Russell got started building his list, and what he learned about spam free emailing....

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32. How to Explain Blockchain to a 5-year old 

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32. How to Explain Blockchain to a 5-year old 

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35. Ten Simple Rules for Choosing Between Industry & Academia

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35. Ten Simple Rules for Choosing Between Industry & Academia

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Citation: Searls DB (2009) Ten Simple Rules for Choosing between Industry and Academia. PLoS Comput Biol 5(6): e1000388. https://doi.org/10.1371/journal.pcbi.1000388

Published: June 26, 2009

Copyright: © 2009 David B. Searls. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The author received no specific funding for this article.

Competing interests: The author has declared that no competing interests exist.

One of the most significant decisions we face as scientists comes at the end of our formal education. Choosing between industry and academia is easy for some, incredibly fraught for others. The author has made two complete cycles between these career destinations, including on the one hand 16 years in academia, as grad student (twice, in biology and in computer science), post-doc, and faculty, and on the other hand 19 years in two different industries (computer and pharmaceutical). The following rules reflect that experience, and my own opinions.

Rule 1: Assess Your Qualifications

If you are a freshly minted Ph.D., you know that you will need a good post-doc or two before you can be seriously considered for a junior faculty position. If you're impatient, you might be thinking of industry as a way to short-circuit that long haul. You should be aware that companies will strongly consider your post-doctoral experience (or lack thereof) in determining your starting position and salary. While you may not relish extending your indentured servitude in academia, any disadvantage, financial and otherwise, can quickly be made up in the early years of your career in industry. In other words, trying to get off the mark quickly is not necessarily a good reason to choose industry over academia.

On the other hand, you may have completed an undergraduate or Master's program with a view to going to industry all along, with never a thought of an academic career. You should still consider the point of the previous paragraph. While abbreviated "practical" bioinformatics training programs can be excellent, a Ph.D. is a significant advantage in all but the most IT-oriented positions in industry, at least at the outset. This is not to discourage anyone from embarking on a fast-track-to-industry program if their heart is in it, but be aware that the further you climb the educational ladder, the higher and faster you can start when you step across to the business ladder, and the better you will compete for a job in the first place. The days are long past when bioinformaticists were in such short supply that any qualification would do.

If you are an old hand and have already notched up a post-doc or two, take stock of your star power. This unspoken but universally understood metric encompasses such factors as whom you've trained with, where you've published (and how much), and what recent results of yours are on everyone's lips. If you are fortunate enough to have significant capital in this department, then the world may be your oyster, but you still need to consider where you will get the greatest leverage. While your stardom may be less taken for granted in industry, my feeling is that academia is a better near-term choice in such circumstances. Consider that it was in academia that you achieved the success you own thus far, so you obviously "get it." The simple fact is that academia is rather more of a star system (as in Hollywood) than is industry.

Finally, if you count among your qualifications a stint in industry already, as an intern or perhaps as part of a collaboration, you will not only be in a better position to compete for a permanent job, but you will be much better prepared to make the decision facing you. Stated another way, if you are seriously considering industry as a career path, you should probably have already taken advantage of the many opportunities out there to dip your toes in the water.

Rule 2: Assess Your Needs

In taking stock of your needs, and perhaps those of your family, a decent living is generally at or near the top of the list. Salaries are still higher in industry, though the gap is not nearly so wide as it once was. If you need a quick infusion of cash, companies may offer signing bonuses, though again these were more common when bioinformatics was a rarer commodity.

Industry offers forms of compensation unavailable in academia, and you will need to consider how to value them relative to your present and future needs. Despite recent bad press, bonus systems are often part of the equation, and depending on your entry point they may constitute a significant percentage of total compensation. There is a tendency among academics to discount bonus programs in their comparison shopping, sometimes to zero, and this is a mistake. Bonuses are considered core aspects of compensation in most companies, and though they always have a performance-based multiplier, the base levels have historically been fairly dependable. That said, these are tough times in industry, and there are no guarantees. Your best strategy is to understand the reward system thoroughly, ask for historical data, and avoid comparing only base salaries unless you are extraordinarily risk-averse.

Share options are another matter. While in the past these were very attractive, and fruitful in practice, most industry types will tell you frankly that any options they've received in the past decade are deep underwater and a deep disappointment. Many consider pharma shares (and therefore options) to be a bargain at the moment, but that's between you and your financial adviser to assess. In any case, it is not a short-term consideration, since options typically take several years to vest.

If you are looking at biotech, however, share options and similar ownership schemes need to be a key consideration, since these are a major rationale for assuming risk-more on that below.

Finally, you may have more specific needs to consider, such as a spouse also in need of a job. The two-body problem has always been tougher in academia than in industry, and probably always will be. If you are both academics, note that industry often has good contacts with local universities, and can facilitate interviews. Being a star certainly helps, so don't be afraid to negotiate. In fact, a general rule of thumb is that it never hurts to make your specific needs known, within reason. Academia will try to accommodate them as a community, while on the other hand business (particularly large, diversified companies) may have resources to address them that you wouldn't have expected. Nobody wants to hear a peremptory demand, but if a company wants you, be sure to let them know anything that might offer them a way to attract you.

Rule 3: Assess Your Desires

There are needs, and then there are desires. Do you want riches? Fame? A life at the frontiers of knowledge? The hurly-burly of the business world? How do you really feel about teaching, publishing, managing, interacting, traveling, negotiating, collaborating, presenting, reporting, reviewing, fundraising, deal-making, and on and on? Though it may seem obvious, this is a good time to decide what really drives you.

First, the obvious. Do you want to teach? If lecturing is in your blood, your decision is made, although if a smattering will suffice you may have the option from within industry of an adjunct academic appointment. (By the same token, if you are not so enchanted with lecturing, grading, tutoring, etc., there are often options for research track professorships that minimize teaching duties.) Do you want to publish? While it will always be "publish or perish" in academia, it is certainly possible to grow your CV in industry, and it can even enhance your career, depending on the company. However, it might be largely on your own time, and you will likely encounter restrictions in proprietary matters, though in practice you can generally find ways to work within them. Ask about publication at the interview, both policies and attitudes, and watch out for any defensiveness.

An important question, surprisingly often overlooked, is how you want to actually spend your time, day by day and hour by hour. In academia, you will immediately be plunged into hands-on science, and your drivers will be to start out on your career by getting results, publishing, networking, and building your reputation with a view to impressing your tenure committee. A career in industry may put more of an early emphasis on your organizational aptitude, people skills, powers of persuasion, ability to strategize and execute to plan, etc.; in terms of growing your reputation, your audience will be the rather narrower community of your immediate management. A somewhat more cynical view would be that in business you will spend seemingly endless hours in meetings and writing plans and reports, while in academia you will spend all that time and more in grantsmanship-in this regard, you must pick your poison.

Finally there is the elephant-in-the-room question: Do you want to make money, or to help people? This is, of course, a false dichotomy, but many people consciously or unconsciously frame the decision in just this way, and you had best deal with it. Try thinking of it not so much in terms of the profit motives of the respective institutions, but in terms of the people with whom you would spend your career. You should have encountered a good sampling of scientists from industry during meetings, internships, collaborations, interviews, etc. (or in any case you should certainly try to do so before making judgments). If you are left in any doubt as to their ethics or sincere desire to relieve human suffering as efficiently as possible, or if you feel these are somehow trumped by the corporate milieu, then by all means choose academia-but only after applying analogous tests to the academics you already know well. In my experience, business doesn't have a monopoly on greed, nor are humanitarian impulses restricted to academia. That said, in the final analysis you must be comfortable with your role in the social order and not finesse the question.

Rule 4: Assess Your Personality

Not surprisingly, some personality types are better-suited to one environment or the other. Raw ambition can be viewed as unseemly in either case, but there is more latitude for it in industry, and greater likelihood of being recognized and rewarded sooner if you are "on the go." In fact, one of the clearest differences between academia and industry are their respective time constants. Although the pace of academia may have quickened of late, it is still stately by comparison with industry, and much more scheduled (so many years to tenure, so many months to a funding decision, etc.). If you are impatient, industry offers relatively fast-paced decision-making and constant change. If you thrive more under structured expectations, academia would be better for you, for although industry has all the trappings of long-range strategies and career planning, the highly reactive environment means these are more honored in the breach. For one thing, reorganizations are common, and in the extreme case mergers (I have experienced two) can reset everything, for good or ill, and devour many months.

This is not to say that all is chaos-industry certainly favors a goal-directed personality, but with plenty of flexibility. On the other hand, flexibility is more the hallmark of academic research, where you will have the opportunity to follow wherever the science leads, once you are running your own shop. In industry, the flexibility is more of the conforming sort, since you won't be able to investigate every promising lead and change your research direction at will. In academia, diverging from the Specific Aims of a grant may be a problem when the time comes to renew, but the risk is yours, as is the reward. In industry, you can make the case for a new program of research, but the decision is management's and will be guided by business considerations. The "lone wolf" or "one-person band" may be increasingly rare in academia in an age of collaboration, but it is unheard of in industry, where being able to work in teams with specialized division of labor is essential. It should be apparent, as well, that mavericks and quirky personalities tend to do better in academia.

The pecking order in industry is deeper and more pyramidal than in academia, and you might end up languishing in a pay grade (or feel like you are), but there are usually plenty of opportunities for lateral moves and a variety of experiences-not to mention that it's easier to switch companies than colleges. In industry, one does need to be able to thrive in a hierarchy; you will always answer to someone, though the degree to which you are monitored will vary. By the same token, if your personality is such that climbing a management ladder and assuming steadily greater responsibility suits you, industry is built for that, and plenty of management training is on offer in larger companies. Learning to manage is much more hit-or-miss in academia; opportunities to lead large organizations are rare (and to manage them actively rather than by consensus, rarer still).

If your personality type is that of a risk-taker, biotechs and/or startups may fit you to a tee. These are the wild and wooly end of the industry spectrum, and the risks and rewards are well-known. You will work longer hours than in large pharma, and maybe even more than in academia. You will most likely share more in ownership, and learn entrepreneurial skills that will serve you well, once the bug has bitten. Bear in mind the very common pattern of faculty spinning off startups or otherwise participating in boards and the like, not to mention staking out intellectual property (shared with their university); thus, you may well be able to scratch this itch from the vantage of academia as well.

A final word about politics. Whether you are an enthusiastically political animal, or abhor this aspect of the human condition, you will encounter plenty of politics in both academia and industry. The flavors differ, to be sure. As a student you doubtless heard the clichés about tedious academic committees and underhanded deans, but you have probably had more exposure to the realities behind those stories than the corresponding ones about the dog-eat-dog corporate world. Company politics, I would hazard to say, are more transparent-the maneuvering more open and the motives more apparent. The results are often more life-altering, unbuffered by tenure and academic convention. Again, it is a matter of taste, but in my opinion the differences are overblown, for the simple reason that people are the same everywhere, in both environments governed by an underlying sense of fair play, but also occasional opportunism.

Rule 5: Consider the Alternatives

As I've suggested, the choice you face is far more fine-grained than simply that between industry and academia. Industry is a spectrum, from large pharma to mature biotech to startup. By the same token, the academic side has at one extreme the research powerhouses, where you will be judged by volume of grants, and at the other the teaching institutions, which may not even have graduate departments. Unless you are very sure of yourself, you'd be well-advised to consider the full range, given the competition you may face.

Also, don't neglect other careers that may value your training. If you love the language, consider science journalism, either writing or editing- Science and Nature have large staffs, and you will often encounter them and representatives of other journals at the same scientific meetings you attend. The same is true of government agencies such as the NIH, NSA, DOE, and so forth, where grants administration is very actively tied to research trends and can be an entrée into the world of science policy. There are many more such positions when foundations, interest groups, and other private funding bodies are included. If you have a knack for business, many management consulting firms have scientific and technical consulting arms that value Ph.D.s and offer intensive training opportunities, and, though it may not be attractive at the moment, a career as a financial analyst specializing in biotech is yet another possibility.

Rule 6: Consider the Timing

The current business environment cannot help but be among your considerations. Pharma has certainly been contributing to the unemployment rolls of late. Corporate strategies, which used to be very similar across the sector, have started to diverge, so that some companies are divesting bioinformatics at the same time that others are hiring computational types disproportionately as they place more of an emphasis on mathematical modeling, systems approaches, pharmacogenomics, drug repurposing, and the like. Overall, though, the industry trend has been to shrink R&D, and this may well continue through a round of consolidation, with several mega-mergers now under way. As noted above, mergers are times of upheaval, carrying both risk and opportunity, and usually a period in limbo as well. At the same time, it is worth bearing in mind that a corollary of downsizing is outsourcing, so that there may be new opportunities for startups and even individual consultants.

For much of the last decade, academia has also been in the doldrums, as NIH budgets have effectively contracted. As I write this, things are definitely looking up, with prospects for renewed funding of science and even near-term benefits to the NIH and NSA from the Obama stimulus package. Whether universities will respond proportionately with faculty hiring, given the losses in their endowment funds and cutbacks in salaries and discretionary spending, remains to be seen. There is a lot of slack to be taken up, and in particular a backlog of meritorious grant applications that are now being reconsidered. Nevertheless, on balance, an academic career has to be somewhat more promising today than a year ago, and a career in pharma rather less so, in the opinion of the author.

Rule 7: Plan for the Long Term

Having noted the current situation in Rule 6, it's important also to say that a career decision should be made with the long haul in mind. The business cycle will eventually reverse itself, and while the business model may need to change irrevocably, the aging population alone dictates that healthcare will be an increasing global priority. Likewise, history shows that growth in government funding for science waxes and wanes, with a time constant somewhat longer than a decade. Trying to optimize a career decision based on current conditions is a bit like trying to time the stock market-you are sure to be overtaken by events.

One approach is to choose some reasonably long time frame, perhaps a decade, and ask yourself whether you'd be content to have lived through the average ups and downs you'd experience in a given job over that period. In academia, that would include a tenure decision (rate your chances), a lot of grant applications with mixed success at best, and maybe some great students and really significant scientific contributions. In pharma or large biotech, it would encompass a couple of promotions, your own group and maybe a department, at least one merger or other big disruption, and several rounds of layoffs. In small business, it might include a failed startup (or two, or three), an IPO if you're lucky, and a lucrative exit strategy or long-term growth if you're really lucky.

If you game these scenarios with various probabilities, and use your imagination, it just might become clear which ones you have no stomach for, and which ones really hold your interest.

Rule 8: Keep Your Options Open

Job-hopping is much more prevalent now than in days of yore, and you should consider this in your scenarios. In industry, there is little stigma attached to changing employers, and if you can tolerate the relocation and/or want to see the world, it is a more or less standard way to advance your career by larger-than-usual increments. This stratagem is far from unknown in academia, but perhaps a bit trickier to execute, though of course it is de rigueur if you fail to get tenure.

Of greater interest is the question of moving between academia and industry. From the former to the latter is fairly easy, but the reverse is not as common, for a variety of reasons. Superstar academics in relevant areas are in great demand in industry, to which they are often exposed through consulting or scientific advisory boards. There are multiple examples of senior academics taking over major R&D organizations in industry, sometimes orders of magnitude larger than anything they managed in academia, and you might even consider this well-trod path as a career goal from the outset.

It is not impossible to return to academia from industry, particularly if you were already quite prominent when you left, but if you start your career in industry you may be at a disadvantage unless you go to great lengths to maintain an academic-style publication record and CV. Important exceptions would be if the work that you did in industry was particularly novel and/or high-profile, or if your business experience is valued in the post you seek. Examples of the latter might be faculty positions with a prominent management component (centers, institutes, core facilities, and the like), or an interface role back to industry, or perhaps a joint business school appointment.

Rule 9: Be Analytic

Approach the decision with the analytic skills you've learned to apply to scientific questions. Gather data from all available sources and organize it systematically. When you interview, don't just impress, but get impressions; record everything down to your gut feelings. Do some bibliometric or even social network analyses of your potential colleagues. Check the industry newsletters and blogs, albeit with a grain of salt, to get a sense of the mood around R&D units (not to be confused with manufacturing, sales and marketing, or other divisions, which may have completely different cultures within the same company).

You might even try out some decision theoretic methodologies, such as decision matrices and Bayesian decision trees, or run simulations on the scenarios of Rule 7. I recommend taking a look at expected utility theory and prospect theory, for an interesting quantitative excursion. But honestly, these suggestions are just a more sophisticated informatics version of the classic advice to "make a list of pros and cons," which always makes one feel a little more in control.

Rule 10: Be Honest with Yourself

Another homily: Now, if ever, is the time to be honest with yourself. Take a hard look at your qualifications, with as much objectivity as you can muster, and use these rules to decide where you would be best-suited and positioned for success. But even more importantly, deal with your emotional responses to industry and academia. If something is nagging at you, tease it out into the open, and try to decide if it is well-founded or not; if you can't decide, then you have to acknowledge it, and realize that it may not go away in the future either.

Finally, try to keep some perspective. Your career choice is important, but not irrevocable, and there are more consequential things in life. Don't let the decision process ruin what should be an exciting time for you.

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Ten Simple Rules for Choosing between Industry and Academia

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Citation: Searls DB (2009) Ten Simple Rules for Choosing between Industry and Academia. PLoS Comput Biol 5(6): e1000388. https://doi.org/10.1371/journal.pcbi.1000388 Published: June 26, 2009 Copyright: © 2009 David B. Searls. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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A model for industry partnerships in academia

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Knowledge Transfer Partnerships (KTPs) link forward thinking businesses with the UK's world class knowledge bases to deliver innovation projects led by inspired graduates

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"KTP projects pave the way to businesses who want to exploit the latest advancements but do not have the expertise or confidence to do so. Through input from academic experts advising on the latest research in the field, and unrelenting support and guidance from Innovate UK [KTN], success is way more attainable."

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Some basic models of Uni / business collaboration - if in doubt ask your commercialisation department

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The Lambert toolkit is currently being updated to reflect changes following the end of the Brexit transition. These changes include the guidance notes on data protection, state aid and competition law and the amended annexes in the model agreements. An updated toolkit will be available shortly.

Overview

The Lambert working group has created this toolkit to assist academic or research institutions and industrial partners who wish to carry out research projects together.

The objectives of the toolkit are to:

facilitate negotiations between potential partners

reduce the time, money and effort required to secure agreement

provide examples of best practice

The toolkit consists of:

a decision guide

7 model research collaboration (one to one) agreements (1-6)

4 consortium (multi-party) agreements (A-D)

heads of terms and variation agreements for both collaboration and consortium agreements

guidance notes

The model agreements are starting points and their use is not compulsory. Each model agreement envisages a different set of circumstances and are not sector specific, allowing for flexible use. However the model agreements can be adapted to meet the particular circumstances of your project.

Benefits from owning IP

The ownership of the IP and the right to use it from a project are important considerations because they can confer a competitive advantage.

Where the results of a project enable the creation of a product with unique characteristics that is attractive to customers, ownership of the IP, or the exclusive right to use it, provides protection for the investment made in developing the product and taking it to market.

Equally knowledge transfer and the engagement of academic or research institutions with industrial partners is acknowledged to be important to the economy of the UK.

Maximising innovation

The aim of the model agreements is to maximise innovation, and encourage the collaboration with industry and the sharing of knowledge. The cornerstone of the 7 model research collaboration agreements is that, at the least, a commercial "partner" (called the collaborator) will have the right to use the results of the project on a non-exclusive basis so as to promote the use of the results and therefore innovation. The agreements are designed to represent a workable and reasonable compromise for both or all parties.

How do I get started?

There are 2 model heads of terms agreements, one for two party collaboration agreements and the other for multi-party consortium agreements. These agreements can be used to start negotiations to agree the basic principles for your project. These help identify major issues early on in the project and identify solutions.

Which agreement should I use?

To help you decide which of the 7 model research collaboration agreements most closely reflects the circumstances of your project, a decision guide will lead you through some of the principles and criteria that you may wish to take into account when deciding on the ownership and rights to exploit IP. You may find it helpful to consult this guide before using any of the model research collaboration agreements.

The decision guide is for use with the bilateral research collaboration agreements only. There is no decision guide for the 4 consortium agreements. This is because there are too many possible alternatives for multi-party research.

Model research collaboration agreements

The 7 model research collaboration agreements cover one to one projects. Each provides a different approach on who is to own, and exploit, the IP in the results or outcome of the project. A decision guide is available to help you decide which agreement you could use.

Agreement 1

Collaborator has non-exclusive rights to use in specified field/territory; no sub-licences

Institution

Agreement 2

Collaborator may negotiate further licence to some or all Institution IP

Institution

Agreement 3

Collaborator may negotiate for an assignment of some Institution IP

Institution

Agreement 4

Institution has right to use for non-commercial purposes

Collaborator

Agreement 4A

Each party has right to exploit certain results created during the project and takes assignment of those results. Institution has right to use for academic and research purposed, the Collaborator for research purposes

Institution and Collaborator

Agreement 5

Contract research: no publication by Institution without Collaborator's permission

Collaborator

Agreement 6

Institution has right to use for academic and research purposes

Collaborator

The model agreements are starting points and their use is not compulsory. You should decide which of the 7 approaches is most suitable and negotiate with the other party before work on the project begins. None of the 7 research collaboration agreements deals with the joint ownership of IP. For examples of joint ownership clauses, see model consortium agreement A.

Model consortium agreements

There are 4 model consortium agreements, for use where more than 2 parties are collaborating.

Agreement A

Each member of the consortium owns the IP in the results that it creates. They grant each other party a non-exclusive licence to use those results for the purposes of the project and any other purpose.

Agreement B

The other parties assign their IP in the results to the lead exploitation party (or the lead exploitation party granted an exclusive licence).

Agreement C

Each party takes an assignment of IP in the results that are germane to its core business and exploits those results.

Agreement D

Each member of the consortium owns the IP in the results that it creates. They grant each other party a non-exclusive licence to use those results for the purposes of the project only. If any member of the consortium wishes to exploit another's IP they must negotiate a license or assignment with the owner of that IP.

The consortium agreements cannot cover all the circumstances that might arise between academic and research institutions and industrial partners in carrying out research. They illustrate terms that might apply in four possible scenarios. You should negotiate with the other parties to achieve consensus and a signed agreement before work on the project begins.

Consortium agreements B and C were drafted for use with Innovate UK's Collaborative R&D Programme. With a few changes, they may be adapted for other circumstances.

Jurisdiction

The Lambert toolkit, including guidance notes and the model agreements, is designed to be used only when the agreements are governed by English law. To use a different legal system you will need to take legal advice from a lawyer qualified to advise on that country's law.

What happens when other party joins the project?

There are 2 model variation agreements, one for two party collaboration agreements and the other for multi-party consortium agreements. These agreements can be used when a new party joins the project. A fundamental principle in all the model agreements is that no variation to the agreement will be valid unless all the existing parties agree to the variation in writing.

Guidance notes

Guidance notes have been developed to help understand when and how to use the Lambert model agreements. The notes will also explain in more detail the roles and terminology of the wording in the agreements.

Guidance notes for the 7 model research collaboration agreements (One to One) and 4 model consortium agreements

Fast track model agreement

A fast track model agreement (MS Word Document, 62.4KB) was produced by Public Health England to evaluate potential treatment options for Ebola and Zika very rapidly and to share the results with stakeholders for a coordinated global response. Following consultations, it is now available as a model agreement that may be adapted for any crisis affecting the health of people, animals and/or the environment.

Fast track model agreement

Institution has right to use for academic and research purposes

Confidential Information excludes Results

Institution can notify Global Stakeholders that they are carrying out the Work, timeline, details of the Developer, Materials, etc.

Institution has right to publish Results (including 'poor' or 'negative' results) and make them available in databases set up by Global Stakeholders

Discount if Product is sold back to the Institution or Commissioning Bodies

Collaborator (or Developer)

Model agreements between the pharmaceutical and biomedical industries, universities and the NHS

The National Institute for Health Research ( NIHR) through its office for Clinical Research Infrastructure provides a suite of model agreements for use in research partnerships involving the pharmaceutical and biotechnology industries, universities and NHS organisations. The Intellectual Property Office worked with NIHR and the Medical Research Council to develop the model Industry Collaborative Research Agreement ( mICRA).

It is designed to support research between universities, the pharmaceutical and biotechnology industries and the NHS by shortening the negotiation and contracting process for IP ownership and management.

The mICRA template model and full guidance information can be downloaded from the NIHR website.

International agreements

The Intellectual Property Office has worked with a number of international parties to create separate collaboration agreements across a number of countries. The Model agreements for collaborative research are available. You will also find guidelines on the management of IP in international collaborations and a European Commission cross border decision guide.

Useful resources

Further examples of other agreements:

These examples have not been subject to the same level of scrutiny as the Lambert toolkit. They are provided for information only.

Feedback is welcome on the Lambert toolkit and your experience of using the model agreements.

Email: lambert@ipo.gov.uk

Lambert materials and copyright

The Lambert agreements and supporting materials are to be treated as crown copyright. They are free for universities, institutions and companies to use, adapt and re-use for the purpose of undertaking collaborative research.

Where practicable, the source of the Lambert Agreements and supporting materials should be cited.

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University and business collaboration agreements: Lambert Toolkit

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The Lambert toolkit is currently being updated to reflect changes following the end of the Brexit transition. These changes include the guidance notes on data protection, state aid and competition law and the amended annexes in the model agreements. An updated toolkit will be available shortly.

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For latest funding opportunities and cross university collaborations

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Funded by Research England's Connecting Capability Fund, this programme supports an innovation platform with focus on the development of solutions to infectious diseases and antimicrobial resistance (AMR).

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The Bloomsbury SET is an innovation partnership bringing together Universities with a strong research reputation to develop new solutions which combat infectious disease and antimicrobial resistance (AMR).

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Mologic - A World Leader In Lateral Flow Technology

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Mologic is a leading developer of lateral flow and rapid diagnostic technologies and products. We work with companies, clinicians...

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Worldwide, 1.1 billion people live with vision loss, but don't need to. This number is set to grow to 1.8 billion in the next 30 years. Simple, cost-effective treatments exist but specialists are in short supply and resources are limited.

Peek Vision is a social enterprise that powers eye health providers working in low- and middle-income countries to optimise their services and strengthen health systems.

Our software, programme design and data intelligence platform helps NGOs, governments and health services become more efficient, more equitable and more effective.

The result? Better vision and eye health for the millions of people worldwide who need it.

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Worldwide, 1.1 billion people live with vision loss, but don't need to. This number is set to grow to 1.8 billion in the next 30 years. Simple, cost-effective treatments exist but specialists are in short supply and resources are limited.

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Guest Speaker - Prof Liam Good / Tecrea 

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Guest Speaker - Prof Liam Good / Tecrea 

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Omega is focused on selling a wide range of specialist products, primarily in the immunoassay, in-vitro diagnostics (IVD) market within three segments: Allergy and Autoimmune, Food Intolerance and Infectious Disease.

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Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, conference, huddle, and training rooms, as well as executive offices and classrooms. Founded in 2011, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done.

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Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, confer…

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Week 2 Worksheet - Impactful Introduction 

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Week 2 Worksheet - Impactful Introduction 

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SOAS Influencing the Corridors of Power 

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SOAS Influencing the Corridors of Power 

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SOAS ICOP - Influencing the Corridors of Power

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The Influencing the Corridors of Power (ICOP) project is hosted by SOAS University of London. The Project aims to promote the impact of ongoing

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Dr Emma Tomlinson Head of Research Development etomlinson@rvc.ac.uk

Mrs Jo Bailey Head of Knowledge Exchange and Commercialisation  jobailey@rvc.ac.uk

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RVC Business - Royal Veterinary College, RVC

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RVC Business - The Royal Veterinary College is Europe's largest veterinary school and one of the most enterprising and innovative research-intensive universities in the world.

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Contacts for KE and commercialisation by schools / discipline can be found here
https://www.soas.ac.uk/researchoffice/

Key Bloomsbury SET contact - Jessica Pavlos - jp61@soas.ac.uk

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SOAS Enterprise (Training, Briefings & Consultancy) at SOAS University of London

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SOAS is a world-renowned academic institution, uniquely positioned to analyse, interpret and respond to some of the most challenging issues facing your organisation today. Our focus on the politics, languages, cultures and societies of Africa, Asia and the Middle East—we have the largest concentrati…

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For translational support and facilitation, KE training and funding, please contact the Innovation Team: Innovation@lshtm.ac.uk

For research commercialisation, and IP queries, please contact Chariot Innovations: info@chariotinnovations.co.uk

Key Bloomsbury SET contact Alexandra Anderson - alexandra.anderson@lshtm.ac.uk

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Bridging the gap between research and enterprise with breakthrough products, research, testing and consultancy from the London School of Hygiene & Tropical Medicine

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Sarah Roberts Sarah.Roberts@lstmed.ac.uk; or
KEF KEF@lstmed.ac.uk

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Liverpool School of Tropical Medicine, founded in 1898, is the first institution in the world dedicated to research and teaching in tropical medicine.