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Web front door - online interactive catalogue of digital tools for HASS researchers

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Web front door - online interactive catalogue of digital tools for HASS researchers

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We are actively developing this online interactive catalogue of digital tools for HASS researchers. Once ready, we will be embedding this tool into the Digital Studio website. Please share your feedback with us at hass-taskforce@unimelb.edu.au!

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Pick the main type using the buttons below. Then let the decision tree guide you to the right tool!

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We are moving towards a demand-driven offering of workshops. Follow this Qualtrics link to request what training you want/need! Open to all UoM researchers (graduate students and academics), as well as research support staff.

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A key challenge for data custodians is to provide data with maximum utility for users but still maintain the confidentiality of the information. Every data release carries some risk of disclosure, so the benefits of each release (its utility or usefulness for research and statistical purposes) must substantially outweigh its risks and be clearly understood. This balancing of risk and utility is something everyone does on a daily basis (for example, when they choose to drive a car). Similarly, data custodians need to approach disclosure risk by managing it, rather than trying to eliminate it.

Confidentiality is breached when a person, group or an organisation is re-identified through a data release or when information can be attributed to them. The likelihood of this happening, or risk of disclosure, is not easily determined. Implicit in this is that the consequences of disclosure are always damaging (to some extent) to the individual or organisation. It is difficult to ascertain the degree of damage, mostly because people differ in the importance they place on information. What may be considered highly confidential to one person is of no consequence to another. The ABS assumes all information it collects to be potentially sensitive and manages it accordingly.

Managing disclosure risk becomes a question of assessing not only the data itself, but also the context in which the data is released. Once the context is clearly understood, it is much easier to determine how to protect against the threat of disclosure. The Five Safes framework provides a structure for assessing and managing disclosure risk that is appropriate to the intended data use.

This framework has been adopted by ABS, several other Australian government agencies as well as national statistical organisations such as the Office of National Statistics (UK) and Statistics New Zealand.

The Five Safes framework takes a multi-dimensional approach to managing disclosure risk. Each safe refers to an independent but related aspect of disclosure risk. The framework poses specific questions to help assess and describe each risk aspect (or safe) in a qualitative way. This allows data custodians to place appropriate controls, not just on the data itself, but on the manner in which data is accessed. The framework is designed to facilitate safe data release and prevent over-regulation

The five elements of the framework are:

Safe people

Is the researcher appropriately authorised to access and use the data?

By placing controls on the way data is accessed, the data custodian invests some responsibility in the researcher for preventing re-identification. Usually, as the detail in the data increases, so should the level of user authorisation required.

Prerequisites for user authorisation usually include:

training in confidentiality and the conditions of data use

signing a legally binding undertaking to maintain data confidentiality

By definition, a safe people assessment would not be required for open data (data that is released into the public domain with no restriction on use).

Safe projects

Is the data to be used for an appropriate purpose?

Users wanting to access detailed microdata should be expected to explain the purpose of their project. For example, in order to access detailed microdata in the ABS DataLab, users must demonstrate to the ABS that their project has a statistical purpose and show it has:

a valid research aim

a public benefit

no capacity to be used for compliance or regulatory purposes

As with safe people, the need for a safe project assessment will depend on the context in which the data is accessed. It would not be required for open data.

Safe settings

Does the access environment prevent unauthorised use?

The environment here can be considered in terms of both the IT and the physical environment. In data access contexts such as open data, safe settings are not required. At the other end of the spectrum however, sensitive data should only be accessed via secure research centres.

Secure research centres may have features such as:

a locked room requiring personal authentication

IT monitoring equipment

auditing and other supervision

Safe settings ensure that data access and use is occurring in a transparent way.

Safe data

Has appropriate and sufficient protection been applied to the data?

At a minimum the removal of direct identifiers (such as name and address) must be applied to data before it is released. Further statistical disclosure controls should also be applied, depending on how the data will be released. Table 1 shows some of the statistical factors that should be considered when assessing disclosure risk.

Data age

Older data is generally less risky

Sample data (e.g. a survey)

Decreases risk

Population data (e.g. a census)

Increases risk

Administrative data

Increases risk

Longitudinal data

Increases risk

Hierarchical data

Increases risk

Sensitive data

Increases risk (sensitive data may be a more attractive target)

Data quality

Poor quality data may offer some protection

Microdata

Main risk: re-identification

Aggregate data

Main risks: attribute disclosure and disclosure from differencing

Key variables

The variables of most interest to users are usually the most disclosive

Source: UK Anonymisation Decision-making Framework

Safe outputs

Are the statistical results non-disclosive?

This is the final check on the information before it is made public, which aims to reduce the risk of disclosure to a minimum. All data made available outside the data custodian's IT environment must be checked for disclosure. For example in the ABS DataLab, statistical experts check all outputs for inadvertent disclosure before the data leaves the DataLab environment.

The Five Safes framework provides a mechanism for data custodians to take necessary and reasonable steps to manage disclosure risk in their data releases. It broadens the approach to data confidentiality by considering not just the treatment of data, but also the manner and context in which data is released.

The safes are assessed independently, but also considered as a whole. They can be thought of as a series of adjustable levers or controls to effectively manage risk and maximise the usefulness of a data release. The degree to which each safe is controlled is critical to assessing the disclosure risk. Tightly controlling all five will be counterproductive because the restrictions applied will not produce a corresponding benefit (useful data).

In practice, the safe data part of the Five Safes should be addressed after the other four are considered. This is because the degree of data treatment required will become evident once it is clear who will be able to access the data, under what conditions, in what circumstances and how the resulting data will be protected in order to be made public. The process is likely to be iterative, as data treatment with a view to maintaining utility may necessitate reassessing one or more of the other four safes.

This table describes how the ABS applies the Five Safes framework to three different data access channels - open data, basic and detailed microdata files.

Website or publication table (open data)

Basic microdata file (via direct download)

Detailed microdata file (via ABS DataLab)

Safe people

No control necessary

Anyone may view the data online

Some control

Users must register to use the data and sign a Declaration of Use
Breaches may be subject to sanctions and/or legal proceedings

High control

Users must undergo training, complete an authorisation process, sign legally binding confidentiality undertakings and a compliance declaration
Breaches of protocols or disclosure of information may be subject to sanctions and/or legal proceedings

Safe projects

No control necessary

Anyone can use the data for their own purposes

Some control

Users sign a declaration regarding the purpose for which they will use the data

High control

Users must detail the purpose for which they will use the data
Purpose can be compared to what is actually produced (see Safe Outputs)

Safe settings

No control necessary

Some control

Users are required to store the data securely and can work on the data in their own physical and IT environment

High control

The DataLab a secure, closed environment, accessed virtually or on-site
Secure login, auditing and monitoring capabilities
No data can be removed without first being checked by ABS staff

Safe data

Very high control

The data is highly aggregated

High control

The data is treated by the ABS to ensure no individual is likely to be identified

Appropriate control

Direct identifiers are removed and the data is further treated where appropriate. Appropriate control of the data optimises its usefulness for statistical and research purposes.

Safe outputs

Very high control

Every table is checked for disclosure before release
(in an Open Data context, the data is the safe output)

Some control

The output is technically controlled by the user, but the ABS provides guidelines or rules about what may be published or shared

High control

All statistical outputs are assessed by the ABS for disclosure before being released to the user. The outputs may also be compared for consistency with the original project proposal.

In all three cases, applying any one safe in isolation is unlikely to provide an effective confidentiality solution. However, when all five safes are considered in combination, the overall disclosure risk becomes very low.

Tabular data is most effectively protected through safe data and safe outputs.

When data is loaded into the user's own environment, some of the safes can wit be more effectively controlled than others. The data custodian cannot directly monitor how the data is used. However, the data custodian mitigates disclosure risk by directly protecting the data. The downside of this approach is that the data can lose some of its utility. Examples of these types of datasets include:

basic microdata files (produced by the ABS)

public use files (PUFs)

The treatment of the microdata files in the ABS DataLab effectively uses all five safes. Safe people, safe projects, safe settings, safe data and safe outputs are all controlled to mitigate the risk of disclosure. This allows appropriately approved researchers to work securely with highly detailed microdata.

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Using safe people, projects, settings, data and output to balance disclosure risk and utility, ABS Fives Safes examples

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Data Services Discovery in Research Data Australia 

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Data Services Discovery in Research Data Australia 

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Find out how to get metadata records into Research Data Australia.

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Researchers spend considerable time, money and effort collecting and interrogating data. Making your data findable, accessible, interoperable and reusable (FAIR) maximises the impact of that investment, including gaining more citations for your data sets.

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FAIR Principles for Research Data Management | ARDC

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Discover why findable, accessible, interoperable and reusable (FAIR) data is important. Learn how to make your data FAIR and how it leads to citations.

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Records Managed SharePoint is a customised version of SharePoint that has been developed by the University for the compliant management of records, including research records.

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SharePoint is part of Microsoft Office 365 and can be used similarly to OneDrive. As SharePoint sites are not associated with any one user’s account, they may be suitable for large or long-term collaborations, with access persisting even when users leave the University. It has additional features fo…

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How can impact strategies be developed that better support universities to address twenty-first-century challenges?

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How can impact strategies be developed that better support universities to address twenty-first-century challenges?

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Position of University of Melbourne: The University of Melbourne is committed to disseminating its research as widely as possible to improve the public good by accelerating the pace of discovery, encouraging innovation, enriching education, and stimulating the economy.

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Position of University of Melbourne: The University of Melbourne is committed to disseminating its research as widely as possible to improve the public good by accelerating the pace of discovery, encouraging innovation, enriching education, and stimulating the economy.

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Learn more about open research, its benefits, and what the funders are saying​.

Practicing Open Research

Open research extends the principles of open access publishing to the whole research cycle, inclusive of research protocols, data, code, software, and much more. It is about being as open as possible, as often as possible, and only being as closed as is necessary. It encompasses all disciplines and types of research. Through planning many elements of the research lifecycle can be made open, transparent, and reproducible.​

The University of Melbourne is committed to disseminating its research as widely as possible and expects researchers to make their publications open access, as outlined in the Principles for Open Access to Research Outputs at the University of Melbourne.

Open Research and the Research Lifecycle

Benefits of Open Research

By engaging with open research you are enabling greater transparency and reproducibility to be applied to your work and research outputs. This, in turn, may lead to​

Creating opportunities for collaboration, both in and outside of your discipline​

Increasing access to research by individuals and organisations outside of academia, thus impacting and progressing real-world problems, impact, and lives faster​

Higher citation rates, as well as more outputs that can be cited. With data, software, and code being made available and being assigned persistent identifiers, a research project can have a larger impact within and beyond academia​

Greater efficiencies in research more broadly as less repetition is needed​

Improving research integrity by limiting the possibility of closed research practices. This in turn increases trust in research​

Compliance with funder mandates and institutional requirements

Funders and Open Research

At the funder mandated level, open research has focused on openly accessible research outputs such as journal articles, book chapters, and monographs. This is commonly referred to as open access. Recently, funders have begun including open data and other potential outputs, such as code and software, in their policies.​ Find out more on our Funder Open Access Policies page.

Further Support

Broadly, this resource has been organised from steps to take from planning your research, through to the publication of results. It can be used in partnership with the Open Research Guide, which provides practical, how-to information on the topics covered here.

For enquiries relating to open research, please email your discipline's Liaison Librarians, or arrange a time to consult a librarian.

Alternatively, you can contact the library, for any questions, feedback and suggestions please complete our Library Enquiries form below:

Return to Open Scholarship

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Learn more about open research, its benefits, and what the funders are saying both in Australia and overseas.

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“Share and communicate research methodology, data and findings openly, responsibly and accurately.” – Principle 3: Transparency.

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“Share and communicate research methodology, data and findings openly, responsibly and accurately.” – Principle 3: Transparency.

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CARE Principles for Indigenous Data Governance

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CARE Principles for Indigenous Data Governance

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Collective benefit Authority to control Responsibility Ethics

The CARE Principles for Indigenous Data Governance guide appropriate use and reuse of Indigenous data. This set of principles indicates the significant and crucial role of data in advancing Indigenous innovation and self-determination.

CARE Principles for Indigenous Data Governance
Guide from the Research Data Alliance International Indigenous Data Sovereignty Interest Group.

Indigenous data
A guide to protocols and research guidelines specific to the Australian context from the ARDC.

Indigenous Data Sovereignty Readiness Assessment and Evaluation Toolkit
This toolkit was designed by the Lowitja Institute, Australia's national institute for Aboriginal and Torres Strait Islander health research and is designed to evaluate the use of Indigenous Data Sovereignty practices (ID-SOV) in research and academic environments. It's goal is to improve individual capabilities and takes a whole-of-organisation approach to embedding ID-SOV practices.

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What do you need to know about data management plans? 

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What do you need to know about data management plans? 

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A data management plan (DMP) is a living document for a research project, which outlines data creation, data policies, access and ownership rules, management practices, management facilities and equipment, and who will be responsible for what.

A Data Management Plan (DMP) describes how research data will be managed throughout your research project. It can begin as a basic plan and further information can be added as the project progresses. It covers areas such as:

What data will be created

Where will the data be stored

Who will own and have access to the data

How will the data be preserved

How will sensitive data be treated

1. it's one of most important things to do and have as a researcher.
2. it helps you do your work and make an impact as a researcher.
3. it can help you manage your research and all aspects of your data.
What else?

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Floridi, L. Soft Ethics and the Governance of the Digital. Philos. Technol. 31, 1–8 (2018). 

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Floridi, L. Soft Ethics and the Governance of the Digital. Philos. Technol. 31, 1–8 (2018). 

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https://doi.org/10.1007/s13347-018-0303-9

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Philosophy & Technology 31, 1-8 (2018) Cite this article

25k Accesses

111 Citations

100 Altmetric

Metrics

Today, in any mature information society (Floridi 2016), we no longer live online or offline but onlife, that is, we increasingly live in that special space, or infosphere, that is seamlessly analogue and digital, offline and online. If this seems confusing, perhaps an analogy may help to convey the point. Imagine someone asks whether the water is sweet or salty in the estuary where the river meets the sea. Clearly, that someone has not understood the special nature of the place. Our mature information societies are growing in such a new, liminal place, like mangroves flourishing in brackish water. And in these 'mangrove societies', machine-readable data, new forms of smart agency and onlife interactions are constantly evolving, because our technologies are perfectly fit to take advantage of such a new environment, often as the only real natives. As a result, the pace of their evolution can be mind-blowing. And this in turn justifies some apprehension. However, we should not be distracted by the scope, depth and pace of technological innovation. True, it does disrupt some deeply ingrained assumptions of the old, exclusively analogue society, e.g. about production, logistics, customization, competition, education, work, health, entertainment, politics and security, just to mention some crucial topics. Yet that is not the most consequential challenge we are facing. It is rather how we are going to design the infosphere and the mature information societies developing within it that matters most. Because the digital revolution transforms our views about values and their priorities, good behaviour, and what sort of innovation is socially preferable-and this is the fundamental issue, let me explain.

To many, what digital innovation will throw up next may seem the real challenge. The question itself is recurrent: what comes next? What is the next disruption? What is the new killer app? Will this be the year of the final battle between Virtual Reality vs. Augmented Reality? Or is it the Internet of Things that will represent the new frontier, perhaps in some combination with Smart Cities? Is it the end of the TV as we know it coming soon? Will healthcare be made unrecognisable by machine learning, or should our attention rather be focused on the automation of logistics and transport? What will the new smart assistants in the home do, apart from telling us what the weather is like, and allowing us to choose the next song? Behind similar questions lies the unspoken assumption that technological innovation leads, and everything else follows, or lags behind: business models, working conditions, standards of living, legislation, social norms, habits, and expectations. Yet this is precisely the distracting narrative we should resist. Not because it is wrong, but because it is only superficially right. The deeper truth is that the revolution has already occurred: the transition from an entirely analogue and offline world to one that is increasingly also digital and online will never happen again in the history of humanity. One day, a quantum computing phone running artificial intelligence apps may be in the pocket of your average teenager, but our generation is the last one that will have seen a non-digital world. And this is the really extraordinary turning point, because that landing in the infosphere happens only once. What this new world will be like, as we create it, is both fascinating, in terms of opportunities, and worrisome, in terms of risks. But the 'exploration' of the infosphere, to indulge in the geographical metaphor a bit longer, no matter how challenging, prompts a much more fundamental question, which is socio-political and truly crucial: what kind of mature information societies do we want to build? What is our human project for the digital age? Looking at our present backwards-that is, from the future to our present-this is the time in history when we shall be seen to have laid down the foundation for our mature information societies. We shall be judged by the quality of our work. So, clearly, the real challenge in no longer digital innovation, but the governance of the digital.

The proof that this is the case is all around us, in the many initiatives addressing the impact of the digital on everyday life and how to regulate it. It is also implicit in the current narrative on the unstoppable and unreachable nature of technological innovation, if one looks just a bit more closely. In the same context where people complain about the speed of innovation and the impossible task of chasing it with some normative framework, one finds that there is equal certainty about the serious risk that the wrong legislation may kill innovation entirely or destroy whole technological sectors and developments. You do not have to be Nietzsche ('Was mich nicht umbringt macht mich stärker'-'what does not kill me makes me stronger' (Nietzsche 2008)) to realise that the inference to be drawn is that updating the rules of the game is perfectly possible-it can have immense consequences-but that reacting to technological innovation is not the best approach. We need to shift from chasing to leading. If then we like the direction in which we move, or where we want to go, then the speed at which we are moving or getting there can actually be something very positive. The more we like where we are going, the feaster we will want to get there. But for this to happen, society needs to stop playing defence and start playing attack. The question is not whether, but how. And to start addressing the how, some clarifications are helpful.

On the governance of the digital, there is much to be said, and even more still to be understood and theorised, but one point is clear: the governance of the digital (henceforth digital governance), the ethics of the digital (henceforth digital ethics, also known as computer, information or data ethics (Floridi and Taddeo 2016)) and the regulation of the digital (henceforth digital regulation) are different normative approaches, complementary, but not to be confused with each other, in the following sense (see Fig. 1 for a visual representation).

Digital governance is the practice of establishing and implementing policies, procedures, and standards for the proper development, use and management of the infosphere. It is also a matter of convention and good coordination, sometimes neither moral nor immoral, neither legal nor illegal. For example, through digital governance, a government agency or a company may (a) determine and control processes and methods used by data stewards and data custodians in order to improve the data quality, reliability, access, security and availability of its services and (b) devise effective procedures for decision-making and for the identification of accountabilities with respect to data-related processes. A typical application of digital governance was the work I co-chaired for the British Cabinet Office in 2016 on a 'Data Science Ethical Framework' (Cabinet Office 2016), which was '[...] intended to give civil servants guidance on conducting data science projects, and the confidence to innovate with data.'1

Digital governance may comprise guidelines and recommendations that overlap with, but are not identical to, digital regulation. This is just another way of speaking about the relevant legislation, a system of rules elaborated and enforced through social or governmental institutions to regulate the behaviour of the relevant agents in the infosphere. Not every aspect of digital regulation is a matter of digital governance and not every aspect of digital governance is a matter of digital regulation. In this case, a good example is provided by the General Data Protection Regulation (GDPR).2 Compliance is the crucial relation through which digital regulation shapes digital governance.

All this holds true of digital ethics, understood as the branch of ethics that studies and evaluates moral problems relating to data and information (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including AI, artificial agents, machine learning and robots) and corresponding practices and infrastructures (including responsible innovation, programming, hacking, professional codes and standards), in order to formulate and support morally good solutions (e.g. good conduct or good values). Digital ethics shapes digital regulation and digital governance through the relation of moral evaluation.

Once the map is understood, several consequences become clear.

First, there is the synecdoche use. Digital governance in Fig. 1 is just one of the three normative forces that can shape and guide the development of the digital. But it is not uncommon to use that part for the whole (a bit like using 'coke' for any variety of cola) and to speak of digital governance as referring to the whole set. It is what I did at the beginning of this article, when I stated that the real challenge today is the governance of the digital. By that I meant to refer not just to digital governance but also to digital ethics and digital regulation, i.e. to the whole normative map. And this is also how I interpret the report 'Data Management and Use: Governance in the 21st Century' that we published in 2017 as a joint British Academy and Royal Society working group (British Academy 2017). As long as the synecdoche is clear, there is no problem.

Second, when policy-makers, both in political and in business contexts, wonder why we should engage in moral evaluation when legal compliance is already available (this is a recurring topic in the discussion of the GDPR), the answer should be clear: compliance is necessary but insufficient to steer society in the right direction. Because digital regulation indicates what the legal and illegal moves in the game are, so to speak, but it says nothing about what the good and best moves could be to win the game-that is, to have a better society. This is the task of both digital ethics, on the side of moral values and preferences, and of digital governance, on the side of best management. This is why, for example, the European Data Protection Supervisor (the EU's independent data protection authority) established the Ethics Advisory Group in 2015, in order to analyse the new ethical challenges posed by digital developments and current legislation, especially in relation to the GDPR. The report we published (EDPS Ethics Advisory Group 2018) should be read as a contribution to, and a stepping stone towards, a normative governance of the infosphere in the EU.

Third, digital ethics may be understood now in two ways, as hard and soft ethics. Hard ethics is what we usually have in mind when discussing values, rights, duties and responsibilities-or, more broadly, what is morally right or wrong, and what ought or ought not to be done-in the course of formulating new regulations or challenging existing ones. In short, hard ethics is what makes or shapes the law. Thus, in the best scenario, lobbying in favour of some good legislation or to improve that which already exists can be a case of hard ethics. For example, hard ethics helped to dismantle apartheid in South Africa and supported the approval of legislation in Iceland that requires public and private businesses to prove that they offer equal pay to employees, irrespective of their gender (the gender pay gap continues to be a scandal in most countries).

Soft ethics covers the same normative ground as hard ethics, but it does so by considering what ought and ought not to be done over and above the existing regulation, not against it, or despite its scope, or to change it, or to by-pass it (e.g. in terms of self-regulation). In other words, soft ethics is post-compliance ethics: in this case, 'ought implies may'. Now, both hard and soft ethics usually presuppose feasibility or, in more Kantian terms, assume that 'ought implies can', given that an agent has a moral obligation to perform an action only if this is possible in the first place. It follows that soft ethics also assumes a post-feasibility approach. Add that any ethical approach, at least in the EU, accepts, as its minimal starting point, the implementation of the Universal Declaration of Human Rights (UDHR) and The Charter of Fundamental Rights of the European Union (within the UDHR, there are rights that may assume greater relevance than others, such as article 3 on security, article 7 on non-discrimination, article 12 on privacy, article 19 on freedom of opinion and expression and article 20 on freedom of association). And the result is that the space of soft ethics is both partially bounded, and yet unlimited. To see why, it is easy to visualise it in the shape of a trapezoid (see Fig. 2), with the lower side representing a feasibility base that is ever-expanding through time-we can do more and more things-the two constraining sides representing legal compliance and human rights, and the open upper side representing the space where what is morally good may happen in general and, in the context of this article, may happen in terms of shaping and guiding the development of our mature information societies.

Soft digital ethics can be rightly exercised in places of the world where digital regulation is already on the good side of the moral vs. immoral divide. But it would be a mistake to argue for a soft ethics approach to establish a normative framework when agents (especially governments and companies) are operating in contexts where human rights are disregarded, e.g. in China, North Korea or Russia, or in contexts where hard ethics is precisely what is needed to change the current regulation, e.g. in the USA and, a fortiori, in the three countries already mentioned. It is really within the European Union (EU) that soft ethics can rightly be exercised, to help companies, governments and other organisations to take more and better advantage, morally speaking, of the opportunities offered by digital innovation, because even in the EU, legislation is necessary but insufficient. It does not cover everything (nor should it), and agents should leverage digital ethics in order to assess and decide what role they wish to play in the infosphere, when regulations provide no simple or straightforward answer, when competing values and interests need to be balanced (or indeed when regulations provide no guidance) and when there is more that can be done over and above what the law strictly requires. This is why it is in the EU that a good use of soft ethics could lead to 'good corporate citizenship' within a mature information society.

Fourth, given the open future addressed by digital ethics, it is obvious that ethics foresight analysis-based on data analytics applied strategically to the ethical impact assessment of digital technologies, goods, services and practices (see Fig. 3)-must become a priority (Floridi 2014). For the task of digital ethics is not simply to 'look into the [digital] seeds of time / And say which grain will grow and which will not' ( Macbeth, I.3, 159-162), it also to determine which ones should grow and which should not.

Or to use a metaphor already introduced above, the best way to catch the technology train is not to chase it, but to be at the next station. In other words, we need to anticipate and steer the ethical development of technological innovation. And we can do this by looking at what is actually feasible, privileging, within this, what is environmentally sustainable, then what is socially acceptable and then, ideally, choose what is socially preferable (see Fig. 4).

We do not yet have an infosphere equivalent for the concept of sustainability of the biosphere, so our current equation is incomplete (see Fig. 5).

In Fig. 4, I suggested that we interpret the x as social 'preferability' but I am aware it is just a place holder for a better idea to come. This may take a while, given that 'the tragedy of the commons' was published in 1968 but the expression 'sustainable development' was only coined by the Brundtland Report almost 20 years later, in 1987 (Brundtland 1987). Yet the lack of conceptual terminology does not make the governance of the digital a mere utopian effort. In particular, digital ethics, with its values, principles, choices, recommendations and constrains already influences the world of technology much more than any other force. This is because the evaluation of what is morally good, right or necessary shapes public opinion-hence the socially acceptable or preferable and the politically feasible, and so, ultimately, the legally enforceable, and what agents may or may not do. In the long run, people (as users, consumers, citizens, patients, etc.) are constrained in what they can or cannot do by organisations, e.g. businesses, which are constrained by law, but the latter is shaped and constrained by ethics, which is where people decide in what kind of society they want to live (see Fig. 6). Unfortunately, such a normative cascade becomes obvious mainly when backlash happens, i.e., mostly in negative contexts, when the public rejects some solutions, even when they may be good solutions. A normative cascade should instead be used constructively, to pursue the design of a mature information society of which we can be proud.

From all this, it follows that ethics in general and digital ethics in particular cannot be a mere add-on, an afterthought, a late-comer, an owl of Minerva that takes its flight only when the shades of night are gathering, only once digital innovation has taken place, and possibly bad solutions have been implemented, less good alternatives have been chosen, or mistakes have been made. Nor can it be a mere exercise of questioning. The building of critical awareness is important but it is also only one of the four tasks of a proper ethical approach to the design and governance of the digital. The other three are signalling that ethical problems matter, engaging with stakeholders affected by such ethical problems, and, above all, providing sharable solutions. Any ethical exercise that in the end fails to provide some acceptable recommendations is only a preamble. So digital ethics must inform strategies for the development and use of digital technologies from the very beginning, when changing the course of action is easier and less costly, in terms of resources and impact. It must sit at the table of policy-making and decision-taking procedures from day one. For we must not only think twice but, most importantly, we must think before taking important steps. This is particularly relevant in the EU, where I have argued that soft ethics can be properly exercised and where SETI (science, engineering, technology and innovation) developments are crucial. If soft digital ethics can be a priority anywhere, this is certainly in Europe. We should adopt it as soon as possible.

Notes

Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), OJEU L119, 04/05/2016.

References

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Authors and Affiliations

1. Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK

Luciano Floridi

2. The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK

Luciano Floridi

Corresponding author

Correspondence to Luciano Floridi.

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Soft Ethics and the Governance of the Digital - Philosophy & Technology

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Philosophy & Technology 31, 1-8 (2018) Cite this article 25k Accesses 111 Citations 100 Altmetric Metrics Today, in any mature information society (Floridi 2016), we no longer live online or offline but onlife, that is, we increasingly live in that special space, or infosphere, that is seamlessly analogue and digital, offline and online.

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What is records disposal?

Disposal is a range of processes associated with implementing records retention, destruction or transfer decisions, which are documented in the University Records Retention and Disposal Authority (RDA).

Who is responsible for disposal?

All University staff share a responsibility for records management, including disposal. Each Faculty or Chancellery work unit is encouraged to integrate a regular records disposal program into their routine operations to ensure that records are disposed of appropriately and in a timely manner.

Note: If you have a substantial amount of disposal work to undertake, Records & Information can do a survey of the records and create a Transfer and Disposal Proposal. This would include an estimate of what your project would cost to your unit using casual staff experienced in records management work, and other requirements. If this option is taken up, we then put you in touch with a suitable person from our casuals register and would provide guidance to them.

When should disposal be undertaken?

It is good practice to attempt short and simple projects and to conduct regular (eg, annual) disposal, rather than to allow records to accumulate. This will help reduce the risks of over-retention. See this KBA for guidance on over-retention of University records (accessible to University staff).

Occasionally, additional ad hoc records disposal activities may be required. For example, when:

Tools for disposal

University staff have two main tools which they can use to help with records disposal.

Retention and Disposal Authority (RDA)

Normal Administrative Practice (NAP)

What is the disposal process?

The process of records disposal can be broken down into the following tasks:

1. Identify records

You need to identify your records before you can make any decisions about how to manage them. This means establishing the following:

The type of records

Where the records are stored

How many records there are

Any other relevant information.

2. Use NAP

Once you have identified the records you want to dispose, review them to see if any are ephemeral, and can be destroyed under Normal Administrative Practice (NAP).
Normal Administrative Practice

3. Sentence using the University Records RDA

The next step is to sentence the records. This process consists of classifying and establishing the record class, retention period and disposal action.

The University Records RDA will guide you through the process.
Using the University Records RDA
In the RDA, records will be classed as either 'Temporary' or 'Permanent':

Temporary - time expired (ie has been kept for the required retention period specified in the RDA)

Destroy

Temporary - not time expired

Store the records until they are time expired

Permanent - still required for business use

Retain in work unit until business use concludes

Permanent - not required for business use

Transfer to Records & Information

4. Undertake disposal action

Once you have sentenced the records to establish the record class and retention requirements, then you can carry out the disposal action:

Further information

If you require further information or advice on records disposal, please contact us: records-info@unimelb.edu.au

Note: Advice on records disposal is limited to University of Melbourne records only. Records created by non-University organisations (except where the University has dealings with them) are not covered.

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NAA Retaining, managing and disposing of data and datasets

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NAA Retaining, managing and disposing of data and datasets

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Article Processing Charges (APC) are charges for making an article open access. In a fully open access journal these may be the only source of income for the journal. In hybrid journals, they are an additional income source to subscriptions.

Author Accepted Manuscript (AAM) is the version of the article accepted for publication including all changes made as a result of the peer review process. It has not gone through the journal's copy-editing process. The AAM is the version of the work that can most commonly be deposited in a repository.

Creative Commons are a set of licences that determine what a user or reader can do with an artefact. Copyright owners can apply the appropriate licence to their work to ensure it is as protected or as open as they wish.

Data custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules.

Data Management Plans are a document that sets out the types of data that will be used and generated within a research project and describes how this will be stored, managed and shared. This is a living document and is likely to change throughout the research project. Some funders require a Data Management Plan for a grant application.

Data stewards have accountability and responsibility for data and processes that ensure effective control and use of data assets.

Embargoes are a restriction imposed by publishers on the public release of an AAM. The length of any given embargo is arbitrary, and embargo lengths differ according to funding sources, disciplines and publishers. Libraries spend time determining and implementing embargoes on AAMs deposited to repositories.

Green open access refers to the deposit of an Author Accepted Manuscript into a repository to make it accessible. In some cases, publishers impose an embargo on the public release of the document.

Grey/Gray literature consists of research outputs that sit outside the published literature. Working papers, discussion papers, technical papers, presentations and submissions are some examples of grey literature.

Gold open access is where an author publishes their article in an online open access journal. The article is immediately accessible upon publication, at no charge to the reader, and will be so in perpetuity. To publish in some of these journals, the publisher charges the author's institution or funding body an Article Processing Charge. However, this is only one business model, and many open access journals will be free to publish in. A comprehensive list of journals is available through the Directory of Open Access Journals (DOAJ).

Hybrid open access refers to a publishing model in which subscription-based journals allow authors to make individual articles open access immediately through payment of an Article Processing Charge. Hybrid open access results in "double dipping" where the university pays twice, both for the subscription to the journal and the APC.

Institutional repositories are hosted within an institution to collect the research outputs of that institution. They often collect a broad range of digital items such as articles, books and chapters, reports, grey literature and creative outputs. Institutional repositories historically have been built on software platforms that were open source, such as DSpace or ePrints, but there are now commercial providers in the market, such as bepress (owned by Elsevier) and Pure (owned by Elsevier). The institutional repository of the University of Melbourne is Minerva Access.

Metadata is information about data. It describes the data and allows the data generator and other to understand the data at a later stage.

Minerva Access is the University of Melbourne's Institutional Repository. It aims to collect, preserve, and showcase the intellectual output of staff and students of the University of Melbourne for a global audience. Minerva Access is indexed by Google Scholar, and other search engines. It provides view and download statistics and a permanent URL for research outputs.

Elements is the University of Melbourne's research outputs management system. It is the tool for the collection of and reporting of data on all research outputs published by the University, and the method by which certain outputs are made publicly available in Minerva Access, the institutional repository.

Offsetting agreements are arrangements between publishers and institutions to mitigate the level of extra payments to publishers above subscription costs. They recognise that research-intensive institutions are spending considerable amounts on APCs in addition to subscription costs. Offsetting agreements are usually in place where there is central management of the payment of APCs. Generally Australian institutions do not have many offsetting agreements with publishers.

Open Access refers to the availability of Research Outputs via the internet, such that any user can find, freely access, read and download the output without charge. Any use or reuse is subject to full and proper attribution, and any licence terms stated to apply, such as the Creative Commons suite of licences. Where open licensing (e.g. Creative Commons) permits, users may also copy, distribute, print, search, link, crawl, mine and otherwise use and reuse the Research Outputs both manually and using automated tools. Open access can be achieved through different methods, see definitions of 'Green Open Access' and 'Gold Open Access'.

Pre-print is a version of an article that is shared on a pre-print server such as arXiv, bioRxiv or the like.

Pre-print servers tend to be discipline specific. The longest established pre-print server is arXiv, which has been operating since 1991. Pre-print servers offer a way for authors to share their work and receive comments from other researchers. Generally, pre-print servers will allow the upload of subsequent versions of the work. A Submitted Manuscript may be preceded by multiple versions of a pre-print.

Published research refers to the following categories from the University of Melbourne "Research Outputs collection Classification Guidelines": A1- Authored research books, A - Other book categories, B1- Research book chapters, B - Other book chapter categories, C1 - Journal articles, refereed, F1 - Conference publications, full written papers, refereed.

Repositories are databases used to share information about research articles and the articles themselves. They generally fall into two categories, institutional repositories and subject based repositories.

Research data is data that is generated in a research project. It is, in itself, a research output. (See definition of Research Data in the Management of Research Data and Records Policy (MPF1242)).

Research Data Management refers to the storage, access and preservation of data produced from a given investigation. Data management practices cover the entire lifecycle of the data, from planning the investigation to conducting it, and from backing up data as it is created and used to long term preservation of data deliverables after the research investigation has concluded. Specific activities and issues that fall within the category of data management include: File naming (the proper way to name computer files); data quality control and quality assurance; data access; data documentation (including levels of uncertainty); metadata creation and controlled vocabularies; data storage; data archiving and preservation; data sharing and reuse; data integrity; data security; data privacy; data rights; notebook protocols (lab or field). RELATED TERM: Data stewards.

Researchers includes all staff employed by the University of Melbourne in a research capacity, including Graduate researchers.

Research outputs are any work produced during the research project. They are diverse in nature and range from research articles, books, book chapters, research data, software, protocols, creative works and others.

Subject repositories are developed by members of a particular discipline. They tend to differ from a pre-print server in that they are a location for the deposit of work rather than providing an ability to comment on the work. Examples include arXiv and Pubmed Central.

Submitted Manuscript is the version of an article sent for peer review. In the past this was sometimes called a pre-print. However, the term pre-prints now has a specific different meaning.

Non Traditional Research Outputs (NTRO) in this context are broader than those NTROs defined by the ARC ( https://dataportal.arc.gov.au/ERA/NationalReport/2018/pages/section1/non-traditional-research-outputs-ntros/) and include the following categories from the University of Melbourne "Research Outputs collection Classification Guidelines": F - Other conference publications, C - Other journal contributions, D - Reference works, G - Reports, J - Original Creative works, Jii - Creative Recorded Works, K - Curated or produced Public Exhibitions and Events.

Version of Record is the final published version of the work. Unless there is a licence that permits sharing of the work, such as a Creative Commons Attribution (CC-BY) licence, then generally this version cannot be shared.

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Article Processing Charges (APC) are charges for making an article open access. In a fully open access journal these may be the only source of income for the journal. In hybrid journals, they are an additional income source to subscriptions.

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Researchers are tasked with collecting, storing and the retention of research data beyond the completion of their research project. Do you know exactly how long you should retain your research data? Carmela Gallo from the University of Melbourne's Legal and Risk team offers key advice on research data management for researchers.

According to Management of Data and Information in Research: A guide supporting the Australian Code for the Responsible Conduct of Research (2.3 Storage, retention and disposal),
"The period for which data should be retained should be determined by prevailing standards for the specific type of research and any applicable state, territory or national legislation. In general, the minimum period for retention of research data is 5 years from the date of publication."
However, researchers should not be misled in believing that all research data retention period requirements are 5 years from the date of publication based on the above statement. As a researcher at the University of Melbourne, you need to be aware that the University of Melbourne Records Retention & Disposal Authority provides the legal mechanism for the retention and disposal of university records in accordance with the Victorian Public Records Act 1973. This includes research data.

By applying the disposal classes to your research data, you will be meeting your compliance obligation. Below is a summary of the retention periods for research data from the University of Melbourne Records and Retention Disposal Authority

Includes data created that is:

part of genetic research, including gene therapy

controversial or of high public interest,

costly or impossible to reproduce

relates to the use of an innovative technique for the first time,

of significant community or heritage value to the state or nation

required by funding or other agreements to be retained permanently

Temporary - Minimum 15 years after completion of research activity

Temporary - Minimum 15 years after the child reaches the age of 18

Temporary - Minimum 5 years after completion of research activity

... under certain circumstances

However, under certain circumstances, there may be other factors that will override these retention periods for research data. Below are two examples:

Are there legal requirements by funders such as government bodies and publishers that require a different retention period than those specified in the University Retention and Disposal authority? If so, is this well documented?

The University has recently adopted a set of open access principles that encourages sharing of data (if appropriate) beyond its publication. If you decided to put your research on open access, consider how long does the research data need to be made accessible and why? The retention period and justification should be documented.

Researchers will need to consider regulatory responsibilities, ethical, legal, cultural and other guidelines when determining how long to retain research data. The University of Melbourne Records Retention and Disposal Authority is one of those guidelines. From a records perspective, it is important that researchers determine and document the retention and disposal requirements from the beginning. It is important that this information is readily available into the future.

For more information on research data management, you can attend this upcoming Researcher@Library webinar, 'File Management 101: Taming the digital chaos' on Thursday 23 July, 2020 at 1pm with Peter Neish from the Digital Scholarship team.

About the author

Carmela Gallo works as a Records Analyst in Records & Information, Legal and Risk at the University of Melbourne. For more information on research data and record-keeping, subscribe to the Records Services Update e-newsletter by emailing records-info@unimelb.edu.au or browse past editions available from their website.
Image credit: Andrew Pons on Unsplash

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Research Data - How long should it be kept?

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Researchers are tasked with collecting, storing and the retention of research data beyond the completion of their research project. Do you know exactly how long you should retain your research data? Carmela Gallo from the University of Melbourne's Legal and Risk team offers key advice on research data management for researchers.

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Sign in with your University of Melbourne account to access Promapp

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Compliance - by securely storing your inactive research data long term, Attica helps you meet any ethics considerations or regulatory requirements attending the data.

Security - access is restricted and the platform is more secure than desktop or USB/disk storage.

Safety - the platform helps to keep your data safe from physical damage, alteration, unauthorised access, deterioration or accidental loss.

Convenience - the University provides and maintains Attica on behalf of researchers, we support any digital file types from databases to images and test results.

Cost effectiveness - the platform is free for researchers and graduate research students.

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Research data management refers to how you handle, organise, and structure your research data throughout the research process. 

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Research data management refers to how you handle, organise, and structure your research data throughout the research process. 

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Data management:Begins with your initial considerations regarding what will be necessary for using or collecting your particular type of data;Includes measures for maintaining the integrity of the data, making sure that they are not lost due to technical mishaps, and that the right people can access the data at the appropriate time;Looks forward to the future, making it clear that you should provide detailed and structured documentation to be able to share your data with other colleagues and prepare them for long-term availability.

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Research data management refers to how you handle, organise, and structure your research data throughout the research process. Data management:

Begins with your initial considerations regarding what will be necessary for using or collecting your particular type of data;

Includes measures for maintaining the integrity of the data, making sure that they are not lost due to technical mishaps, and that the right people can access the data at the appropriate time;

Looks forward to the future, making it clear that you should provide detailed and structured documentation to be able to share your data with other colleagues and prepare them for long-term availability.

To make your research as time-efficient, reproducible and safe as possible, it is important that your data management is well thought through, structured, and documented. A good data management strategy takes into account technical, organisational, structural, legal, ethical and sustainability aspects. The time invested in setting up a good data management strategy pays off when the time comes to reproduce your analysis and results. You will be able to easily find and understand your data, increase your data's reuse potential and comply with funder mandates at the same time.

Data Management Plan

Data Management Plans (DMPs) are a key element of good data management | European Commission, 2016.

Information regarding your data management needs to be easily found and understood, not least if you are working on a project that runs over several years and involves a large team of people. In order to simplify data management, a Data Management Plan (DMP) can be created early in the research process. A DMP is a formal document that provides a framework for how to handle the data material during and after the research project. The way a DMP will look once it is finished is not universal. It is a "living" document that changes together with the needs of a project and its participants. It is updated throughout the project to make sure that it tracks such changes over time and that it reflects the current state of your project.

A lot of diversity exists in DMPs because they are always built around the particular needs of the data collected within your project. Sometimes there are particular requirements from stakeholders that have to be answered in the DMP from stakeholders such as:

Funders may require a Data Management Plan (sometimes called Data Publication Plan (DPP)) to get information on what data you intend to collect and whether (and how) you will make those data accessible to others. In this case you provide the funding agency with whatever information they require, to the extent that they specify. Depending on the nature of the call, such plans may include not only details on the kind and volume of data to be produced but also how the datasets will be documented and shared (along with other research outputs of the project, such as publications, program code, and educational resources). They may specify the length of the DMP or may expect you to include it in the page count of the scientific plan.

A DMP written for the funder is not always the same type of comprehensive DMP which is described in the list of questions to this guide (CESSDA, 2018a). However, the list can be used as a support when writing the DMP/DPP that the funder(s) require(s). Click here for the editable version (CESSDA, 2018b). Note that some funders might require that an updated DMP/DPP to be submitted as a deliverable within a specific time period. See ' Diversity in funder requirements' for more information.

Your institution may have its own policy regarding data management, including what information should be gathered and archived together with research data and publications. It's possible that your institution can support you with writing a DMP, e.g. by providing expertise or (referring to) safe storage services.

The added value of a Data Management Plan

Several researchers who I have been talking to and have looked at the Data Management Planning checklist of the Swedish National Data Service (SND) have said that doing so made them start thinking of data security, data ownership, file formats etc. before the start of their project. By doing so they avoided some possible problems that would otherwise appear later on | Ulf Jakobson, Data manager humanities, SND.

A Data Management Plan (DMP) offers added value in the following ways:

Taking the time to plan ahead can save you a great deal of headache once the project is up and running.

Overall, a DMP helps you plan for the resources, tools, and expertise that are required to store, handle, and manage the given types and volumes of data that are expected to be collected. A DMP serves as a tool to pay careful attention to all aspects of data management. It makes you aware of possible problems at an early stage so that you can work around them. E.g. it reminds you to gain consent for future reuse and sharing from research participants.

By thinking early about various aspects of data management, you can ensure that the material is well-managed already during the data collection period. Structured and well-documented data enable others to understand the materials more easily. This, in turn, facilitates the preparation of the material for archiving, and enables further research after the end of the project.

An important function of a DMP is to work as a one-stop shop to find project-related information. Research becomes so much easier if all of your questions surrounding managing your data are being gathered in one place and project-related details are readily available rather than just vaguely remembered or simply forgotten.

A DMP is an efficient way for the researcher and his/her team to gain control over research data collection and management when the research project is up and running. Regardless of the size of the team there will be a need for easily found data-related information regarding file locations, naming conventions, standards, project description, project roles, backup regimes, versioning and so on. By writing a DMP, the researcher can ensure that the material is well-managed during the research period, which also facilitates the preparation of the material for archiving, and thus enables further research after the research project has ended. Also, it is usually easier to document research material if this is done in close proximity to the steps in the research process that create or change the material.

Project management becomes easier if you also include administrative information such as the names and ORCIDs of the Principal Investigator(s) and project members, information on which institution owns the data, registration numbers for funding and ethics board approvals. Furthermore, a lot of relevant information is kept in log books, code lists, technical reports and other documents. These documents can be referred to in the DMP together with their location information. Keeping all relevant information regarding your project in one place makes future reference a lot easier, whether that future reference is for your own thesis in three years, for an audit in five years or a historical study in fifty years.

Data management is not free. You do not want to find yourself running out of funding before the end of the project because you have ignored or underestimated the cost of structured, detailed, and safe data management. Therefore, an important aspect of a DMP is its use in calculating how much money will be required for managing your research data during your research project.

A DMP can be useful in the process of applying for funding. Grant applications should not only include time and resources for collecting, analysing, and publishing on data in their budget, time and resources for careful documentation as well as server space, backup solutions, and documentation software need to be included as well. A DMP is also useful once funding is granted to plan and manage your expenses. Many research funders require a DMP as part of the application and decision-making process. The arguments for making data available are several, the most popular being that the data produced by public funds should be used to the greatest extent possible and available to the public. Unless there are legal, ethical or commercial barriers, data should also be openly available so that research results can be verified, replicated and reused.

Examples of Data Management cost assessments are given by the University of Utrecht (n.d.) and the Dutch Landelijk Coördinatiepunt Research Data Management ( LCRDM, 2020) inspired by the ' Data management costing tool' by UK Data Service, 2013.

A DMP allows you to think through beforehand how to provide a dataset to a data repository which is as FAIR as possible. A DMP:

Makes structuring and documenting of your datasets simpler, thus making it easier for others as well as your future self to find and understand the material;

Encourages you to think about the data format which is best suited for reuse;

Allows you to think about the reuse license you would want to apply to your data;

Etc.

If you draw up a DMP, you are showing your affiliated institution, funders and project partners a serious approach to research data management, that includes a responsible approach towards research funds and research participants.

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Benefits of data management - Data Management Expert Guide

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Research data management refers to how you handle, organise, and structure your research data throughout the research process.

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https://research.unimelb.edu.au/facilities/support/researcher-development-unit

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The Researcher Development Unit (RDU) is a resource for all members of the University of Melbourne research community, including graduate researchers.

We empower researchers to thrive at all the different stages of their diverse careers by illuminating opportunities and supporting them to negotiate challenges. We recognise that it can sometimes be difficult to see the resources that are available and to find mentors and allies along your chosen path.

We provide meaningful opportunities for development, so that all members of the research community can flourish in mutually beneficial ways leading to discoveries that make an impact by changing the world for the better.

The Research Development Unit supports the co-ordination, delivery and evaluation of programs across all academic career stages and levels. We do this by working in partnership with Faculties, PhD Programs and other professional development providers.

We aim to support researchers to:
Get in touch with us
If you'd like to collaborate with our team on a researcher development offering, or have a question for us, get in touch via email on researcher-development@unimelb.edu.au.

First published on 1 February 2023.

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Researcher Development Unit | Research Support | Melbourne

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The Researcher Development Unit empowers researchers to thrive at all stages of their diverse careers by illuminating opportunities for academic career development.

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23 Research Things is an online learning programme for graduate and academic researchers at the University of Melbourne, showcasing resources, strategies and tools that support research. 23 Research Things can provide you with frameworks for your research practice, new platforms for you to experiment with, and different ways of working. Blogs are written by University experts including academics, librarians, data scientists and research support professionals. Whatever your career stage or discipline, the programme aims to help you develop a strategic approach to managing your research.

The next iteration of 23 Research Things will be released in August 2023. Sign up to the mailing list to receive updates in your inbox and make sure you never miss a post.

23 Research Things was originally launched in March 2014 as a series of weekly posts for all members of the University of Melbourne. Responding to popular demand, 23 Research Things returned in 2017 and 2020 to showcase even more resources for research. 23 Research Things is an initiative of the University of Melbourne Library and was inspired by the Bodleian Library's 23 Things for Research and the DH23 Things programme at the University of Cambridge.

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23 Research Things is an online learning programme for graduate and academic researchers at the University of Melbourne, showcasing resources, strategies and tools that support research. 23 Research Things can provide you with frameworks for your research practice, new platforms for you to experiment with, and different ways of working.

# data[].attributes.attachment_link.readable_text
23 Research Things is an online learning programme for graduate and academic researchers at the University of Melbourne, showcasing resources, strategies and tools that support research. 23 Research Things can provide you with frameworks for your research practice, new platforms for you to experiment with, and different ways of working. Blogs are written by University experts including academics, librarians, data scientists and research support professionals. Whatever your career stage or discipline, the programme aims to help you develop a strategic approach to managing your research.

The next iteration of 23 Research Things will be released in August 2023. Sign up to the mailing list to receive updates in your inbox and make sure you never miss a post.

23 Research Things was originally launched in March 2014 as a series of weekly posts for all members of the University of Melbourne. Responding to popular demand, 23 Research Things returned in 2017 and 2020 to showcase even more resources for research. 23 Research Things is an initiative of the University of Melbourne Library and was inspired by the Bodleian Library's 23 Things for Research and the DH23 Things programme at the University of Cambridge.

# data[].attributes.attachment_link.description
23 Research Things is an online learning programme for graduate and academic researchers at the University of Melbourne, showcasing resources, strategies and tools that support research. 23 Research Things can provide you with frameworks for your research practice, new platforms for you to experiment with, and different ways of working.

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Submit general RDM enquiries via ServiceNow

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Submit general RDM enquiries via ServiceNow

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Alternatively, you can also contact the RDM Program team via email (rdm-program@unimelb.edu.au)

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Sign in with your University of Melbourne account to access ServiceNow

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UoM Research Data Management Policy (MPF1242)

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UoM Research Data Management Policy (MPF1242)

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1.1 The objectives of this policy are to:

(a) Preserve the value of research data and records for researchers, research students, research participants, the University and wider community by defining expected standards for their management; and

(b) Facilitate effective research practices.

2.1. This policy applies to:

(b) Research data and records in any form, including digital formats, paper formats and other physical materials.

2.2. This policy does not apply to:

(a) Research where responsibility for data has been allocated to a third party through a written agreement.

3. Authority

3.1. This policy is made under the University of Melbourne Act 2009 and the Vice-Chancellor Regulation and supports compliance with:

(a) Legislation

(b) Research codes of practice

4.1. Researchers and the University are responsible for ensuring that research data and records are:

(a) Accurate, complete and authentic

(b) Understandable, retrievable and accessible, for as long as is required

(c) Safe and secure

(d) Compliant with ethical and legal obligations

4.2. Failure to comply with this policy may:

(a) Lead to researchers or the University being held legally responsible for breaches of legislation.

(b) Be considered a breach under the Australian Code for the Responsible Conduct of Research and be investigated in line with the Guide to Managing and Investigating Potential Breaches of the Australian Code for the Responsible Conduct of Research.

(c) Be handled in line with the Appropriate Workplace Behaviour Policy () or the Student Conduct Policy () .

5. Procedural Principles

Ownership, responsibility and control

5.1. Researchers must ensure that ownership of and responsibility for research data and records is identified and documented at the start of a research project and reviewed and updated as appropriate, with consideration given to:

(a) Authority to decide on storage, retention, disposal, publication or licensing of research data or records;

(c) Agreements with funders, data providers, research partners and collaborators;

(d) Arrangements for researchers changing institutions or withdrawing from collaborative projects; and

5.2. Researchers must ensure that the University has a record of ownership and responsibility for any research data and records they have transferred into the University's control (e.g., for storage on University facilities).

(a) Where no ownership or responsibility has been recorded, or the recorded responsible party is no longer a University of Melbourne researcher, the relevant Dean or delegate will hold authority to decide on storage, retention, disposal, publication or licensing arrangements in compliance with legal and regulatory obligations.

Storage, retention and disposal

5.3. Researchers must curate and store accurate, complete and authentic research data and records in formats that are understandable, retrievable and accessible to appropriate parties.

(a) Metadata should be stored with research data and records to support interpretation, authenticity and reproducibility.

(b) Where it is not practical to store physical research data and records, durable records documenting or derived from them should be stored in digital formats.

5.4. The University must provide facilities, advisory services and resources for the safe and secure storage and management of research data and records, to support researchers in complying with their ethical and legal obligations.

5.7. Researchers are encouraged to deposit research data and records into suitable University facilities at the conclusion of a research activity/project, to support the meeting of retention obligations. Where research data and records are already stored on University facilities, researchers must ensure that the research activity's completion is recorded in University registers/research management systems to allow for appropriate retention and disposal.

5.8. Researchers leaving the University should ensure that a copy of their research data and records has been deposited into University facilities to support research integrity and retention obligations, as consistent with ethical, contractual or legislative requirements.

Safety and security

5.9. Researchers must ensure the safe and secure management of research data and records to comply with ethical and legal obligations over the life of the research data and records, with consideration given to research data and records with sensitivities, including:

(a) Personal information subject to privacy legislation, including information that may be considered personal information when linked with other information;

(b) Sensitive cultural information, e.g., on sacred cultural practices;

(c) Sensitive ecological information, e.g., the location of vulnerable species;

(d) Information subject to export controls, as regulated by the Defence Trade Control Act ;

(e) Information on security sensitive biological agents (SSBAs), as regulated by the National Health Security Act ; and

(f) Commercial-in-confidence information.

5.10. Researchers should document plans for the safe and secure management of research data and records, and ensure all authorised individuals with access follow documented plans.

5.13. The University must assess University facilities against the University Research Data Classification Framework , to support researchers in determining which facilities are suitable for research data of different sensitivity levels. When utilising suitable University facilities, the safe and secure management of research data is a joint responsibility between researchers and the University.

Access by interested parties

5.15. When making research data or records available to interested parties, consideration should be given to:

(a) Data ownership, e.g. intellectual property rights, Indigenous intellectual and cultural property rights;

(b) Agreements with funders, research partners, data providers or publishers;

(c) Meeting ethical and legal obligations, e.g., preserving privacy, intended use and consent for use of data at the time of collection; and

(d) Ensuring safety and security, e.g., through agreements with interested parties that define required controls;

5.16. Researchers must make available any research data and records substantiating research findings to enable academic discussion or evaluation of research outputs, unless prevented by ethical or legal obligations. Where research data or records have been requested and access refused, the reasons must be transparent and justifiable.

5.18. Researchers are encouraged to publish research data and records to disciplinary, institutional or other established repositories to allow reuse by other researchers and maximise the value of research, unless prevented by ethical or legal obligations.

(b) Researchers should publish research data in formats that meet disciplinary standards, as well as being , accessible , interoperable and .

(c) Researchers should consider applying the least restrictive licensing option that is appropriate for governing the future use of their published research data.

Research students

5.19. The same procedural principles outlined above for researchers also apply to research students, with the exception that all responsibilities are jointly held by research students and their supervisors:

6. Roles and Responsibilities

7. Definitions

POLICY APPROVER

Deputy Vice-Chancellor (Research)

POLICY STEWARD

Pro-Vice-Chancellor (Research Capability)

This policy is to be reviewed by 31 May 2025.

VERSION HISTORY

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1.1 The objectives of this policy are to: (a) Preserve the value of research data and records for researchers, research students, research participants, the University and wider community by defining expected standards for their management; and (b) Facilitate effective research practices. 2.1.

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The Research Data Services Directory also has a provision to group specific services that may be pertinent to researchers in MGSE. The RDM team is able to work with Faculty leadership to accurately determine the relevant service or tools.

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This site uses and shares cookies and similar technologies to personalise your experience, advertise to you and provide content from third-parties as well as analyse our usage. You consent to our use of such technologies by proceeding. You can change your mind or consent choices at any time. Visit o…

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https://gateway.research.unimelb.edu.au/platforms-data-and-reporting/data-and-computation/research-data-management

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ARDC: Data Management PlansWhat are data management plans?

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ARDC: Data Management PlansWhat are data management plans?

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A data management plan (DMP) is a living document for a research project, which outlines data creation, data policies, access and ownership rules, management practices, management facilities and equipment, and who will be responsible for what.

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What are data management plans?

A data management plan (DMP) is a living document for a research project, which outlines data creation, data policies, access and ownership rules, management practices, management facilities and equipment, and who will be responsible for what.

Here are some examples of DMPs specific to a research domain:

Many Australian universities have data management policies and tools available for researchers who need to create a DMP at the start of a research project.
Read publicly available data management policies from Australian research institutions.
Why do I need a data management plan?

Data management planning is an essential component of delivering a successful research project. A DMP can help ensure that research data generated is well managed and can be reused efficiently.

In the past, data management was typically done at the last minute and using the first method that came to mind - an approach that can be time-consuming and error-prone. Taking time at the start of a research project to put in place robust, easy-to-use data management procedures can pay off in the long run.

Inadequate data management can lead to catastrophes like the loss of data or the violation of people's privacy.

Good data management can also be essential for funding. The National Health and Medical Research Council and Australian Research Council both require projects seeking funding to comply with the Australian Code for the Responsible Conduct of Research.

What should a data management plan cover?

A DMP should include:

a back-up strategy

an existing data survey

outline of data to be created

file format guidance

metadata instructions

ownership, access and security information

data organisation and naming conventions

information on managing data transfers and synchronisation between machines

guidelines for collaborative writing with colleagues

version control

data storage locations

hardware budget and management information

bibliography management tools

data sharing, publishing, archiving and licensing instructions

data destruction rules

responsibility allocations

a budget for the overall DMP.

Infrastructure approaches

DMPs are evolving. Institutions are shifting from approaches that create static, stand-alone documents to integrated infrastructure tools connecting a suite of enterprise systems.

Machine readable DMPs can save time for researchers in provisioning research tools, software and safe data storage. A well-formed DMP can streamline the data publication process at journal submission time.

A report on Machine-actionable Data Management Plans (maDMPs) reflects collective thinking on this next generation approach.

Watch an ARDC webinar on infrastructure and DMPs.

Data Management Plans (DMPs) Interest Group

We support the DMPs Interest Group, which has been established for people interested in discussing, building and improving DMPs, including best practice and technical tools.

The group's Australasian membership includes data librarians, repository managers, infrastructure developers and anyone involved in managing or advocating for data management plans.

It meets 3 times a year for an unrecorded chat on DMP infrastructure realtalk: sharing current activities and discussing interesting problems.

Join the DMPs Interest Group.

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Data Management Plans | Australian Research Data Commons | ARDC

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Writing data management plans? Learn why DMPs are key to your research and what to include. Read institutional data management policies currently in force.

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Free training in R, Python, LaTeX, MATLAB, NVivo, 3D printing; accessible to all Unimelb staff and students.

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Drop in sessions: bring any digital HASS research related question!

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Drop in sessions: bring any digital HASS research related question!

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Run by members of MDAP, every Tuesday from 1-3pm in the Digital Studio

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The Digital Studio collaborates on digital and data projects with researchers in many diverse fields, provides support and advice on cutting-edge digital methodologies for humanities and social science research, and hosts international speakers, researchers, and interns. The tiles below provide useful guidance to current projects hosted by or in partnership with the Digital Studio.

Graduate Internship Projects

Each year the Digital Studio hosts a Graduate Internship program in which Arts researchers have the support of an intern to realise a small component of a digital project. In alignment with our strategy to develop emerging digital and data humanists it provides graduates with training and mentoring in digital methods and data science and allows them to contribute to innovations in project development. The tiles below provide insights into the 2020 projects.

Want to see more? Click below to take a look at internship projects from previous years.
All graduate internship projects
Want to get involved with the Digital Studio internship program? We welcome applications from research students to undertake the internship, and from academics and external organisations interested in hosting an internship and supervising an intern.
Find out more

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Digital Studio page in the Faculty of Arts site.

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https://library.unimelb.edu.au/news-and-events/data-publishing-store,-publish,-and-share-digital-materials

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This webinar, delivered by the Digital Stewardship (Research) team and Research Computing Services, is intended for graduate researchers and academic staff. You will learn how to manage, share, and publish research data, other supplementary materials, and non-traditional research outputs (NTROs). You will learn about the benefits and impact of publishing open and citable digital materials using University systems.

Topics include:

What is data publishing and what are the benefits?

How to increase research visibility and impact with open and accessible data.

Accessing and managing private and sensitive data.

Storage and management of research data.

Register for the session using the book now link to the left. Once you're registered, you will receive a Zoom invite. Check your university email account a few days before your session to find your meeting invitation.

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Data Publishing: store, publish, and share digital materials

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Data Publishing: store, publish, and share digital materials

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The Research Data Classification Tool is part of the University of Melbourne's Research Data Classification Framework. Using this tool will help you assess the sensitivity of your research data and provide you with guidance on how to manage your research data safely and securely.

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Secure Research Environment

The University's Secure Research Environment (SRE) is a highly secure, isolated virtual desktop environment with on premises storage and scalable computational resources.

The SRE has been assessed by the UoM Cybersecurity team as being suitable for the processing, analysis and storage of research data classified up to 'Red' by the University's Research Data Classification Framework. The SRE is also in the process of ISO27001 certification, the industry best practice standard for certification of information security management systems.

The SRE offers many features to ensure the protection of sensitive research data while enabling research:
- On-premises dedicated infrastructure with per project isolation
- Secure remote access to your research data 24/7 from anywhere in the world
- Scalable based on your project's computational and storage requirements
- Access by authorised users only with logged and monitored activity.
- Restricted internet and internal UoM network access.
- Data in the SRE never leaves the UoM network and transfer of data out of the system is mediated through a governed approval process with a nominated data custodian. This process ensures only non-identifiable outputs are removed.
- Enhanced cybersecurity controls including multi factor authentication, firewall protection, data encryption, 24/7 monitoring and more.

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The University's Secure Research Environment (SRE) is a highly secure, isolated virtual desktop environment with on premises storage and scalable computational resources. The SRE has been assessed by the UoM Cybersecurity team as being suitable for the processing, analysis and storage of research data classified up to 'Red' by the University's Research Data Classification Framework.

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This is a really important space to bookmark and get your links from on General Administration, Graduate Research, Research Contracts and Funding, Research Outputs, Researcher Profile, Additional Systems & Tools, New Systems & Tools Delivery.

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23 Research Things is an online learning programme for graduate and academic researchers at the University of Melbourne, showcasing resources, strategies and tools that support research. 23 Research Things can provide you with frameworks for your research practice, new platforms for you to experiment with, and different ways of working. Blogs are written by University experts including academics, librarians, data scientists and research support professionals. Whatever your career stage or discipline, the programme aims to help you develop a strategic approach to managing your research.

The next iteration of 23 Research Things will be released in August 2023. Sign up to the mailing list to receive updates in your inbox and make sure you never miss a post.

23 Research Things was originally launched in March 2014 as a series of weekly posts for all members of the University of Melbourne. Responding to popular demand, 23 Research Things returned in 2017 and 2020 to showcase even more resources for research. 23 Research Things is an initiative of the University of Melbourne Library and was inspired by the Bodleian Library's 23 Things for Research and the DH23 Things programme at the University of Cambridge.

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23 Research Things is an online learning programme for graduate and academic researchers at the University of Melbourne, showcasing resources, strategies and tools that support research. 23 Research Things can provide you with frameworks for your research practice, new platforms for you to experiment with, and different ways of working.

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Seek support, training and tailored advice on data stewardship, digital preservation, and exemplar research data management practices.

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https://www.unimelb.edu.au/mdap/hass-support

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Communicate your research in new ways. Experiment, push what your data can do, and make your research impactful by harnessing digital technologies.

Upcoming training

All our training sessions are listed on the Researcher Development Events Portal. If you have bespoke data or digital-related training needs for your staff or graduate research students, please reach out to us to discuss options at hass-taskforce@unimelb.edu.au

Let's visualize your data!

Thursday, 14 September, 2-5pm

In this session, the objective is for you to create a visualization of your data (or we will provide you with data) using your (or our, if you prefer) preferred programming language (R, Python, or Javascript) based on an example found on Google or generated by ChatGPT.
Register now

Web scraping: getting data from websites when there's no better option

Thursday, 21 September, 2-5pm

In this workshop, we will take you through how to plan a web scraping strategy based on the structure of a website and adapt pre-written code to meet your web scraping research needs.
Register now

Past workshops have included Introduction to APIs, small sessions on specific APIs (e.g., Spotify AI), Omeka bulk upload, Constellate, Data Visualisation, TLCMap, and Python for Humanities, Arts, and Social Sciences.

What you can expect

Improve technical expertise in data analytics, coding, data visualisation and more!

Hone critical thinking skills to generate impactful research outcomes

A team that recognises your unique research goals and ideas

Support to identify and overcome challenges or obstacles

Supportive tutors with experience in HASS fields - we speak your language

A supportive community of digital HASS researchers to tap into for support, advice and experience sharing

Access to knowledge, resources, and digital best practice.

Meet the team

Dr Jair Garcia, Research Data Specialist (HASS teaching), Melbourne Data Analytics Platform

Professor David Goodman, Director of the Digital Studio at the Faculty of Arts, and a professor of history at the School of Historical and Philosophical Studies

Dr Mar Quiroga, Research Data Specialist and HASS Taskforce Lead, Melbourne Data Analytics Platform

Contact us

Get in touch at hass-taskforce@unimelb.edu.au

We operate from Melbourne Connect with training delivered at the Digital Studio and other faculty-specific locations.

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Melbourne Centre for Data Science | MCDS

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Melbourne Centre for Data Science facilitates and engages in fundamental data science and interdisiplinary research and research training to drive discovery and enhance outcomes.

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MDAP Quick consultations, advice and short projects

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MDAP Quick consultations, advice and short projects

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Expression of Interest form to collaborate with the Melbourne Data Analytics Platform (MDAP) in 2022

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Researcher funded engagements: MDAP staff can be engaged on a salary cost-recovery secondment model, or a team of data specialists can be engaged at Academic Level C1 rates.

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Researcher funded engagements: MDAP staff can be engaged on a salary cost-recovery secondment model, or a team of data specialists can be engaged at Academic Level C1 rates.

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Below are FAQs for Researcher-funded Engagements. For our annual Collaboration round visit Faculty Supported Collaborations FAQs.

Who is eligible to apply for a Researcher-funded Engagement?

What kind of team will I get with a Researcher-funded Engagement?

What can I expect when collaborating with MDAP on a Researcher-funded Engagement?

How much does it cost to have a Researcher-funded Engagement with MDAP?

When can I submit a Researcher-funded Engagement request?

What happens once I submit a Researcher-Funded Engagement contact form?

What size project would suit a Researcher-funded Engagement with MDAP?

Is my data suitable?

Is this the only way to work with MDAP?

What is a Research Data Specialist?

What is 'data-intensive' research'?

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Below are FAQs for Researcher-funded Engagements. For our annual Collaboration round visit Faculty Supported Collaborations FAQs. Who is eligible to apply for a Researcher-funded Engagement?Currently, Researcher-funded Engagements are only available to University of Melbourne researchers. The lead researcher must hold an academic staff position of at least 0.2 FTE within the University of Melbourne.

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We work with researchers at all stages of the research lifecycle, from research design and data collection, all the way through to analysis, visualisation, and interpretation. With a broad range of backgrounds and experiences, we are uniquely placed to make the most of all the benefits that interdisciplinary research can offer!

We have several ways to help researchers with data and computational needs, from quick questions and short and informal consultations to 9-month formal collaborations and internal secondments. Engage with us.
Subscribe to our monthly newsletter
We hold technical expertise in:

Research study design

Data management

Data stewardship

Data collection methods

Web development

HPC and cloud computing

Digital infrastructure

Data analysis

Machine learning

Data visualisation

Image and video processing

Virtual/augmented reality

Natural language processing

Artificial intelligence
... and much more!

Latest news and events

We encourage researchers across the University to engage with us at any stage of their research project through one or more of the following engagement avenues and funding arrangements.

Research collaborations

Research projects lasting at least 3 months with full or part-time commitment from the MDAP team. There are two main ways to work with us here.

Faculty supported collaborations

Annual competitive round opens in September for collaborations to start the following year.

Researcher funded engagements

MDAP staff can be engaged on a salary cost-recovery secondment model, or a team of data specialists can be engaged at Academic Level C1 rates.
Researcher-funded engagements FAQs
For more information, read the FAQs then get in touch via the form below.

Quick consultations, advice and short projects

Working effectively with data can be complex, and it's easy to get stuck. We offer consultancy, expert advice, advocacy and involvement in shorter term research projects - whether you are planning a new project, deep within it, or wrapping up.

Depending on requirements and availability, we can provide hands-on assistance with small parts of your data-intensive project, either for free or at cost-recovery rates.

Faculty supported

Depending on capacity and capability, we can either help or redirect you to someone who can, including the Melbourne Coder Network.
Enquire here
Researcher funded

If you have research funding, you could consider paying for an engagement at cost-recovery rates (see above).

More about engagement types

Research collaborations

A significant fraction of MDAP is devoted to faculty-funded research collaborations. Once a year we run an open, merit-based based allocation scheme. Researchers can engage with us for 3 to 9 months with costs covered by their faculty.

For more info, watch our Introduction to Collaborations video or visit Faculty Supported Collaborations FAQs.

Researcher-funded engagements

We are available year-round to work with you on researcher-funded projects. There's two main ways to do this:

You can engage individual MDAP staff through secondments, charged at a salary cost-recovery rate.

MDAP can assemble a multi-disciplinary team of data specialists to collaborate on any research topic, charged at Academic Level C1 rates.

There are no set limits on the timing or length of funded engagements, subject to staff availability.

For more information visit Researcher-funded Engagements FAQs.

And...

If we don't have the expertise on hand, we can redirect to other individuals, groups and services across the University who may be able to help. For example the Melbourne Centre for Data Science, Research Computing Services, Digital Skills Training and the Melbourne Coder Network.

2023 Collaborations

Social network mapping social networks in disaster-affected communities

Stories of Hope: A participatory narrative inquiry into the emergence of hope during university transition

Computational methods for artwork authentication using hyperspectral imaging

Increasing comparative genomic resources for amphibians

Trans-diagnostic assessment of neurodevelopment with wearable eye-tracking technology

Analysing decisions with the LATER model: an open-source tool

Infrastructure and methods for forensic audio transcription experiments

Forecasting risks to savanna ecosystems in northern Australia under global change

Developing a convolutional neural network for the analysis of gonadal tissue

Modelling to assist Australia's transition to a more environmentally sustainable diet

Facilitating cognitive research in clinical environments: Automated scoring of the Autobiographical Memory Task

Socio-economic change and the realisation of climate risk

2022 Collaborations

Unlocking diverse herbarium specimen associated data to accelerate biodiversity and evolutionary research

Screening whole genome sequencing datasets for the Eukaryome of globally distributed kelp species

Infectious disease genomics: from database to phylodynamics

FATCAT: a focused pipeline to assess functional changes in coral-associated bacterial communities

Machine learning and patterns of primary care utilisation in cancer diagnosis and outcomes

Artificial intelligence-based image enhancement and segmentation

Pipeline for the refinement of predicted protein structures and evaluation of their accuracy

Tracking changes in conservative political rhetoric over time

Using the capability of deep learning to explore the predictive power of 180,000 eye images for dementia outcomes

Designing and Implementing an Interactive Data Platform for the Monitoring and Evaluating Climate Communication and Education (MECCE) Project

Improving outcomes for people with complex mental health issues: insights from SANE Australia's online and phone services

Fit for Students ... Using Canvas data to tailor disciplinary eLearning approaches

Caring for data now and for the future: Indigenous artistic cultural heritage data management, curation for access, and sustainability

Previous collaborations

If you would like to view our previous collaborations, please step this way.

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Melbourne Data Analytics Platform (MDAP) | Collaboration. Computing. Research Impact.

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We are a team of Research Data Specialists working to enable data-intensive research across the disciplines.

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https://codernetwork.researchsoftware.unimelb.edu.au/accounts/login/?next=/

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NEWS & EVENTS | Melbourne Data Analytics Platform (MDAP)

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Workshop: Introduction to APIs, or how to efficiently collect online data Join this workshop to learn how APIs can help you collect research data from online sources efficiently.

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Data Steward Roles and Responsibilities 

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Data Steward Roles and Responsibilities 

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The University of Melbourne Chief Investigator or Doctoral/Project Supervisor associated with a research project is the Data Steward for that project and is responsible for research data management.

The Data Steward can delegate specific responsibilities, and this should be outlined in the project’s Data Management Plan[1].

In practice, the day-to-day management of research data is likely to be a shared responsibility, with all researchers having a personal responsibility for the data they create or use. In the case of students, these practical responsibilities should be overseen by their doctoral or project supervisor.

In the absence of the Data Steward, responsibility for stewardship of research data, including arbitration on the retention of legacy data held on university systems, will transfer to the Head of Department or Dean of School[2]. See Research Data Management Policy (MPF1242)

Where research is conducted in collaboration with external research partners, or where there are multiple co-investigators at the University, a single Data Steward at the University of Melbourne must still be assigned. This Data Steward[3] is responsible for the management of research data that is under the University’s control.

Before leaving the University of Melbourne at the end of a contract or completion of doctoral studies, researchers must first discuss management and retention of all research data for which they are responsible with their line manager, doctoral or project supervisor, or Head of Department or Dean of School as appropriate, to determine whether a copy of the data will be retained by the University for continued study, and whether the researcher will take a copy[4]. These decisions must conform with requirements and restrictions arising from applicable legislation, funding, contracts, ethical approval, and policies.

[1] DMPMelbourne has been developed to help UoM researchers and teams write data management plans. https://dmp.research.unimelb.edu.au/
[2] 5.2. Researchers must ensure that the University has a record of ownership and responsibility for any research data and records they have transferred into the University’s control (e.g., for storage on university facilities). (a) Where no ownership or responsibility has been recorded, or the recorded responsible party is no longer a University of Melbourne researcher, the relevant Dean or delegate will hold authority to decide on storage, retention, disposal, publication, or licensing arrangements in compliance with legal and regulatory obligations.
[3] The University of Melbourne Digital Stewardship (Research) team aims to modernise scholarly practice via digital techniques and by maximising the value of research data. https://library.unimelb.edu.au/digital-stewardship
[4] See Storage, retention, and disposal - 5.3. Researchers must curate and store accurate, complete, and authentic research data and records in formats that are understandable, retrievable, and accessible to appropriate parties. https://policy.unimelb.edu.au/MPF1242/

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Dimensions - Linked research data from idea to impact 

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Dimensions - Linked research data from idea to impact 

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Dimensions AI | The most advanced scientific research database

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From idea to impact: Navigate the links between grants, publications, datasets, clinical trials, patents, and policy documents.

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Attica  - A cloud-hosted retention platform for depositing inactive research data for long term safe keeping.

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Attica  - A cloud-hosted retention platform for depositing inactive research data for long term safe keeping.

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OMEKA  -web publishing and data curation

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OMEKA  -web publishing and data curation

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Data publication and retention: Figshare

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Data publication and retention: Figshare

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https://gateway.research.unimelb.edu.au/resources/platforms-infrastructure-and-equipment/research-infrastructure-platforms/research-computing-services/services/data-storage-management/figshare

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a DMP helps you plan for the resources, tools, and expertise that are required to store, handle, and manage the given types and volumes of data that are expected to be collected. A DMP serves as a tool to pay careful attention to all aspects of data management.

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DMPMelbourne has been developed to help you write data management plans.

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Our services include the Managing Data @Melbourne resource, training and data management planning, data focused advice on tools, resources, policy, effective process, and grant applications. Find out how to get support, the knowledge base of frequent questions, discover our Managing Data @ Melbourne resource (below), or learn more about the wider program of the Digital Stewardship Research team.

The Digital Stewardship (Research) team is part of a network research support staff at Melbourne. Our aims is to modernise scholarly practice via digital techniques and by maximising the value of research data. The team provides support, services, training, advice and examples of good practice in data stewardship, digital preservation and research data management.

Managing Data @Melbourne is a resource of general practice advice in data management resource. It is also an online course that guides you through the process of writing a data management plan for your research project.

Choose FAQ or Support for information about data management services across the University. Choose "Getting Started" to access the Managing Data @Melbourne resource.

Background

Managing Data @Melbourne is an online program that guides you through the process of writing a data management plan for your research project. It has been developed for graduate researchers, early career researchers, and information professionals.

The program provides Australian context and was initiated by the Digital Scholarship team in Research and Collections at the University of Melbourne.

Managing Data @Melbourne is currently maintained by staff from Digital Scholarship, Research & Collections at The University of Melbourne.

View the Terms of Use section below.

Acknowledgements

The Managing Data @Melbourne program was inspired by and based on the Research Data MANTRA [online course] developed by EDINA and Data Library, University of Edinburgh. The original work can be found here.

We would like to acknowledge the foundational work of the Australian National Data Service (ANDS), the Digital Curation Centre (DCC), and the Research Data Alliance (RDA).

The creators of Managing Data @Melbourne would like to thank everyone who contributed to the project, including the staff who coordinated the early Melbourne MANTRA pilot and the graduate researchers who participated in this, the video interviewees from the University of Melbourne and other colleagues from across the University.

Managing Data @Melbourne content development

Staff from Research & Collections and Scholarly Information at The University of Melbourne.

Editorial assistance

Provided by staff from Research Platforms and Research Ethics and Integrity at The University of Melbourne.

Learning design and educational technology

Provided by staff from Learning Environments at The University of Melbourne.

Marketing and communications

Provided by staff from Student Information at The University of Melbourne.

Terms of use

Please refer to the Terms and Conditions detailed below our Credit listing.

Credits for the Managing Data @Melbourne webpage

Banner: image used under license from Shutterstock.com / Konstantin Yolshin/Shutterstock.com

Credits within specific sections of the Managing Data @ Melbourne program.

Images

Banner: image used under license from Shutterstock.com

Konstantin Yolshin/Shutterstock.com

Modules: images used under license from Shutterstock.com

Dario Lo Presti/Shutterstock.com

YAKOBCHUK VIACHESLAV//Shutterstock.com

Rawpixel.com/Shutterstock.com

Bikeworldtravel/Shutterstock.com

Pixelrain/Shutterstock.com

indukas/Shutterstock.com

Terms of use

Managing Data @Melbourne, University of Melbourne [online course] [http://library.unimelb.edu.au/data], is licensed under a Creative Commons Attribution 4.0 International License.

Except for the logos of the University of Melbourne, the Australian Research Council and the National Health and Medical Research Councils, and the licensed content from Shutterstock (listed above) you are free to re-use part or all of the written content work elsewhere, with or without modification.

You are welcome to link to the Managing Data @Melbourne videos. You can use the videos as permitted under copyright law in your jurisdiction. For any other uses, please contact the University of Melbourne for permission.

In order to comply with the attribution requirements of the Creative Commons license (CC-BY), we request that you cite as above, or be sure to include:

Author/creator: Research & Collections, University of Melbourne

Title of the work: Managing Data @Melbourne [online course]

URL where the original work can be found: http://library.unimelb.edu.au/data

Your feedback on the program is welcomed. Email the Managing Data @Melbourne team.

DOWNLOAD COURSE CONTENT.

The text of the program content is available to download from the University of Melbourne Repository at http://hdl.handle.net/11343/225138.

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Our services include the Managing Data @Melbourne resource, training and data management planning, data focused advice on tools, resources, policy, effective process, and grant applications. Find out how to get support, the knowledge base of frequent questions, discover our Managing Data @ Melbourne resource (below), or learn more about the wider program of the Digital Stewardship Research team.

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Explore the data-focussed resources available to you as a researcher at the University. This is a collection of services and tools which can help you work with and manage your data. Learn more about research data management on the Research Gateway.

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DISCOVER: A showcase of our first year of engagement with researchers from across the University.

We are a team of Academic Specialists who collaborate with researchers to enable data-intensive research across the University. We work with researchers at all stages of the research lifecycle, from research design and data collection, all the way through to analysis, visualisation, and interpretation. With a broad range of backgrounds and experiences, we are uniquely placed to make the most of all the benefits that interdisciplinary research can offer!

If you're working with research data, or thinking about doing so soon, we would love to hear from you about how we might be able to help

We hold technical expertise in:

Research study design

Data management

Data stewardship

Data collection methods

Web development

HPC and cloud computing

Digital infrastructure

Data analysis

Machine learning

Data visualisation

Image and video processing

Virtual/augmented reality

Natural language processing

Artificial intelligence

... and much more!

DISCOVER: Our latest news including research updates, team profiles, community of practice events, and publications.

We encourage researchers across the University to engage with us at any stage of their research project through one or more of the following avenues.

Advice and short projects

Working effectively with data is hard and it is easy to get stuck. We offer advice to move your research forward whether you are planning a new project, deep within it already, or wrapping it up. We might also be able to help with small parts of your data-intensive project, depending on our workload.

Collaborations

Twice a year we invite researchers to collaborate with us on research projects that last three to six months.

Other digital skills projects

Others can help, too. MDAP has recently developed The Melbourne Coder Network as a marketplace for those with digital skills to meet those with digital problems to be solved.

And...

If we don't have the expertise on hand, we can redirect to other groups and services across the University who may help.

We are enabled by the University's Petascale Campus Initiative ( PCI).

Is this not what you are looking for? Try the Research Helpline:

We offer two primary streams of engagement:

1. Collaborations: an allocation scheme for formal collaborative research projects lasting at least three months, with open calls occurring twice yearly. For more information, please review answers to our Frequently Asked Questions.

2. Consultancy, expert advice, advocacy and involvement in shorter term research projects. You can email us at mdap-info@unimelb.edu.au to discuss how MDAP can work with you on your collaborative, data-intensive project.

Expressions of interest are now closed for the 2021A round of collaborations. Proposed collaborations should:

Have an anticipated duration of three to six months;

Be of strategic value to the University, aligning with its research strategy, increasing future research capabilities, and leading to the development of novel tools and techniques that can be applied to other projects and domains;

Require expertise and/or infrastructure that cannot be sourced from existing University Services or platforms; and

Align with available MDAP team capabilities.

Applications will follow a two-stage process, consisting of a short expression of interest followed by a formal proposal. Collaborations will be ranked based on merit by a Resource Allocation Committee made up of academics from a diverse cross-section of University disciplines. The selection criteria used to prioritise collaborations can be found below.

The timeline for this round of EOIs and subsequent applications is:
EOIs open: Monday 26 October 2020
EOIs close: Sunday 15 November 2020, 11:59pm
Applications open: Thursday 26 November 2020
Applications close: Friday 18 December 2020, 11:59pm
If you have any questions, please consult our FAQs and/or contact us at mdap-info@unimelb.edu.au

Research collaborations will be evaluated on these criteria, on a scale from 0 to 10 (low to high score):

Strategic value for the University

(for example but not prescriptive: building new transdisciplinary links, strengthening an emerging area, communicating research impact).

Strategic value for the research group, including capability development

(for example but not prescriptive: training ECRs/staff, gathering pilot data for grant submissions, producing open source software/tools).

Alignment with MDAP capability.

Likelihood of producing stated outcomes within timeframe.

2021 Collaborations

Linking song collections and communities: Open release and interoperability of a song database and linking tool.

Forecasting Bushfire Risk: Integrating a new ground-based sensor network, remote sensing, and weather data to forecast forest fuel dryness

Tackling the canine microbiome in chronic enteropathy: characterising the functionally significant changes that occur with remission of disease

Social Media Disinformation and the Papua Conflict: an Indonesian Language Investigation

Digitally benchmarking public attitudes to secondary use of health data, with NLP data extraction from general practices as a case study

Previous collaborations

If you would like to view our previous collaborations, please step this way.

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Melbourne Data Analytics Platform (MDAP) | Collaboration. Computing. Research Impact.

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We are a team of Academic Specialists who collaborate with researchers to enable data-intensive research across the University. We work with researchers at all stages of the research lifecycle, from research design and data collection, all the way through to analysis, visualisation, and interpretation.

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A gateway to access research support services and resources at the University of Melbourne. Includes research support services, resources, directory, glossary and helpline.