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Guia | Genérico | EN
Descrição: Saiba como elaborar um Plano de Gestão de Dados, em todo o seu cuclo de vida.

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Introduction

This section of the guide provides an overview of the basic concepts, procedures, and vocabulary involved in data management and the development of data management plans (DMP). It can be used as both an introduction to the topic as well as a reference source for those developing a data management plan

Why write a data management plan?

The goal of a data management plan (DMP) is to demonstrate that you have an organized, effective, and well thought-out system to manage your data throughout the research process.

This means going beyond taking care of the data during the collection and analysis phases; your DMP must also address your plans for the data after the research is complete.

In fact, the primary focus of most federal data management plans is on data sharing and data preservation and not on the details of data management during the research. This is because you cannot share or preserve data unless it has been well managed during the research phase.

Before you start:

If you are writing a DMP as part of a grant proposal, read the requirements and keep them on-hand.

Keep in mind that most grant proposals limit your data management plan to just two pages which means that you can't provide too much detail. Grant proposal DMPs need to written as a concise summary to stay within this limit.

Write Your DMP with DMPTool

About this guide

This guide was developed as a resource to introduce scholars and researchers of all experience levels to the basic concepts of data management and to serve as a tool to help researchers write data management plans that accompany grant proposals.

ISU Librarian Megan O'Donnell authored most of the content on this guide and is responsible for periodically updating the content. If you have a question, suggestion, or would like to report an error, please email Megan.

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Library Guides: Data Management Plan (DMP) Guide: Write a DMP

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This section of the guide provides an overview of the basic concepts, procedures, and vocabulary involved in data management and the development of data management plans (DMP).

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ORE Data Guidelines (Share Your Data in 4 Steps)

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ORE Data Guidelines (Share Your Data in 4 Steps)

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Guia | Genérico | EN
Descrição: Informação sobre dados de investigação que precisam de ser incluídos quando se publica um artigo na Open Research Europe, onde os dados podem ser armazenados, e como devem ser apresentados.

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How to identify and assess Research Data Management (RDM) costs

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How to identify and assess Research Data Management (RDM) costs

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Guia | Genérico | EN
Descrição: Saiba como identificar e avaliar os custos da gestão dos de investigação.

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Make a Data Management Plan

Make a DMP before you start creating data; make decisions about managing your data. You can find the template for H2020 DMPs here.

Check if there is a department within your organization to support data management planning.

2 hrs to 2 days, depending on the complexity of your project

1. Data Collection

Do you plan to use existing data, and is the data available at a commercial partner?

Your library may be able to help you acquire a license to a crucial database

In research data repositories, data can be available at no or low costs

Example: A faculty licence on a database for macro-economic analysis: €18.000/y

1. Data Collection

If planned beforehand by developing templates and data entry forms for individual data files (transcripts, spreadsheets, databases) and by constructing clear file structures - low or no additional cost

If needed afterwards - higher cost

Per project organize style, format, names can be done by a student assistant at level 1* salary or data manager at level 2* salary

1. Data Collection
Transcription

Will you transcribe qualitative data (e.g. recorded interviews or focus group sessions) as part of your research; or will you need to do this specifically so data can be more easily shared and reused?

Is full or partial transcription needed?

Is translation needed?

Will you need to develop a standard transcription template or transcription guidelines, to ensure consistent

Example: Time needed for transcription - four to eight hours per hour recording

1. Data Collection
Student assistant at level 1* salary or data manager at level 2* salary

1. Data Collection
Is software or hardware needed for data transfer, for encryption of confidential data before transfer, or for synchronisation of data files across sites?

Free encryption or data transfer software (i.e. SurfFileSender) is available in most cases

2. Data Documentation
Data description and Metadata

Are data in a spreadsheet, database or data warehouse clearly marked with variable, variable labels and value labels, code descriptions, missing value descriptions, etc.?

Are validated questionnaires and standard coding used?

Are labels consistent?

Are files, records and items in the collection clearly described with well-defined metadata or a metadata standard to interpret the relations between them and to quickly select and understand the content.

Do textual data like interview transcripts need description of context, e.g. included as a heading page?

Examples: 4 hrs per single experiment (120 measurements) filling in 60 required metadata fields, with assistance of a data manager at level 2* salary

Two to three weeks are costed into an average two year research grant application to prepare and collate materials for deposit

More information: http://www.data-archive.ac.uk/help/user-faq

2. Data Documentation
Researcher at level 2* salary.

3. Data Storage & Back-up
Institutional backup - included in standard indirect cost/overheadsadditional backup needed - cost according to number of copies to be kept, frequency of backup and storage media needed

Examples: University drive €0.80 per GB/y Cloud: €0.30 per GB/y2 x Harddrive: €0.14 per GB (single purchase)

3. Data Storage & Back-up
Example: Cloud Database as a service:€160/Month (storage 5GB transfer 30GB)Database architect at level 2* salary

4. Data Access & Security
Does remote access via VPN or secure FTP need to be arranged for external people?

Mostly researchers can make use of existing, free services

4. Data Access & Security

Example: TTP (trusted third party), dependent on pseudonymisation type, ca. €1.000- €30.000

Existing encryption services could be used at no costs

5. Data Preservation & Archiving
Researcher at level 2* salary

6. Data Sharing & Reuse

If anonymisation is planned before data collection or transcription/digitisation - cost can be lowered

For audio-visual data - anonymising/editing voices or faces can be very costly and could reduce the usefulness of data

For quantitative data (e.g. survey data) - low cost if identifiers are a priori excluded from data files, are easy to remove, or identifiable variables are coded to avoid disclosure; cost may be higher if variables need recoding afterwards to avoid disclosure

For qualitative textual data (e.g. interview transcripts) - costs can be reduced if anonymisation is carried out during transcription (or at least highlighted/coded during transcription)

Cost depends on how sensitive or complex data are and how much identifying information is recorded in the data- if only removal of names is required, cost is low; pseudonymisation will require more time

For files received of participants, check file properties and edit to remove disclosive information such as editor/author name

Free software is available. AMNESIA is a data anonymization tool, that allows to remove identifying information from data.

Example : Transcribing / simultaneously anonymizing audio (speech): up until one hour per 5 minute fragment (depending on the preciseness level of transcribing)

Student assistant at level 1* salary

6. Data Sharing & Reuse

Is time required to seek copyright clearance?

Is legal advice required?

Juridical advice at level 3* salary

6. Data Sharing & Reuse
Data sharing

Will your data be deposited with a data centre or research data l repository?

Which requirements exist to prepare data to particular standards e.g. regarding documentation or format?

Do structured metadata need to be created when data are shared via a data centre or archive, e.g. completing a deposit form for the UK Data Archive?

What data will be retained and what not?

Examples: Completing a data repository upload form (i.e. Zenodo a free-of-charge repository) may take 15 min to 4 hrs

Dryad €110 once (max 20 GB) DataverseNL €3.60 dper GB/yearCloud Database as a service:€160 /month (storage 5 GB, transfer 30 GB)

6. Data Sharing & Reuse
Data cleaning

Do quantitative data need to be cleaned, checked or verified before sharing, e.g. check validity of codes used, check for anomalous values?

Will data match documentation, e.g. same number of variables, cases, records, files?

Does textual information in data need to be spell-checked?

Do you need to combine your data with other datasets for your research

Example: Data cleaning service: €270 to well over €1800

More information: http://datascopic.net/cost-of-data-cleansing/data-cleansing/

Researcher/data manager at level 2* salary

6. Data Sharing & Reuse
Example: Digitisation €0.50 per page (few pages) OR €320-390 per 1000 pages (OCR included)

If multiple partner institutions, researchers or funders are involved in research - consider cost of data management planning meetings or discussions

Travel costs, lunch, time

Data manager at level 2* salary

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Make a Data Management Plan Make a DMP before you start creating data; make decisions about managing your data. You can find the template for H2020 DMPs here. Check if there is a department within your organization to support data management planning. 2 hrs to 2 days, depending on the complexity of your project 1.

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How to comply with H2020 mandate - for research data

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How to comply with H2020 mandate - for research data

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Guia | Genérico | EN
Descrição: Saiba como cumprir com os requisitos do Horizonte 2020, no que respeita à gestão dos dados de investigação.

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Workshop | Ciências Sociais e Humanidades | EN
Descrição: Especificações do de citação FAIR e recomendações para a citação de dados.

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This round table aims to validate the work done in the SSHOC project and help us refine the specifications of the FAIR SSH citation prototype, as well as inform our Data Citation Recommendations. One of SSHOC's tasks is dedicated to data citation and in particular making data citation machine actionable.

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How do I know if my research data is protected

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How do I know if my research data is protected

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Guia | Genérico | EN
Descrição: Protecção de dados de investigação pelos direitos de propriedade intelectual.

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How do I know if my research data is protected?

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Guides for Researchers - Licences for data

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How to find a trustworthy repository for your data

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How to find a trustworthy repository for your data

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Guia | Genérico | EN
Descrição: Saiba como encontrar um repositório confiável para depositar dados de investigação.

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How to find a trustworthy repository for your data

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How to find a trustworthy repository for your data? by Marjan Grootveld and Gültekin Gurdal How to select a data repository?

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What will it cost to manage and share my data?

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What will it cost to manage and share my data?

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Infografia | Genérico | EN
Descrição: Saiba como identificar e avaliar os custos da gestão dos de investigação.

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O'Connor, Ryan; Delipalta, Alexandra; Jones, Sarah

Infographic providing information for researchers on the costs of research data management, how these can be addressed in advance, and the community resources available.

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What will it cost to manage and share my data?

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O'Connor, Ryan; Delipalta, Alexandra; Jones, Sarah Infographic providing information for researchers on the costs of research data management, how these can be addressed in advance, and the community resources available.

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Guia | Genérico | EN
Descrição: Saiba como preservar os seus dados de investigação.

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Guides for Researchers - Raw data, backup and versioning: What you need to know to preserve your research data by Paula Cristina Marinho Moura and Venkat Shanmugasundaram

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Guia | Genérico | EN
Descrição: Saiba mais sobre licenças para dados de investigação e como aplicá-las.

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Guia | Genérico | EN
Descrição: Saiba como criar um Plano de Gestão de Dados para projetos com finaciamento do programa H2020.

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Taking ARGOS as default DMP tool, which offers DMP templates that match the demands and suggestions of the Guidelines on Data Management in Horizon 2020, it is recommended to refer it the Principal Researcher.

A DMP in Argos consists of key information about the research, such as purpose, objectives and researchers involved, but also about documentation of research datasets, namely Datasets that highlight the steps followed and the means used across data management activities.

* Required field

Once you select Save & Add Dataset, you have successfully created your DMP and will be transferred to the Dataset editor.

Datasets are documented following pre-defined templates which set the content of dataset descriptions. In Argos, a DMP can contain as many dataset descriptions as the datasets it documents.

A DMP in ARGOS can be saved anytime. It can also be shared and it can be downloaded/exported in various formats (in machine readable (.xml) and machine-actionable (.json) formats). A user guide is avaible in the portal at https://argos.openaire.eu/user-guide.

ARGOS is a OpenAIRE service that simplifies the management, validation, monitoring and maintenance of Data Management Plans. It allows actors (researchers, managers, supervisors etc) to create actionable DMPs that may be freely exchanged among infrastructures for carrying out specific aspects of the Data management process in accordance with the intentions and commitment of Data owners.

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Taking ARGOS as default DMP tool, which offers DMP templates that match the demands and suggestions of the Guidelines on Data Management in Horizon 2020, it is recommended to refer it the Principal Researcher.

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Can I reuse someone else’s research data

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Can I reuse someone else’s research data

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Guia | Licenciamento de dados
Descrição: Saiba mais sobre como reutilizar dados de investigação.

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The most common means for the owners of rights in datasets to grant permission is through a licence. A licence is a legal contract that specifies standard restrictions and permissions for a work, which can then be used by anyone the licence is granted to, although the ownership rights are not transferred. Again, keep in mind that licences usually regulate only the copyright and copyright related aspects. Personal data protection cannot be addressed through licences.

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Can I reuse someone else's research data?

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Guides for Researchers - Can I reuse someone else's research data? Learn more on how to reuse research data This guide, a user FAQs for Researchers, about the reuse of research data, is part of the user guide on copyright, open science and data, meant to offer a state of the art, legally advanced, but still manageable set of rules, guidelines, and resources to enable the full potential of OS in the EU research field with a view to addressing copyright and related rights issues.