Research data management

There is no universal definition of the term 'data' or 'research data' - this varies across disciplines and contexts. The Research Council of Norway offers the following definition (NFR, 2017): 

“[research data are] ... the registration/recording/reporting of numerical scores, textual records, images and sounds that are generated by or arise during research projects. These may, for example, be data that are generated through new analysis by combining existing secondary data, or entirely new data that are generated through new data collection. Research data are always a direct result of research activity, regardless of whether the data are based on secondary data or whether they are collected from scratch.”

Whereas the European Open Science Cloud (EOSC) defines research data as: 

“Data collected or produced in the course of scientific research activities and used as evidence in the research process or commonly accepted in the research community as necessary to validate research findings and results”.

“But we don’t have data!!” – you might respond. Some disciplines do not experience working with, generating or collecting ‘research data’ at all. The European Federation of Academies of Sciences and Humanities (ALLEA) has in recent years focused on how humanities researchers can make their research outputs FAIR (the FAIR principles will be explained below), while acknowledging the discomfort with the ‘data’ term in the humanities in particular. Such ambivalence is likely also to apply to other disciplines such as for example legal studies, as well as branches of artistic research.  ALLEA defines research data as:

“broadly as all materials and assets scholars collect, generate and use during all stages of the research cycle”.

In the humanities, researchers have been said to “resist the blanket term ‘data’” for several good reasons, semantic as well as material ones (Edmond and Tóth-Czifra, 2018). Rather, humanities scholars prefer to talk about sources, secondary sources, theoretical documents, bibliographies, critical editions, annotations, notes, etc. They seldom create their sources from scratch or own them, and thus tools for data management can seem very limited in their utility. 

However, researchers within the humanities too have much to gain from becoming more aware of how their sources might be accounted for and even shared. Furthermore, getting familiar with research data management practices surely includes good working routines for managing and structuring documents and manuscripts, having routines for backing up and storing files, which undoubtedly has value for researchers of all fields (Edmond and Tóth-Czifra, 2018).

As such, the varying definitions and tensions above pinpoint the complexity in settling on a definition that applies for all fields. They do, however, address nuances in the term "data" that are important to break down, both in terms of objects or artifacts that can fall within the scope of research data, and the intent or usage of data as a baseline for validating science. 

A useful approach in this regard has been offered by CESSDA, which proposes to distinguish between:

• Data types (how data occurs, are created or expressed through physical or digital objects or forms, such as interview transcripts, images, spreadsheets, samples etc.) 
• Data formats and forms of storage (how digital expressions are manifested in terms of textual, numerical, multimedia files, software code etc. and their formats) 
• Size and complexity (emphasizing the variation between small and large files and datasets, compilations)
• Data stages (research data travel through various life cycle stages from collection or generation through processing, analysis and potential archiving and/or publication, and require considerations pending on stage)

 
The figure above serves as a useful illustration of how research data management can be understood and approached. It shows the different stages of research data throughout the course of a research project or study. Data is transformed, and takes different forms within each stage in the cycle, and each stage requires special considerations with regards to research data management, documentation and storage.

The Fair principles

The shift towards more open research data is intrinsically linked to the wide acceptance of the FAIR principles. The FAIR principles are as a set of guidelines to facilitate further use of research data. The principles were originally introduced in 2016 (Wilkinson et al., 2016), and the Research Council of Norway (RCN) as well as the European Commission (EC) now include the principles when evaluating research proposals for funding. More and more academic publishers are following suit, asking prospective authors how their research complies with the FAIR principles. The Norwegian government also states that the FAIR principles should govern all publicly funded research in Norway. Following the FAIR guidelines is encouraged by many of the Norwegian higher educational institutions, for example the University of Oslo, the University of Bergen, UiT The Arctic University of Norway or the Norwegian University of Science and Technology. 

A key to FAIR research data is to develop and maintain a data management plan (DMP). We will present the essential components of a good DMP below. First, however, we will explain what the FAIR acronym means and what its requirements entail:

In order to be FAIR, data needs to be
F-Findable 
Data (and metadata) should be findable, or possible to discover, by humans and machines. To be findable, data must be uniquely and persistently identifiable, which means that it should be possible to find the same object at any point in time by using persistent links and identifiers (such as a DOI). The data should be described by rich metadata, with clear links to the identifier. Metadata and data should finally be registered or indexed in a searchable database or catalogue.

A-Accessible 
It is not enough for data to be findable, data also need to be possible to access. Accessible data are data that are obtainable by machines and humans after appropriate authorization or authentication, where this is necessary. This means that when data are not openly available, it must be possible to identify if a person or machine seeking access has the right permissions. Access must then be granted through a well-defined protocol.

I-Interoperable 
Interoperable data and metadata means that data are machine-readable and hence formatted according to well-known vocabularies or ontologies, making integration with other data and systems possible.

R-Re-usable 
A final requirement is re-usability, which can only be ensured if the first three requirements are in place. Metadata and data should be described sufficiently well in order to enable linking or integration with other data sources. Published data should have enough metadata to enable correct citation. A clear and accessible data usage license also needs to be in place, explicitly stating the legal terms for reuse.

We acknowledge that making your data FAIR and sharing openly is not always possible. This can be a particular challenge for qualitative research data. The Dutch national center of expertise and repository for research data (DANS) has developed a guidebook on Making Qualitative Data Reusable. An update to this guidebook is expected in 2025.

In sum, managing your data in a FAIR manner will ensure that the data you collect are re-usable and verifiable for fellow researchers, and as such in line with the Norwegian strategy on transparent and reproducible data. For more information about the FAIR principles, visit the GO FAIR webpages, an international bottom-up initiative aiming for a broad comprehensive implementation of the principles. The presentation above is based on GO FAIR .

The CARE principles for Indigenous Data Governance

Although there is wide acceptance and drive for working towards making research data FAIR, the global Indigenous Data Alliance emphasizes that this movement does not fully engage with Indigenous Peoples rights and interests (GIDA, 2025). The FAIR principles, they argue, focus primarily on characteristics of data that will facilitate increased data sharing among entities, ignoring historical contexts and local communities’ empowerment opportunities. The CARE principles are a response to this, ensuring that Indigenous Peoples can assert greater control over the application and use of Indigenous data and Indigenous Knowledge for collective benefit.

The CARE principles emphasize:

C-ollective Benefit

Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.

A-uthority to Control

Indigenous Peoples’ rights and interests in Indigenous data must be recognised and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.

R-esponsibility

Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples

E-thics

Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.

The TRUST principles for digital repositories

A final set of principles relevant for good research data management are enshrined in the TRUST principles for digital repositories, as repositories play a vital role in making data FAIR. In developing these principles, several stakeholders, representing various segments of the digital repository community, have collaboratively developed and endorsed a set of guiding principles to demonstrate digital repository trustworthiness.

These principles cover the following necessary aspects of secure and FAIR archiving:

T-ransparency

To be transparent about specific repository services and data holdings that are verifiable by publicly accessible evidence.

R-esponsibility

To be responsible for ensuring the authenticity and integrity of data holdings and for the reliability and persistence of its service.

U-ser focus

To ensure that the data management norms and expectations of target user communities are met.

S-ustainability

To sustain services and preserve data holdings for the long-term.

T-echnology

To provide infrastructure and capabilities to support secure, persistent, and reliable services.

To be able to share data and align research with the principles mentioned above, data management planning is crucial. A data management plan (DMP) describes how research data will be managed from the start to end of a research project or study. A data management plan should be regarded as a living document in the research process and should be actively maintained throughout. A thorough and updated DMP ensures that you plan for future use of the data, helps to avoid data loss and also makes the potential re-use of your data easier.

Both NFR and the European Commission require that funded research projects provide an updated DMP at various points of the project period. Most of the larger academic institutions in Norway also expect their PhD candidates and research projects to keep a DMP, as part of their Open Science policies. 

A good starting point for a data management and sharing plan is Science Europe’s practical guide. This resource illustrates the core components of a DMP and is in alignment with requirements from both NFR and the European Commission.

Over the past years extensive work has been put into consolidating support and recommendations for DMP-writing and tools across Norway and higher education institutions. Based on the core elements in the Science Europe guide, plan.research-data.no is a national knowledge resource to support Data Management planning at Norwegian research institutions. We recommend that this is your point of departure when starting to put together your DMP, as it also provides access to FAIR WIZARD, a pilot instance to create data management plans. This pilot is a collaboration between the University of Bergen, The Norwegian University of Science and Technology (NTNU), the University of Oslo, and UiT The Arctic University of Norway, and is only available for researchers affiliated with these institutions. However, remember that your institution offers local support if you are unsure when writing your DMP.

Other DMP tools that can be used are:

• Sikt DMP 
• DMPOnline (Look for the Science Europe template)
• Data Stewardship Wizard (particularly suitable for the Life Sciences)

Zenodo and DMPOnline also publish submitted DMPs that you can consult for inspiration when writing your own DMP, but be aware that these are not peer reviewed or curated.
 

Focusing on good data management during the active phase of research is beneficial on several counts. It will benefit others as well as yourself, as it facilitates structured, well-documented, safe and transparent data (or metadata) that can be understood by your future self as well as others wanting to reuse your data. It also eases complying with research ethics and legal requirements. The active phase of research includes the collecting, processing and analysis parts of the research data life cycle. Two central aspects of good RDM will be covered here, and these are data documentation and description, and data structure and organization.

Data documentation and description

Data documentation is an essential part of data management. Documentation is necessary at various levels within a research project or study, such as:

• The project level (title and summary, authors, institutions, funding information, methods, file naming conventions, general ethical or legal considerations, etc.)
• The file level (individual records or data points, dates, locations, context details, interviewee information, etc.)
• The variable/code/theme level (descriptions or inclusion criteria, variable types such as numeric, string, regular expression, date, etc.), measurement units (i.e., cm, kg…), experimental factors, controlled vocabulary, missing values, etc.)

In sum, these levels require various types of metadata. Metadata is information, or data, about the data. There should always be enough metadata to enable you and others to understand how to interpret and use the data. Some data archives or repositories have metadata forms with required fields. If possible, you should use metadata from a widely accepted standard in your field of study; this will make your data easier to find through data search engines. However, scientific disciplines vary greatly in the usage of metadata standards. some don’t even have them. If no other relevant standards come to mind, within your discipline, Dublin Core can serve as a good point of departure.

An important tool aiding in keeping data FAIR and streamlining and making information and documentation accessible is a readMe-file. A readMe-file is a document in the form of a simple text file (.txt) that describes your research data and how you have processed them. A readMe file helps to preserve information about research data, and ensures that others can read, understand and make future use of them for reproduction or further development.

The EPFL (École Polytechnique Fédérale de Lausanne) library has put together a useful guide for writing a readMe-file. Of particular interest are the sections on definitions and best practice for readMe-files. When you have chosen a repository in which to archive your data, the repository may have a standardized template for a readMe-file that has to accompany your data upon submission. See for example the DataverseNO readMe template.

It can also be beneficial to document more unstructured input, reflections, observations or thoughts during your active research process. How this is done varies across disciplines and forms of research, but documenting through research logbooks, diaries or journals, field notes, electronic lab notebooks, as well as through memo writing and annotations is highly recommended as it makes the processes of analysis and interpretation more transparent. How and to what extent it makes sense to share these more unstructured data documents is up to the individual researcher to assess, and how it potentially can accompany shared data or metadata.

Structuring and organizing data

It may seem obvious that data should be structured and organized so that they are easy to understand and reuse. However, common mistakes in this regard include using non-intuitive acronyms and file names, storing different data types together (raw data vs. processed or modified data), and not being transparent about data lineage, meaning how data transforms from raw data through processing and analyses.

Being aware of good routines for file naming, establishing these and maintaining them throughout the project is a key element in order to ensure that your data are possible to share and reuse. The same goes for folder structure and hierarchy.

Try to follow the guidelines below when creating a system for your research data:

• Use understandable, consistent naming conventions e.g. file names such as datatype_date_location.ext. If you use acronyms and abbreviations in file names, headers, etc., explain them in the metadata.
• Avoid names that are too long.
• Avoid special characters.
• For machine readability, use underscore (_) instead of space in file names.
• Use persistent, non-proprietary file formats.
• Keep raw data and processed data in separate folders.
• Keep raw data as ‘read only’ to avoid corruption or overwriting.

The Norwegian institutional archive DataverseNO's guide to depositors provides good guidelines for how to structure research data with clear folder structures and file naming conventions.

For a more visual presentation of key points on how to structure research data, watch this video from UiT.

RDMkit (most relevant for life and natural sciences), CESSDA DMEG (social sciences and humanities) and the Qualitative Data Depository (for qualitative research data) present good resources and tips for good ways to structure and document your data.

The Turing Way is also a good resource for keeping your spreadsheets tidy and organized.

When considering possibilities for publishing your data, remember that the motivation behind open research data is that sharing data enables both new research and the possibility of verifying conclusions. You should therefore consider the rawness of the data you share; for very large or complex datasets a certain degree of data processing before sharing is necessary. However, every step of processing you take could limit the possibilities of future use. This is why it is essential to keep track of data lineage, processing steps and versioning at all points of the project.

If your data can in any way be regarded as sensitive, it is vital that you treat them accordingly. Sensitive data are data that include information in need of special protection, and that needs to be secured with some form of restricted access or access control. Several aspects can make data sensitive: they might contain personal data or special category personal data, they may carry commercial or trade secrets, they may carry information on national or military security, they may include patents or carry with them other IPR related issues, or include other ethical or legal aspects that need to be shielded from the public. OpenAIRE presents a good overview of types of sensitive data, and what you should consider when handling any of the types of data regarded as sensitive.
There are several aspects to consider when dealing with sensitive data. These may be institutional, national or collaborative contexts, relevant legal frameworks, guidelines or other aspects impacting data confidentiality and the level of protection necessary (openscience.no), both in the processing and potential archiving or sharing of the data (or metadata).

In the following section, we will focus on data concerning personal information.

Personal data

When discussing sensitive data, it is often data containing personal information that comes to mind. The legal framework for personal data is the General Data Protection Regulation, incorporated into Norwegian law as the Personal Data Act. To get to know the foundations of personal data and key aspects of data protection, you should familiarize yourself with the aforementioned legal frameworks, as well as the Norwegian Data protection authority and their information about what constitutes personal data (available in Norwegian only). As they explicitly state, all data that in some way identify individuals needs special caution, and you must treat such data with extra care when collecting, processing, transferring or storing them for research purposes.

When planning a project that includes personal data, the first thing you should do is get a clear understanding of the legal basis for your proposed collection of personal data. Secondly, locate your institutional data protection services. Some Norwegian institutions purchase data protection services from Sikt, and some have their own data protection officer. There may be an institutional registration system for projects containing personal data administered by the data protection officer, or there may be the one provided by Sikt, the Notification Form for personal data. You may also need to have your project subjected to a Data Protection Impact Assessment.

If you work within the health or medical field and do research on human beings, human biological material or personal health data, you must seek pre-approval from REK (The Regional Committees for Medical and Health Research Ethics). The project needs to be approved by REK before it can start.

The following categories of data can be considered personal:

• Geographical data: country, county, municipality, institution
• Personal characteristics: body, personality traits, voice
• Demographic data: age, gender, income, education, social status
• Family data: number of children, family structure

Further, some personal data constitute what is called “special category” personal data (in Norwegian: særlige kategorier av personopplysninger) the Norwegian Personal Data Act. Data on the following are such special category personal data:

• Health-related issues
• Genetic information
• Biometric information used for identification
• Criminal offences
• Racial or ethnic origin
• Religious beliefs
• Political views
• Philosophical beliefs
• Union membership
• Sexual matters

If your research project involves the collection and processing of such information, there are some particular conditions that need to be fulfilled (see chapter Three in the Personal Data Act). The Norwegian National Data Protection Authority states however, that many caveats exist, and that it is important to sufficiently understand which legal framework that apply to you and discuss your project with your institutional data protection services. In order to obtain permission to collect personal data, secure ways of storing such material in trusted research environments (such as TSD or SAFE) need to be in place. The IT department at your institution can aid in setting up such infrastructures for you.

Data security

If you are dealing with sensitive data that need some level of protection, you should familiarize yourself with your institution’s framework for classification of research data, and corresponding options for secure research data storage. This may involve both your institution’s data protection officer or responsible agency, as well as your institution’s IT services. Below is a list of the largest universities in Norway and their classification systems and recommendations for secure research data storage:

• UiB storage guide
• UiO data storage guide
• NTNU data storage guide
• UiT Forskningsdataportalen
• OsloMet storage
• Data storage at HVL

Sound and video recording(s)

Video and sound recordings can easily contain personal information, even the voice of the interviewees can be enough to identify them! Cartooning, censoring and voice distortion are ways to anonymize such data, but this option is not viable for all kinds of projects, such as those studying spoken dialect or facial expressions. Note that anonymization must be done on a computer without internet connection, or within a trusted research environment (such as SAFE or TSD). Anonymized data can be processed on a computer with internet but remember that the very content of a dialogue can be enough to identify people.

You need to fully control the equipment you use when recording sound or video. Note that equipment with an internet connection, such as a smartphone, should never be used to record personal information unless you can ensure that all communications are shut off. Equipment without internet connection and which uses removable storage units is generally the best option.

If encryption of the storage media is not an option, make sure that the physical media are securely stored in a safe or the similar, and make sure to transfer any sensitive or personal data to more secure storage, such as a computer without internet connection and with an encrypted hard drive, as soon as possible. Long-time storage of such data for the purpose of further research will need specific secure long-time storage facilities, as well as the approval of such storage from the relevant authorities.

Transferring data between units

Pay attention to the way you transfer your data between units. The transfer of data should be as safe as the storage unit; for example, you cannot send highly sensitive data in e-mails. At the highest levels of security, an encrypted connection is needed, but a physical transfer using a wire or card-reader would also work well. If you transcribe sensitive recorded data, make sure that there is no one in the room with you, looking over your shoulder.

In cases where email is the best solution for transferring personal data, use software such as 7zip for encryption before sending the file. Sensitive data should, as a general rule, never be sent as attachments into e-mail messages via a normal mail server.

Encryption

Encryption of mobile storage media is possible on a normal Windows computer by turning on 'BitLocker' for the selected drive. If you use a Mac, encryption can be done in 'disk utility' after creating an image out of the folder you wish to encrypt. Encryption is necessary for all removable storage media containing high-risk data. The encryption key (the password) must never be accessible to any unauthorized persons and should not be stored in connection with the encrypted disk itself.

Anonymization

By "anonymized data", we mean data where it is impossible to identify any single individual through e.g.

• names
• ID Numbers
• unique characteristics
• background variables
• name/connection key

Data anonymized in accordance with the list above is no longer considered personal data and falls outside the scope of GDPR. However, as a researcher you should make sure that the data are indeed completely anonymized. If you are uncertain, you should get in touch with your institution’s data protection officer for an assessment. When you are considering sharing data however, yet another ethical dimension might emerge if your research participants have not consented to data sharing, even though it has been anonymized. This challenge should be evaluated and balanced against the societal value of making the data available.

For recommendations on how to anonymize adequately, you can consult this resource by the Norwegian Data Protection Authority.

Searching for already existing research data may help you to get to know your field of research better. Even though your research project may not be oriented towards empirical primary or secondary data, you may be surprised to learn what types of data you can find online fully accessible.

There are several reasons as to why informing yourself of existing research data in your field is beneficial: 

• Existing data can be a part of the foundation your research will build upon. 
• You can examine the need for new data.
• If you are planning to collect or generate data, you need to make sure at an early stage that the exact same data have not already been collected.
• You might discover whether there are norms and standards you should follow for data structure and documentation.

Numerous data repositories exist, and they cannot be listed here. However, there are sites where you can search for data sets or repositories relevant to your field or project. 

Data search engines 

DataCite provides services to help researchers locate, identify and cite research data. At https://search.datacite.org/, you can search data sets that are assigned a DOI. Each data set has the citation shown in different reference styles and offer the possibility to export to BibTeX and RIS. 
 
Bielefeld Academic Search Engine is a search engine especially intended for academic web resources. The BASE advanced search allows you to limit the search to datasets.

Data archives

Zenodo and Figshare are multidisciplinary data archives. Their user interface is similar to that of literature databases, and you can use common operators like AND and OR.

Data archive registry

At http://www.re3data.org/ you can search for or browse data repositories by subject, content type or country. Note that not all the repositories are open, but you can use filter to find those that are. The repository details also state terms of use and contact information, so if you find a repository that is not open, you may still get access upon request.

Here is a quick video showing how re3data works (youtube). 

Sources for public, free data 

Sikt has available data sets for personal data, regional data, institutional data, and more. 
 
The Norwegian Institute of Public Health provides data for research and analysis. Data on health records, health surveys and biobanks are available. You will also find information on how to access the data. 
 
Statistics Norway (SSB) can supply you individually with research data.

Citing research data 

When using data collected or generated by others, you need to cite the data set, similar to citing all articles, books and other sources you use in a publication. This facilitates description and information retrieval, access and persistence, verification and reproducibility, and integration with other data sets. Force 11 has developed a set of data citation principles on how and why data need to be cited properly (Data Citation Synthesis Group, 2014). Moreover, many repositories have citation export or clear guidelines on how the data sets should be cited. If not, the citation should include

• the author(s)
• the year
• the title of the dataset
• the data repository or archive
• the version
• the persistent identifier (or url)

Most data repositories will explain which elements should be part of a proper reference to their data, and there is no need to create an exhaustive and hard-to-read reference if you have a persistent identifier leading the reader to all the relevant metadata information.

Information on licensing research data will be provided in the course of fall ‘25.

The research data management sphere is vast, and full of terminology and abbreviations that can seem overwhelming or confusing. Fortunatly, several resources explaining relevant data management vocabulary exist, for instance: 

• The Norwegian Data Protection Authority, which have listed some commonly used Norwegian and English terms used in data management and research activities in the higher education institutions in Norway.
• The Norwegian Association of Higher Education Institutions (UHR), which has created a short dictionary (termbase). In this dictionary, you will find translations of more than 2000 administrative terms from the two written Norwegian languages to English, and vice versa.
• Plan.research-data.no has also assembled an overview of key research data management terms, and refers to Norwegian translations at the openscience.no term base.

CESSDA Data Management Expert Guide (DMEG) provides an expert tour guide on data management. The guide aims to help researchers make their data findable, understandable, sustainably accessible, and reusable.

Opendefinition.org. (2015). Open Definition: Defining open in open data, open content and open knowledge.  Open Definition - Defining Open in Open Data, Open Content and Open Knowledge 

plan.research-data.no. DMP Support Package for Norwegian Higher Education Libraries. plan.research-data.no  

Practical Guide to the International Alignment of Research Data Management - Extended Edition: Practical Guide to the International Alignment of Research Data Management - 

RDMkit: The ELIXIR Research Data Management toolkit for Life Sciences: RDMkit

Data Protection Authority page on personal data (in Norwegian) Personopplysninger | Datatilsynet

Openscience.no Open Science (in Norwegian) | Sensitive data og informasjonssikkerheit

Research Data Alliance in Norway RDA in Norway | RDA

Boczar, J., & Schmidt, L. (2022). Analyzing Faculty Open Access Publishing: A citation analysis of select colleges at the University of South Florida. Journal of Academic Librarianship, 48(2), Article 102493. https://doi.org/10.1016/j.acalib.2022.102493

Carroll, S, et al. (2020). The CARE Principles for Indigenous Data Governance. Data Science Journal, 19: XX, pp. 1–12. https://doi.org/10.5334/dsj-2020-043  

Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. Martone M. (ed.) San Diego CA: FORCE11; 2014  https://doi.org/10.25490/a97f-egyk
Edmond, J., & Tóth-Czifra, E. (2018). Open Data for Humanists, A Pragmatic Guide. Zenodo. https://doi.org/10.5281/zenodo.2657248

GIDA (2025). Global Indigenous Data Alliance, https://www.gida-global.org/

Lin, D., Crabtree, J., Dillo, I. et al. The TRUST Principles for digital repositories. Scientific Data 7, 144 (2020). https://doi.org/10.1038/s41597-020-0486-7
Miguel, E. (2021). Evidence on research transparency in economics. Journal of Economic Perspectives, 35(3), 193-214. https://doi.org/10.1257/jep.35.3.193
The Wilkinson, M.D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A.,... Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, Article 160018. https://doi.org/10.1038/sdata.2016.18