The five stages to data sharing: Depression

Applying the Kübler-Ross model[1] to researchers and data sharing, based on various attitudes and comments we have encountered over the years. Don’t take the presentation seriously, but take the content seriously. Part four in a series of…uh, five.

4. Depression

Symptomatic statement: “I do all the work and someone else gets the credit, why bother with anything?”

Heaven Knows I'm Miserable Now

Heaven Knows I’m Miserable Now

During the fourth stage, the researcher begins to understand the certainty of data sharing. Because of this, the individual may become silent, refuse visitors and spend much of the time crying and grieving over the perceived loss of a Nobel Prize. This process allows the researcher to disconnect from things of love and affection. It is not recommended to attempt to cheer up an individual who is in this stage. It is an important time for grieving that must be processed. Depression could be referred to as the dress rehearsal for the ‘aftermath’. It is a kind of acceptance with emotional attachment. It’s natural to feel sadness, regret, fear, and uncertainty when going through this stage. Feeling those emotions shows that the person has begun to accept the situation.

Data reuse isn’t about “stealing” other people’s work. It’s actually a great opportunity for promotion as people mix, reuse, and re-purpose original data.

Using data without attribution is unacceptable; it’s also known as plagiarism. But using someone’s data with attribution but without permission is also wrong.

Provided you have invested a certain level of creativity and originality in collecting data, you have a right to be recognised as the creator of that data set. If the intellectual property of data is yours (and it’s worth checking with your institution and funder if it is yours because there is be a difference between the moral right of recognised authorship – which you will always have, and the intellectual property right of ownership – which you may not), then you can apply a license to your data protecting and asserting your moral right to be recognised and legal rights that data be used responsibly.

Part of the problem with data sharing, or non-sharing, is the ambiguity as to who can use it, how, and to what ends. Part of this is a consequence of variations in national laws[2] regarding intellectual property rights. Depending on where you are, a user could claim “fair usage[3]/”right to quote[4] or invoke freedom of information[5] laws to access and use data. However, a licence[6] can specify reasonable (i.e. lawful – no human sacrifice requirement please) conditions on what other people can do with the data, thereby bringing clarity to data reuse for both parties. The text you see above in italics isn’t (mostly isn’t) my work, but I have permission to use it in a particular way because the article it came from had a licence[7] allowing me to “Remix—to adapt the work” on condition I attribute the original work in the manner specified by the author or licensor (which I have, but not in any way that suggests that they endorse me or my use of the work – and thank God for that you may be thinking). So even if you plan to make data available to everybody for any use by waiving your rights, then adopt a licence stating you waive your rights so we all know. Alternatively, you may not want commercial companies making money from your data in which case adopt a restriction stating data can only be used for non-profit research or teaching. Licensing data is complicated though; so do not try this at home. Template data licences exist that may be suitable for your research data, for example Creative Commons licences[8] – which while simple in concept and action (you pick and choose from a menu set of conditions), are not designed for research data – hence the “may” qualification. The Open Knowledge Foundation’s Open Data[9] licences are more data/database orientated, but the clue is in the title: “open data”. As previously discussed[10], “open data” in the social sciences is a problematic concept and, again, such a licence may (it’s that word again) not be suitable[11].

One of the good things about archiving your data in a specialist data archive is that the archive will never claim ownership of the data so it remains yours or with the original owner. Furthermore, the archive has experience of managing the legal and bureaucratic side of data reuse on your behalf (now, that can be depressing) with people having to agree to a license (or user agreement) before they access the data – here’s[12] how we do this at GESIS. Bottom line: your data is still yours and should be recognised as yours, even if it is in an archive, even if someone else is using it.

It’s understandable to be depressed if you see sharing your data as something with no professional benefit in a world dependent on being published and cited. However, such feelings are unfounded. Investment is taking place to support better ways of making data citable, tracking citations, elevating data sets to the status of a research output and reuse to the level of citation. In the past the world was much simpler: you cited a publication by referring to its publication – author, title, publication, volume, and page numbers. These days it is more complicated, how do you cite data[13], variables, different versions of datasets, and what happens when websites change address or switch-off their servers? So what? Don’t think that’s a problem? Well, be thankful[14] you aren’t a legal scholar.

With funders[15] investing in long-term reference systems known as persistent resource identifiers[16], we can start (and it is a start, there’s still lots[17] to address[18]) bringing stability[19] to data referencing. For example, GESIS uses a form of identifier called the Digital Object Identifier [20] (DOI®) that allows a fixed, persistent, reference to be applied, providing a standardised reference for citation for data, documentation, and publications. It not only makes work discoverable and citable but also offers a long-term reassurance this remains the case, and if your data is available, discoverable and citable then people will discover and cite[21] it.

Just, ahem...DOI it.

Just, ahem…DOI it.

If you archive and share data, thereby establishing when your research was conducted, in addition to doing the normal expected things a good researcher should be doing anyway to build up a good professional reputation – presenting and writing publications based on that data to make a name for yourself in a field – it’s going to make it so much harder for unscrupulous types to pass-off your work as theirs.

[1] Adapted from under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

[2] Centre for Intellectual Property Law (2011) “The Legal Status of Research Data in the Knowledge Exchange Partner Countries” (Accessed October 09, 2013) from

[3] Association of Research Libraries (2012) “Code of Best Practices in Fair Use for Academic and Research Libraries” (Accessed October 09, 2013) from

[4] Council Regulation (EC) 2001/29 of 22 June 2001 on the harmonisation of certain aspects of copyright and related rights in the information society [2001] OJ L167/10

[5] Whyte, A. (2013) “FOI dataset provisions – what do they mean for RDM?” DCC Blog (Edinburgh: Digital Curation Centre) (Accessed October 10, 2013) from

[6] Ball, A. (2012) “How to License Research Data” DCC How-to Guides (Edinburgh: Digital Curation Centre) (Accessed October 09, 2013) from

[7] Creative Commons (n.d) “Wikipedia:Text of Creative Commons Attribution-ShareAlike 3.0 Unported License” (Accessed October 09, 2013) from

[8] Creative Commons (2013) “About the Licenses” (Accessed October 09, 2013) from

[9] Open Data Commons (2013) “Open Data Commons Attribution License” (Accessed October 09, 2013) from

[10] Archive and Data Management Training Center (2013) “The Five Stages to Data Sharing: Bargaining” (Accessed October 09, 2013) from

[11] Naomi, K., & Oppenheim, C. (2011) “Licensing Open Data: A Practical Guide” (Accessed October 14, 2013) from

[12] GESIS – Leibniz Institute for the Social Sciences (2007) “Usage regulations – Dept. Data Archive for the Social Sciences” (Accessed October 09, 2013) pp.7-8 from

[13] Ball, A., & Duke, M. (2012) “How to Cite Datasets and Link to Publications” DCC How-to Guides (Edinburgh: Digital Curation Centre) (Accessed October 09, 2013) from

[14] Zittrain, J., & Albert, K. (2013) “Perma: Scoping and Addressing the Problem of Link and Reference Rot in Legal Citations” Social Science Research Network

[15] Economic and Social Research Council (n.d) “Data citation: What You Need to Know” (Accessed October 09, 2013) from

[16] Tonkin, E. (2008) “Persistent Identifiers: Considering the Options” Ariadne, 56. (Accessed October 09, 2013) from

[17] CODATA-ICSTI Task Group on Data Citation Standards and Practices (2013) “Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data” Data Science Journal, 12, 1–75. pp.33-36

[18] “DOIs unambiguously and persistently identify published, trustworthy, citable online scholarly literature. Right?” (2013) CrossTech (Accessed October 09, 2013) from

[19] Mooney, H., & Newton, M. P. (2012) “The Anatomy of a Data Citation: Discovery, Reuse, and Credit” Journal of Librarianship and Scholarly Communication, 1(1), 1–16. (Accessed October 09, 2013) from

[20] International DOI Foundation (2013) “The DOI® System” (Accessed October 09, 2013)

[21] Piwowar, H. A., & Vision, T. J. (2013) “Data reuse and the open data citation advantage” PeerJ, 1, e175.


About CESSDA Training

CESSDA Training offers and coordinates training activities for CESSDA, the Consortium of European Social Science Data Archives ( Hosted by the GESIS - Leibniz Institute for Social Sciences, our center promotes awareness throughout the research lifecycle of good research data management practice and emphasizes the importance of long-term data curation.
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