Research Data Curation Bibliography, Version 9

http://digital-scholarship.org/rdcb/rdcb.htm

Includes over 750 English-language works (2009-2017) about research data creation, acquisition, metadata, provenance, repositories, management, policies, support services, funding agency requirements, open access, peer review, publication, citation, sharing, reuse, and preservation.  Includes links and, for CC BY and public domain works, abstracts. Licensed under CC BY 4.0.

The Initiative for Open Citations (I4OC)

The Wikimedia Foundation, in collaboration with 29 publishers and a network of organizations, including the Public Library of Science (PLOS), the Internet Archive, Mozilla, the Bill & Melinda Gates Foundation, the Wellcome Trust, and many others, announced the Initiative for Open Citations (I4OC), which aims to make citation data freely available for anyone to access. DataCite is a founding member.

See Initiave for Open Citations: https://i4oc.org/ 

Related blog post: https://blog.wikimedia.org/2017/04/06/initiative-for-open-citations/

Data citation: two posts by Cameron Neylon

Cameron Neylon has written two useful blog posts on 

1.  the state of the art around data citation, https://researchdata.jiscinvolve.org/wp/2016/04/14/data-citation-what-is-the-state-of-the-art-in-2016/, and
2.  what is a citation in the context of data citation,
   https://rdmetrics.jiscinvolve.org/wp/2016/04/12/what-constitutes-research-data-what-is-citation/

(courtesy of Elizabeth Gadd, Loughborough University via the LIS-BIBLIOMETRICS email distribution group)

Article: Research data explored: an extended analysis of citations and altmetrics

Research data explored: an extended analysis of citations and altmetrics
Peters, Isabella; Kraker, Peter; Lex, Elisabeth; Gumpenberger, Christian; Gorraiz, Juan
Scientometrics, Vol. 107 Issue 2 – 2016: 723 - 744

http://doi.org/10.1007/s11192-016-1887-4

Nature article: The unsung heroes of scientific software

The unsung heroes of scientific software

Creators of computer programs that underpin experiments don’t always get their due — so the website Depsy is trying to track the impact of research code.

For researchers who code, academic norms for tracking the value of their work seem grossly unfair. They can spend hours contributing to software that underpins research, but if that work does not result in the authorship of a research paper and accompanying citations, there is little way to measure its impact.

Enter Depsy, a free website launched in November 2015 that aims to “measure the value of software that powers science”.

Read the full story:

The unsung heroes of scientific software
Singh Chawla, Dalmeet
Nature, Vol. 529 Issue 7584 – 2016: 115 - 116

10.1038/529115a

Global-level data sets may be more highly cited than most journal articles.

Global-level data sets may be more highly cited than most journal articles.

 Chris Belter measured the impact of a few openly accessible data sets and compared to journal articles in his field. His results provide hard evidence that the production, archival, and sharing of data may actually be a more effective way to contribute to the advancement of scientific knowledge.

Blog 

Tracking the impacts of data, beyond citations

How can you tell if data has been useful to other researchers?

Tracking how often data has been cited (and by whom) is one way, but data citations only tell part of the story, part of the time. (The part that gets published in academic journals, if and when those data are cited correctly.) What about the impact that data has elsewhere?

• How data packages are cited
• How to track citations to data packages
• How data papers are cited
• How to find citations for data papers
• Altmetrics for data
• Altmetrics for how data is used on the social web
• Repository- and publisher-based indicators

http://us4.campaign-archive1.com/?u=26fcc4e14b24319755845d9e0&id=9288ccfeff&e=ccf5be4b85

Linking data and publications

Do you want to see if a CrossRef DOI (typically assigned to publications) refers to DataCite DOIs (typically assigned to data)? Here you go:
http://api.labs.crossref.org/graph/doi/10.4319/lo.1997.42.1.0001
Conversely, do you want to see if a DataCite DOI refers to CrossRef DOIs? Voilà:
http://api.labs.crossref.org/graph/doi/10.1594/pangaea.185321

“How can we effectively integrate data into the scholarly record?” This is the question that has, for the past few years, generated an unprecedented amount of handwringing on the part researchers, librarians, funders and publishers. Indeed, this week I am in Amsterdam to attend the 4th RDA plenary in which this topic will no doubt again garner a lot of deserved attention.

We hope that the small example above will help push the RDAs agenda a little further. Like the recent ODIN project, It illustrates how we can simply combine two existing scholarly infrastructure systems to build important new functionality for integrating research objects into the scholarly literature.
Does it solve all of the problems associated with citing and referring to data? Can the various workgroups at RDA just cancel their data citation sessions and spend the week riding bikes and gorging on croquettes? Of course not. But my guess is that by simply integrating DataCite and CrossRef in this way, we can make a giant push in the right direction.

There are certainly going to be differences between traditional citation and data citation. Some even claim that citing data isn’t “as simple as citing traditional literature.” But this is a caricature of traditional citation. If you believe this, go off an peruse the MLA, Chicago, Harvard, NLM and APA citation guides. Then read Anthony Grafton’s, The Footnote? Are you back yet? Good, so let’s continue…

Citation of any sort is a complex issue- full of subtleties, edge-cases exceptions, disciplinary variations and kludges. Historically, the way to deal with these edge-cases has been social, not technical. For traditional literature we have simply evolved and documented citation practices which generally make contextually-appropriate use of the same technical infrastructure (footnotes, endnotes, metadata, etc.). I suspect the same will be true in citing data. The solutions will not be technical, they will mostly be social. Researchers, and publishers will evolve new, contextually appropriate mechanisms to use existing infrastructure deal with the peculiarities of data citation.

Does this mean that we will never have to develop new systems to handle data citation? Possibly But I don’t think we’ll know what those systems are or how they should work until we’ve actually had researchers attempting to use and adapt the tools we have.

Technical background

About five years ago, CrossRef and DataCite explored the possibility of exposing linkages between DataCite and CrossRef DOIs. Accordingly, we spent some time trying to assemble an example corpus that would illustrate the power of interlinking these identifiers. We encountered a slight problem. We could hardly find any examples. At that time, virtually nobody cited data with DataCite DOIs and, if they did, the CrossRef system did not handle them properly. We had to sit back and wait a while.
And now the situation has changed.
This demonstrator harvests DataCite DOIs using their OAI-PMH API and links them in a graph database with CrossRef DOIs. We have exposed this functionality on the “labs” (i.e. experimental) version of our REST API as a graph resource. So…
You can get a list of CrossRef DOIs that refer to DataCite DOIs as follows:
http://api.labs.crossref.org/graph?rel=cites:*&filter=source:crossref,related-source:datacite
And the converse:
http://api.labs.crossref.org/graph?rel=cites:*&filter=source:datacite,related-source:crossref

Caveats and Weasel Words

• We have not finished indexing all the links.
• The API is currently a very early labs project. It is about as reliable as a devolution promise from Westminster.
• The API is run on a pair of raspberry-pi’s connected to the internet via bluetooth.
• It is not fast.
• The representation and the API is under active development.

Read more on the CrossTech Blog...


Linking data and publications by Geoffrey Bilder, unless otherwise expressly stated, is licensed under a Creative Commons Attribution 4.0 International License.

Full disclosure

This ALMOST goes far enough!
If only there was a citation for the data as well - though there is a DOI.

Infographic roundup: data management

Scouring the web we've found seven infographics that look at data management and discuss how you can best manage one of your most important assets - your research data.

• Love your data - practice safe science¹
• Data management checklist² 
• What would you do if you lost you research data tomorrow?³
• Research data management⁴
• Storing healthcare data⁵
• Building a culture of data citation⁶
• Metadata cheat sheet⁷

1. Via Projects. 2. Via eResearch SA. 3. Via University of Leicester. 4. ViaQueen Mary University of London Library. 5. Via HIT Consultant. 6. ViaANDS. 7. Via eResearch SA.

Great infographics courtesy of ERSA: http://www.ersa.edu.au/infographic-roundup-data-managment

Thomson Reuters Collaborates with DataCite to Expand Discovery of Research Data

Published by Sergio Ruiz on 28 August 2014 - 4:50pm

PHILADELPHIA, August 28, 2014 – The Intellectual Property & Science business of Thomson Reuters, the world’s leading source of intelligent information for businesses and professionals, today announced a collaboration with DataCite, a leading global nonprofit organization dedicated to enabling people to find, share, use, and cite data. The collaboration will promote the discovery of research data sets through theData Citation Index SM, a single-point solution providing access to quality research data sets from multi-disciplinary repositories around the world.

This collaboration will connect the Data Citation Index to high quality research data from repositories worldwide that work with DataCite. This will ensure that the valuable content that has been made citable by DataCite is globally discoverable, properly attributed and reusable by other researchers. 

http://www.datacite.org/node/135