Love Your Data

It’s Love Your Data Week, an effort coordinated by one of my amazing data librarian peers, Heather Coates. Love your data week celebrates how prevalent and important data is to research while also acknowledging that we need to give our data some love from time to time.

Each day has a theme and I encourage you to check out the resources on the main site and those appearing on Twitter under the #LYD16 hashtag. I’ve also identified some posts and videos I’ve created over the last few years relating to the 5 topics. Do check them out and think about ways to love your data a little more this week!

Monday – Keep Your Data Safe

Tuesday – Do You Know Where Your Data Is?

Wednesday – Write It Down!

Thursday – Give and Get Credit

Friday – Transforming, Extending, Reusing Data

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Hello, I’m Kristin Briney and I’m number 0000-0003-1802-0184

It’s only January, but it’s looking like one of the biggest trends of 2016 is going to be linking every researcher to a unique ID. I’m speaking, of course, of ORCID numbers and the recent news that even more publishers are now requiring authors to have an ORCID number when they publish articles in their journals. So if you don’t have an ORCID number, now is the perfect time to get one!

So what’s the big deal with these 16-digit numbers and why would anyone want to be a number?

The problem is best illustrated by the John Smith’s and Zhang Wei’s of the world; have you ever tried to find a paper by someone when there are at least 2 people with that name in the same subfield? The problem is further exacerbated by the fact that people change institutions, women change their name after marriage, and that journals don’t abbreviate names in the same way. How is anyone supposed to find someone else’s scholarship, let alone keep track of their own complete scholarly record?

The answer is to correlate a unique number to each researcher. That way, you know that 0000-0003-1802-0184 always means me and only me. I’m lucky to have a pretty unique name, but I’ve also worked at multiple institutions and in two completely different fields (chemistry and librarianship). Having an ORCID number means that someone else can find all of my scholarly work in an easy way.

I’ve been a big fan of ORCID for a while now and am very excited to see these major adoption milestone happening. I know that many people have already grabbed their own ORCID numbers and now is definitely the time to claim your number if you haven’t! Getting an ORCID number is free and pretty straightforward. Registration is quick, though it may take a little time to associate all of your old papers with your new number when you fill out your profile. Once this is done, however, it’s very easy to maintain your ORCID by occasionally adding new publications. More information can be found at orcid.org.

I really expect that the recent news about ORCID integration will only be the tipping point for this useful system. So don’t be surprised if your publisher, funding agency, or other research-associated organization starts asking for your ORCID soon. This means that you’ll want claim an ORCID number of your own, if you haven’t already. 2016 is likely to be the year that you need it.

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My Data Christmas List

In the spirit of the holidays, I want to end the year with a fun blog post – my data Christmas list. Here is what I’m wishing for this year:

  • I wish that all lost datasets may be found. No dataset should be away from home, especially at Christmas.
  • For the data elves to visit in the night and work miracles on documentation.
  • A good backup system under every researcher’s tree.
  • For data to be shared as freely as Christmas joy.
  • Folders all lined up along the mantle in a logical organization structure with a good naming system and filled with brilliant data.
  • Data analysis that is easy to prepare and won’t make a mess of the kitchen; researchers never enjoy an undercooked (or overcooked) analysis.
  • World peace. Or at least making peace with one’s data.

I hope you all have a lovely holiday season. May your data be merry and bright!

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Rethinking Research Data

How do you persuade the average person to care about open research data?

This was the challenge that I faced in my recent TEDxUWMilwaukee talk on “Rethinking Research Data“. The theme of the talk was that whenever we publish the results of research, we also need to publish the corresponding data. There are so many examples – from economic austerity to Joachim Boldt – that make this relevant to everyday people.

Untitled | Flickr - Photo Sharing! : taken from - https://www.flickr.com/photos/136904454@N02/21529103724/Author: TEDxUWMilwaukee Team https://creativecommons.org/licenses/by-nc-nd/2.0/
Untitled | Flickr – Photo Sharing! : taken from – https://www.flickr.com/photos/136904454@N02/21529103724/Author: TEDxUWMilwaukee Team https://creativecommons.org/licenses/by-nc-nd/2.0/

So if you’ve never thought about how open data impacts you or are wary of this new data trend, do check out the video to see why open data is important!

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Getting Credit for Your Research Data

Happy Open Access week! As usual, I’m celebrating the Open Data portion of Open Access, but my special focus this year is on getting credit for your research data. My library did an in-person workshop on the topic earlier this week and I want to share the idea out more widely on the blog because I think it’s important.

The crux of the issue is that more and more researchers are sharing their data (either because their funder/publisher requires it or because the researcher believes in open access to research materials), but not all data sharing venues are made equal. Consider the following sharing pathways[1]:

  1. Sharing data on personal or lab website
  2. Sharing data by request via email or Dropbox
  3. Publishing data as supplemental material for a journal article
  4. Depositing data into a disciplinary data repository
  5. Depositing data into a general research repository or institutional repository.

All of these methods work to distribute your data and many comply with requirements to share. However, not all of these sharing venues will maximize the credit you receive for your data. For example, the last three sharing options will provide researchers with a stable location and a citation for the data, increasing the data’s citability. Option 4, in particular, will probably maximize credit because your research peers are likely to look for your data in a disciplinary repository.

Getting credit for your work is supremely important in research and this doesn’t get any less true when it comes to data sharing. The good news is that sharing data actually increases citation counts on the corresponding article by roughly 10%, with a higher citation boost for older papers.[2] However, this finding likely holds true only if others can actually find your data. Therefore, I encourage you to think about data sharing venues with respect to maximizing your credit.

As a follow up to getting credit for your data, I want to touch on how to actually give credit for using another researcher’s data: data citation. Data citation is very similar to article citation in that you cite the data you used in the works cited section of your article. Where data citation differs is the citation format and that fact that you cite the data separately from the article. Let’s look a little bit at how this works.

At its most basic, a data citation should include the following information:

  • Creator
  • Publication Year
  • Title
  • Publisher
  • Identifier

The format of your data citation can vary across citation styles (APA, Chicago, etc.) but at a minimum should contain these five components. If you don’t have a recommended data citation format, you can use the following:

Creator (PublicationYear): Title. Publisher. Identifier

It’s often considered good practice to cite the corresponding article whenever you cite the dataset, but it’s not strictly necessary. Use your best judgement and always give credit for the content you do use.

I want to wrap up by saying that as we get into a greater regime of data sharing, I hope you start thinking about this topic with respect to maximizing credit. This means placing your data in a location where you’ll get the most credit as well as giving proper credit to others, via data citation, when using their data. Framing data sharing through the lens of credit means that we’ll do right by our data going forward and properly recognize it as an important scholarly product.

 

[1] Many thanks to Lisa Johnston (U Minnesota) for inspiration from her pro/con data sharing exercise
[2] Piwowar HA, Vision TJ (2013) Data reuse and the open data citation advantage. PeerJ 1: e175. http://dx.doi.org/10.7717/peerj.175

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Do You Have an Institutional Data Policy? aka. Who Owns Your Data?

September seems to be publication month and I’m so excited to share my other item that was just published: a research article on institutional data policy.

This paper came out of a question that my collaborators and I had: who owns the data produced by university researchers? We had a sense that some universities made it clear that they owned the data while the answer was ambiguous at other institutions. Complicating matters further was the question of ownership when researchers from different universities collaborate. This was particularly applicable in our case as my colleagues and I all work at different institutions.

So we set down path of trying to find some clarity around research data ownership only to realize that this is a complex question. Data ownership has many facets including: laws, copyright, funding, policy, etc. To simplify things, we decided to start by looking at what universities say about data ownership. This meant studying university data policy.

For this article, we looked at 206 Carnegie “High” and “Very High” research universities in the United States and pulled any policies on research data that we could find. We found that just under half (44%) of the institutions studied had some policy covering research data. Two-thirds of these policies (29% overall) were stand-alone data policies and one-third (15% overall) were IP policies that cover data.

The good news is that we found that the majority of discovered policies (67%) defined the owner of the data; most often this was the university. The bad news is this means that over two-thirds of all institutions studied (71%) offered no guidance on data ownership at all. With so many new requirements around research data in the United States, data ownership is definitely an area where institutions need to step up and offer more clarity.

We’re still on the path for more answers about data ownership, but in the meantime our research article has a lot more to say about institutional data policies and library data services at US research institutions. I encourage you to check out the paper if these topics interest you and to peruse all of the special data issue of the Journal of Librarianship and Scholarly Communication (bonus: it’s Open Access!). The whole issue has definitely jumped to the top of my reading list!

Citation: Briney, K., Goben, A., & Zilinski, L.. (2015). Do You Have an Institutional Data Policy? A Review of the Current Landscape of Library Data Services and Institutional Data Policies. Journal of Librarianship and Scholarly Communication, 3(2), 1–25. DOI: http://doi.org/10.7710/2162-3309.1232

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