On Documentation

by Kristin Briney Posted on 2015-04-29

I just got back from my favorite conference on data, Research Data Access and Preservation (Storify highlights), and am processing all of the great things I learned about there. While some of these things will probably end up in future blog posts, I did want to share a bit on what I talked about during my own panel presentation which is relevant here.

The panel itself was entitled “Beyond Metadata” and I spoke about different methods for teaching documentation types other than metadata. I was particularly excited to be on this panel because I think that librarians’ love of metadata doesn’t always translate into what’s needed in the laboratory. So even though your funder may ask in a data management plan for the metadata schema you plan to use, most of the time that’s not the documentation type you really need.

My general philosophy on research documentation is as follows:

  • Most researchers don’t need formal metadata schemas, unless you have a big (time/size/collaborative) project to organize or are actively sharing your data.
  • Your first strategy for documentation should be to improve your research notes/lab notebook that you are likely already using.
  • That said, you can augment your notes strategically with documentation structures such as README.txt files, data dictionaries, and templates.

It’s actually this latter category of documentation types that you find me talking about a lot, as these are the ones that can really help but that many researchers do no know about.

There are plenty of good reasons to improve your documentation (including giving you the ability to reuse your own data, making sure you don’t lose important details, and being transparent for the sake of reproducibility), but we often don’t teach documentation to researchers beyond the basics. So here are a few resources I’ve created so you can learn to improve your documentation:

Looking over this list, I realize that there are a few gaps in the content of this blog when it comes to documentation practices. So look for future posts on templates and good note taking practices!

Research may yet get to the point where metadata is commonplace but we have many useful documentation structures to employ in the meantime. Research notes in particular have been used effectively for hundreds of years and will continue to be useful. In the end, you should use whatever documentation type that works well for you and ensures that you record the best information you can about your data.

Posted in documentation | 3 Comments

New Data Requirements and How To Meet Them

by Kristin Briney Posted on 2015-03-31

Around the time when I started this blog in 2013, the White House Office of Science and Technology Policy (OSTP) decreed that all major federal funders would soon have to require data management plans and data sharing from their grantees. It’s been almost two years since the OSTP memo came out, but we are finally starting to see the funder’s plans for enacting public access requirements.

The biggest recent announcement came from the NIH. NIH previously had a data sharing requirement for grants over $500,000 per year, but the new policy requires data management plans and data sharing from everyone. This matches the NSF policy on data, which will not change significantly under the new mandates.

In addition to NIH and NSF, other US funding agencies have new data policies. DOE, for example, now requires a data management plan with grant applications and data sharing from funded researchers. Similar requirements now exist for NASA, the CDC, and others. Basically, if you are getting research money from a US agency, you should now plan to write a data management plan and share your data.

So, given these new requirements, how do researchers meet them? In terms of data management plans, I’m pushing people at my university toward the DMPTool. The tool is regularly updated with new policy requirements/templates, contains helpful information for writing a plan, and has features that enable collaboration and review. It’s a great resource for anyone writing a data management plan for a US-based funding group.

The harder part is on the data sharing portion of the new data requirements. This is because a significant number of researchers will have to share data that were not required to do so before. Additionally, funders haven’t been very good about specifying where to share data. So we have a huge need to figure out where to put data and not a lot of recommendations on where that actually should be.

In terms of what I’m doing on my campus, I have three recommendations. First, look for where your funder, journal, or peers recommend you put data. This is likely the best place to put your data. Second, look for lists of data repositories by discipline. I particularly like this one from the new journal Scientific Data and the master repository list at re3data. Finally, you can always contact your local data librarian. I expect finding repositories for people’s data is going to be a big part of how my university is responding to these new requirements.

Overall, I’m very excited about these new requirements as I think that data management will really help researchers take care of their data and data sharing will promote transparency in research. Still, there is not a lot of infrastructure or support behind these new demands. This makes it difficult for both those who support research data and those who generate it.

The good news is that this is an evolving process and that, over time, systems and workflows will develop to make it easier to comply with these requirements. Things will get better. Until then, remember that you likely have assistance at your institutional library.

Posted in dataManagement, government, openData | Leave a comment

Clarification and Correction on My Uniform Guidance Post

by Kristin Briney Posted on 2015-02-18

After talking more yesterday with my university’s compliance person about the new Uniform Guidance, I realize that I misinterpreted the “new” part of the guidance relating to data, A-81 section 200.430, in my last post. Having now read through the guidance several more times (don’t you just love long, dry government documents?), I want to correct my comments on this section.

For clarity, here is the section in question:

(i) Allowable activities. Charges to Federal awards may include reasonable amounts for activities contributing and directly related to work under an agreement, such as delivering special lectures about specific aspects of the ongoing activity, writing reports and articles, developing and maintaining protocols (human, animals, etc.), managing substances/chemicals, managing and securing project-specific data, coordinating research subjects, participating in appropriate seminars, consulting with colleagues and graduate students, and attending meetings and conferences. [emphasis mine]

While I originally interpreted this as meaning all data management expenses can be charged to a federal grant (if you’re at an institute of higher education), really it is only people’s time spent managing data that is allowable. This is part of a larger expansion of allowable personnel charges, such as for administrative staff, under the new Uniform Guidance. My fault for not reading more carefully that this section applies to only people’s time.

Do note that this does not supersede any individual funders’ stipulations that allow a wider variety of data management expenses (eg. storage infrastructure, preservation in a repository, etc.) to be charge to a grant.

While I’m obviously disappointed that my original interpretation is not correct, it is still nice to see the cost of data management explicitly being allowed to be paid for by a federal grant. Because data management certainly requires people’s time to perform. That said, it also usually requires infrastructure and I’d like to see funders do more to cover the total cost of taking care of research data.

Posted in dataManagement, fundingAgencies, government | Leave a comment

New Federal Grants Guidance and How It Effects Data

by Kristin Briney Posted on 2015-02-03

If I made a list of the things I cite the most in the course of my job as a data management specialist, at the top would be ISO 8601, the recent Vines, et al. study on data loss over time, and OMB Circular A-110. I’ve already written about the first two on my blog and I want to finally consider Circular A-110 in this post.

Circular A-110 comes from the White House Office of Management and Budget (OMB) and is the document that defines research data and retention requirements for all research supported by US federal funding. It’s also no longer applicable to federally-sponsored research in the US.

Replacing A-110 and several other Circulars is the new Uniform Guidance, also known as OMB Circular A-81. This document was designed to standardize guidance for everyone receiving federal funding in the US (hence the name “Uniform Guidance”). For this reason, it echoes many of the requirements that were in place before but with a few exceptions. Most of these exceptions concern grants administration and are not relevant to this blog, but I am interested in what the new guidance says about data.

On the whole, the new Uniform Guidance looks a lot like the old A-110. For instance, it includes a verbatim copy of the definition of “research data” from A-110 (see A-81 section 200.315):

(3) Research data means the recorded factual material commonly accepted in the scientific community as necessary to validate research findings, but not any of the following: preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues. This “recorded” material excludes physical objects (e.g., laboratory samples). Research data also do not include:

(i) Trade secrets, commercial information, materials necessary to be held confidential by a researcher until they are published, or similar information which is protected under law; and

(ii) Personnel and medical information and similar information the disclosure of which would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study.

Section 200.315, like the A-110 section 36, also states that the Federal government has a right to access and reproduce data produced under a federal award and delineates how to respond to a Freedom of Information Act request for data.

d) The Federal government has the right to:

(1) Obtain, reproduce, publish, or otherwise use the data produced under a Federal award; and

(2) Authorize others to receive, reproduce, publish, or otherwise use such data for Federal purposes.

(e) Freedom of Information Act (FOIA).

(1) In addition, in response to a Freedom of Information Act (FOIA) request for research data relating to published research findings produced under a Federal award that were used by the Federal government in developing an agency action that has the force and effect of law, the Federal awarding agency must request, and the non-Federal entity must provide, within a reasonable time, the research data so that they can be made available to the public through the procedures established under the FOIA. If the Federal awarding agency obtains the research data solely in response to a FOIA request, the Federal awarding agency may charge the requester a reasonable fee equaling the full incremental cost of obtaining the research data. This fee should reflect costs incurred by the Federal agency and the non-Federal entity. This fee is in addition to any fees the Federal awarding agency may assess under the FOIA (5 U.S.C. 552(a)(4)(A)).

A-81 also still requires a 3-year retention period for all research records (see A-81 section 200.333), though the exceptions differ slightly from those in A-110:

Financial records, supporting documents, statistical records, and all other non-Federal entity records pertinent to a Federal award must be retained for a period of three years from the date of submission of the final expenditure report or, for Federal awards that are renewed quarterly or annually, from the date of the submission of the quarterly or annual financial report, respectively, as reported to the Federal awarding agency or pass-through entity in the case of a subrecipient. Federal awarding agencies and pass-through entities must not impose any other record retention requirements upon non-Federal entities. The only exceptions are the following:

(a) If any litigation, claim, or audit is started before the expiration of the 3-year period, the records must be retained until all litigation, claims, or audit findings involving the records have been resolved and final action taken.

(b) When the non-Federal entity is notified in writing by the Federal awarding agency, cognizant agency for audit, oversight agency for audit, cognizant agency for indirect costs, or pass-through entity to extend the retention period…

On the whole, these requirements are the same (and often verbatim copies of) requirements from OMB A-110.

There is, however, one section of the new Uniform Guidance concerning data that does not appear in Circular A-110. This is A-81 section 200.430, which states that grants to institutions of higher education may include the following items in their budgets:

(i) Allowable activities. Charges to Federal awards may include reasonable amounts for activities contributing and directly related to work under an agreement, such as delivering special lectures about specific aspects of the ongoing activity, writing reports and articles, developing and maintaining protocols (human, animals, etc.), managing substances/chemicals, managing and securing project-specific data, coordinating research subjects, participating in appropriate seminars, consulting with colleagues and graduate students, and attending meetings and conferences. [emphasis mine]

This means that you are allowed to charge data management expenses people’s time spent managing data [ADDED 2015-02-18, see follow up post on this] to your grant. Currently, many US funding agencies requiring data management plans already allow data management-related expenses to be added to the grant budget, but this appears to be an entirely new stipulation at the federal level. Personally, I’m very happy to see this allowance in the new Uniform Guidance because researchers often need funds to manage data properly.

Overall, there’s very little change to the research data landscape under the new Uniform Guidance with the exception that all university researchers can now charge data management expenses to their grants. This is definitely something I plan to promote more to the researchers on my campus!

Posted in fundingAgencies, government | 2 Comments

“Data Is” or “Data Are”?

by Kristin Briney Posted on 2015-01-20

Want to start a disagreement amongst data managers? Ask them if “data” is a singular or plural noun. Does one say “data are” or is it better to say “data is”? Data people often have opinions about which is correct (and will let you know about it).

Personally, I’ve been on the “data are” side of this war for some time. This is partly due to the fact that my PhD advisor drilled into my head that one must never say “spectrums”; it’s either one spectrum or many spectra. Likewise “data” is the plural form. However, I recently had an opportunity to re-evaluate my viewpoint and am starting to lean more toward “data is”.

Much of the reason for my change of opinion came from feedback on my writing. As much as “data are” seems like it should be correct, many people stumble over reading this in a sentence. The meaning of the sentence gets lost as the brain tries to process the grammar. As a writer, this is the last thing that I want. Therefore, I started considering using “data” as a singular noun.

The other thing that moved me toward “data is” was the essay sent to me by a fellow data manager, Amanda, called “Data is a singular noun”. The author makes a good case, based on history and grammar, that it should always be “data is” instead of “data are”.

Part of this author’s reasoning is due to the fact that a word’s usage and evolution in English are more important than how the word’s originating language says the word should be used. So even though Latin suggests “data” should be plural, what matters most is how people actually use the word “data” in English. A second reason for choosing the singular is that we really never use the word “datum” anymore. This presumes that “data” is de facto singular form for this word. Either way, there’s a lot of history behind using “data” as a singular noun.

I’m sure that we’ll eventually reach a point where there is a conclusive answer to this question. Until then, I’m going to try to be more conscious about using “data is”. At least in my writing.

So, “data is” or “data are”? What do you think?

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