NSF Wants You to Use Effective Data Practices

QDR Can Help

In a recent “Dear Colleague Letter”, the National Science Foundation (NSF) encourages researchers to adopt best practices in managing research data. NSF frequently uses “DCLs” to make researchers aware of funding priorities and preferred practices, so if you are thinking of applying for NSF funding, you should pay close attention to such pronouncements.

Endorsed by US Social Science Data Repositories

The Data Preservation Alliance for the Social Sciences (Data-PASS), in which many of the social science data repositories in the US – including QDR – are organized, has strongly endorsed the NSF's efforts on this. Data repositories can be instrumental in helping researchers achieve the "effective practices for data" that NSF is looking for.

Top of NSF DCL on Effective Practices for Data

The NSF points to two specific things it wants researchers to pay close attention to:

1. Persistent IDs for Data

The most important “persistent identifier” in scholarly publishing is the Digital Object Identifier (DOI). You likely have seen DOIs, which always start with “10.” and allow you to create stable links to resources (e.g. the link to my article on DOIs below is its DOI, 10.5281/zenodo.2563130, prefix it with https://doi.org and you have a permanent link to the paper: https://doi.org/10.5281/zenodo.2563130). NSF wants you to share your data with a persistent identifier not just because of the stable linking, but also because permanent identifiers help link different scholarly outputs together and make their metadata accessible (I have written a slightly longer text about DOIs and their usefulness for QMMR).

How QDR can help you: When you deposit data with QDR, your data will automatically receive a DOI. We go even further and provide separate DOIs for every file you deposit. This is particularly important for qualitative data, where someone may want to cite a specific interview or document that is part of your data.


Suggested citation for a QDR data rpoject with DOI
Suggested citation for a QDR data project with DOI

2. Machine-Readable Data Management Plans

NSF has been requiring data management plans (DMPs) to be submitted with every grant application since 2011. Feedback from researchers suggests that NSF program officers are paying increasingly close attention to the content of these plans. In addition to this requirement, NSF now also suggest you make your DMP “machine readable,” i.e. provide it in a format/structure that is easy for computers to parse. Thankfully you don’t have to do this by yourself. If you use the DMP Tool, a very useful online tool that helps you write data management plans, your DMP will automatically be machine readable.

How QDR can help you: QDR has extensive guidance on data management and data management planning, specifically for qualitative data. We will also consult with you on your data management (at no charge) and offer a template for inclusion in your DMP if you’re planning to deposit data from your project with QDR (but please make sure to get in touch with us beforehand to make sure your data are a good fit for QDR).

And there’s more

If you’ve recently visited QDR, you may have seen that we now charge a deposit fee unless your institution is a QDR member (though we do offer waivers and are currently able to do so quite generously).

NSF previously indicated that it would cover such fees for funded research, and reiterates this in its DCL:

In some cases, PIs may have to pay a "data deposit fee" to place data in repositories that then make the data more accessible to others. A "data deposit fee" is a one-time charge paid at the time a dataset is deposited into a data repository. In exchange for this fee, repositories commit to making the data available into the future. NSF has clarified its policies on data deposit fees: these fees are allowable expenses in proposal and award budgets.

We are happy to provide you with a deposit-fee quote for your NSF budget.