USING DATA MANAGEMENT PLANS to EXPLORE VARIABILITY in RESEARCH DATA MANAGEMENT PRACTICES across DOMAINS Jake Carlson, Susan Wells Parham, Patricia Hswe, Brian Westra & Amanda Whitmire IDCC 2016 Amsterdam, Netherlands 24 February 2016

Data management plan As Research Tool (DART) Amanda Whitmire | Stanford University Libraries Jake Carlson | University of Michigan Library Patricia M. Hswe | Pennsylvania State University Libraries Susan Wells Parham | Georgia Institute of Technology Library Brian Westra | University of Oregon Libraries This project was made possible in part by the Institute of Museum and Library Services grant number LG D A R T Team 24 Feb.

DART Premise 24 Feb DMP DATA MANAGEMENT knowledge capabilities practices needs Research Data Services

Project Development 4 24 Feb. Project team testing & revisions Feedback & iterations Advisory board Analytic Tool DART Outputs: 1)An analytic tool to standardize the review of data management plans 2)A study of 500 NSF DMPs from 5 research institutions

24 Feb Performance Level Performance CriteriaComplete / detailed Addressed issue, but incomplete Did not address issue Directorates General Assessment Criteria Describes what types of data will be captured, created or collected Clearly defines data type(s). E.g. text, spreadsheets, images, 3D models, software, audio files, video files, reports, surveys, patient records, samples, final or intermediate numerical results from theoretical calculations, etc. Also defines data as: observational, experimental, simulation, model output or assimilation Some details about data types are included, but DMP is missing details or wouldn’t be well understood by someone outside of the project No details included, fails to adequately describe data types. All Directorate- or division- specific assessment criteria Describes how data will be collected, captured, or created (whether new observations, results from models, reuse of other data, etc.) Clearly defines how data will be captured or created, including methods, instruments, software, or infrastructure where relevant. Missing some details regarding how some of the data will be produced, makes assumptions about reviewer knowledge of methods or practices. Does not clearly address how data will be captured or created. GEO AGS, GEO EAR SGP, MPS AST Identifies how much data (volume) will be produced Amount of expected data (MB, GB, TB, etc.) is clearly specified. Amount of expected data (GB, TB, etc.) is vaguely specified. Amount of expected data (GB, TB, etc.) is NOT specified. GEO EAR SGP, GEO

24 Feb. Distribution of DMPs Across Directorates

Study Limitations 24 Feb Complexity of content Disciplinary shorthand DMP Don’t get whole picture Expectations vs.

Results 24 Feb.

Data Sharing Venues [%] 24 Feb.

Metadata Standards 24 Feb.

Data Archiving Venues [%] 24 Feb.

Results from BIO 24 Feb

24 February DMPs asserting data will be produced – 3 DBI: Biological Infrastructure – 24 DEB: Environmental Biology – 5 EF: Emerging Frontiers – 8 IOS: Integrative Organismal Systems – 6 MCB: Molecular & Cellular Biosciences – 6 undetermined BIO division BIO: Division breakdown 73% of BIO DMPs came from these three divisions

24 February BIO vs. all DMPs: Traits of intent to share data

BIO: Distribution of modes of sharing 24 February

16 GenBank (14) Dryad (12) SRA (11) iDigBio (3) KNB (3) MorphBank (3) NCBI (3) TreeBASE (2) BIO: Repositories mentioned & frequency Named data centers / repositories arguably map most to the top three divisions represented by the DMPs - DEB, IOS, and MCB

24 February BIO: Results on policies for access and sharing Relates to intent to share via data centers & data repositories

24 February BIO: Results on policies for reuse/redistribution

Final thoughts 24 Feb.

Hope for the future? 24 Feb. Does BIO point toward any shift in culture? Lessons from BIO that could be generalized? Will the intent to share lead to the behavior? What’s next? Address the gaps we uncovered Audit DMPs – follow-up with researchers