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IDCC Workshop: Analysing DMPs to inform research data services: lessons from the DART project

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A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).

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IDCC Workshop: Analysing DMPs to inform research data services: lessons from the DART project

  1. 1. Analyzing DMPs to inform research data services Lessons from the DART Project IDCC 2016 | Amanda L. Whitmire | http://orcid.org/0000-0003-2429-8879
  2. 2. 25 Feb. 2016 @DMPResearch | @AWhitTwit 2 Acknowledgements 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-07-13-0328. DARTTeam
  3. 3. US Context for DMPs 25 Feb. 2016 @DMPResearch | @AWhitTwit 3 ~23 Federal agencies now require a DMP with proposals DMPTool offers 30 DMP templates Funding is very limited
  4. 4. 25 Feb. 2016 @DMPResearch | @AWhitTwit 4 DMPs are useful sources of information about researcher knowledge, capabilities, practices & needs* *caveats, etc.
  5. 5. 25 Feb. 2016 @DMPResearch | @AWhitTwit 5
  6. 6. 25 Feb. 2016 @DMPResearch | @AWhitTwit 6
  7. 7. Levels of data services 25 Feb. 2016 @DMPResearch | @AWhitTwit 7 the basics DMP review workshopswebsite mid-level dedicated research services metadata support facilitate deposit in DRs consults high level infrastructure data curation From: Reznik-Zellen, Rebecca C.; Adamick, Jessica; and McGinty, Stephen. (2012). "Tiers of Research Data Support Services." Journal of eScience Librarianship 1(1): Article 5. http://dx.doi.org/10.7191/jeslib.2012.1002
  8. 8. Informed data services development 25 Feb. 2016 @DMPResearch | @AWhitTwit 8 Survey DCPs DMPs DMP
  9. 9. Goal: A tool for consistent & robust analysis of DMPs 25 Feb. 2016 @DMPResearch | @AWhitTwit 9
  10. 10. 25 Feb. 2016 @DMPResearch | @AWhitTwit 10 Performance Level Performance Criteria Complete / detailed Addressed issue, but incomplete Did not address issue Directorates GeneralAssessment 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 NSF Directorate-ordivision- specificassessmentcriteria 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 AGS
  11. 11. 25 Feb. 2016 @DMPResearch | @AWhitTwit 11 Performance Level Performance Criteria Complete / detailed Addressed issue, but incomplete Did not address issue Directorates GeneralAssessment 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 NSF Directorate-ordivision- specificassessmentcriteria 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 AGS
  12. 12. 25 Feb. 2016 @DMPResearch | @AWhitTwit 12 Performance Level Performance Criteria Complete / detailed Addressed issue, but incomplete Did not address issue Directorates GeneralAssessment 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 NSF Directorate-ordivision- specificassessmentcriteria 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 AGS
  13. 13. 25 Feb. 2016 @DMPResearch | @AWhitTwit 13 Performance Level Performance Criteria Complete / detailed Addressed issue, but incomplete Did not address issue Directorates GeneralAssessment 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 NSF Directorate-ordivision- specificassessmentcriteria 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 AGS
  14. 14. 25 Feb. 2016 @DMPResearch | @AWhitTwit 14
  15. 15. 25 Feb. 2016 @DMPResearch | @AWhitTwit 15
  16. 16. 25 Feb. 2016 @DMPResearch | @AWhitTwit 16 https://osf.io/kh2y6/
  17. 17. 25 Feb. 2016 @DMPResearch | @AWhitTwit 17 A few results
  18. 18. Data type & format across disciplines (%) 25 Feb. 2016 @DMPResearch | @AWhitTwit 18 Data types Data formats
  19. 19. Data type & format across disciplines (%) 25 Feb. 2016 @DMPResearch | @AWhitTwit 19 Data types Data formats observational, model results, experimental, qual./quant., geospatial, code, etc. hand-written notes, NetCDF, *.xlsx, *.csv, *.shp, *.shx, *.dbf, *.mp4, R, etc.
  20. 20. Data type & format across disciplines (%) 25 Feb. 2016 @DMPResearch | @AWhitTwit 20 Data types Data formats observational, model results, experimental, qual./quant., geospatial, code, etc. hand-written notes, NetCDF, *.xlsx, *.csv, *.shp, *.shx, *.dbf, *.mp4, R, etc.
  21. 21. Data sharing venues across disciplines (%) 25 Feb. 2016 @DMPResearch | @AWhitTwit 21
  22. 22. Data sharing venues across disciplines (%) 25 Feb. 2016 @DMPResearch | @AWhitTwit 22
  23. 23. Data sharing venues across disciplines (%) 25 Feb. 2016 @DMPResearch | @AWhitTwit 23
  24. 24. Metadata across disciplines (%) 25 Feb. 2016 @DMPResearch | @AWhitTwit 24
  25. 25. Metadata across disciplines (%) 25 Feb. 2016 @DMPResearch | @AWhitTwit 25
  26. 26. 25 Feb. 2016 @DMPResearch | @AWhitTwit 26
  27. 27. Use a digital tool for collecting your assessment data 25 Feb. 2016 @DMPResearch | @AWhitTwit 27 Forces consistency Produces co-located data Facilitates analysis
  28. 28. Assess what the DMP guidelines stipulate, not what you think the DMP should include 25 Feb. 2016 @DMPResearch | @AWhitTwit 28 VS. Ideal DMP guidance Actual DMP guidance
  29. 29. 25 Feb. 2016 @DMPResearch | @AWhitTwit 29 “Provide a description of the data you will collect or re-use, including the file types, dataset size, number of expected files or sets, and content. … Consider the following: • What data will be generated in the research? • What data types will you be creating or capturing? • How will you capture or create the data? • If you will be using existing data, state this and include how you will obtain it. • What is the relationship between the data you are collecting and any existing data? • How will the data be processed? • What quality assurance & quality control measures will you employ? DMPTool guidance on data types “Types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project.” General NSF DMP guidance on data types
  30. 30. 25 Feb. 2016 @DMPResearch | @AWhitTwit 30
  31. 31. 25 Feb. 2016 @DMPResearch | @AWhitTwit 31
  32. 32. 25 Feb. 2016 @DMPResearch | @AWhitTwit 32 http://dmpresearch.library.oregonstate.edu/ https://osf.io/kh2y6/ Amanda Whitmire thalassa@stanford.edu Thank you!
  33. 33. 25 Feb. 2016 @DMPResearch | @AWhitTwit 33 Except where otherwise noted, this work is licensed under http://creativecommons.org/licenses/by/4.0/ Creative Commons & the double C in a circle are registered trademarks of Creative Commons in the United States & other countries. Third party marks & brands are property of their respective holders. Please attribute Amanda Whitmire with a link to this presentation at SlideShare.net

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