Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Data and Donuts: The Impact of Data Management

540 Aufrufe

Veröffentlicht am

Good data management practices are becoming increasingly important in the digital age. Because we now have the technology to freely share research data and also because funding agencies want to do more with decreasing research funds, many funding agencies and journals require authors and grantees to share their research data. To provide training in this area, Tobin Magle, the Morgan Library's Data Management Specialist, is putting on a series of data management workshops called "Data and Donuts". Join us to learn about data management topics throughout the research data lifecycle.

Veröffentlicht in: Daten & Analysen
  • Als Erste(r) kommentieren

Data and Donuts: The Impact of Data Management

  1. 1. The Impact of Data Management C. Tobin Magle, PhD Sept. 29, 2016 9:00-10:00 a.m. Morgan Library Computer Classroom 173
  2. 2. but the same principles apply to both data management != data sharing
  3. 3. Why should I care about data management? Rinehart, AK. “Getting emotional about data” College & Research Libraries News September 2015 vol. 76 no. 8 437-440
  4. 4. *ok not everything, but most things
  5. 5. More researchers https://www.nsf.gov/statistics/2016/nsf16300/digest/nsf16300.pdf
  6. 6. See arXiv:1402.4578 for details
  7. 7. Working Email Data are extant (If status known) Status of data (if response) Response (if email working) doi:10.1016/j.cub.2013.11.014
  8. 8. We are losing vast amounts of data 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 11 1 1 1 1 1 1 1 0 0 0 0 0 0 0 00 0 00 0 1 1 1 1 1 0
  9. 9. Research funding is tight http://www.bu.edu/research/articles/funding-for-scientific-research/
  10. 10. Funders want to do more with less http://figshare.com/blog/2015_The_year_of_open_data_mandates/143
  11. 11. White House’s 2013 OSTP “The Obama Administration is committed to the proposition that citizens deserve easy access to the results of research their tax dollars have paid for. That’s why, in a policy memorandum released today, OSTP Director John Holdren has directed Federal agencies with more than $100M in R&D expenditures to develop plans to make the results of federally funded research freely available to the public—generally within one year of publication.” http://www.whitehouse.gov/blog/2013/02/22/expanding-public-access-results-federally-funded-research
  12. 12. NSF post-award requirements “Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing.” http://www.nsf.gov/pubs/policydocs/pappguide/nsf11001/aag_6.jsp#VID4
  13. 13. In other words… In other words…
  14. 14. It’s good for science • Improves research reproducibility • Improves efficiency • Spurs innovation
  15. 15. It’s good for you • You are the future data user • Your data get used (and cited) • Exposure to collaborators • More competitive grants
  16. 16. But wait… Barriers to data sharing
  17. 17. “But it’s mine, I don’t want to share!” • Usually funded by public money • See White House statement • If you work for CSU, the university actually owns your data • You are the steward • CSU promotes open data
  18. 18. “But my data are too small to be useful”
  19. 19. “But I work with sensitive/private data” • CAN share deidentified data • CAN share summary data • https://clinicaltrials.gov/ • Controlled access • See dbGaP @ NCBI re: NIH genomic data sharing policy • Release metadata so people know the data exist and ask for it • Identifying personal genomes by surname inference • https://www.ncbi.nlm.nih.gov/pubmed/23329047
  20. 20. “But I’m planning applying for a patent!” • Ok data sharing isn’t right for you • But good data management practices have benefits even if you don’t share! • Can share later
  21. 21. What is data management? The policies, practices and procedures needed to manage the storage, access and preservation of data produced from a research project
  22. 22. Where does data management fit into research? Throughout the whole research cycle
  23. 23. Hypothesis The research cycle
  24. 24. Hypothesis Experimental design The research cycle
  25. 25. Hypothesis Data Experimental design The research cycle
  26. 26. Hypothesis Data Experimental design Results The research cycle
  27. 27. Hypothesis Data Experimental design ResultsArticle The research cycle
  28. 28. Hypothesis Data Experimental design ResultsArticle The research cycle
  29. 29. Hypothesis Data Experimental design ResultsArticle Data Management Plans The research cycle
  30. 30. Hypothesis Raw data Experimental design Tidy Data ResultsArticle Data Management Plans Cleaning Analysis The research cycle
  31. 31. Hypothesis Raw data Experimental design Tidy Data ResultsArticle Data Management Plans Cleaning Sharing Analysis Open Data Closed Data Archiving The research cycle
  32. 32. Hypothesis Raw data Experimental design Tidy Data ResultsArticle Data Management Plans Cleaning Sharing Analysis Open Data Code Reproducible Research Closed Data Archiving The research cycle
  33. 33. Hypothesis Raw data Experimental design Tidy Data ResultsArticle Data Management Plans Cleaning Sharing Analysis Open Data Code Reproducible Research Reuse Closed Data Archiving The research cycle
  34. 34. Hypothesis Raw data Experimental design Tidy Data ResultsArticle Data Management Plans Cleaning Sharing Analysis Open Data Code Reproducible Research Reuse Closed Data Archiving The research cycle

×