Strasser "Effective data management and its role in open research"
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This presentation was provided by Carly Strasser of the Chan Zuckerberg Initiative during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
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Strasser "Effective data management and its role in open research"
- 1. Effective data management and its role in open research Carly Strasser, PhD | @carlystrasser Chan Zuckerberg Initiative NISO Effective Data Management September 2021 Images from unsplash.com unless otherwise attributed
- 2. Why open research? What is open research? Data management basics Incentives for openness
- 3. Open Science Advocacy & Education Research
- 4. Why open research? What is open research? Data management basics Incentives for openness
- 12. From Flickr by Alex Roberts We need to update the culture of research to be more open
- 13. Why open research? What is open research? Data management basics Incentives for openness
- 14. Open research is the practice of sharing research processes and outputs publicly in a timely and effective manner, so that other researchers can learn and build on them. proposals preprints datasets images software/code workflows reviews publications What should be shared?
- 16. Open research is the practice of sharing research processes and outputs publicly in a timely and effective manner, so that other researchers can learn and build on them.
- 17. Data Paper just got published! Guess I’ll share my data now
- 18. Modified from https://pacharanero.github.io/2020-howto-opensource-rewired-dev-conf/ D a t a Public domain
- 19. Open data is only useful if it’s FAIR Data
- 20. Getting to FAIR data requires good data management
- 21. Why open research? What is open research? Data management basics Incentives for openness
- 22. Plan Basics
- 23. When do you plan? Yes. manage
- 24. Standard Data Management Plans 1. Types of data 2. Data & metadata standards 3. Policies 4. Plans for preservation 5. Budget More from Maria later!
- 26. Collect & organize data so it’s FAIR from the start (for both the researcher themselves and others) ● Use good naming schema for files ● Organize files logically ● Use spreadsheets thoughtfully ● Use an appropriate metadata schema throughout the project
- 27. Digital context • Name of the data set • The name(s) of the data file(s) in the data set • Date the data set was last modified • Example data file records for each data type file • Pertinent companion files • List of related or ancillary data sets • Software (including version number) used to prepare/read the data set • Data processing that was performed Personnel & stakeholders • Who collected • Who to contact with questions • Funders Scientific context • Scientific reason why the data were collected • What data were collected • What instruments (including model & serial number) were used • Environmental conditions during collection • Temporal & spatial resolution • Standards or calibrations used Information about parameters • How each was measured or produced • Units of measure • Format used in the data set • Precision & accuracy if known Information about data • Definitions of codes used • Quality assurance & control measures • Known problems that limit data use (e.g. uncertainty, sampling problems)
- 28. Plan Collect & organize Store & back up Basics
- 29. What software? What hardware? What personnel? How often? Test system Set reminders
- 30. Plan Collect & organize Store & back up Document processes Basics
- 31. The R in FAIR is the hardest
- 35. The R in FAIR is the hardest …and it’s much easier with code
- 37. Plan Collect & organize Store & back up Document processes Preserve for the long term Basics
- 38. Choose a good data repository
- 39. is best custom data use agreements are bad is okay Use standard, machine-readable licenses and waivers
- 40. Why open research? What is open research? Data management basics Incentives for openness
- 41. What’s the holdup? ● Cost ● Lack of training ● Fear of lost rights or benefits ● No incentives
- 42. ● Cost ● Lack of training ● Fear of lost rights or benefits ● No incentives What’s the holdup?
- 44. ● Cost ● Lack of training ● Fear of lost rights or benefits ● No incentives What’s the holdup?
- 45. Open research is the practice of sharing research processes and outputs publicly in a timely and effective manner, so that other researchers can learn and build on them. proposals preprints datasets images software/code workflows reviews publications
- 46. From Flickr by Selena N. B. H. Journal articles are the LAST output ...and the only thing that “counts”
- 47. Open research is the practice of sharing research processes and outputs publicly in a timely and effective manner, so that other researchers can learn and build on them. proposals preprints datasets images software/code workflows reviews publications How can we get everything to count?
- 48. Tenure & promotion Collaboration Requirements Mandates Better tools & metrics
- 49. Identifiers for everything People Organizations Publications Datasets Protocols Software/code ...
- 50. Identifiers enable citation data software genetic code reagents protocols >
- 51. Identifiers enable citation and credit
- 53. Open science is the future