Strasser "Effective data management and its role in open research"
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The document discusses the importance of effective data management in the context of open research, which involves sharing research processes and outputs publicly to facilitate collaboration and reproducibility. It emphasizes the need for sound data management practices, including planning, organizing, storing, and preserving data, as well as addressing challenges such as cost and lack of training. Additionally, it advocates for the use of identifiers and standard licenses to enhance citation and credit in research outputs.
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
- 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