Good enough practices in data management

Management of research data. Symposium presented at the University of Liège, Belgium.

Transcript

1. Good enough practices in data management Tanja Milotić @milotict June 6th, 2018 Université de Liège
2. Would my colleague be able to take over my project if I suddenly disappeared?
3. Data are hard to find & navigate
4. Digital storage media are fragile
5. Research data are undervalued & neglected
6. Temporary involvement of staff & students
7. Why should you even bother? Help your future self • higher data quality, less mistakes • increased research efficiency • minimized risk of data loss, less frustrations • saved time & money • prevent to collect duplicate data
8. Why should you even bother? Motivation for the near future • required by funders & publishers • increase your visibility (citations!) • easy data sharing “Research data management is part of good research practice”
9. How to start with research data management? Research data management (RDM) concerns the organization of data, from its entry to the research cycle to the dissemination and archiving of valuable results Data management plan (DMP)
10. Research life cycle
11. Research life cycle
12. Data life cycle
13. Data life cycle
14. Data life cycle
15. Data life cycle
16. Data life cycle
17. Data life cycle
18. Data management plan
19. Research data? any information collected/created for the purpose of analysis to verify scientific claims Digital(ized) data: observations, measurements, pictures, audio, models,... Physical data: samples (soil, water, tissue,...), collections,...
20. how about you?
21. Personal data management efforts
22. Time needed to retrieve your own datasets
23. Collaboration with peers
24. data organization
25. Folder structure • Use a well-defined folder structure |- README.md <- The top-level README describing the general layout of the project |- data <- research data | |- raw <- The original, read-only acquired raw data | |- interim <- Intermediate data that has been transformed. | |- processed <- final data products, used in the report/paper/graphs | |_ external <- used additional third party data resources (e.g. vector maps) |- reports <- reported outcome of the analysis as LaTeX, word, markdown,... | |_ figures <- Generated graphics and figures to be used in reporting |- src <- set of analysis scripts used in the analysis “Keep raw data raw!” (Hart et al, 2016)
26. File names • Unique file names • Comprehensible • No special characters • Use _ instead of spaces • YYYY-MM-DD dates • Include initials, project, location, variable, content 2018-02-28_TM_SAFRED_RDM.pdf
27. Keep track of changes In general • Backup changes ASAP • Keep changes small • Share changes frequently Manually track changes • Add a CHANGELOG.txt • Copy entire project after large changes Version control system • Git • GitHub, Bitbucket, Gitlab,... File naming conventions: file_v1, file_v2 Use a version control system (recommended)
28. Data file formats • Non proprietary (open source) formats • Easily reusable • Commonly used Some preferred file formats Tabular data .csv (comma separated values), HDF5, netcdf, rdf Text .txt, html, xml, odt, rtf Still images .tif, jpeg2000, png, pdf, gif, bmp, svg “Love your data, and help other love it, too” (Goodman et al, 2014)
29. data quality
30. Data quality • Standardize data collection • Check data entry • Edit, clean, verify and validate raw data • Peer review • Documentation • Scripting “Data should be structured for analysis” (Hart et al., 2016)
31. documentation
32. Documentation & metadata Why? • Long term usability • Avoid misinterpretation • Collaboration & staff changes • Save your memory for other stuff… @TDXDigLibrary
33. Project documentation Add a README.txt to your project folder • Context • People • Sponsor(s) • Data collection methods • File organization • Known problems, limitations, gaps • Licenses • How to cite
34. Data documentation README.txt for each dataset • Variable names, labels, data type, description • Explain codes & abbreviations • Code & reason for missing values • Code used for derived data • File format • Software • Data standards
35. data preservation
36. Data preservation We all know someone (who knows someone) who has lost data… • Hardware theft or misplacement • Hardware failure • Hardware damage • Software faults • Power failure • Viruses or hacking • Human errors • Backups
37. Still...
39. Time for a backup strategy! • Decide which files • Size? • Responsible • Frequency of full backups • Frequency of partial backups • Organization of backup files “Have a systematic backup scheme” (Hart et al, 2016)
40. Backup guidelines • 3 - 2 - 1 rule: 3 copies - 2 different types of media - 1 offsite • Apply a backup schedule • Test file restores • Do not use CDs or DVDs • Use reliable backup media
41. Storing or archiving? Storing & backing up • Short-term projects • While still active • >1 location: hard drives, servers, laptops,... • Easy to modify files, but also to delete or lose them Archiving & publishing • Long-term monitoring projects • After finishing short-term projects • Deposited in a digital repository • Safeguarded & preserved both!
42. Storage media
43. data publication
44. open data funders demands publishers’ rules innovation & valorization higher citation rates more visibility for your work collaboration application of your findings reduced costs public access to your findings
45. How not to “publish” your data Personal website University website
46. FAIR data Make your data • Findable • Accessible • Interoperable • Reusable Findable • Persistent identifiers (DOI) • Metadata • Naming conventions • Keywords • Versioning Accessible • Choice of datasets • Data repository • Software, documentation • Access status • Retrievable data • Metadata access Interoperable • Standards • Vocabulary • Methodology • References Reusable • Licensing • Provenance • Community standards
47. Where to deposit data? • Depends on data type • Domain specific repositories ◦ GBIF for species occurrences ◦ Genbank for genomic data ◦ ... • General repositories (Zenodo, dataDryad,...) • ORCID integration • DOI for citing datasets
49. @LifeWatchINBO Any questions? tanja.milotic@inbo.be