Good enough practices for Research Data Management

Transcript

1. Good enough practices for
   research data management
   Tanja Milotić
   @oscibio
   Empowering biodiversity research II
   September 27th, 2022
   Brussels
   

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2. Would my colleague
   be able to take over my
   project if I suddenly
   disappeared?
   

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3. Data are hard
   to find & navigate
   

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4. Digital storage
   media are fragile
   

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5. Research data
   are undervalued &
   neglected
   

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6. Temporary
   involvement of
   staff & students
   

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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
   

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8. Why should you even bother?
   Motivation for the near future
   ● required by funders & publishers
   ● increase your visibility (citations!)
   ● easy data sharing
   ● new collaboration opportunities
   “Research data
   management is part of
   good research practice”
   

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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)
   

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10. Research life cycle
    

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11. Data life cycle
    

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12. Data life cycle
    

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13. 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,...
    

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14. data organization
    

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15. 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)
    

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16. File names
    ● Unique file names
    ● Comprehensible
    ● No special characters
    ● Use _ instead of spaces
    ● YYYY-MM-DD dates
    ● Include initials, project,
    location, variable, content
    2022-09-27_EBR_RDM_practices.pdf
    

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17. 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)
    

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18. 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)
    

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19. data quality
    

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20. 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)
    

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21. Tidy data
    ● 80% of data analysis is spent on data cleaning and preparing
    ● Tidy data: structuring datasets to facilitate analysis
    ● Tidy data from the start of the project
    Wickham, 2014
    

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22. From messy to tidy
    Make it a rectangle
    ● Only rows and columns, no additional structure
    ● One column for each type of information
    ● One row for each observation (data point)
    Data carpentry for biologists
    Plot SpeciesA SpeciesB
    1 3 1
    2 2 4
    Messy:
    

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23. From messy to tidy
    Make it a rectangle
    ● Only rows and columns, no additional structure
    ● One column for each type of information
    ● One row for each observation (data point)
    Data carpentry for biologists
    Plot SpeciesA SpeciesB
    1 3 1
    2 2 4
    Messy:
    Plot Species Abundance
    1 A 3
    1 B 1
    2 A 2
    2 B 4
    Tidy:
    

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24. From messy to tidy
    One cell, one value
    ● Every cell contains 1 piece of information
    Data carpentry for biologists
    Mass
    26g
    0.2kg
    Messy:
    

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25. From messy to tidy
    One cell, one value
    ● Every cell contains 1 piece of information
    Data carpentry for biologists
    Mass
    26g
    0.2kg
    Messy:
    Mass Unit
    26 g
    0.2 kg
    Tidy:
    

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26. From messy to tidy
    Don’t mess with the computer
    ● Don’t use visual markings (colors, italics, fonts,...)
    ● Avoid spaces in names, use ‘_’ or CamelCase for multiple words
    ● Avoid special characters (*, @, ^,...)
    Data carpentry for biologists
    Min temp
    5
    4.5
    3.1*
    Messy:
    

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27. From messy to tidy
    Don’t mess with the computer
    ● Don’t use visual markings (colors, italics, fonts,...)
    ● Avoid spaces in names, use ‘_’ or CamelCase for multiple words
    ● Avoid special characters (*, @, ^,...)
    Data carpentry for biologists
    Min temp
    5
    4.5
    3.1*
    Messy:
    min_temp calibration_error
    5 0
    4.5 0
    3.1 1
    Tidy:
    

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28. From messy to tidy
    Be clear and consistent
    ● Use short meaningful names.
    ● Use consistent names, abbreviations, and capitalizations
    ● Use good null values (blanks, NA,... Do not use numbers (0, -999))
    ● Write dates as YYYY-MM-DD or use separate Year, Month, and Day columns
    d s a
    26/02/2022 dior 9
    26/02/2022 disp 1
    May 24, 2022 DIor -999
    May 24, 2022 DISP Missing
    Messy:
    

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29. From messy to tidy
    Be clear and consistent
    ● Use short meaningful names.
    ● Use consistent names, abbreviations, and capitalizations
    ● Use good null values (blanks, NA,... Do not use numbers (0, -999))
    ● Write dates as YYYY-MM-DD or use separate Year, Month, and Day columns
    d s a
    26/02/2022 dior 9
    26/02/2022 disp 1
    May 24, 2022 DIor -999
    May 24, 2022 DISP Missing
    Messy:
    Date Species Abundance
    2022-02-26 dior 9
    2022-02-26 disp 1
    2022-05-24 dior NA
    2022-05-24 disp NA
    Tidy:
    

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30. documentation
    

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31. Documentation & metadata
    Why?
    ● Long term usability
    ● Avoid misinterpretation
    ● Collaboration & staff changes
    ● Save your memory for other stuff…
    @TDXDigLibrary
    

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32. 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
    

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33. 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
    

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34. data preservation
    

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35. 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
    

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36. Storage media
    

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37. data publication
    

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38. 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
    

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39. How not to “publish” your data
    Personal website
    University website
    

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40. 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
    

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41. Where to deposit data?
    ● Depends on data type
    ● Domain specific repositories
    ○ GBIF for species occurrences
    ○ Movebank for movement data
    ○ Genbank for genomic data
    ○ ...
    ● General repositories
    (Zenodo, dataDryad,...)
    ● ORCID integration
    ● DOI for citing datasets
    

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42. Thank you!
    

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