Organising and Documenting Data

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Presented by Stuart Macdonald at the RDM Academic Liaison Library Training event, University of Edinburgh, 15 November 2012.

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  • Is there a filenaming convention for your specific discipline (e.g. The Open Biological and Biomedical Ontologies, DOE’s Atmospheric Radiation measurement (ARM) program )
  • For qualitative data or small-scale surveys, the documentation might exist only in your head. Take the time to write it down while it is fresh in your mind. This may include writing methodology reports, creating codebooks with full variable and value labels, documenting decisions about software, tracking changes to different versions of the dataset, recording assumptions made during analysis.
  • METS, a Digital Library Federation initiative, attempts to build upon the work of MOA2 and provide an XML document format for encoding metadata necessary for both management of digital library objects within a repository and exchange of such objects between repositories (or between repositories and their users) Administrative metadata provides information necessary to allow a repository to manage objects, such as when, how and by whom a resource was created and how it can be accessed. Provenance and licensing
  • Organising and Documenting Data

    1. 1. Organising and Documenting DataStuart MacdonaldEDINA & Data Librarystuart.macdonald@ed.ac.ukRDM Academic Liaison Librarian Training, 15 November 2012
    2. 2. Organising your data•RDM is one of the essential areas of responsible conduct ofresearch.•Research data files and folders need to be organised in asystematic way to be: • identifiable and accessible for yourself, • identifiable and accessible for colleagues, and for future users.•Thus it is important to plan the organisation of your databefore a research project begins.•Doing so will prevent any confusion while research isunderway or when multiple individuals will be editing and / oranalysing the data.
    3. 3. This can be achieved through:•Directory structure & file naming conventions•(File naming conventions for specific disciplines)•File renaming•File version controlFor this to be successful a consistent and disciplinedapproach is required.Easier to accomplish as and when data files are generatedrather than retrospectively attempting to implement.When organization methods become too time consuming,consider automated methods.
    4. 4. File Naming conventions•Naming datasets according to agreed conventions shouldmake file naming easier for colleagues because they will nothave to ‘re-think’ the process each time.•File names should provide context for the contents of the file,making it distinguishable from files with similar subjects ordifferent versions of the same file.•Many files are used independently of their file or directorystructure, so provide sufficient description in the file name.•Suggested strategies: identify the project; avoid specialcharacters; use underscores rather than spaces; include dateof creation or modification in a standard format (e.g.YYYY_MM_DD or YYYYMMDD): use project number•Be consistent! Avoid being cryptic!
    5. 5. Batch (or bulk) renaming• Software tools exist that can organise data files and folders in a consistent and automated way through batch renaming.• There are many situations where batch renaming may be useful, such as: – where images from digital cameras are automatically assigned filenames consisting of sequential numbers – where proprietary software or instrumentation generate crude, default or multiple filenames – where files are transferred from a system that supports spaces and/or non-English characters in filenames to one that doesnt (or vice versa). Batch renaming software can be used to substitute such characters with acceptable ones.
    6. 6. Benefits of consistent data filelabelling are:•Data files are not accidentally overwritten or deleted•Data files are distinguishable from each other within theircontaining folder•Data file naming prevents confusion when multiple people areworking on shared files•Data files are easier to locate and browse•Data files can be retrieved both by creator and by other users•Data files can be sorted in logical sequence•Different versions of data files can be identified•If data files are moved to other storage platform their names willretain useful context
    7. 7. Version ControlIt is important to consistently identify and distinguish versionsof data files.This ensures that a clear audit trail exists for tracking thedevelopment of a data file and identifying earlier versionsespecially if data is frequently updated by multiple users.Suggested strategies:• Use a sequential numbered system: v1, v2, v3, etc.• Dont use confusing labels: revision, final, final2, etc.• Record all changes -- no matter how small• Discard obsolete versions (but never the raw copy)• Use auto-backup instead of self-archiving, if possibleThe alternative is to use version control software. (Bazaar,TortoiseSVN, SubVersion)
    8. 8. Documenting DataThere are many reasons why you need to documentyour data:•To help you remember the details later•To help others understand your research•Verify your findings•Replicate your results•Archive your data for access and re-useSome examples of data documentation are:•Laboratory notebooks•Field notes•Questionnaires•SOPs•Methodologies
    9. 9. Documenting DataLaboratory or field notebooks, for example play animportant role in supporting claims relating tointellectual property developed by Universityresearchers, and even defending claims againstscientific fraud.Research data need to be documented at variouslevels:•Project level • study background, methodologies, instruments, research hypothesis•File or database level • formats, relationships between files•Variable or item level • How variable was generated & label descriptions
    10. 10. Metadata – ‘data about data’The difference between documentation and metadata isthat the first is meant to be read by humans and thesecond implies computer-processing (though may also behuman-readable) to assist location and access to datathrough search interfaces.Three broad categories of metadata are:•Descriptive - common fields such as title, author, abstract,keywords which help users to discover online sources throughsearching and browsing e.g. DC, MARC•Administrative - preservation, rights management, and technicalmetadata about formats.•Structural - how different components of a set of associateddata relate to one another, such as a schema describing relationsbetween tables in a database.
    11. 11. Need for metadata Metadata may not bePublic required if you are working alone on your own computer, but become crucial when dataResearch are shared online.Community Metadata help to place your dataset in a broader context, allowing thoseProject outside your institution, discipline, or research environment toResearcher understand how to interpret your data.
    12. 12. THANK YOU!

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