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S cook ands_ttt2_perth_rdm_training

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Developing a research data Management 101 unit - presented by Sue Cook at the Accelerate your Data training event in Perth 17 May 2018

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S cook ands_ttt2_perth_rdm_training

  1. 1. Developing a Research Data Management (101) Unit INFORMATION MANAGEMENT & TECHNOLOGY Sue Cook, Carmi Cronje, Katie Hannan | Data Librarians 17 May 2018
  2. 2. context • CSIRO • trainers: Research Data Support - Sue and Carmi • audience: Agriculture and Food Data School participants • subject : An introduction to Research Data Management • pilot session for a pilot program 2 |
  3. 3. audience: Agriculture and Food Data school ● Data Literacy ● R ● Data visualisation and exploration ● Version control and git ● Python and systematic programming design ● Statistical Modelling ● HPC ● Databases ● Big Data Practical ● Literate Programming & Jupyter Notebooks ● Bioconductor/Biopython ● Machine Learning ● Networks ● Advanced Programming ● Synthesis project ● Research Data Management 3 |
  4. 4. content building • lots of iteration • 70 hours of work – learning curve • scanned existing materials • started by trying to be modular and reuse others materials • refocused to suit those participants and face to face and workshop • wrote most of the final material from scratch • consultation with organisers changed approach • 4 versions 4 |
  5. 5. filtering • current RDS guides • data management in CSIRO • general RDM materials and modules • open science • FAIR • 5 star data rating • publishing guides • training guides 5 |
  6. 6. content 6 | ● Introductions ● Research data management (drivers, benefits) ● FAIR data principles Activity - Finding other people’s data ● Data governance in CSIRO ● Introduction to CSIRO’s Data Access Portal Activity - Creating a collection in the DAP ● Managing data across the research lifecycle using FAIR data principles Activity - Data management planning
  7. 7. content- details • RDM definition • FAIR • Why manage data? To minimise risks • Why manage data? To share with peers (including you) • Challenges in sharing data • Drivers to manage data • Data governance in CSIRO • DAP • Licences • FAIR during the life cycle • Data management planning 7 |
  8. 8. Processing and analysing - planning 8 | Identifiers > Version control. Are file IDs managed in a systematic way across raw, processed, final data? Can you find the files you need in order to repeat processes? Metadata > Readme text files. Disciplinary metadata standards and vocabularies. Metadata at field and variable level. Can discipline-specific standards be used? Is there enough information associated with each process? Access, storage > Access to different versions of data. Where is the data located, is it backed up? Can you access the files you need in order to repeat processes? + FAIR principles Research Data Management | Research Data Support
  9. 9. some of what we discarded • open science • specific schemas and vocabs • videos • background to FAIR/Force11 • policies of journals and funders 9 |
  10. 10. lessons (for THIS content with THIS audience) • we planned to do ½ day but ended up a full day • but they loved it • generated lots of discussion- that was a goal for the organisers • appreciated that we were a context setting session • KISS • less “hand holding” i.e. less providing the answers • more hands on exercises • next version will be a planned full day • more iterating • define role- facilitation
  11. 11. IMT Carmi Cronje Data Librarian t +61 2 9325 3066 e carmi.cronje@csiro.au w data.csiro.au Thank you! IMT Sue Cook Data Librarian t +61 8 6436 8532 e sue.cook@csiro.au w data.csiro.au

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