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

1. Developing a Research Data Management
(101) Unit
INFORMATION MANAGEMENT & TECHNOLOGY
Sue Cook, Carmi Cronje, Katie Hannan | Data Librarians
17 May 2018

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. 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. 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. 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. 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. 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. 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. some of what we discarded
• open science
• specific schemas and vocabs
• videos
• background to FAIR/Force11
• policies of journals and funders
9 |

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. 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