2018: Transparent data practice for reliable and reproducible research

The first half of workshop begins with a brief presentation about the importance of transparent data practices, then continues with an introduction to making data Findable, Accessible, Interoperable, and Reusable (FAIR). Participants will then have one hour to work with one another and facilitators on the assessment and improvement of an existing dataset. The second half of the workshop begins with an introduction to other aspects of data transparency, including the use of code for analysis, advantages of open-source software, reproducible analytical environments, and the training necessary to implement these approaches. Participants will then have one hour to work with one another and facilitators to incorporate these practices into a proposal for future research.

Participant numbers will be capped for this workshop - first-in secures a place.

Participants should bring: (1) a 'legacy' dataset from research they have completed or that is well underway (e.g., spreadsheets, databases, multimedia archives, etc.); (2) a grant application or research proposal for an upcoming project.

Recommended reading

The Australian Research Data Cloud (formerly the Australian National Data Service) has an excellent 'Working with Data' guide to research data management. Please familiarise yourself with this resource, particularly the FAIR data principles.

Perkel, Jeffrey M. 2018. “A Toolkit for Data Transparency Takes Shape.” Nature 560 (7719): 513–15. https://doi.org/10.1038/d41586-018-05990-5.

Stewart Lowndes, Julia S., Benjamin D. Best, Courtney Scarborough, Jamie C. Afflerbach, Melanie R. Frazier, Casey C. O’Hara, Ning Jiang, and Benjamin S. Halpern. 2017. “Our Path to Better Science in Less Time Using Open Data Science Tools.” Nature Ecology & Evolution 1 (6): s41559–017 – 0160. https://doi.org/10.1038/s41559-017-0160.

Wilkinson, Mark D., Michel Dumontier, Ijsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (March): 160018. https://doi.org/10.1038/sdata.2016.18. 

Mid Career/Senior Researchers

Graduate Research Students

Early Career Researchers