Preparing for data-intensive science across domains.
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Presented at American Institute for Biological Sciences council meeting 8 December 2015. I focus on anecdotes from multiple domains on the kinds of skills and trajectories that empower scientists at multiple levels to become engaged in data-intensive science as data wranglers or tool-builders. Even if they don't have lots of funding from NSF or NIH.
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Preparing for data-intensive science across domains.
- 1. Cynthia Parr @cydparr US Department of Agriculture National Agricultural Library 8 December 2015 Preparing for data-intensive science across domains
- 2. http://blog.thingarage.com/ raw data citable publication The jet engine of science
- 3. Real-time Automated Modified from Peter Wittenberg, Research Data Alliance https://rd-alliance.org/group/data-fabric-ig.html raw data collection Exploration, cleaning, enrichment, analysis registration, preservation temporary data referable data citable data citable publication
- 4. Progressively intense, Multiple domains What to learn, How to learn it Training
- 5. Biodiversity
- 6. Ecology
- 7. Agriculture
- 9. What: Basic data management Purdue Graduate School of Education
- 10. What: Intellectual property Credits: creativecommons.org, www.agilegeoscience.com
- 11. How: Libraries & other units
- 12. How: Libraries & admin
- 13. What: Programming and databases
- 14. What: Specialist tools, like Geographic Information Systems Keith Weller/ARS Image Gallery
- 15. What: Data & metadata standards From Fig. 2. Wieczorek, et al. (2012). "Darwin Core: An Evolving Community-developed Biodiversity Data Standard.". PLoS ONE 7 (1). doi:10.1371/journal.pone.0029715.
- 16. What: Semantics OBOE from http://www.w3.org/2005/Incubator/ssn/wiki/Incubator_Report
- 18. How: Courses
- 19. How: Research sprints Parr and McClain (2014) EOL-BHL-NESCent Research Sprint Report. PeerJ PrePrints 2:e503v1 https://doi.org/10.7287/peerj.preprints.503v1
- 20. What: Expect change http://www.knowledgedirectweb.com/wp- content/uploads/2014/05/learning_objectives. jpg Credits: www.knowledgedirectweb.com Lucie Lang/Shutterstock
- 21. How: Be Agile “The tools needed for doing data- intensive science are in a constant state of flux – it’s hard for practitioners to keep up, let alone keep a curriculum current.” – Leslie Ries • Undergraduate exercises in re-analysis • Graduate curriculum as scaffolded exploration
- 22. How: ImmersionHow: Learning on the job
- 24. Images are my own unless otherwise credited cynthia.parr@ars.usda.gov Ag Data Commons data.nal.usda.gov eol.org/traitbank tdwg.org @cydparr
Notas do Editor
- Preparing for data intensive science across domains I will outline a series of progressive training needs, gleaned through my interactions with long-tail and big data scientists and data managers from a variety of domains. Some needs, such as basic data management and intellectual property concepts, are being addressed by forward-thinking libraries. Other needs, such as basic programming, data standards and semantics, typically are met through participation in short term research sprints, workshops, or courses. Currently, successful researchers expect change and learn informatics informally via their own projects, a perspective that can permeate all training and scientific practice.
- Will keep most of the important URLs to the end
- Level 1: What everyone doing research should know, whatever the domain Basic data life cycle stuff The need to have data management plans Best practices …even the fact that best practices exist
- Level 2: What many researchers should know, get away from spreadsheets, learn some basic programming, some understanding of database principles, maybe even some application programming interfaces. These are very general skills.
- Not everyone need to learn this but for those in many environmental domains some basic GIS understanding is essential, for evolutionary biologists or molecular biologists there are other specialist tools While these can be pretty intense, these like basic programming, can increasingly be taught at high school level
- Useful for data integration and machine learning and text or data mining
- And high performance computing
- Coursera and other courses Titus Brown Training in Data intensive biology
- Like a hackathon
- New data New techniques New tools Level 1: New acronyms Enough to make your head explode But we have gotten used to new phones every couple of years, this isn’t so bad
- Don’t really mean to use agile software development, though that is the method du jour. Rather, learning programming or learning statistics really isn’t about the specific language or algorithm, but learning the general skill of how to learn and enough scaffolding. She also teaches classes with modules where students revisit some published result. In order to do that they have to learn enough about the question, the data, the analysis methods, etc, to redo them. They may or may not take the same approach as the paper. Leslie’s plan for a graduate seminar is to have students each research a new statistical or analysis technique and present it to the class.
- Not to be afraid of using the wrong words
- Go to meetings (tell story of Harry Hochheiser) Pick up the lingo Get to know the sys admin Paired programming
- Expect change and keep learning