preparing for data-intensive science across domains

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Cynthia Parr @cydparr US Department of Agriculture National Agricultural Library 8 December 2015 Preparing for data- intensive science across domains

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Page 1: Preparing for data-intensive science across domains

Cynthia Parr @cydparrUS Department of AgricultureNational Agricultural Library8 December 2015

Preparing for data-intensive science across domains

Page 2: Preparing for data-intensive science across domains

http://blog.thingarage.com/

raw data

citable publication

The jet engine of science

Page 3: Preparing for data-intensive science across domains

Real-timeAutomated

Modified from Peter Wittenberg, Research Data Alliancehttps://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

Page 4: Preparing for data-intensive science across domains

Progressively intense, Multiple domainsWhat to learn, How to learn it

Training

Page 5: Preparing for data-intensive science across domains

Biodiversity

Page 6: Preparing for data-intensive science across domains

Ecology

Page 7: Preparing for data-intensive science across domains

Agriculture

Page 8: Preparing for data-intensive science across domains

Information science

Page 9: Preparing for data-intensive science across domains

What: Basic data management

Purdue Graduate School of Education

Page 10: Preparing for data-intensive science across domains

What: Intellectual property

Credits: creativecommons.org,www.agilegeoscience.com

Page 11: Preparing for data-intensive science across domains

How: Libraries & other units

Page 12: Preparing for data-intensive science across domains

How: Libraries & admin

Page 13: Preparing for data-intensive science across domains

What: Programming and databases

Page 14: Preparing for data-intensive science across domains

What: Specialist tools, like Geographic Information Systems

Keith Weller/ARS Image Gallery

Page 15: Preparing for data-intensive science across domains

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.

Page 16: Preparing for data-intensive science across domains

What: Semantics

OBOE from http://www.w3.org/2005/Incubator/ssn/wiki/Incubator_Report

Page 17: Preparing for data-intensive science across domains

What: Cloud computing

Page 18: Preparing for data-intensive science across domains

How: Courses

Page 19: Preparing for data-intensive science across domains

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

Page 20: Preparing for data-intensive science across domains

What: Expect change

http://www.knowledgedirectweb.com/wp-content/uploads/2014/05/learning_objectives.jpg

Credits: www.knowledgedirectweb.com Lucie Lang/Shutterstock

Page 21: Preparing for data-intensive science across domains

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

Page 22: Preparing for data-intensive science across domains

How: ImmersionHow: Learning on the job

Page 23: Preparing for data-intensive science across domains

How: Be social

https://help.github.com/

Page 24: Preparing for data-intensive science across domains

Images are my own unless otherwise credited

[email protected]

Ag Data Commonsdata.nal.usda.goveol.org/traitbank

tdwg.org

@cydparr