The Value of Collaboration & ACARN's Data Sharing Infrastructure - Sean Smukler, UBC
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Why sharing data is important for agricultural adaptation research and how the data sharing infrastructure developed by ACARN can enable sharing of data sets across regions and researchers.
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The Value of Collaboration & ACARN's Data Sharing Infrastructure - Sean Smukler, UBC
- 1. The value of collaboration and ACARN’s data sharing infrastructure Sean M. Smukler ACARN Annual Workshop December 3, 2019
- 2. Identify and anticipate impacts Develop effective adaptation strategies Provide guidance for policy and planning Climate Change Research Challenges
- 3. 3
- 4. 4 Ray et al. (2019) Climate change has likely already affected global food production. PLOS ONE 14(5) -24.5% 5.1%
- 5. Bruker TENSOR 37 FTIR Diffuse Reflectance Infrared Fourier Transform (DRIFT)
- 6. FT-IR TENSOR with HTS-XT plate reader for automated Mid-IR Micro-plate prepped for FT-MIR analysis
- 7. FTIR Results and Potential 10-20% the cost of standard wet chemistry
- 8. Growing Recognition of Public Data Archiving • Tri-Agency Open Access Policy May 1, 2015 – peer-reviewed publications must be freely accessible within 12 months of publication • CIHR only must deposit bioinformatics, atomic, and molecular coordinate data into the appropriate public database • Databases such as figshare, Dryad, TreeBASE, and GenBank 8
- 9. Benefits of Data-sharing • Prevents loss from hardware malfunction or obsolescence • Preserves data when researchers move to different projects or retire • Facilitates good metadata production • Enables regional analysis and modeling 9
- 10. To Deposit or Not Deposit • Real or perceived costs to share • BC’s Personal Information Protection Act (PIPA) • Collaborator trust 10 Roche DG, Lanfear R, Binning SA, Haff TM, Schwanz LE, et al. (2014) Troubleshooting Public Data Archiving: Suggestions to Increase Participation. PLOS Biology 12(1) Illustration credit: Ainsley Seago
- 11. Development of a BC Agricultural Data-sharing Infrastructure for Climate Adaptation Research December 15th 2017- March 20th, 2018 • Jason Lussier, the BC Agricultural Climate Adaptation Research Network • Dr. Lauchlan Fraser, Thompson Rivers University • Dr. Sean Smukler, University of British Columbia • Dr. Juli Carrillo, University of British Columbia • Serena Black, BC Forage Council 11
- 13. BC Agricultural Soil Data-sharing Working Group • March 24, 2018 PRSSS The Role of Soil in Climate Change Adaptation • October 5, 2018 - Soil Database Development Workshop 13
- 14. Data • BC Nutrient database • ~5000 crop, soil amendment data • ~ 2000 soil data (e.g. past FAIP project) 14
- 15. Sampling across LFV 15 LULC TYPE TOTAL PLOTS Annual Crop 100 Field Margin & Hedgerow 18 Riparian Buffer 14 Grassland 52 Perennial Crop 100 Forest/Forest Patch 15 Wetland 10 Total 309
- 17. Next Steps • Make the soils and amendment data flow operational • Recruit participants • Develop field and lab protocols • Repeat for other datasets 17
Editor's Notes
- Ensure scientists are focused on research that is most relevant for producers and policy Sharing data, methodologies and results among researchers Developing joint coordinated research projects Enhancing knowledge transfer from researchers to producers Training future researchers and outreach specialist
- http://www.science.gc.ca/eic/site/063.nsf/eng/h_F6765465.html?OpenDocument Tri-agency policy 3.1 Peer-reviewed Journal Publications Grant recipients are required to ensure that any peer-reviewed journal publications arising from Agency-supported research are freely accessible within 12 months of publication. Recipients of CIHR funding are required to adhere with the following responsibilities: Deposit bioinformatics, atomic, and molecular coordinate data into the appropriate public database (e.g. gene sequences deposited in GenBank) immediately upon publication of research results.
- Decision Aid System for Integrated Pest Management Molly Thurston Technology and data for adaptation Svan Lembke & Lee Cartier
- The LFV is in the southwest corner of the province We stratified sampling using a conditional latin hyper cube method across 7 LULC types We collected 309 samples The plot methods were similar to the ones I had previously described