1 / 13

Making It Happen

Making It Happen:. Making It Happen. Sustainable Data Preservation and Use. March 19, 2013 Anita de Waard VP Research Data Collaborations, Elsevier RDS a.dewaard@elsevier.com. “What aspects /tools/capabilities/frameworks are related to this idea?” .

lyre
Download Presentation

Making It Happen

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Making ItHappen: Making It Happen Sustainable Data Preservation and Use March 19, 2013 Anita de WaardVP Research Data Collaborations, Elsevier RDS a.dewaard@elsevier.com

  2. “What aspects/tools/capabilities/frameworks are related to this idea?” • There are many different research databases– both generic (Dryad, Dataverse, …) and specific (NIF, IEDA, PDB, …) • There are many systems for creating/sharing workflows (Taverna, MyExperiment, Vistrails, Workflow4Ever etc) • There are many e-lab notebooks (LabGuru, LabArchives, LaBlog, etc) • There are scores of projects, committees, standards, bodies, grants, initiatives, conferences for discussing and connecting all of this (KEfED, Pegasus, PROV, RDA, Science Gateways, Codata, BRDI, Earthcube, etc. etc) • You can make a living out of this ;-)! (and many of us do…)

  3. …but this is what scientists do: Using antibodies and squishy bits Grad Students experiment and enter details into theirlab notebook. The PI then tries to make sense of this, and writes a paper. End of story.

  4. Why save research data? • Data Preservation: • Preserve record of scientific process, provenance • Enable reproducible research • Data Use: • Use results obtained by others • Do better science! • Improve interdisciplinary work • Sustainable Models: • Technology transfer; societal/industrial development • Reward scientists for data creation (credit/attribution) • Long-term archiving

  5. Where The Data Goes Now: PDB: 88,3 k A small portion of data (1-2%?) stored in small, topic-focuseddata repositories PetDB: 1,5 k > 50 My Papers 2 M scientists 2 M papers/year SedDB: 0.6 k MiRB: 25k TAIR: 72,1 k Some data (8%?) stored in large, genericdata repositories Majority of data(90%?) is stored on local hard drives Dataverse:0.6 M Dryad: 7,631 files Datacite: 1.5 M

  6. DEVELOP SUSTAINABLE MODELS Key Needs: PDB: 88,3 k A small portion of data (1-2%?) stored in small, topic-focuseddata repositories PetDB: 1,5 k > 50 My Papers 2 M scientists 2 M papers/year SedDB: 0.6 k MiRB: 25k TAIR: 72,1 k IMPROVE DATA USE Some data (8%?) stored in large, genericdata repositories Majority of data(90%?) is stored on local hard drives Dataverse:0.6 M Dryad: 7,631 files INCREASE DATA PRESERVATION Datacite: 1.5 M

  7. Objections (and rebuttals) to data sharing:

  8. From insular ‘CoSI-Factories’… Prepare Prepare Ponder Ponder Observe Observe Communicate Communicate Analyze Analyze

  9. …to shared experimental repositories: Across labs, experiments: track reagents and how they are used Prepare Prepare Observations Observations Analyze Analyze Communicate Communicate Observations

  10. …to shared experimental repositories: Compare outcome of interactions with these entities Prepare Prepare Observations Observations Analyze Analyze Communicate Communicate Observations

  11. …to shared experimental repositories: Build a ‘virtual reagent spectrogram’ by comparing how different entities interacted in different experiments Prepare Prepare Observations Think Observations Analyze Analyze Communicate Communicate Observations

  12. Some examples: • Grafting tools on workflow: create tailored metadata collection tools on mini-tablets in labsto replace paper notebook • Direct rewards: through ‘PI-Dashboard’:allow immediate access/analysis of shared data: new science! • Data sharing rewards: Data Rescue Challenge:: collect and reward stories/practices of data preservation/use in Earth/Lunar Science • Improve data use: With NIF/Eagle-I: add antibodies as key ‘entities’ to paper, link to AB repository c o n s o r t i u m

  13. How do we make data use happen: • We are creating repositories of shared experiments: you are part of a greater whole! • Collect and share stories and practices re. data use and sustainable systems: “What gets to them?” • Develop system of rewards for data sharing: enable demonstrably better science! • Work with grant agencies, repositories (generic/specific, institutional, cross-national) to integrate and annotate existing datasets and enable cross-use • Collectively pioneer long-term funding options; support/develop ‘shared mission’ funding challenges

More Related