Making It Happen March 19, 2013 Anita de Waard VP Research Data Collaborations, Elsevier RDS Sustainable Data Preservation and Use Making It Happen:

“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, …)research databases There are many systems for creating/sharing workflows (Taverna, MyExperiment, Vistrails, Workflow4Ever etc)workflows There are many e-lab notebooks (LabGuru, LabArchives, LaBlog, etc)e-lab notebooks 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)standardsconferences You can make a living out of this ;-)! (and many of us do…)

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

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

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

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

Objections (and rebuttals) to data sharing: Objection:Rebuttal: “Our lab notebooks are all on paper – it’s how we do things” Graft tools closely on scientists’ daily practice “I need to see a direct benefit of any effort I put in.” Create tools to allow better insight in own and other’s results. “I don’t really trust anyone else’s data – and don’t think they’ll trust mine” Create social networking context and allow data owner to provide granular access control. “I am afraid other people might scoop my discoveries” => Reward system moves from a competition to a ‘shared mission’

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

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

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

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

Grafting tools on workflow: create tailored metadata collection tools on mini-tablets in labs to 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 Some examples: c o n s o r t i u m

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