Ten Habits of Highly Successful Data
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Slides for http://www.discoveryinformaticsinitiative.org/ workshop, Quebec, Sunday July 27 2014

Slides for http://www.discoveryinformaticsinitiative.org/ workshop, Quebec, Sunday July 27 2014

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Ten Habits of Highly Successful Data Presentation Transcript

  • 1. Ten habits of highly effective data: Helping your dataset achieve its full potential Anita de Waard VP Research Data Collaborations a.dewaard@elsevier.com http://researchdata.elsevier.com/ Quebec City. Canada, July
  • 2. Who cares about Research Data? Funding bodies:  Demonstrate impact  Guarantee permanence, discoverability  Avoid fraud  Avoid double funding  Serve general public Research Management/Libary:  Generate, track outputs  Comply with mandates  Ensure availability Phil Bourne, Ass Director for Data Science at NIH: “Foster an ecosystem that enables biomedical research to be done as a digital enterprise.” Mike Huerta, Ass. Director NLM: “Today, the major public product of science are concepts, written down in papers. But tomorrow, data will be the main product of science…. We will require scientists to track and share their data as least as well, if not better, than they are sharing their ideas today.” Researchers:  Derive credit  Comply with mandates  Discover and use  Cite/acknowledge Nathan Urban, PI Urban Lab, CMU, 3/13: “If we can share our data, we can write a paper that will knock everybody’s socks off!” Barbara Ransom, NSF Program Director Earth Sciences: “We’re not going to spend any more money for you to go out and get more data! We want you first to show us how you’re going to use all the data we paid y’all to collect in the past!”
  • 3. What’s the problem? One example: Using antibodies and squishy bits Grad Students experiment and enter details into their lab notebook. The PI then tries to make sense of their slides, and writes a paper. End of story.
  • 4. 7. Trusted (validated/checked by reviewers) Maslow’s Hierarchy of Needs (for Research Data) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 2. Archived (long-term & format- independent) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 3. Accessible (can be accessed by others) 8. Citable (able to point & track citations)
  • 5. 1. Preserve: Data Rescue Challenge • With IEDA/Lamont: award succesful data rescue attempts • Awarded at AGU 2013 • 23 submissions of data that was digitized, preserved, made available • Winner: NIMBUS Data Rescue: – Recovery, reprocessing and digitization of the infrared and visible observations along with their navigation and formatting. – Over 4000 7-track tapes of global infrared satellite data were read and reprocessed. – Nearly 200,000 visible light images were scanned, rectified and navigated. – All the resultant data was converted to HDF-5 (NetCDF) format and freely distributed to users from NASA and NSIDC servers. – This data was then used to calculate monthly sea ice extents for both the Arctic d the Antarctic. • Conclusion: we (collectively) need to do more of this! How can we fund it? 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 8. Citable (able to point & track citations) 3. Accessible (can be accessed by others) 2. Archived (long-term & format- independent)
  • 6. 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 3. Accessible (can be accessed by others) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 2. Archived (long-term & format- independent) 8. Citable (able to point & track citations) 2. Archive: Olive Project • CMU CS & Library: funded by a grant from the IMLS, Elsevier is partner • Goal: Preservation of executable content - nowadays a large part of intellectual output, and very fragile • Identified a series of software packages and prepared VM to preserve • Does it work? Yes – see video (1:24)
  • 7. 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 8. Citable (able to point & track citations) 3. Access: Urban Legend 3. Accessible (can be accessed by others) 2. Archived (long-term & format- independent) • Part 1: Metadata acquisition • Step through experimental process in series of dropdown menus in simple web UI • Can be tailored to workflow of individual researcher • Connected to shared ontologies through lookup table, managed centrally in lab • Connect to data input console (Igor Pro)
  • 8. 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 8. Citable (able to point & track citations) 4. Comprehend: Urban Legend 3. Accessible (can be accessed by others) 2. Archived (long-term & format- independent) • Part 2: Data Dashboard • Access, select and manipulate data (calculate properties, sort and plot) • Final goal: interactive figures linked to data • Plan to expand to more labs, other data
  • 9. 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 8. Citable (able to point & track citations) 5. Discover: Data Discovery Index • NIH interested in creating DDI consortium • Three places where data is deposited: 1. Curated sources for a single data type (e.g.Protein Data Bank, VentDB, Hubble Space Data) 2. Non- or semicurated sources for different data types (e.g. DataDryad, Dataverse, Figshare) 3. Tables in papers: • Ways to find this: – Cross-domain query tools, i.e. NIF, DataOne, etc – Search for papers -> link to data – How to find data in papers?? • Propose to build prototypes across all of these data sources: – Needs NLP, models of data patterns? What else? 3. Accessible (can be accessed by others) 2. Archived (long-term & format- independent) Papers Non-curated DBs Curated DBs
  • 10. 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 8. Citable (able to point & track citations) 6. Reproduce: Resource Identifier Initiative Force11 Working Group to add data identifiers to articles that is – 1) Machine readable; – 2) Free to generate and access; – 3) Consistent across publishers and journals. • Authors publishing in participating journals will be asked to provide RRID's for their resources; these are added to the keyword field • RRID's will be drawn from: – The Antibody Registry – Model Organism Databases – NIF Resource Registry • So far, Springer, Wiley, Biomednet, Elsevier journals have signed up with 11 journals, more to come • Wide community adoption! 3. Accessible (can be accessed by others) 2. Archived (long-term & format- independent)
  • 11. 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 8. Citable (able to point & track citations) 7.Trust: Moonrocks 3. Accessible (can be accessed by others) 2. Archived (long-term & format- independent) How can we scale up data curation? Pilot project with IEDA: • Lunar geochemistry database: leapfrog & improve curation time • 1-year pilot, funded by Elsevier • If spreadsheet columns/headers map to RDB schema, we can scale up curation process and move from tables > curated databases!
  • 12. 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 8. Citable (able to point & track citations) 8. Cite: Force11 Data Citation Principles • Another Force11 Working group • Defined 8 principles: • Now seeking endorsement/working on implementation 3. Accessible (can be accessed by others) 2. Archived (long-term & format- independent) 1. Importance: Data should be considered legitimate, citable products of research. Data citations should be accorded the same importance in the scholarly record as citations of other research objects, such as publications. 2. Credit and attribution: Data citations should facilitate giving scholarly credit and normative and legal attribution to all contributors to the data, recognizing that a single style or mechanism of attribution may not be applicable to all data. 3. Evidence: Where a specific claim rests upon data, the corresponding data citation should be provided. 4. Unique Identification: A data citation should include a persistent method for identification that is machine actionable, globally unique, and widely used by a community. 5. Access: Data citations should facilitate access to the data themselves and to such associated metadata, documentation, and other materials, as are necessary for both humans and machines to make informed use of the referenced data. 6. Persistence: Metadata describing the data, and unique identifiers should persist, even beyond the lifespan of the data they describe. 7. Versioning and granularity: Data citations should facilitate identification and access to different versions and/or subsets of data. Citations should include sufficient detail to verifiably link the citing work to the portion and version of data cited. 8. Interoperability and flexibility: Data citation methods should be sufficiently flexible to accommodate the variant practices among communities but should not differ so much that they compromise interoperability of data citation practices across communities.
  • 13. 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 8. Citable (able to point & track citations) 9. Use: Executable Papers • Result of a challenge to come up with cyberinfrastructure components to enable executable papers • Pilot in Computer Science journals – See all code in the paper – Save it, export it – Change it and rerun on data set: 3. Accessible (can be accessed by others) 2. Archived (long-term & format- independent)
  • 14. 10: Putting it all together: 7. Trusted (validated/checked by reviewers) 6. Reproducible (others can redo experiments) 9. Usable (allow tools to run on it) 4. Comprehensible (others can understand data & processes) 2. Archived (long-term & format- independent) 1. Preserved (existing in some form) 5. Discoverable (can be indexed by a system) 3. Accessible (can be accessed by others) 8. Citable (able to point & track citations) Experimental Metadata: Workflows, Samples, Settings, Reagents, Organisms, etc. Record Metadata: DOI, Date, Author, Institute, etc. Processed Data: Mathematically/computationally processed data: correlations, plots, etc. Raw Data: Direct outputs from equipment: images, traces, spectra, etc. Methods and Equipment: Reagents, settings, manufacturer’s details, etc. Validation: Approval, Reproduction, Selection, Quality Stamp Morecuration Moreusable
  • 15. So how can we help research data be more happy and productive? • Group therapy: Force11, W3C, other fora – shared standards help everyone (we play well with others !) • Financial therapy: find grants to support data-driven processes to support grant proposals; funders like us. • Creative therapy: innovative collaboration projects that expand everyone’s mind – let’s put data together and put it through its paces • Relationship therapy: different communities working together is likely to produce efforts! E.g. ‘Big Mechanism’ effort: scientists + clinicians + publishers + CS =
  • 16. Collaborations and discussions gratefully acknowledged: – CMU: Nathan Urban, Shreejoy Tripathy, Shawn Burton, Ed Hovy – UCSD: Brian Shoettlander, David Minor, Declan Fleming, Ilya Zaslavsky – NIF: Maryann Martone, Anita Bandrowski – Force11: Ed Hovy, Tim Clark, Ivan Herman, Paul Groth, Maryann Martone, Cameron Neylon, Stephanie Hagstrom – OHSU: Melissa Haendel, Nicole Vasilevsky – Columbia/IEDA: Kerstin Lehnert, Leslie Hsu – MIT: Micah Altman Thank you! http://researchdata.elsevier.com/ Anita de Waard a.dewaard@elsevier.com