Ten Habits of Highly Effective Data

Ten Habits of Highly Effective Data

Anita de WaardVP Research Data Collaborations

[email protected]://researchdata.elsevier.com/

mailto:[email protected]

http://researchdata.elsevier.com/

http://researchdata.elsevier.com/

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)

The Maslow Hierarchy for humans:










A Maslow Hierarchy for Data:

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?










http://researchdata.elsevier.com/datachallenge/submission










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)

https://olivearchive.org/demo/








3. Access: Urban Legend



• 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)

http://researchdata.elsevier.com/urbanlegend/app








4. Comprehend: Urban Legend



• 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

http://researchdata.elsevier.com/dashdemo/dashdemo








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?



Papers

Non-curated DBs

Curated DBs

http://www.rcsb.org/pdb/home/home.do

http://www.rcsb.org/pdb/home/home.do

http://www.earthchem.org/ventdb

http://www.cosmos.esa.int/web/hst








6. Reproduce: Resource Identifier InitiativeForce11 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)


https://www.force11.org/Resource_identification_initiative








7.Trust: Moonrocks



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!








8. Cite: Force11 Data Citation Principles• Another Force11 Working group• Defined 8 principles:

• Now seeking endorsement/working on implementation



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.

https://www.force11.org/datacitation








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:



http://www.executablepapers.com/

http://www.elsevier.com/physical-sciences/computer-science/executable-papers

http://www.sciencedirect.com/science/article/pii/S0097849313000575

Experimental Metadata: Objects, Procedures, Properties

10: Let’s allow our data to be happy!










Execute: Direct settings on equipment, circumstances of measurement

Raw Data

Analyze: Mathematical/computational processes and analyticsProcessed

Data

Record Metadata: DOI, Date, Author, Institute, etc.

Prepare: Reagents, species/specimen/cell type, preparation details

Entity IDs

Validation Metadata: Reproduction, Curation; Selection, Citation,

Usage, Metrics

Minimize your metadata footprint!

Reduce:• Every tag needs to be added and read by

someone/thing: this adds cost and waste• Consider ‘return on investment’ per metadata item• TBL: what if “http://” was “h/”?

Reuse: • ‘The good thing about standards is that there are

so many to choose from’• Haendel et al looking at 54 (!!) data standards:

many have only been used once/for one group• Employ a common element set + modular

additions over whole new schema

Recycle: • Make sure you design upstream metadata

with downstream processes in mind• Useful exercise: ‘buy a tag’ where

users/systems that will store/query/cite data say what they need to do their job

• Learn from genetics: one datum can play several different roles!

https://docs.google.com/spreadsheets/d/1tqeRiP74Vy1gecN-62top-KNA7DUgxm3sZupn6Iw6LI/edit%23gid=0