elsevier‘s rdm program: ten habits of highly effective data

14
| 1 Anita de Waard, VP Research Data Collaborations Elsevier RDM Services a.dewaard@ elsevier.com December 1, 2016 Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

Upload: anita-de-waard

Post on 09-Jan-2017

186 views

Category:

Science


5 download

TRANSCRIPT

Page 1: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 1

Anita de Waard, VP Research Data CollaborationsElsevier RDM [email protected]

December 1, 2016

Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

Page 2: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 2

https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data

10. I

nteg

rate

ups

tream

and

dow

nstre

am

– m

ake

met

adat

a to

ser

ve u

se.

Save

Share

Use

9. Re-usable (allow tools to run on it)

8. Reproducible

7. Trusted (e.g. reviewed)

6. Comprehensible (description / method is available)

5. Citable

4. Discoverable (data is indexed or data is linked from article)

3. Accessible

1. Stored (existing in some form)

2. Preserved (long-term & format-independent)

A Maslow Hierarchy for Research Data:

Page 3: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 3

Store, Preserve: Data Rescue Award

Page 4: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 4

Store: Hivebench

www.hivebench.com

Page 5: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 5

https://data.mendeley.com/

Linked to published papers – or not

Linked to Github – or not

Versioning and provenance tracking

Store, Access: Mendeley Data

Different Licenses: GNU-PL, CC-BY CC0,

etc

Page 6: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 6

Access, Cite: Data Linking• Integrated in paper submission process• Supplementary data is never behind a firewall• Closely integrated with > 150 databases:

Page 7: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 7

Access, Discover: Scholix/DLIs• ICSU-WDS/RDA Publishing Data Service Working group,

merged with National Data Service pilot • Cross-stakeholder – with input from CrossRef, DataCite, OpenAIRE, Europe

PubMed Central, ANDS, PANGAEA, Thomson Reuters, Elsevier, and others• Proposed long-term architecture and interoperability framework: www.scholix.org• Operational prototype at http://dliservice.research-infrastructures.eu/#/api

(including 1.4 Million links from various sources)

Page 8: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 8

Cite: Force11

https://www.elsevier.com/connect/data-citation-is-becoming-real-with-force11-and-elsevier

Page 9: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 9

Discover: Datasearch

https://datasearch.elsevier.com

Page 10: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 10

Data articles

Softwarearticles

Methodarticles

Protocols

Video articles

Hardwarearticles

Labresources

Full Researchpaper

• Brief article types designed to communicate a specific element of the research cycle

• Complementary to full research papers

• Easy to prepare and submit• Peer-reviewed and indexed • Receive a DOI and fully citable• Allow citable post-publication updates

• Primarily Open Access (CC-BY) • Published in Multidisciplinary and domain-specific journals

https://www.elsevier.com/books-and-journals/research-elements

Review: Research Elements

Page 11: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 11

11

Reuse: Cortex Registered Reports• Two-step submission process:

• Method and proposed analysis are submitted for pre-registration• Paper is conditionally accepted• Research is executed• Full paper submitted, accepted provided that protocol is followed

• All experimental data made available Open Access• Featured in the Guardian:

Page 12: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 12

Research article

published

Initial inquiry

Share, publish and

link data

Monitor progress and

provide guidance

Generate reports

111110 000111101110 0000

001 100111 011100101

What?

• Service for Research Institutes (esp. librarians) to engage with researchers throughout the research data life cycle.

How?

Offer service for Librarians to interact with researchers regarding the RDM Process to:

• Offer solutions to store, share, link and publish data

• Monitor progress report on posting, citation, downloads of dataset

• Provide monthly reportingDATA LIGHTHOUSE

Metrics for Institutions: Data Lighthouse

Page 13: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 13

10. I

nteg

rate

ups

tream

and

dow

nstre

am

– m

ake

met

adat

a to

ser

ve u

se.

Save

Share

Use

9. Re-usable

8. Reproducible

7. Trusted

6. Comprehensible

5. Citable

4. Discoverable

3. Accessible

1. Stored

2. Preserved

https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data

A Maslow Hierarchy for Research Data:

Data at Risk

Reproducibility Papers

Dat

a Li

ghth

ouse

Page 14: Elsevier‘s RDM Program: Ten Habits of Highly Effective Data

| 14

Links:

• Original Materials:- The original research paper: Kinnings et al, 2010- The paper describing the earlier reproducibility effort: Garijo et al., 2013- A wiki with the reproduction attempt: Gil/Darijo, 2012- Background materials on the reproduction efforts: Garijo, 2012- SMAP Tool: Xie, 2010

• Our rebuild:- Protocol in Hivebench: https://www.hivebench.com/protocols/16483 - Experiment in Hivebench: https

://www.hivebench.com/notebooks/8524/experiments/20562 - Data in Mendeley Data: https

://data.mendeley.com/datasets/r69mvkckmn/draft?preview=1 - MethodsX Paper, with links to protocols and data:

http://www.articleofthefuture.com/methodsx.html