1. Research Data Management From A Publisher’s Perspective
Presentation for RDMI Meeting, Industry Panel
September 14, 2017
Anita de Waard,
VP Research Data Management, RDS Elsevier

2. Outline:
How has your work in data management enabled research and discovery?
What key areas of success has your organization achieved in delivering research data management solutions?
What are the greatest challenges you are facing in developing solutions that meet the needs of research data management?

3. 10 Properties of Highly Effective Research Data
9. Re-usable (allow tools to run on it)
8. Reproducible
Use
7. Trusted (e.g. reviewed)
6. Comprehensible (description / method is available)
10. Integrate upstream and downstream
– make metadata to serve use.
5. Citable
4. Discoverable (data is indexed or data is linked from article)
Share
3. Accessible
2. Preserved (long-term & format-independent)
Save
1. Stored (existing in some form)

4. 10 Properties of Highly Effective Research Data
Data Journals: Research Elements
9. Re-usable (allow tools to run on it)
8. Reproducible
Use
Research Data Guidelines for Journal
7. Trusted (e.g. reviewed)
6. Comprehensible (description / method is available)
10. Integrate upstream and downstream
– make metadata to serve use.
Mendeley Data Repository
5. Citable
4. Discoverable (data is indexed or data is linked from article)
Share
DataSearch
3. Accessible
2. Preserved (long-term & format-independent)
Hivebench Lab Notebook
Save
1. Stored (existing in some form)

5. Research Data Guidelines For Journals:
Research Data Guidelines For Journals:
Option A: Research Data deposit and citation
You are encouraged to:
Deposit your research data in a relevant data repository
Cite this dataset in your article
Option B: Research Data deposit, citation and linking (or Research Data Availability Statement)
You are encouraged to:
Deposit your research data in a relevant data repository
Cite and link to this dataset in your article
If this is not possible, make a statement explaining why research data cannot be shared
Option C: Research Data deposit, citation and linking (or Research Data Availability Statement)
You are required to:
Deposit your research data in a relevant data repository
Cite and link to this dataset in your article
If this is not possible, make a statement explaining why research data cannot be shared
Option D: Research Data deposit, citation and linking
You are required to:
Deposit your research data in a relevant data repository
Cite and link to this dataset in your article
Option E: Research Data deposit, citation and linking (or Research Data Availability Statement); You are required to:
Deposit your research data in a relevant data repository
Cite and link to this dataset in your article.
If this is not possible, make a statement explaining why research data cannot be shared
Peer reviewers are asked to review the data prior to publication

6. HIvebench: Store protocols in an Electronic Lab Notebook.
Edit, export, share
Keep collection of protocols online

7. Hivebench: Run experiments from this Lab Notebook.
Base on saved Protocols
Edit, export, share
Save and Export Outputs

8. Mendeley Data: Export results to a trusted data repository.
Describe how exoeriment can be reproduced
Create DOI for Citation
Link back to protocols
Store up to 5 GB of data in many formats
Keep track of versions of dataset

9. DataSearch: Search over collection of repositories
Chemistry data are retrievable from NIST, but only by going to their page in a browser and using their search tools. What about accessible within other applications, or accessible in assistive devices for those with vision impairment? What guarantee do we have the data will remain accessible in case of government funding problems?

10. Data Journals: E.g. MethodsX
Link to protocols
Data Journals: E.g. MethodsX
Journal focuses on Method reporiduction
Link to Data
Fully OA

11. Currently In Development: Mendeley Data Management Platform: Integration with Existing Standards/Systems at Institution

12. Underway: “Basket of Metrics” & Elsevier Tracking Solutions
Goal:
Metric:
How to measure
More data is saved:
1 Stored, i.e. safely available in long-term repository)
Nr of datasets stored in long-term storage
MD, Pure; Plum Indexes Figshare, Dryad, MD and working on Dataverse.
2. Published, i.e. long-term preserved, accessible via web, have a GUID, citeable, with proper metadata
Nr of datasets published, in some form
Scholix, ScienceDirect/Scopus
3. Linked, to articles or other datasets
Nr of datasets linked to articles
Scholix, Scopus
4. Validated, by a reviewer/curated
Nr of datasets in curated databases/peer reviewed in data articles
Science Direct, DataSearch (for curated Dbses)
More data is seen and used:
5. Discovered: found by users
Nr of datasets viewed in databases/websites/search engines
Datasearch, metrics from other search engines/repositories
6. Identified: Resolved through a GUID Broker
DOI is resolved
DataCite has DOI resolution: made available?
7. Mentioned: Social media and news
Social media and news mentions
Plum and Newsflo
8. Cited: Formal citations of data
Nr of datasets cited in articles
Scopus
9. Downloaded: Distinct downloads
Downloaded from repositories
Downloads from MD, access data from Figshare/Dryad
10. Reused: Dataset is used for new research
Mention of usage in article or other dataset
SD, access to other data repositories

13. We need baselines! Example: University of Manchester
Data sharing = 19% (well above the average of 5.5%)
886 random articles checked
570 articles without any supplementary/associated data (64%); +151 articles with supplementary docs (but not data)
2 data journal articles (0.2%)
86 articles with associated data in repositories (9.7%)
81 articles linked to associated data in a repository (9.1%)
5 articles with no link to a repository (0.6%)
79 articles with supplementary data (8.9%)
9. Re-usable
8. Reproducible
0.2%
7. Trusted
6. Comprehensible
9.1%
5. Citable
4. Discoverable
0.6%
3. Accessible
8.9%
2. Preserved
1. Stored
Random Selection
Articles
886
Links found manually 81
Links found through Scholix 5
Total links
86 (9.8%)
Courtesy Sean Husen and Helena Cousijn (Elsevier)

14. Open Data Report Reveals Some Challenges:
Data sharing survey (with 1167 respondents):
Although 69% of respondents found that sharing data was very important in their field
And 73% wanted to have access to other people’s data,
Only 37% believe there was credit in doing so,
And only 25% felt they had adequate training to properly share their data with others.
The main barriers for sharing data were:
privacy concerns,
ethical issues,
intellectual property rights issues.
Furthermore:
Mandates from publishers or funding agencies were largely not seen as a driving force
=> Gap between desire and practice concerning data sharing.

15. Further Challenge: Who Do We Talk to At An institution?

16. Further Challenge: How do you ‘Play Well With Others’ when there are so many others (e.g. 47 tools on NDS Labs Workbench) and they are mostly ‘academic’ (i.e. OS, constantly renewed, etc etc)?

17. How has your work in data management enabled research and discovery?
Summary:
How has your work in data management enabled research and discovery?
Providing a suite of tools and standards that encourage open, integrated RDM solutions.
What key areas of success has your organization achieved in delivering research data management solutions?
Tools are used (ergo: useful);
Developing institutional solutions and data metrics with partners.
What are the greatest challenges you are facing in developing solutions that meet the needs of research data management?
No great urgency for researchers, inadequate knowledge of possibilities;
Distributed responsibility/decision-making processes for RDM;
Plethora of tools to integrate with;
Difficult to see what the market is (OS, completely? Academic/government?)
> How can publisher play a role?
Feel free to me with any questions!