talk on research data management
TRANSCRIPT
Research Data ManagementFrom A Publisher’s Perspective
Presentation for RDMI Meeting, Industry Panel
September 14, 2017
Anita de Waard, [email protected]
VP Research Data Management, RDS Elsevier
Outline:
1. How has your work in data management enabled research and
discovery?
2. What key areas of success has your organization achieved in
delivering research data management solutions?
3. What are the greatest challenges you are facing in developing
solutions that meet the needs of research data management?
10.
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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)
10 Properties of Highly Effective Research Data
10.
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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)Hivebench
Lab Notebook
Mendeley
Data Repository
DataSearch
Data Journals:
Research Elements
Research Data
Guidelines for
Journal
10 Properties of Highly Effective Research Data
Repeat.
Replicate.
Reproduce.
Reuse.
Review.
HIvebench: Store protocols in an Electronic Lab Notebook.
Keep collection
of protocols
online
Edit, export,
share
https://www.hivebench.com/
Hivebench: Run experiments from this Lab Notebook.
Edit, export,
share
Base on saved
Protocols
Save and
Export Outputs
https://www.hivebench.com/
https://data.mendeley.com/
Mendeley Data: Export results to a trusted data repository.
Describe how
exoeriment can
be reproduced
Keep track of
versions of
dataset
Create DOI for
Citation
Link back to
protocolsStore up to 5
GB of data in
many formats
DataSearch: Search over collection of repositories
https://datasearch.elsevier.com
Data With Journals: Research Data Guidelines For Journals:
https://www.elsevier.com/authors/author-services/research-data/data-guidelines
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
Journal focuses
on Method
reporiduction
Link to protocols
Link to Data
Fully OA
Data Journals: E.g. MethodsX
https://www.journals.elsevier.com/methodsx
Reproducibility: Reproducibility Papers
we have implemented a new publication model for the Reproducibility Section of Information Systems Journal. In this
section,
authors submit a reproducibility paper that explains in detail the computational assets from a previous published
manuscript in Information Systems. Submission is by invitation only.
To increase the practice of reproducibility in computational science, we have two main goals:
1. Usability: development of tools that make it easier and significantly less time-consuming for authors to do
reproducible research, and for reviewers to execute computational artifacts (and modify them) corresponding to
published results.
2. Incentives: a new publication model that recognizes the efforts of making experiments reproducible (for authors)
and verifying published scientific results (for reviewers).
Using Mendeley Data authors also submit their code, data, and optionally a ReproZip package or a Docker
container to make the review process easier. Reviewers not only review the reproducibility paper, but also validate
the results and claims published in the original manuscript.
Once the paper is accepted, reviewers also become co-authors and are encouraged to add a section in the paper that
states the extent to which the software is portable, is robust to changes, and is likely to be usable as a subcomponent
or as a basis for comparison by future researchers. The review is not blinded, so authors and reviewers are
encouraged to engage in a discussion about the validity of the experimental results as many times as
necessary.
Currently In Development: Mendeley Data Management Platform:
Integration with Existing Standards/Systems at Institution
Underway: “Basket of Metrics” & Elsevier Tracking Solutions
Goal: Metric: How to measureMore data is saved:
1 Stored, i.e. safely available in long-term repository)
Nr of datasets stored in long-term storage MD, Pure; Plum IndexesFigshare, 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
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
7. Trusted
6. Comprehensible
5. Citable
4. Discoverable
3. Accessible
2. Preserved
1. Stored8.9%
9.1%
0.6%
0.2%
0102030405060708090
Number of articles with linked data deposited in a data repository for 2015-2017/n=81
Total
Random Selection
Articles 886
Links found manually 81
Links found through
Scholix 5
Total links 86 (9.8%)
We need baselines! Example: University of Manchester
Data sharing = 19% (well above the average of 5.5%)
Courtesy Sean Husen and Helena Cousijn (Elsevier)
Open Data Report Reveals Some Challenges:
https://data.mendeley.com/datasets/bwrnfb4bvh/1
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.
Further Challenge: Who Do We Talk to At An institution?
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)?
Summary:
1. How has your work in data management enabled research and discovery?
• Providing a suite of tools and standards that encourage open, integrated RDM
solutions.
2. 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.
3. 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 email me with any questions!