Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Research Data Management
From A Publisher’s
Perspective
Presentation for RDMI Meeting, Industry Panel
September 14, 2017
A...
Outline:
1. How has your work in data management enabled research and
discovery?
2. What key areas of success has your org...
10.Integrateupstreamanddownstream
–makemetadatatoserveuse.
Save
Share
Use
9. Re-usable (allow tools to run on it)
8. Repro...
10.Integrateupstreamanddownstream
–makemetadatatoserveuse.
Save
Share
Use
9. Re-usable (allow tools to run on it)
8. Repro...
HIvebench: Store protocols in an Electronic Lab Notebook.
Keep collection
of protocols
online
Edit, export,
share
https://...
Hivebench: Run experiments from this Lab Notebook.
Edit, export,
share
Base on saved
Protocols
Save and
Export Outputs
htt...
https://data.mendeley.com/
Mendeley Data: Export results to a trusted data repository.
Describe how
exoeriment can
be repr...
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/...
Journal focuses
on Method
reporiduction
Link to protocols
Link to Data
Fully OA
Data Journals: E.g. MethodsX
https://www.j...
Reproducibility: Reproducibility Papers
we have implemented a new publication model for the Reproducibility Section of Inf...
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 measure
More data is saved:
1 Stored, i.e...
886 random articles checked
570 articles without any supplementary/associated data
(64%); +151 articles with supplementary...
Open Data Report Reveals Some Challenges:
https://data.mendeley.com/datasets/bwrnfb4bvh/1
Data sharing survey (with 1167 r...
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) ...
Summary:
1. How has your work in data management enabled research and discovery?
• Providing a suite of tools and standard...
Upcoming SlideShare
Loading in …5
×

Talk on Research Data Management

276 views

Published on

Talk at https://rdmi.uchicago.edu/

Published in: Science
  • Be the first to comment

  • Be the first to like this

Talk on Research Data Management

  1. 1. Research Data Management From A Publisher’s Perspective Presentation for RDMI Meeting, Industry Panel September 14, 2017 Anita de Waard, a.dewaard@elsevier.com VP Research Data Management, RDS Elsevier
  2. 2. 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?
  3. 3. 10.Integrateupstreamanddownstream –makemetadatatoserveuse. 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) 10 Properties of Highly Effective Research Data
  4. 4. 10.Integrateupstreamanddownstream –makemetadatatoserveuse. 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) 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.
  5. 5. HIvebench: Store protocols in an Electronic Lab Notebook. Keep collection of protocols online Edit, export, share https://www.hivebench.com/
  6. 6. Hivebench: Run experiments from this Lab Notebook. Edit, export, share Base on saved Protocols Save and Export Outputs https://www.hivebench.com/
  7. 7. 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 protocols Store up to 5 GB of data in many formats
  8. 8. DataSearch: Search over collection of repositories https://datasearch.elsevier.com
  9. 9. 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
  10. 10. Journal focuses on Method reporiduction Link to protocols Link to Data Fully OA Data Journals: E.g. MethodsX https://www.journals.elsevier.com/methodsx
  11. 11. 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.
  12. 12. Currently In Development: Mendeley Data Management Platform: Integration with Existing Standards/Systems at Institution
  13. 13. 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
  14. 14. 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. Stored 8.9% 9.1% 0.6% 0.2% 0 10 20 30 40 50 60 70 80 90 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)
  15. 15. 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.
  16. 16. Further Challenge: Who Do We Talk to At An institution?
  17. 17. 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)?
  18. 18. 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! a.dewaard@elsevier.com

×