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

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?

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)

10 Properties of Highly Effective Research Data Repeat. Replicate. Reproduce.  Reuse. Review.  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)

HIvebench: Store protocols in an Electronic Lab Notebook. Edit, export, share Keep collection of protocols online https://www.hivebench.com/

Hivebench: Run experiments from this Lab Notebook. Base on saved Protocols Edit, export, share Save and Export Outputs https://www.hivebench.com/

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 https://data.mendeley.com/

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? https://datasearch.elsevier.com

Data With Journals: Research Data Guidelines For Journals: https://www.elsevier.com/authors/author-services/research-data/data-guidelines Data With 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

Data Journals: E.g. MethodsX Link to protocols Data Journals: E.g. MethodsX Journal focuses on Method reporiduction Link to Data Fully OA https://www.journals.elsevier.com/methodsx

Reproducibility: Reproducibility Papers 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). 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. 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 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

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)

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. https://data.mendeley.com/datasets/bwrnfb4bvh/1

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)?

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 email me with any questions! a.dewaard@elsevier.com