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Publishing data and code openly

FAIRDOM
FAIRDOM

Written and presented by Tom Ingraham (F1000), at the Reproducible and Citable Data and Model Workshop, in Warnemünde, Germany. September 14th -16th 2015.

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Thomas Ingraham
Publishing Editor, F1000Research
thomas.ingraham@f1000.com
Conventional publishing Model
F1000research publishing Model
Enabling early access to results
Pre-print server OA Journal
Early access to results & data Editorial input
Precedence for authors Peer review & indexing
Multiple versions Publication ethics
No Editor in Chief Perfected paper OA
Open peer review
Decision based on
scientific soundness
Versioning
Versioning
Publishing data & code
publishing raw data and source code
Uses
•Exploratory analyses, LOD, reproducibility checks (data,
reanalyses + software)
Linking with Research Articles
•Better metadata/documentation
•Quality control
Software Tool Articles & Data Articles
•Big/small datasets – no analyses
•Software packages/web tools
•Even better metadata/documentation
•Versioning – updates & point releases
•High visibility
•More credit (quantity and quality)
Requirements for data and code
Interoperable formats
Metadata
All?
Workflow & repositories
Submission
Pre-Pub
Check
Publish
Peer
review
Repository
Crosslink
Further requests
F1000Research
curates approved
repo list
Credit for data and software
Data availability:
Repository: Data entry title, PID [Ref]
[Ref] Author Names. Data entry title [v].
Repository. Year. PID
Cite the data or the Data Article?
Clear accessibility info and actively encourage data/software citation
Software availability:
Software access (opt): URL
Latest source code (opt): VCS URL
Archived entry: Repository: Software
title, PID [Ref]
Licence: OSI
[Ref] Author Names. Software title [v].
Repository. Year. PID
INTERGRATION, VISUALIZATION & ANALYSIS OF DATA
Living figures: Data + Code
Colomb & Brembs: http://f1000research.com/articles/3-176/v1
Challenges
Sensitive data
•Protected repositories
•HIPPA-compliant (safe harbour)
•DataSHIELD
Other challenges
•Rawness & granularity
Jisc Bodleian 1-click data submission Project
Reducing the friction data sharing
SUMMARY: F1000RESEARCH’S APPROACH TO OPEN SCIENCE
• Near-immediate publication
Open, invited peer review conducted after
publication
• Transparent peer-review
Signed referee reports and author responses
published alongside each article
• No editorial bias
Publication of all findings, including null results,
software tools and data notes.
• Data included
All research articles accompanied by the data on
which they are based
Publishing data and code openly
ANNOTATOR PLUGIN
Publishing data and code openly
PREDICTS CITATIONS
COLLABORATE

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Publishing data and code openly

Editor's Notes

  1. TL : DR F1000Research is the love child of arxiv, github and an open access journal
  2. All articles ‘New research’ rarely new by the time it’s published Peer review the rate-limiting step Reject for silly reason like scope or not interesting to that particular editor. For a discipline that aims for objectivity, the degree of subjectivity in science publishing is incredible. Alternatives: Peer print
  3. F1000Research combines the advantages of a pre-print server with the advantages of an OA journal on a single, open platform No splintering of discussions etc.
  4. Referees are invited experts Referee reports are visible and named Referee reports are citable: credit We have had reviews longer that the articles themselves Peer review should be a productive discussion, not a ruthless take-down. Reviews put paper in context, strengths and limitations Reduces bias among reviewers (social pressure for objectivity) More constructive, quality reviews Published reports can help teach young researchers Proves peer review has been done! (Bohannon sting) Benefits for reviewers Display informed opinion of the work Demonstrate experience as a reviewer Credit for their report (reports citable)
  5. Average rate of programming errors 15 – 50 per 1000 lines of delivered code (professionals)
  6. Papers and peer reviews cc by Prevent attribution stacking, but still encourage dataset citation (including within article) Reject if not open
  7. Data usage: To date of Figshare 421 uploads, 43000 views (just through Figshare not counting views through F1KR), 1300 downloads. 61% US, 11% UK.
  8. This will appear everywhere – all your colleagues on our project will see it. Colleagues don’t have to be subscribers to participate in projects (they just can’t create their own)