1. Data Publication (in H2020)
Dr Sünje Dallmeier-Tiessen
CERN
Madrid, November 2016

2. Agenda Introduction Research data Relation to H2020 Data Publishing
Examples, developments and lessons learnt from the “real world”
General purpose and disciplinary repositories
Adding journals to the mix
Adding ”reproducibility workflows” to the mix
Lessons learnt

3. Research Data
What is it?
How does it look like?
Does it hurt?

4. Funders’ policies
WELCOMES Open Access to scientific publications as the option by default for publishing the results of publicly funded research; […]
RECOGNISES that the full scale transition towards Open Access should be based on common principles such as transparency, research integrity, sustainability, fair pricing and economic viability; and […]
CALLS on Member States, the Commission and stakeholders to remove financial and legal barriers, and to take the necessary steps for successful implementation in all scientific domains, including specific measures for disciplines where obstacles hinder its progress.
See for example:

5. Mandatory Data Management Plans (DMPs)

6. Journals’ policies
Springer Nature Data policy

7. Data Publishing Paradigms

8. Data Publishing Concepts
Standalone Data (Repository) Traditional article-data linking Data articles/journals
Data
Article
Data
Article
Data

9. Data Publishing components (RDA endorsed)
[2] DOI: /s

10. Another data publishing perspective: establishing context
[2] DOI: /s

11. The FAIR Guiding Principles I
To be Findable:
F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata (defined by R1 below)
F3. metadata clearly and explicitly include the identifier of the data it describes
F4. (meta)data are registered or indexed in a searchable resource
To be Accessible:
A1. (meta)data are retrievable by their identifier using a standardized communications protocol
A1.1 the protocol is open, free, and universally implementable
A1.2 the protocol allows for an authentication and authorization procedure, where necessary
A2. metadata are accessible, even when the data are no longer available
FAIR data

12. The FAIR Guiding Principles II
To be Interoperable:
I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles
I3. (meta)data include qualified references to other (meta)data
To be Reusable:
R1. meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1. (meta)data are released with a clear and accessible data usage license
R1.2. (meta)data are associated with detailed provenance
R1.3. (meta)data meet domain-relevant community standards

13. Various solutions Disciplinary and institutional repositories exist
Choose partners: re3data.org
Article-Data linking
Now easier with Datacite and CrossRef
Data/software journals already exist
With partner repositories
With repository recommendations

14. Data Publishing solutions
Examples, there are more!

16. re3data.org

17. Data Publishing Concepts
Standalone Data (Repository) Traditional article-data linking Data articles/journals
Data
Article
Data
Article
Data

18. All disciplines, institutions
Needs replacement with Zenodo3
Zenodo.org

19. All disciplines, institutions
Figshare screenshot
Figshare.com

20. Established discipplinary databases: life sciences
EBI database screenshot

21. Established disciplinary databases: earth & environmental sciences
Pangaea screenshot
pangaea.de

22. dataverse.org

23. Data Publishing Concepts
Standalone Data (Repository) Traditional article-data linking Data articles/journals
Data
Article
Data
Article
Data

25. Discipline specific data journals
Add another data journal, e.g. ESSD?

27. Considerations for choosing the “right service”
Future purpose: reuse, reproducibility, preservation
Metadata (standards)
Quality
Dependencies (software, methods)
Versioning
Visibility, Discoverability (cf. FAIR principles)
Referencing, data citation capability for all outputs
Persistent links, sustainability

28. In practical terms Discuss with researchers
What are the needs of the group/community
Are there existing services or is there are need for more?
Re3data.org
Don’t shy away from contacting data centres or services directly
Check out what community publishers do
Recommended repositories?
Discuss with partners in computer centre and/or community meetings
What do they do and plan to do; anything you can contribute to or profit from

29. Moving beyond the individual elements
Opening data publishing to reproducible workflows

32. Conditions very discipline specific
Reproducibility
Repeatability
Replicability
Reproducibility
Reusability
Repurposing
In order to reuse/repurpose results,
you sometimes have to reproduce the original results first (to understand the exact details)
An article about computational science in a scientific publication is not the scholarship itself; it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures. ( stanford.edu/doku.php?id=sep:research:reproducible :seg92)
We can reserve the term "replicability" for the regeneration of published results from author provided code and data
Reproducibility is a more general term, implying both replication and the regeneration of findings with at least some independence from the code and/or data associated with the original publication. Both refer to the analysis that occurs after publication.
A third term, “repeatability,” is sometimes used in place of reproducibility, but this is more typically used as a term of art referring to the sensitivity of results when underlying measurements are retaken
To summarize, we need replicability, in part, to resolve differences in outcomes that arise from reproduced computational results, regardless of whether the experiments have been repeated.
Conditions very discipline specific

33. To reproduce or reuse research results a researcher needs…
More than “just” the article
Context, documentation
Links to related research objects: data, code, workflows
Understandable method, processing, software etc.
Steps taken during the research process (versions)

34. Research Lifecycle

35. Seamless integration across the research lifecycle
Who?
When?
Where? ?
project/rcn/194927_en.html
Slide credit to Trisha Cruse, Datacite

36. https://benchling.com/
Docker

37. Example from CERN: CERN Open Data and CERN Analysis Preservation
Future purpose: reuse, reproducibility, preservation What are the components of an analysis (and where are they stored now) How much do these components vary within the collaboration How is quality defined What are the dependencies (software, methods) Versioning Linking Size (10-15TB per analysis)
See CERN presentation later

38. Future Big challenge is adoption  needs all of us to work together
We can help with data curation and services, i.e. guiding researchers to the right services
But we need your expertise to make it an intrinsic process for researchers
Integrated in publishing process
Link objects/resources (DataCite!)
Give data more ❤️ and visibility – make it discoverable

39. Backup slides

40. References THOR project; https://project-thor.eu/ ORCID: orcid.org
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