Researchers, academic institutes and funders are increasingly recognizing the importance of data sharing for reproducible science. However, it is not always straightforward and clear to researchers as to how best to share data in a useful way. At Springer Nature we are working on several initiatives to help facilitate the sharing of research data in a reusable way, with our overarching goal being to publish research that is robust and reproducible. I will talk about the effort that goes into our flagship data journal, Scientific Data, to facilitate best practices in publication and sharing of research data, and share some of our experiences publishing Challenge datasets. I will also describe some of the newer Research Data Services that are now available to help all researchers (not only Springer Nature authors) to share their data in a useful way.

1. The challenge of sharing data well and how publishers can help
Varsha Khodiyar, PhD
DREAM Challenges and Epidemium@RECOMB
workshop, Paris
19.04.2018

2. 1
What are the challenges to sharing data?
12%
16%
20%
23%
28%
Costs of
sharing data
Lack of time
to deposit
data
Not knowing
which
repository to
use
Unsure
about
copyright
and licensing
Organising
data in a
presentable
and useful
wayTotal respondents: 7719
Stuart, D. et al. Practical challenges for researchers in data sharing. Springer Nature
whitepaper (2018) https://doi. org/10.6084/m9.figshare.5975011

3. 2
Scientific Data, a Nature Research journal
nature.com/scientificdata

4. 3
The Data Descriptor
Sections
• Title
• Abstract
• Background & Summary
• Methods
• Data Records
• Technical Validation
• Usage Notes
• Figures & Tables
• References
• Data Citations
• Detailed descriptions of the methods and technical analyses supporting the
quality of the measurements.
• Does not contain tests of new scientific hypotheses
• Data must be archived in a repository at submission
• Peer reviewers are asked to comment on the reusability of the work as presented

5. 4
Data peer review
nature.com/sdata/policies/for-referees
Experimental
Rigor and
Technical Data
Quality
Were data produced in a sound manner?
Technical quality of data – appropriate statistical analyses?
Experimental rigor - appropriate depth, coverage?
Completeness
of the
Description
Sufficient detail to allow others to reproduce these steps?
Sufficient detail to allow others to reuse this data?
Consistent with relevant minimum reporting standards?
Integrity of the
Data Files and
Repository
Record
Do data files appear complete and match manuscript
descriptions?
Are data archived to the most appropriate repository?

6. 5
We capture information about the dataset being described in each Data Descriptor.
During the metadata curation process
• Manuscript re-read
• Data archive checked
• Minor issues with the data and/or manuscript often identified
• Metadata captured in ISA-Tab format
Metadata curation and final data checking

7. 6
ISA-Tab metadata files
scientificdata.isa-explorer.org
Structured Summary table appears after abstract
github.com/ScientificDataLabs/ISA-tab

8. 7
Data Descriptors help researchers share their data in a reusable
way
7
“The Data Descriptor made it easier to
use the data, for me it was critical that
everything was there…all the technical
details like voxel size.”
Professor Daniele Marinazzo

9. 8
Helping researchers know where to share their data
nature.com/sdata/data-policies/repositories
Browse our recommended data repositories online.
• We currently list more than 100 repositories, across biological,
medical, physical and social sciences
• When requested we provide guidance to authors on the best place to
store their data

10. 9
Helping researchers understand data licensing
springernature.com/gp/authors/research-data-policy

11. 10
Reuse of Challenge data published at Scientific Data

12. 11
Other Challenge dataset publications at Scientific Data

13. 12
How can we help Challenge participants?
Visit nature.com/scientificdata
Email scientificdata@nature.com
Tweet @ScientificData