Presentation given at the Weather, climate and air quality BoF at RDA (Research Data Alliance) Plenary 7, 3rd March 2016

1. Varsha Khodiyar, PhD
Data Curation Editor, Scientific Data
Nature Publishing Group
@varsha_khodiyar
@scientificdata
Data sharing as part of the research
ecosystem
Scientific Data’s approach to data publishing
Weather, climate and air quality BoF, 3rd March

2. Why the push to share data?
Research conduct
Publication bias – what is submitted
Experimental design
Statistics
Lab supervision and training
Research reporting and sharing
Gels, microscopy images
Statistical reporting
Methods description
Data deposition and availability
2

3. Generating research data is expensive
Just 18.1% NIH grant applications funded in 2014*
• Hours spent writing grants?
• Hours spent reviewing grants?
Resources are finite/expensive
• Modified animals
• Specialized reagents
Time and effort taken in the laboratory to generate
good, valid data
* report.nih.gov/success_rates/Success_ByIC.cfm

4. • Diversity of analyses and opinion
• New research
• testing of new hypotheses
• new analysis methods
• meta-analyses to create new
datasets
• studies on data collection methods
• Education of new researchers
• Increased return on investment in
research
Vickers AJ: Whose data set is it anyway? Sharing raw
data from randomized trials. Trials 2006, 7:15
Hrynaszkiewicz I, Altman DG: Towards agreement on
best practice for publishing raw clinical trial data.
Trials 2009, 10:17
Sharing data promotes

5. Data needs to be…
Discoverable
Need to
know it’s
there
Accessible
Must be able
to get to the
data
Usable
Require
sufficient
information
about how
the data was
generated
Persistent
Historical
data access
as part of the
scientific
record, as
well as for
new research
Reliable
Data
provenance
informs data
reuse
decisions
Joint Declaration of Data Citation Principles www.force11.org/group/joint-declaration-
data-citation-principles-final
Achieving human and machine accessibility of cited data in scholarly publications Starr et
al. PeerJ Computer Science (2015). doi:10.7717/peerj-cs.1
Making data count Kratz & Strasser. Sci. Data (2015). doi:10.1038/sdata.2015.39
The FAIR guiding principles for scientific data management and stewardship Williams et al.
Sci. Data (in press)

6. Researchers already share data
• Most researchers are sharing
data, and using the data of
others
• Direct contact between
researchers (on request) is a
common way of sharing data
• Repositories are second most
common method of sharing
Kratz and Strasser (2015) doi: 10.1371/journal.pone.0117619 9

7. But…
Sharing of data upon request from published articles
• relies heavily on trust
• when stored informally, disappears at a rate of ~17% per year
(Vines et al. 2014; doi: 10.1016/j.cub.2013.11.014)
Data shared in a repository
• often not reusable due to insufficient context
• may not be possible to determine reliability (peer review?)
• may not be easily findable, if not referenced in a scholarly
article
• no scholarly credit for data producers

8. Synthesis
Analysis
Conclusions
What did I do to generate the data?
How was the data processed?
Where is the data?
Who did what and when?
Methods and technical analyses supporting the quality of the measurements.
Do not contain tests of new scientific hypotheses
Comparison of data paper to traditional article

9. Data papers and journals
• Ensure formal storage in repository
• Allow space for authors to include sufficient context for
reuse
• Peer reviewers often specifically requested to comment
on data archive reusability
• Data paper are formal works, giving scholarly credit to
data producers
• Formal data citations enabling data discovery via
bibliographic indexes that researchers are used to using

10. Data journals and multidisciplinary research
Cross-domain data sharing vital for solving the most pressing world
issues:
• Public health (social science, epidemiology & molecular biology)
• Resource management & sustainability (energy research, policy,
ecology & climate science)
Differences between researchers of vocabulary and expressions of
reliability, mean clear descriptions of data become even more essential
for cross-domain data sharing.
Multidisciplinary data journals (e.g. Data Science Journal, Scientific
Data):
• provide a data sharing outlet to researchers in all domains
• help datasets cross domain boundaries, data is more visible and
searchable i.e. less siloing
10

11. Increasing the discoverability of data
• Is data truly discoverable by
researchers outside the original
authors domain?
• Too many papers to read in each
person’s own field.
• Could increasing the machine
accessibility of data, result in
increased data reuse?

12. Data Descriptors have human and machine readable
components
12
Human readable
representation of
study
i.e. article (HTML &
PDF)
Human readable
representation of
study
i.e. article (HTML
& PDF)
Machine
readable
representation
of study
i.e. metadata

13. • We capture metadata about the data being described in each Data Descriptor
• The manuscript captures human readable metadata needed for data reuse
• The curated metadata records capture machine readable metadata needed for
machine based data discovery
Metadata at Scientific Data

14. Use of community endorsed ontologies and controlled
vocabularies
14
Controlled vocabulary = list of standardized phrases of scientific concepts
Ontology = controlled vocabulary with defined relationships between terms

15. Metadata for data discovery
Search by:
• Data Repositories
• Experiment design
• Measurements made
• Technologies used
• Factor types
• Sample Characteristics
• Organism
• Environment types
• Geographic locations
scientificdata.isa-explorer.org

16. Scientific Data’s Repository List
Browse our recommended data repositories online.
• We currently list almost 80 repositories, across biological, medical,
physical and social sciences
• When required, we provide guidance to authors on the best place to
store their data
www.nature.com/sdata/data-policies/repositories

17. Data citation for humans

18. <ref-list content-type="data-citations">
<ref id="d1">
<element-citation>
<source>Oak Ridge National Laboratory Distributed Active Archive Center</source>
<ext-link ext-link-type="dummy" specific-use="url"
xlink:href="http://dx.doi.org/10.3334/ORNLDAAC/1292">http://dx.doi.org/10.3334/ORNLDAAC/1292</ext-link>
<year>2015</year>
<collab>
<contrib-group>
<contrib>
<name>
<surname>Law</surname>
<given-names>B. E.</given-names>
</name>
</contrib>
<contrib>
<name>
<surname>Berner</surname>
<given-names>L. T.</given-names>
</name>
</contrib>
</contrib-group>
</collab>
</element-citation>
</ref>
</ref-list>
Data citation for machines
• JATS 1.0 XML
• Data citations list marked up as data
citations
• “dummy” value designed to, in the
future, support a tool to generate links
to datasets in approved repositories
from dataset IDs

19. What types of data can be published?
19
Decades
old
dataset
Standalone
dataset
Data that has been
used in an analysis
article
Large
consortium
dataset
Data from a
single
experiment
Data that the
researcher finds
valuable and that
others might find
useful too
Data associated
with a high impact
analysis article

20. When can a Data Descriptor be published?
20
After data
analysis has
been
published
Before analysis
has been
published
Authors not
intending to
analyse data
Data Descriptors can be
submitted and published
at any point in the
research workflow, i.e.
whenever it makes most
sense for your data
After data
analysis has
been
published
Before the
analysis has
been published
Publication
alongside analysis
article

21. Some of our climate sciences Data Descriptors
21
See more at www.nature.com/scientificdata

22. Data as part of the research workflow?
Papers usually written after analyses, key details can be forgotten
• Ideally metadata would be captured during data generation
process
• Takes time and effort to capture adequate metadata of
sufficient quality for data reuse
Machine readable metadata
• Metadata format needs to be decided prospectively
• Researchers require professional expertise and guidance to use
ontologies (essential for machine readability and discovery)
How to ensure data generators are able to capture metadata easily
and in sufficient detail for reuse?
22

23. Data reuse by (some of) the same researchers
23

24. Data reuse by other researchers in the same field
24
“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

25. Data reuse by the non-research community
25
http://www.nytimes.com/interactive/2014/12/30/science/history-of-ebola-in-24-outbreaks.html

26. Discoverable
Machine
based data
discovery
Implement
data citations
Use
community
ontologies
Accessible &
Persistent
Encourage
use of
repositories
Use
persistent
identifiers
for data
Usable
Metadata
capture
during data
generation
process
Encourage
use of
minimal
reporting
standards
Reliable
Encourage
peer
reviewers to
evaluate
data archive
(structure,
format)
alongside
the article
Researcher
incentives
Recognise
data as a
first class
scholarly
work
Provide
tools for
data
visualization
and
discovery
Building infrastructure to promote data sharing as part
of the research workflow

27. Visit nature.com/sdata
Email scientificdata@nature.com
Tweet @ScientificData
Honorary Academic Editor
Susanna-Assunta Sansone
Managing Editor
Andrew L. Hufton
Data Curation Editor
Varsha K. Khodiyar
Advisory Panel and Editorial
Board including senior researchers,
funders, librarians and curators
Supported by