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

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

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

• 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

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)

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

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

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

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

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

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?

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

• 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

Use of community endorsed ontologies and controlled vocabularies

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Controlled vocabulary = list of standardized phrases of scientific concepts Ontology = controlled vocabulary with defined relationships between terms

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

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

Data citation for humans

<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

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

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

Some of our climate sciences Data Descriptors

21

See more at www.nature.com/scientificdata

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

Data reuse by (some of) the same researchers

23

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

Data reuse by the non-research community

25

http://www.nytimes.com/interactive/2014/12/30/science/history-of-ebola-in-24-outbreaks.html

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

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

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