1.
Iain Hrynaszkiewicz, Publisher, Open Research, PLOS
Open Access Week, October 2020
Six things
publishers
can do to
promote
open
research
data

2.
Open research data....for what?
“Open inquiry is at the heart of the scientific
enterprise. Publication of scientific theories - and of
the experimental and observational data on which
they are based - permits others to identify errors, to
support, reject or refine theories and to reuse data
for further understanding and knowledge.
Science’s powerful capacity for self-correction
comes from this openness to scrutiny and
challenge.”
In other words, open research is a
means to conduct and publish better
research

3.
Irreproducible research in biology costs US $28 billion
Freedman LP, Cockburn IM,
Simcoe TS (2015) The
Economics of Reproducibility
in Preclinical Research. PLoS
Biol 13(6): e1002165.
https://doi.org/10.1371/journal
.pbio.1002165

4.
Six things publishers can do, realistically
1. Understand researchers’ needs
2. Raise awareness and help to create change
3. Enable peer reviewer engagement with research data
4. Enhance scholarly communication infrastructure
5. Enhance established incentives - and create new ones
6. Be open and collaborative ourselves

5.
#1 Understanding researchers’ needs
● Many studies have considered researchers’ views and experiences about
sharing research data
● A meta-synthesis of 45 qualitative studies1
identified four major themes: data
integrity, responsible conduct of research, feasibility of sharing data, and
value of sharing data
● Researchers lack time, resources, skills, infrastructure and incentives to
share their data in public repositories
1. Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing
and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182

6.
#1 Understanding researchers’ needs
Science, Digital; Fane, Briony; Ayris, Paul; Hahnel, Mark; Hrynaszkiewicz, Iain; Baynes, Grace; et al. (2019): The State of Open Data
Report 2019. Digital Science. Report. https://doi.org/10.6084/m9.figshare.9980783.v2

7.
Researchers seem satisfied with available tools
Vijghen, Samira; Harney, James; Hrynaszkiewicz, Iain. (2020): Researchers’ priorities for data sharing - PLOS survey dataset (n=724) [dataset] In press
Important, satisfied
Unimportant, satisfied

8.
Motivations for data sharing
Science, Digital; Fane, Briony; Ayris, Paul; Hahnel, Mark; Hrynaszkiewicz, Iain; Baynes, Grace; et al. (2019): The State of Open Data
Report 2019. Digital Science. Report. https://doi.org/10.6084/m9.figshare.9980783.v2

9.
#2 Raise awareness and create change (policy)

10.
PLOS & publisher data policies
● Policies have been in place for specific data types (e.g. protein structural data) for decades
● Publisher policies more common since around 2012
● Since 2014 PLOS has required authors to make all data underlying the findings described
in their manuscript fully available without undue restriction at the time of publication
○ Authors must provide a “Data Availability Statement” (DAS) describing compliance with
PLOS's policy
○ PLOS has published >141,000 papers with a DAS
○ Public data sharing is not mandatory if there are ethical or legal restrictions, e.g., public
participant privacy
○ Reviewers are asked whether authors have complied with the policy

11.
Data availability statements

12.
Steady increase in % authors using repositories
28% authors
using data
repositories
24%21%20%18%

13.
Rapid growth of publisher/ journal data policies
● Publisher wide research data sharing policies covering tens of thousands of journals
have grown rapidly since 2016 (Springer Nature, Elsevier, Taylor & Francis, Wiley,
BMJ, Sage, Hindawi)
● These usually take a tiered approach providing several options for data sharing
policies including less and more stringent policies
● Permits journals, communities, societies to select a policy deemed appropriate for the
researchers they serve

14.
Unintended consequences of policy growth?
● Initiatives from publishers, societies (e.g. American Geophysical Union), funders, and
other groups e.g. FAIR data principles, Transparency and Openness Promotion (TOP)
guidelines, Center for Open Science
● Multiple similar but non-identical policies and terminologies
● Different levels of support and resources available for implementation
● Potential for confusion of researchers and support staff with so many different policy
requirements*
*Naughton, L. & Kernohan, D., (2016). Making sense of journal research data policies. Insights. 29(1), pp.84–89.
DOI: http://doi.org/10.1629/uksg.284

15.
Research Data Alliance (RDA) data policy
standardisation group
https://www.rd-alliance.org/groups/data-policy-standardisation-and-implementation
Iain Hrynaszkiewicz (PLOS), Natasha
Simons (ANDS), Simon Goudie (Wiley),
Azhar Hussain (Jisc), Rebecca Grant
(Springer Nature)
Formed in 2017, Group activities build on
research carried by Jisc, ongoing activities
of Australian Research Data Commons
and work of journal publishers on data
policy

16.
Open development of a framework suitable for all
● 2017: Initiative launch at RDA Plenary
○ Community calls with stakeholders (librarians, researchers, funders,
publishers, editors
● 2018: Public draft made available for comment
○ More than 30 comments received from more than 20 reviewers
○ Elaboration at RDA Plenary meetings
● 2019: Revision of framework
○ Implementation requirements & policy templates Creation of policy
templates
○ Preprint on figshare
● 2020: Publication after peer review

17.
Output: 14 standard features, 6 policy types/ tiers
Hrynaszkiewicz, I., Simons, N., Hussain, A., Grant, R. and
Goudie, S., 2020. Developing a Research Data Policy
Framework for All Journals and Publishers. Data Science
Journal, 19(1), p.5. DOI:
http://doi.org/10.5334/dsj-2020-005
Key:
○ = Information required
● = Information and action required
- = Not applicable

18.
Mandatory policies are far more effective
Data availability statements
mandatory at PLOS & BMC
Data availability statements (DAS)
optional at BMC (~5% compliance)
1. Colavizza et al. PLoS ONE 15(4): e0230416. https://doi.org/10.1371/journal.pone.0230416

19.
There can be costs to stronger policies
● Stronger policies increase data sharing and long-term data availability1,2
● Stronger policies require more resources e.g. editorial or administrative time; systems
enhancements etc to implement3
● Mandatory data sharing policies have been associated with a decrease in submissions
if a journal’s Impact Factor is falling4
● Vital to understand researchers’ expectations and behaviours when designing and
implementing policy and consider your journal(s) objectives in adopting a policy.
○ Raising awareness of an issue; increasing transparency; increasing data sharing;
increasing data accessibility; increasing data quality and reuse ?
1. Vines et al https://doi.org/10.1096/fj.12-218164 (2013)
2. Magee AF,et al. (2014) PLoS ONE 9(10): e110268. https://doi.org/10.1371/journal.pone.0110268
3. Grant, R & Hrynaszkiewicz, IJDC 2018 https://doi.org/10.2218/ijdc.v13i1.614
4. Vines, T & Albert, A https://scholarlykitchen.sspnet.org/2020/08/26/__trashed/ (2020)

20.
Open research (data) is an investment, not a cost
● Investment in better research, the economy and one’s
own reputation
● Cost of not making research data Findable,
Accessible, Interoperable and Reusable (FAIR)
estimated at €10.2B1
● Linking a paper to research data in a repository via
the data availability statement is correlated with a 25%
increase in citations2
1. https://op.europa.eu/en/publication-detail/-/publication/d3766478-1a09-11e9-8d04-01aa75ed71a1
2. Colavizza et al. PLoS ONE 15(4): e0230416. https://doi.org/10.1371/journal.pone.0230416
€10.2B

21.
#3 Enable peer reviewer engagement with data
● Of course, not all journals and peer reviewers will be motivated to request
review of supporting data
● But, making it easy for reviewers to engage with data associated with
manuscripts they review increases their engagement with data1
● Involves systems, policies and guidelines
1. Let referees see the data. Sci Data 3, 160033 (2016). https://doi.org/10.1038/sdata.2016.33

22.
#3 Enable peer reviewer engagement with data

23.
#3 Enable peer reviewer engagement with data
For the Data availability statement (DAS):
● Has an appropriate DAS been provided?
● Is it clear how a reader can access the data?
● Where links are provided in the DAS, are they
working/valid?
● Where data access is restricted, are the access
controls warranted and appropriate?
● Where data are described as being included
with the manuscript and/or supplementary
information files, is this accurate?
For the data files:
● Are the data in the most appropriate repository?
● Were the data produced in a rigorous and
methodologically sound manner?
● Are data and any metadata consistent with file format
and reporting standards of the research community?
● Are the data files deposited by the authors complete
and do they match the descriptions in the
manuscript?
● Do they contain personally identifiable, sensitive or
inappropriate information?
Hrynaszkiewicz, I., Simons, N., Hussain, A., Grant, R. and Goudie, S., 2020. Developing a Research Data Policy Framework for All Journals
and Publishers. Data Science Journal, 19(1), p.5. DOI: http://doi.org/10.5334/dsj-2020-005

24.
#4 Enhance scholarly infrastructure

25.
#4 Enhance scholarly infrastructure
● Partnering and integration with data repositories as part of the peer-review
process increases repository use
○ = Make it easy for authors
● Often requires changes to third party systems such as Editorial Manager,
eJournals Press etc and publishing workflows (cost considerations)
● The most commonly used repositories by PLOS authors:

26.
#4 Enhance scholarly infrastructure
● Make it easier to access
research data that can be reused
in new research
● Increase discoverability of
research data associated with
published articles
● Data-article linking; improved
display of supplementary data
files

27.
#5 Enhance incentives

28.
#5 Enhance incentives: more traditional
- “Traditional” incentives such as citations to research papers and new
authorship opportunities may be the most effective incentives for researchers
to share research data1
- Many publishers offer additional article types to encourage data sharing and
reuse - data papers/descriptors and data journals e.g. Scientific Data,
GigaScience, Earth Systems Science Data
1. Research, Nature; Penny, Dan; Fane, Briony; Goodey, Greg; Baynes, Grace (2019): State of Open Data 2019. figshare. Dataset.
https://doi.org/10.6084/m9.figshare.10011788.v2

29.
#5 Enhance incentives: less traditional
● Many publishers encourage
citation of publicly available
datasets in the same way
one cites published papers,
in the reference list = a “Data
citation”
● Any publicly available
dataset with a persistent
identifier, such as a DOI, can
be cited
● There is no evidence that
citing datasets discourages
citation of papers

30.
Why cite data?
• Provides more specific evidence for claims and sources used in papers
• Gives more credit to data producers and promotes more diverse
contributorship to research
• Supports reproducible research by providing specific links to research
outputs and enabling tracking of provenance
• Promotes data as a “first class” object in scholarly communication
• Ensures robust links to data from papers
• It’s easy from an author’s perspective - no different from citing a paper

31.
#5 Enhance incentives: more experimental
● Offering badges to researchers who
make their research data open with
their publications has been associated
with greatly increased data sharing in
psychology research
Kidwell et al. (2016) PLoS Biol 14(5): e1002456.
https://doi.org/10.1371/journal.pbio.1002456
Open data
badges
introduced
% Papers
with data

32.
#6 Be open and collaborative ourselves
- Open access
- Open licenses (e.g. CC BY)
- Open citations
- Open abstracts
- Open data links (Scholix)
- Open user research

33.
Open for collaboration
The biggest policy and infrastructural challenges that enable open
research can only be tackled by multiple publishers collaborating as an
industry and collaborating with other organisations that support the
conduct and communication of research – repositories, institutions,
funders, societies and infrastructure providers
Hrynaszkiewicz I. (2019) Publishers’ Responsibilities in Promoting Data Quality and Reproducibility. In: Bespalov A., Michel
M., Steckler T. (eds) Good Research Practice in Non-Clinical Pharmacology and Biomedicine. Handbook of Experimental
Pharmacology, vol 257. Springer, Cham. https://doi.org/10.1007/164_2019_290

34.
Example: Research data policy alignment
E.g. Elsevier policy 4
E.g. Springer Nature policy 4,
Elsevier policy 5
E.g. Wiley policy 3, PLOS policy
E.g. Wiley policy 2, TOP level I
E.g. Springer Nature policy 1
E.g. Wiley policy 1, Taylor &
Francis Basic policy
Hrynaszkiewicz, I., Simons, N., Hussain, A.,
Grant, R. and Goudie, S., 2020. Developing a
Research Data Policy Framework for All Journals
and Publishers. Data Science Journal, 19(1), p.5.
DOI: http://doi.org/10.5334/dsj-2020-005

35.
Example: STM Research Data Year
● Publishing industry
association with nearly
150 member publishers
made 2020 the year of
research data
● Promoting and supporting
uptake of standard journal
policies and common
approaches to data
linking and data citation

36.
Six things publishers can do to promote open
research data
1. Understand researchers’ needs
2. Raise awareness and help to create change
3. Enable peer reviewer engagement with research data
4. Enhance scholarly communication infrastructure
5. Enhance established incentives - and create new ones
6. Be open and collaborative ourselves

37.
Thanks for listening
Questions?
ihrynaszkiewicz@plos.org
@iainh_z
linkedin.com/in/iainhz/