This document discusses efforts to standardize journal data policies to improve data sharing. It notes that while many journals now have data policies, they are highly variable, making it difficult for researchers to comply and new journals to adopt policies. International efforts are underway through groups like the Research Data Alliance to identify common policy elements and develop standards. The document also outlines the work of ANDS in Australia to engage stakeholders and develop guidance for journal editors on key policy components. Standardizing major aspects of data policies could help address current issues and make compliance easier for researchers and adoption simpler for journals.

1. [Natasha]
Intro Natasha and Kate
Paper will look at how journal data policies are on the rise but are highly
idiosyncratic which impacts the ability of researchers to comply with policies
and makes it challenging for new journals to adopt a policy.
Look at international efforts to standardise journal data policies and the work
that ANDS is doing at a national level.
Time for a couple of questions at the end.
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2. [Natasha]
An increasing number of publishers and journals are introducing data sharing
policies e.g. PLOS. Intro of PLOS policy caused a stir: on Twitter #PLOSWin
(about time) and #PLOSFail (how dare you!). Data policy is now a standard
part of PLOS paper acceptance.
Other large scholarly publishers have also introduced data sharing policies:
Nature – note that Nature also has a data journal called Scientific Data, a
peer-reviewed, open-access journal for descriptions of scientifically valuable
datasets
Elsevier, Wiley also have data sharing policies.
Publishers and journals are at forefront of data sharing policy initiatives. Some
examples:
COPDESS- Coalition on Publishing Data in the Earth and Space Sciences -
statement of commitment signals important progress and a continuing
commitment by publishers and data facilities to enable open data in the Earth
and space sciences.
Signatories include major publishers Elsevier, Springer and Wiley
TOP Guidelines – Transparency and openness guidelines – includes “data
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3. transparency” guidelines broken into levels - Over 5,000 journals and
organizations have already become signatories of the TOP Guidelines.
JDAP - The Joint Data Archiving Policy (JDAP) describes a requirement that
data supporting publications be publicly available with many journals from
different disciplines adopting the policy.
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4. [Natasha]
A common approach to data sharing by publishers and journals is to ask
authors who submit a manuscript to provide a statement about where data
supporting the results reported in a published article can be found.
Examples of such statements [read from slide]
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5. [Natasha]
Some of the issues with data sharing through journals are that:
data availability declines over time. A 2013 study referenced on this slide
requested data sets from a relatively homogenous set of 516 articles published
between 2 and 22 years ago, and found that availability of the data was
strongly affected by article age. Broken e-mails and obsolete storage devices
were the main obstacles to data sharing.
The same lead author conducted another study which found that mandated
data archiving policies that require the inclusion of a data availability statement
in the manuscript improve the odds of finding the data online almost 1000-fold
compared to having no policy. However archiving rates at journals with less
stringent policies were only very slightly higher than those with no policy at all.
They recommend that policies mandating data archiving at publication are
clearly needed.
Several studies have found data sharing on request has been wanting: not just
because emails can become obsolete but for a host of other reasons from an
unwillingness to share data through to lack of personnel or time to retrieve the
data
Sharing of clinical research data usually happens between individuals and
research groups (non-publicly). Excellent article by Iain Hrynaszkiewicz
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6. (Springer Nature) and colleagues about how journals can still link to metadata
pages about the data where requests for access can be made (see
http://doi.org/10.1186/s41073-016-0015-6).
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7. [Natasha]
Existence of data policy: the higher the Impact Factor of the journal the more
likely they are to have a data availability policy and to enforce it (1)
Data policy aspects: content, discoverability, ease of interpretation,
infrastructure providers, support for compliance (2) – these factors apply even
within the one publishing house - Jisc UK study – ideosyncracies between
policies main reason why Jisc decided to temporarily abandon a project to
build a journal research data policy registry and put efforts into standardising
policies instead
Most data sharing policies do not provide specific guidance on the practices
that ensure data is maximally available and reusable (3) i.e. just because
someone is asked to share their data doesn’t mean it is any good,
interpretable, reusable etc.
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8. [Natasha]
Range of effects, probably most significant are:
Can be difficult for journal editors to develop and support a data policy (where
do I start? What do I include? A one page policy that is tailored to my discipline
please)
Difficult for researchers in understanding and complying with data policies
Challenges for infrastructure providers and research support staff to assist
data policy compliance
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9. [Natasha]
So to try and answer the question this talk poses: how can journal data
policies be more prevalent and effective?
There is clear benefit in a more standardised approach (1). Comes through
strongly from the work and research conducted by Jisc UK and ties in with the
conclusion of Springer Nature and other publishers such as Wiley.
Greater standardisation could also facilitate the construction of a register of
data policies, similar to the SHERPA registers for funder and publisher policies
on open access (1).
Springer Nature research data policy types as standardisation example: they
have 4 policy types which vary in strength from encouraged to required that
journal editors can choose to implement; more than 1,000 (~45%) Springer
Nature journals have adopted a standard policy (2).
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10. [Natasha]
There are international efforts underway to standardize journal data policies.
One effort is being made through the Research Data Alliance – RDA - which is
a community-driven organization building the social and technical
infrastructure to enable open sharing of data.
RDA has over 6,000 members from 130 countries (September 2017), and
provides a neutral space where its members can come together through
focused global Working and Interest Groups to develop and adopt
infrastructure that promotes data-sharing and data-driven research.
At the Denver RDA plenary is September last year a number of interested
persons got together for a BoF on the topic of data policy standardisation. BoF
led by Iain and I was part of this. As a result, we formed the RDA Data Policy
Standardisation and Implementation Interest Group: main focus is journal data
policies but also funder policies are in scope.
Co-chairs: Iain Hrynaszkiewicz, Chair (Springer Nature), Natasha Simons
(ANDS), Simon Goudie (Wiley), Azhar Hussain (Jisc)
Group activities are building on and are informed by research carried out by
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11. Jisc, ongoing activities of ANDS and work of Springer Nature and Wiley on
data policy
Activities: RDA plenary discussions, community calls to identify journal data
policy components etc. More to come!
Now I’ll hand over to Kate to talk about ANDS activities and how you can get
involved
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12. [Kate]
ANDS has co-sponsored two national roundtables this year which have
brought together journal editors, publishers, associations, infrastructure
providers and more to discuss journal data policies. One focused on social
sciences and one on health and medical.
Outcomes: follow up with individual journal editors and infrastructure providers.
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13. [Kate]
ANDS has developed a Research data for journal editors Guide. This guide is
intended to provide a starting point for editors considering developing or
improving data policies for their journals. The guide outlines 10 elements to
consider in developing or refining a data policy: what to deposit, how to make
sensitive data available, where when and how to deposit, if and when the data
should be peer reviewed, advice on data licensing and data citation,
compliance and consequences of non- compliance, policy development
process and timing.
There is additional information on the ANDS website and ANDS is working
individually with Editors to develop/enhance data policies.
One example: discussions with Editor-In-Chief of a leading clinical nursing
journal has led to a block of work with Human Resource Ethics Committees –
because the decision of who you can share data with is made at the
ethics/participant consent level and asking for the data at the journal level can
be too late in the process.
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14. [Kate]
So how can those at research institutions engage with these activities?
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15. [Kate]
Thank you and please feel free to contact us.
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