1. Persistent Identifiers, Discoverability, and Open Science Fiona Murphy, Institute for Environmental Analytics, University of Reading, Reading, UK, Kerstin Lehnert, Lamont-Doherty Earth Observatory, Columbia University, NY, USA, Brooks Hanson, American Geophysical Union, Washington DC, USA, gu.org David Mellor, Center for Open Science, Virginia, USA,
Problem Statement
The traditional research paper is the only widely accreditable research output.
Because of this, researchers face few incentives to make other parts of their research accessible.
Solutions
Funders need to recognize a wide range of research outputs (including data collection, management planning and enrichment, reviews, software, etc) as contributing to the knowledge cannon.
The goals of reproducibility and interoperability should be woven into research funding calls.
Producers of these outputs should be rewarded accordingly.
Altmetrics to form part of the assessment framework in a transparent form
Researchers to be encouraged to submit a range of outputs for assessment
Editors need to know how data can be cited
Publishers need to inform authors and editors about data publication, draw up and enact practical policies, and work with partners to ensure robust links between publications and related outputs housed by other platforms.
All stakeholders need to share best practices for citing data, and other materials.
Suggest Best Practices for Journals
The Coalition on Publishing Data in the Earth and Space Sciences (COPDESS) develops and recommends best practices for journal author instructions around data and identifiers as a resource to the community. These best practices are consistent with and based on the COPDESS Statement of Commitment and have been developed with guidance from participants in COPDESS.
Data Policy Statement
Data Citation
Sample Citation and Identification
Crossref Funder Registry
ORCIDs
Data Policy Statement:
XXX journal has endorsed the Statement of Commitment of the Coalition on Publishing Data in the Earth and Space Sciences (COPDESS).  All data and software necessary to understand, evaluate, replicate, and build upon the reported research must be made available and accessible at the time of publication as far as possible. Data should, to the extent possible, be stored in appropriate domain repositories that are widely recognized and used by the community, follow leading practices for data curation, and can provide additional data services.
Best Practices:
Include a prominent statement about data availability and link to COPDESS.
Encourage editors, reviewers, and staff to evaluate data and affirm availability.
Statement above is one minimal version of data availability requirements.  Other examples are provided here:
Software availability should be included as part of data availability.
Adoption and Next Steps
Work with funders to incentivize the sharing, publication and re-use of data
We need to work with relevant societies, publishers and with individual journal editors to ensure these policies are widely disseminated and, where necessary, that community responses can refine or adapt them to become more suitable for specific disciplines.
Training, researchers, peer review.
What Can You Do?
Contact details
Lost Knowledge
The data used in publications can often be hard to access.
Data collected but never used in a publication are even harder to use.
Other parts of the research lifecycle face similar obstacles: if they’re not included in the final publication, they will be lost.
TOP
Eight Standards:
Citation Standards
Data Transparency
Analytical Methods and Code Transparency
Research Material Transparency
Design and Analysis Transparency
Preregistration of Studies
Preregistration of Analysis Plans
Replication
Three Levels: Disclosure, Requirement, Verification
Logistical Challenges
Technical and Educational
This isn’t simply a technical challenge, although it has technical aspects. For reproducibility and interoperability to be meaningful and well-understood, appropriate standards need to be agreed. Amongst these, Persistent Identifiers (PIDs) could well form a key part.
However, in order to do so they need to be understood by the respective research communities. In addition, workflows need to be optimized (and as far as possible automated) for accurate use and, if there are implications for workloads, consideration given to resourcing and training requirements.
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Signatories
Eight Standards:
Citation Standards
Data Transparency
Analytical Methods and Code Transparency
Research Material Transparency
Design and Analysis Transparency
Preregistration of Studies
Preregistration of Analysis Plans
Replication
Three Levels: Disclosure, Requirement, Verification
Head 3, to label the table below
Head 3, to label the table below
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