Data sharing in biological anthropology

Guiding principles and best practices

Bibliographic Data

ID8313987
AuthorsT R Turner (0000-0001-8615-442X, Department of Anthropology University of Wisconsin – Milwaukee Milwaukee Wisconsin, corresponding author), C J Mulligan (0000-0002-4360-2402, Department of Anthropology University of Florida Gainesville Florida)
Year2019
Volume170
Issue1
Pages3-4
Publication date2019-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Physical Anthropology (JOURNAL)
Journal identifiersISSN: 0002-9483 • E-ISSN: 1096-8644
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ajpa.23909
PMID31368116
OpenAlexW2966864431
LanguageEN
Citations received10

In 2017 the AAPA established an ad hoc committee on data access and data sharing; committee members were Trudy Turner (Chair), Connie Mulligan (Co-Chair), Doug Boyer, Eric Delson, and William Leonard. With support from a NSF grant to the association, the committee led a workshop on data sharing in biological anthropology on February 8–9, 2019 in Milwaukee, WI. Forty participants representing all aspects of the field were able to reach consensus on data sharing. The goal of the workshop was to formalize a set of guiding principles and best practices in order to increase and normalize data sharing within biological anthropology. Here we present the results of the workshop and we invite comments through Letters to the Editor of AJPA from the biological anthropology community. We believe data sharing is essential for the advancement of the field of biological anthropology. Data used for research in our field are collected from physical materials and phenomena that are temporally or geographically limited and may not be able to be collected again. These data often represent unique evolutionary, ecological and cultural histories. We have a responsibility to maintain and share these resources for purposes of scientific integrity, world heritage, and advancing knowledge. Data sharing improves research transparency and promotes replicability, reproducibility, and correction. We believe data sharing leads to more equitable access to resources and responsible use of research materials and funded information. Public sharing of data also brings the discipline into compliance with requirements from funders and publishers. As a field, we should strive to make our research data “findable, accessible, interoperable, and reusable”, or FAIR, as outlined by the H2020 programme guidelines on FAIR Data Management in Horizon 2020, July 26, 2016 (http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf). We maintain that open and public access of data is the ultimate goal, but limitations exist with respect to specific data sets, institutional policies, cultural heritage considerations, and international conventions. Thus, data access should be “as open as possible, as closed as necessary” (H2020 Programme, 2016). At a minimum, data that are used in a publication should be made publicly available no later than time of publication. Ideally, data should be made publicly available in time for the data set identifier (e.g., accession numbers or DOIs) to appear in the publication to ensure easy access to the data. All data required for replicating statistical analyses and underlying summary statistics, including but not limited to raw data, metadata, and a codebook of variables, should be shared whenever possible. Sharing summary statistics is not sufficient. All digital resources used to generate quantitative data required for replicating statistical analyses, including but not limited to digital imagery, custom software programs, scripts and annotated code, should be shared whenever possible. Data should be posted in a “trusted” data repository—see https://docs.google.com/document/d/11wj0y8I6H7OfwGmtvwsX3bwGqs0Durvt8n9ldnIlwdw/edit for a list of recommended repositories. An accessible, enduring, digital file format should be used that is appropriate to each data type or data set. Authors may embargo access to archived data from a published paper that are referenced by a DOI or other persistent link for up to 6 months post-publication date, when justified. Ideally, all data (raw, summary, metadata) from a project, that is, not just data used in a publication, should be made publicly available. Due to issues of participant confidentiality, and ethical and legal considerations, it may not be possible to make all data publicly available at the time of publication, but efforts should be made to make as many data sets publicly available and as open as possible. The following information should also be shared: the presence and identity of other stakeholders, the conditions of data collection, and the limits these factors may place on archiving and sharing of each data set used or collected in a grant or publication

Best practice · Data science · Data sharing · Engineering ethics · Field (mathematics) · Political science · Sociology · Transparency (behavior) · Computer Science · Engineering · Law · Medicine · Research Data Management Practices

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