“The Science Journals support the Transparency and Openness Promotion (TOP) guidelines to raise the quality of research published in Science and to increase transparency regarding the evidence on which conclusions are based….All data used in the analysis must be available to any researcher for purposes of reproducing or extending the analysis. Data must be available in the paper, deposited in a community special-purpose repository, accessible via a general-purpose repository such as Dryad, or otherwise openly available….”
“A year ago the world’s leading medical-journal editors announced plans to require their authors to share with other scientists the data associated with their published articles about clinical trials. “I realistically think this will take several years” for the right environment to be in place, said Darren B. Taichman, secretary of the International Committee of Medical Journal Editors, which proposed the now-abandoned data-sharing requirement. The benefits of an open-data system are widely accepted by scientists. Sharing the data that underlie a journal article helps colleagues confirm the accuracy of the published finding, speed and expand their own research, and credit the originators, advocates have said. But the coalition of journal editors, also known as the ICMJE, said last week that a rash of complaints from scientists about the proposed requirement had led it to conclude that the research community still was not ready for the mandate….”
Open data is a vital pillar of open science and a key enabler for reproducibility, data reuse, and novel discoveries. Enforcement of open-data policies, however, largely relies on manual efforts, which invariably lag behind the increasingly automated generation of biological data. To address this problem, we developed a general approach to automatically identify datasets overdue for public release by applying text mining to identify dataset references in published articles and parse query results from repositories to determine if the datasets remain private. We demonstrate the effectiveness of this approach on 2 popular National Center for Biotechnology Information (NCBI) repositories: Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). Our Wide-Open system identified a large number of overdue datasets, which spurred administrators to respond directly by releasing 400 datasets in one week.
“Advances in genetic sequencing and other technologies have led to an explosion of biological data, and decades of openness (both spontaneous and enforced) mean that scientists routinely deposit data in online repositories. But researchers are only human and may forget to tell a repository to release the data when a paper is published.
“Psychological Science is now introducing some minor changes designed to increase the frequency and ease with which editors and reviewers of submissions can access data and materials as part of the peer-review process. I anticipate that, in addition to enhancing the review process, these changes will further increase the percentage of Psychological Science articles for which researchers can quickly and easily access data and materials postpublication. The changes we are introducing are tweaks and nudges, not radical shifts. In the following, I explain the changes and why they are worth undertaking.”
“Published in Science in 2015, the TOP [Transparency and Openness Promotion] guidelines include eight modular standards, each with three levels of increasing stringency. Journals select which of the eight transparency standards they wish to adopt for their journal, and select a level of implementation for each standard. These features provide flexibility for adoption depending on disciplinary variation, but simultaneously establish community standards….”
“Data Descriptors, Scientific Data‘s primary article type, describe scientifically valuable datasets. These datasets must be made available to editors and referees at the time of submission, and must be shared with the scientific community as a condition of publication. Here, we provide information on the types of data that should be archived, guidance for authors on selecting a suitable repository for their data, and how to archive sensitive data.
Scientific Data’s data policies are compatible with the standardised research data policies set out by Springer Nature.
Please read on for our data deposition policies, and please contact us if you would like additional advice on how best to meet these requirements for your own data….”
“PLOS journals require authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception.
When submitting a manuscript online, authors must provide a Data Availability Statement describing compliance with PLOS’s policy. If the article is accepted for publication, the data availability statement will be published as part of the final article.
Refusal to share data and related metadata and methods in accordance with this policy will be grounds for rejection. PLOS journal editors encourage researchers to contact them if they encounter difficulties in obtaining data from articles published in PLOS journals. If restrictions on access to data come to light after publication, we reserve the right to post a correction, to contact the authors’ institutions and funders, or in extreme cases to retract the publication.
Methods acceptable to PLOS journals with respect to data sharing are listed below, accompanied by guidance for authors as to what must be indicated in their data availability statement and how to follow best practices in reporting. If authors did not collect data themselves but used another source, this source must be credited as appropriate. Authors who have questions or difficulties with the policy, or readers who have difficulty accessing data, are encouraged to contact the relevant journal office or firstname.lastname@example.org.
The data policy was implemented on March 3, 2014….”
Abstract: Background. There is wide agreement in the biomedical research community that research data sharing is a primary ingredient for ensuring that science is more transparent and reproducible. Publishers could play an important role in facilitating and enforcing data sharing; however, many journals have not yet implemented data sharing policies and the requirements vary widely across journals. This study set out to analyze the pervasiveness and quality of data sharing policies in the biomedical literature. Methods. The online author’s instructions and editorial policies for 318 biomedical journals were manually reviewed to analyze the journal’s data sharing requirements and characteristics. The data sharing policies were ranked using a rubric to determine if data sharing was required, recommended, required only for omics data, or not addressed at all. The data sharing method and licensing recommendations were examined, as well any mention of reproducibility or similar concepts. The data was analyzed for patterns relating to publishing volume, Journal Impact Factor, and the publishing model (open access or subscription) of each journal. Results. 11.9% of journals analyzed explicitly stated that data sharing was required as a condition of publication. 9.1% of journals required data sharing, but did not state that it would affect publication decisions. 23.3% of journals had a statement encouraging authors to share their data but did not require it. There was no mention of data sharing in 31.8% of journals. Impact factors were significantly higher for journals with the strongest data sharing policies compared to all other data sharing mark categories. Open access journals were not more likely to require data sharing than subscription journals. Discussion. Our study confirmed earlier investigations which observed that only a minority of biomedical journals require data sharing, and a significant association between higher Impact Factors and journals with a data sharing requirement. Moreover, while 65.7% of the journals in our study that required data sharing addressed the concept of reproducibility, as with earlier investigations, we found that most data sharing policies did not provide specific guidance on the practices that ensure data is maximally available and reusable.