An Editorial published this week at Nature Genetics endorsed the goals of the DivSeek initiative and issued a call for wider sharing of plant phenotype data – particularly phenotypic data associated with important genetic and genomic studies. Scientific Data supports this call, and we invite researchers to submit manuscripts to our journal describing and releasing such datasets. Continue reading
At Scientific Data we have been considering how we might develop our scope and editorial policies to better accommodate clinical research data. Publication of clinical research presents a number of challenges, which we will not be the first to have attempted to solve. In particular, we might need to support linking of our primary article type, the data descriptor, to non-public datasets – datasets that cannot be open access due to patient privacy or other legitimate constraints. While we advocate setting the default for research data to open, we are also conscious that full anonymisation of clinical data is often impossible to achieve with certainty. Continue reading
This week, Nature and the Nature research journals made some important updates to their data availability policies: updates that strengthen the editorial links between Nature journals and Scientific Data; updates that provide better resources and support for authors wishing to better support reproducible research; and updates that leverage the work of Scientific Data to curate datasets and identify suitable data repositories for more authors. See the related editorial published at Nature. Continue reading
Today, we are pleased to announce our first Advisory Panel and Editorial Board members from the social sciences. Researchers from these communities are now invited to submit Data Descriptor manuscripts describing quantitative datasets, particularly those that may be of use for integrative analyses that stretch across the traditional boundaries between the natural and social sciences. Continue reading
Big data are, it seems, everywhere and attracting much attention, but in terms of size are hard to define. Scientific research generates a lot of “small data” too – the average file size for all datasets deposited in our partner repository figshare, for example, is just 1.35 Mb. However, big data are ironically somewhat agnostic of file size, and instead are more about complexity – of the processing techniques and sources the data are derived from. Scientific Data is, for the data underlying our publications, also size agnostic. We welcome data big and small and, in response to feedback from our Editorial Board, have updated our frequently asked questions and scope statement to reflect this. Continue reading
Data Matters presents a series of interviews with scientists, funders and librarians on topics related to data sharing and standards.
As we move toward greater openness in science, the team at Scientific Data believe we need to fully grasp the benefits of and barriers to sharing data across the academic spectrum. To help us better our understanding of why the practice of sharing data is so varied across different disciplines, institutions, and geographies we interviewed numerous people who support the principle – from palaeontologists to neuroscientists and ecologists (to name just a few) but also those working with funding bodies and libraries. Continue reading
We are very pleased to share that NPG has endorsed the Joint Declaration of Data Citation Principles. These principles are a synthesis of previous guidelines and have been released by the Data Citation Synthesis Group a collaboration involving CODATA, the Research Data Alliance, members of the Force11 community, publishers and others.
The guiding principles stress the importance of data resources in scientific communication and the need for citation to facilitate credit and attribution to those who contribute to data generation – summed up in the Joint Declaration of Data Citations preamble below.
Sound, reproducible scholarship rests upon a foundation of robust, accessible data. For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record. In other words, data should be considered legitimate, citable products of research. Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse. Continue reading
Earlier this month, Scientific Data published its first two Data Descriptors. These pre-launch articles recently cleared peer-review and we have decided to publish them before our formal launch in May 2014. They were published using a simplified article template, but they will be transferred to our more feature-rich publication platform in May, and will retain the same citation information and DOIs.
Both of these works present valuable, previously unpublished datasets. We are also actively considering Data Descriptor manuscripts that expand on previous publications (e.g. releasing important datasets in more detail, or making them more reusable), and we expect to have some excellent examples of these types of follow-up works for our launch in May.
Today, we released Scientific Data’s ISA-Tab metadata specification, a document describing in detail the format we use to capture and distribute machine-readable metadata content with our Data Descriptor articles.
Most authors will not need to understand our metadata specification in detail. Metadata records will be created with the help of our in-house curation support, after manuscripts have been peer-reviewed and accepted for publication, and authors will not need to have any special knowledge regarding metadata creation.
Advanced users, however, will be able to submit machine-readable directly with their Data Descriptor manuscripts with the help of this metadata specification. This specification document will also be invaluable to scientists that wish to mine the metadata associated with our publications.
Each published Data Descriptor will be accompanied by machine-readable metadata designed to help advanced users mine and search our content. These metadata will include basic information about the Data Descriptor article, as well as terms that describe key aspects of the experiments or procedures in the study. See Box 1 for a brief outline of this information.
After discussions with the community, and our Advisory Panel, we have decided to share this information under the Creative Commons Zero waiver (CC0), which is designed to free information of copyright restrictions. By applying the CC0 waiver to Data Descriptor metadata, we allow others to reuse it without legal limitation. Indeed, much of the content in these metadata files could be considered collections of “facts”, and may not be copyrightable in the first place – but, there can be substantial legal grey areas. The CC0 licence helps to remove ambiguity. Simply put, we don’t want data miners to have to hire a lawyer before using our metadata.
The main human-readable content of Data Descriptors — the body text of the main article, figures, etc — will remain covered by one of three open-access Creative Commons licences selected by the authors, all of which explicitly require attribution for any reuse (CC BY, CC BY-NC, & CC BY-NC-SA). The actual primary data files associated with Data Descriptors will be stored in one or more external repositories, which will have their own terms of use or licencing policies. Continue reading