News & Comment

A significant challenge facing rare disease communities is finding a sufficient quantity and variety of data to develop and test disease-specific hypotheses. Here we describe an approach to data sharing in which stakeholders from the neurofibromatosis (NF) research community collaborated to develop a disease-focused data portal with the goal of supporting scientists within and outside the community as well as clinicians and patient advocates.

We outline a principled approach to data FAIRification rooted in the notions of experimental design, and whose main intent is to clarify the semantics of data matrices. Using two related metabolomics datasets associated to journal articles, we perform retrospective data and metadata curation and re-annotation, using community, open, interoperability standards. The results are semantically-anchored data matrices, deposited in public archives, which are readable by software agents for data-level queries, and which can support the reproducibility and reuse of the data underpinning the publications.

In the past decade, there has been a surge in the number of sensitive human genomic and health datasets available to researchers via Data Access Agreements (DAAs) and managed by Data Access Committees (DACs). As this form of sharing increases, so do the challenges of achieving a reasonable level of data protection, particularly in the context of international data sharing. Here, we consider how excessive variation across DAAs can hinder these goals, and suggest a core set of clauses that could prove useful in future attempts to harmonize data governance.

Climate change cannot be addressed without improving the energy efficiency of the buildings in which we live and work. The papers in this collection describe and release a series of datasets that help us understand how occupants influence and experience building energy use, both to aid future research and policy-development, and to spark wider data sharing in this important area.

A special collection on multi-omics data sharing, launched today at Scientific Data, offers to the scientific community a compendium of multi-omics datasets ready for reuse, which showcase the diversity of multi-omics projects and highlights innovative approaches for preprocessing, quality control, hosting and access.

UniProt continues to support the ongoing process of making scientific data FAIR. Here we contribute to this process with a FAIRness assessment of our UniProtKB dataset followed by a critical reflection on the challenges and future directions of the adoption and validation of the FAIR principles and metrics.

The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard.

The Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

Although increasingly recognized as critical to genomic research, genomic data sharing is hindered by an absence of standards regarding timing, patient privacy, use agreement standards, and data characterization and quality. Only after months of identifying, permissioning for use, committing to terms restricting use and sharing, downloading, and assessing quality, is it possible to know whether or not a dataset can be used. In this paper, we evaluate the barriers to data sharing based on the Treehouse experience and offer recommendations for use agreement standards, data characterization and metadata standardization to enhance data sharing and outcomes for all pediatric cancer patients.

Scientific Data published its first batch of papers five years ago this week. Here, we reflect on our progress and thank all those that have helped us along the way.

There is an urgent need to improve integrity of large industrial infrastructure. Sharing data can support better understanding of accidents such as recent mining dam collapses, making them less likely to occur, and contributing to sustainability.

The number of chemical compounds and associated experimental data in public databases is growing, but presently there is no simple way to access these data in a quick and synoptic manner. Instead, data are fragmented across different resources and interested parties need to invest invaluable time and effort to navigate these systems.

The past two decades have seen a revolution in digital imaging techniques for capturing gross morphology, offering an unprecedented volume of data for biological research. Despite the rapid increase in scientific publications incorporating those images, the underlying datasets remain largely inaccessible. As the technical barriers to data sharing continue to fall, we face a more intimate, and perhaps more complicated, obstacle to open data – the one in our minds.

Starting last month, publications at Scientific Data now include data citations in the main reference list, rather than in a separate data citations section. This change will be supported by changes to the underlying structure of our content to promote machine readability and reuse of links between scholarly articles and datasets. This aligns the journal with a roadmap for data citation co-developed by representatives of the academic community and several publishers, which seeks to make data citation a standard part of the scholarly publishing process.