Aims and scope

Scientific Data is an open access journal dedicated to data, publishing descriptions of research datasets and articles on research data sharing from all areas of natural sciences, medicine, engineering and social sciences. It aims to advance the sharing and reuse of scientific data, promote wider data sharing and reuse, and to credit those that share.

Find out more about the key principles that drive Scientific Data

Scientific Data primarily publishes Data Descriptors. These provide detailed descriptions of research datasets, including the methods used to collect the data and technical analyses supporting the quality of the measurements. Data Descriptors focus on helping others reuse data, rather than testing hypotheses, or presenting new interpretations, methods or in-depth analyses.

Scientific Data also welcomes original articles on the technologies, policies, data standands, platforms and workflows used to support data sharing within a repository use case. 

Scientific Data uses a thorough peer-review process that evaluates the rigour and quality of the experiments used to generate the data and the completeness of the description of the data. The actual data are stored in one or more public, community-recognized repositories, and release of the data is verified as a condition of publication.

Data Descriptors may describe data from new or published studies, and can be published alongside traditional research works. Data Descriptors that describe previously published datasets should provide new content sufficient to merit further publication: for example, updates to important datasets, fuller release of a dataset, or additional information that aids reuse. Please see our policies on complementary and prior publication.

Special Issue on Data for epigenetics research
截稿日期: 2024-09-30

Epigenetics research over the last 20 years has led to an extraordinary range of discoveries, which are continuing to grow in number and scope. Fields of research span biochemistry, molecular biology, genomics, epigenomics, transcriptomics, medicine, therapeutics, our understanding of reproduction and ageing, developmental biology and evolutionary biology. This Collection presents data within epigenetics research including, but not limited to, data generated through techniques such as ChIP-seq, ATAC-seq, whole-genome DNA methylation such as WGBS or RRBS, RNA sequencing, single-cell RNA-seq (scRNA-seq), single-cell epigenetics/epigenomics, spatial transcriptomics/epigenomics, and the role of non-coding RNAs in epigenetic modulation.

Special Issue on Politics and elections data
截稿日期: 2024-09-30

Trust in election processes and government function are vital to democratic society, with the need to perform and disseminate research into their workings crucial for improving our understanding and awareness of democratic functions. However, access to data at sufficient levels of privacy, granularity and temporal/regional specificity can very across jurisdictions, limiting our ability to such conduct studies. This article Collection aims to showcase recent efforts in election and political science data creation, describing datasets sourced from primary government data sources or quantitative research methods with the aim of advancing areas such as of policy, political engagement and other fields.

Special Issue on Epidemiological data
截稿日期: 2024-09-30

Epidemiological data collection plays a crucial role in advancing public health research and guiding evidence-based interventions. These datasets serve as foundational resources for studying disease patterns, risk factors, and health outcomes across populations, species, and environments. This Collection presents a series of articles describing epidemiological datasets spanning diverse populations, ecosystems, and disease contexts. Data are presented without hypotheses or significant analyses, and can be derived from population surveys, health registries, electronic health records, field sampling, or other sources. All described datasets are assessed to ensure their open availability (where possible) or secure access controls (where required) via Scientific Data’s editorial and peer review processes.

Special Issue on Neuroscience data to understand human behaviour
截稿日期: 2024-10-31

The coupling of neuroscience techniques with experiments assessing psychology, cognitive ability, and social and demographic factors have been instrumental in advancing our understanding of human behaviour. This Collection presents descriptions of datasets combining brain imaging or neurophysiological data performed alongside real-world tasks or exposure to different stimuli. It will cover single- or multi-modal studies from humans or animal models.

Special Issue on Datasets for gait analysis
截稿日期: 2025-03-20

Gait analysis plays an essential role in advancing medical research and improving clinical interventions for conditions affecting mobility and balance. This Collection of articles focuses on curated datasets related to gait patterns, encompassing a variety of populations and health contexts. Datasets are presented without hypotheses or conclusions, and analyses should focus on validating the dataset to ensure reliability and quality for reuse in the community.

Data sources may include information from clinical assessments, motion capture technology, and wearable sensors, among others. For all datasets that contain data from participants, please complete our Human Data Checklist at the time of submission and supply a copy with the manuscript files.