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 Datasets for gait analysis
Submission Date: 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.