Journal Information

Scientific Data

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Impact Factor:
6.9
Publisher:
Springer
ISSN:
2052-4463
Viewed:
22041
Tracked:
1

Call For Papers

Scientific Data is an academic journal published by Springer. (ISSN 2052-4463, impact factor 6.9).

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. We believe that: Open research data sharing is crucial for facilitating scientific advancement. Articles describing how to source and use data can greatly facilitate data discovery and reuse, underpinning the aims of the FAIR principles, across any subject discipline. If data are scientifically valid and of potential interest to someone it should be described and made available to the research community. Manuscripts must make an original contribution but they are not assessed based on their perceived significance, importance or impact. This is especially true for Data Descriptors (see below), which do not present hypotheses or conclusions.
Last updated by Dou Sun on

Special Issues

Special Issue on Datasets in education Submission Date: 2026-08-19 Education research increasingly relies on data to inform policy, improve pedagogy, and understand learning outcomes. This Collection invites Data Descriptors that describe the generation, curation, and validation of open datasets related to educational systems, practices, and outcomes across diverse contexts and populations. We welcome submissions presenting datasets from classroom observations, standardized assessments, digital learning platforms, educational interventions, teacher training programs, curriculum analyses, and more. Relevant data might include student performance metrics, longitudinal learning trajectories, multimodal data from learning environments (e.g., video, eye-tracking, keystroke logs), or survey responses from students, educators, or institutions. Submissions should include detailed methodological descriptions and information about data repositories to ensure accessibility and reuse. We are especially interested in datasets that support equity in education, cross-cultural comparisons, or interdisciplinary approaches—such as those integrating education with psychology, linguistics, or technology-enhanced learning. Ethical considerations, including anonymisation and consent, must be thoroughly addressed.
Last updated by Dou Sun on
Special Issue on Data from Unmanned Aerial Vehicles (UAVs) Submission Date: 2026-09-01 This collection welcomes submissions describing datasets generated using Unmanned Aerial Vehicles (UAVs), also known as drones, across a wide range of scientific disciplines. UAVs have become indispensable tools for data acquisition in fields such as environmental monitoring, agriculture, archaeology, disaster response, urban planning, and beyond. Their ability to capture high-resolution spatial, spectral, and temporal data has opened new frontiers in research and resource management. We invite Data Descriptors that detail the collection, processing, and validation of UAV-derived datasets, including but not limited to aerial imagery, LiDAR scans, thermal and multispectral data, 3D reconstructions, and time-series observations. Submissions should clearly document the methods used for data acquisition, calibration, georeferencing, and quality control, as well as provide information on data accessibility and reuse potential. By assembling high-quality, well-documented UAV datasets, this collection aims to support reproducible research and foster cross-disciplinary collaboration. We encourage contributions from researchers, engineers, and practitioners working to advance the use of UAVs in scientific data collection and to make these valuable datasets openly available to the global research community.
Last updated by Dou Sun on
Special Issue on Data from archaeological samples, museum collections and historical records Submission Date: 2026-09-08 This Collection invites submissions that describe datasets derived from the rich and diverse sources of our shared past, including: archaeological samples, museum holdings, and historical records. These datasets are vital for understanding human history, cultural evolution, and environmental change across time. We welcome Data Descriptors that detail the curation, digitisation, and validation of data from excavated materials, artefact inventories, archival documents, and other historical sources. Submissions may include datasets from archaeological fieldwork, radiocarbon dating, palaeoenvironmental reconstructions, digitized museum catalogues, historical maps, and transcribed archival records. All datasets should be openly accessible, reusable, and accompanied by rigorous documentation of their provenance, structure, and quality control processes. We encourage contributions from researchers, curators, archivists, and data stewards working to preserve and share the legacy of historical and archaeological data.
Last updated by Dou Sun on

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