期刊信息

Scientific Data

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影响因子:
6.9
出版商:
Springer
ISSN:
2052-4463
浏览:
19753
关注:
0

征稿

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.
由 Dou Sun 最后更新于

Special Issues

Special Issue on Transcriptomics data from clinical isolates 截稿日期: 2026-07-08 Transcriptomic datasets from clinical isolates provide vital insights into pathogen biology, host-pathogen interactions, and antimicrobial resistance. Captured directly from patient-derived samples, these datasets reflect gene expression patterns shaped by complex clinical environments. This Collection features articles describing transcriptomic data from a range of microbial pathogens, including bacteria, viruses, fungi, and parasites isolated in clinical settings. Submissions are presented without hypotheses or conclusions, with a focus on dataset validation to ensure quality and reusability. Data may be generated using RNA sequencing, microarrays, or other transcriptomic technologies, and should be accompanied by detailed descriptions of sample origin and processing methods. Authors submitting datasets involving human participants must complete the Human Data Checklist and include it with their manuscript files.
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Special Issue on Genomics in freshwater and marine science 截稿日期: 2026-07-23 Freshwater and marine ecosystems support a rich diversity of organisms, many of which remain genomically understudied. Advances in transcriptomics and genome assembly are enabling deeper insights into the physiology, adaptation, and evolutionary history of aquatic species, from phytoplankton to fish and benthic invertebrates. This Collection features articles describing transcriptomic datasets and genome assemblies from freshwater and marine taxa. Datasets are presented without hypotheses or conclusions, and analyses should focus on validating the data to ensure quality, reproducibility, and reusability. Submissions should include detailed metadata on sample collection, sequencing technologies, and assembly methods to support transparency and reuse across the research community.
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Special Issue on Plant transcriptomic responses 截稿日期: 2026-07-28 Studying plant transcriptomic responses allows us to understand how plants regulate gene expression in response to environmental changes, stress, and developmental processes. Predominantly through RNAseq, researchers can identify genes involved in traits such as drought resistance, disease defence, and growth regulation. These datasets help improve crop resilience, enhance agricultural productivity, and inform breeding strategies for climate adaptation. Additionally, transcriptomic data provides insights into plant signalling pathways, metabolic processes, and responses to biotic and abiotic stressors. This Collection presents a series of such datasets, as this information is crucial for developing sustainable agricultural practices, conserving plant biodiversity, and advancing biotechnology for food security and ecosystem health.
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Special Issue on Biogeochemistry in terrestrial environments 截稿日期: 2026-07-29 Terrestrial biogeochemistry plays a critical role in regulating Earth’s climate, nutrient cycles, and ecosystem health. As environmental change accelerates, the availability of high-quality, well-documented datasets is essential for monitoring, modelling, and managing these complex systems. We invite Data Descriptors that detail the collection, curation, and validation of datasets from forests, grasslands, wetlands, agricultural systems, and other land-based environments. Relevant datasets may include, but are not limited to, measurements of soil chemistry, greenhouse gas fluxes, nutrient cycling, plant-soil interactions, microbial community composition, and long-term ecological monitoring. Submissions should provide clear documentation of sampling methods, analytical protocols, data quality assurance, and repository access. By bringing together openly accessible datasets from across the terrestrial biosphere, this Collection aims to support reproducible research and foster interdisciplinary collaboration in ecology, environmental science, and Earth system science. We encourage contributions from researchers, field stations, monitoring networks, and institutions working to make terrestrial biogeochemical data more transparent, standardised, and reusable.
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Special Issue on Glaciology, sea ice and inland waters ice 截稿日期: 2026-08-12 Glaciers, sea ice, lake ice, and river ice are critical components of the Earth system, influencing global sea levels and ocean circulation. Their rapid transformation due to climate warming has significant environmental and societal consequences, including rising sea levels, altered freshwater and surface energy fluxes, disrupted marine and riverine ecosystems, and changes in regional weather patterns and local transport routes. Observing and understanding the dynamics of glaciers, sea ice, and river ice is essential for tracking climate change and predicting its global impacts. This Collection features a series of articles describing datasets related to glaciology, sea ice, and river ice. Datasets are presented without hypotheses or conclusions, and analyses should focus on validating the dataset to ensure reliability and quality for reuse within the research community. Data may be sourced from satellite remote sensing, field measurements, autonomous platforms, or numerical models, with Data Descriptors providing full details of data acquisition, processing methods, and any associated computational tools.
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Special Issue on 10 years of the FAIR principles 截稿日期: 2026-08-14 The FAIR principles, which provide foundational guidance for sharing data and metadata, were published in Scientific Data in 2016 (Wilkinson, et al). Since then, the core concepts of Findable, Accessible, Interoperable, and Reusable data have become embedded in the design of key infrastructure across the data-sharing ecosystem, including repositories, data standards, policies, and workflows for curating and sharing machine-readable data. To celebrate this milestone, Scientific Data invites researchers to submit manuscripts related to FAIR-aligned infrastructure, policy, or standardisation to this collection. Submissions are welcome under our "Article" manuscript type only (see Aims and Scope).
由 Dou Sun 最后更新于
Special Issue on Datasets in education 截稿日期: 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.
由 Dou Sun 最后更新于
Special Issue on Data from Unmanned Aerial Vehicles (UAVs) 截稿日期: 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.
由 Dou Sun 最后更新于
Special Issue on Data from archaeological samples, museum collections and historical records 截稿日期: 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.
由 Dou Sun 最后更新于

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