Scientific data - Springer Nature | 2024 Impact Factor:6.9 | Cite Score:8.4 | Q1

Impact Factor and Journal Rank of Scientific data

• About: Scientific Data is an open-access journal published by Springer Nature that focuses on publishing data descriptors, which are detailed descriptions of datasets. The journal aims to make valuable research data more accessible, discoverable, and reusable by providing a platform for sharing and disseminating datasets across a wide range of scientific disciplines.
• Content Types: Detailed, peer-reviewed articles that describe datasets, including the methodology for data collection and processing, technical validation, and usage notes. These descriptors provide comprehensive context to help other researchers understand and reuse the data. Articles discussing trends, challenges, and advancements in data sharing, data management, and data policies within the scientific community.
• High Standards and Impact: All submissions undergo rigorous peer review to ensure the quality and reliability of the data described. As an open-access journal, Scientific Data ensures that all articles are freely available to the global research community, promoting widespread dissemination and use of the data. Emphasizes adherence to high standards of data quality, documentation, and ethical considerations in data sharing.
• Global Reach: Attracts submissions from researchers worldwide, fostering a global exchange of valuable datasets and best practices in data sharing. Ensures that datasets and their descriptions are accessible to a wide audience, including researchers in developing countries and those outside traditional academic institutions.
• Significance: Scientific Data is a leading open-access journal that plays a critical role in the scientific community by promoting the sharing, discoverability, and reusability of research datasets. Its focus on high-quality data descriptors, interdisciplinary reach, and commitment to open access make it an essential resource for researchers, data scientists, and policymakers dedicated to advancing data-driven science and improving research transparency and reproducibility.

• Journal Home:  Journal Homepage

• Editor-in-Chief:  Guy Jones

• Scope: The journal is dedicated to promoting the sharing and reuse of research data by publishing detailed descriptions of datasets, known as Data Descriptors. These descriptors provide information on the methods used to collect, process, and validate the data, enabling researchers to understand and utilize the datasets effectively. The scope of the journal includes a wide range of scientific disciplines, ensuring comprehensive coverage of various types of data.
• Key areas covered by the journal include, but are not limited to:
• Life Sciences: Datasets related to DNA sequencing, gene expression, genome assembly, and genetic variation. Data on protein expression, protein interactions, metabolite profiling, and metabolic pathways. Datasets on cell structure, function, signaling pathways, and cellular processes. Data on ecosystems, biodiversity, environmental monitoring, and conservation.
• Physical Sciences: Datasets on fundamental physics experiments, astronomical observations, cosmology, and astrophysics. Data related to chemical reactions, molecular structures, spectroscopy, and materials science. Datasets on geology, geophysics, meteorology, oceanography, and climate science.
• Social Sciences and Humanities: Data on cognitive processes, behavioral studies, and psychological assessments. Datasets on social behavior, cultural studies, and human interactions. Data related to economic models, financial markets, political behavior, and public policy.
• Health and Medicine: Datasets on clinical trial outcomes, patient records, disease outbreaks, and public health studies. Data on imaging techniques, diagnostic tests, and biomedical devices. Datasets on drug efficacy, pharmacokinetics, and toxicology.
• Engineering and Technology: Data on mechanical systems, electrical circuits, robotics, and automation. Datasets on algorithms, software systems, cybersecurity, and artificial intelligence. Data on material properties, nanomaterials, and fabrication techniques.
• Multidisciplinary and Interdisciplinary Research: Data on complex networks, system dynamics, and interdisciplinary studies. Datasets that involve large-scale data analysis, machine learning, and data mining techniques. Data on educational methods, learning outcomes, and student assessments.
• Latest Research Topics for PhD in Computer Science

• Print ISSN:  2052-4463

  Electronic ISSN:  

• Abstracting and Indexing:  Scopus, SCIE

• Imapct Factor 2024:  6.9

• Subject Area and Category:  Computer Science,Computer Science Applications,Information Systems,Decision Sciences,Statistics, Probability and Uncertainty Mathematics,Statistics and Probability,Social Sciences,Education,Library and Information Sciences

• Publication Frequency:  Continuous

• H Index:  142

• Best Quartile:

  Q1:  Computer Science Applications

  Q2:  

  Q3:  

  Q4:  

• Cite Score:  8.4

• SNIP:  2.113

• Journal Rank(SJR):  1.867

• Latest Articles:   Latest Articles in Scientific data

• Guidelines for Authors: Scientific data Author Guidelines

• Paper Submissions: Paper Submissions in Scientific data

• Publisher:  Nature Publishing Group

  Country:  United Kingdom

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