TY  - SER
AU  - Chawla,Nitesh V.
AU  - Chawla,Nitesh V.
AU  - Chawla,Nitesh V.
ED  - SpringerLink (Online service)
TI  - Discover Data
SN  - 2731-6955
CY  - Cham
PB  - Springer International Publishing, Imprint: Discover.
KW  - Data structures (Computer science)
KW  - Information theory
KW  - Data protection
KW  - Quantitative research
KW  - Sampling (Statistics)
KW  - Artificial intelligence—Data processing
KW  - Data Structures and Information Theory
KW  - Data and Information Security
KW  - Data Analysis and Big Data
KW  - Methodology of Data Collection and Processing
KW  - Data Science
N2  - Discover Data is a broad, open access journal publishing research into the theory and application of data science and data analytics across all fields of research. The journal welcomes submission of papers detailing the application of existing data science-driven techniques to novel problems in science, industry and society, as much as primary research into data theory, management and analysis. The journal also welcomes the submission of data notes describing novel, open datasets and the methodologies used in the acquisition and processing of these. These datasets should have scientific value to the research community, and must be available for reuse as per the FAIR criteria. The journal aims to serve a wide range of communities from computer and information scientists and statisticians as well as researchers engaged with data-driven approaches to all areas of science, including the natural sciences, clinical and medical research and social science disciplines. The journal particularly welcomes research that contributes to achieving the global aims of the United Nations Sustainable Development Goals. Topics Discover Data covers topics including (but not limited to) the following primary areas: data collection and data acquisition data processing data analysis data maintenance and data integrity data curation data management systems data compression As well as data-driven applications to natural sciences healthcare and medicine humanities and social sciences Content Types Discover Data welcomes a variety of article types – please see our submission guidelines for details. The journal also publishes guest-edited Topical Collections of relevance to all aspects of data science research. For more information, please follow up with our journal publishing contact
UR  - https://doi.org/10.1007/44248.2731-6955
ER  -