Data Science in Science (DSS)

Data Science in Science is an open access, international journal publishing original research and reviews at the intersection of Science and Data Science. Its aim is to advance: new ideas for experimental and observational data-driven learning and discovery that help address fundamental questions at the frontiers of Science and scientific inference; quantification and summarization of uncertainty from data-driven theories and complex Data Science models, algorithms, and workflows; and new practices for scientific reproducibility and replicability enabled through Data Science. It promotes the intrinsically multidisciplinary nature of the field of Data Science and seeks explicitly science-driven advances in Data Science, and their novel, significant, or transformative applications to Science. It fosters dedicated collaboration and convergence between the broadly defined fields of Science and Data Science through: (i) deep domain, (ii) broader inter-domain, and (iii) trans-domain collaborative research. It encourages collective scientific learning through new collaborative and scientific methods and theories that have the potential to inform the knowledge among and strengthen the data practices of domain and data scientists.

Data Science in Science (DSS) is :-

• International, Peer-Reviewed, Open Access, Refereed, deep domain, broader inter-domain, Data Science, trans-domain collaborative research, insightful applications across disciplines, specific considerations , Online , Continuously Journal

• UGC Approved, ISSN Approved: P-ISSN E-ISSN: 2694-1899, Established: 2022,
• Provides Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE