Data Sharing and Privacy in Neuroinformatics (Scopus) Dataset
Description
The Data Sharing and Privacy in Neuroinformatics Dataset was curated from the widely recognized Scopus academic database. It includes data from 4,245 research articles in English across 28 academic disciplines, such as Medicine, Computer Science, Neuroscience, Engineering, and Biochemistry, Genetics, and Molecular Biology. The dataset spans publications from 2002 through January 18, 2024, and is unrestricted by publication type, encompassing diverse research outputs, including articles, conference papers, reviews, book chapters, editorials, books, and more. Each document in the dataset includes six attributes: Title, Year, DOI, Abstract, Author Keywords, and Index Keywords. This dataset was developed to identify parameters relevant to the academic perspectives on data sharing and privacy in neuroinformatics. It is part of our comprehensive research and development strategy focused on multiperspective parameter discovery and autonomous systems development [1]. Our approach leverages big data, deep learning, and digital media to explore and analyze cross-sectional, multi-perspective insights, supporting improved decision-making and more effective governance frameworks. These perspectives span academic, public, industrial, and governmental domains. We have applied this approach across various fields and sectors, including energy[2], education[3], healthcare[4]–[6], transportation[7], labor markets[8], [9], tourism [10], service industries [11], and others. References [1] doi: 10.54377/95e5-08b3 [2] doi: 10.3389/FENRG.2023.1071291. [3] doi: 10.3389/FRSC.2022.871171/BIBTEX. [4] doi: 10.3390/SU14063313. [5] doi: 10.3390/TOXICS11030287. [6] doi: 10.3390/app10041398. [7] doi: 10.3390/SU14095711. [8] doi: 10.3390/JOURNALMEDIA4010010. [9] doi: 10.1177/00368504231213788. [10] doi: 10.3390/SU15054166. [11] doi: 10.3390/SU152216003.