Data science landscape : towards research standards and protocols
著者
書誌事項
Data science landscape : towards research standards and protocols
（Studies in big data, v. 38）
Springer, c2018
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注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains.
The idea of this work germinated from the two day workshop on "Big and Open Data - Evolving Data Science Standards and Citation Attribution Practices", an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group.
In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume.
目次
Chapter 1. Data Science Landscape - Tracking the Ecosystem.- Chapter 2. Open Data Infrastructure for Research and Development.- Chapter 3. Managing Research Data by R&D community in Nuclear Data Science in India.- Chapter 4. Big Data in Astronomy and Beyond.- Chapter 5. Preserving for a More Just Future: Tactics of Activist Data Archiving.- Chapter 6. Little Data from Big Data for Disaster Risk Reduction in India.- Chapter 7. Data Marketplace as a Platform for Sharing Scientific Data.- Chapter 8. ICSSR Data Service: A National Initiative for Sharing of Social Science Research Data.- Chapter 9. Prismatic Consumer Insights through Big Data: A Case Study of National Consumer Helpline.- Chapter 10. Big Data in the Context of Smart Cities: Exploring Urban Planning and Governance.- Chapter 11. Crowd sourcing for Municipal Governance.- Chapter 12. Effective Business Development for In-Market IT Innovations with Industry-driven API Composition.- Chapter 13. The Data that Get Forgotten.- Chapter 14. Big data and Predictive Analytics: A facilitator of Talent Management.- Chapter 15. Privacy Preserving Data Mining Techniques for Hiding Sensitive Data - A Step Towards Open Data.- Chapter 16. Role of Credible Data in Economic Decision Making.- Chapter 17. Big Data in the International System: Indian, American and Other Perspectives.- Chapter 18. Evolving an Industrial Digital Ecosystem - A Case of Leather Industry.- Chapter 19. Applying Big Data Analytics in Governance to Achieve Sustainable Development Goals (SDGs) in India.- Chapter 20. Open Data: India's Initiative for Researchers, Research and Innovation.- Chapter 21. Data Science Management: Some Perspectives.
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