Results 21 to 30 of about 53,144,553 (313)

The Role of Data Science in Web Science [PDF]

IEEE Intelligent Systems, 2016
Web science relies on an interdisciplinary approach that seeks to go beyond what any one subject can say about the World Wide Web. By incorporating numerous disciplinary perspectives and relying heavily on domain knowledge and expertise, data science has emerged as an important new area that integrates statistics with computational knowledge, data ...
Christopher Phethean, Elena Simperl, Thanassis Tiropanis, Ramine Tinati, Wendy Hall 0001   +4 more
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Why geographic data science is not a science [PDF]

Geography Compass, 2020
Abstract “Data Science” has taken many disciplines by storm. And for a good reason: New forms and unseen quantities of data enter nearly every scientific field, substantially changing the ways how scientists do science, and potentially allowing them to answer old questions or to pose them in novel ways.
Scheider, Simon, Nyamsuren, Enkhbold, Kruiger, Han, Xu, Haiqi   +3 more
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Academic Data Science Alliance: Member Book 2022-2023

, 2023
The Academic Data Science Alliance is a community network for data science leaders, practitioners, and educators who take responsibility for a just, equitable future where data science approaches are thoughtfully applied in all domains for the benefit of
Academic Data Science Alliance
core   +1 more source

Data integration with the Climate Science Modelling Language [PDF]

, 2005
The Climate Science Modelling Language (CSML) has been developed by the NERC DataGrid (NDG) project as a standards-based data model and XML markup for describing and constructing climate science datasets. It uses conceptual models from emerging standards
O'Neill K, Lawrence, B., Latham, S., M. Gutierrez, O'Neill, K., Latham S, Woolf, A., Cramer, R., R. Lowry, Stephens AG, S. Latham, Lowe D, Stephens A, Kondapalli, S., Gutierrez M, Gutierrez, M., K. Kleese van Dam, R. Cramer, K. O'Neill, Lawrence B, Woolf A, Kleese van Dam, K., D. Lowe, Lowry R, S. Kondapalli, Lowe, D., Cramer R, Kondapalli S, Lowry, R., Kleese van Dam K, A. Woolf, A. Stephens, B. Lawrence, Stephens, A.   +33 more
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Enabling quantitative data analysis through e-infrastructures [PDF]

, 2009
This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can ...
Sinnott, Richard, Lambert, P.S., Jones, Simon, Maxwell, M., Warner, G., Gayle, V., Tan, K., Lambert, Paul, Tan, K.L.L., Tan, Koon Leai Larry, Bowes, A., Sinnott, Richard O., Bell, D.N.F., Jones, S., Blum, Jesse, Turner, K. J., Turner, K.J., Sinnott, R., Lambert, Paul S., Jones, Simon B., Warner, Guy, Lambert, P, Gayle, Vernon, Jones, S.B., Blum, J., Turner, Kenneth J, Blum, Jesse Michael, Sinnott, R.O., Turner, Ken J.   +28 more
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Towards Data Science

Data Science Journal, 2015
Currently, a huge amount of data is being rapidly generated in cyberspace. Datanature (all data in cyberspace) is forming due to a data explosion. Exploring the patterns and rules in datanature is necessary but difficult.
Yangyong Zhu, Yun Xiong
doaj   +1 more source

Risk Assessment for Scientific Data

Data Science Journal, 2020
Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use.
Matthew S. Mayernik, Kelsey Breseman, Robert R. Downs, Ruth Duerr, Alexis Garretson, Chung-Yi (Sophie) Hou, Environmental Data Governance Initiative (EDGI) and Earth Science Information Partners (ESIP) Data Stewardship Committee   +6 more
doaj   +1 more source

Thoughts on Starting the CODATA Data Science Journal

Data Science Journal, 2023
This essay discusses some of the considerations that led to the founding of the [CODATA] Data Science Journal. Three factors were most relevant to the founding.
John Rumble
doaj   +1 more source

Data science [PDF]

Communications of the ACM, 2017
While it may not be possible to build a data brain identical to a human, data science can still aspire to imaginative machine thinking.
openaire   +5 more sources

Human Data Science

Patterns, 2020
Most data science is about people, and opinions on the value of human data differ. The author offers a synthesis of overly optimistic and overly pessimistic views of human data science: it should become a science, with errors systematically studied and ...
DL Oberski
doaj   +1 more source