The Data Deluge: An e-Science Perspective

By A. Hey, Anne E. Trefethen. 2003

579 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/fd6d8762bd76c58226653af12597fbcb893f183f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Stefan Schweter
2. George K. Karagiannidis
3. James C. Robinson
4. Christine L. Borgman
5. Sami Hakam Muhaidat
6. Geoffrey C. Bowker
7. Paul N. Edwards
8. Matthew S. Mayernik

In Information Systems

1. George K. Karagiannidis
2. Christine L. Borgman
3. Geoffrey C. Bowker
4. Paul N. Edwards
5. Matthew S. Mayernik
6. Archer L. Batcheller

In Information Systems and Management

1. Christine L. Borgman
2. Geoffrey C. Bowker
3. Paul N. Edwards
4. Matthew S. Mayernik
5. Archer L. Batcheller

Citations in the corpus

• Science friction: Data, metadata, and collaboration (2011, background): “Metadata are often described as ‘data about data’ (Hey and Trefethen, 2003) or ‘information about data’ (Michener, 2006).”
• Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0 (2022, background): “, 2021) and the expansive commodification of data (Hey and Trefethen, 2003), have enabled a new mode of historical inquiry that capitalises on the ‘big data of the past’ (Kaplan and Di Lenardo, 2017).”
• Economic implications of alternative scholarly publishing models : exploring the costs and benefits. JISC EI-ASPM Project. A report to the Joint Information Systems Committee (JISC) (2009, background): “problem oriented, multidisciplinary research being conducted in a wider range of settings, and greater use of a wider range of digital objects from images to large data collections (Gibbons et al. 1994; Etzkowitz and Leydesdorff 1997; Hey and Trefethen 2003).”
• Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014, unknown): “big data) and powerful computational resources.(19)”
• Efficient Machine Learning for Big Data: A Review (2015, methodology): “However, when we add the gene sequence data (data on the 100,000 or so translated proteins and the 32,000,000 amino acids), the relevant data volume can easily expand to an order of about 200 giga-byte [11].”
• Bayesian inverse problems for functions and applications to fluid mechanics (2009, background): “The explosion of data gathering over the last few decades has been, and will continue to be, phenomenal [35].”
• Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’ (2014, background): “As these datasets grow exponentially, researchers, politicians, and the private sector have begun to focus on how ‘big data’ might allow potentially unprecedented insights into our world (Hey and Trefethen, 2003; Anderson, 2008; Floridi, 2012).”
• Metabarcoding From Microbes to Mammals: Comprehensive Bioassessment on a Global Scale (2020, background): “As the advancement of genomic tools and other technologies facilitate rapid data generation, we will be confronted with the enormous task of dealing with this deluge of information (Hey and Trefethen, 2003; Bell et al., 2009).”