Beyond the Data Deluge

By Gordon Bell, Tony (Anthony) John Grenville Hey, A. Szalay. 2009

573 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management

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Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “care records, the deluge of data [1], [2], [3] has pervaded every walk of life.”
Science friction: Data, metadata, and collaboration (2011, background): “…emerging ‘fourth paradigm’ of data-driven, interdisciplinary research is augmenting the existing paradigms of experimental, theoretical, and computational science (Atkins and National Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure, 2003; Bell et al., 2009; Hey et al., 2009).”
ScholarBERT: Bigger is Not Always Better (2022, background): “…rate of scientiﬁc publication places a tremendous cognitive burden on researchers (Teplitskiy et al., 2022), as valuable information is now being created at a rate faster than any individual researcher can process (Price, 1963; Bornmann and Mutz, 2015; Hey and Tre-fethen, 2003; Bell et al., 2009).”
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).”
Principal Component Analysis (2018, methodology): “Such trends are now known as the data deluge [Bell et al. 2009]. However, such vast quantities of data are, by themselves, of no great avail unless appropriate methods are applied to identify the most relevant information contained in such repositories, a process known as data mining [Hand 2007]. In”
Collaboration and Productivity in Scientific Synthesis (2011, background): “Second, many disciplines now grapple with a situation that would have seemed ludicrous even two decades ago: too much data (Carlson 2006, Bell et al. 2009).”
Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter (2011, background): “. Accurate description through quantitative measurement is essential to the advancement of any scientific field, and the shift from being data scarce to data rich has revolutionized many areas”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, background): “Data-intensive science [18] is emerging as the fourth scientific paradigm in terms of the previous three, namely empirical science, theoretical science and computational science.”
Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “Moving beyond the commercial sphere, many have recognized the importance of data management in many scientific disciplines, where petabyte-scale datasets are also becoming increasingly common [21].”
FireWorks: a dynamic workflow system designed for high‐throughput applications (2015, background): “Scientists are embarking on a new paradigm for discovery employing massive computing to generate and analyze large data sets [1].”
Big Data computing and clouds: Trends and future directions (2013, background): “This data deluge is often referred to as Big Data [12, 13, 14]; a term that conveys the challenges it poses on existing infrastructure in respect to storage, management, interoperability, governance, and analysis of the data.”