The Promise and Potential of Big Data: A Case for Discovery Informatics
Open accessAbstract
The emergence of “big data” offers unprecedented opportunities for not only accelerating scientific advances, but also enabling new modes of discovery. While we understand how to automate routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort. We argue that realizing the full potential of data to accelerate discovery calls for a concerted effort in advancing Discovery Informatics: (i) understanding, formalization, and information processing descriptions of the entire scientific process; (ii) design, development, and evaluation of the computational artifacts (representations and processes) that embody such understanding; and (iii) application of the resulting artifacts and systems to advance science (by augmenting individual or collective human efforts, or by fully automating science).
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Scientific knowledge can also be used to advance other aspects of data science, such as the design of scientific workflows or the generation of model simulations.
“Scientific knowledge can also be used to advance other aspects of data science, e.g., the design of scientific work-flows [83], [84] or the generation of model simulations [85].”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [83]
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Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
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- Authors
- Honavar, V. G.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Honavar, Vasant G.
- ORCID
- Honavar, Vasant G.
- Venue
- Review of Policy Research
- Publisher
- Wiley
- Published
- 2014-07
- Volume
- 31
- Issue
- 4
- Pages
- 326-330
- ISSN
- 1541-132X (ISSN-L)
- 1541-1338
- OpenAlex
- W2126681669
- Cited by
- 24
- References
- 22
- Citations by year
- 2026: 1 · 2022: 1 · 2021: 3 · 2020: 1 · 2019: 4 · 2018: 2 · 2017: 3 · 2016: 5 · 2015: 4
- FWCI
- 7.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Review of Policy Research (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (0711356)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- big data, scientific method, scientific discovery, data management, data analytics, computational artifacts
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