scholarly journals Big science, big data, and a big role for biomedical informatics

2012 ◽  
Vol 19 (e1) ◽  
pp. e1-e1 ◽  
Author(s):  
L. Ohno-Machado
Author(s):  
Catherine Jayapandian ◽  
Chien-Hung Chen ◽  
Aman Dabir ◽  
Samden Lhatoo ◽  
Guo-Qiang Zhang ◽  
...  

JAMIA Open ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Philip R O Payne ◽  
Elmer V Bernstam ◽  
Justin B Starren

Abstract There are an ever-increasing number of reports and commentaries that describe the challenges and opportunities associated with the use of big data and data science (DS) in the context of biomedical education, research, and practice. These publications argue that there are substantial benefits resulting from the use of data-centric approaches to solve complex biomedical problems, including an acceleration in the rate of scientific discovery, improved clinical decision making, and the ability to promote healthy behaviors at a population level. In addition, there is an aligned and emerging body of literature that describes the ethical, legal, and social issues that must be addressed to responsibly use big data in such contexts. At the same time, there has been growing recognition that the challenges and opportunities being attributed to the expansion in DS often parallel those experienced by the biomedical informatics community. Indeed, many informaticians would consider some of these issues relevant to the core theories and methods incumbent to the field of biomedical informatics science and practice. In response to this topic area, during the 2016 American College of Medical Informatics Winter Symposium, a series of presentations and focus group discussions intended to define the current state and identify future directions for interaction and collaboration between people who identify themselves as working on big data, DS, and biomedical informatics were conducted. We provide a perspective concerning these discussions and the outcomes of that meeting, and also present a set of recommendations that we have generated in response to a thematic analysis of those same outcomes. Ultimately, this report is intended to: (1) summarize the key issues currently being discussed by the biomedical informatics community as it seeks to better understand how to constructively interact with the emerging biomedical big data and DS fields; and (2) propose a framework and agenda that can serve to advance this type of constructive interaction, with mutual benefit accruing to both fields.


2014 ◽  
Vol 57 (7) ◽  
pp. 13-15 ◽  
Author(s):  
Alex Wright
Keyword(s):  
Big Data ◽  

2010 ◽  
Vol 40 (2) ◽  
pp. 183-224 ◽  
Author(s):  
Elena Aronova ◽  
Karen S. Baker ◽  
Naomi Oreskes

This paper discusses the historical connections between two large-scale undertakings that became exemplars for worldwide data-driven scientific initiatives after World War II: the International Geophysical Year (1957––1958) and the International Biological Program (1964––1974). The International Biological Program was seen by its planners as a means to promote Big Science in ecology. As the term Big Science gained currency in the 1960s, the Manhattan Project and the national space program became paradigmatic examples, but the International Geophysical Year provided scientists with an alternative model: a synoptic collection of observational data on a global scale. This new, potentially complementary model of Big Science encompassed the field practices of ecologists and suggested a model for the natural historical sciences to achieve the stature and reach of the experimental physical sciences. However, the program encountered difficulties when the institutional structures, research methodologies, and data management implied by the Big Science mode of research collided with the epistemic goals, practices, and assumptions of many ecologists. By 1974, when the program ended, many participants viewed it as a failure. However, this failed program transformed into the Long-Term Ecological Research program. Historical analysis suggests that many of the original incentives of the program (the emphasis on Big Data and the implementation of the organizational structure of Big Science in biological projects) were in fact realized by the program's visionaries and its immediate investigators. While the program failed to follow the exact model of the International Geophysical Year, it ultimately succeeded in providing a renewed legitimacy for synoptic data collection in biology. It also helped to create a new mode of contemporary science of the Long Term Ecological Research (LTER Network), used by ecologists today.


2015 ◽  
Vol 17 (4) ◽  
pp. 63-65 ◽  
Author(s):  
James J. Hack ◽  
Michael E. Papka
Keyword(s):  
Big Data ◽  

Author(s):  
Bradley E. Alger

This chapter reviews distinctions between kinds of science, which is especially relevant to the book’s topic because it is an area that Karl Popper did not consider in detail. This creates a problem since critics of the hypothesis often do not distinguish between true hypothesis-based science and other kinds that don’t depend on hypotheses, and the traditional divisions of science miss the main points. The chapter distinguishes among several modern kinds of science including Big Science/Small Science and how they relate to Big Data and Little Data, and why Discovery Science is different from hypothesis-testing science. It separates “exploratory” from “confirmatory” studies and explains why this terminology can create confusion in trying to understand science. The differences between applied and basic science are genuine and meaningful because these two kinds of science have different goals and apply different, though overlapping, standards to achieve their goals.


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