scholarly journals Big Data Challenges in Big Science

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Andreas Heiss
Keyword(s):  
Big Data ◽  
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.


2019 ◽  
Vol 95 (6) ◽  
pp. 1326-1337 ◽  
Author(s):  
Julio Saez-Rodriguez ◽  
Markus M. Rinschen ◽  
Jürgen Floege ◽  
Rafael Kramann
Keyword(s):  
Big Data ◽  

Author(s):  
Aidan Lyon

In this chapter, the author reviews some of the philosophical issues that arise in connection with the concept of data. He first asks what data are, and he evaluates a number of different answers to this question that have been given. The author then examines the concept of so-called big data and the corresponding concept of big science. It has been claimed that the advent of big science calls for a fundamental change to science and scientific methodology. The author argues that such claims are too strong. Finally, the author reviews the distinction between data and phenomena, due to Bogen and Woodward 1988, and discusses some of its connections to big data and big science.


ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  

Find Out About 'Big Data' to Track Outcomes


2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.


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