scholarly journals Czy GIS podnosi rangę dyscyplin geograficznych? Znaczenie GIS i GIScience dla geografii

2018 ◽  
pp. 5-23
Author(s):  
Piotr Werner
Keyword(s):  
Social Sciences ◽  
Data Driven

Geografowie postrzegają technologie informacyjne i komunikacyjne, w tym oprogramowanie GIS, jako element nieuchronnych przemian w geografii. Część uważa, że są instrumentami pomocniczymi geografii, część zauważa „cyfrowy zwrot” (digital turn), który odnawia zainteresowanie i związki z takimi dziedzinami, jak computational social sciences oraz data-driven geography w celu uzyskania głębszego wglądu w badania ilościowe, w skali czasowej, wielorozdzielcze i wieloskalowe. Współcześnie różnorodność cyfrowych urządzeń, platform, aplikacji i usług jest nieodłącznym, normalnym i oczekiwanym elementem codziennego życia, a technologie cyfrowe są również standardem medialnym generowania i analizy wiedzy w badaniach jakościowych. W miarę postępu, komercjalizacji i popularyzacji technologii geoprzestrzennych, one same przyczyniają się do rozwoju ontologii i epistemologii przestrzennych. Analiza semantyczna artykułów opublikowanych w najważniejszych czasopismach naukowych dedykowanych różnym dyscyplinom geografii (w latach 2014–2018) dowodzi, że w geograficznym dyskursie naukowym najbardziej aktywne jawią się czasopisma geograficzne podejmujące tematykę interdyscyplinarną, metodologiczną lub z zakresu geografii stosowanej.

scholarly journals Big data need big theory too

2016 ◽  
Vol 374 (2080) ◽  
pp. 20160153 ◽  
Author(s):  
Peter V. Coveney ◽  
Edward R. Dougherty ◽  
Roger R. Highfield
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Conceptual Knowledge ◽  
Structural Characteristics ◽  
Multiscale Modelling ◽  
Neural Nets ◽  
Data Driven ◽  
Scientific Methods

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their ‘depth’ and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote ‘blind’ big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.

scholarly journals O fim da teoria: o confronto entre a pesquisa orientada por dados e a pesquisa orientada por hipóteses | The end of theory: the confrontation between data-driven research and hypothesis-driven research

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Luís Fernando Sayão ◽  
Luana Farias Sales
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Scientific Method ◽  
Computational Analysis ◽  
Data Driven ◽  
Present Essay ◽  
Contemporary Science ◽  
The Social ◽  
Analysis Strategies ◽  
New Knowledge

RESUMO A ciência contemporânea e seus fundamentos metodológicos têm sido impactados pelo fenômeno do big data, que proclama que na era dos dados medidos em petabytes, de supercomputadores e sofisticados algoritmos, o método científico está obsoleto e que as hipóteses e modelos estão superados. As estratégias do big data científico confia em estratégias de análises computacionais de massivas quantidades de dados para revelar correlações, padrões e regras que vão gerar novos conhecimentos, que vão das ciências exatas até as ciências sociais, humanidade e cultura, delineando um arquétipo de ciência orientada por dados. O presente ensaio coloca em pauta as controvérsias em torno da ciência orientada por dados em contraposição à ciência orientada por hipóteses, e analisa alguns dos desdobramentos desse embate epistemológico. Para tal, tomo como metodologia os escritos de alguns autores mais proximamente envolvidos nessa questão.Palavras-chave: Big Data; Método Cientifico; Ciência Orientada por Dados; Ciência Orientada por Hipóteses.ABSTRACT Contemporary science and its methodological foundations have been impacted by the big data phenomenon that proclaims that in the age of data measured in petabytes, supercomputers and sophisticated algorithms the scientific method is obsolete and that the hypotheses and models are outdated.The strategies of the scientific big data rely on computational analysis strategies of massive amounts of data to reveal correlations, patterns and rules that will generate new knowledge, ranging from the exact sciences to the social sciences, humanity and culture, outlining an archetype of data-driven science. The present essay addresses the debates around data-driven science as opposed to hypothesis-oriented science and analyzes some of the ramifications of this epistemological confrontation. For this, the writings of some authors who are more closely involved in this question are taken as methodology.Keywords: Big Data; Scientific Method; Data-Driven Science; Hypothesis-Driven Science.

scholarly journals AIMSS: An Architecture for Data Driven Simulations in the Social Sciences

2007 ◽  
pp. 1098-1105 ◽  
Author(s):  
Catriona Kennedy ◽  
Georgios Theodoropoulos ◽  
Volker Sorge ◽  
Edward Ferrari ◽  
Peter Lee ◽  
...  
Keyword(s):  
Social Sciences ◽  
Data Driven ◽  
The Social

scholarly journals Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities

2021 ◽  
Vol 13 (3) ◽  
pp. 59
Author(s):  
Albert Weichselbraun ◽  
Philipp Kuntschik ◽  
Vincenzo Francolino ◽  
Mirco Saner ◽  
Urs Dahinden ◽  
...  
Keyword(s):  
Social Sciences ◽  
Domain Knowledge ◽  
Data Driven ◽  
Media Criticism ◽  
Domain Experts ◽  
Social Sciences And Humanities ◽  
The Social ◽  
Recent Developments ◽  
Selection Of

Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.

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