scholarly journals Moving back to the future of big data-driven research: reflecting on the social in genomics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Melanie Goisauf ◽  
Kaya Akyüz ◽  
Gillian M. Martin
Keyword(s):  
Big Data ◽  
Data Driven ◽  
The Social ◽  
The Future

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that in the age of data measured in petabytes, supercomputers and sophisticated algorithms the scientific method is obsolete and that the hypotheses and models are outdated.The strategies of the scientific big data rely on computational analysis strategies of massive amounts of data to reveal correlations, patterns and rules that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.

Ihde’s Missing Sciences

2016 ◽  
Vol 20 (2) ◽  
pp. 137-152
Author(s):  
Daniel Susser ◽  
Keyword(s):  
Big Data ◽  
Information Technologies ◽  
Natural World ◽  
Data Driven ◽  
Human Sciences ◽  
Advanced Technologies ◽  
Contemporary Science ◽  
The Social ◽  
The Family ◽  
The Way

In Husserl’s Missing Technologies, Don Ihde urges us to think deeply and critically about the ways in which the technologies utilized in contemporary science structure the way we perceive and understand the natural world. In this paper, I argue that we ought to extend Ihde’s analysis to consider how such technologies are changing the way we perceive and understand ourselves too. For it is not only the natural or “hard” sciences which are turning to advanced technologies for help in carrying out their work, but also the social and “human” sciences. One set of tools in particular is rapidly being adopted—the family of information technologies that fall under the umbrella of “big data.” As in the natural sciences, big data is giving researchers in the human sciences access to phenomena which they would otherwise be unable to experience and investigate. And like the former, the latter thereby shape the ways those scientists perceive and understand who and what we are. Looking at two case studies of big data-driven research in the human sciences, I begin in this paper to suggest how we might understand these phenomenological and hermeneutic changes.

scholarly journals Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research

Sociology ◽  
2017 ◽  
Vol 51 (6) ◽  
pp. 1132-1148 ◽  
Author(s):  
Susan Halford ◽  
Mike Savage
Keyword(s):  
Big Data ◽  
Social Science ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Data ◽  
The Social ◽  
The Future

Recent years have seen persistent tension between proponents of big data analytics, using new forms of digital data to make computational and statistical claims about ‘the social’, and many sociologists sceptical about the value of big data, its associated methods and claims to knowledge. We seek to move beyond this, taking inspiration from a mode of argumentation pursued by Piketty, Putnam and Wilkinson and Pickett that we label ‘symphonic social science’. This bears both striking similarities and significant differences to the big data paradigm and – as such – offers the potential to do big data analytics differently. This offers value to those already working with big data – for whom the difficulties of making useful and sustainable claims about the social are increasingly apparent – and to sociologists, offering a mode of practice that might shape big data analytics for the future.

scholarly journals Digital Civics

2017 ◽  
Author(s):  
Taylor Shelton
Keyword(s):  
Big Data ◽  
Civic Engagement ◽  
Public Participation ◽  
Digital Technologies ◽  
Status Quo ◽  
Data Driven ◽  
Political Status ◽  
Data Practices ◽  
The Social ◽  
The Way

This chapter reviews some of the key ways that civic engagement and public participation have become increasingly digitized and data-driven. But rather than engaging in debates over the democratic potential of digital technologies, it’s arguably more productive to look at the range of ways that this emerging ‘digital civics’ is reconfiguring how we conceptualize and practice citizenship in the era of big data. The chapter first turns to discussing how citizenship is increasingly defined in relation to data and data practices, and how these redefinitions have precipitated larger changes in the way citizenship is conceptualized and operationalized. Second, the chapter identifies three ongoing, interrelated changes to the digital civics landscape that are worthy of greater attention moving forward. These include the spatialization of digital civics, the corporatization of digital civics, and the growing prominence of oppositional uses of digital civics that seek to challenge the social and political status quo.

scholarly journals Beyond Privacy: Protecting Data Interests in the Age of Artificial Intelligence

2021 ◽  
Vol 3 (1) ◽  
pp. 96-108
Author(s):  
Matt Bartlett
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Economic Value ◽  
Social Dimension ◽  
Data Driven ◽  
Data Control ◽  
The Social ◽  
User Data ◽  
General Data

Serious challenges are raised by the way in which technology companies like Facebook and Google harvest and process user data. Companies in the modern data economy mine troves of data with sophisticated algorithms to produce valuable behavioural predictions. These data-driven predictions provide companies with a powerful capacity to influence and manipulate users, and these risks are increasing with the explosive growth of ‘Big Data’ and artificial intelligence machine learning. This article analyses the extent to which these challenges are met by existing regimes such as Australia and New Zealand’s respective privacy acts and the European Union’s General Data Protection Regime. While these laws protect certain privacy interests, I argue that users have a broader set of interests in their data meriting protection. I explore three of these novel interests, including the social dimension of data, control and access to predictions mined from data and the economic value of data. This article shows how existing frameworks fail to recognise or protect these novel interests. In light of this failure, lawmakers urgently need to frame new legal regimes to protect against the worst excesses of the data economy.

scholarly journals Little Data Streams to the Big Data River: Data-Based Solutions to Non-Data Questions and Their Implications for the CHIA Project

2015 ◽  
Author(s):  
Andrew Behrendt
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Historical Data ◽  
Data Driven ◽  
Clear Pattern ◽  
Social Composition ◽  
The Social ◽  
Historical Text ◽  
Correct Angle

This article offers an example of how a “traditional” reading of an historical text can invite, and be enhanced by, a data-driven analysis. It suggests that historians who do not work primarily in data keep in mind the possibility that their research, viewed from the correct angle, may contribute to the collection of world-historical data. The data on the National Hungarian Weekend Association, overwhelmingly qualitative, nonetheless permitted construction of a useful dataset. The social composition of leadership in the organization revealed an unexpectedly narrow and clear pattern through an orderly investigation of organizational registration lists. 

scholarly journals Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Smart Cities ◽  
Data Driven ◽  
Sustainable Cities ◽  
Short Term ◽  
Science Data ◽  
Urban Computing ◽  
The Future

AbstractSustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

Current Postvention Research and Priorities for the Future

Crisis ◽  
2017 ◽  
Vol 38 (3) ◽  
pp. 202-206 ◽  
Author(s):  
Karl Andriessen ◽  
Dolores Angela Castelli Dransart ◽  
Julie Cerel ◽  
Myfanwy Maple
Keyword(s):  
Social Life ◽  
Online Survey ◽  
Research Priorities ◽  
Future Research ◽  
Physical And Mental Health ◽  
Intercultural Collaboration ◽  
The Social ◽  
The Future ◽  
Suicide Bereavement ◽  
Ethical Approval

Abstract. Background: Suicide can have a lasting impact on the social life as well as the physical and mental health of the bereaved. Targeted research is needed to better understand the nature of suicide bereavement and the effectiveness of support. Aims: To take stock of ongoing studies, and to inquire about future research priorities regarding suicide bereavement and postvention. Method: In March 2015, an online survey was widely disseminated in the suicidology community. Results: The questionnaire was accessed 77 times, and 22 records were included in the analysis. The respondents provided valuable information regarding current research projects and recommendations for the future. Limitations: Bearing in mind the modest number of replies, all from respondents in Westernized countries, it is not known how representative the findings are. Conclusion: The survey generated three strategies for future postvention research: increase intercultural collaboration, increase theory-driven research, and build bonds between research and practice. Future surveys should include experiences with obtaining research grants and ethical approval for postvention studies.

Party preferences and political participation: the emergence of the Turkish-origin German voter

2008 ◽  
Vol 5 (1) ◽  
pp. 21-40
Author(s):  
Vera Eccarius-Kelly
Keyword(s):  
Political Participation ◽  
Voting Behavior ◽  
Green Party ◽  
Missed Opportunities ◽  
Social Democrats ◽  
Political Landscape ◽  
Turkish Origin ◽  
The Social ◽  
The Future ◽  
Specific Interests

The article examines trends in voting preferences and voting behavior of Turkish-origin German voters. Despite only representing a small percentage of the total German electorate, Turkish-origin voters are gaining an opportunity to shape the future political landscape. While the Social Democrats have benefited most directly from the minority constituency so far, this author suggests that the Green Party is poised to attract the younger, better educated, and German-born segment of the Turkish-origin voters. All other dominant national parties have ignored this emerging voting bloc, and missed opportunities to appeal to Turkish-origin voters by disregarding community-specific interests. 

Sign in / Sign up

Export Citation Format

Share Document