A view from anthropology: Should anthropologists fear the data machines?

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110436
Author(s):  
Kristoffer Albris ◽  
Eva I Otto ◽  
Sofie L Astrupgaard ◽  
Emilie Munch Gregersen ◽  
Laura Skousgaard Jørgensen ◽  
...  
Keyword(s):  
Data Science ◽  
Ethnographic Methods ◽  
Social Data ◽  
Computational Tools ◽  
Future Role ◽  
Digital Methods

If you are an anthropologist wanting to use digital methods or programming as part of your research, where do you start? In this commentary, we discuss three ways in which anthropologists can use computational tools to enhance, support, and complement ethnographic methods. By presenting our reflections, we hope to contribute to the stirring conversations about the potential future role(s) of (social) data science vis-a-vis anthropology and ethnography, and to inspire other anthropologists to take up the use of digital methods, programming, and computational tools in their own research.

scholarly journals Correction to: An ethico-legal framework for social data science

2021 ◽  
Author(s):  
Nikolaus Forgó ◽  
Stefanie Hänold ◽  
Jeroen van den Hoven ◽  
Tina Krügel ◽  
Iryna Lishchuk ◽  
...  
Keyword(s):  
Data Science ◽  
Legal Framework ◽  
Social Data

Social Data Science Xennials

2020 ◽  
Author(s):  
Gian Marco Campagnolo
Keyword(s):  
Data Science ◽  
Social Data

Critical Digital Humanities

2019 ◽  
Author(s):  
James E. Dobson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Methods ◽  
Digital Humanities ◽  
Data Science ◽  
Computational Thinking ◽  
Major Question ◽  
Computational Tools ◽  
Multiple Dimensions ◽  
Selection For

This book seeks to develop an answer to the major question arising from the adoption of sophisticated data-science approaches within humanities research: are existing humanities methods compatible with computational thinking? Data-based and algorithmically powered methods present both new opportunities and new complications for humanists. This book takes as its founding assumption that the exploration and investigation of texts and data with sophisticated computational tools can serve the interpretative goals of humanists. At the same time, it assumes that these approaches cannot and will not obsolete other existing interpretive frameworks. Research involving computational methods, the book argues, should be subject to humanistic modes that deal with questions of power and infrastructure directed toward the field’s assumptions and practices. Arguing for a methodologically and ideologically self-aware critical digital humanities, the author contextualizes the digital humanities within the larger neo-liberalizing shifts of the contemporary university in order to resituate the field within a theoretically informed tradition of humanistic inquiry. Bringing the resources of critical theory to bear on computational methods enables humanists to construct an array of compelling and possible humanistic interpretations from multiple dimensions—from the ideological biases informing many commonly used algorithms to the complications of a historicist text mining, from examining the range of feature selection for sentiment analysis to the fantasies of human subjectless analysis activated by machine learning and artificial intelligence.

scholarly journals Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

2018 ◽  
Vol 2 ◽  
pp. 247054701774755 ◽  
Author(s):  
Isaac R. Galatzer-Levy ◽  
Kelly V. Ruggles ◽  
Zhe Chen
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Research Domain Criteria ◽  
Computational Tools ◽  
Machine Learning Methods ◽  
Current Review ◽  
Health And Illness ◽  
Multiple Levels ◽  
Complex Relationships ◽  
Research Domain

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria initiative provides a theoretical framework to understand health and illness as the product of multiple interrelated systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, and environmental factors) as they relate to outcomes that are free from prior diagnostic benchmarks represent a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals.

scholarly journals GeCoAgent: A Conversational Agent for Empowering Genomic Data Extraction and Analysis

2022 ◽  
Vol 3 (1) ◽  
pp. 1-29
Author(s):  
Pietro Crovari ◽  
Sara Pidò ◽  
Pietro Pinoli ◽  
Anna Bernasconi ◽  
Arif Canakoglu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Low Cost ◽  
Data Extraction ◽  
End Users ◽  
Genomic Information ◽  
Computational Tools ◽  
Human Genomics ◽  
Novel Approach ◽  
General Data

With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools. This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.

scholarly journals Stitching together the heterogeneous party: A complementary social data science experiment

2017 ◽  
Vol 4 (2) ◽  
pp. 205395171773633 ◽  
Author(s):  
Anders Blok ◽  
Hjalmar B Carlsen ◽  
Tobias B Jørgensen ◽  
Mette M Madsen ◽  
Snorre Ralund ◽  
...  
Keyword(s):  
Data Science ◽  
Social Data

scholarly journals An ethico-legal framework for social data science

2020 ◽  
Author(s):  
Nikolaus Forgó ◽  
Stefanie Hänold ◽  
Jeroen van den Hoven ◽  
Tina Krügel ◽  
Iryna Lishchuk ◽  
...  
Keyword(s):  
Data Science ◽  
Legal Framework ◽  
Social Data
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