Data and documentaries: Methodological hybridizations in activism

2020 ◽  
Vol 12 (2) ◽  
pp. 315-332 ◽  
Author(s):  
Miren Gutiérrez
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Court Cases ◽  
Migration Policies ◽  
Critical Data ◽  
Science And Art ◽  
Documentary Studies

Hybridization permeates all fields of communication: documentaries are no exception. One example is The Left-to-Die Boat by Forensic Architecture (FA), an audio-visual account of how 63 refugees lost their lives in 2011 when their ship was adrift in the waters of Libya. The Left-to-Die Boat combines documentary techniques with data analysis and visualizations to expose a tragedy, change migration policies and sustain court cases. Based on critical data and documentary studies, this article inspects the methodological hybridization proposed by FA in six documentaries. The questions are: how does FA hybridize? What are the outcomes of this hybridization? What is hybridization today? The analysis links these documentaries’ characteristics with functions and outcomes, offering a taxonomy that can be employed beyond this study. The findings indicate that activism is taking multidimensional forms, blurring the boundaries separating documentaries, data science and art in search of impact.

Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science

2021 ◽  
Author(s):  
Nikolai West ◽  
Jonas Gries ◽  
Carina Brockmeier ◽  
Jens C. Gobel ◽  
Jochen Deuse
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Quality Criteria ◽  
Industrial Data ◽  
Analysis Quality ◽  
Integrated Data Analysis

scholarly journals Service Assessment Planning for the Hunt Library Dataspace

2019 ◽  
Author(s):  
Mia Partlow ◽  
Karen Ciccone ◽  
Margaret Peak
Keyword(s):  
North Carolina ◽  
Statistical Analysis ◽  
Data Science ◽  
Planning Process ◽  
Service Development ◽  
State University ◽  
University Libraries ◽  
New Service Development ◽  
Data Intensive ◽  
Critical Data

Presentation given at TRLN Annual Meeting, Durham, North Carolina, July 1, 2019. The Hunt Library Dataspace was launched in August 2018 to provide students with access to the tools and support they need to develop critical data skills and perform data intensive tasks. It is outfitted with specialized computing hardware and software and staffed by graduate student Data Science Consultants who provide drop-in support for programming, data analysis, statistical analysis, visualization, and other data-related topics.Prior to launching the Dataspace the Libraries’ Director of Planning and Research worked with the Data & Visualization Services department to develop a plan for assessing the new Dataspace services. The process began with identifying relevant goals based on NC State University and the NC State University Libraries’ strategic priorities. Next we identified measures that would assess our success in relation to those goals. This talk describes the assessment planning process, the measures and methods employed, outcomes, and how this information will be used to improve our services and inform new service development.

scholarly journals How R helps Airbnb make the most of its data

2017 ◽  
Author(s):  
Ricardo Bion ◽  
Robert Chang ◽  
Jason Goodman
Keyword(s):  
Data Analysis ◽  
Predictive Models ◽  
Data Science ◽  
Recent Survey ◽  
Specific Advice

At Airbnb, R has been amongst the most popular tools for doing data science in many different contexts, including generating product insights, interpreting experiments, and building predictive models. In a recent survey of the Airbnb team, 73% of Data Scientists and Analysts rated themselves as closer to “Expert” than “Beginner” in using R, and 58% regularly use R as a language for data analysis. Airbnb supports R usage by creating internal R tools and by creating a community of R users. At the end of the post, the authors provide some specific advice for practitioners who wish to incorporate R into their day-to-day workflow.

Data Science and Big Data Analytics in Financial Services

Web Services ◽  
2019 ◽  
pp. 1301-1329
Author(s):  
Suren Behari ◽  
Aileen Cater-Steel ◽  
Jeffrey Soar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Financial Services ◽  
Data Science ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Big Data Analysis ◽  
Future Trends ◽  
The Right

The chapter discusses how Financial Services organizations can take advantage of Big Data analysis for disruptive innovation through examination of a case study in the financial services industry. Popular tools for Big Data Analysis are discussed and the challenges of big data are explored as well as how these challenges can be met. The work of Hayes-Roth in Valued Information at the Right Time (VIRT) and how it applies to the case study is examined. Boyd's model of Observe, Orient, Decide, and Act (OODA) is explained in relation to disruptive innovation in financial services. Future trends in big data analysis in the financial services domain are explored.

Data Analysis through Visualization

2021 ◽  
pp. 107-132
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
University Of California ◽  
Science Class ◽  
Temporal Data ◽  
Santa Cruz ◽  
State Action ◽  
Spatio Temporal ◽  
Visualization Systems ◽  
Visualization Techniques

This chapter discusses the topic of how one can use visualization techniques to analyze game data. Specifically, the chapter delves into the development of heatmaps to analyze spatio-temporal data. The chapter also discusses spatio-temporal visualizations and state-action transition visualizations. We also discuss two visualization systems that we have developed within the GUII lab: Stratmapper and Glyph. We provide you with a link that allows you to explore the use of these visualizations with real game data. This chapter is written in collaboration with Riddhi Padte and Varun Sriram, based on their work in Dr. Seif El-Nasr’s game data science class at Northeastern University; Erica Kleinman, PhD student at University of California at Santa Cruz; and Andy Bryant, software engineer at GUII Lab. The chapter also includes labs where you get to experience the analysis of game data through visualization.

scholarly journals Critical Care, Critical Data

2019 ◽  
Vol 10 ◽  
pp. 117959721985656 ◽  
Author(s):  
Christopher V Cosgriff ◽  
Leo Anthony Celi ◽  
David J Stone
Keyword(s):  
Artificial Intelligence ◽  
Critical Care ◽  
Clinical Data ◽  
Continuous Monitoring ◽  
Data Science ◽  
Clinical Decision ◽  
Critical Care Medicine ◽  
Data Driven ◽  
Critical Data

As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

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