infrastructure for supporting computational social science

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Infrastructure Research to Support Computational Social Science Derek Hansen & Kevin Tew Brigham Young University

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Slides for KredibleNet workshop, April 9, 2013

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Page 1: Infrastructure for Supporting Computational Social Science

Infrastructure Research to Support Computational Social Science

Derek Hansen & Kevin TewBrigham Young University

Page 2: Infrastructure for Supporting Computational Social Science

Current Options for ResearchersData Sources Code it Yourself

Use Free 3rd Party Tools

Social Scientists

Computer Scientists

APIs

Scrapers

Software Libraries

Use Corporate Tools

Page 3: Infrastructure for Supporting Computational Social Science

Problems with Current Approach

• Non-coders have limited opportunities• Corporate tools not designed for research needs and high cost• Major duplication of effort– Extra work for researchers– More resource intensive for companies

• APIs not available, constantly changing, or rate limited• Creating and maintaining 3rd party tools is hard– Ongoing funding is challenging in a research environment– Contribution not always recognized in academia

• Inconsistency in legal & ethical approaches

Page 4: Infrastructure for Supporting Computational Social Science

A Large-Scale Solution?

Enabling a Better Understanding of Continental-Scale EcologyNEON is designed to gather and synthesize data on the impacts of climate change, land use change and invasive species on natural resources and biodiversity… NEON will combine site-based data with remotely sensed data and existing continental-scale data sets (e.g. satellite data) to provide a range of scaled data products that can be used to describe changes in the nation’s ecosystem through space and time.

Free and Publicly Accessible ResourcesNEON’s open-access approach to its data and information products will enable scientists, educators, planners, decision makers and the public to map, understand and predict the effects of human activities on ecology and effectively address critical ecological questions and issues.

Page 5: Infrastructure for Supporting Computational Social Science

Non-Trivial Infrastructure

Page 6: Infrastructure for Supporting Computational Social Science

Non-Trivial User Experience

Page 7: Infrastructure for Supporting Computational Social Science

Infrastructure Research

• Data Handling and Processing– “Big Data” storage and analysis (e.g., scalable,

real-time)– Customized programming language(s)

• Human-Computer Interaction– Support usability and encourage high quality work– Visualization

• Legal and Social– Legal framework for companies & IRBs– Community-building among researchers

Page 8: Infrastructure for Supporting Computational Social Science

Collaboration Opportunities

Center for the Advanced Study of Communities and Information (CASCI)

2013 Digital Societies and Social Technologies (DSST) Summer Institute