National Science Foundation contributes to four international projects in data-intensive social science and humanities research

Ten international research agencies, including the United State’s National Science Foundation (NSF), recently announced the winners of the third Digging into Data Challenge.

The Digging into Data program gives research teams the ability to develop new insights, tools and skills in innovative social science and humanities research using large-scale data analysis.

Fourteen teams representing Canada, the Netherlands, the United Kingdom and the United States will receive grants to investigate how computational techniques can be applied to `big data` in social sciences and the humanities. Each team represents collaborations among scholars, scientists and information professionals from leading universities and libraries in Europe and North America.

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Digging into Data projects receiving NSF funding are highlighted below.

Organizing and Uniting Linguistic Databases (the COULD project)

Abstract: The COULD project has 5 goals. (1) It seeks to transfer existing linguistic data from a variety of different formats into a universal format that will allow linguists to combine and share information, not only with other linguists but also with the public at large. (2) The project will build applications that automatically correct errors, draw attention to inconsistencies and fill gaps in the data. (3) These automated mechanisms will provide new tools to detect patterns that are not obvious
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Field Mapping: An Archival Protocol for Social Science Research Findings

Abstract: In this project, psychology and management scholars from the United States and Canada will collaborate with an expert in online research and classification methods to devise a web application that will (1) enable the encoding of millions of individual findings in a multidisciplinary social science research domain, (2) facilitate complex analyses and (3) provide open access to members of the scholar community and the general public. The project provides protocols
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Legal Structures

Abstract: This project takes a radically novel approach to the problem of measuring and visualizing differences among legal systems: it focuses on machine coding of internal references in codes and laws. Internal referencing is an inherent characteristic of codes. Already the Code of Hammurabi, almost 3800 years ago, was structured as a numbered list of laws with at least one cross-reference. The intuition behind this approach is that fundamental differences among legal systems
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MIning Relationships Among variables in large datasets from CompLEx systems (MIRACLE)

Abstract: Social scientists have used agent-based models (ABMs) to explore the interaction and feedbacks among social agents and their environments. The bottom-up structure of ABMs enables simulation and investigation of complex systems and their emergent behavior with a high level of detail; however the stochastic nature and potential combinations of parameters of such models create large non-linear multidimensional `big data,` which are difficult to analyze using traditional statistical methods. The proposed project seeks to address this challenge by developing algorithms and web-based analysis and visualization tools
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