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Human Networks and Data Science – Infrastructure

Human Networks and Data Science – Infrastructure (HNDS-I). Infrastructure proposals will address the development of data resources and relevant analytic techniques that support fundamental Social, Behavioral and Economic (SBE) research. Successful proposals will, within the financial resources provided by the award, construct user-friendly large-scale next-generation data resources and relevant analytic techniques and produce a finished product that will enable new types of data-intensive research.

Deadline: 

Thursday, February 3, 2022
Thursday, February 2, 2023
Thursday, February 1, 2024
Thursday, February 6, 2025

Research on the Science and Technology Enterprise: Indicators, Statistics, and Methods Dissertation Grant

NCSES welcomes efforts by the research community to use NCSES or other data to conduct research on the S&T enterprise, develop improved survey methodologies that could benefit NCSES surveys, explore alternate data sources that could supplement NCSES data, create and improve indicators of S&T activities and resources, strengthen methodologies to analyze S&T statistical data, and explore innovative ways to communicate S&T statistics.

Deadline: 

Tuesday, January 18, 2022
Tuesday, January 17, 2023
Tuesday, January 16, 2024
Tuesday, January 21, 2025

Research on the Science and Technology Enterprise: Indicators, Statistics, and Methods

NCSES welcomes efforts by the research community to use NCSES or other data to conduct research on the S&T enterprise, develop improved survey methodologies that could benefit NCSES surveys, explore alternate data sources that could supplement NCSES data, create and improve indicators of S&T activities and resources, strengthen methodologies to analyze S&T statistical data, and explore innovative ways to communicate S&T statistics.

Deadline: 

Tuesday, January 18, 2022
Tuesday, January 17, 2023
Tuesday, January 16, 2024
Tuesday, January 21, 2025

Revamped Bayesian Inference

This research project will make Bayesian statistical computation much faster. Bayesian methods have not gained much traction in the social sciences, in part because the approach is so computationally intensive. Many researchers who could usefully apply these techniques choose not to do so because the analysis is too costly. This project will improve the computational efficiency of Bayesian methods by harnessing a critical theorem that has long been overlooked by statisticians but proven by one of the twentieth century's greatest mathematicians.

Improving Representativeness in Non-Probability Surveys and Causal Inference with Regularized Regression and Post-Stratifiation

The proposed project has a broad aim of working with the increasing complexities of survey statistics with decreasing response rate. We focus specifically on non-probability samples (samples of convenience) due to their increasing popularity, but note that these non-probability samples are simply an extreme case of a probability based survey with high non-response, and so our methods could be expected to generalize.

RAPID: Flexible, Efficient, and Available Bayesian Computation for Epidemic Models

Decisions about coronavirus response are necessarily based on statistical models of prevalence, transmission risks, case fatality rate, projection of future spread of infection, and estimated effects of medical and social interventions. Much of this modeling and inference is being done using the Bayesian framework, an approach to statistics that is well suited to integration of information from different sources and accounting for uncertainty in predictions that can be input into decision analysis.

Collaborative Research: PPoSS: Planning: Scalable Systems for Probabilistic Programming

Statistical methods have had great successes for exploring data, making predictions, and solving problems in a wide range of problems. But in the world of big data, methods need to be scalable, so as to handle larger problems while modeling the real-world problems of messy and nonrepresentative data. The project?s novelties are developments in software and hardware facilitating full-stack integration of Bayesian inference to allow complex and realistic models to be fit to large datasets.

NSF Program on Fairness in Artificial Intelligence (AI) in Collaboration with Amazon (FAI)

NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. Specific topics of interest include, but are not limited to transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, validation of fairness, and considerations of inclusivity. Funded projects will enable broadened acceptance of AI systems, helping the U.S.

Deadline: 

Tuesday, August 3, 2021

Uncommon Methods & Metrics

A primary data collection initiative to uncover how entrepreneurial ecosystems affect entrepreneurs

Deadline: 

Tuesday, July 11, 2017

CI-SUSTAIN: Stan for the Long Run

Stan is a software package that transforms scientific discovery by allowing scientists to quickly and easily explore, evaluate, and refine rich scientific hypotheses tailored to their particular research question and data collection mechanism. For computational reasons, analyses of data (big or otherwise) have tended to be simple and focused more on the difficulties of manipulating the data than on realistic scientific models.

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