Abstract

This paper describes an initial cognitive framework that captures the reasoning involved in scientific data analyses, drawing from close collaborations with scientists in different domains over many years. The framework aims to automate data analysis for science. In doing so, existing large repositories of data could be continuously and systematically analyzed by machines, updating findings and potentially making new discoveries as new data becomes available. The framework consists of a cycle with six phases: formulating an investigation, initiating the investigation, getting data, analyzing data, aggregating results, and integrating findings. The paper also describes our implementation of this framework and illustrates it with examples from different science domains.

Metadata

publication

CogSci, 2022

year

2022

publication date

2022

authors

Yolanda Gil, Deborah Khider, Maximiliano Osorio, Varun Ratnakar, Hernan Vargas, Daniel Garijo, Suzanne A Pierce

link

https://dgarijo.com/papers/gil_et_al_cogsci_2022.pdf

resource_link

https://dgarijo.com/papers/gil_et_al_cogsci_2022.pdf

conference

CogSci