Epistemological implications of the use of massive amount of data in the process of scientific discovery

• Grant number: 20/03134-1
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: September 01, 2020
• End date: August 31, 2024
• Field of knowledge: Humanities - Philosophy - Epistemology
• Principal Investigator: Maria Eunice Quilici Gonzalez
• Grantee: Mariana Vitti Rodrigues
• Host Institution: Faculdade de Filosofia e Ciências (FFC). Universidade Estadual Paulista (UNESP). Campus de Marília. Marília , SP, Brazil
• Associated scholarship(s): 23/01408-5 - Discovery by abduction and serendipity in the context of open science Practices, BE.EP.PD

Abstract

The aim of this project is to investigate possible epistemological implications of applying massive amount of data, known as Big Data, to the process of scientific discovery. The central problem that guides our investigation can be formulated as follows: What are the possible epistemological implications of employing Big Data analytics in the process of scientific discovery? From this central question, two specific questions arise: (i) To what extent can semantic and pragmatic aspects present in the process of scientific discovery be automated by applying Big Data analytics?; (ii) What criteria are involved in the selection of relations (causal and/or correlational) in large databases available to the scientific analysis? To investigate these questions, we will present the contemporary debate about epistemological implications of using Big Data analytics for scientific investigation. We will then elucidate the concepts of data, information, and meaning, in the contexts of philosophy of information, with emphasis on the works of Charles S. Peirce, Fred Dretske, and Sabina Leonelli. We will also analyze the concept of causality, indicating similarities between causal and informational relations in scientific research. Finally, we will propose an analysis of the process of scientific discovery in the Big Data era, highlighting the role of information in abductive and diagrammatic reasoning. Illustrative examples of scientific discovery in the Big Data era will be analyzed during this research. (AU)