Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Abstract

Data science receives increasing attention in a variety of geoscience disciplines and applications. Many successful data-driven geoscience discoveries have been reported recently, and the number of geoinformatics and data science sessions have begun to increase in many geoscience conferences. Across academia, industry, and governmental sectors, there is a strong interest to know more about the current progress as well as the potential of data science for geoscience. To address that need, this article provides a review from the perspective of a data life cycle. The key steps in the data life cycle includes concept, collection, preprocessing, analysis, archive, distribution, discovery, and repurpose. Those subjects are intuitive and easy to follow even for geoscientists with very limited experience of cyberinfrastructure, statistics, and machine learning. The review includes two key parts. The first is about the fundamental concepts and theoretical foundation of data science, and the second is the summary of highlights and sharable experience from existing publications centered on each step in the data life cycle. At the end, a vision about the future trends of data science applications in geoscience is discussed, including topics on open science, smart data, and science of team science. We hope this review will be useful to data science practitioners in the geoscience community, and will lead to more discussions on the best practices and future trends of data science for geoscience.

OpenMindat: Open and FAIR mineralogy data from the Mindat database

“…Such data‐intensive scientific pursuits will be significantly leveraged with the open data provided by OpenMindat. In our previous studies, we have also summarized experience and best practices of applying data science in geoscience, including the abductive process (Hazen, 2014), visual exploratory analysis (Ma et al, 2017) and Agile‐style datathon activities (Ma, 2022). Other scientists using OpenMindat data, if needed, may adapt those experience and best practices in their own studies.…”

“…The research outputs on mineral evolution and ecology (Cleland et al, 2021; Hazen et al, 2019; Hystad et al, 2015) are good examples illustrating the key components and steps in the methodology of mineral informatics. Nevertheless, the practitioners, including the authors of this paper, also found that accessing adequate reliable open data often remains a challenge for mineral informatics (Ma, 2022). Through community activities (Golden et al, 2019; Prabhu et al, 2021; Wyborn et al, 2021), although researchers are gradually improving the FAIRness (Findable, Accessible, Interoperable and Reusable) (Wilkinson et al, 2016) of open data in the field of mineralogy, more work still needs to be done to fully meet the needs of mineral informatics, particularly for machine‐to‐machine interactions (Musen, 2022; Wyborn & Brownlee, 2022).…”

Extensible, Customizable and Trustworthy SocialNetwork Data Sharing Platform: IPFS Approach

“…the provider needs to share data with a small set of consumers). In essence, it becomes increasingly difficult to gauge fair pricing for a given data stream as more worker nodes cooperate to deliver the data [3][4][5].…”

“…The ubiquity of Social Network Services (SNS) is the result of their ability to promote virtual connectivity and boost economic and social outcomes [1][2][3]. As such, SNS platforms have become the solicited and prominent communication media enabling users to generate and share massive amount of data [3][4][5]. This data could be utilized by individuals and businesses in many different applications such as entertainment and data-driven decision-making [6,7].…”

“…This level of specification makes them more robust and performant but less customizable and extensible [8,9,[17][18][19]. Thus, users are forced to work within the parameters set by the solution developers which might not always be feasible [3][4][5]. For example, different consumers might: (a) want to receive data in formats not supported by the data provider or (b) need the data to be pre-processed in some way before receiving it [8,9].…”