The OpenMindat v1.0.0 R package: a machine interface to Mindat open data to facilitate data-intensive geoscience discoveries - X. Que; J. Zhang; W. Chen; J. Ralph; X. Ma (Geoscientific Model Development)
Technologies such as machine learning and deep learning are powering the discovery of meaningful patterns in Earth science big data. In the field of mineralogy, Mindat (“mindat.org”) is one of the largest databases. Although its front-end website is open and free, a machine interface for bulk data query and download had never been set up before 2022. Through a project called OpenMindat, an application programming interface (API) to enable open data query and access from Mindat was set up in 2023. To further lower the barrier between Mindat open data and geoscientists with limited coding skills, we developed an R package (OpenMindat v1.0.0) on top of the API. The Mindat API includes multiple data subjects such as geomaterials (e.g., rocks, minerals, synonyms, variety, mixture, and commodity), localities, and the IMA-approved (International Mineralogical Association) mineral list. The OpenMindat v1.0.0 package wraps the capabilities of the Mindat API and is designed to be user-friendly and extensible. In addition to providing functions for querying data subjects on the API, the package supports exporting data to various formats. In real-world applications, these functions only require minor coding for users to get desired datasets, and various other packages in the R environment can be used to analyze and visualize the data. The OpenMindat v1.0.0 package, which includes detailed tutorials and examples, is available on GitHub under the MIT license. The field of mineralogy and many other geoscience disciplines are facing opportunities enabled by open data. Various research topics such as mineral network analysis, mineral association rule mining, mineral ecology, mineral evolution, and critical minerals have already benefited from Mindat's open data efforts in recent years. We hope this R package can help accelerate those data-intensive studies and lead to more scientific discoveries.