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Abstract The mindat.org website (Mindat) has been operating since October 2000 as a free, crowd-sourced, and expert-curated database particularly focused on mineral species and their occurrences worldwide. The project has transformed from a hobbyist site in the beginning into a resource that has found use in various scientific research projects and educational programs. Together with other open data resources, Mindat has helped accelerate scientific discoveries in many fields, such as mineral evolution, mineral ecology, and the co-evolution of the geosphere and biosphere. Recently, through open data efforts, machine interfaces and software packages have been established to enable flexible data discovery and download from Mindat. We assume that the data access and usage will further scale up in the next years. Although Mindat is curated by a team of geoscience and database experts across the world, the crowd-sourced records in Mindat possess some bias. In this paper, we first present an overview of the primary data subjects in Mindat and then give extensive details about the characteristics and partiality of three of the most popular data subjects: locality, mineral species, and mineral occurrence. In the discussion, we also give an outlook on appropriate data usage and future extension of data records. We hope users can obtain a more comprehensive view of the Mindat database through this paper and thus better plan their data use. We also hope more people will be inspired to contribute to the data curation work to make Mindat a sustained data ecosystem for geoscience research. 

Abstract. 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. 

Variations in the Dolivo-Dobrovol’sky symmetry index for minerals through time reveal several factors that influence the emergence of crystalline symmetry in natural processes. Of special interest in this regard are the numerous paragenetic modes—different processes of mineral genesis that reflect changes in physical, chemical, and ultimately biological environments that foster the emergence of new mineral species. Here, we consider the roles of hydrogen content, rarity, formation temperature and pressure, and age on the average symmetry of minerals from 57 different modes of formation (i.e., paragenetic modes). We find four significant trends in the average mineral symmetry index for all minerals in each paragenetic mode: specifically, this average index is (1) lower for minerals with greater hydrogen content; (2) greater for minerals formed at higher pressure; (3) lower for minerals of greater rarity; and (4) greater for older paragenetic modes. These findings elucidate some of the intricate relationships among paragenetic modes, average mineral attributes, and the Dolivo-Dobrovol’sky symmetry index, providing insights into the geological processes governing mineral formation. 

The integration of machine learning (ML) and deep learning (DL) into geoscience has experienced a pronounced uptick in recent years, a trend propelled by the intricate nature of geosystems and the abundance of data they produce. These computational methods have been harnessed across a spectrum of geoscientific challenges, from climate modeling to seismic analysis, exhibiting notable efficacy in extracting valuable insights from intricate geological datasets for applications such as mineral prediction. A thorough analysis of the literature indicates a marked escalation in AI-centric geoscience research starting in 2018, characterized by a predictive research orientation and a persistent focus on key computational terms. The thematic network and evolution analyses underscore the enduring prominence of “deep learning” and “machine learning” as pivotal themes, alongside progressive developments in “transfer learning” and “big data”. Despite these advancements, other methodologies have garnered comparatively lesser focus. While ML and DL have registered successes in the realm of mineral prediction, their amalgamation with domain-specific knowledge and symbolic reasoning could further amplify their interpretability and operational efficiency. Neuro-Symbolic AI (NSAI) emerges as a cutting-edge approach that synergizes DL’s robust capabilities with the precision of symbolic reasoning, facilitating the creation of models that are both powerful and interpretable. NSAI distinguishes itself by surmounting traditional ML constraints through the incorporation of expert insights and delivering explanatory power behind its predictive prowess, rendering it particularly advantageous for mineral prediction tasks. This literature review delves into the promising potential of NSAI, alongside ML and DL, within the geoscientific domain, spotlighting mineral prediction as a key area of focus. Despite the hurdles associated with infusing domain expertise into symbolic formats and mitigating biases inherent in symbolic reasoning, the application of NSAI in the realm of critical mineral prediction stands to catalyze a paradigm shift in the field. By bolstering prediction accuracy, enhancing decision-making processes, and fostering sustainable resource exploitation, NSAI holds the potential to significantly reshape geoscience’s future trajectory.