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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. 

A<sc>bstract</sc> In the dark dimension scenario, which predicts an extra dimension of micron scale, dark gravitons (KK modes) are a natural dark matter candidate. In this paper, we study observable features of this model. In particular, their decay to standard matter fields can distort the CMB and impact other astrophysical signals. Using this we place bounds on the parameters of this model. In particular we find that the natural range of parameters in this scenario is consistent with these constraints and leads to the prediction that the mean mass of the dark matter today is close to a few hundred keV and the effective size of the extra dimension is around 1–30 μm. 

Abstract The locations of minerals and mineral-forming environments, despite being of great scientific importance and economic interest, are often difficult to predict due to the complex nature of natural systems. In this work, we embrace the complexity and inherent “messiness” of our planet's intertwined geological, chemical, and biological systems by employing machine learning to characterize patterns embedded in the multidimensionality of mineral occurrence and associations. These patterns are a product of, and therefore offer insight into, the Earth's dynamic evolutionary history. Mineral association analysis quantifies high-dimensional multicorrelations in mineral localities across the globe, enabling the identification of previously unknown mineral occurrences, as well as mineral assemblages and their associated paragenetic modes. In this study, we have predicted (i) the previously unknown mineral inventory of the Mars analogue site, Tecopa Basin, (ii) new locations of uranium minerals, particularly those important to understanding the oxidation–hydration history of uraninite, (iii) new deposits of critical minerals, specifically rare earth element (REE)- and Li-bearing phases, and (iv) changes in mineralization and mineral associations through deep time, including a discussion of possible biases in mineralogical data and sampling; furthermore, we have (v) tested and confirmed several of these mineral occurrence predictions in nature, thereby providing ground truth of the predictive method. Mineral association analysis is a predictive method that will enhance our understanding of mineralization and mineralizing environments on Earth, across our solar system, and through deep time. 

Abstract Minerals are information-rich materials that offer researchers a glimpse into the evolution of planetary bodies. Thus, it is important to extract, analyze, and interpret this abundance of information to improve our understanding of the planetary bodies in our solar system and the role our planet’s geosphere played in the origin and evolution of life. Over the past several decades, data-driven efforts in mineralogy have seen a gradual increase. The development and application of data science and analytics methods to mineralogy, while extremely promising, has also been somewhat ad hoc in nature. To systematize and synthesize the direction of these efforts, we introduce the concept of “Mineral Informatics,” which is the next frontier for researchers working with mineral data. In this paper, we present our vision for Mineral Informatics and the X-Informatics underpinnings that led to its conception, as well as the needs, challenges, opportunities, and future directions of the field. The intention of this paper is not to create a new specific field or a sub-field as a separate silo, but to document the needs of researchers studying minerals in various contexts and fields of study, to demonstrate how the systemization and enhanced access to mineralogical data will increase cross- and interdisciplinary studies, and how data science and informatics methods are a key next step in integrative mineralogical studies. 

Abstract Ecological observations and paleontological data show that communities of organisms recur in space and time. Various observations suggest that communities largely disappear in extinction events and appear during radiations. This hypothesis, however, has not been tested on a large scale due to a lack of methods for analyzing fossil data, identifying communities, and quantifying their turnover. We demonstrate an approach for quantifying turnover of communities over the Phanerozoic Eon. Using network analysis of fossil occurrence data, we provide the first estimates of appearance and disappearance rates for marine animal paleocommunities in the 100 stages of the Phanerozoic record. Our analysis of 124,605 fossil collections (representing 25,749 living and extinct marine animal genera) shows that paleocommunity disappearance and appearance rates are generally highest in mass extinctions and recovery intervals, respectively, with rates three times greater than background levels. Although taxonomic change is, in general, a fair predictor of ecologic reorganization, the variance is high, and ecologic and taxonomic changes were episodically decoupled at times in the past. Extinction rate, therefore, is an imperfect proxy for ecologic change. The paleocommunity turnover rates suggest that efforts to assess the ecological consequences of the present-day biodiversity crisis should focus on the selectivity of extinctions and changes in the prevalence of biological interactions. 

Abstract The open data movement has brought revolutionary changes to the field of mineralogy. With a growing number of datasets made available through community efforts, researchers are now able to explore new scientific topics such as mineral ecology, mineral evolution and new classification systems. The recent results have shown that the necessary open data coupled with data science skills and expertise in mineralogy will lead to impressive new scientific discoveries. Yet, feedback from researchers also reflects the needs for better FAIRness of open data, that is, findable, accessible, interoperable and reusable for both humans and machines. In this paper, we present our recent work on building the open data service of Mindat, one of the largest mineral databases in the world. In the past years, Mindat has supported numerous scientific studies but a machine interface for data access has never been established. Through the OpenMindat project we have achieved solid progress on two activities: (1) cleanse data and improve data quality, and (2) build a data sharing platform and establish a machine interface for data query and access. We hope OpenMindat will help address the increasing data needs from researchers in mineralogy for an internationally recognized authoritative database that is fully compliant with the FAIR guiding principles and helps accelerate scientific discoveries.