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 in order 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 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. In order to systematize and synthesize the direction of these efforts, we introduce the concept of “Mineral Informatics”. Mineral Informatics is the next frontier for researchers working with mineral data. In this paper, we present our vision for Mineral Informatics, the X-Informatics underpinnings that led to its conception, the needs, challenges, opportunities, and future directions. 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 increased 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.

The five divisions of NASA’s Science Mission Directorate (SMD) represent a very broad spectrum of academic disciplines, ranging from Astronomy, to Planetary science, to Heliophysics, Earth science, Biology and Physical science with measurement scales ranging from components of atoms to the structure of the entire universe. In addition, the systems that support access to these data range from systems based on formal and broadly accepted OWL ontologies, to those based on current and historical disciplinary metadata standards, to ad-hoc or bespoke systems dating back to NASA’s very earliest missions; all generally developed to support the mission or, more recently, discipline focussed data users. Consequently the access mechanisms, data structures, vocabularies, terms in use, etc. vary widely across the divisions making cross-disciplinary research at best difficult if not impossible. Currently NASA SMD is working to improve support for cross-disciplinary/transdisciplinary research by developing a system that supports discovery across all of SMD’s data products, a model that can be extended to all forms of scientific output including software, tools, models, publications, etc. The core underpinnings of such a system is an information model being developed using the methodology developed by Dr. Peter Fox and Dr. Deborah McGuinness. Here we discuss the model (a knowledge graph), lessons learned along the way, and key findings for other systems attempting to bridge across broad disciplinary challenges.

The discipline of informatics, generically cast as the science and engineering of information system within a socio-technical framework, originating in the middle of last century has undergone generational adaptations as computer hardware, networks and software have evolved. Within the “eScience” era of the last two decades, discipline-specific fields of informatics have flourished, such as geoinformatics, mineral informatics and many more. In fact even in geosciences, there may be few fields of study that have not added an informatics sub-field. Over the same time, efforts at systematizing the common (or core, i.e. discipline neutral) aspects of informatics have been successful: use cases, human-centered design, iterative approaches, information models and more are some of the key elements. However new pressures are being placed on functional and non-functional requirements of information systems: with the now somewhat routine underlying data that are high dimensional, heterogeneous, sparse and with uncertain quality. However, demands have arisen from renewed attention to hub/ server/ cloud-based provision of the application of machine learning, neural networks and artificial intelligence in general. Those methods as implemented in software libraries producing results to be assessed and interpreted (often leading to decisions made) by “humans-in-the-loop”. Informatics, revisited is a possible answer. This presentation features some history of informatics, recent disruptions that need to be addressed, and offers ideas for new directions, with the goal of advancing geoscience in general.

The usage of the term Knowledge Graph (KG) has gained significant popularity since 2012, when Google introduced its own knowledge graph, and how they used it to enhance their searches and question answering systems. While various definitions and interpretations for knowledge graphs have been presented, what remains consistent is that knowledge graphs are commonly used with reasonsers to make inferences about data, based on assertions and axioms written by human experts. But knowledge graphs, which store complex, multi-dimensional data contain hidden patterns and trends that cannot be explored simply using reasoners. In such a case it becomes necessary to extract parts of the knowledge graph (focusing on the instances related to one property at a time) and analyze them individually in order to conduct a focused but tractable exploration of the domain. In this presentation, we present one way to gain insights from knowledge graphs, using network science. To achieve this goal, we have formalised the partitioning of knowledge graphs to unipartite knowledge networks, and present various ways to explore and analyse such knowledge networks to form scientific hypotheses, gain scientific insights and make discoveries.