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Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. The needed transformation of the LTG data culture requires emphasis in student education on science and management of data.

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. [ABSTRACT FROM AUTHOR]

Integrating mineralogy with data science is critical to modernizing Earth materials research and its applications to geosciences. Data were compiled on 95 650 garnet sample analyses from a variety of sources, ranging from large repositories (EarthChem, RRUFF, MetPetDB) to individual peer-reviewed literature. An important feature is the inclusion of mineralogical "dark data" from papers published prior to 1990. Garnets are commonly used as indicators of formation environments, which directly correlate with their geochemical properties; thus, they are an ideal subject for the creation of an extensive data resource that incorporates composition, locality information, paragenetic mode, age, temperature, pressure, and geochemistry. For the data extracted from existing databases and literature, we increased the resolution of several key aspects, including petrogenetic and paragenetic attributes, which we extended from generic material type (e.g., igneous, metamorphic) to more specific rock-type names (e.g., diorite, eclogite, skarn) and locality information, increasing specificity by examining the continent, country, area, geological context, longitude, and latitude. Likewise, we utilized end-member and quality index calculations to help assess the garnet sample analysis quality. This comprehensive dataset of garnet information is an open-access resource available in the Evolutionary System of Mineralogy Database (ESMD) for future mineralogical studies, paving the way for characterizing correlations between chemical composition and paragenesis through natural kind clustering (Chiama et al., 2022; 10.48484/camh-xy98). We encourage scientists to contribute their own unpublished and unarchived analyses to the growing data repositories of mineralogical information that are increasingly valuable for advancing scientific discovery. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

The Gakkel ridge, which extends under the Arctic ice cap for ~1,800km, is the slowest spreading ocean ridge on Earth. Its spreading created the Eurasian basin, which is isolated from [...]

Sampling the natural world and built environment underpins much of science, yet systems for managing material samples and associated (meta)data are fragmented across institutional catalogs, practices for identification, and discipline-specific (meta)data standards. The Internet of Samples (iSamples) is a standards-based collaboration to uniquely, consistently, and conveniently identify material samples, record core metadata about them, and link them to other samples, data, and research products. iSamples extends existing resources and best practices in data stewardship to render a cross-domain cyberinfrastructure that enables transdisciplinary research, discovery, and reuse of material samples in 21st century natural science. [ABSTRACT FROM AUTHOR]

Material samples form an important portion of the data infrastructure for many disciplines. Here, a material sample is a physical object, representative of some physical thing, on which observations can be made. Material samples may be collected for one project initially, but can also be valuable resources for other studies in other disciplines. Collecting and curating material samples can be a costly process. Integrating institutionally managed sample collections, along with those sitting in individual offices or labs, is necessary to faciliate large-scale evidence-based scientific research. Many have recognized the problems and are working to make data related to material samples FAIR: findable, accessible, interoperable, and reusable. The Internet of Samples (i.e., iSamples) is one of these projects. iSamples was funded by the United States National Science Foundation in 2020 with the following aims: 1. enable previously impossible connections between diverse and disparate samplebased observations; 2. support existing research programs and facilities that collect and manage diverse sample types; 3. facilitate new interdisciplinary collaborations; and 4. provide an efficient solution for FAIR samples, avoiding duplicate efforts in different domains (Davies et al. 2021) The initial sample collections that will make up the internet of samples include those from the System for Earth Sample Registration (SESAR), Open Context, the Genomic Observatories Meta-Database (GEOME), and Smithsonian Institution Museum of Natural History (NMNH), representing the disciplines of geoscience, archaeology/anthropology, and biology. To achieve these aims, the proposed iSamples infrastructure (Fig. 1) has two key components: iSamples in a Box (iSB) and iSamples Central (iSC). The iSC component will be a permanent Internet service that preserves, indexes, and provides access to sample metadata aggregated from iSBs. It will also ensure that persistent identifiers and sample descriptions assigned and used by individual iSBs are synchronized with the records in iSC and with identifier authorities like International Geo Sample Number (IGSN) or Archival Resource Key (ARK). The iSBs create and maintain identifiers and metadata for their respective collection of samples. While providing access to the samples held locally, an iSB also allows iSC to harvest its metadata records. The metadata modeling strategy adopted by the iSamples project is a metadata profilebased approach, where core metadata fields that are applicable to all samples, form the core metadata schema for iSamples. Each individual participating collectionis free to include additional metadata in their records, which will also be harvested by iSC and are discoverable through the iSC user interface or APIs (Application Programming Interfaces), just like the core. In-depth analysis of metadata profiles used by participating collections, including Darwin Core, has resulted in an iSamples core schema currently being tested and refined through use. See the current version of the iSamples core schema. A number of properties require a controlled vocabulary. Controlled vocabularies used by existing records are kept, while new vocabularies are also being developed to support high-level grouping with consistent semantics across collection types. Examples include vocabularies for Context Category, Material Category, and Specimen Type (Table 1). These vocabularies were also developed in a bottom-up manner, based on the terms used in the existing collections. For each vocabulary, a decision tree graph was created to illustrate relations among the terms, and a card sorting exercise was conducted within the project team to collect feedback. Domain experts are invited to take part in this exercise here, here, and here. These terms will be used as upper-level terms to the existing category terms used in the participating collections and hence create connections among individual participating collections. iSample project members are also active in the TDWG Material Sample Task Group and the global consultation on Digital Extended Specimens. Many members of the iSamples project also lead or participate in a sister research coordination network (RCN), Sampling Nature. The goal of this RCN is to develop and refine metadata standards and controlled vocabularies for the iSamples and other projects focusing on material samples. We cordially invite you to participate in the Sampling Nature RCN and help shape the future standards for material samples. Contact Sarah Ramdeen (sramdeen@ideo.columbia.edu) to engage with the RCN. [ABSTRACT FROM AUTHOR]

Many have argued that datasets resulting from scientific research should be part of the scholarly record as first class research products. Data sharing mandates from funding agencies and scientific journal publishers along with calls from the scientific community to better support transparency and reproducibility of scientific research have increased demand for tools and support for publishing datasets. Hydrology domain‐specific data publication services have been developed alongside more general purpose and even commercial data repositories. Prominent among these are the Hydrologic Information System (HIS) and HydroShare repositories developed by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). More broadly, however, multiple organizations have been involved in the practice of data publication in the hydrology domain, each having different roles that have shaped data publication and reuse. Bibliographic and archival approaches to data publication have been advanced, but both have limitations with respect to hydrologic data. Specific recommendations for improving data publication infrastructure, support, and practices to move beyond existing limitations and enable more effective data publication in support of scientific research in the hydrology domain include: improving support for journal article‐based data access and data citation, considering the workflow for data publication, enhancing support for reproducible science, encouraging publication of curated reference data collections, advancing interoperability standards for sharing data and metadata among repositories, developing partnerships with university libraries offering data services, and developing more specific data management plans. While presented in the context of CUAHSI's data repositories and experience, these recommendations are broadly applicable to other domains. This article is categorized under:Science of Water > Methods [ABSTRACT FROM AUTHOR]

Earth Science researchers require access to integrated, cross-disciplinary data in order to answer critical research questions. Partially due to these science drivers, it is common for disciplinary data systems to expand from their original scope in order to accommodate collaborative research. The result is multiple disparate databases with overlapping but incompatible data. In order to enable more complete data integration and analysis, the Observations Data Model Version 2 (ODM2) was developed to be a general information model, with one of its major goals to integrate data collected by in situ sensors with those by ex-situ analyses of field specimens. Four use cases with different science drivers and disciplines have adopted ODM2 because of benefits to their users. The disciplines behind the four cases are diverse - hydrology, rock geochemistry, soil geochemistry, and biogeochemistry. For each case, we outline the benefits, challenges, and rationale for adopting ODM2. In each case, the decision to implement ODM2 was made to increase interoperability and expand data and metadata capabilities. One of the common benefits was the ability to use the flexible handling and comprehensive description of specimens and data collection sites in ODM2's sampling feature concept. We also summarize best practices for implementing ODM2 based on the experience of these initial adopters. The descriptions here should help other potential adopters of ODM2 implement their own instances or to modify ODM2 to suit their needs. [ABSTRACT FROM AUTHOR]

There is growing recognition that unambiguous citation and tracking of physical samples allows previously impossible linking of samples to data and publications, linking and integration of sample-based observations across data systems, and paves the road towards advanced data mining of sample-based data. And in recent years, there has been an uptake in the use of Persistent Identifiers (PIDs) for physical samples to support such citation and tracking. The IGSN (International Geo Sample Number) is a PID for physical samples. It was originally developed for the solid earth sciences, and has evolved into an international PID system with members in five continents and a network of active allocating agents. It has been adopted by a growing number and range of stakeholders worldwide, including national geological surveys, research infrastructure providers, collection curators, researchers, and data managers, and by other disciplines that need to refer to physical samples. Nearly 6.9 million samples have been registered with IGSNs so far. The IGSN system uses the Handle System (Kahn and Wilensky 1995; see also Handle.Net ) and has an international organization, IGSN e.V., to manage its governance structure and the technical architecture. The recent expansion of the IGSN beyond the geosciences into other domains such as biodiversity, archeology, and material sciences confirms the power of its concept and implementation, but imposes substantial pressures on the existing capacity and capabilities of the IGSN architecture and its governing organization. Modifications to the IGSN organizational and technical architecture are necessary at this point to keep pace with the growing demand and expectations. These changes are also necessary to ensure trustworthy and sustainable services for PID registration and resolution in a maturing research data ecosystem. The essential criteria for a trustworthy system include an organizational foundation that ensures longevity, sustainability, proper governance, and regular quality assessment of registration services. It also includes a reliable and secure technical platform, based on open standards, which is sufficiently scalable and flexible to accommodate the growing diversity of specimen types, use cases, and stakeholder requirements. In 2018, a major planning project for the IGSN was funded by the Alfred P. Sloan Foundation. An international group of experts participates in re-designing and improving the existing organization and technical architecture of the IGSN system, revising the current business model of the IGSN e.V. and professionalizing its operations. The goal is for the IGSN system to be able to respond to, and support in a sustainable manner, the rapidly growing demands of a global and increasingly multi-disciplinary user community, and to ensure that the IGSN will be a trustworthy, stable, and adaptable persistent identifier system for material samples, both technically and organizationally. The end result should also satisfy and facilitate participation across research domains, and will be a reliable component of the evolving research data ecosystem. Finally, it will ensure that the IGSN is recognized as a trusted partner by data infrastructure providers and the science community alike. [ABSTRACT FROM AUTHOR]

The article focuses on the benefit of Deep-time Digital Earth project in understanding the geoscientific research regarding deep-time and data sharing.

The composition and state of the earth's lithosphere through time has had profound effect on past and present biodiversity and will continue to do so into the future. Environments ranging from deep sea hydrothermal vents to active continental volcanic centres provide a wide range of ecosystems that have shaped the planet we know. Catastrophic events relating to movements of the lithosphere and events deep in the mantle have also caused major biodiversity changes such as mass extinctions. Our museum collections contain rock and fossil specimens collected from many of these environments and suites of samples specifically collected in order to better understand the evolution of our planet. Requests to carry out geochemical investigations on these samples are common and a large amount of data is generated as a result. Currently there are no natural history collections management systems tailored towards recording and delivering these datasets and the result is that the data is recorded in various distributed systems and cannot be easily assessed and used. It is important for these analyses on our museum collections to be delivered in a standard way so that the importance and relevance of these collections can be demonstrated and large datasets generated to answer big questions regarding the geological evolution of our planet. Examples of these questions include "how and when will volcanic eruptions will occur?" and "what has been the role of volcanism during mass extinction?". Other geochemical studies such as oxygen isotope studies have been carried out on museum collections in order to investigate past oceanic environments and the effects of changes in climate on our oceans. Geochemical data aggregators such as Earth Chem have made great strides in working towards international data standards and providing portals for delivering this type of data. As we progress towards one European Collection (DiSSCo) it is vital that we recognise the importance of these natural history collections related geochemical datasets and include delivering them on the general roadmap [ABSTRACT FROM AUTHOR]

Life sciences research, and even more specifically biodiversity sciences research, has yet to coalesece on a single system of identifiers for specimens (physical samples collected for research) or even a single set of standards for identifiers. Diverse identifier systems lead to duplication and ambiguity, which in turn lead to challenges in finding specimens, tracking and citing their usage, and linking them to data. Other research disciplines provide experience that biodiversity sciences could use to overcome these challenges. Earth sciences/geology may be the most advanced discipline in this regard, thanks to the use of the International GeoSample Number (IGSN) system, which was established to provide globally unique identifiers for geological samples. The original motivation of IGSN was to overcome duplication of sample numbers reported in the scientific literature and to support the correlation of observations on the same samples carried out by different laboratories and reported in different publications. The IGSN system is managed through a small set of 'allocating agents' who act on behalf of a national agency or community, under the overall coordination of the IGSN Organization - a volunteer group representing a mixture of research institutions and agencies. Similar to widely-recognized Digital Object Identifiers (DOIs), the primary requirement of an allocating agent is to maintain the mapping from an IGSN to a web 'landing page' corresponding to each sample. A standard (minimal) schema for describing samples registered with IGSN has been developed, but individual IGSN allocating agents will often supplement the base metadata with additional information. Other efforts are working on cross-disciplinary sample metadata schemas, but no single core standard has been agreed upon yet. An important part of the development of the IGSN system has been an engagement with scholarly publishers, with a goal of making each mention of an IGSN within a report or paper be a hyperlink, and also for links to other observations relating to the same sample to be automatically highlighted by the publisher. [ABSTRACT FROM AUTHOR]

The use of globally unique, persistent, and resolvable identifiers for samples ensures unambiguous identification of samples and actionable links from publications to online metadata profiles (landing pages) and to other data generated by other studies of the same sample. In recent years, the International Geo Sample Number (IGSN) has seen increasing uptake as a Persistent Identifier (PID) for physical samples that ensures unambiguous citation and tracking of physical samples and links samples to data and publications, allowing previously impossible linking and integration of sample-based observations across data systems and paving the road toward advanced data mining of sample-based data. Originally developed for the solid Earth Sciences with funding from the US National Science Foundation, the IGSN has evolved into an international PID system that is now adopted by a growing number and range of stakeholders worldwide, including researchers, collection curators, and data managers, and by other disciplines that need to refer to physical samples. More than 6.7 million samples have been registered so far. The recent expansion of the IGSN beyond the geosciences confirms the power of its concept and implementation, but imposes substantial pressures on the existing capacity and capabilities of the IGSN architecture and its governing organization. Significant updates to the IGSN organizational and technical architecture are necessary at this point to keep pace with the growing demand and expectations. As stated by Wittenburg et al. [2017], "in order to be useful and reliable, PID registration and resolution systems need to be trustworthy and sustainable". Essential criteria for trustworthiness include an organizational foundation that ensures longevity, sustainability, proper governance, and regular quality assessment of registration services, as well as a reliable and secure technical platform based on open standards that is sufficiently scalable and flexible to accommodate the growing diversity of specimen types, use cases, and stakeholder requirements.A major planning activity is currently underway funded by a recent award from the Sloan Foundation with participation of an international group of experts to re-design and improve the existing organization and technical architecture of the IGSN for it to be able to respond to, and support in a sustainable manner, the rapidly growing demands of a global and increasingly multi-disciplinary user community in a landscape of maturing research data infrastructures. This will include a revision of the current business model of the IGSN e.V. as it is no longer able to sustain the organization through the necessary professionalization of its operations. The goal is to ensure that the IGSN will be a trustworthy, stable, and adaptable persistent identifier system for material samples, both technically and organizationally, that attracts, facilitates, and satisfies participation within and beyond the Geosciences, that will be a reliable component of the evolving research data ecosystem, and that is recognized as a trusted partner by data infrastructure providers and the science community alike.Wittenburg, P., Hellström, M., Zwölf, C.-M., Abroshan, H., Asmi, A., Di Bernardo, G., Weigel, T.. (2017). Persistent identifiers: Consolidated assertions. https://doi.org/10.5281/zenodo.1116189. [ABSTRACT FROM AUTHOR]

Data facilities in the Earth, Space, and Environmental sciences provide indispensable services to the science community ensuring discovery, access, reusability, attribution, and preservation of data as essential products of and resources for scientific research. Most data facilities these days are challenged with meeting the growing demands of users and funders, and of a maturing research data ecosystem, from the rapidly expanding volumes of data that the facilities need to manage, requiring new levels of storage and network capacity and, consequentially, resources and team expertise; to the need to comply with international guidelines for trustworthy operation of services, including transparent and standards-based data curation procedures, licensing, risk management, security, and sustainability; to continuously evolving opportunities or requirements for enhancing machine access and interoperability of data holdings; to the need to maintain a diverse staff that keeps up with new and emerging technologies. In addition, they struggle with stagnant or declining funding and longer-term sustainability. Establishing infrastructure services that are shared among multiple data facilities can potentially help each facility better address these challenges, making operations more efficient and sustainable, while also leading to better alignment of technologies, policies, and procedures across data facilities, and opening opportunities for joint developments and innovation to meet future needs.The Council of Data Facilities (CDF) is a federation of existing and emerging geoscience data facilities that serves as an effective foundation for the US NSF EarthCube program and related contributors to delivering cyberinfrastructure for earth system science. One of the declared goals of the CDF since its inception has been to identify and support the development and utilization of shared infrastructure services, including computing and storage services, professional staff development and training services, and related activities. In 2018, a CDF Working Group was established to explore priorities and first steps for developing shared infrastructure. A preliminary survey was completed by 11 data facilities in 2018 and identified as priorities for shared infrastructure: cloud services (VMs, storage, computing, shared licenses); shared subscriptions for DOI minting and access to ORCID services; access to a shared pool of technical capacity (expertise & person hours) as well as shared expertise for repository certification; and joint training and knowledge sharing mechanisms. A more detailed assessment of facilities' needs for shared infrastructure is currently underway and will be completed in January 2019. This presentation will report on the results of this assessment and the CDF's plans for the next steps toward implementation of shared infrastructure services. [ABSTRACT FROM AUTHOR]

Integrated access to and analysis of data for cross-domain synthesis studies are hindered because common characteristics of observational data, including time, location, provenance, methods, and units are described differently within different information models, including physical implementations and exchange schema. We describe a new information model for spatially discrete Earth observations called the Observations Data Model Version 2 (ODM2) aimed at facilitating greater interoperability across scientific disciplines and domain cyberinfrastructures. ODM2 integrates concepts from ODM1 and other existing cyberinfrastructures to expand capacity to consistently describe, store, manage, and encode observational datasets for archival and transfer over the Internet. Compared to other systems, it accommodates a wider range of observational data derived from both sensors and specimens. We describe the identification of community information requirements for ODM2 and then present the core information model and demonstrate how it can be formally extended to accommodate a range of information requirements and use cases. [ABSTRACT FROM AUTHOR]