See also the associated Data Descriptor published in Nature Scientific Data: www.nature.com/articles/s41597-023-01965-y

This data set covers global extraction of coal and metal ores on an individual mine level. It covers
1171 individual mines in 80 different countries, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well as mineral processing capacities (smelters and mineral refineries) and production is included. The data was gathered manually from more than 1900 openly available sources, such as annual or sustainability reports of mining companies. All datapoints are linked to their respective source documents. After manual screening and entry of the data, automatic cleaning, harmonization and data checking was conducted. Geoinformation was obtained either from coordinates available in company reports, or by retrieving the coordinates via Google Maps API and subsequent manual checking. For mines where no coordinates could be found, other geospatial attributes such as province, region, district or municipality were recorded, and linked to the GADM data set, available at www.gadm.org.

The data set, found in the "data" sub-folder, consists of 12 tables. The table “facilities” contains descriptive and spatial information of mines and processing facilities, and is available as a GeoPackage (GPKG) file. All other tables are available in comma-separated values (CSV) format. If you are working in Excel or have problems handling the GeoPackage file, it can be converted to Excel with an online tool, such as https://mygeodata.cloud/converter/gpkg-to-xlsx.

A schematic depiction of the database is provided in the file database_model.pdf. A description of all variables of all tables is provided in the Excel file variables_descriptions.xlsx, and all materials for which production is reported in the database are listed in the file materials_covered.xlsx.

For convenience, global and national coverage shares for every material and country with recorded production in the database is provided in the file coverage_table.pdf. These coverage shares were calculated by comparing the production values of this database to official production statistics reported in the UNEP IRP Global Material Flows Database, to be found under https://www.resourcepanel.org/global-material-flows-database. For significant raw material producing countries, these coverage shares are also visualised in the file coverage_national_area_charts.pdf.

Source: https://zenodo.org/record/7369478

This data set covers global extraction of coal and metal ores on an individual mine level. It covers 1171 individual mines in 80 different countries, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well as mineral processing capacities (smelters and mineral refineries) and production is included. The data was gathered manually from more than 1900 openly available sources, such as annual or sustainability reports of mining companies. All datapoints are linked to their respective source documents. After manual screening and entry of the data, automatic cleaning, harmonization and data checking was conducted. Geoinformation was obtained either from coordinates available in company reports, or by retrieving the coordinates via Google Maps API and subsequent manual checking. For mines where no coordinates could be found, other geospatial attributes such as province, region, district or municipality were recorded, and linked to the GADM data set, available at www.gadm.org.

The data set, found in the "data" sub-folder, consists of 12 tables. The table “facilities” contains descriptive and spatial information of mines and processing facilities, and is available as a GeoPackage (GPKG) file. All other tables are available in comma-separated values (CSV) format. If you are working in Excel or have problems handling the GeoPackage file, it can be converted to Excel with an online tool, such as https://mygeodata.cloud/converter/gpkg-to-xlsx.

A schematic depiction of the database is provided in the file database_model.pdf. A description of all variables of all tables is provided in the Excel file variables_descriptions.xlsx, and all materials for which production is reported in the database are listed in the file materials_covered.xlsx.

For convenience, global and national coverage shares for every material and country with recorded production in the database is provided in the file coverage_table.pdf. These coverage shares were calculated by comparing the production values of this database to official production statistics reported in the UNEP IRP Global Material Flows Database, to be found under https://www.resourcepanel.org/global-material-flows-database. For significant raw material producing countries, these coverage shares are also visualised in the file coverage_national_area_charts.pdf.

Chemical looping is an innovative technique that relies, to a large extent, on the possibility of finding new oxygen carriers. Until now, these materials have primarily been identified via experimental techniques and therefrom derived insights. However, this is both costly and time-consuming. To speed-up this process, we have applied a computational screening approach based on energetic data retrieved from the Open Quantum Materials Database. In particular, we have considered combinations of all mono-, bi-, and trimetallic alloys and mixed oxides with up to three distinctive phases. Here, we specifically focus on a technique referred to as chemical looping oxygen uncoupling, which is especially suitable for solid fuels, e.g., combustion of biomass for negative CO2 emissions. The formation energies obtained for the materials of interest were used to identify phase transitions that are likely to occur under conditions relevant for chemical looping oxygen uncoupling. Given these criteria, the initial list of 300000 materials is reduced by a factor of 20, and after filtering out rare, radioactive, toxic, or harmful elements only 1000 remain. When considering the abundance of elements in the ranking criteria, most of the highest ranking phases include Cu, Mn, and Fe. This adds credibility to the procedure, as many viable oxygen carriers for chemical looping oxygen uncoupling that have been studied experimentally contain these elements. While Cr-based materials have not been widely explored for this application, our study suggests that this might be worthwhile since these occur more frequently than Fe. Other elements that would be interesting as additional components include Ba, K, Na, Al, and Si.

As semiconductor devices are miniaturized, the importance of atomic layer deposition (ALD) technology is growing. When designing ALD precursors, it is important to consider the melting point, because the precursors should have melting points lower than the process temperature. However, obtaining melting point data is challenging due to experimental sensitivity and high computational costs. As a result, a comprehensive and well-organized database for the melting point of the OMCs has not been fully reported yet. Therefore, in this study, we constructed a database of melting points for 1,845 OMCs, including 58 metal and 6 metalloid elements. The database contains CAS numbers, molecular formulas, and structural information and was constructed through automatic extraction and systematic curation. The melting point information was extracted using two methods: 1) 1,434 materials from 11 chemical vendor databases and 2) 411 materials identified through natural language processing (NLP) techniques with an accuracy of 86.3%, based on 2,096 scientific papers published over the past 29 years. In our database, the OMCs contain up to around 250 atoms and have melting points that range from −170 to 1610 °C. The main source is the Chemsrc database, accounting for 607 materials (32.9%), and Fe is the most common central metal or metalloid element (15.0%), followed by Si (11.6%) and B (6.7%). To validate the utilization of the constructed database, a multimodal neural network model was developed integrating graph-based and feature-based information as descriptors to predict the melting points of the OMCs but moderate performance. We believe the current approach reduces the time and cost associated with hand-operated data collection and processing, contributing to effective screening of potentially promising ALD precursors and providing crucial information for the advancement of the semiconductor industry.

Materials database of Curie and Néel magnetic phase transition temperatures.

Each record contains a field indicating the type of phase transition (Curie or Néel), a compound or label, extracted and normalised values and units, the article title, journal, DOI, and authors of the source article. Furthermore, if the record was retrieved using the modified Snowball quaternary relationship extraction stage, the record contains the associated confidence score.

The database can be downloaded in MongoDB, CSV or JSON formats.

The vastness of materials space, particularly that which is concerned with metal–organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

The Service géologique du Luxembourg (Geological Survey of Luxembourg) SGL, a division of the Administration des ponts et chaussées (Roads and bridges administration), collects information about past and current mineral resource occurrence and extraction sites in Luxembourg. This information is assembled in a spatial database (about 800 records). The majority of these are building and dimension stone quarries (sandstone, limestone) and iron ore mines. Accessorily, there are slate quarries and a few other metallic ore mines. In the context of MINTELL4EU, about 550 validated records on quarries, gravel pits and mines were extracted and harmonized according to the MINTELL4EU guidelines.

No description is available. Visit https://dataone.org/datasets/sha256%3Af2aeeb477c68f0da4a5e7303fd4552f70f57785be8e6ffdbe0f395ba992c2fc0 for complete metadata about this dataset.

Three known binary compounds of tin (Sn) and sulfur (S), namely, SnS, SnS2, and Sn2S3, have been extensively studied for potential application in energy generation and conversion applications. Inspired by the existence of many metastable phases of SnS, we explore the chemical space of nine crystalline solids with chemical composition SnxS1–x (x falls between 0.25 and 0.75), predicting that Sn3S is thermodynamically stable in a metallic Pmn21 phase. Due to the layered structure of this phase, Sn3S is a quasi two-dimensional material, characterized by highly anisotropic electronic-related properties. Moreover, the discovered metastable structures of Sn3S2, Sn2S, and Sn5S2 are just about 5 meV/atom above the stability limit, and may potentially be realized. The data set of 369 low-energy structures of nine SnxS1–x crystalline solids reported in this work is a reliable sample of the low-energy sector of the chemical space, and thus being useful for the currently established materials databases, providing a playground for future data-mining works in materials discoveries.

This is a condensed version of HTEM database downloaded via HTEM API from National Renewable Energy Laboratory. Due to network constraints, all entries without XRD entries are discarded.

Dataset Overview

The index file contains experiment conditions of 1400+ experiments performed by the high-throughput experiment platform in NREL. Each experiments contains 44 samples, whose associated data are stored in the samples folder. The 44 samples in each experiment all have different thin film thickness and composition. Depending on the experiment setup, the sample data files may contain data from X-ray Fluorescence (thin film composition), X-ray Diffraction (crystalline structure), electronic measurement (thin film conductivity), and optical spectra (light absorption).

This dataset provides a complete record of experimental condition, structural characterization, and properties measurement, making it a valuable resource for data-mining for a better understanding of complex process-structure-property relationships in thin film materials.

Please cite: A. Zakutayev, N. Wunder, M. Schwarting, J. D. Perkins, R. White, K. Munch, W. Tumas and C. Phillips, Sci Data 5, 180053 (2018).

Data were provided by geological Survey of Serbia - GSS.

A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) processes are still needed to efficiently access and exploit this accumulated knowledge, paving the way towards data-driven materials design. Herein, we present SuperMat (Superconductor Materials), an annotated corpus of linked data derived from scientific publications on superconductors, which comprises 142 articles, 16052 entities, and 1398 links that are characterised into six categories: the names, classes, and properties of materials; links to their respective superconducting critical temperature (Tc); and parametric conditions such as applied pressure or measurement methods. The construction of SuperMat resulted from a fruitful collaboration between computer scientists and material scientists, and its high quality is ensured through validation by domain experts. The quality of the annotation guidelines was ensured by satisfactory Inter Annotator Agreement (IAA) between the annotators and the domain experts. SuperMat includes the dataset, annotation guidelines, and annotation support tools that use automatic suggestions to help minimise human errors.

This study presents an experimental database focused on the cyclic response of mine tailings compiled from various sources. The database is organized considering three classes. Class A contains cyclic simple shear (CSS), triaxial (Tx), and cone penetration testing (CPTu) information, Class B has CSS and Tx or CPTu information, and Class C has only CSS information. Most materials belong to Class A. Of note, Class C is comparable with most databases for natural soils; hence, highlighting the uniqueness of our database. This data can give valuable insight into the field and lab scale response of mine tailings to cyclic loading.

Tiedot saatiin geologinen Survey of Serbia - GSS.

Machine learning transforms the landscape of 2D materials design, particularly in accelerating discovery, optimization, and screening processes. This review has delved into the historical and ongoing integration of machine learning in 2D materials for electrochemical energy applications, using the Knowledge Discovery in Databases (KDD) approach to guide the research through data mining from the Scopus database using analysis of citations, keywords, and trends. The topics will first focus on a “macro” scope, where hundreds of literature reports are computer analyzed for key insights, such as year analysis, publication origin, and word co-occurrence using heat maps and network graphs. Afterward, the focus will be narrowed down into a more specific “micro” scope obtained from the “macro” overview, which is intended to dive deep into machine learning usage. From the gathered insights, this work highlights how machine learning, density functional theory (DFT), and traditional experimentation are jointly advancing the field of materials science. Overall, the resulting review offers a comprehensive analysis, touching on essential applications such as batteries, fuel cells, supercapacitors, and synthesis processes while showcasing machine learning techniques that enhance the identification of critical material properties.

mined_10_properties.tsv — All validated property entries for the ten target properties: optical bandgap, exciton binding energy, total/radiative/non-radiative exciton lifetime, exciton linewidth, exciton diffusion length, exciton diffusion coefficient, exciton Bohr radius, and exciton reduced mass. Each row records the source DOI, material name, a discriminator capturing the measurement context, property name, value, and unit.mined_dielectric_constant.tsv — Static dielectric constants collected in a separate mining pass, restricted to documents from which at least one exciton binding energy was mined, so that each value can be paired with a binding energy from the same source.eval_extraction_only.xlsx, eval_extraction_validation.xlsx, eval_extraction_validation_ebe_only.xlsx — Manually curated ground truth and per-trial predictions for the exciton binding energy, used to compute the precision, recall, and F1 scores of the extraction and validation steps. Based on 1,200 randomly selected articles containing 236 distinct binding energy entries across 156 articles.eval_screening_triplet.xlsx — Evaluation set of 202 total exciton lifetime entries (34 triplet, 168 non-triplet) with ground truth labels, used to assess the semantic screening step.Units — Values are standardized per property: eV for optical bandgap, binding energy, and linewidth; ns for lifetimes; nm for Bohr radius and diffusion length; cm²/s for diffusion coefficient; m₀ for reduced mass; dimensionless for dielectric constant.Scope — Only extracted factual values are distributed. Source full texts obtained through publisher APIs are not included for licensing reasons; entries can be traced to their source through the DOI field.

Since 1999, the Geologic Survey of Baden-Württemberg publishes a statewide geological map series 1 : 50 000 "Karte der mineralischen Rohstoffe 1 : 50 000 (KMR 50)". On it, the distribution of near-surface mineral raw material prospects and occurrences (mainly) and deposits (subordinate) is shown. This continuously completed and updated map currently covers around 60% of the federal state. It is the base for the regional associations in the task of mineral planning.

The prospects and occurrences are classified according to different raw material groups (e.g. raw material for crushed stone (limestone, igneous rocks, metamorphic rocks, sand and gravel), raw materials for cement, dimension stone, high purity limestone, gypsum ...). Their spatial delineation is based on various group-specific criteria such as minimum workable thickness, minimum resources, ratio overburden/workable thickness, and so on. It is assumed that they contain deposits as a whole or in parts. In the vast majority of cases, the data is not sufficient for the immediate planning of mining projects, but it does facilitate the selection of exploration areas.

The name of each area (e.g. L 6926-3) consists of three parts. L = roman rnumeral fo 50, 6926 = sheet number of the topographic map 1 : 50 000, 3 = number of the area/mineral occurrence shown on this sheet.

Co-occurring land-use conflicts, e.g. water protection areas and nature conservation areas, forestry and agriculture, are not taken into account in the processing of KMR 50. Their assessment is the task of land use planning, the licensing authorities and the companies interested in mining.

The data is stored in the statewide raw material area database "olan-db" of the LGRB.

MIN4EU DB se compose de deux parties: Données de l’inventaire des minéraux et données de l’Annuaire des minéraux. L’inventaire des minéraux couvre les occurrences minérales et les mines en Europe (à terre). L’Union européenne a identifié la sécurité d’approvisionnement, l’amélioration de la gestion de l’environnement et l’utilisation efficace des ressources comme des défis majeurs pour le secteur des matières premières. Les données sur l’emplacement et la distribution spatiale des matières premières primaires et secondaires en relation avec l’exploration, l’exploitation, la production et les activités commerciales constituent la base de la prise de décisions au sein du gouvernement et de l’industrie. Compte tenu de la nature dynamique de ces données, des mises à jour régulières d’informations complètes, fiables et harmonisées au-delà des frontières sont nécessaires, car il existe plusieurs sources de données non harmonisées avec différentes couvertures développées au cours des dernières décennies par des projets nationaux et internationaux à des fins différentes. Les données ont été préparées et collectées dans le cadre des projets Minerals4EU, EURare, ProSUM, ORAMA, Reseerve et MINTELL4EU, et d’autres, et sont partagées dans l’infrastructure européenne de données géologiques (EGDI).

This dataset contains the materials used to develop and evaluate SciSchema.org: a collection of machine-actionable schemas for describing scientific processes. It includes the final expert-annotated schemas, intermediate model-generated schemas, domain-expert feedback, source materials, community-building documents, and analysis outputs.

Navigating the dataset

• core/ contains one folder for each of the 16 scientific processes. For each process, master-schema/ holds the final expert-annotated schema in JSON Schema (.json) and SHACL, serialized in Notation3 (.n3). The stage-* folders preserve the process descriptions, model-generated schemas, and expert feedback from successive development stages; metadata.csv records process-level metadata.
• raw-expert-feedback/ contains the exported expert-feedback form responses in TSV format.
• community-building/ contains calls for participation and instructions used during community data collection and schema development.
• paper-analysis/ contains analysis data, scripts, and generated figures used for the accompanying paper. This includes token-length analyses, structural schema-complexity analyses, final expert-rating analyses, and figure-generation scripts. The subfolder paper-analysis/figure_3/ contains the data, script, and outputs for the Data Overview section figure in the paper, summarizing the structural characteristics of the 16 final expert-annotated master schemas.

For direct reuse, begin with core/. Use the staged materials, raw feedback, and paper-analysis/ files when examining how a master schema was developed or reproducing the analyses reported in the accompanying paper.

Online access

• A live version of SciSchema.org is actively maintained.
• Persistent access to the collection is provided through the SciSchema W3ID namespace.
• The schema catalogue provides persistent access to the published schemas.
• The development repository contains the JSON Schema files served by the website.

This archive is a versioned research snapshot intended to accompany its Zenodo record and the associated paper. Use the live resource for current schemas and this archived version for reproducible reference.

License

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise stated. Reusers may share and adapt the materials, including the schemas, provided that appropriate credit is given and adaptations are distributed under the same license.

Analysis scripts included in this archive are provided for reproducibility. Unless otherwise indicated, they may be reused under the same license as the dataset.

Related Data Descriptor

This dataset accompanies the Data Descriptor SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions.

Citation

If you reuse this dataset, please cite the Zenodo record.

The published Data Descriptor citation is forthcoming.

Данните са събрани от Геоложкото проучване на Албания в рамките на проект RESEERVE.