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.

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.

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.

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).

The following data was used in the paper "Schiller & Roscher (2023). Impact of Urbanization on construction material consumption: A global analysis" to calculate material consumption of non-metallic mineral construction materials. This dataset provided data on the extraction of mineral construction materials that are used for further processes. The data represent the extraction of raw materials from the environment by country. They are based on reported data on the one hand and estimated data on the other. The specific assumptions and factors for the estimates can be found in Krausmann et al. (2009). Growth in global material use, GDP and population during the 20th century. Ecological Economics 68(10) 2696-2705. doi: 10.1016/j.ecolecon.2009.05.007. In the MFA methodology, some categories of materials are defined as "unused extraction" because they are not economically used or further processed. For example, unused materials include overburden from mining activities and unused residues from biomass extraction (OECD, 2008). These are not included in presented data. The following materials belong to the group of construction minerals: asphalt, chert and flint, common clay, clay for bricks etc., crushed stone, igneous rock, lava sand, limestone, marl, shell, loam, marble, travertines, sand and gravel, sandstone, slate and turfaceous rock (see Lutter, S., Lieber, M., & Giljum, S. (2016). Global Materialflow database. Material extraction data. Technical Report, Version 2015.1. retrieved February 2018 from www.materialflows.net). The status of the given data is spring 2018 and was downloaded from www.materialflows.net at this time. Due to restructuring of the website, 2018 data is no longer available online. Current data on material extraction and consumption can be found on United Nations Environment Programme, International Resource Panel (IRP). (2023). Global Material Flows Database. https://www.resourcepanel.org/global-material-flows-database.

The research for the related journal article was supported by basic funding from the Leibniz Institute for Ecological Urban and Regional Development, Dresden.

Data were collected by Geological Survey of the Republic of Srpska, Bosnia and Herzegovina, in frame of RESEERVE project.

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.

China PPI: Mining: Non Metal Mineral: Chemical Materials Ores Mining data was reported at 99.700 Prev Year=100 in Jun 2026. This records a decrease from the previous number of 100.800 Prev Year=100 for May 2026. China PPI: Mining: Non Metal Mineral: Chemical Materials Ores Mining data is updated monthly, averaging 96.900 Prev Year=100 from Jan 2014 (Median) to Jun 2026, with 150 observations. The data reached an all-time high of 142.400 Prev Year=100 in Jul 2022 and a record low of 93.700 Prev Year=100 in Jan 2014. China PPI: Mining: Non Metal Mineral: Chemical Materials Ores Mining data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IE: Producer Price Index: Same Month PY=100.

Since the end of the 1980ies the geological, areal and production data of operating mining sites have been collected systematically by LGRB. The periodic update of this information is carried out every four or five years. Main reasons are 1) the preparation of the periodic follow-up of the 12 regional development plans, 2) the work on the near-surface mineral raw material maps published by LGRB, and 3) the periodical editing of the state report for near-surface mineral raw materials published by LGRB at the start of each new election period. The geological data include a detailed documentation of the thickness, petrography and quality of mined rock(s) and the overburden as well as geochemical data gained from rock samples. The areal data refer both to the permitted mining area (zones of recultivation, work and expansion) and to possible areas for the mine expansion (the latter are confidential). Due to the quick spatiotemporal variability of these data, here all mining sites are shown as point data. The confidential annual production data are the basis for the periodic raw material report. In addition, another data are collected, e.g. for the mining permission, the delivery area and the subsequent land use. All these data are stored in the mining site database of the LGRB (Rohstoffgewinnungs-stellendatenbank = RGDB). This one comprises also the data for abandoned mining sites and mines. In total, actual (2021) about 14.000 data records are stored. The name of each mining site (e.g. RG 6826-3) consists of three parts. RG is the abbreviation for "Rohstoffgewinnungsstelle". the following four-digit number means the number of the relevant topographic map 1 : 25.000. The last number means the serial number of the mining site; serial numbers 1-99 mark operating mining sites gathered since the end of the 1980ies ( (today partially already closed) , such > 100 mark abandoned mining sites collected before 1980 and such > 300 mark data of mining sites and mines collected in the course of actual raw material mapping.
The mintell4eu data set comprises all mining sites with serial numbers 1-99. In addition, the most important abandoned mines of former or probably still ongoing economic importance.

Recently, as the demand for electric vehicles has rapidly grown, concerns regarding the safety of liquid electrolytes used as battery materials have increased. Rechargeable batteries made of liquid electrolytes pose a risk of fire and may explode due to the decomposition reaction of the electrolyte. Accordingly, interest in solid-state electrolytes (SSEs), which have greater stability than liquid electrolytes, is increasing, and research into finding stable SSEs with high ionic conductivity is actively being conducted. Consequently, it is essential to obtain a large amount of material data to explore new SSEs. However, the data collection process is highly repetitive and time-consuming. Therefore, the goal of this study is to automatically extract the ionic conductivities of SSEs from published literature using text-mining algorithms and use this information to construct a materials database. The extraction procedure includes document processing, natural language preprocessing, phase parsing, relation extraction, and data post-processing. For performance verification, the ionic conductivities are extracted from 38 studies, and the accuracy of the proposed model is confirmed by comparing extracted conductivities with the actual ones. In previous research, 93% of battery-related records were unable to distinguish between ionic and electrical conductivities. However, by applying the proposed model, the proportion of undistinguished records was successfully reduced from 93 to 24.3%. Finally, the ionic conductivity database was constructed by extracting the ionic conductivity from 3258 papers, and the battery database was reconstructed by adding eight pieces of representative structural information.

Abstract: This repository/dataset provides a suite of Python scripts to generate a simulated relational database for inventory management processes and transform this data into object-centric event logs (OCEL) suitable for advanced process mining analysis. The primary goal is to offer a synthetic yet realistic dataset that facilitates research, development, and application of object-centric process mining techniques in the domain of inventory control and supply chain management. The generated event logs capture common inventory operations, track stock level changes, and are enriched with key inventory management parameters (like EOQ, Safety Stock, Reorder Point) and status-based activity labels (e.g., indicating understock or overstock situations).

Overview: Inventory management is a critical business process characterized by the interaction of various entities such as materials, purchase orders, sales orders, plants, suppliers, and customers. Traditional process mining often struggles to capture these complex interactions. Object-Centric Process Mining (OCPM) offers a more suitable paradigm. This project provides the tools to create and explore such data.

The workflow involves:

1. Database Simulation: Generating a SQLite database with tables for materials, sales orders, purchase orders, goods movements, stock levels, etc., populated with simulated data.
2. Initial OCEL Generation: Extracting data from the SQLite database and structuring it as an object-centric event log (in CSV format). This log includes activities like "Create Purchase Order Item", "Goods Receipt", "Create Sales Order Item", "Goods Issue", and tracks running stock levels for materials.
3. OCEL Post-processing and Enrichment:
   • Calculating standard inventory management metrics such as Economic Order Quantity (EOQ), Safety Stock (SS), and Reorder Point (ROP) for each material-plant combination based on the simulated historical data.
   • Merging these metrics into the event log.
   • Enhancing activity labels to include the current stock status (e.g., "Understock", "Overstock", "Normal") relative to calculated SS and Overstock (OS) levels (where OS = SS + EOQ).
   • Generating new, distinct events to explicitly mark the moments when stock statuses change (e.g., "START UNDERSTOCK", "ST CHANGE NORMAL to OVERSTOCK", "END NORMAL").
4. Format Conversion: Converting the CSV-based OCELs into the standard OCEL XML/OCEL2 format using the pm4py library.

Contents:

The repository contains the following Python scripts:

• 01_generate_simulation.py:

  • Creates a SQLite database named inventory_management.db.
  • Defines and populates tables including: Materials, SalesOrderDocuments, SalesOrderItems, PurchaseOrderDocuments, PurchaseOrderItems, PurchaseRequisitions, GoodsReceiptsAndIssues, MaterialStocks, MaterialDocuments, SalesDocumentFlows, and OrderSuggestions.
  • Simulates data for a configurable number of materials, customers, sales, purchases, etc., with randomized dates and quantities.

• 02_database_to_ocel_csv.py:

  • Connects to the inventory_management.db.
  • Executes a SQL query to extract relevant events and their associated objects for inventory processes.
  • Constructs an initial object-centric event log, saved as ocel_inventory_management.csv.
  • Identified object types include: MAT (Material), PLA (Plant), PO_ITEM (Purchase Order Item), SO_ITEM (Sales Order Item), CUSTOMER, SUPPLIER.
  • Calculates "Stock Before" and "Stock After" for each event affecting material stock.
  • Standardizes column names to OCEL conventions (e.g., ocel:activity, ocel:timestamp, ocel:type:).

• 03_ocel_csv_to_ocel.py:

  • Reads ocel_inventory_management.csv.
  • Uses pm4py to convert the CSV event log into the standard OCEL XML format (ocel_inventory_management.xml).

• 04_postprocess_activities.py:

  • Reads data from inventory_management.db to calculate inventory parameters:
    • Annual Demand (Dm)
    • Average Daily Demand (dm)
    • Standard Deviation of Daily Demand (σm)
    • Average Lead Time (lm)
    • Economic Order Quantity (EOQ): (2⋅Dm⋅S)/H (where S is fixed order cost, H is holding cost)
    • Safety Stock (SS): z⋅σm⋅lm (where z is the z-score for the desired service level)
    • Reorder Point (ROP): (dm⋅lm)+SS
  • Merges these calculated parameters with ocel_inventory_management.csv.
  • Computes an Overstock level (OS) as SS+EOQ.
  • Derives a "Current Status" (Understock, Overstock, Normal) for each event based on "Stock After" relative to SS and OS.
  • Appends this status to the ocel:activity label (e.g., "Goods Issue (Understock)").
  • Generates new events for status changes (e.g., "START NORMAL", "ST CHANGE UNDERSTOCK to NORMAL", "END OVERSTOCK") with adjusted timestamps to precisely mark these transitions.
  • Creates a new object type MAT_PLA (Material-Plant combination) for easier status tracking.
  • Saves the enriched and transformed log as post_ocel_inventory_management.csv.

• 05_ocel_csv_to_ocel.py:

  • Reads the post-processed post_ocel_inventory_management.csv.
  • Uses pm4py to convert this enriched CSV event log into the standard OCEL XML format (post_ocel_inventory_management.xml).

Generated Dataset Files (if included, or can be generated using the scripts):

• inventory_management.db: The SQLite database containing the simulated raw data.
• ocel_inventory_management.csv: The initial OCEL in CSV format.
• ocel_inventory_management.xml: The initial OCEL in standard OCEL XML format.
• post_ocel_inventory_management.csv: The post-processed and enriched OCEL in CSV format.
• post_ocel_inventory_management.xml: The post-processed and enriched OCEL in standard OCEL XML format.

How to Use:

1. Ensure you have Python installed along with the following libraries: sqlite3 (standard library), pandas, numpy, pm4py.
2. Run the scripts sequentially in a terminal or command prompt:
   • python 01_generate_simulation.py (generates inventory_management.db)
   • python 02_database_to_ocel_csv.py (generates ocel_inventory_management.csv from the database)
   • python 03_ocel_csv_to_ocel.py (generates ocel_inventory_management.xml)
   • python 04_postprocess_activities.py (generates post_ocel_inventory_management.csv using the database and the initial CSV OCEL)
   • python 05_ocel_csv_to_ocel.py (generates post_ocel_inventory_management.xml)

Potential Applications and Research: This dataset and the accompanying scripts can be used for:

• Applying and evaluating object-centric process mining algorithms on inventory management data.
• Analyzing inventory dynamics, such as the causes and effects of understocking or overstocking.
• Discovering and conformance checking process models that involve multiple interacting objects (materials, orders, plants).
• Investigating the impact of different inventory control parameters (EOQ, SS, ROP) on process execution.
• Developing educational materials for teaching OCPM in a supply chain context.
• Serving as a benchmark for new OCEL-based analysis techniques.

Keywords: Object-Centric Event Log, OCEL, Process Mining, Inventory Management, Supply Chain, Simulation, Synthetic Data, SQLite, Python, pandas, pm4py, Economic Order Quantity (EOQ), Safety Stock (SS), Reorder Point (ROP), Stock Status Analysis.

Seawater represents a potential resource for raw materials extraction. Although NaCl is the most representative mineral extracted other valuable compounds such as Mg, Li, Sr, Rb and B and elements at trace level (Cs, Co, In, Sc, Ga and Ge) are also contained in this “liquid mine”. Most of them are considered as Critical Raw Materials by the European Union. Solar saltworks, providing concentration factors of up-to 20 to 40, offer a perfect platform for the development of minerals and metal recovery schemes taking benefit of the concentration and purification achieved along the evaporation saltwork ponds. However, the geochemistry of these elements in this environment has not been yet thoroughly evaluated. Their knowledge could enable the deployment of technologies capable to achieve the recovery of valuable minerals. The high ionic strengths expected (0.5–7 mol/kg) and the chemical complexity of the solutions imply that only numerical geochemical codes, as PHREEQC, and the use of Pitzer model to estimate the activity coefficients of the different species in solution can be adopted to provide valuable description of the systems. In the present work, for the first time, PHREEQC Pitzer code database was extended to include the target minor and trace elements using Trapani saltworks (Sicily, Italy) as a case study system. The model was able to predict: i) the purity in halite and the major impurities contained, mainly Ca,Mgand sulphate species; ii) the fate of minor components as B, Sr, Cs, Co, Ge and Ga along the evaporation ponds. The results obtained pose a fundamental step in critical raw materials mining from seawater brine, for process intensification and combination with desalination.

A key technical challenge inhibiting fossil-free energy generation, which is especially pronounced in solar plants, is the inherent intermittency. Since thermochemical energy storage represents a solution that has yet to see large-scale utilization, the goal of the study at hand has been to find suitable materials using a hybrid high-throughput approach, encompassing both data mining and first principles calculations. Through these efforts, thermal properties have been estimated for more than 50 000 mono-, bi-, and trimetallic oxides, as well as alloys, retrieved from the Materials Project database.Thereafter, close to 18 000 convex hulls were created, each representing a unique metal ratio, in order to determine the most stable phases at different oxygen chemical potentials. In turn, this allowed almost 300 000 potential transitions to be identified. After filtering out potentially toxic and rare compositions, a little over 51 000 remained, which were further reduced, to about 3000, by requiring that the phase transformation must occur at a temperature between 400 °C and 1300 °C.A thorough analysis of the results led to the discovery of several promising energy storage materials. In addition, Chromium; manganese; calcium; and magnesium were found to be associated with high reaction enthalpies. When the sensible heat was taken into account, however, light elements such as lithium; boron; iron; and sodium dominated among the top-ranking candidates. This study therefore demonstrates how high-throughput data mining can be efficiently enhanced through first-principles calculations, enabled by machine-learning interatomic potentials, to facilitate material discovery.

1.Framework overview. This paper proposed a pipeline to construct high-quality datasets for text mining in materials science. Firstly, we utilize the traceable automatic acquisition scheme of literature to ensure the traceability of textual data. Then, a data processing method driven by downstream tasks is performed to generate high-quality pre-annotated corpora conditioned on the characteristics of materials texts. On this basis, we define a general annotation scheme derived from materials science tetrahedron to complete high-quality annotation. Finally, a conditional data augmentation model incorporating materials domain knowledge (cDA-DK) is constructed to augment the data quantity.2.Dataset information. The experimental datasets used in this paper include: the Matscholar dataset publicly published by Weston et al. (DOI: 10.1021/acs.jcim.9b00470), and the NASICON entity recognition dataset constructed by ourselves. Herein, we mainly introduce the details of NASICON entity recognition dataset.2.1 Data collection and preprocessing. Firstly, 55 materials science literature related to NASICON system are collected through Crystallographic Information File (CIF), which contains a wealth of structure-activity relationship information. Note that materials science literature is mostly stored as portable document format (PDF), with content arranged in columns and mixed with tables, images, and formulas, which significantly compromises the readability of the text sequence. To tackle this issue, we employ the text parser PDFMiner (a Python toolkit) to standardize, segment, and parse the original documents, thereby converting PDF literature into plain text. In this process, the entire textual information of literature, encompassing title, author, abstract, keywords, institution, publisher, and publication year, is retained and stored as a unified TXT document. Subsequently, we apply rules based on Python regular expressions to remove redundant information, such as garbled characters and line breaks caused by figures, tables, and formulas. This results in a cleaner text corpus, enhancing its readability and enabling more efficient data analysis. Note that special symbols may also appear as garbled characters, but we refrain from directly deleting them, as they may contain valuable information such as chemical units. Therefore, we converted all such symbols to a special token

Under the Act of June 9, 2011, Geological and Mining Law (Journal of Laws No. 163, item 981, as amended) Polish Geological Institute – National Research Institute (PIG-PIB) performs the role of Polish Geological Survey. One of the tasks of PIG-PIB as Geological Survey is to create and maintain geological databases, in this including the system MIDAS (the System of management and protection of mineral resources in Poland – MIDAS). System MIDAS is the primary source of information on mineral resources of Poland, the exploitation of deposits and it is the source of data for the project Mintell4eu. The System contains information on deposits (and its spatial location), raw materials in deposits, raw materials resources and on the raw materials volumes of exploitation. In addition, it also contains data on mining areas and exploatation permits (concessions) as well as their spatial location. The owner of the colected data is the State Treasury represented by the proper minister resposible for geology. The original MIDAS database structure has been modified and adapted to the structure given in Mintell4eu specification.

Indonesia Wholesale Price Index: Mining & Quarrying: Stone for Manufacturing Materials data was reported at 173.440 2010=100 in Dec 2018. This stayed constant from the previous number of 173.440 2010=100 for Nov 2018. Indonesia Wholesale Price Index: Mining & Quarrying: Stone for Manufacturing Materials data is updated monthly, averaging 188.560 2010=100 from Jan 2013 (Median) to Dec 2018, with 72 observations. The data reached an all-time high of 207.490 2010=100 in Apr 2014 and a record low of 101.740 2010=100 in Feb 2013. Indonesia Wholesale Price Index: Mining & Quarrying: Stone for Manufacturing Materials data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IB003: Wholesale Price Index: by Sector: Mining and Quarrying.

Under the Act of June 9, 2011, Geological and Mining Law (Journal of Laws No. 163, item 981, as amended) Polish Geological Institute – National Research Institute (PIG-PIB) performs the role of Polish Geological Survey. One of the tasks of PIG-PIB as Geological Survey is to create and maintain geological databases, in this including the system MIDAS (the System of management and protection of mineral resources in Poland – MIDAS). System MIDAS is the primary source of information on mineral resources of Poland, the exploitation of deposits and it is the source of data for the project Mintell4eu. The System contains information on deposits (and its spatial location), raw materials in deposits, raw materials resources and on the raw materials volumes of exploitation. In addition, it also contains data on mining areas and exploatation permits (concessions) as well as their spatial location. The owner of the colected data is the State Treasury represented by the proper minister resposible for geology.

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.

Czech Republic IPI: NACE 1.1: Mining and Quarrying: Energy Producing Materials data was reported at 90.563 2000=100 in Dec 2008. This records a decrease from the previous number of 99.926 2000=100 for Nov 2008. Czech Republic IPI: NACE 1.1: Mining and Quarrying: Energy Producing Materials data is updated monthly, averaging 97.202 2000=100 from Jan 2000 (Median) to Dec 2008, with 108 observations. The data reached an all-time high of 113.906 2000=100 in Nov 2001 and a record low of 74.988 2000=100 in Jul 2007. Czech Republic IPI: NACE 1.1: Mining and Quarrying: Energy Producing Materials data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.B013: Industrial Production Index: NACE 1.1: 2000=100.