Please see the individual layers below to access the detailed metadata.This feature layer contains three datasets:The Mining Boreholes dataset contains GIS points depicting mining boreholes digitized from the U.S. Bureau of Mines (USBM) Illinois Mineral Development Atlas (IMDA) for Jo Daviess County, Illinois. Each point includes a link to a corresponding log (if available). This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.The named mines dataset contains GIS polygons depicting surveyed outlines of known (named) mine diggings from the U.S. Bureau of Mines (USBM) Illinois Mineral Department Atlas (IMDA) for Jo Daviess County, Illinois. This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.The unnamed mines dataset contains GIS polygons depicting unsurveyed inferred outlines of unknown (unnamed) mine diggings from the U.S. Bureau of Mines (USBM) Illinois Mineral Development Atlas (IMDA) for Jo Daviess County, Illinois. This is one of several datasets complied for the Karst Feature Database of Jo Daviess County, IL and hosted by the U.S. Fish and Wildlife Service.

This dataset includes locations and associated information about mines and mining activity in the contiguous United States. The database was developed by combining publicly available national datasets of mineral mines, uranium mines, and minor and major coal mine activities. This database was developed in 2013, but temporal range of mine data varied dependent on source. Uranium mine information came from the TENORM Uranium Location Database produced by the US Environmental Protection Agency (U.S. EPA) in 2003. Major and minor coal mine information was from the USTRAT (Stratigraphic data related to coal) database 2012, and the mineral mine data came from the USGS Mineral Resource Program.

Interactive database and visual map of lignite and hard coal mines in Poland. Explore production volumes, methane emissions, closure dates and location of mining sites.

Abandoned railroads and infrastructure from the anthracite coal mining industry are significant features in abandoned mine lands and are an important part of history; however, these features are often lost and masked by the passage of time and the regrowth of forests. The application of modern light detection and ranging (lidar) topographic analysis, combined with ground-truthing "boots on the ground" mapping, enable recovery of the location of these historical features. Waste rock piles and abandoned mine lands from historical mining locally appear as distinct features on the landscape depicted on the percent slope map. Abandoned, and in many places demolished, infrastructure such as breakers, turntables, rail beds, water tanks, tram piers, and bridge abutments, to name a few, were confirmed in the field and located with a global positioning system (GPS) receiver. This map captures the locations of many of the abandoned features from the coal mining industry near Forest City, Pennsylvania, and preserves a time that was an important part of the industrial revolution and a way of life that has been quiet for over half a century. The data layers should be used in conjunction with lidar data available separately at https://www.pasda.psu.edu.

This Web Mapping Service (WMS) contains a collection of information extracted from the National Coal Mining Database. This data represents a snapshot in time taken from the National Coal Mining Database and has been derived from current information available to the Coal Authority at that time. The data is supplied free of charge under the terms of the Open Government Licence (http://www.nationalarchives.gov.uk/doc/open-government-licence/) subject to the following overriding re-use condition: re-use is not permitted for any activity that is part of the Coal Authority's public task responsibilities.

The Data Mining Market is Segmented by Component (Tools [ETL and Data Preparation, Data-Mining Workbench, and More], Services [Professional Services, and More]), End-User Enterprise Size (Small and Medium Enterprises, Large Enterprises), Deployment (Cloud, On-Premise), End-User Industry (BFSI, IT and Telecom, Government and Defence, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

The Critical Minerals in Archived Mine Samples Database (CMDB) contains chemistry and geologic information for historic ore and ore-related rock samples from mineral deposits in the United States. In addition, the database contains samples from archetypal deposits from 27 other countries in North America, South America, Asia, Africa and Europe. Samples were obtained from archived ore collections under the U.S. Geological Survey's project titled “Quick Assessment of Rare and Critical Metals in Ore Deposits: A National Assessment” (2008 to 2013) in an effort to begin an assessment of the Nations' previously mined ore deposits for critical minerals. Mineralized and altered rock samples were provided by the Colorado School of Mines—Ransome collection, Mackay School of Mines—Stanford and Keck collections, and by the personal collections of Don Bryant and David Leach.

This map shows the NC Mining Permits which includes the Pending, Active, Inactive, Release, and Revoked Mining permits in North Carolina. There is additional information within the dataset that includes commodity type, and permitted acres. Each permit is labeled with the permittee and the name of the mine.List of Layers:NC Mining PermitsContacts:Data and Map Contact: Adam Parr (adam.parr@ncdenr.gov)Data is maintained by North Carolina Department of Environmental Quality, Division of Energy, Mineral and Land Resources, Mining Program.

Data for (i) active mine sites and (ii) inactive mine sites are stored are stored as Excel spreadsheets. NB the number of active/inactive mines shown in the spreadsheets is less than that reported in Table S1, because proprietary data sources have not been included (i.e. MRDS, BRITPITS and S&P). Each spreadsheet lists mine names (column A), mine status i.e. active or inactive (column B), the principal commodity mined (column C), and lat/long coordinates (columns D & E). Data for (iii) TSFs and (iv) TDFs are stored as zipped Shapefiles. Data should be uncompressed and then imported into any GIS programme that can read Shapefiles. Modelling was implemented procedurally in MATLAB v9.9.0 (R2020b) with the open source TopoToolbox MATLAB program for the analysis of digital elevation models (https://topotoolbox.wordpress.com). Modelling workflow is presented in SI Figure S8 with example code available in the WAPHA database (Macklin et al code.pdf). Citations to software sources are giv...

"The Africa Power–Mining Database 2014 shows ongoing and forthcoming mining projects in Africa categorized by the type of mineral, ore grade, size of the project. The database draws on basic mining data from Infomine surveys, the United States Geological Survey, annual reports, technical reports, feasibility studies, investor presentations, sustainability reports on property-owner websites or filed in public domains, and mining websites (Mining Weekly, Mining Journal, Mbendi, Mining-technology, and Miningmx). Comprising 455 projects in 28 SSA countries with each project’s ore reserve value assessed at more than $250 million, the database collates publicly available and proprietary information.

It also provides a panoramic view of projects operating in 2000–12 and anticipated demand in 2020. The analysis is presented over three timeframes: pre-2000, 2001–12, and 2020 (each containing the projects from the previous period except for those closing during that previous period)."

This Web Mapping Service (WMS) contains a collection of information extracted from the National Coal Mining Database.

This data set covers global extraction and production of coal and metal ores on an individual mine level. It covers 1171 individual mines, 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 sources. 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 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. A schematic depiction of the database is provided as in PNG format in the file database_model.png.

The Data Mining Tools Market size is expected to reach a valuation of USD 3.33 billion in 2033 growing at a CAGR of 12.50%. The Data Mining Tools market research report classifies market by share, trend, demand, forecast and based on segmentation.

This Web Mapping Service (WMS) contains a collection of information extracted from the National Coal Mining Database. This data represents a snapshot in time taken from the National Coal Mining Database and has been derived from current information available to the Coal Authority at that time. The data is supplied free of charge under the terms of the Open Government Licence (http://www.nationalarchives.gov.uk/doc/open-government-licence/) subject to the following overriding re-use condition: re-use is not permitted for any activity that is part of the Coal Authority's public task responsibilities.

A global surge in ‘artisanal’, small-scale mining (ASM) threatens biodiverse tropical forests and exposes residents to dangerous levels of mercury. In response, governments, and development agencies are investing millions (USD) on ASM formalization; registering concessions and demarcating extraction zones to promote regulatory adherence and direct mining away from ecologically sensitive areas. This data publication contains data used to examine patterns of mining-related deforestation associated with ASM formalization efforts in the Department of Madre de Dios in the Peruvian Amazon. Using satellite images and government-issued spatial layers on mining formalization, we tracked changes in mining activities from 2001 to 2014 when agencies: (a) issued 1701 provisional titles and (b) tried to restrict mining to a > 5000 square kilometer (km²) ‘corridor’. The data reported in this publication are based on the centroids of a 25 hectare (ha) hexagon grid covering the 20,850 km² study area and includes variables related (1) mining deforestation from years 2001 to 2014, (2) mining concession status, (3) location relative to the mining corridor, as well as (4) location relative to time-invariant variables and access (geology, distance to river), administrative units (district, native communities), and conservation designation (protected areas).Data were compiled and analyzed to examine patterns of mining-related deforestation associated with formalization efforts in the Department of Madre de Dios, Perú.For more information about this study and these data, see Álvarez-Berríos and L'Roe (2021).

Québec geoscientific database collected over the past 150 years. Every year, it grows richer with additional data coming from geological mapping surveys, prospection and exploration activities undertaken by the Ministry, mining companies and universities.

The Midcontinent Rift System (MRS) of North America is one of the world’s largest continental rifts and has an age of 1.1 Ga (giga-annum). The MRS hosts a diverse suite of magmatic and hydrothermal mineral deposits in the Lake Superior region where rift rocks are exposed at or near the surface. As part of the construction of a database summarizing information on mineral deposits in the MRS, data from regional mineral deposits were downloaded from the U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), the USGS Mineral Deposit Database (USMIN), and the Ontario Ministry of Energy, Northern Development and Mines Mineral Deposit Inventory (MDI). Deposits related to MRS rocks or mineralizing events were identified and compiled into a database to develop a space/time classification for MRS-related mineral deposits.

Locations of over 31,000 types of minerals throughout NSW, including coal, gas, gemstones and construction materials. This spatial dataset is a derivative product of the New South Wales Mineral Occurence Database (also known as New South Wales MetIndEx). It records over 31,000 known mineral occurrences throughout NSW. These records have been compiled from resource and production data compiled from both historic and current mining operations records held by the Geological Survey of NSW. Data records start from a state-wide Metallogenic Mapping program developed by the Survey in the late 1960s and are continuously improved via mandatory reporting. The alternative name MetIndEx refers to the METallics, INDustrial minerals and Exploration discoveries database of economic mineral deposits and occurrences in New South Wales. The data has been migrated over various databases from Microsoft Access to Oracle and SQL Server. The location data has also had multiple datum changes since the original database was compiled. The data is being continuously improved and refined by the custodians on an irregular basis. This data is part of the New South Wales Geoscientific Data Warehouse (NSW GDW) series. Data and Resources

The data in this repository is available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/

This repository includes two datasets. The first is a collection of polygons covering mines globally and the associated forest cover loss from 2000 to 2019. The polygons were derived by merging the "global-scale mining polygons version 2" (Maus et al., 2022) and mining and quarry polygon features extracted from the OpenStreetMap database (OpenStreetMap contributors, 2017). To remove double counting of areas the overlaps between the datasets were resolved by uniting intersecting features into single polygon features, i.e. keeping only the external borders of intersecting features. A random visual check was conducted, and a few small manual editing of polygons was performed where errors were identified.

The resulting dataset is encoded as a Geopackage in the file 'global_mining_polygons.gpkg'. The GeoPackage includes a single layer with 192,584 entries called 'mining_polygons' with the following attributes:

• id unique feature identifier
• isoa3 ISO 3166-1 alpha-3 country codes
• country country names
• area area of the polygon in squared kilometres
• geom the geometry of the features in geographical coordinates WGS84

The second dataset provides annual time series of global tree cover loss within mines from 2000 to 2019, covering all polygons in the above dataset. The area of tree cover loss for each polygon was calculated from the Global Forest Change database (Hansen et al., 2013). Each polygon also has additional string attributes with biomes derived from Ecoregions 2017 ^{© Resolve} (Dinerstein et al., 2017) and the level of protection derived from The World Database on Protected Areas (UNEP-WCMC and IUCN, 2022).

This dataset is encoded in CSV format in the file 'global_mining_forest_loss.csv', which includes 416,412 entries and 53 variables, such that:

• id unique feature identifier
• id_hcluster unique feature identifier
• list_of_commodities a comma-separated list of commodities
• isoa3 ISO 3166-1 alpha-3 country codes
• country country names
• ecoregion ecoregion name
• biome biome name
• year the year
• area_forest_loss_XXX_YYY

The values of tree cover loss are disaggregated per initial percentage of tree cover (XXX) and per protection level (YYY).

• XXX can take one of:
  • 000: total tree cover loss independently from the initial tree cover
  • 025: tree cover loss on pixels with initial tree cover between 0 and 25%
  • 050: tree cover loss on pixels with initial tree cover between 25 and 50%
  • 075: tree cover loss on pixels with initial tree cover between 50 and 75%
  • 100: tree cover loss on pixels with initial tree cover between 75 and 100%
• YYY can take one of:
  • la: tree cover loss within strict nature reserve
  • Ib: tree cover loss within wilderness area
  • II: tree cover loss within national park
  • III: tree cover loss within natural monument or feature
  • IV: tree cover loss within habitat/species management area
  • V: tree cover loss within protected landscape/seascape
  • VI: tree cover loss within PA with sustainable use of natural resources
  • p: tree cover loss within any type of protection, including not applicable, not assigned, or not reported
  • none: when YYY is omitted, total tree cover loss within the polygon

For details about the protection levels definition see the UNEP-WCMC and IUCN (2022). The id can be used to link polygons to forest loss data.

Data Mining Tools Market Outlook

According to our latest research, the global Data Mining Tools market size reached USD 1.93 billion in 2024, reflecting robust industry momentum. The market is expected to grow at a CAGR of 12.7% from 2025 to 2033, reaching a projected value of USD 5.69 billion by 2033. This growth is primarily driven by the increasing adoption of advanced analytics across diverse industries, rapid digital transformation, and the necessity for actionable insights from massive data volumes.

One of the pivotal growth factors propelling the Data Mining Tools market is the exponential rise in data generation, particularly through digital channels, IoT devices, and enterprise applications. Organizations across sectors are leveraging data mining tools to extract meaningful patterns, trends, and correlations from structured and unstructured data. The need for improved decision-making, operational efficiency, and competitive advantage has made data mining an essential component of modern business strategies. Furthermore, advancements in artificial intelligence and machine learning are enhancing the capabilities of these tools, enabling predictive analytics, anomaly detection, and automation of complex analytical tasks, which further fuels market expansion.

Another significant driver is the growing demand for customer-centric solutions in industries such as retail, BFSI, and healthcare. Data mining tools are increasingly being used for customer relationship management, targeted marketing, fraud detection, and risk management. By analyzing customer behavior and preferences, organizations can personalize their offerings, optimize marketing campaigns, and mitigate risks. The integration of data mining tools with cloud platforms and big data technologies has also simplified deployment and scalability, making these solutions accessible to small and medium-sized enterprises (SMEs) as well as large organizations. This democratization of advanced analytics is creating new growth avenues for vendors and service providers.

The regulatory landscape and the increasing emphasis on data privacy and security are also shaping the development and adoption of Data Mining Tools. Compliance with frameworks such as GDPR, HIPAA, and CCPA necessitates robust data governance and transparent analytics processes. Vendors are responding by incorporating features like data masking, encryption, and audit trails into their solutions, thereby enhancing trust and adoption among regulated industries. Additionally, the emergence of industry-specific data mining applications, such as fraud detection in BFSI and predictive diagnostics in healthcare, is expanding the addressable market and fostering innovation.

From a regional perspective, North America currently dominates the Data Mining Tools market owing to the early adoption of advanced analytics, strong presence of leading technology vendors, and high investments in digital transformation. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid industrialization, expansion of IT infrastructure, and growing awareness of data-driven decision-making in countries like China, India, and Japan. Europe, with its focus on data privacy and digital innovation, also represents a significant market share, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions modernize their operations and adopt cloud-based analytics solutions.

Component Analysis

The Component segment of the Data Mining Tools market is bifurcated into Software and Services. Software remains the dominant segment, accounting for the majority of the market share in 2024. This dominance is attributed to the continuous evolution of data mining algorithms, the proliferation of user-friendly graphical interfaces, and the integration of advanced analytics capabilities such as machine learning, artificial intelligence, and natural language pro