Uranium rose to 71.40 USD/Lbs on July 22, 2025, up 0.21% from the previous day. Over the past month, Uranium's price has fallen 7.93%, and is down 14.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Uranium - values, historical data, forecasts and news - updated on July of 2025.

Uranium is a common element throughout the Earth’s crust, soils, and oceans. Uranium resources are naturally occurring deposits that may have a sufficient concentration of uranium to support mining operations. Canada has about 8% of the world’s unmined uranium resources, but accounts for some 25% of the global primary uranium production. Canada’s uranium mines are located in the Athabasca Basin of northern Saskatchewan, which has ore grades as high as 21% uranium metal, an order of magnitude larger than any other deposits in the world. The nuclear industry provides about 15% of Canada’s electrical power (50% of Ontario’s). The map shows districts with potential for uranium development, small occurrences of uranium, locations of uranium mines and facilities, and locations of nuclear facilities that generate electrical power.

Nuclear Energy Index rose to 41.40 USD on July 23, 2025, up 2.70% from the previous day. Over the past month, Nuclear Energy Index's price has risen 6.70%, and is up 50.55% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Nuclear Energy Index.

This data set contains uranium–lead (U-Pb) isotopic data and ages, hafnium (Hf) isotope data, and trace element concentrations of zircon from igneous rocks collected from the Darby Mountains area of the Seward Peninsula, Alaska. The sample suite was collected as part of geological mapping and supporting geochemical and geochronological analysis conducted in 2017. The data tables accompanying this data release report the isotopic composition of U and thorium (Th) measured in each grain, ratios of 207Pb and 206Pb and 235U and 238U, and the age of each grain, plus trace element abundances from the same ablated aliquot, including phosphorus (P), vanadium (V), strontium (Sr), high field strength elements (HFSE), and rare earth elements (REE). Reported Hf (and lutetium, Lu) isotope data includes 176Hf/177Hf, 176Lu/177Hf, and 178Hf/177Hf ratios, calculated epsilon Hf values, and age-corrected isotope ratios and epsilon values.

Abstract From samples of the Paleozoic Tamatán Group (Huizachal–Peregrina Anticlinorium, Tamaulipas, Mexico), petrographic (qualitative and modal) and geochemical analyses (major, trace, and rare earth elements) were conducted. The first U–Pb geochronological data on detrital zircons of the Tamatán Group were generated using four samples. The data presented here contains a broad overview of photomicrography, recalculated modal point-count data, raw geochemical data, and simple statistics of selected geochemical parameters. The data presented in this article are interpreted and discussed in the research article titled “Provenance and tectonic setting of the Tamatán Paleozoic sequence, NE Mexico: Implications for the closure of the Rheic Ocean at the northwestern part of Gondwana” [1]. Value of the data • Important data available for researchers conducting research on the Northwestern margin of Gondwana and adjacent areas. • Data collection available for sedimentologists, working with geochemical data. • Data made availabla to construct integrated geological models for the Northwestern Margin of Gondwana and adjacent areas. • A complete geochemical dataset for the Tamatán Group. • Tectonic activity, weathering, and provenance data of the Tamatán Group are provided. • First U-Pb geochronological data of the Tamatán Group Data This article provides data from 105 samples. From 70 samples, photomicrographs were taken and point-counted and modal analyses on recalculated petrographic parameters were provided were provided. Geochemical analyses (major, trace, and rare earth elements [REE]) of 73 samples were conducted. Four samples for U–Pb geochronological zircon analyses were made. The sample location is given with the geographical and UTM coordinates of each sample. Each sample is located on a geological map. The petrographic and geochemical data are presented as raw data and displayed as a simple statistic of the selected petrography and geochemical parameters, respectively. Additionally, outcrop photographs are provided. Acknowledgements Financial support for this work was provided by a Ph.D. fellowship from the National Council of Science and Technology (CONACYT). The first author, a Ph.D. student at the postgraduate program of the Facultad de Ciencias de la Tierra, Universidad Autónoma de Nuevo León (FCT/UANL), wants to thank Sergio Padilla-Ramírez, Centro de Investigación Científica y de Educación Superior de Ensenada B.C, México and Susana Rosas-Montoya and Daniela Tazzo (CICESE) for their help in the preparation and analysis of the geochronological data. Special thanks to L.A. Elizondo-Pacheco, N.Z. Morales-Alemán, and D.C. Rodríguez-Campero y M. Rodríguez-Escamilla (FCT/UANL) for their assistance in the field. The geochemical and geochronological analyses were supported by the PAICyT projects CT-129-09 and CN-940-19, which was granted by the Universidad Autónoma de Nuevo León.

Statewide maps of the probability of exceeding a given concentration of either uranium (U) or radon (Rn) in New Hampshire groundwater (represented as statewide rasters) are the product of statistical analyses and are available here. The dependent variables in these statistical models were either 1) the natural log of Rn concentrations or 2) a dichotomous variable indicating the exceedance of 1 microgram per liter (μg/L) U in groundwater samples. In the case of U, a dichotomous variable was used because of the large number of measured samples below the detection limit. A boosted Regression Tree (BRT) model was selected for the U analysis with an exceedance probability of 1 ug/L because that was near the center of observed measured concentrations. Ordinary Least Squares was selected for the Rn analysis, which permitted predictions for multiple exceedance values (300, 2000, and 4000 ug/L selected) from a single model. Raster datasets included in this data release are: 1) rasters of input predictor variables for the state of New Hampshire at 30-m resolution. – Each 30-meter cell has a unique cell value (GRID_CODE) and the associated attributes used as predictors. One set of 3 rasters (North, Central, and South) is used for Rn exceedance probability calculations and the other set of 3 rasters were used for U exceedance probability calculations; 2) output rasters of the probabilities of exceeding 200, 2000, and 4,000 pCi/L for radon and 1 μg/L for uranium, at 30-m resolution. – These exceedance probability rasters are presented in integer format to store the data more efficiently. For this reason, the value of each cell must be divided by 100,000 to obtain the actual exceedance probability (a number that can range from 0 to 1).

The Geological Survey of South Australia has used SA Geodata to compile cleaned datasets of selected maximum downhole geochemistry for state-wide display on SARIG. Geochemical maps consist of drill hole locations, and sampled geochemical data... The Geological Survey of South Australia has used SA Geodata to compile cleaned datasets of selected maximum downhole geochemistry for state-wide display on SARIG. Geochemical maps consist of drill hole locations, and sampled geochemical data transformed from single element values (obtained from whole rock ppm/ppb conversion) normalised to times average crustal abundance. The maximum uranium value from each drill hole has then been selected and displayed on SARIG.

Some of the highest grade uranium (U) deposits in the United States are hosted by solution-collapse breccia pipes in the Grand Canyon region of northern Arizona. These structures are named for their vertical, pipe-like shape and the broken rock (breccia) that fills them. Hundreds, perhaps thousands, of these structures exist. Not all of the breccia pipes are mineralized; only a small percentage of the identified breccia pipes are known to contain an economic uranium deposit. An unresolved question is how many undiscovered U-bearing breccia pipes of this type exist in northern Arizona, in the region sometimes referred to as the “Arizona Strip”. Two principal questions remain regarding the breccia pipe U deposits of northern Arizona are: (1) What processes combined to form these unusual structures and their U deposits? and (2) How many undiscovered U deposits hosted by breccia pipes exist in the region? A piece of information required to answer these questions is to define the area where these types of deposits could exist based on available geologic information. In order to determine the regional processes that led to their formation, the regional distribution of U-bearing breccia pipes must be considered. These geospatial datasets were assembled in support of this goal.

The Alaska Geochemical Database Version 3.0 (AGDB3) contains new geochemical data compilations in which each geologic material sample has one best value determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database Version 2.0 before it, the AGDB3 was created and designed to compile and integrate geochemical data from Alaska to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, element concentrations and associations, environmental impact assessments, and studies in public health associated with geology. This relational database, created from databases and published datasets of the U.S. Geological Survey (USGS), Atomic Energy Commission National Uranium Resource Evaluation (NURE), Alaska Division of Geological & Geophysical Surveys (DGGS), U.S. Bureau of Mines, and U.S. Bureau of Land Management serves as a data archive in support of Alaskan geologic and geochemical projects and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 112 laboratory and field analytical methods on 396,343 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. Most samples were collected by personnel of these agencies and analyzed in agency laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various agency programs and projects from 1938 through 2017. In addition, mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are included in this database. The AGDB3 includes historical geochemical data archived in the USGS National Geochemical Database (NGDB) and NURE National Uranium Resource Evaluation-Hydrogeochemical and Stream Sediment Reconnaissance databases, and in the DGGS Geochemistry database. Retrievals from these databases were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. In other words, the data of the AGDB3 supersedes data in the AGDB and the AGDB2, but the background about the data in these two earlier versions are needed by users of the current AGDB3 to understand what has been done to amend, clean up, correct and format this data. Corrections were entered, resulting in a significantly improved Alaska geochemical dataset, the AGDB3. Data that were not previously in these databases because the data predate the earliest agency geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB3 and will be added to the NGDB and Alaska Geochemistry. The AGDB3 data provided here are the most accurate and complete to date and should be useful for a wide variety of geochemical studies. The AGDB3 data provided in the online version of the database may be updated or changed periodically.

Garber-Wellington - Groundwater Wells with Maximum Trace Metal Concentrations

Assessment of Distribution of Arsenic, Chromium, Selenium and Uranium in Groundwater in the Garber-Wellington Aquifer - Central Oklahoma.The Garber Wellington Aquifer (GWA) in central Oklahoma is a major bedrock aquifer comprised of interbedded sandstone, shale and mudstone that yields significant quantities of water for municipal, industrial, agriculture and domestic beneficial uses. This aquifer also is characterized by locally elevated naturally occurring levels of arsenic, chromium, selenium and uranium. The aquifer underlies parts or all of Cleveland, Lincoln, Logan, Oklahoma and Pottawatomie counties, and these major cities: Oklahoma City, Edmond, Del City, Guthrie, Midwest City, Moore, Nichols Hills, and Norman. These communities, and industries (such as Tinker Air Force Base) and businesses within their confines rely wholly or partly on the aquifer for water supply. In addition, domestic wells supply drinking water to thousands of individuals where public water supply distribution systems do not exist. Unlike public water supply entities that regularly have their municipal water tested and are required to provide water to their customers that meets the Environmental Protection Agency's mandated maximum contaminant levels (MCLs) for these constituents, domestic supply wells are not required to be regularly tested and many homeowners or prospective homeowners relying on water from domestic wells may be completely in the dark to the potential of drinking water with elevated levels of these constituents. The purpose of this project is to provide public access to increase awareness of water quality indicator information of these trace metals. The project indicator information to be disseminated to the public constitutes historical laboratory analytical records "data mined" from water agency data bases matched with their sampled water well locations. It is not the purpose or intent of this report to describe or interpret the results of these analytical results per se.A significant amount of scientific research of this aquifer has been conducted by the United States Geological Survey (USGS), et al. Many scientific publications available online and in print describe in great detail the causative factors for the presence of these naturally occurring trace elements in the aquifer, locally, at elevated levels that exceed EPA’s MCLs for public water supplies. The primary objective of this dataset is to disseminate information about groundwater quality trace metal occurrence (arsenic, chromium, selenium and uranium) in the Garber-Wellington to enhance public access and increase awareness to the potential of exposure to these naturally occurring elements in water wells. Historical, laboratory analytical data for samples collected from water wells across the Garber-Wellington Aquifer were obtained from the Oklahoma Department of Environmental Quality (ODEQ), United States Geological Survey (USGS) and the Association of Central Oklahoma Governments (ACOG). Water well metals data that could be associated with a unique earth coordinate (latitude/longitude) were used to create the dataset. Through data review and reduction of the original data sets received, it was determined that 1,835 project wells could be associated with a unique earth coordinate and contained an analytical report for at least one of the 4 primary project metals. For these 1,835 project wells, there are over 4,300 associated laboratory analytical results. *It is important to note that many of the well locations were derived from address locations and legal descriptions, not actual GPS locations. Therefore, this dataset is not intended to be used for site specific applications or matching wells to properties. Due to the variation of well depths, screened zones of wells, and formation variations in the Garber-Wellington aquifer, this dataset should not be used for interpolating values between wells.The attribute table contains information regarding the source collection agency and the historical maximum concentration level sampled for each metal. Additional fields contain the following three categories for laboratory analytical levels of reporting: (3) Metals data with a concentration that exceeds the maximum contaminant level (MCL); (2) Metals data with a concentration that is less than the MCL (1); Metals data with a concentration that was reported as less than the laboratory detection limit (0) A fourth classification was created to indicate that the well was not sampled for a particular metal. Note: The MCLs of Arsenic, Chromium, Selenium and Uranium are 10, 100, 50, and 30 micrograms/liter respectively.This project was funded through the 2009 604(b) Water Quality Management Program and the American Recovery and Reinvestment Act of 2009 (ARRA). The OWRB would like to thank the Oklahoma Department of Environmental Quality (ODEQ), the Association of Central Oklahoma Governments (ACOG), and the United States Geological Survey (USGS) for contributing data for this project.

This dataset is a compilation of metallic mineral occurrences of economic interest that have been found in Alberta either by government or industry exploration. The dataset comprises a diverse suite of metals including precious metals, base metals, uranium and iron. The occurrences are classified based on commodity and development stage and status into producers, past producers, projects with resource estimate, prospects, showings, and anomalies. This dataset is part of a package of datasets produced to support the creation of AGS Map 590, Minerals of Alberta. The package includes datasets of Prospective Areas for Mineral Exploration (DIG 2019-0025), Industrial Mineral Occurrences of Alberta (DIG 2019-0027), Kimberlite and Ultrabasic Intrusions of Alberta (DIG 2019-0028), Lithium Content in Groundwater and Formation Water in Alberta (DIG 2019-0029), references (txt), and a description of the methodology (Map 590 Information Document).

The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). Radiometrics can tell us about the distribution of certain soils and rocks. Geologists and geophysicists routinely use it as a geological mapping tool to tell them where certain rock types change. It is also useful for the study of geomorphology and soils. The data collected are processed via standard methods to ensure the response recorded is that due only to the rocks in the ground. The results produce datasets that can be interpreted to reveal the geological structure of the sub-surface. The processed data is checked for quality by GA geophysicists to ensure that the final data released by GA are fit-for-purpose.

Data was compiled from published sources by US Geological Survey geoscientists Mark J. Mihalasky, Susan M. Hall and Robert A. Zielinski. This dataset was provided to the U.S. Energy Information Administration in February of 2019 to facilitate updating of national uranium resource distribution maps. The location of uranium provinces, districts and select important deposits located outside of these broader regions was taken from a variety of sources listed alphabetically below.Adams S.S.; Smith R.B., 1981, Geology and recognition criteria for sandstone uranium deposits in mixed fluvial-shallow marine sedimentary sequences, South Texas; U.S. Department of Energy Report GJBX-4(81), 145 p.Colorado Geological Survey, 2018, Uranium Districts – Colorado; published on the Colorado Geological Survey website at http://coloradogeologicalsurvey.org/energy-resources/uranium2/map/.Chenoweth, W.L., 1980, Uranium in Colorado; Rocky Mountain Association of Geologists, 1980 Symposium, p. 217-224Gloyn, R.W.; Bon, R.L.; Wakefield, S.; Krahulec, K., 2005, Uranium and vanadium map of Utah; Map 215, Utah Department of Natural Resources, Utah Geological Survey, 1:750,000 scale, 1 sheet. Metadata download at: https://gis.utah.gov/data/energy/uranium/Gregory R.W., 2016, Uranium: Geology and Applications; Wyoming State Geological Survey Public Information Circular No 46, 36 p.Keith, S.B.; Gest, D.E.; DeWitt, E; 1983, Metallic mineral districts of Arizona; Arizona Bureau of Geology and Mineral Technology, Geological Survey Branch, Tucson, AZ, 1:1,000,000 scale, 1 sheetKyle L, Beahm D, 2013, NI 43-101 preliminary economic assessment update (revised), Coles Hill uranium property, Pittsylvania County, VA USA; prepared by Lyntek Incorporated, Lakewood, CO; 2013, 126 p. Figure 1.1.McLemore, V.T. and Chenoweth, W.L., 1989, Uranium resources in New Mexico; New Mexico Bureau of Mines and Minerals Resources, Resource Map 18, 36 p. Available at: https://geoinfo.nmt.edu/faq/mining/home.html

These data show the intensity of gamma rays released by Uranium, Thorium and Potassium in different soils and rocks in Ireland. Different soils and rock types can then be mapped. The data were collected between 2005 and 2021.Several surveys were merged to create this dataset. (1) Tellus Northern Ireland 2005-2006(2) Cavan-Monaghan, 2006(3) Tellus Border, 2011-2012(4) Tellus North Midlands, 2014-2015(5) Block A1, 2015(6) Block A2, 2016(7) Waterford, 2016(8) Block A3, 2017(9) Block A4, 2017(10) Block A5, 2018-2019(11) Block A6, 2018-2019(12) Block A7, 2019(13) Block A8 2020-2021(14) Block A9 2021The data were collected using an airplane. The airplane flies at 60 m flight height along lines that are 200 m apart. Gamma ray spectrometer data are recorded at around 60 m intervals along the flight lines. The spectrometer system mounted on the airplane records the number of gamma rays emitted per second by rocks and soils. The gamma ray intensity changes depending on the amount of Uranium, Thorium and Potassium in rocks and soil beneath the aircraft. For example, rocks such as granite contain a large amount of Uranium, Thorium and Potassium, while limestone rocks contain low amounts of these elements.The data are collected as points in XYZ format. X and Y are the airplane coordinates. Z is the different recorded data, which include gamma ray intensity and aircraft flight height. The XYZ data for each line contains thousands of points. The data from separate lines are merged to create grids of gamma ray counts and Uranium, Thorium and Potassium contents for each survey block. All the survey blocks are then merged to create final grids for Ireland.This data shows how much uranium is contained in the ground which can then be mapped.Colours are used to show gamma ray counts, Uranium, Thorium and Potassium concentration ranges. The values are defined in counts-per-second for gamma ray counts, parts per million for Uranium and Thorium concentrations and percent for Potassium concentration. Pinks and reds show the highest values. Greens and blues show lowest values.This is a raster dataset. Raster data stores information in a cell-based manner and consists of a matrix of cells (or pixels) arranged into rows and columns. The format of the raster is a grid. The grid cell size is 50 m by 50 m. This means that each cell (pixel) represents an area on the ground of 50 metres squared. Each cell has a value which is the average value of all the points located within that cell.The Tellus project is a national survey which collects geochemical and geophysical data across Ireland. It allows us to study the chemical and physical properties of our soil, rocks and water. It is managed by the Geological Survey Ireland.

This map service provides access to most of the Resource Map datasets shown on the GeoAtlas application. **Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse. They are therefore identical and share the same refresh schedule. This map service is used by the GeoATLAS web application, sub-section Resource Map in the Mineral Exploration theme. It includes Base Metals Potential, Coal Potential, Gold Potential, Helium Potential, Bitumen (Oil Sands) Potential, Lithium Potential, Potash and Salt Resource Potential, Rare Earth Elements Potential and Uranium Potential schema in Production Data Warehouse. Note: Oil and Gas pools are found in the /Petroleum service.

Open pit uranium mining in Atascosa, Karnes, and Live Oak Counties in the Texas gulf coast region was active during the second half of the 20th century. Understanding the history of these mining operations is important for proper management and restoration. Although some mines have extensive records documenting the locations and extents of mining pits and mine waste-rock piles, and provide descriptions of reclamation activities, abandoned mines with little to no such documentation are present on the landscape. A multiple lines of evidence approach using lidar derivatives and multispectral remote sensing temporal analysis (Stengel, 2022) was developed to (1) identify uranium mine waste-rock, wastewater, and land disturbance due to mining, to (2) differentiate between abandoned and reclaimed mine features, and to (3) help understand the life cycle of mining activities on the Texas landscape. This data release provides the 2013 and 2018 lidar data used to derive derivative terrain pa ...

Uranium concentrations in a large number of marine sediment samples of different types with world-wide spatial distribution have been determined using the rapid, precise and nondestructive technique of counting the delayed neutrons emitted during U235 fission induced with thermal neutrons. Several interesting relationships were apparent. 1) A direct proportionality was observed between percentage of organic carbon and uranium in sediments deposited in an anoxic environment in the Pettaquamscutt River in Rhode Island with concentrations ranging from 7 per cent organic carbon and 7 ppm uranium to 14 per cent organic carbon and 30 ppm uranium. A similar relationship was found in cores of sediments deposited on the Sigsbee Knolls in the Gulf of Mexico. 2) For manganese nodules a direct relationship can be seen between uranium and calcium concentrations and both decrease with increasing depth of deposition. For nodules from 4500 m in the Pacific, concentrations are 3 ppm uranium and 0.3 per cent calcium compared with 14 ppm uranium and 1.5 per cent calcium at 1000 m. 3) Relatively high uranium concentrations were observed in carbonates deposited in the deepest parts of the Gulf of Mexico, with the >88 ? carbonate fraction in Sigsbee Knoll cores having as much as 1.20 ppm. A model to explain the observed variations must include uranium enrichment in near shore environments via an anoxic pathway, followed by redeposition in a deep ocean environment with dilution either by low-uranium-bearing foraminiferal or silicious oozes or, along the continental margins, dilution with high-uranium-bearing carbonate sands.

The data comprise measurements of the 'soluble', 'chemically exchangeable' and 'isotopically exchangeable' U concentrations in a diverse set of soils following experimental addition of UO22+ and incubation in the laboratory under controlled temperature conditions for ca. 1.7 years. The long term behaviour of U in aerobic soils was studied by conducting a laboratory-based experiment in which a set of twenty topsoils from central England with contrasting properties (e.g. pH, organic matter content, land use) were contaminated with a solution containing UO22+ in soluble form and incubated in the dark, in a moist but aerobic condition, at a temperature of 10 deg C for 619 days. The transformations of U in each soil microcosm were periodically monitored by means of soil extractions conducted on subsamples of incubated soils. The resulting dataset enabled quantification of the kinetics of UO22+ transformations in aerobic soils and the relationships with soil properties and land uses (arable, grassland and moorland/woodland). The dataset will be useful in developing models of long-term U bioavailability in aerobic soils under temperate conditions. Full details about this dataset can be found at https://doi.org/10.5285/0d8b2aea-574c-4cff-a8bd-17115a0b90fc

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Uranium Energy (YCA): The Sun's Yellow Cake, or Just a Fool's Gold Investment?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This data release compiles the whole-rock geochemistry, X-ray diffraction, and electron microscopy analyses of samples collected from the uranium ore bodies of two mined-out deposits in the Grand Canyon region of northwestern Arizona - the Hack II and Pigeon deposits. The samples are grab samples of ore collected underground at each mine by the U.S. Geological Survey (USGS) during the mid-1980s, while each mine was active. The Hack II and Pigeon mines were remediated after their closure, so these data, analyses of samples in the archives of the USGS, are provided as surviving, although limited representations of these ore bodies. The Hack II and Pigeon deposits are similar to numerous other uranium deposits hosted by solution-collapse breccia pipes in the Grand Canyon region of northwest Arizona. The uranium-copper deposits occur within matrix-supported columns of breccia (a "breccia pipe") that formed by solution and collapse of sedimentary strata (Wenrich, 1985; Alpine, 2010). The regions north and south of the Grand Canyon host hundreds of solution-collapse breccia pipes (Van Gosen and others, 2016). Breccia refers to the broken rock that fills these features, and pipe refers to their vertical, pipe-like shape. The breccia pipes average about 300 ft (90 m) in diameter and can extend vertically for as much as 3,000 ft (900 m), from their base in the Mississippian Redwall Limestone to as stratigraphically high as the Triassic Chinle Formation. The breccia fragments are blocks and pieces of rock units that have fallen downward, now resting below their original stratigraphic level. In contrast to many other types of breccia pipes, there are no igneous rocks associated with the northwestern Arizona breccia pipes, nor have igneous processes contributed to their formation. Many of these breccia pipes contain concentrated deposits of uranium, copper, arsenic, barium, cobalt, lead, molybdenum, nickel, antimony, strontium, vanadium, and zinc minerals (Wenrich, 1985), which is reflected in this data set. The Hack II and Pigeon mines were two of thirteen breccia pipe deposits in the Grand Canyon region mined for uranium from the 1950s to present (2020) (Alpine, 2010; Van Gosen and others, 2016). While hundreds of breccia pipes in the region have been identified (Van Gosen and others, 2016), six decades of exploration across the region has found that most are not mineralized or substantially mineralized, and only a small percentage of the breccia pipes contain economic uranium deposits. The most recent mining operation in a breccia pipe deposit in the region is the Canyon mine, located about 6.1 miles (10 km) south-southeast of Tusayan, Arizona. In 2018, Energy Fuels completed a mine shaft and other mining facilities at the Canyon deposit, a copper- uranium-bearing breccia pipe (Van Gosen and others, 2020); however, this mining operation is currently (2020) inactive, awaiting higher market prices for uranium oxide. The Hack II deposit is one of four breccia pipes mined in Hack Canyon near its intersection with Robinson Canyon (Chenoweth, 1988; Otton and Van Gosen, 2010), approximately 30 miles (48 km) southwest of Fredonia and 9 miles (14.5 km) north-northwest of Kanab Creek. Hack Canyon incised and exposed part of the "Hacks" (or "Hack Canyon") breccia pipe, which was discovered and mined as a surface mine in the early 1900s for copper and silver. The original Hacks mine and adjacent Hack I deposit were later mined underground for uranium from 1950 to 1954 (Chenoweth, 1988). The Hack II deposit was discovered in the late 1970s along Hack Canyon about 1 mile (1.6 km) upstream of the Hacks and Hack I mines. The Hack II mine is located at latitude 36.58219 north, longitude -112.81059 west (datum of WGS84). Mining began at Hack II in 1981 and ended in May 1987. The USGS collected the ore samples reported in this data release in 1984 from underground exposures in the Hack II mine while it was in operation. Reclamation of the four mines in the area (Hacks, Hack I, Hack II, and Hack III) was planned and completed from March 1987 to April 1988, including infilling of the shafts and adits. Total production from the Hack II mine was reported as 7.00 million pounds (3.2 million kilograms) of uranium oxide from ore that had an average grade of 0.70 percent uranium oxide. This represents the largest uranium production from a breccia pipe deposit in the Grand Canyon region thus far (Otton and Van Gosen, 2010). The Pigeon mine was discovered along Kanab Creek in 1980. The site was prepared and developed from 1982 to 1984, and mining began in December 1984. The pipe was mined out in late 1989 and reclamation begun shortly thereafter. The former mine site is located at latitude 36.7239 north, longitude -112.5275 south (datum of WGS84). The Pigeon mine reportedly produced 5.7 million pounds (2.6 million kilograms) of ore that had an average grade of 0.65 percent uranium oxide. The five Pigeon deposit samples reported in this data release were collected by the USGS from underground exposures in the Pigeon mine in 1985, while the mine was in operation. Fourteen samples of Hack II ore and two samples of Pigeon ore were analyzed for major and trace elements by a laboratory contracted by the USGS. Concentrations for 59 elements were determined by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES). Additionally, carbonate carbon (inorganic carbon), total carbon, total sulfur, iron oxide, and mercury concentrations were determined using other element-specific analytical techniques. These 16 samples and an additional four Hack II ore samples and three Pigeon ore samples were analyzed by X-ray diffraction (XRD) to determine their mineralogy. Polished thin sections cut from six of the Hack II ore samples were examined using a scanning electron microscope equipped with an energy dispersive spectrometer (SEM-EDS) to identify the ore minerals and observe their relationships at high magnification. The EDS vendor's auto identification algorithm was used for peak assignments; the user did not attempt to verify every peak identification. The spectra for each EDS measurement are provided in separate documents in Portable Data Format (pdf), one document for each of the six samples that were examined by SEM-EDS. The interpreted mineral phase(s), which is based solely on the judgement of the user, is given below each spectrum. References cited above: Alpine, A.E., ed., 2010, Hydrological, geological, and biological site characterization of breccia pipe uranium deposits in northern Arizona: U.S. Geological Survey Scientific Investigations Report 2010-5025, 353 p., 1 plate, scale 1:375,000. Available at http://pubs.usgs.gov/sir/2010/5025/ Chenoweth, W.L., 1988, The production history and geology of the Hacks, Ridenour, Riverview and Chapel breccia pipes, northwestern Arizona: U.S. Geological Survey Open-File Report 88-648, 60 p. Available at https://pubs.usgs.gov/of/1988/0648/report.pdf Otton, J.K., and Van Gosen, B.S., 2010, Uranium resource availability in breccia pipes in northern Arizona, in Alpine, A.E., ed., Hydrological, geological, and biological site characterization of breccia pipe uranium deposits in northern Arizona: U.S. Geological Survey Scientific Investigations Report 2010-5025, p. 23-41. Available at http://pubs.usgs.gov/sir/2010/5025/ Van Gosen, B.S., Johnson, M.R., and Goldman, M.A., 2016, Three GIS datasets defining areas permissive for the occurrence of uranium-bearing, solution-collapse breccia pipes in northern Arizona and southeast Utah: U.S. Geological Survey data release, https://doi.org/10.5066/F76D5R3Z Van Gosen, B.S., Benzel, W.M., and Campbell, K.M., 2020, Geochemical and X-ray diffraction analyses of drill core samples from the Canyon uranium-copper deposit, a solution-collapse breccia pipe, Grand Canyon area, Coconino County, Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9UUILQI Wenrich, K.J., 1985, Mineralization of breccia pipes in northern Arizona: Economic Geology, v. 80, no. 6, p. 1722-1735, https://doi.org/10.2113/gsecongeo.80.6.1722