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160 results for “geochemical”
Eight Mile Lake Research Watershed, Thaw Gradient: Geochemical data collected daily in Panguingue Creek during spring thaw and summer rain events, 2018-2019
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites each with a different degree of change due to permafrost thawing. As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. Geochemical analysis of river waters complements the overarching aim of this study by targeting the organic component transported laterally from the catchment and understanding the association between this component and mineral elements. These mineral element-organic carbon associations must be accounted for when considering the stability of organic carbon transported laterally from the catchment.
Geochemical flux on McMurdo Dry Valleys Commonwealth glacier
As part of a geochemical study of the Commonwealth Glacier in Taylor Valley, Antarctica, two 3-meter snow pits were dug in the accumulation zone and analyzed for major ions by ion chromatography. This dataset shows the mean annual atmospheric flux of chloride, sulfate, nitrate, and calcium to the Commonwealth Glacier. Determination of the atmospheric flux of these ions to the glacier surface aids in assessing the chemical composition of precipitation to the McMurdo Dry Valleys and the role of glaciers in the geochemical cycles of the region.
Geochemical data for fluids collected in Acoculco Geothermal Field
<p>The dataset <strong>CO<sub>2</sub>_flux_measurements_Acoculco </strong>contains data on CO<sub>2</sub> fluxes, coordinates (UTM), air temperature, atmospheric pressure measured in selected sites belonging to the Acoculco Geothermal Field: in particular, the areas named Lagunilla, Alcaparrosa, Los Azufres and also the area between them were investigated. CO<sub>2</sub> flux measurements were performed using the accumulation chamber method.</p> <p>The dataset <strong>Field_meas_Acoculco_waters</strong> reports the ID, coordinates (UTM), Altitude (m.a.s.l.), temperature, flow rate, pH, Electrical Conductivity and Dissolved Oxygen for water samples collected in the central sector of the Acoculco geothermal field, but also in other sectors located inside and outside the Acoculco caldera. Total depth is also included for samples collected from water wells.</p> <p>The dataset <strong>Chemical_isotopic_data_Acoculco_waters</strong> reports major and minor chemical components and stable isotopic composition for hydrogen and oxygen determined in collected water samples in Acoculco geothermal field. Calculated partial pressures (in bars and log<sub>10</sub>-value) and CO<sub>2</sub> concentrations of dissolved CO<sub>2</sub> were also included.</p> <p>The dataset <strong>Chemical_isotopic_data_Acoculco_gas </strong>reports chemical and isotopic data for collected samples from Los Azufres and Alcaparrosa natural gas manifestations.</p>
Namibian reference ochres description and geochemical data
<p>The datasets present the macroscopic description and ICP-OES and ICP-MS geochemical data of Namibian reference ochres. </p>
Insights into the operation of the solid Earth system from analysis of compiled geochemical data (Video)
<p>This is the first session video recording of the Goldschmidt 2020 Virtual Workshop: Earth Science meets Data Science - Services & Systems, Policies & Procedures, Tools & Techniques for Geochemistry. Moderated by Kerstin Lehnert (Columbia University)</p>
Radionuclide, organic matter, geochemical and colorimetric properties of potential source material and target sediment for conducing sediment fingerprinting approaches in the Dzoumogné reservoir, Mayotte Island, France
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Colorimetric properties analysed with a portable diffuse reflectance spectrophotometer (Konica Minolta CM-700d) and geochemical contents obtained with an energy dispersive X-ray fluorescence spectrometer (ED-XRF Epsilon 4), organic matter and stable isotopes were analysed by EA-IRMS and radionuclides using coaxial N- and P- type HPGe detectors (Canberra/Ortec). These properties were analysed in potential source material that may supply sediment to the Dzoumogné reservoir, Mayotte island, France. Three potential soil source materials (n = 57) were considered: cropland (n = 29), forest (n = 13) and subsurface material originating from channel bank collapse, landslides, badlands (n = 16). A sediment core was collected in the Dzoumogné reservoir (Target) on the 8th October 2021 and 20 layers were sampled.</p><p>The current dataset comprises two Excel files including the metadata description and the data itself.</p>
Shuttle Radar Topography Mission digital elevation models, data points, radiocarbon dates, and geochemical data for the Rub' al Khali Desert
Open the record for dataset details and reuse information.
Geochemical and physical characterization of lithic raw materials in the Olduvai Basin, Tanzania
<p>The invention and proliferation of stone tool technology in the Early Stone Age (ESA) marks a watershed in human evolution. Patterns of lithic procurement, manufacture, use, and discard have much to tell us about ESA hominin cognition and land use. However, these issues cannot be fully explored outside the context of the physical attributes and spatio-temporal availability of the lithic raw materials themselves. The Olduvai Basin of northern Tanzania, which is home to both a wide variety of potential toolstones and a rich collection of ESA archaeological sites, provides an excellent opportunity to investigate the relationship between lithic technology and raw material characteristics. Here, we examine two attributes of the basin's igneous and metamorphic rocks: spatial location and fracture predictability. A total of 244 geological specimens were analyzed with non-destructive portable XRF (pXRF) to determine the geochemical distinctiveness of five primary and secondary sources, while 110 geological specimens were subjected to Schmidt rebound hardness tests to measure fracture predictability. Element concentrations derived via pXRF show significant differences between sources, and multivariate predictive models classify geological specimens with 75–80% accuracy. The predictive models identify Naibor Soit as the most likely source for a small sample of three lithic artifacts from Bed II, which supports the idea that this inselberg served as a source of toolstone during the early Pleistocene. Clear patterns in fracture predictability exist within and between both sources and rock types. Fine-grained volcanics show high rebound values (associated with high fracture predictability), while finer-grained metamorphics and coarsegrained gneisses show intermediate and low rebound values, respectively. Artifact data from Bed I and II suggest that fracture predictability played a role in raw material selection at some sites, but other attributes like durability, expediency, and nodule size and shape were more significant.</p>
Geochemical significance of Acropora death assemblages in the northern South China Sea: implications for environmental reconstruction using branching corals
<p>The datasets contain the Sr/Ca, Li/Mg, 𝛅<sup>11</sup>B, B/Ca, 𝛅<sup>13</sup>C and 𝛅<sup>18</sup>O determinations of the <em>Acropora</em> death assemblages collected off south Hainan Island in the northern South China Sea. </p>
Supporting Information for "Geochemical evidence for diachronous uplift and synchronous collapse of the high elevation Variscan hinterland"
<p>Supplemental data for GLR manuscript submission.</p>
Geochemical and isotopic analyses of a ~14 000 year old sediment core collected from Lake Simcoe, Canada
<p>This dataset contains measurements of modern water and ancient core materials from Lake Simcoe, the fourth largest lake wholly in Ontario, Canada. These data consist of: <em>(i)</em> oxygen, hydrogen and carbon isotope (<em>δ</em><sup>18</sup>O, <em>δ</em><sup>2</sup>H and <em>δ</em><sup>13</sup>C) compositions for modern water samples; <em>(ii)</em> physical measurements of one piston core, PC-5; <em>(iii) δ</em><sup>13</sup>C and <em>δ</em><sup>18</sup>O values of ostracods collected from PC-5, and <em>(iv) δ</em><sup>13</sup>C and <em>δ</em><sup>18</sup>O values of ancient DIC and water, respectively, inferred from item <em>(iii)</em>. Physical measurements performed on core PC-5 include magnetic susceptibility, mineralogy and grain size. Mass accumulation rates are also reported.</p>
Machine learning based on whole-rock geochemical data: An indication for the porphyry Cu-Au deposits in Central Asian Orogenic Belt
<div> <h2>Introduction</h2> </div> <p>Welcome to the PorphyryAuML project! This repository is dedicated to exploring the mechanism of gold (Au) enrichment in porphyry systems within the Central Asian Orogenic Belt using machine learning. We apply models like XGBoost and Random Forest to analyze whole-rock geochemical data, aiming to classify porphyry deposit types and highlight key geochemical indicators.</p> <div> <h2>Key Features 🌟</h2> </div> <ul> <li><strong>Principal Component Analysis (PCA):</strong> Reduce dimensionality to discover the most significant variables.</li> <li><strong>Machine Learning Models:</strong> Utilize XGBoost and Random Forest for robust classification.</li> <li><strong>Feature Importance Analysis:</strong> Identify crucial geochemical markers for Au presence and quantity.</li> <li><strong>Visualization:</strong> Detailed plots to illustrate model outcomes and geochemical patterns.</li> </ul> <div> <h2>Data 📊</h2> </div> <div> <h3>DATA.xlsx</h3> </div> <p>The <code>DATA.xlsx</code> file contains crucial geochemical data used in our analysis, organized across three sheets:</p> <ul> <li><strong>group1</strong>: Represents the Cu-Au (Copper-Gold) porphyry deposits. This sheet contains all relevant geochemical markers and measurements specific to this group.</li> <li><strong>group2</strong>: Corresponds to Cu(-Au±Mo) (Copper with minor Gold and possibly Molybdenum) porphyry deposits. It includes a detailed set of data focusing on the variations and characteristics of these mixed element deposits.</li> <li><strong>group3</strong>: Contains data related to Cu-Mo (Copper-Molybdenum) porphyry deposits, focusing on the distinct geochemical profiles that typify these deposits.</li> </ul> <p>Each sheet is named to reflect the group it represents and is vital for our machine learning analysis to classify and predict porphyry deposit types based on their geochemical properties.</p>
Data for "Uncertainty quantification in geochemical mapping: a review and recommendations"
<p>Data for "Uncertainty quantification in geochemical mapping: a review and recommendations".</p>
Geochronological and geochemical data of volcanic deposits of the Central Main Ethiopian Rift
<p>This database represents a collection of geochronological data, geochemistry of bulk rocks and melt inclusions from a large set of samples collected in the framework of two field surveys (2019, 2020) in the Central Main Ethiopian Rift.</p> <p>This dataset provides supporting information to the paper:<br>Franceschini Z., Cioni R., Scaillet S., Prouteau G., Corti G., Sani F., Mondanaro A., Frascerra D., Melaku A.A., Scaillet B., Oppenheimer C., Duval F. (2024) "Pulsatory volcanism in the Main Ethiopian Rift and its environmental consequences", Nature Communications Earth and Environment.</p> <p>The samples have been prepared for the different analyses at the Department of Earth Sciences of the University of Florence. The geochronological analyses building up the dataset have been performed at the Argon facility at the Institut des Sciences de la Terre d'Orléans (ISTO), in France. The analythical methods related to the 40Ar/39Ar analyses are described in detail in the above mentioned paper.<br>The geochemical dataset contains the results of major and trace elements concentrations on whole rock samples (performed at the Service d'Anlayse des Roches et des Mineraux -CNRS - CRPG of Nancy, in France) and major elements and volatile concentrations of melt inclusions belonging to four ignimbrite samples (analysed with an Electron Micro Probe Analyzer housed at the laboratories of the Institut des Sciences de la Terre d'Orléans, in France).</p> <p>These datasets have been performed in the frame of a PhD fellowship (granted to Zara Franceschini) funded by the Università Italo Francese (Bando Vinci 2018, n. C3-1717) and also partially supported by the Italian Ministero dell'Università e della Ricerca (MiUR) through PRIN grant 2017P9AT72.</p>
Geochemical data for protolith classification testing
<p>Global major element geochemical data for igneous and sedimentary rocks. This dataset is used to train via machine learning a classifier for igneous and sedimentary protoliths. It contains the major element chemistry, ilr transformed chemistry, rock type, protolith class, and reference to the original data. Codes to train a protolith classifier and analyze the results are found in the GitHub repository github.com/dhasterok/global_geochemistry/</p>
The coincidence degree between the geochemical behavior of elements and the periodic variation of elements based on geochemical data of C2 coal seam in the Fengfeng mining area of the Handan Coalfield in Hebei, China
<p>In this data-set, based on the geochemical data of C2 coal seam in the Fengfeng mining area of the Handan Coalfield in Hebei (China), where provided ideal coal samples changing continuously from low-rank metamorphic coal to high-rank metamorphic coal, the coincidence degree (or similarity degree) between the geochemical behavior of 57 elements and the periodic variation of elements during the thermal metamorphism process is calculated.</p>
Geochemical evidence for high volatile fluxes from the mantle at the end of the Archean: matlab code
<p>This is the matlab code used to compute past fluxes of mantle-derived Xe to account for the isotopic evolution of atmospheric Xe. </p>
Geochemical data of fine bed-sediment from downstream sediment cores and upstream source sub-catchments in a catchment-wide flood in the Brantian
<b>Description: </b><p>Geochemical datasets were obtained from fieldwork carried out in the Brantian catchment between June 2013 and November 2016 under the hydrology component of the SAFE Project. The project has two key components: (1) Geochemical profiles down historical sediment cores at seven downstream locations organised in a nested hierarchical arrangement; and (2) Geochemical data for sediment deposited by the single extreme high magnitude flood event of 12 September 2016 in all sub-catchment source areas sampled around the Brantian including all downstream sediment core locations referred to in (1) <br>Sample collection and preparation method:<br>Fluvial sediment cores were obtained from seven downstream sites located within a nested hierarchical (dendritic) arrangement with the study catchment outlet draining 377 km2 of the upper Brantian. Core sites 4 and 5 in the west were nested within core site 2; core sites 6 and 7 in the east were nested within core site 3; core sites 2 and 3 were in turn nested within core site 1 at the study catchment outlet. Areas upstream at each drainage hierarchy varied from 30-135km2 (core sites 4-7); 150-200km2 (core sites 2-3) and 377km2 (core site 1).<br>Sediment cores were obtained within the bankfull channel at sites inundated by high-flow events with the progressive accumulation of fine bed-sediment monitored by repeat measurements of surface profile. Pits were dug to create a shelf surface from which to obtain large (200g to 1100g) bulk samples of sediment integrated over depth intervals of 2cm. The shelf technique permits larger samples while depths are absolute and not affected by core liner compression. Core depths ranged from 102 cm to 210 cm.<br>Sediment samples deposited in the high-flow event of 12 September 2016 were obtained using pre-installed surface horizon marker grids. Surface-layer (0-2cm) scrape samples were composited over a 10-20 m2 area. At sites with depths of fresh sediment >2cm small sediment cores representing sediment deposited in that event were obtained using the shelf technique. Field replicates were obtained from the same elevation and at either higher or lower elevation within the channel.<br>All sediment samples were oven-dried at temperatures no higher than 40 C before dry-sieving to obtain the fine-sediment <63um size fraction. Other size fractions were obtained from nested sieve stacks in order to calculate bulk particle size distribution from the entire sample. Samples of dried <63um sediment were thoroughly mixed by hand (not ground) before a sub-sample transferred to a standard 32mm outer-diameter plastic pot pre-fitted with a 3um thick Prolene XRF analytical film window, compacted to 20Nm torque pressure and sealed. Mass (g) and total thickness (mm) of prepared samples were recorded. <br>Geochemical analysis method:<br>Total elemental concentration of each sample (ppm) was measured using a Niton XL3t GOLDD+ 900 Energy-Dispersive X-Ray Fluorescence (ED-XRF) analyser in a laboratory stand with count periods of 180 seconds for 'soils' (Compton scatter) mode (60s in each of three energy band filters: low medium and high). Measurements were also made in 'mining' mode (Fundamental Parameters calibration) using helium purging to obtain concentrations of light elements Mg-S. In both modes, two measurements were obtained for each sample by repositioning the sample window exposed to the XRF beam after the first measurement (position 1 and position 2). <br>ED-XRF analysis returns total elemental concentration (ppm) of elements Mg-U. Measurement error is reported by the Niton XRF in terms of 2sigma (two times standard deviation) for each element. All measurements were higher than limit of detection. Variability between the two measurement positions 1 and 2 reflects geochemical environmental variability in sediment (ie sampling error) and analytical error.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/133"><b>Assessing erosional impacts of logging and conversion to oil palm in the Brantian catchment using sediment fingerprinting and radioisotope dating.</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Doctoral Training Grant, NE/L501827/1)</li><li>British Geomorphological Society (Postgraduate Research Grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.3(149))</li><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.5(145))</li><li>Sabah Forestry Department (Research licence 100-14/18/2KLT.29(37))</li><li>Maliau Basin Management Committee (MBMC) (Research licence 2015/29(165))</li><li>Sabah Biodiversity Council (Export licence JKM/MBS.1000-2/3 JLD.2(86))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3402746">here</a></p><p><b>Files: </b>This consists of 1 file: SamHigton_Geochem_fluvial_sediment_data.xlsx</p><p><b>SamHigton_Geochem_fluvial_sediment_data.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Sediment core geochemistry data</b> (described in worksheet Sediment_core_geochem_data)</p><p>Description: XRF analysis data of geochemical composition of sediment from 7 core sites. There is one sample for every 2cm depth interval down each core (with one set of bulk particle size data) and then two separate repeat XRF measurements of each sample.</p><p>Number of fields: 69</p><p>Number of data rows: 947</p><p>Fields: </p><ul><li><b>Core_number</b>: Core number (Field type: id)</li><li><b>Sample_Site</b>: Corresponds to sample site number in the locations tab (Field type: location)</li><li><b>Upstream_Area_km2</b>: Upstream area (Field type: numeric)</li><li><b>Sampling_date</b>: Date sediment sample taken (Field type: date)</li><li><b>Upper_Depth_cm</b>: Upper depth of sample slice relative to the sediment surface; for surface samples this will always be zero (Field type: numeric)</li><li><b>Lower_Depth_cm</b>: Lower depth of each sample slice relative to the sediment surface; in increments of 2cm (Field type: numeric)</li><li><b>BulkPS_<63um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_63-125um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_125um-2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_>2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_mass_g</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_thick_mm</b>: Mass of XRF sample analysed (Field type: numeric)</li><li><b>EDXRF_measurement_number_1or2</b>: Refers to XRF measurement 1 or 2 - each sample was analysed in two different positions across the sample measurement surface producing two measurements of element concentration and 2SD error for each sample (Field type: categorical)</li><li><b>Mg</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Al</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Si</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>P</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>S</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>K</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ca</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ti</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>V</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Fe</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ni</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cu</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>As</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Rb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cd</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Te</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cs</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ba</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Pb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Th</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>U</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mg.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Al.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Si.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>P.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>S.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>K.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ca.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ti.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>V.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Mn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Fe.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ni.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cu.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>As.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Rb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cd.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Te.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cs.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ba.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Pb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Th.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>U.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li></ul></li><li><p><b>Sept2016 flood geochemical data</b> (described in worksheet Sept2016_flood_geochem_data)</p><p>Description: XRF analysis of geochemical composition of sediment from a single flood event around the Brantian. Most samples have depth of 0-2cm since this represents surface layer material, but at many sites there were also 'mini' cores which is why the depths vary. 28 elements analysed as above and each sample measured twice, with error columns and bulk particle size etc.</p><p>Number of fields: 70</p><p>Number of data rows: 222</p><p>Fields: </p><ul><li><b>Sample_Site</b>: Corresponds to sample site number in the locations tab (Field type: location)</li><li><b>Site_Type</b>: Site Type - U: Upstream site (sample taken at one of the upstream source sites 9 to 29) ; C: Core site (sample at a sediment core site which are only sites numbered 1 to 7); RC: Field replicate from the sediment core site itself; RL: Field replicate at that site but from a relatively lower elevation position; RH: Field replicate at that site but from a relatively higher elevation position) (Field type: categorical)</li><li><b>Upstream_Area_km2</b>: Total drainage area upstream of each sample site (Field type: numeric)</li><li><b>Sampling_date</b>: Date sediment sample taken (Field type: date)</li><li><b>Upper_Depth_cm</b>: Upper depth of sample slice relative to the sediment surface; for surface samples this will always be zero (Field type: numeric)</li><li><b>Lower_Depth_cm</b>: Lower depth of each sample slice relative to the sediment surface; in increments of 2cm (Field type: numeric)</li><li><b>BulkPS_<63um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_63-125um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_125um-1mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_1mm-2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_>2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_mass_g</b>: Mass of XRF sample analysed (Field type: numeric)</li><li><b>XRF_sample_thick_mm</b>: Thickness of prepared sample for XRF analysis (to nearest 0.5cm) (Field type: numeric)</li><li><b>EDXRF_measurement_number_1or2</b>: Refers to XRF measurement 1 or 2 - each sample was analysed in two different positions across the sample measurement surface producing two measurements of element concentration and 2SD error for each sample (Field type: categorical)</li><li><b>Mg</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Al</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Si</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>P</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>S</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>K</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ca</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ti</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>V</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Fe</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ni</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cu</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>As</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Rb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cd</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Te</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cs</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ba</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Pb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Th</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>U</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mg.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Al.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Si.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>P.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>S.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>K.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ca.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ti.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>V.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Mn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Fe.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ni.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cu.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>As.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Rb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cd.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Te.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cs.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ba.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Pb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Th.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>U.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2013-06-21 to 2019-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Mineralogy and Geochemical Characteristics of Lateritic Nickel Deposit in Wiwirano District, North Konawe, SE Sulawesi, Indonesia
<p><span>XRF data of laterite was taken from 29 drilling points for each 1 m interval from top to bottom of the profiles.</span></p> <p><span>If you have any further questions, feel free to contact me at <a href="mailto:riocj@uho.ac.id">riocj@uho.ac.id</a></span></p>
Data from : Geochemical and Documentary Topography of a Medieval Silver Valley
<p>These data are the source of an interdisciplinary investigation (archaeology, geochemistry, history) of a medieval silver and lead production site located in southern France, in the Minier valley (Occitanie, Aveyron, Le-Viala-du-Tarn). In order to identify the production sites, in situ geochemical surveys were carried out using a portable X-ray fluorescence spectrometer and differential GPS, guided on the analysis of medieval archival sources. The cartographic representation of the metal concentrations in the surface horizons shows significant enrichment of zinc and lead in the vicinity of the mines. This first type of enrichment makes it possible to highlight the activities of separation of sphalerite and silver-bearing galena. The galena thus isolated on the hillsides is then transported to the vicinity of watercourses, where it is crushed, washed, and smelted. These secondary activities result in a last type of enrichment in which only lead is found in large quantities. The cross-referencing of the information made it possible to overcome the challenges related to the location of the mineral processing workshops, which were often invisible on the surface. The medieval workshops have been located and a function suggested, outlining the first trends in the spatial and social division of labour and providing a solid corpus for future archaeological excavations. Finally, this study highlights the persistence of significant metal contamination in the soils of a rural valley and encourages the consideration of former mining areas when examining the environmental impact of metal production.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.