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55 results for “Lithology”
Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale
<p>Continental Europe surface lithology based on <strong><a href="http://www.europe-geology.eu/onshore-geology/geological-map/onegeologyeurope/">EGDI / OneGeology map</a></strong> at 1:1M scale produced by <a href="https://egdi.geology.cz/record/basic/5729ffdf-2558-48fc-a5d2-645a0a010855">GEOZS, Slovenia</a>. European datasets harvested from national WFS for geologic units or national geological units datasets, based on OneGeology and <strong><a href="https://inspire.ec.europa.eu/codelist/LithologyValue/">INSPIRE Lithology</a></strong> and Geochronologic Era URI codelists. Layers include:</p> <ul> <li>EGDI_GE_GeologicUnit_EN_1M_Surface_LithologyPolygon_v2_250m_epsg.3035.tif = original EGDI surface lithology map;</li> <li>dtm_surface.lithology_egdi.1m_c_250m_s_20000101_20221231_eu_epsg.3035_v20240530.tif = gap filled surface lithology map;</li> </ul> <p>Missing values in the original EGDI lithology map have been imputed by training a random forest classifier model based on parameters derived from DTM and soil regions map from Die Bundesanstalt für Geowissenschaften und Rohstoffe (BGR). By generating 1 million random points, geographically balanced over the whole pan-EU land area, each class in the map was covered properly. Classes whose number of samples is less than 10 were discarded from the model training. The hyperparameter tuning of the model was carried out via a Bayesian approach with a criteria to maximize accuracy of 5k-fold cross validation. The tuned random forest model achieved an accuracy of 47% (Kappa=0.43) for the testing data, 20% of the generated sample points. The lithology of Turkey, on the other hand, was digitised from the available geology map produced by the General Directorate of Mineral Research and Exploration (MTA). The available raster map was post-processed and classified as 20 lithology classes using the k-means algorithm. These classes were harmonized with the classes in the EGDI lithology map.</p> <p>Acknowledgment: GEOZS, Continental Shelf Department at the Ministry for Transport and Infrastructure.</p>
Global landform and lithology class at 250 m based on the USGS global ecosystem map
<p>Layers include: lithology (15) and landform (7) indicator maps (0-100%). Derived from the <a href="https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/">USGS Global Ecosystem Map</a>, i.e. the EcoTapestry map. Water bodies masked out. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>dtm = theme: digital terrain models / relief and soil,</li> <li>lithology = variable: lithological class,</li> <li>usgs.ecotapestry = determination method: USGS Global Ecosystem Map,</li> <li>p = probability 0-100%,</li> <li>250m = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2014 = time reference: year 2014,</li> <li>v1.0 = version number: 1.0,</li> </ul>
The LILY Database: Linking Lithology to IODP Physical, Chemical, and Magnetic Properties Data
<p>During each expedition of the International Ocean Discovery Program and its precursor, the Integrated Ocean Drilling Program (jointly referred to as IODP), vast arrays of data are collected from drill cores. These data, which are accessible from the IODP LIMS (Laboratory Information Management System) database, include physical, chemical, and magnetic properties collected semi-continuously along cores using automated track systems, as well as a variety of analyses conducted on discrete subsamples taken from the cores. In addition, the lithology of all cores is described based on visual characteristics of the surface of split cores, visual examination of smear slides and thin sections, and compositional or mineralogical information derived from geochemical analyses. We extract basic lithologic information from this complex array of descriptive information and then tie that information to all other measurements. This new database is referred to as <strong>LI</strong>MS with <strong>L</strong>itholog<strong>y</strong> (LILY). LILY currently contains over 34 million data from 89 km of core recovered on 42 expeditions conducted 2009-2019. Some uses of LILY include identifying the abundance of different lithologies, finding data from core intervals with a specific lithology, assessing the efficacy of coring systems in different lithologies, or characterizing and analyzing physical, chemical, and magnetic properties based on lithology. We illustrate the use of LILY by computing the grain density by lithology from over 24,000 moisture and density measurements and then use those grain densities, along with the large IODP bulk density dataset, to compute a new high-resolution porosity dataset with over 3.7 million new porosity estimates.</p> <h2>CONTENT DESCRIPTION:</h2> <p><strong>The main LILY database is stored in the files with the suffix DataLITH.csv.</strong> Each file contains IODP LIMS data with lithology and other metadata added. The file prefix gives the type of data. For example, AVS_DataLITH.csv contains the Automated Vane Shear (AVS) shear strength data paired with lithology and other metadata. A list of all data types is given in Supporting Information Table S1 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). There are a total of 23 DataLITH files.</p> <ul> <li>AVS_DataLITH.csv: automated vane shear; shear strength measurements.</li> <li>CARB_DataLITH.csv: total carbon, hydrogen, nitrogen, and sulfur, inorganic carbon (carbonate), and organic carbon measured on discrete samples.</li> <li>GE_DataLITH.csv: gas elements from gas chromatography.</li> <li>GRA_DataLITH.csv: gamma ray attenuation bulk density from the Whole-Round Multisensor Logger (WRMSL).</li> <li>ICP_DataLITH.csv: Inductively-coupled plasma data.</li> <li>IW_DataLITH.csv: interstitial water chemistry.</li> <li>JR6A_DataLITH.csv: discrete magnetic measurements from the JR6A spinner magnetometer.</li> <li>KAPPA_DataLITH.csv: Kappabridge susceptibility meter measurements.</li> <li>MAD_DataLITH.csv: moisture and density from discrete samples.</li> <li>MS_DataLITH.csv: magnetic susceptibility from the WRMSL.</li> <li>MSP_DataLITH.csv: point magnetic susceptibility from the Section Half Multisensor Core Logger (SHMSL).</li> <li>NGR_DataLITH.csv: natural gamma radiation from the Natural Gamma Radiation Logger (NGRL).</li> <li>PEN_DataLITH.csv: pocket penetrometer compressional strength measurements.</li> <li>PWB_DataLITH.csv: P-wave velocity from the bayonet system.</li> <li>PWC_DataLITH.csv: P-wave velocity from the caliper system.</li> <li>PWL_DataLITH.csv: P-wave velocity from the WRMSL.</li> <li>RGB_DataLITH.csv: Red-Green-Blue color from the Section Half Imaging Logger (SHIL).</li> <li>RSC_DataLITH.csv: reflectance spectroscopy from the SHMSL.</li> <li>SRA_DataLITH.csv: source rock analyzer measurements.</li> <li>SRM_DataLITH.csv: Superconducting Rock Magnetometer (SRM) measurements of split-core sections.</li> <li>SRMD_DataLITH.csv: SRM measurements of discrete samples.</li> <li>TCON_DataLITH.csv: thermal conductivity measured with the Teka Berlin TK04 probe.</li> <li>TOR_DataLITH.csv: Torvane shear strength measurements.</li> </ul> <p>Other compressed data folders contain multiple files used in creating the LILY database:</p> <p>RawDESC.zip: Contains 7,940 .csv files derived from the raw text content of the DESClogik Excel worksheets that was extracted, converted to comma separated value (.csv) format, and put into files with a consistent naming convention, without applying any corrections or conversions to the original text. Each file is the direct extraction of a tab from the DESC workbooks, available at <a href="https://web.iodp.tamu.edu/DESCReport/">https://web.iodp.tamu.edu/DESCReport/</a></p> <p>CoreSUMM.zip: Contains one file with Core Summary information, which includes the expedition, site, hole, core, coring type, top and bottom depths drilled, advances and recoveries, time and date of recovery, and the number of sections. These data are further paired with additional metadata (expanded core type, latitude, longitude, and water depth). Coordinates and water depth for each hole are derived from LIMS (and the JANUS database at <a href="http://www-odp.tamu.edu/database/">http://www-odp.tamu.edu/database/</a> for older expeditions).</p> <p>RawDATA.zip: Contains the raw track/discrete dataset downloaded by expedition from IODP LIMS database and placed in folders for each type of data (AVS, CARB, SRM, etc.) </p> <p>RawLITH.zip: Contains 42 .csv files, with one file for each expedition. Each file contains all lithologic description (prefix, principal and suffix, etc.) information for an entire expedition, as it was originally described. These have been transformed to a consistent format and paired with consistent identification information and additional metadata. Headers are normalized across all expeditions and SampleID information is standardized.</p> <p>CleanLITH: Contains 42 .csv files. Each file contains all lithologic description (prefix, principal and suffix) information for an entire expedition. The lithologic descriptions have been standardized to a consistent nomenclature using the dictionary given in Support Information Table S4 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). These data are further paired with additional metadata (e.g., degree of consolidation, expanded core type, latitude, longitude, and water depth).</p> <h2>GitHub Repository:</h2> <ul> <li>Contains a few notebooks to demonstrate how to work with the LILY database</li> <li><a href="https://github.com/IODP/LILY">IODP LILY GitHub Repository</a></li> </ul>
A new Geo-Lithological Map (Geo-LiM) for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy)
<p><strong>We introduce a new geo-lithological map of Central Europe (Geo-LiM) elaborated adopting a lithological classification compliant to the methods more used in the litterature for estimating the consumption of atmospheric CO2 due by chemical weathering. <br> Geo-LiM represents a novelty if compared with published global geo-lithological maps. The first novelty is due by the attention paid in discriminating metamorphic rocks that were classified according to the chemistry of protoliths. The second novelty is that the procedure used for the definition of the map is made available on the web to allow the replicability and reproducibility of the product.</strong></p>
A dataset of Earth Observation Data for Lithological Mapping using Machine Learning
<p><strong>Dataset Information</strong></p> <p>Machine Learning (ML) algorithms had successfully contributed in the creation of automated methods of recognizing patterns in high-dimensional data. Remote sensing data covers wide geographical areas and could be used to solve the problem of the demand of various in-situ data. Lithologicall mapping using remotely sensed data is one of the most challenging applications of ML algorithms. In the framework of the “AI for Geoapplications” project , ML and especially Deep Learning (DL) methodologies are investigated for the identification and characterization of the lithology based on remote sensing data in various pilot areas in Greece. In order to train and test the various ML algorithms, a dataset consisting of 30 ROIs selected mainly from low -vegetated areas, that cover 2% of the total area of Greece was created</p> <p><strong>Dataset Preprocessing</strong></p> <p>Dataset preprocessing was executed using a combination of SNAP, QGIS and ENVI tools.</p> <p>Preprocessing steps:</p> <p>Defining areas with the following properties:</p> <ul> <li> <p>Zero cloud and snow coverage</p> </li> <li> <p>No water bodies</p> </li> <li> <p>Minimum vegetation</p> </li> </ul> <p>For the Aster Images:</p> <ul> <li> <p>Subset on defined areas</p> </li> <li> <p>Mosaic images when needed</p> </li> <li> <p>Digitising clouds</p> </li> </ul> <p>For the Labels:</p> <ul> <li> <p>We got the Soil map from YPEN (<a href="https://ypen.gov.gr/">https://ypen.gov.gr/</a>)</p> </li> <li> <p>Subset on defined areas</p> </li> <li> <p>All categories are represented with good analogies</p> </li> <li> <p>Clip label files with digitised clouds</p> </li> <li> <p>Rasterize</p> </li> </ul> <p> </p> <p>For the Labels we have eighteen categories for the twenty-eight areas that we collected data. We use the following coding for the Labels of our <strong>Dataset</strong>:</p> <table> <tbody> <tr> <td> <p><strong>Alluvial deposits</strong></p> </td> <td> <p><strong>0</strong></p> </td> </tr> <tr> <td> <p><strong>Limestone colluvial deposits</strong></p> </td> <td> <p><strong>1</strong></p> </td> </tr> <tr> <td> <p><strong>Limestones</strong></p> </td> <td> <p><strong>2</strong></p> </td> </tr> <tr> <td> <p><strong>Schists</strong></p> </td> <td> <p><strong>3</strong></p> </td> </tr> <tr> <td> <p><strong>Quaternary sediments</strong></p> </td> <td> <p><strong>4</strong></p> </td> </tr> <tr> <td> <p><strong>Gneiss</strong></p> </td> <td> <p><strong>5</strong></p> </td> </tr> <tr> <td> <p><strong>Slope fan debris</strong></p> </td> <td> <p><strong>6</strong></p> </td> </tr> <tr> <td> <p><strong>Mixed flysch</strong></p> </td> <td> <p><strong>7</strong></p> </td> </tr> <tr> <td> <p><strong>Flysch shale and cherts</strong></p> </td> <td> <p><strong>8</strong></p> </td> </tr> <tr> <td> <p><strong>Dolomites</strong></p> </td> <td> <p><strong>9</strong></p> </td> </tr> <tr> <td> <p><strong>Granite</strong></p> </td> <td> <p><strong>10</strong></p> </td> </tr> <tr> <td> <p><strong>Sandstone flysch</strong></p> </td> <td> <p><strong>11</strong></p> </td> </tr> <tr> <td> <p><strong>Flysch colluvial deposits</strong></p> </td> <td> <p><strong>12</strong></p> </td> </tr> <tr> <td> <p><strong>Peridotite and Gabbro</strong></p> </td> <td> <p><strong>13</strong></p> </td> </tr> <tr> <td> <p><strong>River bed deposits</strong></p> </td> <td> <p><strong>14</strong></p> </td> </tr> <tr> <td> <p><strong>Gneiss colluvial deposits</strong></p> </td> <td> <p><strong>15</strong></p> </td> </tr> <tr> <td> <p><strong>Not available</strong></p> </td> <td> <p><strong>-100</strong></p> </td> </tr> <tr> <td> <p><strong>cloud coverage</strong></p> </td> <td> <p><strong>-999</strong></p> </td> </tr> </tbody> </table> <p>The following table lists the available <strong>areas </strong>and the <strong>categories </strong>that each contains<strong>: <a href="https://docs.google.com/spreadsheets/d/17q0L5Ltz7V4uBY9i6DhULJsJCtf7BOY1nbblB-hf3Pw/edit?usp=share_link">Lithology_Dataset</a> </strong></p> <p> </p> <p>For the <strong>Sentinel-2 images</strong>, we made the following process:</p> <ul> <li> <p><strong>Resampling 10m</strong></p> </li> <li> <p><strong>Subset on defined areas</strong></p> </li> </ul> <p>The Sentinel-2 map contains: Sentinel 2 false colour composite 11/8/4 with OSM background</p> <p>The Final step is the collocation of the previous into a datacube i.e a multidimensional array with 25 bands (datacube dimensions differentiate for every area) using the Aster image as base (15m spatial resolution). </p> <ul> <li> <p>Bands 1-14: Aster</p> </li> <li> <p>Bands 15-24: S2</p> </li> <li> <p>Band 25: Label</p> </li> </ul> <p>The code for preprocessing the dataset in order to be used for machine learning algorithms can be found in the following link: </p> <p><a href="https://github.com/georgegiannop/Lithology">https://github.com/georgegiannop/Lithology</a></p> <p><strong>Citation</strong></p> <p>If you use this dataset in your work, please cite our paper:</p> <p>Vernikos, I., Giannopoulos, G., Christopoulou, A., Begaj, A., Stefouli, M., Bratsolis, E., and Charou, E.: A dataset of Earth Observation Data for Lithological Mapping using Machine Learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17570, <a href="https://doi.org/10.5194/egusphere-egu23-17570">https://doi.org/10.5194/egusphere-egu23-17570</a>, 2023.</p> <p> </p> <p> </p>
Fig. 4. Detailed lithological column around the D–C in Rugose corals across the Devonian-Carboniferous boundary in NW Turkey
Fig. 4. Detailed lithological column around the D–C boundary (DCB) in the Topluca section (unit ET-DC in Fig. 2). The stratigraphic distribution of some guide taxa is also indicated.
Figure 4 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 4. Columnar sections of the Paratirolites Limestone in the Aras Valley, Ali Bashi 4 and Ali Bashi 1 sections with their conodont and ammonoid zonation as well as the weight % of CaCO3 (determined by the weight loss–acid digestion method) of the Ali Bashi 1 section.
Figure 1 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 1. (A) Geographical position of Permian–Triassic boundary sections in the Transcaucasus and in NW Iran (after Arakelyan et al., 1965); sections investigated in this study are highlighted. (B) Palaeogeographic position of the Julfa area (after Stampfli and Borel, 2002).
Figure 3. Ali Bashi 4 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 3. Ali Bashi 4 section and columnar sections of the entire Changhsingian in Ali Bashi 4, Ali Bashi 1 and Ali Bashi M sections with their conodont zonation.
Lithologic Compilation of Basins Sampled for Cosmogenic 10Be in the Greater Caucasus Mountains
<p>Document ('Litho_Compilation.pdf') describing the geologic map compilation process for areas covering a suite of catchments sampled for cosmogenic 10Be within the Greater Caucasus Mountains. A shapefile ('mapunits.shp') which includes these mapped regions is provided.</p>
Figure 6 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 6. Characteristic Changhsingian conodonts from the Julfa region (scale bars equal to 100 µm); all specimens stored in the collection of the Ferdowsi University, Mashhad. (A) Clarkina orientalis (Barskov and Koroleva, 1970); FUM#1J192.1; upper Julfa beds (Vedioceras beds), Ali Bashi 1 section. (B) Clarkina subcarinata Sweet, 1973; FUM#4J142.8; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (C) Clarkina changxingensis Wang and Wang, 1981; FUM#4J153.1; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (D) Clarkina bachmanni Kozur, 2004; FUM#AJ185.23; Paratirolites Limestone (Ali Bashi Formation), Aras Valley section. (E) Clarkina nodosa Kozur, 2004; FUM#G249.16; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi M section. (F) Clarkina yini Mei, 1998; FUM#AJ192.4; Paratirolites Limestone (Ali Bashi Formation), Aras Valley section. (G) Clarkina abadehensis Kozur, 2004; FUM#1J248.9; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 1 section. (H) Clarkina hauschkei Kozur, 2004, FUM#1J249D.9; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 1 section. (I) Hindeodus eurypyge Nicoll, Metcalfe and Wang, 2002, FUM#1J255.7 (cusp broken); Zal Member (Ali Bashi Formation), Ali Bashi 1 section. (J) Hindeodus typicalis Sweet, 1970, FUM#G233.5; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi M section. (K) Hindeodus typicalis Sweet, 1970, FUM#4J200.56; Paratirolites Limestone (Ali Bashi Formation), Ali Bashi 4 section. (L) Hindeodus julfensis Sweet, 1973, FUM#1J198.4; Zal Member (Ali Bashi Formation), Ali Bashi 4 section. (M) Hindeodus praeparvus Kozur, 1996, FUM#G274.6 (cusp broken); Aras Member (Elikah Formation), Ali Bashi M section. (N) Hindeodus changxingensis Wang, 1995, FUM#4J201.6 (cusp broken); Aras Member (Elikah Formation), Ali Bashi 4 section. (O) Merrillina ultima Kozur, 2004, FUM#AJ204.13; Aras Member (Elikah Formation), Aras Valley section. (P) Hindeodus parvus Kozur and Pjatakova, 1976, FUM#4J213.1; Elikah Formation; Ali Bashi 4 section.
Figure 5 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 5. The correlation of the conodont schemes by Kozur (2005, 2007), Shen and Mei (2010) and own results with the ammonoid stratigraphy by Shevyrev (1965) and own results.
Figure 7 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 7. Characteristic Changhsingian ammonoids from the Julfa region (scale bars equal to 5 mm); all specimens stored in the collection of the Museum für Naturkunde, Berlin. (A) Phisonites triangulus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22703; × 1.0. (B) Iranites transcaucasius (Shevyrev, 1965) from the Aras Valley section, specimen MB.C.22704; × 1.0. (C) Dzhulfites nodosus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22705; × 1.0. (D) Shevyrevites nodosus Shevyrev, 1965 from the Aras Valley section, specimen MB.C.22706; × 1.0. (E) Paratirolites trapezoidalis Shevyrev, 1965 from the Ali Bashi 4 section, specimen MB.C.22707; × 0.75. (F) Stoyanowites dieneri (Stoyanow, 1910) from the Aras Valley section, specimen MB.C.22708; × 1.0. (G) Paratirolites vediensis Shevyrev, 1965 from the Ali Bashi N section, specimen MB.C.22709; × 0.75. (H) Abichites stoyanowi (Kiparisova, 1947) from the Ali Bashi N section, specimen MB.C.22710; × 1.25. (I) Arasella minuta (Zakharov, 1983) from the Ali Bashi N section, specimen MB.C.22711; × 1.25.
Text-fig. 1. A – outline map of the Slovak Republic, Gombasek Quarry marked; B – Gombasek needle – Gombasecká ih a in the Gombasek Quarry, the sample was collected at the right side of the pillar; C – typical lithology, gray coloured clay. in Tracing Of Palynomorphs In The Eastern Slovakian Karst
Text-fig. 1. A – outline map of the Slovak Republic, Gombasek Quarry marked; B – Gombasek needle – Gombasecká ih a in the Gombasek Quarry, the sample was collected at the right side of the pillar; C – typical lithology, gray coloured clay.
Text-fig. 3. Borehole section in the Blansko Graben with lithology, distribution of palynomorphs, macroflora and macrofauna (modified after Čech, unpublished report). 1 – Spesovicornea pacltovae, 2 – Platanus sp., 3 – Myrtophyllum angustum (VEL.) KNOBOCH, 4 – Gleichenia sp.), 5 – percentage of land-derived palynomorphs, 6 – percentages of marine palynomorphs, 7 – glauconite, 8 – pyrite nodules, 9 – macrofauna, 10 – productive palynological samples, 11 – carbonized roots, 12 – conglomerate, 13 – sandstone, 14 – claystone, 15 – coal, 16 – granite and granodiorite of the Brno pluton. in Spesovicornea Pacltovae Gen. Nov. Et Sp. Nov., A New Elateroid Sporomorph From The Bohemian Cenomanian (Czech Republic)
Text-fig. 3. Borehole section in the Blansko Graben with lithology, distribution of palynomorphs, macroflora and macrofauna (modified after Čech, unpublished report). 1 – Spesovicornea pacltovae, 2 – Platanus sp., 3 – Myrtophyllum angustum (VEL.) KNOBOCH, 4 – Gleichenia sp.), 5 – percentage of land-derived palynomorphs, 6 – percentages of marine palynomorphs, 7 – glauconite, 8 – pyrite nodules, 9 – macrofauna, 10 – productive palynological samples, 11 – carbonized roots, 12 – conglomerate, 13 – sandstone, 14 – claystone, 15 – coal, 16 – granite and granodiorite of the Brno pluton.
Text-fig. 1 Simplified geological map of NW Portugal (adapted from Portuguese Geological Survey 1:500.000 lithological map). in Overview Of The Stratigraphy And Initial Quantitative Biogeographical Results From The Devonian Of The Albergaria-A-Velha Unit (Ossa-Morena Zone, W Portugal)
Text-fig. 1 Simplified geological map of NW Portugal (adapted from Portuguese Geological Survey 1:500.000 lithological map).
Fig. 1 in Census of dinosaur skin reveals lithology may not be the most important factor in increased preservation of hadrosaurid skin
Fig. 1. Part (A) and counterpart (B) skin of hadrosaurid Kritosaurus sp. (YPM PU 016969) showing the typical dinosaurian morphology of non-imbricating, polygonal tubercles. Courtesy of the Peabody Museum of Natural History, Yale University, New Haven, USA.
Fig. 2. Lithological details for Section B in Ordovician ostracods from east central Iran
Fig. 2. Lithological details for Section B of the Shirgesht Formation in the Derenjal Mountains on the west side of the Dahaneh Kolut valley. The position of fossil sample points, and the stratigraphical distribution of ostracods and selected trilobites, brachiopods and conodonts are also shown. Within lithological Unit B5, ostracod sample B−D/2 was taken from the base of the unit, sample B−D/3 at 18.85 m and sample B−D/4 at 46.7 m above the base of the unit. Ostracod sample B−D/5 was taken at the base of lithological Unit B6. The dashed lines for Liomegalaspides winsnesi, Neseuretinus birmanicus, and Nicolella sp. indicate that this fossil material was collected from loose blocks directly adjacent to the upper part of lithological Unit B5 in the field.
Datasets and code for "To heal or not to heal? Part II: The moment-recurrence time behavior of Oklahoma lithologies is consistent with laboratory healing behavior"
<p>The following files are included:</p> <ul> <li>PulseWidths.csv: Pulse width measurements for each earthquake at each stations</li> <li>family_df_withMoment.csv: Average measurements of pulse width and moment for each earthquake as well as family information and timing (Datetime, recurrence time, time until next event)</li> <li>MTSpec_Prague.ipynb: A jupyter notebook with code for measuring corner frequency from spectral ratios using the joint fit procedure described in the manuscript. Produces the figures shown in the manuscript and supplement. <ul> <li>files needed to run this code are: families_df_1_25_0.95.csv, EQ_catalog_sp.csv, Catalog.txt, Repeater_Sig.csv, the waveforms supplied at Okamoto et al., 2022 (<a href="10.5281/zenodo.6658257">10.5281/zenodo.6658257</a>) <p> </p> </li> </ul> </li> </ul>
Fig. 6 in New Stratigraphic Data from the Erlian Basin: Implications for the Division, Correlation, and Definition of Paleogene Lithological Units in Nei Mongol (Inner Mongolia)
Fig. 6. Sketch profiles from (above) Camp Margetts and (below) 7 miles southwest (235°) of Camp Margetts (Granger, 1930).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.