Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
55
datasets available to search
ShareScore release 0.7.1
Dataset results
55 results for “Lithology”
Supporting GIS file for: Tectonic landform and lithologic age impact uncertainties in fault displacement hazard models
<p>This project aims to understand how the error in mapped fault location and the residual between the modeled and observed coseismic displacements vary with tectonic landform and the surficial lithologic age. We focus on four historical earthquakes: the M6.9 Borah Peak, 2014 M6.0 Napa, 2016 M7.0 Kumamoto, and 2016 M7.8 Kaikoura earthquakes.</p> <p>The GIS shape file contains information about the tectonic landform, the surficial landscape age, the observed and modelled coseismic displacement, fault location error, and the confidence ranking of the mapped fault trace. Each entry corresponds to a location where a displacement measurement was made following the earthquake of focus. Additional detail is given in the readme.</p> <p>The entries in the GIS file are collected from the following references:</p> <p>Chiou, B., Chen, R., Thomas, K., Milliner, C. W. D., Dawson, T., & Petersen, M. D. (2022). Surface Fault Displacement Models for Strike-Slip Faults. <em>Natural Hazards Risk and Resiliency Research Center B. John Garrick Institute for the Risk Sciences University of California, Los Angeles</em>, <em>Report GIRS‐2022‐07</em>, 186. https://doi.org/10.34948/N3RG6X</p> <p>Crone, A. J., Machette, M. N., Bonilla, M., Lienkaemper, J. J., Pierce, K., Scott, W., & Bucknam, R. (1987). Surface faulting accompanying the Borah Peak earthquake and segmentation of the lost river fault, central Idaho. <em>Bulletin of the Seismological Society of America</em>, <em>77</em>.</p> <p>Graymer, R. W., Brabb, E., Jones, D. L., Barnes, J., Nicholson, R. S., & Stamski, R. E. (2007). <em>Geologic Map and Map Database of Eastern Sonoma and Western Napa Counties, California</em> (No. U.S. Geological Survey Scientific Investigations Map 2956). Retrieved from https://doi.org/10.3133/sim2956</p> <p>Heron, D. W. (2018). Geological Map of New Zealand 1:250 000. GNS Science Geological Map 1 (2nd ed.) Lower Hutt, New Zealand. GNS New Zealand. Retrieved from https://www.gns.cri.nz/data-and-resources/geological-map-of-new-zealand/</p> <p>Hoshizumi, H., Ozaki, M., Miyazaki, K., Matsuura, H., Toshimitsu, S., Uto, K., et al. (2004). Geological Map of Japan 1:200,000: Kumamoto. Geological Survey of Japan. Retrieved from https://www.gsj.jp/Map/EN/geology2-6.html#Kumamoto</p> <p>Janecke, S. U., & Wilson, E. (1992). Geologic map of the Borah Peak, Burnt Creek, Elkhorn Creek, and Leatherman Peak 7.5’ quadrangles, Custer County, Idaho, Scale 1:24,000. Idaho Geological Survey Technical Report 92-5. Retrieved from https://www.idahogeology.org/product/T-92-5</p> <p>Kuehn, Nicolas, Kottke, A., Madugo, C., Sarmiento, A., & Bozorgnia, Y. (2022). Report GIRS 2022-06: UCLA–PG&E Fault Displacement Model. https://doi.org/10.34948/N3X59H</p> <p>Lewis, R. S., Link, P., Stanford, L. R., & Long, S. P. (2012). <em>Geologic Map of Idaho</em>. Moscow, Boise, Pocatello: Idaho Geologic Survey. Retrieved from https://www.idahogeology.org/maps-pubs-data/state-geologic-map</p> <p>Ponti, D. J., Blair, J. L., & Rosa, C. M. (2019). Digital Datasets Documenting Fault Rupture and Ground Deformation Features Produced by the Mw 6.0 South Napa Earthquake of August 24, 2014 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7P26W84</p> <p>Sarmiento, A., Madugo, D., Bozorgnia, Y., Shen, A., Mazzoni, S., Lavrentiadis, G., et al. (2021). Fault Displacement Hazard Initiative Database. <em>Report No. GIRS-2021-08, Revision 3.3 Dated 29 May 2024. Los Angeles, CA: The B. John Garrick Institute for the Risk Sciences at UCLA Engineering</em>. https://doi.org/10.34948/N36P48</p> <p>Scott, C., Adam, R., Arrowsmith, R., Madugo, C., Powell, J., Ford, J., et al. (2023). Evaluating how well active fault mapping predicts earthquake surface-rupture locations. <em>Geosphere</em>, <em>19</em>(4), 1128–1156. https://doi.org/10.1130/GES02611.1</p> <p>Scott, C. P., Arrowsmith, J. R., Nissen, E., Lajoie, L., Maruyama, T., & Chiba, T. (2018). The <em>M</em> 7 2016 Kumamoto, Japan, Earthquake: 3-D Deformation Along the Fault and Within the Damage Zone Constrained From Differential Lidar Topography. <em>Journal of Geophysical Research: Solid Earth</em>, <em>123</em>, 6138–6155. https://doi.org/10.1029/2018JB015581</p> <p>Vincent, K. R. (1995). Implications for models of fault behavior from earthquake surface displacement along adjacent segments of the Lost River fault, Idaho<em>:</em> University of Arizona.</p> <p>Wagner, D., & Gutierrez, C. (2017). <em>Preliminary Geologic Map of the Napa and Bodega Bay 30’ x 60’ Quadrangles, California</em>. California Department of Conservation. Retrieved from https://ngmdb.usgs.gov/Prodesc/proddesc_105819.htm</p> <p>Zinke, R., Hollingsworth, J., Dolan, J. F., & Van Dissen, R. (2019). Three‐Dimensional Surface Deformation in the 2016 M <sub>W</sub> 7.8 Kaikōura, New Zealand, Earthquake From Optical Image Correlation: Implications for Strain Localization and Long‐Term Evolution of the Pacific‐Australian Plate Boundary. <em>Geochemistry, Geophysics, Geosystems</em>, <em>20</em>(3), 1609–1628. https://doi.org/10.1029/2018GC007951</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (SeisRox Pro modelling projects - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets used for seismic modelling based on geomodels created by interpretation of DOMs.</p> <p>The dataset includes:</p> <ul> <li>Five different SeisRox modelling projects of the Landnørdingsvika model;</li> </ul> <p>1) base-case model and base-case+Noise in one project,</p> <p>2) 30Hz model,</p> <p>3) no_thin_units model,</p> <p>4) no_karst model,</p> <p>5) perfect_illumination model.</p> <ul> <li>Two different SeisRox modelling projects of the Treskelodden model;</li> </ul> <p>1) base-case model, base-case+Noise, 30Hz model, perfect_illumination model all in one project,</p> <p>2) model with 10x thicker Kapp Starostin layer in the overburden model.</p> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Synthetic seismic models - supplementary data)
<p>This dataset includes pre-loaded seismic models based on digital outcrop models (DOMs) representing 11 different case scenarios with varying signal processing setting (frequency, illumination angle, noise) and different geological models for the DOMs. The case scenarios are set up to investigate detection tresholds in seismic models of thin beds with a high degree of lithological variation. </p> <p>The dataset includes:</p> <ul> <li>A Petrel 2022 project with 11 pre-loaded seismic models based on two different geomodels presented in the paper.</li> <li>The input SGY files for each seismic section and PSF used.</li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Analysing detection thresholds of lithological complexity and karst overprinting in outcrop-scale seismic models (Well data for elastic rock parameters - supplementary data)
<p>This dataset includes pre-loaded and integrated data sets of the well data from boreholes on Spitsbergen. it also includes various maps of Svalbard to see where the boreholes are located. Rock parameters were plotted and determined using the blueback toolbox add-on to Petrel. </p> <p>The dataset used for rock parameter statisitcs includes:</p> <ul> <li>A Petrel 2022 project with well data (gamma ray, density, sonic, P-velocity, lithology) from the two boreholes Reindalspasset I (7816/2-1) and Tromsøbreen II (7617/1-2).</li> <li>Excel sheets with the wireline-log data used from the two boreholes. </li> </ul> <p>This dataset is related to a manuscript currently in review for Marine and Petroleum Geology.</p>
Figure 2. Lithological profile. A in Osteology and phylogenetic relationships of Ligabuesaurus leanzai (Dinosauria: Sauropoda) from the Early Cretaceous of the Neuquén Basin, Patagonia, Argentina
Figure 2. Lithological profile. A schematic log of the the lower section of the Cullin Grande Member (Bajada del Agrio Group, Lohan Cura Formation, Lower Cretaceous, Albian) that outcrops at the Cerro de los Leones locality (modified from Martinelli et al., 2007). Abbreviations: CS, crevasse channel; FF, floodplain fines; FL, fossiliferous level; LA, lateral accretion; LS, laminated sand sheets; LV, levee; SB, sandy bedforms. Architectural element codes follow Miall (1996).
Data from: Problems with using rock outcrop area as a paleontological sampling proxy: rock outcrop and exposure area compared with coastal proximity, topography, land use, and lithology.
Open the record for dataset details and reuse information.
Data from: An experimental study of the influence of lithology on compaction behaviour of broken waste rock in coal mine backfill
The research aims to explore the influences of lithology on the compaction behaviours of broken waste rocks. For this purpose, a WAW1000D servo test machine and a self-made bidirectional loading test system for granular materials were used to conduct axial and lateral compaction tests on four typical types of broken waste rocks: sandstone, mudstone, limestone, and shale. On this basis, we analysed the relationships between lateral and axial stress with the strain in, and porosity of, the four types of broken waste rocks. In addition, the relationship of axial stress with lateral stress and lateral pressure coefficient, and the changes in the particle size distribution of broken waste rocks before, and after, compaction were discussed. The test results demonstrated that the samples of higher strength were found to have low lateral and axial strains as well as a lower porosity in axial and lateral loading tests; while samples of lower strength showed low lateral stress and lateral pressure coefficient under axial load. After being compacted, the samples of the four types of broken waste rocks were found to have a higher proportion of small particles, indicating some particle crushing. Moreover, the samples of lower strength were broken to a greater extent.
Lithology and Distance to fault maps
Open the record for dataset details and reuse information.
Text-fig. 2. Lithology of basal Upper Cretaceous sediments at Kaňk – Na Vrších (a). 1 – conglomerates; 2 – limestones; 3 – erosional surface with borings and mineralization; 4 – limestone layer with nodule-like bodies; 5 – calcareous claystones. (after Žítt, 1992; slightly modified). in Sabellidae And Serpulidae (Polychaeta, Canalipalpata) From The Locality Kaňk - Na Vrších In Kutná Hora (Upper Cenomanian - Lower Turonian, Bohemian Cretaceous Basin - The Czech Republic)
Text-fig. 2. Lithology of basal Upper Cretaceous sediments at Kaňk – Na Vrších (a). 1 – conglomerates; 2 – limestones; 3 – erosional surface with borings and mineralization; 4 – limestone layer with nodule-like bodies; 5 – calcareous claystones. (after Žítt, 1992; slightly modified).
Lithological and geochemical data of the Naqing section
Open the record for dataset details and reuse information.
Fig. 5 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. 5. Stratigraphic correlations of the Nuhetingboerhe-Huheboerhe area (NHA). See Fig. 1B for the locations of the sections.
Data from: An experimental study of the influence of lithology on compaction behaviour of broken waste rock in coal mine backfill
Open the record for dataset details and reuse information.
Multi-stage evolution of the South Australian Craton: petrological constraints on the architecture, lithology, and geochemistry of the lithospheric mantle
<p>APPENDIX_A: LA-ICP-MS and EPMA analyses for pyrope garnet and Cr diopside used in study.</p>
Data from "Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics"
<p>Datasets and script of the manuscript “Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics” authored by R. Muñoz*, M. Enríquez, F. Bongers, R.D. López-Mendoza, C. Miguel-Talonia & J.A. Meave*, published in Frontiers in Forests and Global Change (2023).</p> <p>* Correspondence: R. Muñoz (rod.munozaviles@gmail.com) & J.A. Meave (jorge.meave@ciencias.unam.mx)</p> <p>The original publication can be found in https://doi.org/10.3389/ffgc.2023.1082207</p> <p> </p> <p><strong>TERMS OF USE FOR THE CURRENT DATASETS AND SCRIPTS</strong></p> <p>All data and scripts associated with the current publication are intended ONLY for the reproduction and validation of the analyses conducted in the manuscript cited above. Use of this data for other purposes (for example, other publications or meta-analyses) is strictly forbidden without prior consent from the corresponding authors (R. Muñoz and/or J.A. Meave, contact details above).</p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The ZIP folder is structured in the following manner:</p> <p>– Munoz et al 2023 Frontiers.zip</p> <p> – READ ME.txt</p> <p> – Script Munoz et al 2023 Frontiers.R</p> <p> – Data source</p> <p> – Dataset Munoz et al 2023 Frontiers stand data.csv</p> <p> – Dataset Munoz et al 2023 Frontiers species matrix.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ONI.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ENSO events.csv</p> <p> </p> <p><strong>DESCRIPTION OF SCRIPT</strong></p> <p>The script provided in the root of the ZIP folder (Script Munoz et al 2023 Frontiers.R) allows to reproduce the analyses, figures and tables supporting the original publication in Frontiers. When executed in full, the script generates a new folder named “Figures” where all figures are stored in their raw, unedited version. The figures for publication were later edited in Adobe Illustrator to enhance their visual appearance.</p> <p> </p> <p><strong>DESCRIPTION OF DATASETS</strong></p> <p>Four datasets are provided in this ZIP file (“Data source” folder):</p> <p>1. Dataset Munoz et al 2023 Frontiers stand data.csv (<em>Stand data</em>)</p> <p>2. Dataset Munoz et al 2023 Frontiers species matrix.csv (<em>Species matrix</em>)</p> <p>3. Dataset Munoz et al 2023 Frontiers ONI.csv (<em>ONI</em>)</p> <p>4. Dataset Munoz et al 2023 Frontiers ENSO events.csv (<em>ENSO events</em>)</p> <p> </p> <p><em>STAND DATA </em>contains information about the seven forest attributes included in the study, per substrate and year. It contains the following variables:</p> <ol> <li>Year: Year of measurement</li> <li>Plot: Plot code</li> <li>Set: Can only be “MatCan” (Mature Canopy)</li> <li>Subset: Either “Lim” (limestone) or “Phy" (phyllite)</li> <li>Dynamics: Whether there is a previous measurement allowing the estimation of dynamic rates (e.g., net change; FALSE/TRUE) </li> <li>Basal: Basal area expressed in m2/ha</li> <li>DeltaBasal: Annual net change in basal area</li> <li>R.basal: Annual change in basal area due to recruitment</li> <li>G.basal: Annual change in basal area due to growth</li> <li>M.basal: Annual change in basal area due to mortality</li> <li>AGB: Aboveground biomass expressed in Mg/ha, estimated from the allometric equation of Chave et al. 2014 (including DBH, height and WD)</li> <li>DeltaAGB: Annual net change in AGB</li> <li>R.agb: Annual change in AGB due to recruitment</li> <li>G.agb: Annual change in AGB due to growth</li> <li>M.agb: Annual change in AGB due to mortality</li> <li>Dens: Tree density expressed in individuals/ha</li> <li>DeltaDens: Annual net change in tree density</li> <li>R.dens: Annual change in tree density due to recruitment</li> <li>G.dens: Annual change in tree density due to “growth”. Here, “growth” is a term introduced to account for small differences in tree densities between years due to changes in the extrapolation factor of a tree. Due to the nested sampling design of the vegetation survey, sometimes trees change their extrapolation factor as they grow larger. Thus, is a tree changes extrapolation factor, those differences (that are neither recruitment or mortality) are added up here.</li> <li>M.dens: Annual change in tree density due to mortality</li> <li>Species: Species richness expressed in spp/plot. Redundant with “q0” column.</li> <li>DeltaSpecies: Annual net change in species richness</li> <li>R.species: Annual change in species richness due to recruitment</li> <li>M.species: Annual change in species richness due to mortality</li> <li>Height: Average plot canopy height expressed in m</li> <li>q0: Hill number of order 0 expressed in species effective number (species richness)</li> <li>q1: Hill number of order 1 expressed in species effective number (typical species)</li> <li>q2: Hill number of order 2 expressed in species effective number (dominant species)</li> </ol> <p> </p> <p><em>SPECIES MATRIX</em> contains an abundance matrix per species, plot and year. It contains the following variables:</p> <ol> <li>PlotYear: This column actually does not have a name to it in the file, but is the first column in the dataset, It contains the three-character identifier for the plot and the four numbers of the year of measurement. For instance, “BER2008” would represent the observations made for the plot BER in 2008.</li> <li>treat: This indicates whether the plot is located on limestone (1) or phyllite (2) substrate</li> <li>sp001-sp127: indicates the abundance (in number of individuals per plot) of a given species. Species numbers were assigned randomly, thus they do not match the order of the table provided in Supplementary Material 3 of the publication in Frontiers.</li> </ol> <p> </p> <p><em>ONI</em> contains the Oceanic El Niño Index values per month and year. It is a “year by month” contingency matrix, where years are presented in the rows name, and months are presented in the columns name. ONI values are given in Celsius degrees, and they represent the 3-month rolling average of the temperature anomaly in the Nino3.4 region. The data source and details of this dataset can be found at the NOAA webpage (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).</p> <p> </p> <p><em>ENSO EVENTS</em> contains the occurrence of events of El Niño (warm and dry episodes) and La Niña (cold and wet episodes). It contains the following variables:</p> <ol> <li>Year: Year</li> <li>Month: Month</li> <li>ONI: Oceanic El Niño Index (see ONI dataset description above)</li> <li>Year.cont: Time as a continuous variable (instead of having years and months separately, for plotting)</li> <li>Nino: El Niño (warm and dry) episode occurrence (“1” indicates occurrence)</li> <li>Nina: La Niña (cold and wet) episode occurrence (“1” indicates occurrence)</li> </ol>
Data - Bedding Plane Geometry and Lithological Variations Influencing Rainfall-Triggered Landslide Dynamics in the Chittagong Fold Belt of Bangladesh
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.