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.
56
datasets available to search
ShareScore release 0.7.1
Dataset results
56 results for “porosity”
Salt River Wetlands denitrification rate, dissimilatory nitrate reduction to ammonium rate, dissolved organic carbon concentration in June 2016 as well as soil porosity and bulk density
Raw and derived data used to calculate denitrification and dissimilatory nitrate to ammonium (DNRA) from push-pull experiments with added isotopically labelled nitrate. Experiments were conducted in 2016 in the Salt River Accidental Wetlands in three different patch types: Unvegetated, dominated by Ludwigia peploides, and dominated by Typha species (T. domingensis and T. latifolia). Data include start and end of incubation concentration of nitrate, ammonium, atom percent 15N in ammonium, dissolved organic carbon, excess mass 29-N2, and excess mass 30-N2. Soil data was collected from the same patch types including soil moisture, porosity, and bulk density.
Benthic chlorophyll, density, porosity, and organic content concentrations and gross oxygenic photosynthesis rates in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from January, April, June and July 2001
Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll, density, porosity, and organic content were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll, density, porosity, and organic content were measured in the bulk surface centimeter depth of sediment. Several relatively pristine sites on Sapelo Island (Moses Hammock and Dean Creek) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration across bank and marsh zones with higher photosynthetic biomass in the spring season.
Benthic chlorophyll, density, porosity, and organic content concentrations in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from June and August 2002
Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll, density, porosity, and organic content were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll, density, porosity, and organic content were measured in the bulk surface centimeter depth of sediment. Several relatively pristine sites on Sapelo Island (Moses Hammock and Dean Creek) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration across bank and marsh zones with higher photosynthetic biomass in the spring season.
Bulk carbon, nitrogen, porosity, chlorophyll a and phaeopigments in sediments of the Parker and Rowley Rivers, Newbury and Rowley, Massachusetts, PIE LTER.
Measurements of bulk sediment carbon, nitrogen, chlorophyll a, phaeopigments and porosity 1993 - 2017 at a variety of sites in the Parker and Rowley Rivers, Newbury and Rowley Massachusetts.
Corrected IODP Gamma Ray Attenuation (GRA) densities and calculated porosities derived from the LILY Database
<div>The dataset <strong>GRA_Densities_Corrected_and_Porosities_2023-12-26.csv</strong> is derived from an analysis of data from the LILY Database (<a href="https://doi.org/10.5281/zenodo.8408296">https://doi.org/10.5281/zenodo.8408296</a>) as described in Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). The file contains over 3.7 million corrected gamma ray attenuation (GRA) bulk density data derived from the LILY database file GRA_DataLITH.csv. It also contains over 3.7 million porosity estimates that are computed from the corrected GRA bulk density using grain densities computed for each lithology from Moisture and Density (MAD) grain densities (derived from LILY file MAD_DataLITH.csv).</div> <div> </div> <div><strong>Citation: </strong>Please cite Childress et al. (2024) when using these data:</div> <div>Childress, L.B., Acton, G.D., Percuoco, V.P., Hastedt, M., 2024. The LILY Database: Linking Lithology to IODP Physical, Chemical, and Magnetic Properties Data, <em>Geochemistry, Geophysics, Geosystems, 25</em>, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>.</div> <div> </div> <div><strong>GRA_Densities_Corrected_and_Porosities_2023-12-26.csv</strong> file size uncompressed is 950 Mb.</div> <div> </div> <div><strong>Data File format:</strong></div> <ul> <li>Exp: expedition number</li> <li>Site: site number</li> <li>Hole: hole number</li> <li>Core: core number</li> <li>Type: Type indicates the coring tool used to recover the core (typical types are F, H, R, X; see Table S3 in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>).</li> <li>Sect: section number</li> <li>Offset (cm): position of the observation, measured relative to the top of a section.</li> <li>Depth CSF-A (m): location of the observation expressed relative to the top of a hole.</li> <li>Bulk density (GRA): bulk GRA density measured on whole core sections in g/cm^3.</li> <li>Timestamp (UTC): date and time the observation was made.</li> <li>Instrument: abbreviation or mnemonic for the GRA sensing device used to make this observation (GRA1 or GRA2).</li> <li>Instrument group: abbreviation or mnemonic for the data collection device (logger) used to acquire this observation (WRMSL).</li> <li>Text ID: automatically generated unique database identifier for a sample, visible on printed labels.</li> <li>Prefix: Prefix of the lithology</li> <li>Principal: Principal lithology</li> <li>Suffix: Suffix of the lithology</li> <li>Full Lithology: full lithologic name = Prefix + Principal + Suffix</li> <li>Simplified Lithology: categorization of lithologies (see Supporting Information in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>)</li> <li>Lithology Type: Sedimentary, Igneous, or Metamorphic</li> <li>Degree of Consolidation: consolidation state of the lithology.</li> <li>Lithology Subtype: categorization of lithologies (see Supporting Information in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>).</li> <li>Expanded Core Type: the actual coring type used, because some coring types were incorrectly grouped in the "Type" column (see Childress et al., 2024 for an explanation)</li> <li>Latitude (DD): Latitude in decimal degrees</li> <li>Longitude (DD): Longitude in decimal degrees</li> <li>Water Depth (mbsl): water depth in meters below sea level</li> <li>Grain Density: grain density associated with the Principal lithology, computed from MAD data</li> <li>Mean MAD Bulk Density: mean MAD bulk density associated with the Principal lithology.</li> <li>Std MAD Bulk Density: standard deviation in the MAD bulk densities for each Principal lithology.</li> <li>Correction Basis: the GRA bulk densities are corrected based on coring tool used. If the RCB was used, then the lithology cored by the RCB is used in determining the size of the correction.</li> <li>Median Difference: The correction that will be applied based on the median difference between the raw GRA bulk density and the colocated MAD bulk density for a specific Correction Basis.</li> <li>GRA Bulk Density Corrected: The corrected GRA bulk density in g/cm^3.</li> <li>Porosity: porosity computed from the corrected GRA bulk densities and grain density.</li> <li>Deviation: difference between "GRA Bulk Density Corrected" and "Mean MAD Bulk Density", which is the deviation the corrected density has from that expected for its Principal lithology.</li> <li>N Deviations: The number of standard deviations by which the observation differs from the expected value (= Deviation/(Std MAD Bulk Density)), which is useful for identifying outliers.</li> </ul> <h3>GitHub Repository:</h3> <ul> <li>Contains a few notebooks to demonstrate how to work with the LILY database</li> <li><a title="IODP LILY GitHub Repository" href="https://github.com/IODP?tab=repositories">IODP LILY GitHub Repository</a> </li> </ul>
Measurement data used in "Thermal and porosity properties of meteorites: A compilation of published data and new measurements".
<p>Measurement data used in “Thermal and porosity properties of meteorites: A compilation of published data and new measurements”. Includes the measurement data as a csv file, as well as 3D models and images of the measured meteorites as zip archives.</p>
Porosity distribution in sub-skin boundary area of the powderbed additively manufactured parts
<p>Repository contains measurement results of the experimental investigationn of the sub-skin porosity in additively manufactured parts. Specimens were manufactured using machine manufacturer's suggested process parameters. Four different machines (EOS M400, TRUMPF TruPrint 1000, SLM 280 and DMG MORI LASERTEC 30 2nd gen.) and four different powder materials (mararging steel 1.2709, aluminum alloy AlSi10Mg, Titanium grade 5 and stainless steel 1.4404) are covered. Influences of the relative orientation of the hatch and boundary scanning tracks was investigated. Efficiency of the mitigation strategy againts sub-skin porosity issues through distance variation between hatch and boundary tracks was evaluated.</p> <p>Please refer to README.MD (or .PDF) for more detailed information about this dataset.</p>
Data set: On the porosity-dependent permeability and conductivity of triply periodic minimal surface based porous media
<p>This file contains all processed data from the simulations and calculations.</p>
Results of inter-laboratory testing on COSC-1 cores and reference samples (density, porosity, thermal properties)
<p>The data files contain the results of comparative measurements of thermal properties of four widely used rock references and nine core samples originating from the ICDP COSC-1 borehole, using a steady-state and a transient divided-bar device, a transient plane source device, a modified Angstrom device, as well as two optical thermal conductivity scanners. In addition, the Dewar method provided benchmark values for specific heat capacity. </p> <p>participating institutions and methods (acronyms used in file headers)</p> <p>Chalmers University (CU) transient plane source (TPS)<br>Geological Survey of Sweden (SGU) optical scanning (TCS) <br>Ruhr-Universit ̈at Bochum (RUB) Dewar method, modified Angstrom (mAng), optical scanning (TCS)<br>Aarhus University (AU) transient divided bar (TDB)<br>University College Dublin (UCD) divided bar (DB)</p>
Datasets for the paper: "Rock-matrix porosity and permeability of the hydrothermally altered, upper oceanic crust, Oman ophiolite"
<p>This dataset includes all the data used for the paper submitted to the Journal of Geophysical Research: Solid Earth, entitled: <strong>Rock-matrix porosity and permeability in the hydrothermally altered, upper oceanic crust, Oman ophiolite. </strong></p> <p>One excel file is provided with the following tables on separate sheets:</p> <p>Table 1: Sample locations, alteration types, and petrophysical analyses</p> <p>Table S1: Trace element Zirconium and Yttrium analysis for this study’s sample set in Figure S1</p> <p>Table S2: Outcrop upscale results from image analysis</p> <p>Table S3: Sample bulk rock geochemistry</p>
Longyearbyen CO2 Lab - Porosity, permeability, density and mechanical data
<p>Porosity, permeability, density and mechanical testing measurements on core samples taken from the Longyearbyen CO2 Lab boreholes/drillcore.</p> <p>Sources are indicated in the file name, as are the included data types.</p>
Dataset for publication "Sol-gel derived silicate-phosphate glass SiO2–P2O5–CaO–TiO2: the effect of titanium isopropoxide on porosity and thermomechanical stability"
<p>In this work, sol-gel silica-phosphate glasses (30 mol.% P<sub>2</sub>O<sub>5</sub>) with defined porosity from 2.5 to 7.5 mol.% TiO<sub>2</sub> modifications were synthesized. Thee dimensional stability with respect to the thermal stability of porous silica-phosphate glasses modified by titanium dioxide (TiO<sub>2</sub>) through titanium isopropoxide (TTIP) precursor was studied.</p> <p>The data of thermomechanical and thermogravimetric analysis, X-ray diffraction, SEM -EDX, mercury intrusion porosimetry, N2 physisorption, micro-computed tomography, ultra-small-angle X-ray scattering are published.</p>
Porosity of the porous carbonate rocks in the Jingfengqiao-Baidiao area based on finite automata
<p>This study is based on the processing of computed microtomography images of rock samples. In this study, a finite automation is constructed using the gray value, RGB value, and Euler number of polarized images of carbonate rocks from the Jingfengqiao-Baidiao area. The finite automaton is used to perform black and white binary processing of the polarized images of the carbonate rocks. The porosity of the carbonate rock is calculated based on the black and white binarization processing results of the polarized images of the carbonate rocks. The obtained porosity is compared with the carbonate porosity obtained by use of the traditional carbonate research method (TCRM). When the two porosities are close, the image processing threshold of the finite automata is considered to be credible. Based on the finite automata established using the image processing threshold, the black and white binary images of the polarized images of the carbonate rocks are used to establish a rock pore image using ImageJ2X. The polarized images of the carbonate rocks are classified according to their red–green–blue (RGB) values using the finite automata for the porosity classification, and the obtained images are used as textures to paste onto a cube to construct a 3D data model of the carbonate rocks. This study also uses 16S rDNA analysis to verify the formation mechanism of the carbonate pores in the Jingfengqiao-Baidiao area. The results of the 16S rDNA analysis show that the pores in the carbonate rocks in the Jingfengqiao-Baidiao area are closely related to microorganisms, represented by denitrifying bacteria.</p>
Effect of Sediment Form and Form Distribution on Porosity
<p>Data to accompany the journal article "Effect of Sediment Form and Form Distribution on Porosity: A Simulation Study Based on the Discrete Element Method" by Rettinger et al. (2022).</p> <p>The data set contains simulation input and results obtained by the particle packing studies using the discrete element method, as described in the article.</p> <p>The folder `surface_meshes` features 63 processed scans of gravel sediments from the Rhine river in Germany. The original scans have been converted to a convex surface mesh, and then simplified to feature at most 300 surface triangles. Additionally, they were oriented along their principle axes of inertia and scaled such that their size, according to the applied definition, is 1.</p> <p>The folder `single_form` contains the simulation results of the single shape/form studies, where all particles featured a single shape, given by one of the meshes. Three different size distributions have been used: U1 is a unimodal one with a single size class, U7 is also unimodal but with seven size classes, B50 is a bimodal one with approximately half fine and half coarse particles. Each corresponding csv file contains the obtained porosity (`porosity`) of the actual packing and of the packing using equivalent ellipsoids (`porosityEquivEllip`). Furthermore, the considered form factors according to their respective definition, as well as their inverse (`invX`), are given. See the article for the definition and the meaning of the abbreviation.</p> <p>The folder `form_distribution` contains the simulation results of the form distribution study, where the particle form was prescribed by flatness and elongation which were sampled from truncated normal distributions. Again two different size distributions have been used: U1 and U7. Each corresponding csv file contains the obtained porosity (`porosity`) and statistical information about the form factors, given as number-based mean (`mean`) and standard deviation (`std`). In addition to the inverse of each form factor (`invX`), the inverse of each statistical quantity has been evaluated (`1/(X)`) and is contained as column.</p>
A small CO2 leakage may reactivate a sub-seismic fault in a good-porosity clastic saline aquifer
<p>Seismograms.h5: HDF5 data set that contains the raw seismograms for all the 19 events (named ‘/event_X’ for the X-th event). Each seismogram contains 909 channels and 7000 samples for each trace, with a sampling frequency 1 kHz. The channel identification is stored in ‘/receiver_coordinates’, a 909 by 6 array, with the six columns having receiver line number, station number within the line, station name, UTM (zone 54H) easting (meters), UTM (zone 54H) northing (meters), and elevation (meters).</p>
Bombardment history of the Moon constrained by crustal porosity
<p>The source data is the modeling result regarding the origin of crustal porosity for the Moon. In this study, we find that the crustal porosity of the early Moon was likely to be high, generated by large basins in its early bombardment history. This high porosity can be reduced over time by smaller impacts or overburden pressure. Our porosity evolution model is based on the observed GRAIL (Gravity and Recovery Interior Laboratory) datasets and global lunar crater catalog and can explain the porosity distribution in the present-day lunar crust.</p>
Spectral effects of regolith porosity in the mid-IR - Forsteritic olivine
<p>This dataset contains laboratory spectra of olivine in the mid-Infrared (MIR; 5-35 micron) wavelength region as described in Martin et al., 2022. </p> <p>Files are labeled accordingly: mineral_largest particle size_regolith porosity</p> <p>Example: The file labeled OLV1_45_10.txt contains spectra of olivine (OLV1 in the paper), with 20-45 micron particle sizes, has 10% regolith porosity. </p>
Spectral effects of regolith porosity in the Mid-IR - Pyroxene [part 2]
<p>This dataset is one of three that contains laboratory spectra of pyroxene in the mid-Infrared (MIR; 5-35 micron) wavelength region as described in Martin et al., 2023. Specifically, this dataset (part 2) has .txt files of HEN1, HEN2, and AEG spectra.</p> <p>Dataset part 1 (10.5281/zenodo.11398016) contains spectra of ENS, DIOP1, and DIOP2.</p> <p>Dataset part 3 (10.5281/zenodo.11402393) contains spectra of AUG and MXT.</p> <p>Files are labeled accordingly: mineral_largest particle size_regolith porosity</p> <p>Example: The file labeled AEG_45_10.txt contains spectra of aegirine (AEG in the paper), with 20-45 micron particle sizes, has 10% regolith porosity. </p>
Spectral effects of regolith porosity in the Mid-IR - Pyroxene [part 1]
<p>This dataset is one of three that contains laboratory spectra of pyroxene in the mid-Infrared (MIR; 5-35 micron) wavelength region as described in Martin et al., 2023. Specifically, this dataset (part 1) has .txt files of ENS, DIOP1, and DIOP2 spectra.</p> <p>Dataset part 2 (10.5281/zenodo.11402380) contains spectra of HEN1, HEN2, and AEG.</p> <p>Dataset part 3 (10.5281/zenodo.11402393) contains spectra of AUG and MXT.</p> <p>Files are labeled accordingly: mineral_largest particle size_regolith porosity</p> <p>Example: The file labeled DIOP1_45_10.txt contains spectra of diopside (DIOP1 in the paper), with 20-45 micron particle sizes, has 10% regolith porosity. </p>
Spectral effects of regolith porosity in the Mid-IR - Pyroxene [part 3]
<p>This dataset is one of three that contains laboratory spectra of pyroxene in the mid-Infrared (MIR; 5-35 micron) wavelength region as described in Martin et al., 2023. Specifically, this dataset (part 3) has .txt files of AUG and MXT spectra.</p> <p>Dataset part 1 (10.5281/zenodo.11398016) contains spectra of ENS, DIOP1, and DIOP2.</p> <p>Dataset part 2 (10.5281/zenodo.11402380) contains spectra of HEN1, HEN2, and AEG.</p> <p>Files are labeled accordingly: mineral_largest particle size_regolith porosity</p> <p>Example: The file labeled AUG_45_10.txt contains spectra of augite (AUG in the paper), with 20-45 micron particle sizes, has 10% regolith porosity. </p>
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.