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Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")
<p>This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in:<br> "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".</p> <p> <br> 1. Absorbance data (200-730 nm wavelengths):</p> <p> * Average absorbance compensated for measured concentrations (average absorbance value per concentration)<br> * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)<br> * Average raw absorbance measured (average absorbance value per concentration)<br> * Raw absorbance measured (all absorbance values for all concentrations)</p> <p> Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.<br> <br> 2. Measured concentration data:</p> <p> * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)</p> <p> </p>
Data from: Reproducibility of the Quantification of Reversible Wall Interactions in VOC Sampling Lines
<p>Dataset from the following publication (<a href="https://doi.org/10.3390/atmos12020280">https://doi.org/10.3390/atmos12020280</a>). In the paper, a method to quantify the amount of substance segregated by reversible interactions on sampling lines is proposed. The areic amount of a VOC (Acetone) interacting with the pipe is measured for a commercial test pipe (Sulfinert®) as the amount of substance per unit area of the internal surface of the test pipe segregated from the flowing gas mixture. The areic amount is function of numerical integrals estimated under different conditions and reproducibility is evaluated. The data used to estimate the integrals described in this work is organised in folders. Each folder correspond to a sample. Sample information is available on Table 3 of the paper.</p>
Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples
<p># Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples"</p> <p>SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes.</p> <p>SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results.</p> <p>SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID.</p> <p>SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows:<br> * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020.<br> * rab.all: median clr value for all samples in the feature<br> * rab.win.conditionA: median clr value for the condition A of samples<br> * rab.win.conditionB: median clr value for the condition B of samples<br> * diff.btw: median difference in clr values between A and B conditions<br> * diff.win: median of the largest difference in clr values within A and B conditions<br> * effect : median effect size: diff.btw / max(diff.win) for all instances<br> * overlap : proportion of effect size that overlaps 0 (i.e. no effect)<br> * we.ep: Expected P value of Welch’s t test<br> * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test<br> * wi.ep: Expected P value of Wilcoxon rank test<br> * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test<br> * parts: gene name<br> * conditionA: label of condition A that is compared against condition B<br> * conditionB: label of condition B that is compared against condition A<br> * conditions.A.vs.B: label to explain condition A compared against condition B<br> NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs</p> <p>SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences.</p> <p>SD6: FASTA file containing all unique consensus sequences reported in the manuscript.</p> <p>SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.</p>
Single-crystal X-ray diffractometry data for a sample of [Cu(HF₂)(pyrazine)₂]PF₆ collected on beamline I19-2 at Diamond Light Source
<p>Single-crystal X-ray diffractometry data for a sample of [Cu(HF₂)(pyrazine)₂]PF₆.</p> <p>These data were collected at Diamond Light Source, on beamline I19 (experiments hutch 2), on 2022-01-30, and are particularly useful for testing data reduction routines. They are known to produce good merging statistics and final structure refinement.</p> <p>The sample was prepared as follows:<br> Ammonium hexafluorophosphate (NH₄PF₆) (0.310 g, 1.9 mmol), ammonium hydrogen difluoride ((NH₄)HF₂) (0.109 g, 1.9 mmol) and pyrazine (C₄H₄N₂) (0.300 g, 3.7 mmol) were dissolved in 5 mL of deionised water. The obtained colourless solution was slowly added to a blue solution of copper(II) nitrate prepared by dissolving copper(II) nitrate hemipentahydrate (Cu(NO₃)₂ · 2.5(H₂O)) (0.425 g, 1.8 mmol) in 5 mL of deionised water. The solutions were mixed in a plastic beaker at room temperature. The formation of blue crystals of [Cu(HF₂)(pyrazine)₂]PF₆ on the side of the beaker started after few seconds and continued for about 24 hours during which the sealed beaker was not moved.</p> <p>The sample was measured at room temperature and the illuminating beam had a wavelength of 0.4859 Å (25.52 keV).</p> <p>Beamline I19-2 at Diamond Light Source, a four-circle κ-geometry diffractometer (see <a href="https://onlinelibrary.wiley.com/doi/10.1107/97809553602060000936">[Kern 2019]</a>) with an undulator source, is described in <a href="https://doi.org/10.1107/S0909049512008801">[Nowell 2012]</a> but has since been upgraded to use a Dectris Eiger2 X 4M CdTe hybrid photon counting detector. The data are written in the <a href="https://manual.nexusformat.org/classes/applications/NXmx.html">NXmx variant</a> of the <a href="https://www.nexusformat.org/">NeXus format</a>, and so include metadata with a functionally complete description of the diffractometer.</p> <p>Inventory of data:</p> <ul> <li><strong><code>01_CuHF2pyz2PF6b_Phi.tar.xz</code></strong><br> A single 1750-image 350° φ rotation scan from -175° to 175° with 0.2° rotation per image, an exposure time of 0.1 s per image, ω = -90°, κ = 0° and 2θ = 0°.</li> <li><strong><code>02_CuHF2pyz2PF6b_2T.tar.xz</code></strong><br> A single 1750-image 350° φ rotation scan from -175° to 175° with 0.2° rotation per image, an exposure time of 0.1 s per image, ω = -90°, κ = 0° and 2θ = 20°.</li> <li><strong><code>03_CuHF2pyz2PF6b_P_O.tar.xz</code></strong><br> Two sequential rotation scans: <ul> <li><strong><code>CuHF2pyz2PF6b_P_O_01.nxs</code></strong><br> A 1750-image 350° φ scan from -175° to 175° with ω = -90°, κ = 0° and 2θ = 0°.</li> <li><strong><code>CuHF2pyz2PF6b_P_O_02.nxs</code></strong><br> A 600-image 120° ω scan from -125° to -5° with φ = -90°, κ = 45° and 2θ = 0°.</li> </ul> Both scans had 0.2° rotation per image and an exposure time of 0.1 s per image.</li> </ul> <p>The same sample was used for all these measurements. Throughout, the sample-to-detector distance was 85 mm and the beam was attenuated to 0.2% of its full intensity.</p> <p>For each rotation scan, the data comprise a single top-level NXmx-format NeXus file named <code><filename>.nxs</code>, one or more image files named <code><filename>_00000n.h5</code>, where <code>n</code> is a numeral, and a single detector metadata file named <code><filename>_meta.h5</code>. The NeXus file contains an HDF5 virtual data set that links to the data in the image file(s), and several HDF5 external links to data in the detector metadata file.</p> <p>For internal reference of Diamond Light Source staff, these data were collected as part of commissioning visit CM31144-1. Some file names and corresponding HDF5 link targets have been altered from their original names for consistency with the file contents.</p>
Whole-genome genotype data for French Large White pigs from two distinct sampling times
<p>Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909.</p> <p>Two different genotype datasets were obtained from the raw WGS:</p> <p>1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90%</p> <p>2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software.</p> <p>More details about these datasets and their use can be found in the following study:</p> <p>Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.</p>
DCSsim (simulated) and DCSsub (sub-sampled) ChIP-seq data from different chromosomes.
<p>These data are the results from three independent runs of DCSsim and DCSsub for TF, sharp and broad mark signals in 50:50 regulation scenarios for mm10 chr1, chr8, chr11, chr19 and chrX.</p> <p>Simulated data from DCSsim: simulated_ChIP-seq_data.zip</p> <p>set1: TF 50:50 chr11<br> set4: TF 50:50 chr8<br> set7: TF 50:50 chrX<br> set10: TF 50:50 chr1<br> set22: TF 50:50 chr19</p> <p>set2: Sharp mark 50:50 chr11<br> set5: Sharp mark 50:50 chr8<br> set8: Sharp mark 50:50 chrX<br> set11: Sharp mark 50:50 chr1<br> set23: Sharp mark 50:50 chr19</p> <p>set3: Broad mark 50:50 chr11<br> set6: Broad mark 50:50 chr8<br> set9: Broad mark 50:50 chrX<br> set12: Broad mark 50:50 chr1<br> set24: Broad mark 50:50 chr19</p> <p><br> Sub-sampled data from DCSsub: sub-sampled_ChIP-seq_data.zip</p> <p>Set1: C/EBPa-ChIP-seq 50:50 chr11<br> Set2: C/EBPa-ChIP-seq 50:50 chr8<br> Set3: C/EBPa-ChIP-seq 50:50 chrX<br> Set4: C/EBPa-ChIP-seq 50:50 chr1</p> <p>Set5: H3K27ac-ChIP-seq 50:50 chr11<br> Set6: H3K27ac-ChIP-seq 50:50 chr8<br> Set7: H3K27ac-ChIP-seq 50:50 chrX<br> Set8: H3K27ac-ChIP-seq 50:50 chr1</p> <p>Set9: H3K36me3-ChIP-seq 50:50 chr11<br> Set10: H3K36me3-ChIP-seq 50:50 chr8<br> Set11: H3K36me3-ChIP-seq 50:50 chrX<br> Set12: H3K36me3-ChIP-seq 50:50 chr1</p> <p><br> C/EBPa-ChIP-seq 50:50 chr19 can be found in sub-sampled_ChIP-seq_data.zip of the FRIP data set (DOI: 10.5281/zenodo.6042902 set8)<br> H3K27ac-ChIP-seq 50:50 chr19 can be found in sub-sampled_ChIP-seq_data.zip of the FRIP data set (DOI: 10.5281/zenodo.6042902 set9)<br> H3K36me3-ChIP-seq 50:50 chr19 can be found in sub-sampled_ChIP-seq_data.zip of the FRIP data set (DOI: 10.5281/zenodo.6042902 set10)</p>
NYC telematics data with asynchronous sampling
<p>This telematics data are used in the paper "Alrassy, P., Jang, J., and Smyth, A. W. (2021). "OBD-data-assisted cost-based map-matching algorithm for low-sampled telematics data in urban environments." IEEE Transactions on Intelligent<br> Transportation Systems. doi:10.1109/TITS.2021.3109851</p> <p>The data were collected in New York City. Each trajectory represents 15 to 30 minutes of driving. An in-vehicle sensing hardware package is developed and comprises a Raspberry PI 3 Model B+ microcomputer, a microSD 32GB SD card, an OBDCheck BLE OBD-II scanner, and a GPS module. This sensor configuration aims to collect timestamps, GPS positioning, and instantaneous speed data. Each sensor module has a dedicated data collection algorithm to collect data whenever new data updates are available on the sensor node. The structure of the data follows:</p> <p>Data Type, timestamp, values</p> <p>GPS, timestamp, latitude, longitude, altitude</p> <p>OBD, timestamp, RPM, speed</p>
D4.1. YouCount open data sample from the evaluation – current stand
<p>The H2020 YouCount project runs from February 2021 to January 2024. The overarching objectives are to generate new knowledge and innovations to increase the social inclusion of youth through co-creative youth citizen social science (Y-CSS) and to provide evidence of the actual outcomes of Y-CSS. Multiple case studies—consisting of 10 co-creative Y-CSS projects with young citizen scientists (YCS) aged between about 13-29 years old across nine countries in Europe—will provide knowledge about the positive drivers of social inclusion in general. The cases will further produce knowledge as well as innovations in relation to social participation, social belonging, and citizenship.</p> <p>The YouCount evaluation design for process and outcome evaluation of Y-CSS is a multi-method approach that spans across the whole duration of the project and is carried out by the WP4 of UNIVIE. It therefore is to be classified as current work in progress, as some methods only just have been implemented and will be analyzed in the future, to estimated cross-case comparisons. The deliverable aims at making the research design, as well as the current stand of the evaluative studies, transparent and publicly available. This happens in the spirit of open science, with the goal of doing “Science for and with Society”. Hereby outlined is the theoretical design, the way of carrying it out, and the current stand of each study implementation in the overall project.</p> <p>Moreover, the D4.1 includes open data regarding the outcome methodology (pre-survey questionnaire) and a sav.file with a sample of open data collected from the current pre-survey data. See more details in the report. The attached sav.-file can provide knowledge about the used variables, to estimate occurring answering patterns very roughly, ad to familiarize with the implementation of such a pre-post-survey. However, it is to be noted that this data set is exemplary, anonymized and potentially also not complete yet and must be handled and used accordingly. Due to the relatively low number of participants (yet), this research is to be characterized as early stage research and only depicts a moment in time. This being said, the YouCount project is designed to gather a huge quantity of data that promises a variety of concrete research outputs, so future data samples will be richer for in-depth analyses. At this point, more quantitative as well as qualitative data is needed to estimate real impacts of Y-CSS in all its facettes.</p>
Data of FigS7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS7, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS7.PNG). The Corresponding raw data and subsequent data analysis obtained contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt) and one file as csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv).</p>
Data Providers Samples DB v2
<p>This dataset describes the data samples uploaded by the data providers to the REACH Data Catalogue for both OC1 and OC2. </p>
Data for conductance-based simulations of "Cortical oscillations support sampling-based computations in spiking neural networks"
<p>This repository contains the full data generated by the conductance-based simulations described in: <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009753">Cortical oscillations support sampling-based computations in spiking neural networks</a>. The code is accessible via <a href="https://doi.org/10.5281/zenodo.5512526.">this repository</a>.</p>
Raw luminescence data for samples from Abri 122/1200 (Vârghiș Gorges, Romania)
<p>The files contain the raw luminescence data used to calculate the equivalent doses and ages cited in the study by Schmidt et al.: Evidence for the oldest Middle Palaeolithic cave occupation in the Romanian Carpathians.</p> <p>The .seq files contain the measurement parameters, while the .binx files contain the results (.binx files can be read by the Analyst software thta can be downloaded for free here: https://users.aber.ac.uk/ggd/).</p> <p>DRT: Dose recovery test</p> <p>PHP: Preheat plateau test</p> <p> </p> <p>The .csv files contain all parameters used to calculate the final ages, which were derived by using the software DRAC (Durcan et al., 2015). The two scenarios considering the shielding of the cave overburden for calculation of the cosmic dose rate refer to the two different .csv files, according to their name.</p> <p> </p>
HipFT Sample Input Dataset for Convective Flows and Data Assimilation
<p>This file package is a sample data set for running <a href="https://www.github.com/predsci/hipft">HipFT</a> with convective flows and data assimilation. </p> <p>The convective flows were generated with the <a href="https://www.github.com/predsci/conflow">ConFlow</a> code (soon to be released), while the data assimilation maps were processed from HMI M720s LOS data using the <a href="https://www.github.com/predsci/MagMAP">MagMAP</a> package (also soon to be released).</p> <p>See the enclosed README file on how to run an example included in the HipFT package that uses the two data sets.</p>
Data for "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere"
<p>Colony count data collected from Petri dishes, as described in "A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere." See the associated article for more information.</p>
CyTOF data of PBMC samples of patients with metastatic pancreatic ductal adenocarcinoma
<p>These two CyTOF datasets are a part of the manuscript by M. Baretti "E<span>ntinostat in combination with nivolumab in metastatic pancreatic ductal adenocarcinoma: a phase 2 clinical trial" accepted in Nature Communications. The datasets contain FCS files of PBMCs samples of patients with metastatic pancreatic ductal adenocarcinoma treated with entinostat and nivolumab. PBMC samples were run with myeloid- and lymphoid-oriented panels.<br></span></p>
Sample data for analysis of sequence variation in HIV
<p>These are downsampled interleaved paired fastq datasets from Jair et. 2019 (<a href="https://doi.org/10.1371/journal.pone.0214820">https://doi.org/10.1371/journal.pone.0214820</a>). The datasets were prepared by:</p> <ol> <li>Downloading original data from NCBI SRA (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA517147)</li> <li>Trimming contaminating Nextera adapters using trim-galore</li> <li>Mapping reads against nxb2 reference of HIV genome (K03455.1) with BWA MEM</li> <li>Restricting mapped reads to <em>pol</em> gene vicinity (K03455.1:2000-5100)</li> <li>Downsampling mapped data to ~10% of the original with Picard's DownsampleSam</li> <li>Converting BAM to Interleaved Fastq with Picard's SamToFastq</li> <li>Gzipping resultant interleaved paired fastq files</li> </ol>
Raw metagenomic data from sweep net samples collected in 2016 as part of the Slikok Creek Watershed Biotic Inventory
<p>We set out to inventory vascular plants, bryophytes, lichens, birds, arthropods, and earthworms on a grid of sites in the portion of Slikok Creek watershed that is on the Kenai National Wildlife Refuge, Kenai Peninsula, Alaska. Occurrence data, images, and field data sheets from this project are available via an <a href="https://arctosdb.org/">Arctos</a> project page at <a href="http://arctos.database.museum/project/10002227">http://arctos.database.museum/project/10002227</a>.</p> <p>This dataset includes the raw FASTQ files from metagenomic processing and associated collection data. Of the 160 sweep net samples collected, 125 were selected for High Throughput Sequencing and shipped to RTL Genomics (<a href="http://rtlgenomics.com">http://rtlgenomics.com</a>) for extraction and sequencing steps. Sequencing was performed on an Illumina MiSeq platform and reads were processed using RTL Genomics’ standard methods with the mlCOIlintF/HCO2198 primer set of Leray et al. (2013), yielding a 313 bp region of the COI gene.</p> <p>Collection data are included in the file <code>ArctosData_43C6167EB1.csv</code> downloaded from Arctos. Extraction methods and sequencing methods provided by RTL Genomics are included in the files <code>Bowser 4869.pdf</code> and <code>Illumina MiSeq Two-Step Method 454 profile only.docx</code>. Primers used are provided in the file <code>Bowser_4869M.txt</code>. The archive <code>FASTQ.zip</code> contains all of the resulting FASTQ files.</p> <p>These sequence data have also been been published to GenBank's Sequence Read Archive in accessions <a href="http://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10454582">SRR10454582</a>–<a href="http://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10454706">SRR10454706</a> under BioProject <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA427721">PRJNA427721</a>.</p>
Data supporting publication: Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples
<p>This repository includes the data corresponding to the figures shown in the journal article entitled Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples, published in small. </p>
Hyperspectral data of Vigo sediment samples
<h2>Abstract</h2> <p>Reflectance and Radiance converted hyperspectral data of 9 sediment samples. The samples were collected by UPORTO and IGME from Vigo Campaign fieldwork in Sept 2023 but they were scanned at Ecotone lab in Trondheim by UHI in February 2024. The data was scanned for both dry and wet sediments.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Hyperspectral data of Vigo sediment samples</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Reflectance and Radiance converted hyperspectral data of 9 sediment samples. The samples were collected by UPORTO and IGME from Vigo Campaign fieldwork in Sept 2023 but they were scanned at Ecotone lab in Trondheim by UHI in February 2024. The data was scanned for both dry and wet sediments.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Reflectance estimated hyperspectral data, Radiance converted hyperspectral data, sediments, sand, mineral resource</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Report, photo</p> <p>Raw data: <a href="https://doi.org/10.5281/zenodo.13462199">https://doi.org/10.5281/zenodo.13462199</a></p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Mineral resources</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>3.04.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>3.04.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>HDF5</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>1.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone AS (info@ecotone.com)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS (info@ecotone.com)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Experimental data for fracture toughness analysis of sandstone and granite samples under fluid saturation conditions
<p>This database includes experimental results from mode I fracture toughness (KIC) tests conducted on saturated rock specimens. Three lithologies were studied: a porous siliceous sandstone (Corvio, C) and two high-strength, low-porosity granites (Blanco Mera, BM and Blanco Alba, BA). Tests were conducted at room pressure and temperature using the pseudo-compact tension (pCT) methodology. Seven different fluids were used: deionized water, methanol, NaCl-saturated water, mineral oil, diesel fuel, an acidic HCl solution, and a caustic NaOH solution.</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.