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.
13,064
datasets available to search
ShareScore release 0.9.0
Dataset results
13,064 results for “predictions”
Dataset of Move, Rove, Love: Color Cues Help Learning Novel English Words When Pronunciation Is Not Predictable From Spelling
Open the record for dataset details and reuse information.
Dataset and machine learning models for seismic response predictions of small-to-medium continuous girder bridges
<p>This upload includes the dataset and machine learning models (based on Matlab platform) for longitudinal seismic response predictions of multi-span highway girder bridges, which have a typical span length of 30 m supported by reinforced concrete (RC) bridge bents and abutments through spherical steel bearings. The input variables (features) are five structural parameters of studied bridges and seven intensity measures of earthquakes. The output variables (labels) are peak column drifts and peak bearing deformations. The dataset is developed by conducting a total number of 720 nonlinear time-history analyses considering the uncertainty of bridges and earthquakes. Machine learning models are developed using two popular machine learning algorithms named artificial neural network (ANN) and support vector regression (SVR).</p>
Dataset for the article Artificial intelligence for earthquake prediction: a preliminary system based on periodically trained neural networks using ionospheric anomalies
<p>Training and validation data sets along with the corresponding trained convolutional neural network in the article "Artificial intelligence for earthquake prediction: a preliminary system based on periodically trained neural networks using ionospheric anomalies" by Sergio Baselga published in <em>Appl. Sci.</em> <strong>2024</strong>, <em>14</em>(23), 10859; https://doi.org/10.3390/app142310859</p>
Code and Training Data for "Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands based on In-situ Internet of Things Monitoring"
Open the record for dataset details and reuse information.
Creep behavior of short glass fiber reinforced poly(butylene terephthalate) and its prediction based on the matrix creep using an elementary volume approach
<p><span>The creep compliances of short glass fiber reinforced poly(butylene terephthalate) composites (SFRC PBT) plates processed by conventional injection molding and push pull processing were determined parallel and perpendicular to the flow direction using a tensile creep stand. The fiber weight contents of the plates were 0, 20 and 30 %. Tensile test bars were taken out of the plates parallel and perpendicular to the flow direction to determine short-term mechanical properties, fiber length distribution and fiber orientation distribution necessary for subsequent modelling. An elementary volume approach was used to calculate the longitudinal and transverse creep compliances in the main axes system showing that their time dependencies are governed by the creep of the PBT matrix only. As the plates hardly exhibit any fiber orientation in the thickness direction, the components of the 2D compliance tensor were transformed according to RM Jones’ <em>Mechanics of composite materials</em> to take into account the fiber misalignment in SFRC. This transformation introduces the unknown shear modulus G<sub>12</sub> to the components of the transformed compliances tensor. This problem could be overcome as G<sub>12</sub> can be expressed in terms of the transverse compliance J<sub>22</sub> and a shear correction factor within the EV approach. Comparing the predicted creep compliances to measured ones resulted in an underestimation of 15 to 30 % parallel and 5 to 15 % perpendicular to the flow direction. This underestimation can be attributed to a large extent to a non-perfect fiber matrix adhesion. Furthermore, SEM pictures of fracture surfaces show different failure behavior parallel to the fiber axis and perpendicular to it. Thus, the fiber matrix adhesion seems to depend on the direction of stress with respect to the fiber axes. The time range of well correspondence of matrix creep and composites creep can be expressed by the creep time limit which decreases exponentially with increasing creep stresses.</span></p>
FESOM-REcoM model data: Predicting future distribution of Antarctic toothfish (Dissostichus mawsoni), with implications for Marine Protected Areas in the Southern Ocean
<p>This data set belongs to <strong>"Predicting future distribution of Antarctic toothfish (<em>Dissostichus mawsoni</em>), with implications for Marine Protected Areas in the Southern Ocean"</strong> by <a>Rebecca Konijnenberg, Cara Nissen, Casper Kraan, Jilda Cavacco, Peter Yates, Philippe Ziegler, and Katharina Teschke (in preparation). </a></p> <p>Contact for data set: cara.nissen@colorado.edu</p> <p>The data provided here are post-processed from the raw FESOM1.4-REcoM2 model output which can be obtained <br>at the World Data Center for Climate (WDCC): <a href="https://www.wdc-climate.de/ui/project?acronym=HighRes_highLat_SO">https://www.wdc-climate.de/ui/project?acronym=HighRes_highLat_SO</a></p> <p>simA (historical simulation): <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a><br>simA-ssp126: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC</a><br>simA-ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC</a><br>simA-ssp370: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC</a><br>simA-ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a><br>simB (control simulation): <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC</a></p> <p>Here, we provide decadal monthly climatologies of the following variables: sea-ice fraction, surface small-phytoplankton chlorophyll, surface diatom chlorophyll, as well as potential temperature, practical salinity and oxygen concentrations (all at the surface, at the bottom, at 100m, at 250m, at 500m, at 1000m, and at 1500m). For example, "tos" is temperature in ocean at surface", while "tob" is "temperature in ocean at bottom" (with <em>bottom</em> here being the deepest available model grid cell at each location).</p> <p>We also provide the script used to re-grid the original model output to the regular 0.25° x 0.0625° mesh used in this study. </p>
predicted_protein_complex_structures_datasets
Open the record for dataset details and reuse information.
FIGURE 4 in Macropterous species of genus Melanabropsis Wang & Liu, 2020 (Orthoptera: Anostostomatidae: Anabropsini) from the localities predicted in a previous study
FIGURE 4. Habitat environment of Melanabropsis formosensis sp. nov. A) Surrounding environment of a collection site B) a tree trunk on which a specimen was found. Photographed by Li-Yen Ho.
FIGURE 3 in Macropterous species of genus Melanabropsis Wang & Liu, 2020 (Orthoptera: Anostostomatidae: Anabropsini) from the localities predicted in a previous study
FIGURE 3. Living Melanabropsis formosensis sp. nov. A) Adult female B) adult male C) nymph D) an adult male found on a tree trunk around 2 meters in height. A) Photographed by Yen-Min Lin; B), C) and D) photographed by Yuan-Teng Wang.
FIGURE 1 in Macropterous species of genus Melanabropsis Wang & Liu, 2020 (Orthoptera: Anostostomatidae: Anabropsini) from the localities predicted in a previous study
FIGURE 1. Morphological features of Melanabropsis guizhouensis sp. nov. A) male head in frontal view B) male head and pronotum in lateral view C) same in dorsal view D) male sternum in ventral view E) male paraprocts in posterior view F) male subgenital plate in ventral view.
FIGURE 2 in Macropterous species of genus Melanabropsis Wang & Liu, 2020 (Orthoptera: Anostostomatidae: Anabropsini) from the localities predicted in a previous study
FIGURE 2. Morphological features of Melanabropsis formosensis sp. nov. A) male head in frontal view B) male head and pronotum in lateral view C) same in dorsal view D) male sternum in ventral view E) male paraprocts in lateral view F) male subgenital plate in ventral view G) male right tegmen H) female ovipositor and subgenital plate in lateral view I) same in ventral view.
SBC Predictive Model
<p>Data and code used for the development of an <strong>ethnoarchaeological inductive predictive model</strong> in the central italian Alps (Upper Brembo Valley, province of Bergamo, Italy). SBC is for "Sorgenti del Brembo di Carona" (Source of the Carona branch of the River Brembo).</p> <p>The position of modern summer farms, along with a sample of random points, and physical landscape data, are used to calculate an inductive predictive model centered on pastoral activities. The calculation is done mainly with GRASS GIS and R/RStudio. Some data are pre-processed using QGIS.</p> <p>The model creation and output is <strong>discussed in:</strong> CROCE, E., CARRER, F. & ANGELUCCI, D.E., 2025, Ethnoarchaeological Inductive Predictive Model: A Field Test in the Italian Alps<em>,</em> <em>Journal of Archaeological Method and Theory</em> 32, 43. <a href="https://doi.org/10.1007/s10816-025-09712-w">https://doi.org/10.1007/s10816-025-09712-w</a></p> <p>The inductive predictive model was <strong>originally created as part of a PhD Thesis</strong>: <em>CROCE E., 2022, Archeologia d'alta quota alle sorgenti del Brembo, </em>PhD Thesis, A.A. 2020/2021, Università di Trento<em>.</em> <a href="https://dx.doi.org/10.15168/11572_350299" rel="nofollow">https://dx.doi.org/10.15168/11572_350299</a></p> <p>The methodology is <strong>based upon the previous work of F. Carrer</strong> (which co-authored also the present model): <em>CARRER F., 2013, </em>An ethnoarchaeological inductive model for predicting archaeological site location: a case-study of pastoral settlement patterns in the Val di Fiemme and Val di Sole (Trentino, Italian Alps)<em>, Journal of Anthropological Archaeology, 32, pp. 54-62.</em> <a href="https://doi.org/10.1016/j.jaa.2012.10.001" rel="nofollow">https://doi.org/10.1016/j.jaa.2012.10.001</a></p> <p> </p> <p> </p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Datasets for understanding the importance of conformation in property prediction models
<p>Descriptor and conformer data sets for molecular property and reaction selectivity prediction tasks. The PQC data set was created based on a part of the PubChemQC PM6 dataset (J. Chem. Inf. Model. 2020, 60, 12, 5891–5899), which contains two- and three-dimensional descriptors and conformers. The APTC data sets are based on the data sets for asymmetric phase transfer catalysts with enantio-selectivity (<a href="https://github.com/Laboratoire-de-Chemoinformatique/3D-MIL-QSSR/tree/main/datasets" target="_blank" rel="noopener">https://github.com/Laboratoire-de-Chemoinformatique/3D-MIL-QSSR/tree/main/datasets</a>). The melting point data set was created from the Jean-Claude Bradley Double Plus Good (Highly Curated and Validated) Melting Points Dataset (<a href="https://doi.org/10.6084/m9.figshare.1031638.v1">https://doi.org/10.6084/m9.figshare.1031638.v1</a>).</p> <p>They contained descriptors and conformers to train and validate machine learning models.</p> <p>Detailed explanations on how to use these datasets are found in the Github repository: <a href="https://github.com/YuHamakawa/Conformation-Importance-ML-Models">https://github.com/YuHamakawa/Conformation-Importance-ML-Models</a>. </p> <p> </p> <p> </p>
Genome size predicts diatom abundance in the polar ocean
<p>This repository contains the datasets, code, and results for:</p> <p>Roberts et al. 2024. Genome size predicts diatom abundance in the polar ocean.</p> <p> </p> <p> </p>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD p025, 2020–2022</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Spatiotemporal prediction of soil organic carbon density (SOCD) for pan-Europe (2000-2022) in 3D+T
<h2><strong>Sub-dataset: SOCD mean, 2016–2020</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the Spatiotemporal prediction of soil organic carbon density for Europe (2000-2022) in 3D+T dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil Organic Carbon Density (SOCD) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 SOCD maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in kg/m<sup>3</sup> (scaled 10x). </li> <li><strong>Organic carbon content based on dry combustion weight percentage (WPCT) (2000-2022, 4-year intervals):</strong><br> This data includes mean, p975, and p025 WPCT maps for four depth ranges (0-20cm, 20-50cm, 50-100cm, and 100-200cm) in percentage (scaled 10x). </li> </ul> <h3>Related identifiers</h3> <ul> <li><strong>SOCD mean:</strong><br> <a href="https://doi.org/10.5281/zenodo.13754343">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13771721">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13771841">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13771911">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13771967">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13772054">2020-2022</a> </li> <li><strong>SOCD p025:</strong><br> <a href="https://doi.org/10.5281/zenodo.13779539">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13774064">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13774089">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13774114">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13774167">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774196">2020-2022</a> </li> <li><strong>SOCD p975:</strong><br> <a href="https://doi.org/10.5281/zenodo.13778472">2000-2004</a> <a href="https://doi.org/10.5281/zenodo.13773396">2004-2008</a> <a href="https://doi.org/10.5281/zenodo.13773765">2008-2012</a> <a href="https://doi.org/10.5281/zenodo.13773828">2012-2016</a> <a href="https://doi.org/10.5281/zenodo.13773953">2016-2020</a> <a href="https://doi.org/10.5281/zenodo.13774003">2020-2022</a> </li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> 2000–2022, in 4-year intervals (last period covers 2020–2022).</li> <li><strong>Type of data:</strong> Spatiotemporal soil organic carbon data cube, with depth ranges and weighted percentage data for soil carbon assessments.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using machine learning models.</li> <li><strong>Statistical methods used:</strong> Quantile Random Forest</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> oc = organic carbon</li> <li><strong>variable procedure combination:</strong> iso.10694.1995.mg.cm3 = ISO method 10694:1995, with values in mg/cm<sup>3</sup> for SOCD | iso.10694.1995.wpct = ISO method 10694:1995, with values in weighted percentage of organic carbon content.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | p975 = percentile 97.5 | p025 = percentile 2.5</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> b0cm..20cm = depth range from 0 to 20cm</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20041231 = 2004-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240804 = version from 2024-08-04</li> </ol>
Data for the publication "Predicting the three-dimensional stratigraphy of an ice rise".
<p><span>The input and output data for the simulations presented in "Predicting the three-dimensional stratigraphy of an ice rise" are organised in this repository as follows:</span></p> <p> </p> <p><span>Model Data:</span></p> <p> </p> <p><span>- Remesh: The code necessary for remeshing the domain for a higher resolution in the area encompassing the ice rise. </span></p> <p><span>- Init: Initialisation of the model.</span></p> <p><span>- Glen_n3: The code for running simulations with a Glen's flow law exponent of n=3 </span></p> <p><span>- Glen_n4: The code for running simulations with a Glen's flow law exponent of n=4 </span></p> <p><span>- SensitivitySimulations: The code for running further sensitivity simulations</span></p> <p> </p> <p><span>Figure Data:</span></p> <p> </p> <p><span>- The data used to produce each figure.</span></p> <p> </p> <p><span>Data Pre-Processing:</span></p> <p> </p> <p><span>- The data used in the pre-processing process. </span></p>
Morphological profiling data resource enables prediction of chemical compound properties
<p><span><span>Morphological profiling with the Cell Painting assay has </span><span>emerged</span><span> as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid prediction of compound </span></span><span><span><span>bioactivity</span></span></span><span><span>. We present a comprehensive morphological profiling resource using the carefully curated and well-annotated EU-OPENSCREEN Bioactive Compounds. </span><span>The data was generated across four imaging sites with high-throughput confocal microscopes using the </span><span>Hep</span><span> G2 as well as the U2 OS cell line.</span><span> We employed an extensive assay optimization process to achieve high data quality across the different sites. An analysis of the extracted profiles </span><span>validates</span><span> the robustness of the generated data. <span>We used this resource to compare the morphological features of the different cell lines. By correlating the profiles with overall activity, cellular toxicity, several specific mechanisms of action (MOAs), and protein targets, we </span><span>demonstrate</span><span> the dataset's potential for </span><span>facilitating</span><span> more extensive exploration of mechanisms of action.</span> </span></span></p>
Electronic Supplement to: A Predictive Model for Divalent Element Partitioning between Clinopyroxene and Basaltic Melt and a Europium-in-Plagioclase-Clinopyroxene Oxybarometer for Cumulate Rocks
<p>Contents</p> <p>Supplementary Figures</p> <p>Supplementary Calculator Spreadsheet</p> <ul> <li>A calculator for divalent element partitioning between the clinopyroxene M2 site and silicate melt</li> <li>An fO2-, temperature- and composition- dependent clinopyroxene-melt Eu partition coefficient calculator for many samples, each at a single fO2</li> <li>An fO2-, temperature- and composition- dependent clinopyroxene-melt Eu partition coefficient calculator for a single sample at many fO2s</li> <li>A Eu-in-clinopyroxene-melt oxybarometer</li> <li>A Eu-in-plagioclase-clinopyroxene oxybarometer</li> </ul> <p>Supplementary Code</p> <ul> <li>A Eu-in-plagioclase-clinopyroxene oxybarometer</li> <li>A Monte Carlo-based fO2 uncertainty calculator</li> </ul>
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.