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.
6,452
datasets available to search
ShareScore release 0.9.0
Dataset results
6,452 results for “Application”
PyGBe-LSPR—Computational nanoplasmonics for biosensing applications (problem datasets)
<p>Problem folders including all the input files necessary to reproduce the computations in the paper PyGBe-LSPR—Computational nanoplasmonics for biosensing applications. </p>
A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications
<p>## GDSC dataset</p> <p>**GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p> <p>## PMID17185464 (Bortezomib) dataset</p> <p>**PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p>**PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).</p>
Supplemental data for "Applications and Limitations of Portable Density Meter Measurements Of Na-Ca-Mg-K-Cl-SO4 Brines"
<p>Geochemistry, brine density measurements, environmental observations and geochemical modeling of the Bonneville Salt Flats and surrounding area. </p>
Artifact of MADUSA: Mobile Application Demo Generation based on Usage Scenarios
<p>Artifact of MADUSA: Mobile Application Demo Generation based on Usage Scenarios</p>
Putting green clipping yield, canopy reflectance, and vegetative indices by time from colorant and spray oil combination product application
<p>Multispectral radiometry resolutely quantifies canopy attributes of similarly managed monocultures over wide and varied temporal arrays. Likewise, liquid phthalocyanine-containing products are commonly applied to turfgrass as a spray pattern indicator, dormancy colorant, and/or product synergist. While perturbed multispectral radiometric characterization of putting greens within 24 h of treatment by synthetic phthalocyanine colorant has been reported, explicit guidance on subsequent use is absent from the literature. Our objective was to assess creeping bentgrass (Agrostis stolonifera L. 'Penn G2') putting green reflectance and growth one to 14 d following semi-monthly treatment by synthetic Cu II phthalocyanine colorant (Col) and petroleum-derived spray oil (PDSO) combination product at a 27 L ha–1 rate and/or 7.32 hg ha–1 soluble N treatment by one of two commercial liquid fertilizers. As observed in a bentgrass fairway companion study, mean daily shoot growth and canopy dark green color index (DGCI) increased with Col+PDSO complimented N treatment. Yet contrary to the fairway study results, deflated mean normalized differential red edge (NDRE) or vegetative index (NDVI) resulted from an associated Col+PDSO artifact that severely impeded near infrared (810-nm) putting green canopy reflectance. Regardless of time from Col+PDSO combination product treatment, the authors strongly discourage turfgrass scientists from employing vegetative indices that rely on 760- or 810-nm canopy reflectance when evaluating such putting green systems.</p>
Fairway reflectance and clipping yield from: Reduced creeping bentgrass fairway reflectance following synthetic colorant application
<p>Repeated measures using multispectral radiometry resolutely quantify canopy attributes of identical turfgrass cultivars under similar management. Concern regarding multispectral radiometric characterization of turfgrass canopies <24 h following synthetic phthalocyanine colorant application has been affirmed and is accordingly now avoided; yet explicit guidance on subsequent employ, at time(s) >24-h post-application, is lacking. Our objective assessed how petroleum-derived spray oil (PDSO) and synthetic Cu II phthalocyanine colorant (C) combination product influences creeping bentgrass (<em>Agrostis stolonifera</em> L.) reflectance up to 10 d following application. A maintained fairway received semi-monthly 9.76 kg ha<sup>–1</sup> soluble N treatments alone or in combination with 27 L PDSO+C ha<sup>–1</sup>. Treatment by PDSO+C increased mean shoot growth (kg ha<sup>–1</sup>) and dark green color index (DGCI) calculated by visible waveband reflectance. Yet reduced far red and near infrared reflectance from PDSO+C treated plots artificially deflated mean normalized differential vegetative indices (NDVI). Cautious interpretation of vegetative indices relying on 710- to 810-nm canopy reflectance is encouraged when evaluating fairways treated by PDSO and Cu II phthalocyanine combination product(s).</p>
Data for MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications
<p>This repository contains the results of simulations exposed in the article <strong>MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications.</strong></p> <p>Notebooks for easy plotting of the results are contained in this repository. The README.txt contains light explainations regarding how to use those notebooks.</p> <p>This article is yet to be published at the time of writing.</p> <p> </p>
Spatial data for creating a thermal inertia index and incorporating it for conservation applications
<p>This repository contains supporting material for a journal article being submitted to one of the journals published by the American Geophysical Union, titled Earth's Future. The repository contains the following items:</p> <p>1. README file of what is in the repository including methods associated with the geodatabase</p> <p>2. File Geodatabase</p> <p><strong>1. README file</strong></p> <p>The files collected here relate to a study being submitted to the American Geophysical Union's journal, Earth's Future. The title of the paper being submitted is, "The contribution of Microrefugia to landscape thermal inertia for climate-adaptive conservation and adaptation strategies."</p> <p>The study was conducted across 40,250 km<sup>2</sup> of complex mountainous terrain in Northern California. The objective of the study was to consider whether it was possible to identify the relative strength of microrefugia systematically in order to provide conservation and climate-adaptation strategies with information that could help with prioritizing actions. We selected an operational scale of 10 ha (25 acres) as a scale that is suitable for various types of landscape planning exercises, and created a hexagon grid for the region. We calculated the mean value for multiple variables and appended them into the hexagons. For thermal inertia, we calculated the mean elevation per hexagon and then its coolest (highest) point using an environmental lapse rate. We also calculated solar energy loading, calculated the mean solar load per hexagon, and calculated its effect on air temperature. We combined these two temperature metrics to identify how much thermal buffering capacity each hexagon contains, as measured by how much warming it could experience before the mean temperature, as determined from a baseline time period, is no longer found anywhere within the hexagon. We tied the mean annual temperature from 1981–2010 to the mean elevation in each hexagon, as well as a temperature from an earlier period, and from several future periods, based on global circulation models.</p> <p>The study shows how long current (baseline) climate conditions found in each hexagon may persist and shows how the resulting map of landscape thermal inertia can be used when considering natural vegetation types for conservation, identifying which parts of high-priority wildlife corridors have the greatest capacity to retain their current climate conditions, and what the potential for retaining baseline climate conditions is for areas with late-seral forest conditions as represented by forest canopy height.</p> <p class="MsoNormal">The methods section below describes the data used in the study to create the data in the geodatabase that is posted here. The Geodatabase itself provides all the data needed to replicate the various results presented in the paper. Further information can be found in Thorne et al. 2020. That report is more extensive than the results in our associated paper, but it contains more information on the calculation of various metrics associated with and was the foundation from which we developed this study. The report is provided here in order to keep all the relevant materials compiled for potential use by others. </p> <p><strong>2. File Geodatabase</strong></p> <p>The geodatabase is provided as a separate file.</p> <p>Name: ThermalInertiaIndex.gdb</p> <p>Contents:</p> <ul> <li>AllHexagons <ul> <li>A feature class containing all 408,948 hexagon grids used in this study</li> <li>Fields within the feature class:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>Id</p> </td> <td> <p>A unique ID for each hexagon</p> </td> </tr> <tr> <td> <p>Watershed</p> </td> <td> <p>Watershed the hexagon falls within</p> </td> </tr> <tr> <td> <p>DomWHR</p> </td> <td> <p>Habitat type (WHR) that had the majority coverage within the hexagon</p> </td> </tr> <tr> <td> <p>WHR_Name</p> </td> <td> <p>Descriptive name of the habitat type</p> </td> </tr> <tr> <td> <p>WHR_GroupName</p> </td> <td> <p>Major vegetation type</p> </td> </tr> <tr> <td> <p>CanopyHt_Score</p> </td> <td> <p>Canopy Height Score ranging from 1 (under 1m) to 5 (over 25m)</p> </td> </tr> <tr> <td> <p>CanopyHt_m</p> </td> <td> <p>Average canopy height within the hexagon (m)</p> </td> </tr> <tr> <td> <p>Conn_Score</p> </td> <td> <p>Connectivity Score ranging from 1 (low) to 5 (high)</p> </td> </tr> <tr> <td> <p>dem10m</p> </td> <td> <p>Average elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_min</p> </td> <td> <p>Minimum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_max</p> </td> <td> <p>Maximum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>SRtemp_min</p> </td> <td> <p>The lowest Solar Radiation load within the hexagon (degree C)</p> </td> </tr> <tr> <td> <p>ElevLR_NegEff2</p> </td> <td> <p>Effect of elevation on air temperature (degree C)</p> </td> </tr> <tr> <td> <p>Thermal_Inertia</p> </td> <td> <p>Hexagon buffering capacity (degree C)</p> </td> </tr> <tr> <td> <p>tave_5180</p> </td> <td> <p>Average temperature 1951-1980</p> </td> </tr> <tr> <td> <p>tave_8110</p> </td> <td> <p>Average temperature 1981-2010</p> </td> </tr> <tr> <td> <p>tave_1039mi8</p> </td> <td> <p>Average temperature 2010-2039 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069mi8</p> </td> <td> <p>Average temperature 2040-2069 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099mi8</p> </td> <td> <p>Average temperature 2070-2099 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_1039cn8</p> </td> <td> <p>Average temperature 2010-2039 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069cn8</p> </td> <td> <p>Average temperature 2040-2069 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099cn8</p> </td> <td> <p>Average temperature 2070-2099 (CNRM-CM5 RCP 8.5)</p> </td> </tr> </tbody> </table> </div> <p> </p> <ul> <li>Connectivity_Scores <ul> <li>90m raster containing all 3 connectivity scores</li> <li>Fields within the raster:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>TNC_Conn_Score</p> </td> <td> <p>Connectivity Score from reclassed TNC/Omniscape</p> </td> </tr> <tr> <td> <p>CEHC_Score</p> </td> <td> <p>Connectivity Score from reclassed California Essential Habitat Connectivity</p> </td> </tr> <tr> <td> <p>Combined_Score</p> </td> <td> <p>Overall Connectivity Score</p> </td> </tr> </tbody> </table> </div> <p> </p>
Evaluation of Ortho VITROS and Roche Elecsys S and NC immunoassays for SARS-CoV-2 serosurveillance applications
<p>SARS-CoV-2 seroprevalence studies are instrumental in monitoring epidemic activity and require <span>well-characterized, high-throughput assays, and appropriate testing algorithms.</span> The U.S. Nationwide Blood Donor Seroprevalence Study performed monthly cross-sectional serological testing from July 2020 to December 2021, implementing evolving testing algorithms in response to changes in pandemic activity. With high vaccine uptake, anti-Spike (S) reactivity rates reached > 80% by May 2021, and the study pivoted from reflex Roche anti-nucleocapsid (NC) testing of Ortho S-reactive specimens to parallel Ortho S/NC testing. We evaluated the performance of the Ortho NC assay as a replacement for the Roche NC assay and compared performance of parallel S/NC testing on both platforms. Qualitative and quantitative agreement of Ortho NC with Roche NC assays was evaluated on pre-selected S/NC concordant and discordant specimens. All 190 Ortho S+/Roche NC+ specimens were reactive on the Ortho NC assay; 34% of 367 Ortho S+/Roche NC- specimens collected prior to vaccine availability and 43% of 37 Ortho S-/Roche NC+ specimens were reactive on the Ortho NC assay. Performance of parallel S/NC testing using Ortho and Roche platforms was evaluated on 200 specimens collected in 2019 and 3,903 study specimens collected in 2021. All 200 pre-COVID 2019 specimens tested negative on the four assays. Agreement of S and NC reactivity on specimens was 96.4% (3,769/3,903); most discordant results had reactivity close to the cutoffs on the alternate assays. These findings, and higher efficiency and throughput, support use of parallel S/NC testing on either Roche or Ortho platforms for large serosurveillance studies.</p>
Interpretable Geotechnical Artificial Intelligence (XGeoT-AI) Application to Demystify Image Recognition of Soil Cracks [Datasets]
<p>Here is the test data for the paper "Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition".</p>
A New Open-source Geomagnetosphere Propagation Tool (OTSO) and its Applications - Data
<p>Data files for the computations done with OTSO for the asymptotic cones and effective cut-off rigidities for several neutron monitor stations during three ground-level enhancement events (GLE 66, 70, 71). The computations for GLE 66 and 71 were done using three external geomagnetic field models (TSY89, TSY96, TSY01). Data for the global map of effective cut-off rigidities during GLE70 is also included. Data is in CSV format.</p>
Supplementary Information for "Modeling the circular economy in environmentally extended input–output: A web application"
<p>Supplementary Information for "Modeling the circular economy in environmentally extended input–output: A web application"</p> <p>Annex I: This supporting information provides the software review for native and web-based tools that could be used for Circular Economy and environmental analysis.</p> <p>Annex II: This supporting information provides the use-case result analysis.</p>
Dataset for the pubblication "Impact of DC Transient Disturbances on Harmonic Performance of Voltage Transformers for AC Railway Applications"
<p>This is the dataset for the paper published:</p> <p>Letizia, P.S.; Signorino, D.; Crotti, G. Impact of DC Transient Disturbances on Harmonic Performance of Voltage Transformers for AC Railway Applications. <em>Sensors</em> <strong>2022</strong>, <em>22</em>, 2270. https://doi.org/10.3390/s22062270</p> <p> </p>
Benchmark table 3D scanning applications
<p>Benchmark results of the native Leica software and several iPad LiDAR scanning solutions, measured within the 2.17 classroom of the Product Development campus at the University of Antwerp</p>
Dataset for: Exploring the legacy effect of biochar application on soil nitrous oxide emissions
<p><span>This dataset contains data for exploring the legacy effects of biochar addition on soil nitrous oxide emissions and their effects on functional gene abundance associated with soil nitrogen cycling. The dataset contains two data files, one is a data table on the impact of biochar application on soil nitrous oxide emissions, which contains the coordinates of the study site, climate, basic physical and chemical properties of the soil, biochar characteristics and study duration, crop type and management. The other is the data table on the effect of biochar application on the abundance of functional genes related to soil nitrogen cycling, which contains information such as test site coordinates, climate, biochar characteristics, crop type and management. This data can be referenced and reused without any legal or ethical considerations</span>.</p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 13 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 13 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 08 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 8 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 06 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 6 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 04 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 4 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 10 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 10 after drought stress application. </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.