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278 results for “Validated dataset”
Dataset Validation Images for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)
<p>Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)</p> <p>This is the Validation Image part. </p> <p>If you use this dataset in your research, you must cite the papers in the References below !!!</p>
SSiB5/TRIFFID/DayCent-SOM datasets for the paper's in-situ validations and global evaluations
<p>The various data used for the paper "A plant carbon-nitrogen interface coupling framework in a coupled biophysical-ecosystem-biogeochemical model: Its parameterization, implementation, and evaluation" submitted to Geoscientific Model Development for publication are shared here.</p>
Dataset for the optimization and validation of a gas chromatography-mass spectrometry method to analyze acetate, propionate and butyrate in the systemic circulation.
<p>This dataset contains data about the optimization and validation of a gas chromatography method to analyze acetate, propionate and butyrate in blood. Validation parameters include linearity, precision, accuracy and recovery. The method's applicability was demonstrated with the analysis of the short-chain fatty acids in human blood samples that were collected in a dietary intervention study.</p>
Data S6. Cell Size Validation Dataset
<p>This dataset contains the input values to compute the 2010 MTI in different cell sizes options, and the final MTI values. </p>
Datasets Energy Citizenship Scale Development and Validation (Task 2.4, D2.2, D2.3)
<p>The datasets are part of the Reports on the development (D2.2) and validation (D2.3) of the energy citizenship scale. They are also part of the publication "Energy citizenship as people's perceived (collective) rights and responsibilities in a just and sustainable energy transition - scale development and validation" (https://doi.org/10.1016/j.jenvp.2024.102310). </p>
Dataset used for the validation of stemv: An R package for calculating tree stem volume in Japan
<p>This repository contains the dataset for validating an R package "stemv", which provides the functions for calculating tree stem volume in Japan.<br>The source of the package can be found on <a href="https://github.com/dulvrq/stemv" target="_blank" rel="noopener">GitHub </a>(https://github.com/dulvrq/stemv).</p>
Dataset with manually validated version histories of Stack Overflow posts
<p>We used this dataset to evaluate different string similarity metrics for SOTorrent (http://sotorrent.org/). For the versions published 2018-11-01 and 2018-12-14, we double-checked and updated the ground truth files.</p> <p>The dataset has been created with this tool: https://github.com/sotorrent/posthistory-gt</p> <p>The dataset has been validated with this tool: https://github.com/sotorrent/posthistory-comparator-gt-cs</p> <p>The dataset has been used in this project: https://github.com/sotorrent/metric-evaluation</p> <p>The most recent version of the files can always be found here: https://github.com/sotorrent/metric-evaluation/tree/master/testdata/samples_comparison</p>
Global River Ice Dataset - validation dataset
<p><strong>Documentation for nws_breakup_nogeo.csv and nws_freezeup_nogeo.csv</strong></p> <p>Alaskan river ice records from National Weather Service (NWS), including <strong>nws_breakup_nogeo.csv</strong> containing location (description) and dates of ice breakup and related conditions and <strong>nws_freezeup_nogeo.csv </strong>containing location (description) and dates of ice freeze-up and related conditions. Note that both dataset do not contain exact geolocations of the observation. We thank Dr. Scott Lindsey at the Alaska-Pacific River Forecast Center for providing these datasets.</p> <p><strong>Documentation for landsat_river_ice_validation.csv</strong></p> <p>This file contains 20,687 same-day river ice condition from Landsat and from in situ, and consists of the following associated properties for each comparison:</p> <ol> <li>date: The date on which both the Landsat river ice (length) fraction and in situ river ice condition were observed (data type: string; format: "YYYY-MM-DD").</li> <li>ice_in_situ: The ice condition on rivers observed in situ. For records from NWS, we assumed river has been ice covered between the date of "first_ice" to the date of "breakup" in the following year and ice-free between the date of "breakup" and the following "first_ice" date. For records from Water Survey of Canada, river was treated as ice-covered whenever the daily "Flow" data were flagged with "B"–meaning backwater effect (data type: integer; range: 0 (ice-free) or 1 (ice-covered)).</li> <li>ice_landsat: The river ice length fraction derived from Landsat image (data type: float; range: [0, 1]).</li> <li>cloud_landsat: The cloud fraction derived from Landsat image (data type: float; range: [0, 0.25]).</li> <li>LANDSAT_SCENE_ID: The unique Landsat TOA image identifier (data type: string).</li> <li>site_id: The ID of the site in its original dataset.</li> <li>dat_source: The source of the in situ river ice record (data type: string; values: ("National Weather Service (Alaska)", "Water Survey of Canada").</li> <li>longitude: The longitude of the site (data type: float, format: decimal degree).</li> <li>latitude: The latitude of the site (data type: float, format: decimal degree).</li> </ol> <p>A subset (N = 18,930) of this dataset was used in the evaluation of the river ice classification. This subset was calculated by applying the following two constraints on the full dataset in the <strong>landsat_river_ice_validation.csv</strong>:</p> <ol> <li><span class="math-tex">\(cloud\_landsat ≤ 0.05\)</span></li> <li><span class="math-tex">\(site\_id \neq 10BE013\)</span> & <span class="math-tex">\(site\_id \neq 08KE016\)</span></li> </ol> <p>The second constraint exclude two Canadian sites from the evaluation as via manual inspection, we found that the Landsat-derived ice fraction for this two sites came from river reaches that were different from where the in situ records were observed.</p>
Dataset 1. Validated natural and cryptic mRNA splicing mutations
<p>Source data computed by the Shannon pipeline and Veridical, displayed on the ValidSpliceMut (<a href="https://validsplicemut.cytognomix.com/">https://validsplicemut.cytognomix.com/</a>) website.</p>
Dissimilarity-adaptive cross-validation experiments and datasets
<p>This data includes all datasets and codes for implementing dissimilarity-adaptive cross-validation experiments, datasets and code, Reademe.txt explains each file's meaning. Appendix includes the descriptions of datasets. </p>
Dataset for article: Gait Speed Assessment in the 10-meter Walk Test for Older Adults Using a Computer Vision-based System: A Cross-sectional Study on Validity, Reliability, and Usability
<p>This dataset provides the Validity, Reliability, and Usability for an assessment of gait speed detection system in the 10-meter Walk Test for Older Adults.</p> <p>The dataset is formatted for easy import into microsoft excel software consist of:<br>Supplementary1.xlsx - Validity <br>Supplementary2.xlsx - Reliability<br>Supplementary3.xlsx - Usability test</p>
Mutually validated Tilt Angle Dataset
<h1>Mutually-Validated Tilt Angle Dataset</h1> <p>This dataset provides reliable sunspot group/active region(AR) tilt angle data and several other parameters from 1996 to 2018.</p> <h1>Overview</h1> <p>The dataset is generated by mutually validating two existing datasets: (1) <strong>A-live-homogeneous-database-of-solar-active-regions</strong> by Ruihui Wang, Jie Jiang and Yukun Luo (<strong>WJL</strong>), and (2) <strong>Debrecen Photoheliographic Data sunspot catalogue</strong> by Baranyi, T., Győri, L. and Ludmány, A. (<strong>DPD</strong>). The original datasets can be downloaded via their websites: <a href="https://github.com/Wang-Ruihui/A-live-homogeneous-database-of-solar-active-regions">A-live-homogeneous-database-of-solar-active-regions</a> and <a href="http://fenyi.solarobs.epss.hun-ren.hu/en/databases/DPD/">Debrecen Photoheliographic Data</a></p> <p>The mutual validation is automatically accomplished by Python. The code, mutual<strong>_validation.py</strong>, is attached. For a more detailed description of the mutual validation method, see Lang Qin, Jie Jiang, Ruihui Wang, Mutual Validation of Datasets for Analyzing Tilt Angles in Solar Active Regions(APJ under review)</p> <div> <h1>Data Description</h1> </div> <p>The mutually validated datset is in the file <strong>mutually_validated_tilt_angle_dataset.xlsx</strong>. There are 14 columns, each contains a type of parameter. The meanings of each header are as follows:</p> <blockquote> <p>date_dpd: date of observation in DPD dataset</p> <p>lat_dpd: heliographic latitude of the sunspot group</p> <p>lon_dpd: (Carrington) heliographic longitude of the sunspot group</p> <p>area_dpd: the total area of the sunspot group in μMSH</p> <p>tilt_dpd: the tilt angle of the sunspot group</p> <p>date_wjl: date of observation in WJL dataset</p> <p>lat_wjl: heliographic latitude of the active region</p> <p>lon_wjl: (Carrington) heliographic longitude of the active region</p> <p>area_wjl: the total area of the active region in μMSH</p> <p>flux_wjl: the total unsigned magnetic flux of the active region in Mx</p> <p>tilt_wjl: the tilt angle of the active region</p> <p>number: the NOAA number</p> <p>CR: the Carrington rotation number of the sunspot group/active region</p> <p>consistency: the marker of whether the two tilt angle values are close enough to be considered consistent. "1" means consistent and "0" means inconsistent</p> </blockquote> <div> <h1>Author</h1> </div> <p>Lang Qin, Jie Jiang, Yukun Luo</p>
POI comments validation dataset
<p>This is the dataset produced during the validation of the Viarota scenario for analysing feedback with respect to specific POIs from certain user groups. We will collect and process online data, regarding the RADON validation activities for processing anonymised opinions with respect to experiences when receiving the services offered in a certain POI.</p>
R scripts for the practical exercises with QGIS in the book "Land Use Cover Datasets and Validation Tools"
<p>This dataset includes a series of R scripts required to carry out some of the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.</p> <p>The scripts have been designed within the context of the R Processing Provider, a plugin that integrates the R processing environment into QGIS. For all the information about how to use these scripts in QGIS, please refer to Chapter 1 of the book referred to above.</p> <p>The dataset includes 15 different scripts, which can implement the calculation of different metrics in QGIS:</p> <ul> <li>Change statistics such as absolute change, relative change and annual rate of change (Change_Statistics.rsx)</li> <li>Areal and spatial agreement metrics, either overall (Overall Areal Inconsistency.rsx, Overall Spatial Agreement.rsx, Overall Spatial Inconsistency.rsx) or per category (Individual Areal Inconsistency.rsx, Individual Spatial Agreement.rsx)</li> <li>The four components of change (gross gains, gross losses, net change and swap) proposed by Pontius Jr. (2004) (LUCCBudget.rsx)</li> <li>The intensity analysis proposed by Aldwaik and Pontius (2012) (Intensity_analysis.rsx)</li> <li>The Flow matrix proposed by Runfola and Pontius (2013) (Stable_change_flow_matrix.rsx, Flow_matrix_graf.rsx)</li> <li>Pearson and Spearman correlations (Correlation.rsx)</li> <li>The Receiver Operating Characteristic (ROC) (ROCAnalysis.rsx)</li> <li>The Goodness of Fit (GOF) calculated using the MapCurves method proposed by Hargrove et al. (2006) (MapCurves_raster.rsx, MapCurves_vector.rsx)</li> <li>The spatial distribution of overall, user and producer’s accuracies, obtained through Geographical Weighted Regression methods (Local accuracy assessment statistics.rsx).</li> </ul> <p>Descriptions of all these methods can be found in different chapters of the aforementioned book.</p> <p>The dataset also includes a readme file listing all the scripts provided, detailing their authors and the references on which their methods are based.</p>
Data for the practical exercises in the book "Land Use Cover Datasets and Validation Tools"
<p>This dataset contains all the data that is required to carry out the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.</p> <p>The dataset includes data for three different case studies: The Asturias Central Area (Spain), the Ariège Valley (France) and Marqués de Comillas (Mexico). For the Asturias Central Area and the Ariège Valley, the dataset includes Land Use Cover (LUC) maps for several years of reference as well as data (simulation outputs, model drivers) for different modelling exercises. For Marqués de Comillas, the dataset includes a LUC map and a set of reference points used to validate it.</p> <p>The dataset includes a readme file listing all the files it contains and auxiliary files describing the data. For further information on the study area and the files used in the practical exercises, users are referred to Chapter 1 of the book.</p>
Nitrofurans Method Validation Dataset
<p>Nitrofurans Method Validation Dataset</p>
WHUS2-CD+ dataset for sentinel-2 cloud detection validation
<p>WHUS2-CD+ is a cloud validation detection dataset for Sentinel-2A images. WHUS2-CD+ contains 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2A images evenly distributed over China mainland.</p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. Wu, Z. Hu, C. Jian, S. Luo, L. Mou, X. Zhu, and M. Molinier, "A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features," in IEEE Transactions on Geoscience and Remote Sensing, 2021. <a href="https://doi.org/10.1109/TGRS.2021.3069641">https://doi.org/10.1109/TGRS.2021.3069641</a>.</p> <p>[2] Z. Wu, J. Li, Y. Wang, Z. Hu and M. Molinier, "Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 10, pp. 1792-1796, Oct. 2020. <a href="https://doi.org/10.1109/LGRS.2019.2955071">https://doi.org/10.1109/LGRS.2019.2955071</a>.</p> <p>The training and testing list is (The challenging senes are marked in bold):</p> <table> <tbody> <tr> <td>Training set</td> </tr> <tr> <td>S2A_MSIL1C_20190714T043711_N0208_R033_T46TFN_20190714T073938</td> </tr> <tr> <td>S2A_MSIL1C_20191219T040151_N0208_R004_T47SQU_20191219T055033</td> </tr> <tr> <td>S2A_MSIL1C_20190630T045701_N0207_R119_T45SWC_20190630T080543</td> </tr> <tr> <td>S2A_MSIL1C_20191215T042151_N0208_R090_T46RGV_20191215T065406</td> </tr> <tr> <td>S2A_MSIL1C_20180930T044701_N0206_R076_T45SXR_20180930T074413</td> </tr> <tr> <td>S2A_MSIL1C_20200317T024541_N0209_R132_T51TWM_20200317T053350</td> </tr> <tr> <td>S2A_MSIL1C_20180816T053641_N0206_R005_T44TKK_20180816T093424</td> </tr> <tr> <td>S2A_MSIL1C_20191023T040821_N0208_R047_T47TQF_20191023T074550</td> </tr> <tr> <td>S2A_MSIL1C_20180824T031541_N0206_R118_T50TKL_20180824T061636</td> </tr> <tr> <td>S2A_MSIL1C_20191118T025011_N0208_R132_T50RMN_20191118T071843</td> </tr> <tr> <td>S2A_MSIL1C_20190916T023551_N0208_R089_T50RQS_20190916T042547</td> </tr> <tr> <td>S2A_MSIL1C_20190819T031541_N0208_R118_T49SFU_20190819T065332</td> </tr> <tr> <td>S2A_MSIL1C_20190815T051651_N0208_R062_T44TPN_20190815T090034</td> </tr> <tr> <td>S2A_MSIL1C_20200410T022551_N0209_R046_T51TXG_20200410T042047</td> </tr> <tr> <td>S2A_MSIL1C_20191002T025551_N0208_R032_T50TQQ_20191002T054113</td> </tr> <tr> <td>S2A_MSIL1C_20180429T032541_N0206_R018_T49SCV_20180429T062304</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T51UWS_20200506T043639</td> </tr> <tr> <td>S2A_MSIL1C_20200325T034531_N0209_R104_T47RQL_20200325T065315</td> </tr> <tr> <td>S2A_MSIL1C_20190928T031541_N0208_R118_T49RBJ_20190928T061248</td> </tr> <tr> <td>S2A_MSIL1C_20180827T032541_N0206_R018_T48RYV_20180827T062627</td> </tr> <tr> <td>S2A_MSIL1C_20200222T030731_N0209_R075_T49QEE_20200222T060244</td> </tr> <tr> <td>S2A_MSIL1C_20180722T030541_N0206_R075_T49RFP_20180722T060550</td> </tr> <tr> <td>S2A_MSIL1C_20180729T025551_N0206_R032_T49RGL_20180729T055945</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T50SPE_20200506T052918</td> </tr> <tr> <td>Testing set</td> </tr> <tr> <td>S2A_MSIL1C_20180930T030541_N0206_R075_T49QDD_20180930T060706</td> </tr> <tr> <td>S2A_MSIL1C_20191105T023901_N0208_R089_T51STR_20191105T054744</td> </tr> <tr> <td>S2A_MSIL1C_20190812T032541_N0208_R018_T48RXU_20190812T070322</td> </tr> <tr> <td>S2A_MSIL1C_20190602T021611_N0207_R003_T52TES_20190602T042019</td> </tr> <tr> <td>S2A_MSIL1C_20190328T033701_N0207_R061_T49TCF_20190328T071457</td> </tr> <tr> <td>S2A_MSIL1C_20191001T050701_N0208_R019_T45TXN_20191002T142939</td> </tr> <tr> <td>S2A_MSIL1C_20200416T042701_N0209_R133_T46SFE_20200416T074050</td> </tr> <tr> <td>S2A_MSIL1C_20200528T050701_N0209_R019_T44SPC_20200528T082127</td> </tr> <tr> <td><strong>S2A_MSIL1C_20210207T023851_N0209_R089_T52UCU_20210207T040210</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20210126T052111_N0209_R062_T44SNE_20210126T063836</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20210102T054231_N0209_R005_T43SFB_20210102T065941</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20201206T041141_N0209_R047_T47SMV_20201206T053320</strong></td> </tr> </tbody> </table>
Dataset used for validation of the Danish 20-item Spiritual Needs Questionnaire.
<p>This material comprises the datasets to support the validation works of the Danish 20-item Spiritual Needs Questionnaire (DA-SpNQ-20). There is data on the questionnaire instruments: Spiritual Needs Questionnaire (danish translation of questionnaire will be published in Zenodo later) and the World Health Organization Well Being Index (WHO-5). All data has been fully anonymized and thus stripped of age, gender and religious/spiritual denominations. The sample is a convenience sample of (healthy) adult Danes with a large body of university students in the sample. Mean age of sample is 43,9 (sd: 16,3) and 73% were women.</p> <p>Information on the SpNQ is available at www.spiritualneeds.net</p> <p>Files included:</p> <p>(1) dataset of the full sample.</p> <p>(2) dataset of a randomly selected half (sample A) that was used for the exploratory factor analysis.</p> <p>(3) dataset of a randomly selected half (sample B) that was used for the confirmatory factor analysis.</p>
Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders: Synthetic dataset for validation of trace gas retrieval algorithms
<p>This data set is described in detail in a paper submitted to AMTD:</p> <p><strong>Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders - Part I: Synthetic dataset for validation of trace gas retrieval algorithms</strong></p> <p>by Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and<br> Bernhard Mayer</p> <p> </p> <p>The subdirectory <em>boxcloud</em> includes synthetic reflectances for clearsky, 1D cloud and box cloud.</p> <p>The subdirectory <em>les_cloud</em> includes synthetic reflectances for the LES cloud scenario for low earth orbit (<em>leo</em>) and geostationary orbit (<em>geo</em>).</p> <p>All data are provided in <em>netcdf</em> format.</p> <p> </p>
``HiPen'': a new dataset for validating (S)QM/MM free energy simulations
<p>Calculating free energy differences between levels of theory (i.e., <span class="math-tex">\(\Delta A^{low \to high}\)</span>) is integral to performing indirect (S)QM/MM free energy simulations. However, connecting levels of theory via free energy simulations has proved difficult due to (1) bond/angle degrees of freedom, (2) dihedral degrees of freedom, and (3) solvent arrangement differences between levels of theory, largely due to partial charge differences between levels of theory. In order to improve calculation of (S)QM/MM free energy simulations, the free energy simulation community should begin to compare methods based on convergence success relative to overall computational time and resource requirements. We have begun to compile such a dataset by calculating <span class="math-tex">\(\Delta A^{MM \to SCC-DFTB}\)</span> in gas phase for 22 drug-like molecules, as seen in our recent publication, Kearns, et al. <strong>2018</strong>, <em>Molecules</em>, Submitted, and we hope that future practitioners will do the same. With this work we hope to provide a standard for comparison for future FES methodologies; additionally, in the near future we hope to continue to add to this dataset including results in more complicated environments such as in solution and in enzyme. All data can be found in our publication and in the accompanying Supporting Information; raw data (such as simulation trajectories and raw data files) can be made available upon request. The purpose of this dataset publication is to make available all starting coordinates, topologies, parameter sets, and input files necessary to replicating the results published in our work.</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.