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Dataset results
8,038 results for “validation”
Validation of C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/ab9a564e-8c55-4ba2-828a-9577e9948803. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/9241fbf2-995f-4ece-be64-2302efaae280. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/b61ec3bb-0eb9-4803-bd70-2dd052c5bcfb. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/ceed8392-014f-48e6-a7f7-eb00d0b4ddd9. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v202312 vs C3S SM combined v202212 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v202312 vs C3S SM combined v202212 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/b80aa8db-e6a7-4205-a9bd-8478e1861608. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/24418dc9-98ae-4b96-a272-87c4c6583df3. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/ed647646-8513-4bca-8d0a-1db534c145fd. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v202312 vs C3S SM combined v202212 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v202312 vs C3S SM combined v202212 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/e7156ae6-e229-47ee-acb7-bb75fb8f12b1. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/62c2f4f1-572a-4932-b4c9-cf0d29da156b. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/d6936b1a-e01c-4908-8f71-ea4be275c4fd. Produced on QA4SM (https://qa4sm.eu)
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>
How can we biochemically validate protein function predictions with the Ras GTPase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-74ad-345f">How can we biochemically validate ProteinCartography with the Ras GTPase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> using human HRas and KRas as our inputs for a single run (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P01112/entry">P01112</a> and <a href="https://www.uniprot.org/uniprotkb/P01116/entry">P01116</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
How can we biochemically validate protein function predictions with the deoxycytidine kinase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-1e5d-e272">How can we biochemically validate ProteinCartography with the deoxycytydine kinase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> on the deoxycytidine kinase (dCK) using human dCK as our input (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P27707/entry">P27707</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
Data for "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024)
<p>This archive contains the data and scripts needed to reproduce the analysis in "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024).</p> <p>Contents</p> <p>data/: Input data and posterior distributions of different model fits</p> <p>datasets/: Observational datasets</p> <p>figures/: Folder to save figures in</p> <p>fluxes/: Saved lightcurves for auxiliary plotting purposes</p> <p>archive_paper_plotter.ipynb: Example script to plot fitted eclipse maps</p> <p>eclipse_pixel_sampling.py: Script to fit eclipse map and test k-fold CV score</p> <p>paper_eclipse_suite.py: Script to use simulated or observational data to fit an eclipse map</p>
Validating Instructional Practice Scale (IPS) for University Instructors: Confirmatory Factor Analysis
<p>.csv dataset for the study "validating instructional practice scale for instructors in some selected Ethiopian public universities: confirmatory factor analysis"</p>
Rumsey Train and Validation Data for ICDAR'24 MapText Competition
<p>Data set of 2Kx2K image tiles cropped from maps of the <a href="https://davidrumsey.com">David Rumsey collection</a> for the <a href="https://rrc.cvc.uab.es/?ch=28">ICDAR'24 Competition on Historical Map Text Detection, Recognition, and Linking</a>.</p> <p>Annotations and images follow the format described at the competition website and can be evaluated using the official <a href="https://github.com/icdar-maptext/evaluation">evaluation repository</a> script.</p> <p><strong>Important</strong>: v1.1 fixes an image channel order error, superseding the prior version. v1.2 corrects group links among several annotations. v1.3 strips markup that inadvertently remained in some annotation transcriptions.</p> <table> <tbody> <tr> <td> </td> <td><strong>Train</strong></td> <td><strong>Validation</strong></td> </tr> <tr> <td>Annotations</td> <td><code>rumsey_train.json</code></td> <td><code>rumsey_val.json</code></td> </tr> <tr> <td>Images</td> <td><code>train.zip</code></td> <td><code>val.zip</code></td> </tr> <tr> <td>Files</td> <td><code>rumsey/train/*.png</code></td> <td><code>rumsey/val/*.png</code></td> </tr> <tr> <td>Tiles</td> <td>200</td> <td>40</td> </tr> <tr> <td>Map Sheets</td> <td>196</td> <td>40</td> </tr> <tr> <td>Words</td> <td>34,518</td> <td>5,544</td> </tr> <tr> <td>Label Groups</td> <td>21,205</td> <td>3,502</td> </tr> <tr> <td>Illegible Words</td> <td>1,870</td> <td>313</td> </tr> <tr> <td>Truncated Words</td> <td>3,582</td> <td>628</td> </tr> <tr> <td>Valid Words</td> <td>30,563</td> <td>4,860</td> </tr> </tbody> </table> <p> </p> <p><strong>Annotations</strong>: Copyright 2024 UMN Knowledge Computing Lab, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC-BY-NC-SA 4.0 International.</a><br><strong>Images</strong>: David Rumsey Map Collection, David Rumsey Map Center, Stanford Libraries. <a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC-BY-NC-SA 3.0 Unported</a>.</p>
[Supplementary-material] Validation of Inter-Parameter Dependencies in API Gateways
<p>This is the supplementary material for the paper Validation of Inter-Parameter Dependencies in API Gateways.</p>
Molecular markers for taxonomic validation of 7 deep water invertebrate animals
<p>File 1: MolecularBarcodeSeqs_forValidation.fasta</p> <p>Molecular markers extracted from transcriptome assemblies for validation of animal taxonomic identification.</p> <p>Files 2-4: 2022-03-29-01_JAB_FALKOR_RNA_1, 2022-03-29-01_JAB_FALKOR_RNA_2, 2022-03-29-01_JAB_FALKOR_RNA_2.</p> <p>TapeStation data for total RNA quality assessment. Identifiers in the Sample Description column are linked to identifiers in the published table, <span>Table 2. RNA and sequence statistics for each specimen, from the manuscript: </span><em><span>Transcriptome sequencing of seven deep marine invertebrates</span></em></p>
Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio-logging methods
<p>Data and R Scripts for the manuscript titled "Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio-logging methods".</p> <p>Code is for labelling accelerometer data and running a random forest model.<br>Script01. Label the accelerometer data with behavioural labels from BORIS<br>Script02. Create summary metrics and resample frequencies. This includes code adapted from (Clark, 2019; Clark et al., 2022). https://ore.exeter.ac.uk/repository/handle/10871/120152, https://www.int-res.com/abstracts/meps/v701/p145-157/<br>Script03. Run random forest for each frequency and smoothing window.</p> <p>AccelerometerData.zip containes Files grouped by individual. For each individual there is:</p> <p>Accelerometer_data: Accelerometer data.</p> <p>ID: BORIS behaviour output</p> <p>ID_labs: Labelled accelerometer data</p>
Data used for validation of the Local Subtraction Approach
<p>This dataset contains the volume conductor models used in the evaluation of the local subtraction approach for the EEG and MEG forward problem, as detailed in the corresponding manuscript (Published in SIAM Journal on Scientific Computing: <a href="https://epubs.siam.org/doi/full/10.1137/23M1582874" target="_blank" rel="noopener">https://epubs.siam.org/doi/full/10.1137/23M1582874</a>).</p> <p> </p> <p>This first archive contains the four-layer sphere models, the EEG and MEG sensors, and the dipoles used in the validation studies against analytical solutions.</p> <p> </p> <p>The second archive contains the realistic volume conductor model we used for comparing the analytical and local subtraction approaches. </p> <p> </p> <p>Details on the creation and interpretation of the data are given in README files contained in the dataset.</p>
ScienceDex guides
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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.