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7,515
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Dataset results
7,515 results for “screenings”
Data Set on the Literature Screening for a Scoping Review of Evacuation Training Methods in Buildings
<p>This data set contains all retrieved literature records of a scoping review on fire evacuation training methods in buildings together with the reasoning for their in- or exclusion in the review.</p>
Dataset for the Moesin antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Moesin protein, encoded by the MSN gene. The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.4724169">https://doi.org/10.5281/zenodo.4724169</a>).</em></p>
Dataset for the Midkine antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterization antibodies for the Midkine protein, encoded by the MDK gene. The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.5644321">https://doi.org/10.5281/zenodo.5644321</a>), and published on F1000Research (<a href="https://doi.org/10.12688/f1000research.130587.4">https://doi.org/10.12688/f1000research.130587.4</a></em><em>).</em></p>
Dataset for the Secreted frizzled-related protein 1 antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Secreted frizzled-related protein 1 protein, encoded by the SFRP1 gene. The original study is also available on Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.6370454">https://doi.org/10.5281/zenodo.6370454</a>).</em></p>
Dataset for the Transmembrane protein 106B antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Transmembrane protein 106B protein, encoded by the TEM106b gene. The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.7459629">https://doi.org/10.5281/zenodo.7459629</a>).</em></p>
Genetically adjusted PSA levels for prostate cancer screening
<p>Prostate-specific antigen (PSA) screening for prostate cancer remains controversial because it increases overdiagnosis and overtreatment of clinically insignificant tumors. Accounting for genetic determinants of constitutive, non-cancer PSA variation has potential to improve screening utility. We discovered 128 genome-wide significant associations (<em>P</em><5×10<sup>-8</sup>) in a multi-ancestry GWAS meta-analysis of 95,768 men and developed a PSA polygenic score (PGS<sub>PSA</sub>) that explains 9.61% of constitutive PSA variation. Here we provide full GWAS summary statistics for the multi-ancestry meta-analysis and ancestry-stratified summary statistics for ~1.2 million variants used to derive a genome-wide PGS<sub>PSA.</sub></p>
Dataset for the TDP-43 antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the TDP-43 protein. The study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.7249802">https://doi.org/10.5281/zenodo.7249802</a>).</em></p>
Dataset for the Profilin-1 antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Profilin-1 protein. The study is available on Zenodo (<a href="https://doi.org/10.5281/zenodo.7249258">https://doi.org/10.5281/zenodo.7249258</a>).</em></p>
A Serious Game to Anticipate Handwriting Difficulties Screening Through Visual Perception Assessment - DATASET
<p>Each row in the dataset represents a subject. It contains:</p> <ul> <li>The answers to a characterization questionnaire</li> <li>The performance in the game described in the article</li> </ul>
Dataset for the Ubiquilin-2 antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Ubiquilin-2 protein. The study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.7459541">https://doi.org/10.5281/zenodo.7459541</a>).</em></p>
APNIWAVE: A Dataset Collected Using a Radar-Based Sleep-Apnea Screening Device for Use at Home
<p>The "APNIWAVE: A Dataset Collected Using a Radar-Based Sleep-Apnea Screening Device for Use at Home" dataset was created by collecting data from 11 patients with Obstructive Sleep Apnea and Hypopnea Syndrome (OSAHS) using an UWB sensor, which was the X4M200 UWB radar sensor by Novelda and a Raspberry Pi 3 device, for the purposes of APNIWAVE project (EIT Health RIS Scheme, Project ID: Project ID 2021-RIS_Innovation-066). The dataset comprises 1,011 selected samples of 10 sec each (a total of about 3 hours), from three main events that are the most frequent during sleep:</p> <ul> <li><em>Normal breathing</em> – periodic movement of the patient’s torso as a result of the inhale and exhale. (label in "<em>Labels Three Events.csv</em>" file equal to 0)</li> <li><em>Apnea event</em> – based on the Apnea type, e.g. central Apnea or hypopnea, no torso movement or a periodic torso movement but weaker than normal breathing, respectively. (label in "<em>Labels Three Events.csv</em>" file equal to 1)</li> <li><em>Other event</em> – events that may occur during data collection, such as change of posture, leaving the bedroom etc. (label in "<em>Labels Three Events.csv</em>" file equal to 2)</li> </ul> <p>Data from five distances are included for each 10 sec sample. More specifically, the central distance is the distance of the user from the radar; on top of that we added two more discrete distance steps (as designated by the radar range resolution) in front of and another two behind the user, thus totaling five distances from the radar. These five distances were selected from a set of 165 distance steps (from 0.5m to 9 m) considering a distance step of about 0.05144 m and a sampling rate of 17 Hz. As a consequence, the size of each sample (of 10 sec duration) is 5 x 170, which leads to a total size of the uploaded "<em>Multiple Distances Three Events.csv</em>" file of 5 x 171870.</p>
Dataset for the Sequestosome-1 antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Sequestosome-1 protein. The study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.4818440">https://doi.org/10.5281/zenodo.4818440</a>).</em></p>
Dataset for Superoxide dismutase 1 Cu/Zn (SOD1) antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Superoxide dismutase 1 Cu/Zn (SOD1) protein. The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.5061103">https://doi.org/10.5281/zenodo.5061103</a>).</em></p>
Screening for side effects of COVID-19 drug candidates on cardiovascular development -RAW DATA qPCR RESULTS
<p>Raw Data relating to Figures 4 and Supplemental Figures S7 and S8 of the article </p> <p><strong>Screening for side effects of COVID-19 drug candidates on cardiovascular development </strong></p> <p>Alexander Ernst<sup>1#</sup>, Indre Piragyte<sup>1,2#</sup>, Ayisha Marwa MP<sup>1,2</sup>, Ngoc Dung Le<sup>3</sup>, Denis Grandgirard<sup>3</sup>, Stephen L. Leib<sup>3</sup>, Andrew Oates<sup>4</sup>, Nadia Mercader<sup>1,2,5</sup></p> <p> </p> <p> </p> <p> </p> <p><sup>1</sup> Institute of Anatomy, University of Bern, Switzerland</p> <p><sup>2</sup> Department for Biomedical Research DBMR, University of Bern, Switzerland</p> <p><sup>3</sup> Institute for Infectious Diseases, University of Bern, Switzerland</p> <p><sup>4 </sup>School of Life Sciences, École polytechnique fédérale de Lausanne, Switzerland</p> <p><sup>5</sup> Centro Nacional de Investigaciones Cardiovasculares, CNIC, Madrid, Spain</p> <p># shared first-authorship</p>
Dataset for Optineurin antibody screening study
<p>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Optineurin protein. The study is available on Zenodo (https://doi.org/10.5281/zenodo.4730992).</p>
Screening the maize rhizobiome for consortia that improve Azospirillum brasilense root colonization and plant growth outcomes
<p>Data corresponds to results from: <em>Barua N, Clouse KM, Ruiz Diaz DA, Wagner MR, Platt TG and Hansen RR (2023) Screening the</em> <em>maize rhizobiome for consortia that improve Azospirillum brasilense root colonization and plant growth outcomes. Front. Sustain. Food Syst. 7:1106528. doi: 10.3389/fsufs.2023.1106528</em></p>
Dataset for the hVPS35 antibody screening study
<p>This project contains the following underlying data included in a study aiming at characterizing antibodies for the hVPS35 protein. The study is available on Zenodo (https://doi.org/10.5281/zenodo.7671730).</p>
Dataset for the TIA1 antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the following underlying data included in a study aiming at characterizing antibodies for the TIA1 protein, encoded by the TIA1 gene. The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.7671718">https://doi.org/10.5281/zenodo.7671718</a>).</em></p>
Computational Screening of Supported Metal Oxide Nanoclusters for Methane Activation: Insights into Homolytic versus Heterolytic C-H Bond Dissociation
<p>Optimized geometries (in XYZ format) of methane activation over supported [M<sub>1</sub>OM<sub>2</sub>]<sup>2+</sup> complexes where M<sub>1</sub>, M<sub>2</sub> = Cu, Zn, Ni, Co, Fe, and Mn. 'SM' in the file name stands for spin multiplicity. Calculations were performed using Gaussian 16 and M06-L functional. The def2-SVP basis set was used for C, O, H, whereas the def2-TZVP basis set was employed for all metal elements. </p>
Deep Reinforcement Learning Enables Better Bias Control in Benchmark for Virtual Screening
<p>This compressed file contains all datasets made for the validation of MUBDsyn.</p><ul><li>datasets_int_val: 17 cases in this folder are derived from <a href="https://github.com/jwxia2014/ULS-UDS">MUBD for GPCRs</a>. MUBDreal was made by <a href="https://github.com/jwxia2014/MUBD-DecoyMaker2.0">MUBD-DecoyMaker2.0</a> and MUBDsyn was made by <a href="https://github.com/taoshen99/MUBDsyn">MUBD-DecoyMakersyn</a>.</li><li>datasets_ext_val_classical_VS: Five cases in this folder are derived from the shared cases of MUV and DUD-E. The active sets of MUV were taken as the input to make corresponding MUBD datasets. Files in SBVS are raw molecular docking results by smina.</li><li>datasets_ext_val_SI_classical_VS: DeepCoy and TocoDecoy were used to make the datasets corresponding to the same five cases above. The data of DeepCoy was directly retrieved from <a href="https://opig.stats.ox.ac.uk/resources">DeepCoy resources at OPIG</a> while topology decoys of TocoDecoy_9W were made based on the scripts provided at <a href="https://github.com/5AGE-zhang/TocoDecoy">TocoDecoy GitHub Repository</a>. Files in SBVS are raw molecular docking results by smina.</li><li>datasets_ext_val_ML_VS: Ten cases in this folder are derived from <a href="http://nrlist.drugdesign.fr/">NRLiSt-BDB</a>. Corresponding MUBD datasets were made as described above.</li></ul><p>All these datasets can be used for the reproduction of validation performed in the manuscript or to benchmark various virtual screening methods.</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.