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195
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ShareScore release 0.9.0
Dataset results
195 results for “RC”
An Open-Label Study of JZP-458 (RC-P) in Patients With Acute Lymphoblastic Leukemia (ALL)/Lymphoblastic Lymphoma (LBL)
ClinicalTrials.gov study NCT04145531. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Closed Loop DBS Implanted RC+S Study
ClinicalTrials.gov study NCT03815656. IPD Sharing: YES. Countries: 1. Publications: 1.
Supplementary material 3 from: Gbedomon RC, Salako VK, Schlaepfer MA (2020) Diverse views among scientists on non-native species. NeoBiota 54: 49-69. https://doi.org/10.3897/neobiota.54.38741
Distribution of respondents across the clusters regarding their opinions on values associated to non-native species
Dataset: Seismic Performance of Slender RC U-shaped Walls with a Single-Layer of Reinforcement
<p>This dataset contains the experimental data for two large-scale reinforced concrete U-shaped wall specimens tested at the Earthquake Engineering and Structural Dynamics Laboratory (EESD Lab), École Polytechnique Féderale de Lausanne (EPFL) in Switzerland. This dataset contains supplementary material for the journal paper titled, "Seismic Performance of Slender RC U-shaped Walls with a Single-Layer of Reinforcement", which contains some of the experimental observations and results. Thus, this dataset also contains MATLAB files associated with calculating the results presented in the journal paper and the corresponding figures and plots. The abstract for the journal paper is below:</p> <p>Reinforced concrete walls are typically used to resist the lateral loading induced by wind and earthquake actions. While most walls feature two vertical reinforcement layers, in some regions the use of slender reinforced concrete walls with a single vertical layer of reinforcement is current construction practice or has been in the past. The seismic performance of such elements is largely unknown given the paucity of experimental research that has been conducted on walls with a single layer of reinforcement, particularly for non-rectangular walls, such as the popular U-shaped wall. This paper presents the results of two slender reinforced concrete U-shaped walls tested at the Earthquake Engineering and Structural Dynamics Laboratory (EESD Lab), École Polytechnique Féderale de Lausanne (EPFL) in Switzerland. Both wall specimens were unconfined and detailed with a single-layer of vertical reinforcement to replicate some of the current construction practices being conducted in Colombia. Both walls reached ultimate drifts larger than 2.5-3.0% and initially failed due to buckling of the longitudinal reinforcement at the flange ends. The buckling length was 700-800 mm, which corresponded to 44-50 bar diameters.</p>
rc modelomschechtfab
Source: Objaverse 1.0 / Sketchfab
pianta museo diocesano rc
Source: Objaverse 1.0 / Sketchfab
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - Irr+RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that werethat were irradiated to ablate adult neurogenesis and housed in a regular cage (Irr+RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were female. Refer to the original publication for additional information. </p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that were housed in a regular cage (RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>Refer to the original publication for additional information. The sex of individual mice is as follows:</p> <table> <tbody> <tr> <td><strong>Group</strong></td> <td><strong>Mouse #</strong></td> <td><strong>Sex</strong></td> </tr> <tr> <td>RC</td> <td>M1</td> <td>M</td> </tr> <tr> <td> </td> <td>M2</td> <td>M</td> </tr> <tr> <td> </td> <td>M3</td> <td>M</td> </tr> <tr> <td> </td> <td>M4</td> <td>F</td> </tr> <tr> <td> </td> <td>M5</td> <td>F</td> </tr> </tbody> </table>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - RetroAAV-RC
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from retro-AAV injected mice that were housed in a regular cage (RC). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were male. Refer to the original publication for additional information. </p>
Damage to RC Structures and Intensity Measures from the 2023 Türkiye Earthquake
<p>This database is associated with the following paper:</p> <p><strong>Hariri-Ardebili, M.A., and Sattar, S. (2024).</strong> <em>Data-Driven Insights into Post-Earthquake Reconnaissance Findings: 2023 Türkiye Earthquake Sequence</em>, Earthquake Spectra.</p> <p>The database contains information on over 240 reinforced concrete structures surveyed after the 2023 Türkiye earthquake sequence. The data is divided into two main categories: (1) building-related information and observed damage, and (2) various ground motion intensity measures from different shocks and aftershocks, calculated using either the Kriging method or the USGS ShakeMap.</p> <p>Part (1) of the database was primarily developed as part of the ACI Committee 133 initiative and was also presented in the following paper. However, several missing data points were supplemented by the developers of this new database based on further investigation of the individual buildings and engineering judgment:</p> <p><strong>Pujol et al. (2024).</strong> <em>Quantitative Evaluation of the Damage to RC Buildings Caused by the 2023 Southeast Turkey Earthquake Sequence</em>, Earthquake Spectra.</p>
OpenFE 1.0rc release: Benchmarking results
<p>These are benchmarking results of the OpenFE 1.0rc release. Deposited are the calculated differences in binding free energy for ligands from 15 datasets from the Protein Ligand Benchmark set. The DDG values can be found in the "csv_files" folder, plots of the calculated DDG and DG values against the experimental data can be found in the folder "plots".</p> <p>Input structures for the benchmark were taking from the protein ligand benchmark repository: </p> <p>https://github.com/openforcefield/protein-ligand-benchmark</p> <p>Settings used in this benchmark: </p> <ul> <li>Kartograf atom mapper</li> <li>Minimal spanning networks </li> <li>allowing element changes</li> <li>Partial charge assignment using OpenEye ELF10 (user charges)</li> <li>Small molecule forcefield: OpenFF 2.1</li> </ul> <p>The ligand networks (including mappings and partial charges) that were run to obtain these results can be found here: </p> <p>https://zenodo.org/records/13165855</p>
DBS for TRD With the Medtronic Summit RC+S
ClinicalTrials.gov study NCT04106466. IPD Sharing: YES. Countries: 1. Publications: 5.
Comparing the Effectiveness of a Treat-to-target (T2T) Disease Management Strategy vs. Routine Care (RC) in Adult Patients With Moderate to Severe Rheumatoid Arthritis (RA) Treated With Subcutaneous A
ClinicalTrials.gov study NCT03274141. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Placebo Controlled, Randomized Safety and Efficacy Study of RC-1291 in Cancer Anorexia/Cachexia.
ClinicalTrials.gov study NCT00267358. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Prognostic Effect of Whether Doing PLND During RC for High-risk NMIBC
ClinicalTrials.gov study NCT05123625. IPD Sharing: NO. Countries: 1. Publications: 25.
Impact Advanced Recovery® for Radical Cystectomy (RC) Patients: a Pilot Study
ClinicalTrials.gov study NCT01868087. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Jovenes Sanos: Preventing IPV and RC
ClinicalTrials.gov study NCT03660514. IPD Sharing: NO. Countries: 2. Publications: 1.
Radical Cure (RC) With Tafenoquine or Primaquine After Semi-quantitative G6PD Testing: A Feasibility Study in Peru
ClinicalTrials.gov study NCT05361486. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
MPN-RC 118 AVID200 in Myelofibrosis
ClinicalTrials.gov study NCT03895112. IPD Sharing: NO. Countries: 1. Publications: 1.
Safety and Efficacy of RC-1291 HCl in Patients With Cancer Related Anorexia and Weight Loss
ClinicalTrials.gov study NCT00219817. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.