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60 results for “residence time”
Mapping soil microbial residence time at the global scale
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Data from: Competitive ability of native and alien plants: effects of residence time and invasion status
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Inter- and intraspecific selection in alien plants: how population growth, functional traits and climate responses change with residence time
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RRR input and output files corresponding to "Global patterns in river water storage dependent on residence time"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR input and output files that were used in the study reported in:</p> <ul> <li>Collins, E. L., C. H. David, R. Riggs, G. H. Allen, T. M. Pavelsky, P. Lin, M. Pan, D. Yamazaki, R. K. Meentemeyer, and G. M. Sanchez (2024), Global patterns in river water storage dependent on residence time.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Input datasets</strong></p> <p>The dataset herein benefited from and was built upon other datasets:</p> <ul> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> <li>GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/ZRIHVF29X43C">10.5067/ZRIHVF29X43C</a></li> <li>GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/QN80TO7ZHFJZ">10.5067/QN80TO7ZHFJZ</a></li> <li>GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/SGSL3LNKGJWW">10.5067/SGSL3LNKGJWW</a></li> </ul> <p><strong>Known bugs and limitations in this dataset or the associated manuscript</strong></p> <p>In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, two river reaches do not have a corresponding catchment: COMID=31000001 and COMID=61000003. These two river reaches were removed from our analysis, hence bringing the total number of river reaches used from 2,938,143 to 2,938,141, the same value as the total number of catchments.</p> <p>In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, the attribute table for the river reach with COMID=67072432 contains only four upstream reaches (COMID=64072433, COMID=64072735, COMID=64073772, and COMID=64073786), while the reach actually has five upstream reaches on the map (those above and COMID=64073793). This was modified herein because all connectivity in this dataset was created by associating upstream and downstream nodes rather than using the attribute table.</p> <div> <div> <div> <p>Our monthly dataset of river gauges with 95% daily data availability for 1980–2009 and with average discharge greater than or equal to 100 m3/s contains 1,148 stations. After mapping gauges to the MERIT Hydro river network using a joint criteria of 1) within a distance of 0.05 degrees and 2) uncorrected 30-yr average simulated discharge of the river reach within one order of magnitude relative to the observed average discharge of the gauge; the number of gauges reduces to 1129. In cases of multiple gauges per reach, we selected the gauge that had the closest observed average discharge to the uncorrected 30-yr averages simulated, resulting in 1,001 gauges. The dataset was further split in calibration gauges (702) and validation gauges (299). Three specific gauges are located in places with 0 accumulation of runoff in their specific sub-basin, they are located at river reaches with ID 41003992, 41001545, and 24005996. These three gauges were removed from the overall analysis at all gauges, resulting in 998 total gauges. All three gauges are part of the 299 validation gauges, not the 702 calibration gauges. They were retained in the analysis of 299 validation gauges. </p> <p> </p> </div> </div> </div> <p><strong>Files included in this version</strong></p> <p>All monthly discharge observation files for 1148 gauges, in shapefile and CSV formats:</p> <ul> <li>sites_1980-01_2009-12_100cms_095p.zip</li> <li>timeseries_obs_1980-01_2009-12_100cms_095p_monthly.zip</li> </ul> <p>All CSV files corresponding to the hydrography of all pfaf_level_02 regions of MERIT-Hydro v0.7 Basins v1.0 (i.e. the 61 values of <em>ii</em>):</p> <ul> <li>coords_pfaf_<em>ii</em>.zip</li> <li>kfac_pfaf_<em>ii</em>_1km_hour.zip</li> <li>k_pfaf_<em>ii</em>_low.zip</li> <li>k_pfaf_<em>ii</em>_nrm.zip</li> <li>k_pfaf_<em>ii</em>_hig.zip</li> <li>rapid_catchment_pfaf_<em>ii</em>.zip</li> <li>rapid_connect_pfaf_<em>ii</em>.zip</li> <li>rapid_coupling_pfaf_<em>ii</em>_GLDAS.zip</li> <li>riv_bas_id_pfaf_<em>ii</em>_topo.zip</li> <li>sort_pfaf_<em>ii</em>_topo.zip</li> <li>xfac_pfaf_<em>ii</em>_0.1.zip</li> <li>x_pfaf_<em>ii</em>_low.zip</li> <li>x_pfaf_<em>ii</em>_nrm.zip</li> <li>x_pfaf_<em>ii</em>_hig.zip</li> </ul> <p>All netCDF4 files with monthly surface and subsurface runoff that were combined from GLDAS 2.0 and corresponding to the following land surface models for the years 1980 to 2009 (both included):</p> <ul> <li>GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_ENS_M_1980-01_2009-12_utc.zip (the 3-model ensemble average based on the three above models).</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the GLDAS data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly discharge (based on the m3_riv data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>Qout_pfaf_<em>ii</em>_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly storage (based on the Qout_pfaf_<em>ii</em>_GLDAS_ENS data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig):</p> <ul> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_hig.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above):</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_GLDAS_ENS.zip</li> </ul> <p>All monthly discharge observation files for 1148 gauges, in shapefile format, with observed monthly mean appended:</p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ.zip</li> </ul> <p>All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, in shapefile and CSV formats, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR.zip</li> <li>obs_tot_id_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> <li>Qobs_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> </ul> <p>All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, separated into calibration (CAL) and validation (VAL) gauges, in shapefile and CSV formats, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_CAL.zip</li> <li>obs_bas_id_1980-01_2009-12_100cms_095p_meanQ_CAL_pfaf_<em>ii</em>.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_VAL.zip</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for calibration (CAL) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for all correction (COR) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly storage (based on the Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig):</p> <ul> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_hig.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip data above):</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_GLDAS_COR.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) catchments that have been dissolved and of which the perimeter was extracted, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code; as well as global combined files:</p> <ul> <li>cat_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_perim.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_perim.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network retaining only those reaches that flow to the global coast, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_coast.zip</li> </ul>
Drug-target residence time data
<p>Drug-target residence time data:<br> - <strong>residence_time_data.xlsx </strong>contains information about target residence time and other binding kinetics coefficients, basic ligand properties like simplified molecular-input line-entry system (SMILES) or International Chemical Identifier (InChI) string, as well as the reference literature in PubMed</p> <p>- <strong>structures.zip</strong> contains structure files: the protein molecule along with other components such as water molecules and metal ions in the <em>pdb</em> format, while the ligand (drug) in Structure Data Format (<em>sdf</em>)</p> <p>All files can be opened via either a molecular visualization system (eg. PYMOL, Chimera) or a text editor.</p>
Phosphorus allocation to and resorption from leaves regulate the residence time of phosphorus in aboveground forest biomass on Mount Kinabalu, Borneo
<p>1. The residence time of phosphorus (P) in trees is a consequence of plant adaptation to P deficiency, with longer P residence time on soils with low P availability. P residence time has been studied at the leaf or canopy level but seldom at the whole-tree level. Whereas P residence time at the leaf or canopy level is largely determined by leaf longevity and the resorption of P before leaf abscission, P residence time at the whole-tree level will also be influenced by differences in P allocation to different plant parts because leaves and woody organs have distinct longevities.</p> <p>2. We estimated the residence time of P in aboveground tree biomass (AGB) as the ratio of P mass (i.e. leaves plus wood) to the annual flux of P via litterfall (i.e. fine litter plus coarse woody debris) for seven tropical rain forests with different soil P availabilities on Mount Kinabalu, Borneo. We analysed the effects of P allocation to and resorption from leaves on P residence time along a soil P gradient.</p> <p>3. P residence time (2.7–9.8 years) was approximately one fifth of biomass residence time (AGB/annual litterfall mass; 19.8–48.8 years). This was due to a disproportionately greater relative allocation of P to leaves (P mass in leaves/P mass in AGB; 0.11–0.46), which had a smaller fraction of biomass (leaf biomass/AGB; 0.02–0.05) but a shorter longevity (1.0–1.8 years).</p> <p>4. The relative allocation of P to leaves was often high on low-P soils, and P residence time was expected to be short. By contrast, the resorption rate of P from leaves was also high on low-P soils, which extended P residence time with P deficiency. Consequently, P residence time was nearly constant across the forests.</p> <p>5. The short residence time of P relative to biomass indicates that P residence time depends largely on relative P allocation among plant organs. Similar P residence times among sites were maintained because greater P allocation to leaves on low-P soils was effectively offset by higher P-resorption efficiency.</p>
A global map of microbial residence time
<p>Soil microbes are the fundamental engine for carbon (C) cycling. Microbial residence time (MRT) therefore determines the mineralization of soil organic C, releasing C as heterotrophic respiration and contributing substantially to the C efflux in terrestrial ecosystems. We took use of a comprehensive dataset (2627 data points) and calculated the MRT based on the basal respiration and microbial biomass C. Large variations in MRT were found among biomes, with the largest MRT in boreal forests and grasslands and smallest in natural wetlands. Biogeographic patterns of MRT were found along climate (temperature and precipitation), vegetation variables (root C density and net primary productivity), and edaphic factors (soil texture, pH, topsoil porosity, soil C, and total nitrogen). Among environmental factors, edaphic properties dominate the MRT variations. We further mapped the MRT at the global scale with an empirical model. The simulated and observed MRT were highly consistent at plot‐ (R<sup>2</sup>=0.86), site‐ (R<sup>2</sup>=0.88), and biome‐ (R<sup>2</sup>=0.99) levels. The global average of MRT was estimated to be 38 (±5) days. A clear latitudinal biogeographic pattern was found for MRT with lower values in tropical regions and higher values in the Arctic. The biome‐ and global‐level estimates of MRT serve as valuable data for parameterizing and benchmarking microbial models.</p>
Time to Defibrillation Using Automated External Defibrillators by Pediatric Residents in Simulated Cardiac Arrests
ClinicalTrials.gov study NCT00640354. IPD Sharing: Not stated. Countries: 1. Publications: 12.
Residence Time of Biomarkers of Semen Exposure
ClinicalTrials.gov study NCT00984555. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Analysis of the Impact on Surgical Residency Programs in Times of Pandemic in Argentina
ClinicalTrials.gov study NCT04703400. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Residents' Learning Curve of Intraoperative Transit-time Flowmetry and High-frequency Ultrasound in CABG (LEARNERS)
ClinicalTrials.gov study NCT06589323. IPD Sharing: NO. Countries: 1. Publications: 16.
A global map of microbial residence time
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Phosphorus allocation to and resorption from leaves regulate the residence time of phosphorus in aboveground forest biomass on Mount Kinabalu, Borneo
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Data from: A residence time theory for biodiversity
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Does autotext usage decrease documentation time among resident physicians? A retrospective analysis of EHR usage data
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Physical processes determine spatial structure in water temperature and residence time on a wide reef flat
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Supplementary material 1 from: Gruntman M, Segev U (2024) Effect of residence time on trait evolution in invasive plants: review and meta-analysis. NeoBiota 91: 99-124. https://doi.org/10.3897/neobiota.91.109251
Supplementary information
Dataset (IX) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>Well-tempered metadynamics simulation data of compounds <strong>1 </strong>and<strong> 2</strong> of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (X) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>Well-tempered metadynamics simulation data of compounds <strong>1 </strong>and<strong> 2</strong> of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Real-time Experiences, Physical Activity and Biological Outcomes in Personal Recovery Residents (EMPOWER-RES)
ClinicalTrials.gov study NCT06914622. IPD Sharing: YES. Countries: 1. Publications: 0.
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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.