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12,346 results for “derivatives”
CLDF dataset derived from Walker and Ribeiro's "Bayesian Phylogeography of the Arawak expansion" from 2011
<p>Cite the source of the dataset as:</p> <blockquote> <p>Robert S. Walker and Lincoln A. Ribeiro. 2011. Bayesian phylogeography of the Arawak expansion in lowland South America. *Proceedings of the Royal Society B: Biological Sciences*. Volume 278, Number 1718, 2562--2567. DOI: 10.1098/rspb.2010.2579</p> </blockquote>
CLDF dataset derived from Lama's "Subgrouping of Nisoic (Yi) Languages" from 2012
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lama, Ziwo Q.-F. (2012). Subgrouping of Nisoic (Yi) Languages: A Study from the Perspectives of Shared Innovation and Phylogenetic Estimation. Ph.D. Thesis. The University of Texas at Arlington.</p> </blockquote>
CLDF dataset derived from Koelle's "Polyglotta Africana" from 1854
<p>Cite the source of the dataset as:</p> <blockquote> <p>Koelle, Sigismund W. (1854). Polyglotta Africana or Comparative Vocabulary of Nearly Three Hundred Words and Phrases in more than One Hundred Distinct African Languages. London: Church Missionary House.</p> </blockquote>
Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features
<p>This dataset supports the study titled <em>"Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features"</em>, published in <em>Urban Climate</em> (<a href="https://doi.org/10.1016/j.uclim.2024.102102" target="_new" rel="noopener">DOI: 10.1016/j.uclim.2024.102102</a>).</p> <p> </p> <p><strong>Content Overview:</strong></p> <ul> <li> <p><strong>Building Label Data for Footprint Detection</strong>:</p> <ul> <li><em>Amsterdam_BDG_Label.rar</em></li> <li><em>MiamiDade_BDG_Label.rar</em></li> </ul> <p>These are the label datasets used for training the building detection segmentation models. They have been instrumental in accurately detecting building footprints in Amsterdam.</p> </li> <li> <p><strong>Amsterdam_3D_Buildings.rar</strong>: CityGML file of 3D building models for Amsterdam, derived from LiDAR data and U-Net3+ model.</p> </li> </ul> <ul> <li> <p><strong>Morphological Features.rar</strong>: Contains urban morphological features (in raster format) extracted from LiDAR data used in the study.</p> </li> <li> <p><strong>Training and Test Data for Air Temperature Estimation</strong>:</p> <ul> <li><em>Train_Test_AvgTemp_Amsterdam.rar</em></li> <li><em>Train_Test_MaxTemp_Amsterdam.rar</em></li> <li><em>Train_Test_MinTemp_Amsterdam.rar</em></li> </ul> <p>This dataset includes training and testing data for estimating air temperatures in three scenarios: average daily temperature, minimum daily temperature, and maximum daily temperature for the city of Amsterdam.</p> </li> </ul>
D-PLACE dataset derived from NASA TERRA/MODIS 'Net Primary Productivity'
<p>Cite the source of the dataset as:</p> <blockquote> <p>NASA. Net Primary Productivity (1 month - TERRA/MODIS)</p> </blockquote>
Results for datasets with simulated error (derived from Datasets 1-5)
<p>Datasets w/ simulated error derived from Datasets 1-5.</p> <p>Version 2: Datasets were calculated with an Rstandard (R_std) value of 0.0036765 (<span>Skrzypek, G. and Dunn, P. J. H., Rapid Commun. Mass Spectrom., 34, e8890, <a href="https://doi.org/10.1002/rcm.8890">https://doi.org/10.1002/rcm.8890</a>, 2020</span>), which is more accurate than the atom percent value used in Version 1. Updating the R_std value had essentially no effect on the average values calculated using these datasets.</p> <p>There are 1000 datasets per combination of error level and skewness type.</p> <ul> <li>e1: Low error, no skew</li> <li>e2: Medium error, no skew</li> <li>e3: High error, no skew</li> <li>e4: Low error, left skew</li> <li>e5: Medium error, left skew</li> <li>e6: High error, left skew</li> <li>e7: Low error, right skew</li> <li>e8: Medium error, right skew</li> <li>e9: High error, right skew</li> </ul>
At-a-station hydraulic geometry exponent b derived from Landsat river width and discharge observation
<h2>Description</h2> <p>Global at-a-station hydraulic geometry exponent b dataset derived from Landsat river width (Feng et al., 2022) and discharge observation (Lin et al., 2019).</p> <p> </p> <p>For more details, please refer to:</p> <div> </div> <p><span>Zimin Yuan, Peirong Lin, Xiwei Guo, Kai Zhang, Hylke E. Beck. Revisiting At-a-Station Hydraulic Geometry Using Discharge Observations and Satellite-Derived River Widths. <em>J Remote Sens.</em> 2024;4:0271. DOI:<a href="https://doi.org/10.34133/remotesensing.0271">10.34133/remotesensing.0271</a></span></p> <p> </p> <h2>Contacts</h2> <ul> <li>Zimin Yuan, <a href="mailto:ziminyuan@pku.edu.cn">ziminyuan@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Dataset for article: Potent and reversible open-channel blocker of NMDA receptor derived from dizocilpine with enhanced membrane-to-channel inhibition
<div>This version shows the correct number of n for Figure 6.</div> <div> </div>
Satellite derived discharge for 27 river sections
<p>Discharge time series derived from remote sensing data with upper and lower uncertainty for 27 studied rivers.</p> <p>The filename is based on the upstream and downstream SWORD v15 node_id used in the computation:<br>River_DownstreamNodeID_Upstream_NodeID_discharge.csv</p> <p>When using this data please cite <em>Scherer D., Schwatke C., Dettmering D., Seitz F.</em>: <strong>Monitoring river discharge from space: An optimization approach with uncertainty quantification for small ungauged rivers. </strong>Remote Sensing of Environment. In Review.</p>
3D Coordinates of the isoxazole derivative 3a against FXR and hLIFR receptors
<p>This repository contains the 3D Coordinates (Docking and Molecular Dynamics best cluster) in pdb extension of the isoxazole derivative 3a against FXR and hLIFR receptors. </p>
Multisensory Number Channels Derived from Individual Differences
<p>This is the dataset related to the article: "Multisensory Number Channels Derived from Individual Differences"</p> <p>The file contains subjects data for the three experiments</p> <p>sub_ID is the participant's identification number<br><br>The subsequent columns indicate Wfs for each tested numerosity in each experiment.</p> <p>M = Digit to Actions Task<br>F = Digit to Visual Sequence Task<br>D = Visual Dot estimation Task<br><br>E.g. M10 indicates the Wf measured for that subjects with tested number 10 in the Motor condition </p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/24°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/72°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from MIOST geostrophic velocities (1/24°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from MIOST geostrophic velocities (1/72°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/24°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/72°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
Figure 8 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 8. Treatment Group with resveratrol tempeh + 2-ME after induced by Beta-Amyloid (10 x10).
Figure 5 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 5. Treatment group: 2- ME (10 x10).
Figure 9. Treatment Group with 2 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 9. Treatment Group with 2-ME after induced by Beta-Amyloid (10 x10).
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.