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1,036 results for “Palm”
Trees and alignments for: A robust phylogenomic framework for the calamoid palms
<p>Target file, alignments, gene trees and species trees from phylogenomic analyses in Kuhnhäuser et al. (2021), A robust phylogenomic framework for the calamoid palms, Molecular Phylogenetics and Evolution. <a href="https://doi.org/10.1016/j.ympev.2020.107067">https://doi.org/10.1016/j.ympev.2020.107067</a>.</p> <p>Raw sequence data are deposited in the European Nucleotide Archive of the European Bioinformatics Institute (<a href="https://www.ebi.ac.uk/ena">https://www.ebi.ac.uk/ena</a>) under project number PRJEB40689. Scripts for all phylogenetic analyses are available at <a href="https://github.com/BenKuhnhaeuser/PhyloFrame">https://github.com/BenKuhnhaeuser/PhyloFrame</a>.</p>
Native tree growth and reproduction in response to reduction in the coconut palm (Cocos nucifera) canopy at Palmyra Atoll
These data describe competition for light (open solar path) between introduced coconut palm trees (Cocos nucifera) and native tree species between 2004 and 2008 at Palmyra Atoll, Northern Line Islands, Pacific Ocean. Data are contained in one table, including values from the start, end, and intermediate samples. The dataset measures the change in tree growth (DBH and height) and reproductive potential (flower and fruit production) in relation to time and open solar path value. Two treatments are considered: OSP values less than 50% created by C. nucifera removal, and OSP values greater than 50%.
NOAA Daily Surface Meteorologic Data at NCDC Royal Palm Ranger Station (ID-087760)(FCE LTER), South Florida, USA, May 1949 - ongoing
The National Climatic Data Center's (NOAA) daily mean, maximum, and minimum air temperatures and daily precipitation collected at Royal Palm Ranger Station (Coop ID- 087760).
Insights in the structural hierarchy of statically crystallized palm oil
<p>This dataset contains all data obtained on palm oil samples and used in the publication "Insights in the structural hierarchy of statically crystallized palm oil". See paper for more information on the methods for obtaining the data.</p> <p> </p> <p>Abbreviations used:</p> <p>PO = palm oil</p> <p>PPP = tripalmitin</p> <p>I = intensity (in X-ray scattering)</p> <p>q = scattering vector (in X-ray scattering)</p> <p>SEM = Scanning Electron Microscopy</p> <p>DSC = Differential Scanning Calorimetry</p> <p>WAXS = Wide Angle X-ray Scattering</p> <p>SAXS = Small Angle X-ray Scattering</p> <p>USAXS = Ultra Small Angle X-ray Scattering</p> <p>PLM = Polarized Light Microscopy</p> <p>FC = fast cooling = 20°C/min</p> <p>SC = slow cooling = 1°C/min</p> <p>TAG = triglyceride</p> <p>FA = fatty acid</p>
Challenges of constructing and selecting the "perfect" initial and boundary conditions for the LES model PALM
<p><strong>README</strong></p> <p>All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:<br>1. IBC-pre-post-process-revised.zip which contains:<br> - Radio sounding data used for vertical profile statistical and visual comparison. They are stored as "CHMU-soundings.dat" in the CHMU_soundings directory<br> - code for making the figures for vertical profile comparison between the WRF and PALM model<br> - code for performing the statistical analysis for the vertical profiles of PALM and the WRF model<br> - code for making the scatter plots of PALM and WRF vertical profiles<br> - code for making the heatmaps of the PALM model data</p> <p>2. PALM_code.zip contains the source code for the current version of the PALM model used for this experiment</p> <p>3. palm_inputs.zip contains:<br> - static driver file<br> - dynamic driver file<br> - configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr)<br>for each of the performed simulations</p> <p>4. postproc.zip contains:<br> - the code for performing statistical analysis for minimum (min), average (Avg), and maximum (max) three-day averaged differences for the WRF and PALM model outputs<br> - the code for making figures of the differences between selected pairs of WRF and PALM model outputs</p> <p>5. wrf_namelist.zip contains:<br> - list of files in which the setups/configuration for the WRF ensemble used in this experiment</p> <p><strong>PALM MODEL INSTALLATION AND USAGE GUIDE</strong></p> <p>A. Installation:</p> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <p>B. Usage:</p> <p>After a successful installation, the executables for all packages have been linked into the directory /bin and a default PALM configuration file can be found at /.palm.config.default. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p>
Ecological, flowering phenology, morphological and seed production of three sympatric dioecious Chamaedorea palms from Costa Rica
<p>The data in the file was used to estimate the factors shaping seed production in three sympatric dioecious Chamaedorea palms in Costa Rica during the 2011-2012 season. The file contains the following fields:</p> <ol> <li>Species. The name of the species: C. costaricana, C. macrospadix and C. tepejilote</li> <li>ID. Identifier for each studied individual female plant.</li> <li>infl. Identifier for each sampled inflorescence from each sampled female.</li> <li>census.date: flowering date of each inflorescence.</li> <li>days.since.oct14: number of days since the first Chamaedorea inflorescence flowered.</li> <li>days.since.1st.flr: number of days since the first Chamaedorea inflorescence of each species flowered.</li> <li>sync.costa: flowering overlap with C. costaricana males.</li> <li>sync.macro: flowering overlap with C. macrospadix males.</li> <li>sync.tepe: flowering overlap with C. tepejilote males.</li> <li>neartest.female: distance to the nearest synchronously flowering <span>conspecific </span>female.</li> <li>male.5m: number of synchronously flowering <span>conspecific </span>male individuals in a 5m radius</li> <li>male.10m: number of synchronously flowering <span>conspecific </span>male individuals in a 10m radius</li> <li>female.5m.edco: number of synchronously flowering <span>conspecific </span>female individuals in a 5m radius, after applying Ripley's (1977) edge correction.</li> <li>female.10m.edco: number of synchronously flowering <span>conspecific </span>female individuals in a 10m radius, after applying Ripley's (1977) edge correction.</li> <li>male.5m.edco: number of synchronously flowering <span>conspecific </span> male individuals in a 5m radius, after applying Ripley's (1977) edge correction.</li> <li>male.10m.edco: number of synchronously flowering <span>conspecific </span>male individuals in a 10m radius, after applying Ripley's (1977) edge correction.</li> <li>no.stems: specific for C. costaricana, number of stems per individual.</li> <li>height: height of the flowering stem in cm.</li> <li>leaves: number of leaves of the flowering stem</li> <li>leaflets: number of leaflets of the youngest leaf of the flowering stem</li> <li>leaf.rachis: length in cm of the youngest leaf of the flowering stem</li> <li>floral.rachis: length in cm of the inflorescence's rachis</li> <li>peduncle: length in cm of the inflorescence's peduncle</li> <li>no.spikes: number of spikes of the inflorescence</li> <li>no.flowers: number of flowers per inflorescence</li> <li>no.fruits: number of single-seeded fruits per inflorescence</li> </ol>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
Annotation of the the assembled genome of Fusarium oxysporum f. sp. albedinis strain 133, the causal agent of date palm dieback.
<p>Annotation of the the assembled genome of <em>Fusarium oxysporum f. sp. albedinis</em> strain 133 (Khayi et al., 2020). Gene prediction and annotation were carried out using funnotate pipeline v1.8.1 (Stajich, 2020), which includes masking, ab initio gene-prediction training, using Augustus and Genmark, with the EST dataset reported to the Ganoderma mycocosm repository, gene prediction, and the assignment of functional annotation to protein-coding gene models.</p>
Challenges of high-fidelity air quality modeling in urban environments - PALM sensitivity study during stable conditions (TURBAN)
<h3>Introduction</h3> <p>This dataset contains the PALM model inputs and the source code used to create the simulations for Prague-Legerova scenarios performed in the scope of the <strong>TURBAN</strong> project (<a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>). Detailed description of the simulations is provided in the referencing scientific paper.</p> <h3>List of simulations</h3> <table> <tbody> <tr> <td><strong>Scenario name</strong></td> <td><strong>Days simulated</strong></td> <td><strong>IBC</strong></td> <td><strong>Configuration changes</strong></td> </tr> <tr> <td>legerovas_s6_sens_base</td> <td>13–15 February 2023</td> <td>ICON</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_dtmax</td> <td>13 February 2023</td> <td>ICON</td> <td>dt_max=0.2</td> </tr> <tr> <td>legerovas_s6_sens_heat</td> <td>13 February 2023</td> <td>ICON</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_sgs</td> <td>13 February 2023</td> <td>ICON</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_stg</td> <td>13 February 2023</td> <td>ICON</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_alad</td> <td>13–15 February 2023</td> <td>ALADIN</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_alad_heat</td> <td>13 February 2023</td> <td>ALADIN</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_alad_sgs</td> <td>13 February 2023</td> <td>ALADIN</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_alad_stg</td> <td>13 February 2023</td> <td>ALADIN</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_wrf</td> <td>13–15 February 2023</td> <td>WRF</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_wrf_heat</td> <td>13 February 2023</td> <td>WRF</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_wrf_sgs</td> <td>13 February 2023</td> <td>WRF</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_wrf_stg</td> <td>13 February 2023</td> <td>WRF</td> <td>STG_PROFILES added</td> </tr> </tbody> </table> <h3>Directory structure</h3> <p>The directory inputs contains the model inputs and it is further divided into these subdirectories:</p> <p>- inputs/common: The PALM static driver and the emission drivers for the parent and child domains. These files are common to all simulations</p> <p>- inputs/dynamic/*: These directories contain the dynamic drivers for the parent and child domanis, which contain the initial and boundary conditions (IBC) as well as external radiation data. The three subdirectories aladin, icon and wrf contain IBCs created from the respective mesoscale model outputs. </p> <p>- inputs/legerovas_s6_sens_*: These directories contain the PALM model configuration (p3d) for both domains for each simulation.</p> <p>- inputs/build_config: The included .palm.iofiles configuration file ensures that the files STG_PROFILES are correctly copied from the input directory.</p> <p>The directory palm_sources contains the exact model source used for the simulations. It is derived from the PALM model release 23.04 with additional bugfixes. There are two source archives:</p> <p>- heat.tar.gz: PALM source further modified to include anthropogenic heat from cars, used for the simulations legerovas_s6_sens_*_heat</p> <p>- standard.tar.gz: PALM source used for all other included simulations.</p> <h3>Reproducing the simulations</h3> <p>In order to reproduce the simulations, unpack the respective source code archive and follow the standard installation, configuration and build procedures described in the README.md file within the archive and on the PALM model website http://www.palm-model.org/. Then copy the input files for the respective simulation in the JOBS directory. The common files and the dynamic driver files need to be renamed so that they match the prefix given by the name of the simulation, as is described in the PALM model documentation.</p>
3D PALM LexA-PAmCherry in E.coli MG1655
<p>3D PALM in near TIRF conditions of LexA-PAmCherry fusion chromosomally-tagged in MG1655 background<br><br>Data set used in</p> <p><em><span>Anisotropic DBSCAN for 3D SMLM Data Clustering</span></em></p> <p> </p>
Crystallization behavior and structural build-up of palm stearin - wax hybrid fat blends
<p>This dataset was used in the publication <em>"Crystallization behavior and structural build-up of palm stearin - wax hybrid fat blends"</em>. An overview of the abbreviations and the dataset can be found below.</p>
RattanID - a molecular identification toolkit for rattan palms
<p>This repository contains laboratory protocols, reference datasets, auxiliary files (target file and sequencing adapters) and example data for the RattanID molecular identification toolkit (https://github.com/BenKuhnhaeuser/RattanID). It also contains a dataset detailing rattan occurrence records at species level, rattan uses and extinction risk predictions, as well as distribution maps built based on the rattan occurrence records dataset.</p>
Murchison Falls National Park Uganda Woody Plant and Palm Inventory Plots 2022
Murchison Falls National Park (MFNP) is a protected area in northern Uganda along the border with the Democratic Republic of the Congo and straddling the Victoria Nile. This project was designed to assess the accuracy of woody cover maps developed in (Nagelkirk & Dahlin, 2020). We determined an area of interest and then identified 40 plots that we expected would range from zero to nearly 100% woody cover. Due to restrictions related to the COVID-19 pandemic, we could only spend six days in the field, and so our sampling time was limited. We were able to collect 36 30x30 m square plots (four plots were not measured due to safety or accessibility issues). In each, we collected data describing woody plant species, when possible, diameter at breast height (DBH) or basal diameter depending on the size of the plant, and two crown diameter measurements: one at the widest width and another approximately perpendicular to the first. Together these measurements allow us to estimate woody plant canopy cover and basal area, along with species diversity both by count and by basal area. With additional information, aboveground biomass, functional diversity, and phylogenetic diversity could also be estimated in the future. Although this project was limited in scope, since eastern African savannas are underrepresented in global databases of woody cover and aboveground biomass, this data set will contribute to our overall understanding of vegetation patterns and processes.
Fig. 7 in Revision of the palm-pollinating weevil genus Elaeidobius Kuschel, 1952 (Curculionidae, Curculioninae, Derelomini) with descriptions of two new species
Fig. 7. Adults of Elaeidobius plagiatus (Fåhraeus, 1844) comb. nov. (left) and E. subvittatus (Faust, 1898) (right) feeding on male inflorescence of Elaeis guineensis Jacq.
Fig. 3 in Revision of the palm-pollinating weevil genus Elaeidobius Kuschel, 1952 (Curculionidae, Curculioninae, Derelomini) with descriptions of two new species
Fig. 3. Head and prothorax in lateral view of species of Elaeidobius Kuschel, 1952 (Part 2). A. E. pilimargo Haran & Kuschel sp. nov. (JHAR00409). B. E. piliventris Haran & Kuschel sp. nov. (JHAR02182). C. E. plagiatus (Fåhraeus, 1844) comb. nov. (JHAR00272). D. E. singularis (Faust, 1898) comb. nov. (JHAR00283). Not to scale.
Fig. 6 in Revision of the palm-pollinating weevil genus Elaeidobius Kuschel, 1952 (Curculionidae, Curculioninae, Derelomini) with descriptions of two new species
Fig. 6. Preliminary phylogenetic tree of the genus Elaeidobius Kuschel, 1952 inferred from COII fragment sequences (730 bp). Bootstrap support values were obtained for 1000 replicates. * = bootstrap values above 70; ** = bootstrap values above 90.
Genetic structuring in a Neotropical palm analyzed through an Andean orogenesis‐scenario
<p>Andean orogenesis has driven the development of very high plant diversity in the Neotropics through its impact on landscape evolution and climate. The analysis of the intraspecific patterns of genetic structure in plants would permit inferring the effects of Andean uplift on the evolution and diversification of Neotropical flora. In this study, using microsatellite markers and Bayesian clustering analyses, we report the presence of four genetic clusters for the palm <em>Oenocarpus bataua</em> var. <em>bataua </em>which are located within four biogeographic regions in northwestern South America: (a) Chocó rain forest, (b) Amotape-Huancabamba Zone, (c) northwestern Amazonian rain forest, and (d) southwestern Amazonian rain forest. We hypothesize that these clusters developed following three genetic diversification events mainly promoted by Andean orogenic events. Additionally, the distinct current climate dynamics among northwestern and southwestern Amazonia may maintain the genetic diversification detected in the western Amazon basin. Genetic exchange was identified between the clusters, including across the Andes region, discarding the possibility of any cluster to diversify as a distinct intraspecific variety. We identified a hot spot of genetic diversity in the northern Peruvian Amazon around the locality of Iquitos. We also detected a decrease in diversity with distance from this area in westward and southward direction within the Amazon basin and the eastern Andean foothills. Additionally, we confirmed the existence and divergence of <em>O. bataua</em> var. <em>bataua</em> from var. oligocarpus in northern South America, possibly expanding the distributional range of the latter variety beyond eastern Venezuela, to the central and eastern Andean cordilleras of Colombia. Based on our results, we suggest that Andean orogenesis is the main driver of genetic structuring and diversification in <em>O. bataua</em> within northwestern South America.</p>
High resolution global industrial and smallholder oil palm map for 2019
<p>The dataset contains 634 100x100 km tiles, covering areas where oil palm plantations were detected. The file '<em>grid.shp</em>' contains the grid that covers the potential distribution of oil palm. The file '<em>grid_withOP.shp</em>' shows the 100x100 grid squares with presence of oil palm plantations. The classified images (‘<em>oil_palm_map</em>’ folder, in geotiff format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. The images have a spatial resolution of 10 meters and contain three classes: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm. The file ‘<em>Validation_points_GlobalOilPalmLayer_2019.shp</em>’ includes the 13,495 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the labelled class assigned by visual interpretation, and the attribute ‘predClass, which reflects the predicted class by the convolutional neural network. The ‘Class’ and ‘predClass’ values are the same as the raster files: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm.</p> <p>See article for additional information:</p> <p>Descals, Adrià, et al. "High-resolution global map of smallholder and industrial closed-canopy oil palm plantations." <em>Earth System Science Data</em> 13.3 (2021): 1211-1231.</p> <p> </p> <p>Changelog v1:</p> <p>- The analysis was extended to Sri Lanka, South India, and countries in Eastern Africa where oil palm can potentially grow.</p> <p>- The validation dataset only includes the points drawn by simple random sampling and stratified random sampling in the grid cells where the IUCN industrial layer detected oil palm.</p> <p>- The 'Class' and 'predClass' values in the validation dataset were reclassified with the same values as the raster images: [1] Industrial plantations, [2] Smallholder plantations, and [3] Other land covers/uses.</p>
PsPM-SCRV10: Skin conductance responses to loud sounds, simultanously recorded from palm, fingers and foot
<p>This dataset includes skin conductance response (SCR) measurements recorded from 3 locations (see section 'Data files' below) for each of 26 healthy unmedicated participants (12 males and 14 females aged 24.4+/-4.9 years) in response to an auditory stimulus (single white noise bursts, 1s length; 10ms ramp; ~85dB). Participants are asked to press a foot pedal upon hearing a stimulus.</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.