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1,891 results for “Field data”
Fig. 17 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Fig. 17. Known distribution of the core group of the Pholcus halabala species group.
Fig. 110 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Fig. 110. Known distribution of the Pholcus krabi species group.
Data from a field plot experiment with the canola pathogen Leptosphaeria maculans including disease severity at the leaf spot and canker stages of the epidemic, and population composition as isolates infectivity pathotypes.
<p><strong>Data set</strong></p> <p>Data are from an experiment simulating how differences in <em>Brassica napus</em> resistance deployment strategies and landscape connectivity influence epidemic severity and pathogen population composition of the fungus <em>Leptosphaeria maculans</em> on field plots inoculated with combinations of stubble in 2016 at CSIRO Canberra, ACT, Australia. Disease severity was assessed on the 60 field plots [Data_severity.csv] and 1490 isolates were sampled and assessed for infectivity [Data_infectivity.csv]. This dataset is described and analyzed in Bousset et al. (2018).</p> <p>Treatments were factorial combination of Resistance, Genetic Connectivity and Spatial Connectivity, replicated in 4 blocks (B1 to B4). Resistance has 3 categories (Rlm4, Rlm6 LepR1) differing by the resistance genes in oilseed rape varieties. Genetic Connectivity has 2 levels (HighGC, LowGC) differing by the pre-adaptation of the stubble populations to the host variety. Spatial Connectivity has two levels (HighSC, LowSC) differing by the stubble load. Control plots had NoStubble.</p> <p><strong>Data files</strong></p> <p>[Data_severity.csv] Disease severity was assessed on the 60 field plots at leaf spot and canker stages of the epidemic. Leaf spots data are counts. Canker data are numbers of stems in 12 categories defined by the cankered area on cross section (0 = no canker to 100 = fully cankered).</p> <p>[Data_infectivity.csv] Two types of isolates (122 from 3 stubble sources with contrasting preadaptation and 1368 from leaves sampled on 50 field plots) were tested for infectivity response (V = infective; A = non-infective) on the three host varieties (Rlm4, Rlm6 LepR1), at the seedling stage in greenhouse.</p> <p><strong>Associated publication</strong></p> <p>Bousset L, Sprague S, Thrall PH, Barrett LG (2018). Spatio-temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen <em>Leptosphaeria maculans. Evolutionary Applications</em> [ DOI: 10.1111/eva.12630 ].</p> <p><strong>Funding information</strong></p> <p>This work benefited from the financial support of INRA – the French National Institute for Agronomical Research, a CSIRO Sir Frederick McMaster fellowship to L. Bousset (Impact of inoculum carry-over on landscape dynamics of the blackleg canola pathogen) and the Grains Research & Development Corporation (GRDC Grant CSP00192)</p>
Data for the paper "Small-scale variabilities in the electric field and the particle precipitation and their impacts on Joule heating"
<p>This dataset works specifically for the paper Zhu et al., (2018), Small-scale variabilities in the electric field and the particle precipitation and their impacts on Joule heating. </p> <p>Notes:</p> <p>1) fig1 file for data generating fig 1</p> <p>2) fig2 zip for data generating fig 2 </p> <p>3) fig3 file for data generating fig 3</p> <p>4) fig4 zip for data generating fig 4 (In that zip file, Run 1 for fig 4a, Run2 for fig 4b, Run3 for fig 4c)</p> <p> </p>
Additional data for the "In-situ full field measurement during inter-facial debonding in single fiber composite under transverse load"
<p>The following document is an extension of the <em>In-situ full field measurement during inter-facial debonding in single fiber composite under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiment of epoxy matrix and PTFE fiber, epoxy matrix and galvanized steel matrix, modified epoxy matrix and PTFE fiber and modified epoxy matrix and galvanized steel matrix. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
Data and code for article "Excitonic Emission of Monolayer Semiconductors Near-Field Coupled to High-Q Microresonators"
<p>Data, code and sample fabrication details for article "Excitonic Emission of Monolayer Semiconductors Near-Field Coupled to High-Q Microresonators".</p> <p>The code was tested with Matlab R2015b.</p>
LFP Data to estimate post-synaptic potentials from local field potentials with reverse neural mass model
<p>All data used in the paper name "Reconstruction of post-synaptic potentials by reverse modeling of local field potentials" that is under submission at PLOS Computational Biology.</p> <p>All data are given in Matlab files (.mat) and text files (.csv)</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL field pea
<p>This is model output from LPJmL for field pea as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
Additional data for "In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load"
<p>The following document is an extension of the <em>In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiments and bundle of carbon fiber ones. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
Supporting data for "Taking connected mobile-health diagnostics of infectious diseases to the field"
<p>Raw and intermediate data used to create figures 1 and 4 of Wood, C.,<em> et al., "</em>Taking connected mobile-health diagnostics of infectious diseases to the field", <strong>Nature</strong> (2019).</p>
Data of Listening Experiments for Azimuthal Localisation in (Local) Sound Field Synthesis
<p>Data of two listening experiments conducted at University of Rostock, Germany. The study investigated the four (Local) Sound Field Synthesis techniques</p> <ul> <li>Wave Field Synthesis</li> <li>Near-Field-Compensated Higher-Order Ambisonics</li> <li>Local Wave Field Synthesis using Spatial Bandwidth Limitation</li> <li>Local Wave Field Synthesis using Virtual Secondary Sources</li> </ul> <p>The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the directory `brs`. The employed noise stimulus is contained in `stimuli`. The localisation results are stored in `results`. The `analysis` directory includes scripts for parsing the data.</p>
VERTEX simulator snapshot with pre-generated data and analysis scripts for electric field stimulation simulations
<p>This dataset contains a snapshot of the VERTEX Matlab toolbox for simulating spiking neural networks, along with Matlab files for pre-generated results from simulations run using VERTEX. The scripts and results files here are to accompany the manuscript 'Predicting the impact of electric field stimulation in a detailed computational model of cortical tissue' and pertain to simulations of electric field stimulation on a cortical tissue model. Analysis scripts for the simulation results along with scripts for setting up and running simulations are included.</p>
Field-scale rainfall-runoff experiments data
<p>This repository holds the data required to reproduce results in “Estimation of field-scale variability in soil saturated hydraulic conductivity from rainfall-runoff experiments” by Goyal et al. (2019). The data includes the experimental observations from 16 rainfall events near the University of Perugia, Italy, in the autumn/winter conditions of 2013/2014. Each text file includes:</p> <ul> <li>Event date</li> <li>Rainfall observations at 30-minute intervals</li> <li>Runoff observations at 30-minute intervals</li> <li>The average value of the initial moisture content in the study plot</li> <li>The soil-water potential at the wetting front</li> </ul> <p> </p> <p>Additional details about the experiments are provided in Flammini et al. (2018).</p> <p>Use of this data should be acknowledged appropriately.</p> <p> </p> <p><strong>References:</strong></p> <p>Goyal, A., Morbidelli, R., Flammini, A., Corradini, C., & Govindaraju, R. S. (2019). Estimation of field‐scale variability in soil saturated hydraulic conductivity from rainfall‐runoff experiments. <em>Water Resources Research</em>, 55, 7902-7915. <a href="https://doi.org/10.1029/2019WR025213">https://doi.org/10.1029/2019WR025213</a></p> <p>Flammini, A., Morbidelli, R., Saltalippi, C., Picciafuoco, T., Corradini, C., & Govindaraju, R. S. (2018). Reassessment of a semi-analytical field-scale infiltration model through experiments under natural rainfall events. <em>Journal of Hydrology</em>, 565(August), 835–845. <a href="https://doi.org/10.1016/j.jhydrol.2018.08.073">https://doi.org/10.1016/j.jhydrol.2018.08.073</a></p>
Long-term data from field erosion plot studies in eastern Austria
<p>Long-term runoff plot experiments were conducted at three sites in eastern Austria to evaluate the impact of conservation tillage. Following soil tillage treatments were investigated: 1) conventional tillage with ploughing in fall (CT), 2) mulch tillage with cover crops during winter period (MT), and 3) no tillage with cover crops during winter period (NT). Soil textures range from silt loam to loam.</p> <p>Datasets include for each site and each year the period of operation of the plots, the planted crop, the annual precipitation, surface runoff, soil loss, surface losses of nitrogen, phopshorus and soil organic carbon as well as absolute and relative crop yields.</p>
Long-term data from field erosion plot studies in eastern Austria
<p>Long-term runoff plot experiments were conducted at three sites in eastern Austria to evaluate the impact of conservation tillage. Following soil tillage treatments were investigated: 1) conventional tillage with ploughing in fall (CT), 2) mulch tillage with cover crops during winter period (MT), and 3) no tillage with cover crops during winter period (NT). Soil textures range from silt loam to loam.</p> <p>Datasets include for each site and each year the period of operation of the plots, the planted crop, the annual precipitation, surface runoff, soil loss, surface losses of nitrogen, phopshorus and soil organic carbon as well as absolute and relative crop yields.</p>
l-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of large size.</p>
s-sized Training and Evaluation Data for Publication "Using Supervised Learning to Classify Metadata of Research Data by Field of Study"
<p>Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This is the cleaned and vectorized version with a feature selection of small size.</p>
Zea mays MAGIC RIL population - field trial phenotyping data
<p>MIAPPE compliant ISA-tab files of the Zea mays MAGIC RIL population derived from eight genetically diverse founder lines.</p>
IMF clock angle proxy: orbital averaged magnetic field data from MAVEN in Martian magnetosheath
<p>The By and Bz components can be used as IMF clock angle proxy (IMF direction in MSO y-z plane). See the publication by Dong et al. 2019 for details: Dong, Y., Fang, X., Brain, D. A., Hurley, D. M., Halekas, J. S., Espley, J. R., et al. (2019). Magnetic field in the Martian magnetosheath and the application as an IMF clock angle proxy. <em>Journal of Geophysical Research: Space Physics</em>, 124, 4295–4313. <a href="https://doi.org/10.1029/2019JA026522">https://doi.org/10.1029/2019JA026522</a></p> <p>Formats: IDL save file and ASCII file</p> <p>Data descirption:</p> <p>Time span: Nov 11 2014 - May 21 2024</p> <p>Time: averaged unix time of MAVEN in magnetosheath of each s/c orbit</p> <p># of data points: # of data points (time resolution: 4s) taken in magnetosheath of each s/c orbit. Suggest using only # of data points >150 (i.e. duration > 10 min)</p> <p>Bx, By, Bz in MSO coordinate system</p> <p> </p>
Dataset for plant species richness estimation in a wet grassland field using UAV data features
<p>This dataset supports the estimation of plant species richness in a wet grassland field using features extracted from UAV (Unmanned Aerial Vehicle) data. It includes field and plot shapefiles, pre-processed input data, model performance metrics, spatial predictions (RASTER files).The dataset also contains geospatial imagery in the form of input and scaled GeoTIFF images, as well as two additional CSV files: <code>date.csv</code>, which records the cutting dates relevant to the study, and <code>merged_obs.csv</code>, which consolidates all the features with canopy height information extracted from Digital Elevation Model (DEM) data with field observed plant species richness.</p> <ul> <li> <p><strong>Summary:</strong></p> <ul> <li><strong>BIomass_Samples_Shapefiles:</strong> Contains shapefiles for field and plot-level data.</li> <li><strong>Results:</strong> <ul> <li><strong>ALLDATA:</strong> Pre-processed input data for RF and PLS models.</li> <li><strong>MODELPERF:</strong> Performance metrics and variable importance for RF and PLS models.</li> <li><strong>RASTER:</strong> Spatially-explicit predictions (maps) for plant species richness estimation.</li> <li><strong>GLCM:</strong> Pre-processed Gray Level Co-occurrence Matrix (texture features).</li> <li><strong>VI:</strong> Pre-processed Vegetation Indices.</li> </ul> </li> <li><strong>TIF:</strong> Input and scaled geotiff images. <ul> <li><strong>rescaled:</strong> Rescaled geotiff images.</li> <li><strong>resampled:</strong> Resampled geotiff images.</li> </ul> </li> <li><strong>date.csv:</strong> Contains cutting dates for the field.</li> <li><strong>merged_obs.csv:</strong> Contains DEM and species richness data (number of species).</li> </ul> </li> </ul> <p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the Digital Agriculture Knowledge and Information System (DAKIS) Project [Grant number 031B0729E]. </p>
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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.