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361 results for “proportions”
Figure 4 in Locomotion in terrestrial mammals: the influence of body mass, limb length and bone proportions on speed
Figure 4. Regression plots of independent contrasts for hind limb parameters. Log running speed is in km h-1. a, Hind limb length in mm/3÷body mass in kg; b, metatarsus/femur ratio; c, cnemial crest height in mm/3÷body mass in kg; d, calcaneal tuber length in mm/3÷body mass in kg. Regression lines fitted to the contrasts by means of least squares (model I) analysis.
Figure 2 in Locomotion in terrestrial mammals: the influence of body mass, limb length and bone proportions on speed
Figure 2. Plots of standardized contrasts to their standard deviations. a, log forelimb length in mm; b, radius/humerus ratio; c, metacarpus/humerus ratio; d, tibia/femur ratio; e, cnemial crest height in mm/3÷body mass in kilograms; f, calcaneal tuber/metatarsus ratio.
Sentinel2 RGB chips over Colombia (NE) with ESA World Cover for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong></p> <p>We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a></p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a><br> <br> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a> </p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
Sentinel2 RGB chips over BENELUX with ESA World Cover for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</strong></p> <p>We use the communes administrative division which is standardized across Europe by EUROSTAT at:<br> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a><br> This is roughly equivalent to the notion municipalities in most countries.</p> <p>From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp.</p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a><br> <br> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a></p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
Sentinel2 RGB chips over Colombia (NE) with JRC GHSL Population Density 2015 for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong></p> <p>We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a></p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: Global Human Settlement Layers, Population Grid 2015</strong></p> <p> labels range from 0 to 31, with the following meaning:<br> label value original value in GEE dataset<br> 0 0<br> 1 1-10<br> 2 11-20<br> 3 21-30<br> ...<br> 31 >=291 </p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a></p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre> <p> </p>
Sentinel2 RGB chips over BENELUX with JRC GHSL Population Density 2015 for Learning with Label Proportions
<p>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</p> <p>We use the communes adminitrative division which is standardized across Europe by EUROSTAT at:<br> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a><br> This is roughly equivalent to the notion municipalities in most countries.</p> <p>From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp.</p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds acoording to QA60 band following the example<br> given in GEE dataset info page at:<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: Global Human Settlement Layers, Population Grid 2015</strong></p> <p> labels range from 0 to 31, with the following meaning:<br> label value original value in GEE dataset<br> 0 0<br> 1 1-10<br> 2 11-20<br> 3 21-30<br> ...<br> 31 >=291 </p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a><br> </p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
Accompanying data for paper Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments
<p>Links</p> <ul> <li>Data DOI: <a href="https://doi.org/10.5281/zenodo.7729452">10.5281/zenodo.7729452</a></li> <li>Article <em>Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments</em>, DOI: <a href="https://doi.org/10.46298/jtcam.8913">10.46298/jtcam.8913</a></li> </ul> <p>Authors</p> <ul> <li>Xiang Kong, <a href="mailto:xiang.kong@minesparis.psl.eu">xiang.kong@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-0835-3826">0000-0002-0835-3826</a></li> <li>Jianqiang Chen, Pratt & Whitney Canada, 1000 Boul. Marie-Victorin, Longueuil, QC J4G 1A1 Canada</li> <li>Yazid Madi, <a href="mailto:yazid.madi@minesparis.psl.eu">yazid.madi@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-3530-8668">0000-0002-3530-8668</a></li> <li>Djamel Missoum-Benziane, <a href="mailto:djamel.missoum-benziane@minesparis.psl.eu">djamel.missoum-benziane@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-9877-8261">0000-0002-9877-8261</a></li> <li>Jacques Besson, <a href="mailto:jacques.besson@minesparis.psl.eu">jacques.besson@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0003-1975-2408">0000-0003-1975-2408</a></li> <li>Thilo F. Morgeneyer, <a href="mailto:thilo.morgeneyer@minesparis.psl.eu">thilo.morgeneyer@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-0278-9565">0000-0002-0278-9565</a></li> </ul> <p>Language</p> <ul> <li>English</li> </ul> <p>Licence</p> <ul> <li>CC BY 4</li> </ul> <p>Contributions</p> <ul> <li>Conception and design of study, revising the manuscript critically for important intellectual content: TFM, JB.</li> <li>Acquisition of data: XK, JC, YM.</li> <li>Analysis and/or interpretation of data: XK, DMB, TFM.</li> </ul> <p>Associated article</p> <p>Xiang Kong, Jianqiang Chen, Yazid Madi, Djamel Missoum-Benziane, Jacques Besson, Thilo Morgeneyer "Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments" Journal of Theoretical, Computational and Applied Mechanics (JTCAM), March 13, 2023, DOI: <a href="https://doi.org/10.46298/jtcam.8913">10.46298/jtcam.8913</a>, HAL: <a href="https://hal.science/hal-03497233v3">hal-03497233v3</a></p> <p>Keywords</p> <ul> <li>plastic anisotropy</li> <li>mechanical testing</li> <li>non-proportional loading</li> <li>static loading</li> <li>ductile fracture</li> </ul> <p>Data collection: period and details</p> <ul> <li>Sept 2018 - June 2022, PhD period of Xiang Kong</li> <li>Mechanical experiments mainly were performed at the Centre des Materiaux in Evry, France, except the laminographic experiment which was performed at the ESRF ID19b in Grenoble, France, while numerical simulations were performed in <a href="http://www.zset-software.com/">Z-set/Zebulon</a> on the cluster at the Centre des Materiaux.</li> </ul> <p>Recommended citation line for the data</p> <p>Xiang Kong, Jianqiang Chen, Yazid Madi, Djamel Missoum-Benziane, Jacques Besson, & Thilo F. Morgeneyer. (2023). Accompanying data for paper Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments [Data set]. <a href="https://doi.org/10.5281/zenodo.7729452">10.5281/zenodo.7729452</a></p> <p>Funding sources</p> <ul> <li>ANR (Lambda project: ANR17-CE08-0051 and Alicandte project)</li> </ul> <p>Data structure and information</p> <ul> <li>The output data were used to produce Figures 6-9, 16-19, 23 from the associated article.</li> <li>Folder/files structure: <ul> <li><code>Experiments_Simulations_results/</code> <ul> <li><code>2198T3R/</code> - folder containing results for material AA2198T3R</li> <li><code>T3R_EXP_*.csv</code> - experimental data files</li> <li><code>T3R_SIM_*.csv</code> - simulation output</li> <li><code>README.md</code></li> <li><code>2198T8R/</code> - folder containing results for material AA2198T8R</li> <li><code>T8R_EXP_*.csv</code> - experimental data files</li> <li><code>T8R_SIM_*.csv</code> - simulation output</li> <li><code>README.md</code></li> <li><code>plot_T3R.py</code> - python script plotting results for material AA2198T3R</li> <li><code>plot_T8R.py</code> - python script plotting results for material AA2198T8R</li> <li><code>README.md</code></li> </ul> </li> <li><code>Simulation_input_files/</code> <ul> <li><code>2198T3R.mat,2198T8R.mat,steel.mat</code> - material properties files for Z-set/Zébulon</li> <li><code>postprocess.inp</code> - postprocessing input file for Z-set/Zébulon</li> <li><code>*.inp</code> - FE solver input files for Z-set/Zébulon</li> <li><code>post.py</code> - Python script to plot processed data</li> <li><code>README.md</code></li> <li><code>mesh_files/</code> <ul> <li><code>scale.inp</code> - Z-set/Zébulon mesh modifier</li> <li><code>ST_*.geof</code> - mesh files in Z-set/Zébulon format</li> <li><code>README.md</code></li> </ul> </li> </ul> </li> <li><code>.solidipes/</code> - curation tool metadata (not a part of the dataset)</li> </ul> </li> </ul>
Data from: Female preference for males with lower pattern contrast follows Weber's law of proportional processing in an ornate jumping spider
<p>According to Weber's law of proportional processing, perceptual discrimination between stimuli of different magnitudes is based on their proportional differences in magnitude (not absolute differences). Proportional processing operates in various sensory modalities and behavioural contexts. However, whether female mate preference for colour patterns in animals follows Weber's law of proportional processing remains untested. </p> <p>We addressed this research gap using the jade jumping spider, <em>Siler semiglaucus</em>, whose males exhibit remarkable sexually selected colour patterns and whose females show preferences for males with low abdomen pattern contrast (pattern contrast is defined as the spatial feature of the relative abundance of two adjacent colour patches). By manipulating the dorsal abdomen colour patterns of <em>S. semiglaucus</em> males, we created males with varying abdomen pattern contrasts. We then assessed female preference for males that varied in both absolute and proportional differences in pattern contrast.</p> <p>We found that females preferred males with lower abdomen pattern contrasts and discriminated between males based on both absolute and proportional differences in pattern contrast. While proportional difference alone was not a significant predictor of female mate choice, discrimination based on proportional difference, coupled with absolute difference had a greater influence on female mate preference than absolute difference alone. </p> <p>Hence, our findings suggest that <em>S. semiglaucus</em> female preference for males with lower pattern contrast follows Weber's law, and female discrimination may have the potential to limit the exaggeration of sexually selected colour patterns. </p>
Supporting code and data for: Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds
<p>This is the R code and shapefiles of the coniferous HUC-12 watersheds and their western US boundary for the publication, "Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds" </p>
Repetitive elements of Erebia and Carex and their genomic proportions
<p>Datasets of repetitive elements in the genomes of <em>Erebia </em>and <em>Carex</em>, detected and annotated using RepeatExplorer2 (Novák et al., 2010, 2020) using low-coverage (0.1X) short read sequencing data. Data from the article "Holocentric repeat landscapes: from microevolutionary patterns to macroevolutionary associations with karyotype evolution":</p> <p>Cornet, C., Mora, P., Augustijnen, H., Nguyen, P., Escudero, M., & Lucek, K. (2023). Holocentric repeat landscapes: From micro-evolutionary patterns to macro-evolutionary associations with karyotype evolution. Molecular Ecology, 00, 1–19. <a href="https://doi.org/10.1111/ mec.17100">https://doi.org/10.1111/ mec.17100</a></p> <p>47 <em>Erebia </em>and 14 <em>Carex </em>species were analysed in genus-level analyses ("Erebia" and "Carex" folders).<br> In addition, individuals of 4 <em>Erebia </em>species ("Erebia cassioides", "Erebia tyndarus", "Erebia nivalis" and "Erebia pronoe" folders) from different populations were analysed in species-level analyses. </p> <p>Subfolders "Individuals" and "Comparative" represent the two modes in which RepeatExplorer2 was run: the individual mode identifies repeats in each sample separately, and the comparative mode identifies repeats in all samples simultaneously, allowing comparisons between individuals and species. </p> <p>Files named "CLUSTER_TABLE..." are the raw output of RepeatExplorer2 and represent the overall number of reads in each cluster of repetitive element, and their annotation.<br> Files named "COMPARATIVE_ANALYSIS_COUNTS..." are the raw output of RepeatExplorer2 in comparative mode, representing the number of reads in each cluster for each sample included in the analysis. <br> Files named "Genome_proportion..." are the genomic proportion of each repeat annotation, calculated as the proportion of reads with the same annotation.</p> <p>Refer to Cornet et al. (2023) in Molecular Ecology for more details on how the data was generated, the downstream analyses and the sample names (see Tables S1, S2 and S3). </p> <p>References:</p> <p>Novák, P., Neumann, P., & Macas, J. (2010). Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data. BMC Bioinformatics, 11(1), 378. https://doi.org/10.1186/1471-2105-11-378</p> <p>Novák, P., Neumann, P., & Macas, J. (2020). Global analysis of repetitive DNA from unassembled sequence reads using RepeatExplorer2. Nature Protocols, 15(11), Article 11. https://doi.org/10.1038/s41596-020-0400-y</p>
Data from: Female preference for males with lower pattern contrast follows Weber’s law of proportional processing in an ornate jumping spider
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Hibiscus bullseyes reveal mechanisms controlling petal pattern proportions that influence plant-pollinator interactions
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Supporting code and data for: Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds
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EEG Data for: "Learning from Label Proportions in Brain-Computer Interfaces"
<p>Data about two experiments is contained in this repository. An EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects was conducted in addition to a smaller study with N=5 subjects performing both an auditory and a visual ERP paradigm. </p> <p>The dataset is used and described in the following journal article:</p> <p><em>Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856.</em></p> <p><strong>Please cite the above article when using the data.</strong></p> <p>The larger data set with N=13 is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article.</p> <p>In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article:</p> <p><em>Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021.</em></p> <p>The following files are available:</p> <p>description.pdf: <strong>Full description of the dataset</strong><br> offline_auditory.zip: <strong>Data from the auditory offline study with N=5 subjects</strong><br> offline_visual.zip: <strong>Data from the visual offline study with N=5 subjects</strong><br> online_study_1-7.zip: <strong>Data from the online study for subjects 1-7</strong><br> online_study_8-13.zip: <strong>Data from the online study for subjects 8-13</strong><br> sequence.mat: <strong>Sequence data necessary for applying LLP to the online study. It is the same for all subjects</strong></p> <p>We will create a git repository with example code soon.</p>
After the 'Black Summer' fires: faunal responses to megafire depend on fire severity, proportional area burnt, and vegetation type
<ol> <li>Climate change and human activities have disrupted historical fire regimes, leading to complex and far-reaching impacts on global ecosystems. Despite extensive research in fire ecology, studies exploring vertebrate responses to megafires, and to nuanced fire characteristics, remain limited.</li> <li>We collected camera trap data 3–27 months following Australia's 2019–20 'Black Summer' megafires from 30 burnt sites and 10 unburnt sites. Our data included 14 animal species/groups, encompassing mammalian predators, small and medium-sized mammals, large herbivores, and birds. We used generalised additive mixed models to assess the influence of time-since-the-fires, burn status, fire severity, proportional area burnt, and vegetation type on species' activity.</li> <li>Models that included fire variables were well-supported for all species. The proportional cover of low-moderate or high-extreme severity fire had substantial support for five species, particularly herbivores, which generally showed a preference for burnt sites but at differing fire severities. The proportional area burnt, disregarding severity, was well supported for four species. At highly burned sites, fox activity peaked shortly after the fires, while small to medium-sized mammal activity increased more gradually. Vegetation type strongly influenced the response of four species to fire; in particular, wet forest birds preferred unburnt areas.</li> <li> <em>Policy implications</em>. We document variable short- to medium-term responses of a range of species to fire which could help guide management interventions. We demonstrate that animal species' responses to fire are diverse and better captured using broader landscape-scale fire variables. We found that species were strongly influenced by proportional area burnt, fire severity, and vegetation type. Introduced foxes were attracted to recently burnt areas, so timely predator control may benefit vulnerable prey species. Wet forest species were sensitive to fires and could benefit from preservation and restoration of these habitats. Some species exploited low-moderate severity burnt areas, while others preferred high-severity burns. This suggests that species will face diverse challenges and opportunities in future extreme fire events. We emphasise the importance of using multi-faceted approaches to account for the complex responses of co-occurring species to fire events.</li> </ol>
Number and proportion of social workers in the cantonal and national parliaments of Switzerland in 2024
<p>Dieser Datensatz trägt für den Stichtag 1.1.2024 die Anzahl und Anteile von Sozialarbeitenden in allen 26 kantonalen Parlamenten sowie im nationalen Parlament (National- und Ständerat) der Schweiz zusammen. Die Anzahl und Anteile werden mit anderen Berufsangaben (z.B. Jurist:innen, Landwirt:innen, Lehrpersonen) verglichen. Die Berufsangaben wurden den kantonalen Staatskalendern oder den offiziellen Webseiten der jeweiligen Parlamente entnommen. Die Berufsangaben wurden von den jeweiligen Parlamentsmitgliedern selbst gemacht und sind teils nicht eindeutig zuzuordnen (z.B. "Unternehmer:in", "Geschäftsleiter:in" etc.).</p> <p>Für eine zusammenfassende Darstellung der Ergebnisse siehe: Kindler, T. (2024). Democratic advocacy: Representation of social workers in the cantonal and national parliaments of Switzerland. Eastern Switzerland University of Applied Sciences. https://doi.org/10.5281/zenodo.10413049</p>
Dataset for "Overwinter and spring thaw nitrous oxide fluxes in a northern Prairie cropland are limited but a significant proportion of annual emissions"
<p>This dataset contains the data used in the publication "Overwinter and spring thaw nitrous oxide fluxes in a northern Prairie cropland are limited but a significant proportion of annual emissions" in Global Biogeochemical Cycles. This study presented micrometeorological N2O fluxes measured using the flux-gradient method over 4 years in Saskatchewan, Canada, to evaluate the magnitude of freeze-thaw N2O emissions and investigate its driving factors. The files contain the daily average N2O emissions and supporting environmental data.</p>
The accuracy of length measurements made using imaging SONAR is inversely proportional to the beam width
<p>Multibeam imaging SONARs have been used for a range of measurement applications, such as measurements of fish lengths. This study aimed to quantify the accuracy of imaging SONAR systems, that varied in frequency and beam geometry, to measure the length of synthetic targets positioned perpendicularly. Blueprint Oculus imaging SONAR systems, with four different (centre) frequencies (750 kHz, 1.2 MHz, 2.1 MHz, and 3 MHz), were used to measure the length of three targets of nominal lengths: 10 cm, 20 cm, and at ranges between 1 m and 15.5 m. The effect of beam geometry on measurement error was then examined using regression analysis. This study found that there was an overestimation of the actual length of the target for all measurements that was inversely proportional to the horizontal beam width. The measurement error can be reduced by normalising for beam width. However, the variation of measurements (i.e. the precision), was found to also increase with range, which was attributed to the increasing beam separation. It is important that the effect of beam width on the accuracy of target length measurements, by imaging SONARs is acknowledged in future studies. One approach to mitigating this problem, is to limit the range at which length measurements are made to a beam width that produces an estimated level of error that is acceptable for that study. Keywords: acoustic camera, imaging SONAR, underwater acoustics, underwater measurements.</p>
Large and exaggerated sexually selected weapons comprise high proportions of metabolically inexpensive exoskeleton
<p>The cost-minimization hypothesis proposes that positive allometry in sexually selected traits can be explained if the proportional energetic maintenance costs of weapons decrease as traits increase in size. Energetic maintenance costs are the costs of maintaining homeostasis. They are slow, persistent energy sinks that are distinct from ephemeral costs of growth. Because some tissues expend more energy on maintenance than others, energetic maintenance costs can be inferred from proportional tissue composition. For example, soft tissues require more energy for maintenance than exoskeleton, so an arthropod claw that is 50% soft tissue and 50% exoskeleton would have higher energetic maintenance costs than one that is 30% soft tissue and 70% exoskeleton. I tested the cost-minimization hypothesis using proportional tissue composition as a proxy for energetic maintenance costs in snapping shrimp ( Alpheus heterochaelis and Alpheus estuariensis ) and fiddler crabs ( Uca pugilator ). As predicted, larger weapons comprised proportionally less soft tissue mass and more exoskeleton mass than smaller weapons. Furthermore, I extended cost-minimization to explain trait exaggeration: individuals might exaggerate traits by investing more mass in exoskeleton. As predicted, exoskeleton mass proportional to weapon mass increased as exaggeration increased. These results support and extend the cost-minimization hypothesis to explain positive allometry and weapon exaggeration.</p>
Imputed Multiple Sequence Alignment used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'
<p>Multiple Sequence Alignment of imputed SARS-CoV-2 sequences used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'</p>
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.