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Synthetic dataset from - Bar et al., Sifting through the haystack - efficiently finding rare behaviors in large-scale datasets, WACV 2025
<p>This is a synthetic dataset emulating pose estimation data, introduced in the associated paper. </p> <p>Briefly, each sample in the dataset is a sequence of 5-keypoints with 9 timesteps. Movement of each keypoint in time is determined by a sinus with some amplitude A and some frequency f, this is loosely inspired by larval zebrafish swimming movement. <br>There are two types of behaviors - a common behavior (aka forming the majority of the samples in the dataset) where the frequency of the sinus is larger than the amplitude, and a rare one where the amplitude is larger than the frequency. <br>We vary the similarity between the rare and common behaviors by relaxing the standard deviation of the gaussian from which we draw these movement parameters (behavior similarity, sd= [0.5, 1.5, 2.5, 5]). We also test different levels of data imbalance, varying the frequency of the rare behavior (rarity=[1.5%,5%,12%,24%]). </p> <p>Thus we created 16 datasets with all possible combinations.</p> <p>The data generation code will become available in our code repository: https://github.com/shir3bar/SiftingTheHaystack</p> <p>The data was used to create a controlled experimental sandbox in which we could test our pipeline for detecting rare behaviors. Sounds interesting? Read our paper and check out the code :)</p> <p> </p> <p> </p>
Accompanying data for the paper "Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling" : Mode-I
<h2>Contributions</h2> <ul> <li>Manon Voisin--Leprince: Contributed to writing scripts, launching simulations, and analyzing results</li> <li>Joaquin Garcia-Suarez: Contributed to helping analyze results</li> <li>Guillaume Anciaux: Contributed to supervising the project</li> <li>Jean-François Molinari: Contributed to supervising the project</li> </ul> <p>All authors reviewed the results and contributed to the manuscript</p> <h2>Funding sources</h2> <ul> <li>Grant 200021_197152, entitled <code>Wear across scales</code> by the Swiss National Science Foundation. </li> </ul> <h2>FEM-DEM coupling applications</h2> <p>The data_mode_I folder is composed of:</p> <p>1- The DEM folder which contains the scripts to generate the DEM samples used in the simulations (Mode_I and Mode_II)</p> <p>2- The Mode_I folder which is composed of:</p> <ul> <li> <p>mode_I: Contains the scripts and data of the section "Mode I crack propagation" presented in the paper</p> </li> <li> <p>post_processing_mode_I: Contains the files to conduct the post processing relative to the section "Mode I crack propagation"</p> </li> </ul> <p>Additional README.md files are provided in the subfolders</p> <p>The notebook folder contains scripts to plot the results of the section "Mode I crack propagation". </p> <h2>Mode_II complementary dataset</h2> <p>The Mode-II part of the study can be found at https://doi.org/10.5281/zenodo.14264611</p>
Accompanying data for the paper "Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling" : Mode-II
<h2>Contributions</h2> <ul> <li>Manon Voisin--Leprince: Contributed to writing scripts, launching simulations, and analyzing results</li> <li>Joaquin Garcia-Suarez: Contributed to helping analyze results</li> <li>Guillaume Anciaux: Contributed to supervising the project</li> <li>Jean-François Molinari: Contributed to supervising the project</li> </ul> <p>All authors reviewed the results and contributed to the manuscript</p> <h2>Funding sources</h2> <ul> <li>Grant 200021_197152, entitled <code>Wear across scales</code> by the Swiss National Science Foundation. </li> </ul> <h2>FEM-DEM coupling applications</h2> <p>The data folder contains the Mode_II folder which is composed of:</p> <ul> <li> <p>mode_II: Contains the scripts and data of the section "Surface wear during relative sliding" presented in the paper. Only data for the largest case is not provided.</p> </li> <li> <p>post_processing_mode_II: Contains the files to conduct the post processing relative to the section "Surface wear during relative sliding"</p> </li> </ul> <p>Additional README.md files are provided in the subfolders</p> <p>The notebook folder contains scripts to plot the results of the section "Surface wear during relative sliding". </p>
Digital repository for: Large-scale forest disturbance and associated management shape bird communities in Central European spruce forests
<p>Repository containing R-script and data to reproduce analysis and main figures on the effect of large-scale forest disturbance and associated pre- and post-disturbance management on bird communities in the Harz Mountains, Germany.</p> <p>R-script includes:</p> <ul> <li>indicator species analysis (R package indicspecies; Cáceres & Legendre, 2009)</li> <li>non-metric multidimensional scaling (R package vegan; Oksanen et al., 2016)</li> <li>rarefaction- and extrapolation of Hill numbers (R package iNEXT; Hsieh et al., 2019)</li> <li>multi-species community distance sampling (R package sp Abundance; Doser et al., 2023)</li> </ul> <p>Attached files:</p> <ul> <li><strong>bird_data_Graser_et_al.csv </strong>(row data of bird species point counts per distance category)</li> <li><strong>bird_data_abundance_100_Graser_et_al.csv </strong>(abundance of species per sampling site, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>siteCovs_Graser_et_al.csv</strong> (environmental variables for each sampling point)</li> <li><strong>A_species_matrix_100_new_Graser_et_al.csv</strong> (species-site matrix of <strong>bark-beetle disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>B_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>windthrow disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>C_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle/windthrow disturbance, underplanted, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>D_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, salvage-unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>E_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, underplanted, salvage-unlogged </strong>sites for rarefaction and extrapolation, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong> F_species_matrix_100_new_Graser_et_al.cs</strong>v (species-site matrix of <strong>mature spruce plantation </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>msHDS_bird_data_management_model_Graser_et_al.rds</strong> (R-data set for multi-species community distance sampling of the effect of different pre- and post-disturbance management groups)</li> <li><strong>msHDS_bird_data_stand_age_model_Graser_et_al.rds </strong>(R-data set for multi-species community distance sampling of the effect of post-disturbance forest succession)</li> </ul> <p>A more detailed description of the data can be found in the README.txt document.</p> <p><span>References:</span></p> <p><span>Cáceres, M. D., & Legendre, P. (2009). </span><span>Associations between species and groups of sites: Indices and statistical inference. <em>Ecology</em>, <em>90</em>(12), 3566–3574. https://doi.org/10.1890/08-1823.1</span></p> <p><span>Doser, J. W., Finley, A. O., Kéry, M., & Zipkin, E. F. (2023). spAbundance: An R package for single‐species and multi‐species spatially explicit abundance models. <em>Methods in Ecology and Evolution</em>, <em>15</em>(6), 1024–1033. https://doi.org/10.1111/2041-210X.14332</span></p> <p><span>Hsieh, T. C., Ma, K. H., & Chao, A. (2019). <em>iNEXT-package: Interpolation and extrapolation for species diversity</em>. https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.html</span></p> <p><span>Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Wagner, H., Minchin, P. R., Gavin, L., & Henry, H. (2016). Vegan: Community ecology package. R package version 1.17-4. <em>Http://CRAN. R-Project. </em></span><em><span>Org/Package=vegan</span></em><span>.</span></p> <p></p> <p></p>
Datasets for Ash et al. (2024): Testing the Breakdown of the Asteroseismic Scaling Relations in Luminous Red Giants
<p>Datasets used for the high-alpha sequence and cluster analysis in Ash et al. (2024) which explores the efficacy of the asteroseismic scaling relations with a focus on the luminous giant regime. These datasets are a subset of the APOKASC3 dataset, which is publiclly available (Pinsonneault et al. (2024)). The data selection methods to genrate these data subsets are available in Ash et al. (2024). The high-alpha sequence and open cluster datasets are used to compare seismic masses computed with various methods to seismic-independent masses. <a href="https://zenodo.org/api/records/14008022/draft/files/NGC6791_APO_giants+rc_GaiaDR3_membership.txt/content" target="_blank" rel="noopener noreferrer">NGC6791_APO_giants+rc_GaiaDR3_membership.txt</a> and <a href="https://zenodo.org/api/records/14008022/draft/files/NGC6819_APO_giants+rc_GaiaDR3_membership.txt/content" target="_blank" rel="noopener noreferrer">NGC6819_APO_giants+rc_GaiaDR3_membership.txt</a> contains asteroseismic data for red clump and red giant branch stars in the open clusters NGC6791 and NGC6819. The <a href="https://zenodo.org/api/records/14008022/draft/files/high_alp_sequence.txt/content" target="_blank" rel="noopener noreferrer">high_alp_sequence.txt</a> file contains asteroseismic data, including the several methods to compute seismic masses, for Milky Way high-alpha sequence stars. </p> <p> </p> <p>APOKASC 3 paper may be found at: https://arxiv.org/abs/2410.00102</p> <p> </p> <p> </p> <p> </p>
Effectively controlling for sample relatedness in large-scale GWAS: application to 79 EHR-derived longitudinal traits
<p><span>Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPA<sub>GRM</sub>, which controls for sample relatedness via a precise approximation of the joint distribution of genotypes. SPA<sub>GRM</sub> can utilize GRM-free models and thus is applicable to various trait types and statistical methods, including linear mixed models and generalized estimation equations for longitudinal traits. A hybrid strategy incorporating saddlepoint approximation greatly increases the accuracy to analyze low-frequency and rare genetic variants, especially in unbalanced phenotypic distributions. We also introduce SPA<sub>GRM(CCT)</sub> to aggregate the results following different models via Cauchy combination test. Extensive simulations and real data analyses demonstrated that SPA<sub>GRM</sub> maintains well-controlled type I error rates and SPA<sub>GRM(CCT)</sub> can serve as a broadly effective method. Applying SPA<sub>GRM</sub> to 79 longitudinal traits extracted from</span><span> </span><span>UK Biobank primary care data, we identified 7,463 genetic loci, making a pioneering attempt to conduct GWAS for these traits as longitudinal traits.</span></p>
Supplementary data for: Chromosome-scale genome assemblies of aphids reveal extensively rearranged autosomes and long-term conservation of the X chromosome
<p><strong><em>Myzus persicae </em>clone O v2 frozen release</strong></p> <p>Genome assembly: Myzus_persicae_O_v2.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Myzus_persicae_O_v2.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Myzus_persicae_O_v2.0_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae de novo</em> repeat library: Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff.out</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae</em> <em>de novo r</em>epeat library (gff format): Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Acyrthosiphon pisum</em> clone JIC1 v1 frozen release</strong></p> <p>Genome assembly: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Acyrthosiphon_pisum_JIC1_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum</em> <em>de novo</em> repeat library: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.out</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum de novo</em> repeat library (gff format): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Rhodnius prolixus</em> DNA zoo chromosome-scale genome assembly annotation</strong></p> <p><em>R. prolixus </em>chromosome-scale genome assembly was obtained here: <a href="https://www.dnazoo.org/assemblies/Rhodnius_prolixus">https://www.dnazoo.org/assemblies/Rhodnius_prolixus</a>.</p> <p>Genome assembly: Rhodnius_prolixus-3.0.3_HiC.fasta</p> <p>BRAKER2 gene models: Rhodnius_prolixus-3.0.3_HiC.braker2.gff</p> <p>BRAKER2 protein sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.cds.fa</p> <p><strong><em>Triatoma rubrofasciata</em> chromosome-scale genome assembly annotation</strong></p> <p><em>T. rubrofasciata </em>chromosome-scale genome assembly was obtained here: <a href="http://dx.doi.org/10.5524/100614">http://dx.doi.org/10.5524/100614</a></p> <p>Genome assembly: zhuichun_assembly.fasta</p> <p>BRAKER2 gene models: zhuichun_assembly.braker2.gff</p> <p>BRAKER2 protein sequences: zhuichun_assembly.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): zhuichun_assembly.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: zhuichun_assembly.braker2.gff.cds.fa</p> <p><strong>Hemiptera orthogroups and species tree</strong></p> <p>OrthoFinder was used to cluster proteomes of 14 Hemiptera into orthogroups for phylogenomic analysis. All proteomes were reduced to the longest transcript per gene. See here for full details:</p> <p>Species included, taxon IDs and data source:</p> <p>Mcer = Myzus cerasi v1.1 (<a href="https://bipaa.genouest.org/sp/myzus_cerasi/">https://bipaa.genouest.org/sp/myzus_cerasi/</a>)</p> <p>MperO = Myzus persicae clone O v2 (This study)</p> <p>Dnox = Diuraphis noxia Thorpe et. al. gene predictions (<a href="https://bipaa.genouest.org/sp/diuraphis_noxia/">https://bipaa.genouest.org/sp/diuraphis_noxia/</a>)</p> <p>Apis = Acyrthosiphon pisum JIC1 v1 (This study)</p> <p>Pnig = Pentalonia nigronervosa (This study)</p> <p>Rmai = Rhopalosiphum maidis v0.1 (<a href="http://gigadb.org/dataset/100572">http://gigadb.org/dataset/100572</a>)</p> <p>Rpad = Rhopalosiphum padi v1.0 (<a href="https://bipaa.genouest.org/sp/rhopalosiphum_padi/">https://bipaa.genouest.org/sp/rhopalosiphum_padi/</a>)</p> <p>Agly = Aphis glycines biotype 4 v2.1 (<a href="https://zenodo.org/record/3453468#.XnpL5JOgLRY">https://zenodo.org/record/3453468#.XnpL5JOgLRY</a>)</p> <p>BtabMEAM1 = Bemissia tabacci MEAM1 v1.2 (<a href="http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi">http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi</a>)</p> <p>Trub = Triatoma rubrofasciata (This study)</p> <p>Rpro = Rhodnius prolixus (This study)</p> <p>Ofas = Oncopeltus fasciatus OGS v1.0 (<a href="https://i5k.nal.usda.gov/Oncopeltus_fasciatus">https://i5k.nal.usda.gov/Oncopeltus_fasciatus</a>)</p> <p>Sfuc = Sogatella furcifera v1 (<a href="http://dx.doi.org/10.5524/100255">http://dx.doi.org/10.5524/100255</a>)</p> <p>Nlug = Nilaparvata lugens (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42</a>)</p> <p>Files:</p> <p>Proteomes included in the analysis: proteomes.tar.gz</p> <p>Orthogroups: Orthogroups.txt</p> <p>Gene counts per orthogroup, per species: Orthogroups.GeneCount.csv</p> <p>Single copy conserved orthogroups used for species tree: SingleCopyOrthogroups.txt</p> <p>Species tree alignment: SpeciesTreeAlignment.fa</p> <p>r8s configuration file (includes time calibrations and OrthoFinder ML species tree with branch lengths): species_tree_rooted.r8s.nex</p> <p>r8s time calibrated species tree: r8s_tree.nwk</p>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
SCALE-AMPS data: Simulations of SHEBA case
<p>This contains all the 2D idealized simulation results of SHEBA mixed-phase clouds by SCALE-AMPS, which are mainly used for validating the model.</p>
Data from: Disentangling the drivers of ground-dwelling macro-arthropod metacommunity structure at two different spatial scales
<p>The goal of this study was to explore the community assembly rules at local and regional scales.</p> <p> </p> <p><strong><em>Site description </em></strong></p> <p>All sampling locations were selected within the black soil region (Fig. 1), which is predominantly located in the temperate continental monsoon climatic zone in North China. It is characterized by a dry and cold winter and warm and humid summer. The soil was classified as black soil following the Chinese Soil Classification System, which is equivalent to a Typic Hapludoll in the USDA Soil Taxonomy. More specific details for this soil (such as black soil coverage area, geographical and ecological resources, etc.) can be obtained from Wen and Liang (2001). Samples were collected from three municipal districts: Bei'an, Hulan and Dehui.</p> <p> </p> <p><strong><em>Sampling design and setup</em></strong></p> <p>We conducted field sampling of ground-dwelling macro-arthropods and measured a set of environmental and spatial variables across all sampling locations three times: in May, July and September 2015. In total, 15 plots (five plots in each of the three municipal districts) were selected and sampled. At each plot, we further selected five sampling sites (approximately 10 m away from each other).</p> <p>We collected additional samples for estimating soil abiotic parameters at each site. Soil samples (5 × 5 cm and 10 cm depth) were collected near each pitfall trap site. The exact geographic coordinates of each sampling site were obtained by GPS.</p> <p>Ground-dwelling macro-arthropods were sampled by a pitfall trapping method. For pitfall traps, we used plastic cups (7 cm in diameter and 12 cm deep), which were partially filled with saturated salt water. The traps were exposed for one week in each sampling month. All collected ground-dwelling macro-arthropods were removed from the pitfall traps, sorted and preserved in a 95% alcohol solution. All adult macroarthropods from pitfalls were identified at the species or genus level using appropriate keys (e.g., Simon (1879), Martens (1978) and Barrientos (2004) for Opiliones; Roberts (1993, 1995) for Lycosidae; and Forel and Leplat (2001) and Ortuño and Marcos (2003) for Carabidae) and then were counted. Juvenile ground-dwelling arthropods were excluded from all analyses due to difficulties with their identification (Gao et al., 2016).</p> <p> </p> <p><strong><em>Environmental and spatial variables</em></strong></p> <p>Environmental variables used in our analysis included soil organic matter, soil total nitrogen, water content, pH, temperature. Soil water content (SWC%) was measured in the laboratory after the fresh soil was loaded into an aluminium box. Prior to estimating soil total nitrogen (TN) (Kjeldahl's method described by Duchaufour (1975)), soil organic matter (SOM) (Anne's method described by Duchaufour (1975)) and pH (Pansu and Gautheyrou, 2003), the collected soil samples were air-dried at 25℃ for one week and sieved (1 mm mesh size). Local temperature values were obtained from the publicly available datasets (The Local Chronicles of Bei’an, Hulan and Dehui). Geographic coordinates were recorded for further spatial modelling analysis.</p> <p> </p> <p>We have seven data files:</p> <p>env BAHLDH may.csv</p> <p>env BAHLDH july.csv</p> <p>env BAHLDH september.csv</p> <p>sp BAHLDH may.csv</p> <p>sp BAHLDH july.csv</p> <p>sp BAHLDH september.csv</p> <p>Geospatial coordinates.csv</p> <p> </p> <p>Explanation of the variables in the datasets:</p> <p>Site: Bei’an, Hulan, Dehui represent sampling district; I-V represent sampling plot; 1-5 represent replicate</p> <p>SOM: soil organic matter</p> <p>pH: soil pH</p> <p>SWC: Soil water content</p> <p>TN: soil total nitrogen</p>
Data and analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui"
<p>Butterfly scale data and data analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in <em>Vanessa cardui</em>."</p> <p> </p> <p>It is recommended to download all files and folders into a single root folder for use in MATLAB.<br> This code was prepared for use in MATLAB R2019b, and some scripts or functions require the Image Processing Toolbox.</p>
nal; E', F', internal, G'-J', Pal. 1341 (peripheral 8); G', H', external; I', J', internal; K'-N', Pal. 1343 (peripheral 9); K', L', external; M', N', internal; O'-R', Pal. 1345 (peripheral 10); O', P', external; Q', R', internal; S'-V', Pal. 1348 (peripheral 11); S', T', external; U', V', internal views; W', reconstruction of carapace Thick lines correspond to scute sulci, dotted lines denote plate sutures, and oblique lines indicate missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Spr, supracaudal; Ve, vertebral. Scale bars: A-V', 1 cm; W', 2.5 cm. in Fossil turtles from the early Miocene localities of Mokrá-Quarry (Burdigalian, MN4), South Moravian Region, Czech Republic
nal; E', F', internal, G'-J', Pal. 1341 (peripheral 8); G', H', external; I', J', internal; K'-N', Pal. 1343 (peripheral 9); K', L', external; M', N', internal; O'-R', Pal. 1345 (peripheral 10); O', P', external; Q', R', internal; S'-V', Pal. 1348 (peripheral 11); S', T', external; U', V', internal views; W', reconstruction of carapace Thick lines correspond to scute sulci, dotted lines denote plate sutures, and oblique lines indicate missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Spr, supracaudal; Ve, vertebral. Scale bars: A-V', 1 cm; W', 2.5 cm.
sal; W, visceral; X-Z, Pal. 1308 (costal 5); X, Y, dorsal; Z, visceral; A'-C', Pal. 1309 (costal 6); A', B', dorsal; C', visceral; D'-F', Pal. 1310 (costal 8); D', E', dorsal; F', visceral; G'-J', Pal. 1312 (peripheral 1); G', H', dorsal; I', J', visceral; K'-N', Pal. 1313 (peripheral 7); K', L', dorsal; M', N', visceral; O'-R', Pal. 1314 (peripheral 8); O', P', dorsal; Q', R', visceral views; S', reconstruction of carapace. Thick lines indicate to scute sulci, dotted lines sutures and oblique lines denote missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Ve, vertebral. Scale bars: 1 cm. in Fossil turtles from the early Miocene localities of Mokrá-Quarry (Burdigalian, MN4), South Moravian Region, Czech Republic
sal; W, visceral; X-Z, Pal. 1308 (costal 5); X, Y, dorsal; Z, visceral; A'-C', Pal. 1309 (costal 6); A', B', dorsal; C', visceral; D'-F', Pal. 1310 (costal 8); D', E', dorsal; F', visceral; G'-J', Pal. 1312 (peripheral 1); G', H', dorsal; I', J', visceral; K'-N', Pal. 1313 (peripheral 7); K', L', dorsal; M', N', visceral; O'-R', Pal. 1314 (peripheral 8); O', P', dorsal; Q', R', visceral views; S', reconstruction of carapace. Thick lines indicate to scute sulci, dotted lines sutures and oblique lines denote missing plate portions. Abbreviations: Ce, cervical; co, costal; Ma, marginal; ne, neural; nu, nuchal; per, peripheral; Pl, pleural; py, pygal; sp, suprapygal; Ve, vertebral. Scale bars: 1 cm.
Dataset: Planetary-scale waves seen in thermal infrared images of Venusian cloud top
<p>The data archive contains data used in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al.</p> <p>The contents of the directories are as follows.</p> <p>"data_used_in_figures" : The table data used in the figures are given in Excel and CSV. The table format is described in the data files. An image in NetCDF format is also included.</p> <p>"time_series_of_brightness_temperature_gradient" : The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper (Table S1 in the Supporting Information). The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.</p>
Economies of scale shape energetics of solitary and group living spiders and their webs
<p>Metabolic scaling, whereby larger individuals use less energy per unit mass than smaller ones, may apply to the combined metabolic rate of group-living organisms as group size increases. Spiders that form groups in high disturbance environments can serve to test the hypothesis that economies of scale benefit social groups.</p> <p>Using solitary and group-living spiders, we tested the hypothesis that spiders exhibit negative allometry between body or colony mass and the standing mass of their webs and whether, and how, such a relationship may contribute to group-living benefits in a cooperative spider.</p> <p>Given the diverse architecture of spider webs—orb, tangle, and sheet-and-tangle, and associated differences in silk content, we first assessed how standing web mass scales with spider mass as a function of web architecture and whether investment in silk differs among web types. As group-living spiders are predominantly found in clades that build the presumably costlier sheet-and-tangle webs, we then asked whether cost-sharing through cooperative web maintenance contributes to a positive energy budget in a social species.</p> <p>We found that larger spiders had a relatively smaller investment in silk per unit mass than smaller ones, but more complex sheet-and-tangle webs contained orders of magnitude more silk than simpler orb or tangle ones. In the group-living species, standing web mass per unit spider mass continued to decline as colony size increased with a similar slope as for unitary spiders. When web maintenance activities were considered, colonies also experienced reduced mass-specific energy expenditure with increasing colony size. Activity savings contributed to a net positive energy balance for medium and large colonies after inputs from the cooperative capture of large prey were accounted for.</p> <p>Economies of scale have been previously demonstrated in animal societies characterized by reproductive and worker castes, but not in relatively egalitarian societies as those of social spiders. Our findings illustrate the universality of scaling laws and how economies of scale may contribute to setting limits on social behaviour and hunting strategies.</p>
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
Ontologizing Health Systems at Scale: Making Translational Discovery a Reality (Recorded talk)
<p>This entry contains the recorded talk that was presented at the 2021 American Medical Informatics Association Virtual Informatics Summit (<a href="https://amia.org/education-events/amia-2021-virtual-informatics-summit">https://amia.org/education-events/amia-2021-virtual-informatics-summit</a>).</p>
Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network
<p>Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, <em>"Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"</em></p>
Fig. 4 in Scale-Morphometry Study To Discriminate Gibel Carp (Carassius Gibelio) Populations In The Balaton-Catchment (Hungary)
Fig. 4. Relationship between Canonical Variates and environmental variables based on sampling sites separation
Fig. 2 in Scale-Morphometry Study To Discriminate Gibel Carp (Carassius Gibelio) Populations In The Balaton-Catchment (Hungary)
Fig. 2. Landmarks used to define the shape of the scales (Prussian carp). The areas of the scales are described with respect to the fish position
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.