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866 results for “attack”
FAW attack algorithm
<p>The algorithm of FAW attack</p>
DDoS Attacks Data Set - Consolidated from CICDDOS2019 and CICIDS2017
<p>This dataset is a transformation of the <a href="https://www.unb.ca/cic/datasets/ddos-2019.html">CICDDOS2019</a> collection of datasets by <a href="https://ieeexplore.ieee.org/abstract/document/8888419">Iman Sharafaldin et al. (2019)</a>. All datasets have been combined and have had their labels standardised. Infinity values have been removed. A supplement of Benign tuples has been introduced from <a href="https://www.unb.ca/cic/datasets/ids-2017.html">CICIDS2017</a>, another collection of datasets by <a href="https://fardapaper.ir/mohavaha/uploads/2018/07/Fardapaper-Toward-Generating-a-New-Intrusion-Detection-Dataset-and-Intrusion-Traffic-Characterization.pdf">Iman Sharafaldin et al. (2018)</a>, to increase the proportion of Benign tuples within the dataset.</p> <p>Our paper: <a href="https://doi.org/10.1063/5.0133063">source1</a>, <a href="https://www.researchgate.net/publication/370975966_Evaluating_classifiers'_performance_on_a_consolidated_DDoS_data_set">source 2 </a>(preprint).</p>
The implementation of systemic insecticides and increased irrigation against invasive species attacking Ficus trees in Hawai'i.
<p>This contains the data collected for the publication submitted to the Journal of Applied Entomology. </p>
SoK: Taxonomy of Attacks on Open-Source Software Supply Chains - Visualization Tool Screenshots & Selected Papers
<p>This artifact complements the paper "SoK: Taxonomy of Attacks on Open-Source Software Supply Chains", submitted at IEEE S&P 2023.</p> <p>The papers selected during the Systematic Literature Review (SLR) are presented in the CSV file.</p> <p>This screenshots display the main features of the visualization tool that allows to explore the taxonomy of attacks on OSS supply chains, as well as the related safeguards and the selected references.</p>
Data from "Evidence of attack deflection suggests adaptive evolution of wing tails in butterflies"
<p><span>Predation is a powerful selective force shaping many behavioural and morphological traits in prey species. The deflection of predator attacks from vital parts of the prey usually involves the coordinated evolution of prey body shape and colour. Here, we test the deflection effect of hindwing tails in the swallowtail butterfly <em>Iphiclides podalirius</em>. In this species, hindwings display long tails associated with a conspicuous colour pattern. By surveying the wings within a wild population of <em>I. podalirius</em>, we observed that wing damage was much more frequent on the tails. We then used a standardised behavioural assay employing dummy butterflies with real <em>I. podalirius </em>wings to study the location of attacks by great tits <em>Parus major.</em> Wing tails and conspicuous coloration of the hindwings were struck more often than the rest of the body by birds. Finally, we characterised the mechanical properties of fresh wings and found that the tail vein was more fragile than the others, suggesting facilitated escape ability of butterflies attacked at this location. Our results clearly support the deflective effect of hindwing tails and suggest that predation is an important selective driver of the evolution of wing tails and colour pattern in butterflies.</span></p>
Does prey scarcity increase the risk of wolf attacks on domestic dogs?
<p>Gray wolf (Canis lupus) predation on domestic dogs (Canis familiaris) is a considerable wolf-human conflict issue in several regions of Europe and North America but has not been well documented in the scientific literature. Livestock depredations by wolves may be related to the abundance of wild prey. Regardless of the presumed motivations of wolves for attacking dogs (likely due to interference competition and predation), the abundance of wild prey populations may also influence the risk of wolf attacks on dogs. We examined whether the annual number of tatal attacks by wolves on dogs was related to the abundance of primary prey, including wild boar (<em>Sus scrofa</em>) and roe deer (<em>Capreolus capreolus</em>) in Estonia, as well as the abundance of moose (<em>Alces alces</em>) in Finland. Statistical models resulted in significant negative relationships, thus providing evidence that the risk of attacks in both house yards (Estonia) and hunting situations (Finland) was highest when the density of wild prey was low. Wild ungulates cause damage to agriculture and forestry, but they seem to mitigate conflicts between wolves and humans; therefore, it is necessary to develop a holistic, multispecies management approach in which the importance of wild ungulates for large carnivores is addressed. </p>
Data from: Single, but not dual, attack by a biotrophic pathogen and sap-sucking insect affects the oak leaf metabolome
<p>Plants interact with a multitude of microorganisms and insects, both belowand above ground, which might influence plant metabolism. Despite this, we lack knowledge of the impact of natural soil communities and multiple aboveground attackers on the metabolic responses of plants, and whether plant metabolic responses to single attack can predict responses to dual attack. We used untargeted metabolic fingerprinting (gas chromatographymass spectrometry, GC-MS) on leaves of the pedunculate oak, <em>Quercus robur</em>, to assess the metabolic response to different soil microbiomes and aboveground single and dual attack by oak powdery mildew (<em>Erysiphe alphitoides</em>) and the common oak aphid (<em>Tuberculatus annulatus</em>). Distinct soil microbiomes were not associated with differences in the metabolic profile of oak seedling leaves. Single attacks by aphids or mildew had pronounced but different effects on the oak leaf metabolome, but we detected no difference between the metabolomes of healthy seedlings and seedlings attacked by both aphids and powdery mildew. Our findings show that aboveground attackers can have species-specific and non-additive effects on the leaf metabolome of oak. The lack of a metabolic signature detected by GC-MS upon dual attack might suggest the existence of a potential negative feedback, and highlights the importance of considering the impacts of multiple attackers to gain mechanistic insights into the ecology and evolution of species interactions and the structure of plant-associated communities, as well as for the development of sustainable strategies to control agricultural pests and diseases and plant breeding.</p>
Spring phenology and pathogen infection affect multigenerational plant attackers throughout the growing season
<p>Climate change has been shown to advance spring phenology, increase the number of insect generations per year (multivoltinism), and increase pathogen infection levels. However, we lack insights into the effects of plant spring phenology and the biotic environment on the preference and performance of multivoltine herbivores and whether such effects extend into the later part of the growing season. To this aim, we used a multifactorial growth chamber experiment to examine the influence of spring phenology on plant pathogen infection, and how the independent and interactive effects of spring phenology and plant pathogen infection affect the preference and performance of multigenerational attackers (the leaf miner Tischeria ekebladella and the aphid Tuberculatus annulatus) on the pedunculate oak in the early, mid and late parts of the plant growing season. Pathogen infection was highest on late phenology plants, irrespective of whether inoculations were conducted in the early, mid or late season. The leaf miner consistently preferred to oviposit on middle and late phenology plants, as well as healthy plants, during all parts of the growing season, whereas we detected an interactive effect between spring phenology and pathogen infection on the performance of the leaf miner. Aphids preferred healthy, late phenology plants during the early season, healthy plants during the mid season, and middle phenology plants during the late season, whereas aphid performance was consistently higher on healthy plants during all parts of the growing season. Our findings highlight that the impact of spring phenology on pathogen infection and the preference and performance of insect herbivores is not restricted to the early season, but that its imprint is still present – and sometimes equally strong – during the peak and end of the growing season. Plant pathogens generally negatively affected herbivore preference and performance, and modulated the effects of spring phenology. We conclude that spring phenology and pathogen infection are two important factors shaping the preference and performance of multigenerational plant attackers, which is particularly relevant given the current advance in spring phenology, pathogen outbreaks and increase in voltinism with climate change. </p>
Hennessy et al-Supplementary file 5-Attack video
<p>Video recording of the male (Shanto) attempting to attack the observer through the glass observation window of the enclosure.</p>
Live video/audio recording of Hamas rocket attack and rushing for bomb shelter at Weizmann Institute campus, Rehovot, Israel
<p>In the early morning of October 7, 2024, terrorists launched an attack with rockets and death squads on Israel. For several days, an enormous number of rockets was launched by Hamas terrorists in the Gaza strip on Israel.</p> <p>This is a real live video recording of escaping from an apartment in Rehovot, Israel, to the nearby Luftschutzbunker (Shelter).</p> <div> <div>This was one of the massive (Qassam) rocket attacks by Hamas terrorists launched from Gaza strip towards north of Israel, here at Rehovot near Weizmann Institute of Science. The rockets were intercepted by the iron dome system. Several families with children were already inside the Shelter.</div> </div> <div>The scene was recorded in the evening of 7th October 2023. Several attacks happened before that first day of the 2023 Israel-Hamas war. The earliest was 06:30 am in the morning on the Shabat.</div> <div>The rocket attacks stopped at night after the Israeli Air Force had begun raids on the Gaza strip.</div>
Reproduction Package for "Preventing Refactoring Attacks on Software Plagiarism Detection through Graph-Based Structural Normalization"
<p>This repository stores all data used in the evaluation of the master's thesis "Preventing Refactoring Attacks on Software Plagiarism Detection through Graph-Based Structural Normalization". It ensures the continuous reproducibility of the results of the thesis.</p> <p>Content:</p> <ul> <li>JPlag v5.1.0 including the Java CPG frontend <ul> <li>Code base</li> <li>Runnable JAR</li> </ul> </li> <li>Data sets used for evaluation</li> <li>Evaluation results</li> <li>R script used to process the results</li> <li>Graphics and tables generated from the results</li> </ul> <p>The data sets were generated by Nils Niehues and Moritz Brödel and were originally published here:</p> <ul> <li><a href="../records/10430322">Supplementary Material for "Detecting Automatic Software Plagiarism via Token Sequence Normalization" (zenodo.org)</a></li> <li><a href="../records/10149536">Reproduction package for: Intelligent Match Merging to Prevent Obfuscation Attacks on Software Plagiarism Detectors (zenodo.org)</a></li> </ul> <p>The data sets are based partly on PROGPedia, available here:</p> <ul> <li><a href="../records/7449056">PROGpedia (zenodo.org)</a></li> </ul> <p>Visit <a title="State-of-the-Art Software Plagiarism & Collusion Detection" href="jplag.github.io/JPlag/" target="_blank" rel="noopener">JPlag</a> on GitHub for the current version.<br>See the thesis document for more information.</p> <p>Read about similar publications about Plagiarism Detection <a title="JPlag" href="https://jplag.github.io/MinimalLandingPage/" target="_blank" rel="noopener">here</a>.</p>
Cyber Attacks in the Ukrainian Conflict 2013-2020 Dataset
<p>Aggregated dataset on 76 cyberattacks conducted against Ukraine between late 2013 and 2020. The dataset is based on the following sources:</p> <p>Maness RC, Valeriano B, Hedgecock K, Jensen BM, Macias JM. Codebook for the Dyadic Cyber Incident and Campaign Dataset (DCID) Version 2.0. 2022.<br>Baezner M. Cyber and Information warfare in the Ukrainian conflict, Version 2. Zürich: Center for Security Studies (CSS), ETH; 2018.<br>Passeri P. Hackmageddon - Information Security Timelines and Statistics. 2024. https://www.hackmageddon.com/.</p> <p> </p>
IR data of the compounds published in "Bioinspired Nucleophilic Attack on a Tungsten-Bound Acetylene: Formation of Cationic Carbyne and Alkenyl Complexes"
Open the record for dataset details and reuse information.
Fig 1 in Antimicrobial activity of pandanus leaves extract to against Aeromonas hydrophila which attacked catfish
Fig 1: Inhibition test of Aeromonas hydrophila bacteria
Decomposition of bark beetle-attacked trees after mortality varies across forests
<p>Data are from a 2 year experiment examining differences in decomposition processes between bark beetle-attacked trees and trees not attacked by bark beetles in three sites spanning a broad geographic area. Specifically, in Honduras, and Mississippi and Arizona, USA, we felled one recently bark beetle-attacked and one apparently healthy conspecific tree at each site that was cut into 120 experimental logs. Logs of each tree (attacked or unattacked) were assigned one of three metal mesh covering treatments: 1) fully covered to exclude all macroinvertebrates, 2) covered from above to exclude secondary bark beetle colonization, 3) no cover to allow all detrital food web organisms. Half of all logs at each site was collected after 1 and 2 years and the density loss, insect visual damage rating, and abundance of termites, ants, and beetles was measured.</p>
A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period
<div> <div></div> </div> <div> <div> <div> <p><span>Machine learning methodologies are increasingly popular in health care research. This shift to integrated data science approaches necessitates professional development of the existing health care data analyst workforce. To enhance a smooth transition, educational resources need to be developed. Barriers to accessing real healthcare datasets, vital for health care data analyses methodologies training purposes, include financial, ethical and patient confidentiality concerns. Synthetic datasets mimicking real-world complexities offer a simpler solution.</span></p> <p>We present a synthetic dataset which mirrors routinely collected primary care data on heart attack and stroke among the adult population. The data incorporates much of the practical challenges encountered in routinely collected primary care systems such as missing data, informative censoring, interactions, variable irrelevance, and noise and can be used for training in methods which handle these difficulties. The intent is for the user to build models of heart/stroke risk using survival-based methodologies.</p> <p>By sharing this synthetic dataset openly, our goal is to contribute a transformative asset for professional training in health and social care data analysis. The dataset covers demographics, lifestyle variables, comorbidities, systolic blood pressure, hypertension treatment, family history of cardiovascular diseases, respiratory functioning, and experience of heart-attack and/or stroke. This initiative aims to bridge the gap in sophisticated healthcare datasets for training, fostering professional development of the health and social care research workforce.</p> <p>This study is funded by the National Institute for Health and Care Research ARC Wessex and the National Centre for Research Methods. The views expressed in this summary are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.</p> <p> </p> </div> </div> </div>
Modeling Sulfate Attack in Modern Concrete for Building Sustainable and Resilient Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17CTAM01. Abstract of the final report is stated below for reference:</p> <p>"External sulfate attack is a complex phenomenon and is manifested in the form of large expansion, cracking, and spalling depending on the exposure solution and material constituent properties. Several models were developed in the past to demonstrate sulfate attack mechanisms that account for the diffusion of sulfate ions into the porous concrete and the successive deformation triggered by the chemical reaction and precipitation of expansive agents. However, none of these models accounts for the effect of the migration of solvent water from the low solute concentration solution to high solute concentration solution driven by the osmotic pressure. Osmotic pressure is believed to cause spalling and cracking of concrete substrates coated with semipermeable membrane that prohibits diffusion of ions from the surroundings into the porous body. In order to determine the effect of osmotic pressure on the deformation of concrete exposed to sulfate solution, a coupled poromechanical model has been developed. Sensitivity analysis has been performed to investigate the effect of material constituent properties and exposure solution on the osmotic pressure induced damage propensity of concrete. It has been found that concrete surface can exhibit high instantaneous tensile stress developed by the gradient in the salt concentration between the pore solution and external surroundings."</p>
FAN-GHETS24: A Flying Ad Hoc Network Dataset for Early Time Series Classification of Grey Hole Attacks
<p>Flying ad-hoc networks (FANETs) consist of multiple unmanned aerial vehicles (UAVs) that rely on multi-hop routes for communication. These routes are particularly susceptible to grey hole attacks, necessitating swift and accurate defense to preserve the network's quality of service. This novel dataset, FAN-GHETS24, is designed for early time series classification of various grey hole attack scenarios. The dataset is derived from sequences of packet interactions between UAVs within the network, generated through multiple simulations. These sequences undergo post-processing via two methods: firstly, an anonymization procedure that replaces IP addresses with standard string variables, allowing for offline model training and universal deployment across UAVs; and secondly, the application of feature engineering techniques to format the data for machine learning model integration.</p> <div> <div>The dataset is split across several zip files, combine and extract them by issuing these command:</div> </div> <div> <div>$ zip -FF fan-ghets24.zip --out fan-ghets24-combined.zip</div> <div>$ unzip fan-ghets24-combined.zip</div> </div>
Processed data supporting the manuscript "Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution"
<div><strong>Processed data supporting the manuscript: </strong>Cutting the sap: first molecular phylogeny of twig-girdler longhorn beetles (Coleoptera: Cerambycidae: Lamiinae: Onciderini) suggests shifts in host plant attack behaviors contributed to morphological evolution</div> <div> </div> <div><strong>By:</strong> Diego de S. Souza 1, 2, Rowan L. K. French 3, José O. Silva Júnior 4, Eugenio H. Nearns 5, Luciane Marinoni 4, Ian P. Swift 6, Kelly B. Miller 7, Felix A. H. Sperling 2 & Marcela L. Monné 1</div> <div> </div> <div>1 Department of Entomology, National Museum, Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil.</div> <div>2 Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.</div> <div>3 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.</div> <div>4 Department of Zoology, Federal University of Paraná, Curitiba, Paraná, Brazil.</div> <div>5 National Museum of Natural History, Smithsonian Institution, Washington, DC, USA.</div> <div>6 California State Collection of Arthropods, Sacramento, California, USA.</div> <div>7 Department of Biology and Museum of Southwestern Biology, University of New Mexico, Albuquerque, New Mexico, USA.</div> <div> </div> <div>Corresponding author: Diego de S. Souza, dsouza@fieldmuseum.org. Current affiliation: Field Museum of Natural History, Chicago, Illinois, USA.</div> <div> </div> <div> </div> <div><strong>List of Contents: </strong></div> <div> </div> <div><strong>Onciderini_concat_matrix.phy</strong></div> <div>Concatenated matrix (cox1, Wg and CPS) used for the phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini). </div> <div> </div> <div><strong>PartitionFinder_AICc_best_scheme.txt</strong></div> <div>Results from PartitionFinder v2.1.1, containing the best partitioning scheme for the concatenated matrix of Onciderini, identified using the corrected Akaike Information Criterion (AICc), with model definitions for use in the phylogenetic analyses.</div> <div> </div> <div><strong>RAxML_Onciderini_concat_matrix (zip file)</strong></div> <div>- Onciderini_concat_matrix.phy: concatenated matrix (cox1, Wg and CPS) used in the RAxML phylogenetic analyses of Onciderini (Coleoptera: Cerambycidae: Lamiinae: Onciderini).</div> <div>- Partitions_AICc_RAxML.txt: partitioning scheme used in the RAxML analysis as predefined by PartitionFinder v2.1.1 using the corrected Akaike Information Criterion (AICc).</div> <div>- RAxML_bestTree.Onciderini_concat_matrix_ML: best-scoring maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini.</div> <div>- RAxML_bipartitions.Onciderini_concat_matrix_final: bipartitions (clades) of the maximum likelihood tree inferred by RAxML with support values estimated from 1,000 pseudoreplicates.</div> <div>- RAxML_bipartitionsBranchLabels.Onciderini_concat_matrix_final: final maximum likelihood tree inferred by RAxML for the concatenated matrix of Onciderini, with labeled branches showing bootstrap support values.</div> <div>- RAxML_bootstrap.Onciderini_concat_matrix_bootstrap: bootstrap trees generated from a non-parametric bootstrap analysis in RAxML based on 1,000 pseudoreplicates.</div> <div>- RAxML_info.Onciderini_concat_matrix_bootstrap: log file containing details of the bootstrap analysis, including the settings and parameters used in the non-parametric bootstrap runs in RAxML.</div> <div>- RAxML_info.Onciderini_concat_matrix_final: log file summarizing the RAxML analysis, including settings and convergence statistics for the final maximum likelihood tree.</div> <div>- RAxML_info.Onciderini_concat_matrix_ML: log file containing details of the maximum likelihood tree search, including the parameters and models applied during the maximum likelihood analysis conducted by RAxML.</div> <div>- RAxML_log.Onciderini_concat_matrix_ML: log file of the maximum likelihood tree search for the concatenated matrix of Onciderini.</div> <div>- RAxML_parsimonyTree.Onciderini_concat_matrix_ML: parsimony starting tree used by RAxML during the maximum likelihood analysis for the concatenated matrix of Onciderini.</div> <div>- RAxML_result.Onciderini_concat_matrix_ML: maximum likelihood tree inferred by RAxML from the concatenated matrix of Onciderini, summarizing the tree topology and likelihood score for the best tree obtained.</div> <div> </div> <div><strong>BI_AICc_Onciderini_concat_matrix (zip file)</strong></div> <div>- BI_AICc_Onciderini_concat_matrix.nex: nexus file containing the concatenated matrix of Onciderini used for Bayesian Inference (BI), including the best-fit model scheme identified by PartitionFinder and MCMC parameters for running the analysis in MrBayes.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex_r1_r2_combined_consensus.tree: consensus tree from two combined independent Bayesian Inference (BI) runs based on the concatenated matrix of Onciderini, after discarding the first 25% of initial generations as burn-in.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run1.p: log file containing parameter values and likelihood scores from the first run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div>- BI_AICc_Onciderini_concat_matrix.nex.run2.p: log file containing parameter values and likelihood scores from the second run of the Bayesian Inference (BI) based on the concatenated matrix of Onciderini.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_lognormal (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_lognormal.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- BEAST2_Onciderini_BD_lognormal_run[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution.</div> <div>- TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a lognormal distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_exponential (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_exponential.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- BEAST2_Onciderini_BD_exponential_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution.</div> <div>- TreeAnnotator_Onciderini_BD_exponential_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and an exponential distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>BEAST2_Onciderini_BD_uniform (zip file)</strong></div> <div>- BEAUTi_Onciderini_BD_uniform.xml: XML file generated by BEAUTi for running BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- BEAST2_Onciderini_BD_uniform_[1-8].log: log files from eight independent runs of BEAST2, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.out: TreeAnnotator output file combining the results of eight BEAST2 runs based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution.</div> <div>- TreeAnnotator_Onciderini_BD_uniform_run1-run8_consensus.tre: consensus tree from eight combined BEAST2 runs, based on the concatenated matrix of Onciderini, using a birth-death (BD) process model and a uniform distribution, after discarding the first 10% of initial generations as burn-in.</div> <div> </div> <div><strong>Comparative_analyses (zip file)</strong></div> <div><strong>RawData (folder):</strong> raw morphometric and girdling data, plus tree that was later pruned for downstream comparative analyses; these data were used as input for the OncidHeadDimorphism-DatasetPREP-FINAL.R data cleaning script. </div> <div>- Onciderini_BD_lognormal_run1-run8_consensus.nwk: newick version of TreeAnnotator_Onciderini_BD_lognormal_run1-run8_consensus.tre (outputted as a newick file by importing the .tre file into FigTree and exporting in newick format).</div> <div>- Measurements_Onciderini_Raw_Final.csv: individual-level raw morphometric data for Onciderini.</div> <div>- Behav_Matrix_2_states_trimmed_2022-12-15.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as girdlers (2 behavioral states across Onciderini species). </div> <div>- Matrix_3_states_trimmed_final.csv: species-level data on girdling status for Onciderini species, with all Lochmaeocles species classified as facultative girdlers (3 behavioral states across Onciderini species). </div> <div>- Matrix_2_states_trimmed_1LochGirdler_Final.csv: species-level data on girdling status for Onciderini species, with only one Lochmaeocles species (L. tessellatus) classified as a girdler (2 behavioral states across Onciderini species). </div> <div> </div> <div><strong>ProcessedData (folder): </strong>filtered data and pruned trees outputted by the OncidHeadDimorphism-DatasetPREP-FINAL.R script</div> <div>- oncid_f_36spp_clean.csv: dataset of species means and log-ratios for morphometric traits in females, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_m_42spp_clean.csv: dataset of species means and log-ratios for morphometric traits in males, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_sd_35spp_clean.csv: dataset of species means for sexual dimorphism in morphometric traits, plus girdling data; only includes species that have girdling data and are in the phylogenetic tree</div> <div>- oncid_girdlingbehav_allingroupspp_clean.csv: full dataset of girdling behavior for 56 Onciderini species that are in the phylogenetic tree; includes separate columns for the three alternative girdling classification schemes</div> <div>- oncid_tree_behavfull_56spp.nwk: pruned phylogenetic tree for the full girdling dataset (56 species)</div> <div>- oncid_tree_f_36spp.nwk: pruned phylogenetic tree for the female morphometric dataset (36 species)</div> <div>- oncid_tree_m_42spp.nwk: pruned phylogenetic tree for the male morphometric dataset (42 species)</div> <div>- oncid_tree_mf_43spp.nwk: pruned phylogenetic tree for all species with morphometric data for males or females; used for the stochastic character map next to the heatmap plot (Fig 4)</div> <div>- oncid_tree_sd_35spp.nwk: pruned phylogenetic tree for the sexual dimorphism dataset (35 species)</div> <div> </div> <div><strong>FittedModels (folder): </strong>fitted models (mvgls, model comparison analyses, OUM models, simmaps) outputted by the OncidHeadDimorphism-Analysis-FINAL.R script </div> <div>- MacroModelFits_logRtraits_f_36spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to female morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits_logRtraits_m_42spp-2024-10-12.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to male morphometric data across 100 stochastic character maps of girdling behavior</div> <div>- MacroModelFits-SDDI-35spp_2024-10-11.Rdata: summary of model comparison results for Brownian Motion (BM), single-peak Ornstein-Uhlenbeck (OU), multipeak OU (OUM), and multi-rate Brownian motion (BMM) models (univariate and multivariate) fitted to sexual dimorphism data across 100 stochastic character maps of girdling behavior</div> <div>- mvgls-results-headsize-mf-2024-10-12.Rdata: fitted mvgls regression models for male and female traits (analyzed separately)</div> <div>- mvgls-results-sddi-2024-10-12.Rdata: fitted mvgls regression models for sexual dimorphism</div> <div>- OUM_headtraits_f_36spp-2024-10-12.Rdata: fitted OUM models and summary statistics for female head traits</div> <div>- OUM_headtraits_m_42spp-2024-10-12.Rdata: fitted OUM models and summary statistics for male head traits</div> <div>- OUM-SDDI-35spp-2024-10-12.Rdata: fitted OUM models and summary statistics for sexual dimorphism in head traits</div> <div>- simmaps_ard_full_2state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - all Lochmaeocles species are classified as girdlers</div> <div>- simmaps_ard_full_3state.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with three behavioral states (girdling, non-girdling, or facultative girdling)</div> <div>- simmaps_ard_full_1Loch.RDS: stochastic character maps of girdling behaviour for all 56 species with girdling data, with two behavioral states (girdling or non-girdling) - only one Lochmaeocles species (L. tessellatus) is classified as a girdler</div> <div>- simmaps_ard_m.RDS: stochastic character map for the 42 species used in the analyses of male morphometric traits</div> <div>- simmaps_ard_f.RDS: stochastic character map for the 36 species used in the analyses of female morphometric traits</div> <div>- simmaps_ard.RDS: stochastic character map for the 35 species used in the sexual dimorphism analyses; 2 behavioral states.</div> <div> </div> <div><strong>Rscripts (folder): </strong>R scripts used to process data and run phylogenetic comparative analyses of head size and girdling behavior.</div> <div>- OncidHeadDimorphism-DatasetPREP-FINAL.R: R script used to filter data and prune trees from the RawData folder for downstream phylogenetic comparative analyses; outputs of this script are in the ProcessedData folder.</div> <div>- OncidHeadDimorphism-Analysis-FINAL.R: R script used to analyze data in the ProcessedData folder to answer questions about the origin and evolution of girdling behavior and the relationship between girdling and head size or head size sexual dimorphism; fitted models outputted by this script are in the FittedModels folder</div> <div>- OncidHeadDimorphism-Plots-FINAL.R: R script used to generate plots for the manuscript<br><br></div>
Simulation Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<p>This is a supplementary simulation dataset to reproduce Fig. 3C,D of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <p>The zip folder contains the following 3 files in h5 format: front attack (out_Npred1_pred_angle0.0.h5), side attack (<span>out_Npred1_pred_angle1.5707963267948966.h5), </span><span>back attack (out_Npred1_pred_angle3.141592653589793.h5).</span></p> <p>Each file has the following "keys": KeysViewHDF5 ['circ_seg', 'end', 'endD', 'end_PosVel', 'fount', 'part', 'partD', 'pavas', 'pred', 'predD', 'start', 'start_fountain', 'start_pred', 'swarm', 'swarm_pred0', 'swarm_predD'].</p> <p>The key necessary to reproduce Fig. 3C,D of the aforementioned paper is "fount" (<HDF5 dataset "fount": shape (40, 1200, 100, 8), type "<f8">). Namely, "fount" data array consists of 40 simulation runs, 1200 time points, 100 agents, and 8 metrics. The metric with index "0" depicts the value of the Euclidean distance from the agent <em>i</em> to the simulated predator. The metric with index "1" depicts the value of the position angle (theta 1 in rad) of the agent <em>i</em> relative to the simulated predator. The metric with index "2" depicts the value of the flee angle (theta 2 in rad) of the agent <em>i</em> relative to the simulated predator.</p> <p>To estimate the start and the end of the fountain evasion, one can use the following script in Python:</p> <p>import numpy as np</p> <p>m = h5py.File(filename, "r")<br><br>for key in m.keys():<br> print(key)</p> <p>fount_runs = m[key]["fount"]</p> <p>for j in range(40):<br> fnt_start[j] = np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][0] <br> fnt_end[j] = np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][-1]</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.