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866 results for “attack”
Empirical Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<h3>Description of the data and file structure</h3> <p>The files contain the source data for Fig. 1 and Fig. 3A,B of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <h4>Files and variables</h4> <h5>File: Fountain_Fish_coordinates.zip</h5> <p><strong>Description:</strong> </p> <p>The zip file contains 30 folders, each containing information on one predator attack and respective prey evasion. Each folder is named according to the drone ID used for the filming (e.g., DJI _1, DJI _2, DJI _3) and the respective frame number (e.g., f930) from the video recording. </p> <h5>File naming</h5> <p>Each folder contains JPG and CSV files. </p> <p>The JPG files show the image from the footage at the corresponding frame indicated in the files' name (e.g., frame_001_im).</p> <p>There are 2 types of CSV files. Files with the name 'polygon.csv' contain coordinates (in pixels) of points (x, y) defining the polygon outlining the prey school at the particular frame as indicated in the files' name (e.g., frame001) and corresponding to the image in the JPG file with the same frame number. Files with the name 'sardines_and_marlin.csv' contain coordinates (in pixels) of points (x, y), defining the head (columns 1 and 2) and the dorsal fin (columns 3 and 4) of single sardine individuals (by rows), and of the respective attacking marlin: marlin's head (columns 5 and 6), marlin's dorsal fin (columns 7 and 8), and marlin's tip of the bill (columns 9 and 10). These coordinates correspond to the respective image with the same frame number.</p>
Terrorist Attack Network Datasets
<p>## TNA_NETS</p> <p>Terrorist Attack Network Datasets</p> <p>This collection consists of annotated networks developed from documents related to various terrorist attacks in India. Each dataset is named after a specific case and visualizes relationships and interactions among individual entities involved in these incidents. These networks are instrumental for analyzing key actors, hierarchies, and patterns within terrorist organizations. Below is an overview of each file:</p> <ul> <li><strong>TerroristData1</strong> : Captures the network associated with the 2001 Parliament attack in India. This dataset outlines connections between key individuals, detailing both direct and inferred associations critical for understanding the network structure behind this event.</li> <li><strong>TerroristData2</strong>: Represents the network surrounding the 1996 Dausa blast, in which a bomb exploded in a Rajasthan State Transport Corporation (RSTC) bus traveling from Agra to Bikaner. The explosion occurred at Samleti village in Dausa district, Rajasthan, killing 14 people and injuring 37. The incident took place a day after a similar blast in Delhi’s Lajpat Nagar. This dataset captures the interactions among involved individuals and entities, shedding light on the logistical and operational aspects of this tragic event.</li> <li><strong>TerroristData3</strong> : Documents the network based on the 2008 Mumbai 26/11 attacks. It highlights the coordination among individuals and entities involved in the planning and execution, giving insights into the operational dynamics of the group.</li> <li><strong>TerroristData4</strong>: Visualizes the network behind the assassination of former Prime Minister of India, Mr. Rajiv Gandhi. The dataset details the connections and hierarchy within the network involved in the plot, showcasing the organizational structure and chain of command.</li> <li><strong>TerroristData5</strong>: Maps the network involved in the 1993 Bombay blasts, displaying connections between perpetrators, facilitators, and key locations involved in the coordinated attacks across Mumbai.</li> </ul> <p>These datasets are a resource for studying the structure and influence of clandestine networks in terrorist operations. They can support research on network centrality, influence analysis, and resilience, offering insights into the organizational dynamics of terror groups</p>
Discoid decorations function to shield juvenile Argiope spiders from avian predator attacks
Decorating behavior is common in various animal taxa and serves a variety of functions from camouflage to communication. One predominant function cited for decoration is to avoid predators. Conspicuous, disc-like (discoid) silk decorations spun by orb-web Argiope juvenile spiders are hypothesized, among others, to defend spiders against visual predators by concealing spider outlines on the web, deflecting attacks, shielding them from view or masquerading as bird-droppings. However, the direct evidence is limited for a specific mechanism by which discoid decorations may deter predators. Here we evaluate the mechanisms by which discoid decorations may defend Argiope juveniles against naïve chicks. Using visual modelling, we show that avian predators are able to distinguish spiders from discoid decorations. Using chick predation experiments, we found that the naïve chicks readily pecked any objects, ruling out the possibility of their neophobia. Significantly more chicks attacked spiders when they were exposed to chicks, regardless of whether their webs had discoid decorations, but few chicks attacked spiders when they were behind the decorations. We also found that significantly few chicks attacked decorations when spiders were absent or behind the decorations. We thus conclude that discoid decorations function to deter avian predators by shielding the spider from view or distracting, not by deflecting attacks, concealing the spider's outline or masquerading as bird-droppings. This study sheds light on the study of other similar anti-predator strategies, in a wide range of spider species and other animals that use decorating strategies. --
Datatset: Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS
<p>This dataset accompanies the paper "Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS". It was used to experimentally prove the presented attack strategies on real hardware. The corresponding source code for all three attacks is also publicly available.</p> <p>A detailed description of how the data was obtained can be found in the paper. Section 4 precisely describes the experimental setup.</p> <p> </p> <p>Prerequisites:</p> <pre><code class="language-bash">sudo apt-get install p7zip</code></pre> <p> </p> <p>Extract the data:</p> <pre><code class="language-bash">7z x galactics_attack_data.7z</code></pre> <p> </p> <p>Running the attacks:</p> <p>The source code to run the three presented attacks can be found on Github. The instructions on how to use the python code can be obtained from the corresponding README.</p> <p> </p> <p>Re-using the dataset:</p> <p>The dataset consists of <em>.pickle</em> and <em>.bin</em> files. The <em>.pickle</em> files can be read using <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_pickle.html">Pythons Pandas library</a>. Python access functions for the <em>.bin</em> files are also provided.</p>
Figures 4–6 in The Western Palaearctic species of Psilophrys Mayr (Hymenoptera, Chalcidoidea: Encyrtidae), parasitoids of kermesids (Hemiptera, Coccoidea: Kermesidae) attacking oaks (Quercus spp.)
Figures 4–6. Psilophrys bella, ♀. (4) Ovipositor. (5) Antenna. (6) Fore wing.
Figures 1–3 in The Western Palaearctic species of Psilophrys Mayr (Hymenoptera, Chalcidoidea: Encyrtidae), parasitoids of kermesids (Hemiptera, Coccoidea: Kermesidae) attacking oaks (Quercus spp.)
Figures 1–3. Psilophrys aristotelei, ♀. (1) Antenna. (2) Fore wing. (3) Ovipositor.
FIGURE 3 in Attack and defense in a gamasid-oribatid mite predator-prey experiment - sclerotization outperforms chemical repellency
FIGURE 3: Body size and mass of adults and tritonymphs of Archegozetes longisetosus. Stars indicate significant differences (Welch two-sample t-test, ***p<0.001).
FIGURE 1 in Attack and defense in a gamasid-oribatid mite predator-prey experiment - sclerotization outperforms chemical repellency
FIGURE 1: Consumption [%] of the predatory mite Stratiolaelaps miles feeding on differently treated adults and tritonymphs of Archegozetes longisetosus (armed= control group; disarmed= hexane treated specimens). Stars indicate significant differences (Mann-Whitney-U-test, ***p<0.001).
FIGURE 2 in Attack and defense in a gamasid-oribatid mite predator-prey experiment - sclerotization outperforms chemical repellency
FIGURE 2: Normalized amount of defensive secretion of attacked and control group Archegozetes longisetosus. Stars indicate significant differences (Mann-Whitney-U-test, ***p<0.001).
Fig. 5. Reproductive territorial male attacking a in Social and reproductive physiology and behavior of the Neotropical cichlid fish Cichlasoma dimerus under laboratory conditions
Fig. 5. Reproductive territorial male attacking a non reproductive territorial male.
Supplemental material for: Increased large vessel occlusive strokes following the Christchurch 2019 March 15 terror attack
<div class="WordSection1"> <p><b>Background:</b> The impact of sudden catastrophic events such as terror attacks on stroke is unclear. We investigated the association between the 2019 March 15 Christchurch terror attack and ischaemic stroke admissions and reperfusion therapies.</p> <p><b>Methods:</b> We used a Bayesian Poisson model to estimate the effect of the terror attack on ischaemic stroke admissions, the numbers of intracranial large vessel occlusion (LVO) and reperfusion therapy, in the week after the attack compared with weekly data from 1st January 2018 until 21st April 2019. These analyses were repeated for the rest of New Zealand with Christchurch data omitted. The probability of the rate observed in the week following the terror attack being higher than the background rate was calculated for each measure, with a probability higher than 99.5% providing strong evidence of an effect.</p> <p><b>Results:</b> In the week starting on Monday, three days after the terror attack, there was an increase in the number of patients with intracranial LVOs (10, background mean rate=2.4, probability P>99.9%) and those receiving reperfusion therapy (9, mean rate=2.6, P=99.9%), compared with the baseline rates. There was no difference in the total number of ischemic stroke admissions (26, mean rate=27). There was no strong evidence of a change in the numbers of reperfusion therapies or ischaemic stroke admissions in the rest of New Zealand.</p> <p><b>Conclusion: </b>Sudden catastrophic events such as terror attacks may increase the numbers of patients developing intracranial LVO requiring stroke reperfusion therapies within the affected community.</p> </div>
Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France
<h1>Description</h1> <p>This repository contains the supplementary materials (<em>Supplementary_information.pdf</em>, <em>Supplementary_figures.pdf</em>, <em>Supplementary_tables.pdf</em>) of the manuscript: "<strong>Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France</strong>". This repository also provides the R codes and datasets necessary to run the analyses described in the manuscript. </p> <h1>Datasets</h1> <p>We provide the spatially anonymized R datasets to respect confidentiality. Therefore, the preliminary preparation of the data is not provided in the public codes. These datasets, all geolocated and necessary to the analyses, are:</p> <ul> <li><em>Attack_sf.RData</em>: 19,302 analyzed wolf attacks on sheep <br> - ID: unique ID of the attack <br> - DATE: date of the attack <br> - PASTURE: the related pasture ID from "Pasture_sf" where the attack is located <br> - STATUS: column resulting from the preparation and the attribution of attacks to pastures (part 2.2.4 of the manuscript); not shown here to respect confidentiality </li> <li><em>Pasture_sf.RData</em>: 4987 analyzed pastures grazed by sheep <br> - ID: unique ID of the pasture <br> - CODE: Official code in the pastoral census <br> - FLOCK_SIZE: maximum annual number of sheep grazing in the pasture <br> - USED_MONTHS: months for which the pasture is grazed by sheep </li> <li><em>Removal_sf.RData</em>: 232 analyzed single or multiple wolf removals <br> - ID: unique ID of the removal <br> - OVERLAP: are they single removal ("single" in the manuscript => "NO" here), or not ("multiple" in the manuscrit, here "SIMULTANEOUS" for removals occurring during the exact same operation or "NON-SIMULTANEOUS" if not). <br> - DATE_MIN: date of the single removal or date of the first removal of a group <br> - DATE_MAX: date of the single removal or date of the last removal of a group <br> - CLASS: administrative type of the removal according to definitions from 2.1 part of the manuscript <br> - SEX: sex of the removed wolves if known <br> - AGE: class age of the removed wolves if known <br> - BREEDER: breeding status of the removed female wolves, "Yes" for female breeder, "No" for female non-breeder. Males are "No" by default, when necropsied; dead individuals with NA were not found. <br> - SEASON: season of the removal, as defined in part 2.3.4 of the manuscript <br> - MASSIF: mountain range attributed to the removal, as defined in part 2.3.4 of the manuscript </li> <li><em>Mountain_range_buff_sf.RData</em>: one row for each mountain range, corresponding to the buffered mountain ranges where removal control events could be sampled, as defined in part 2.3.3 of the manuscript </li> <li><em>Area_to_exclude_sf.RData</em>: one row for each mountain range, corresponding to the area too close from other mountain ranges, terrestrial and maritime limits, where removal control events could not be sampled, as defined in part 2.3.3 of the manuscript </li> <li><em>Overlapping_removal_sf.RData</em> corresponds to the spatial dataset necessary to run the supplementary figures about overlapping removals (S9) </li> </ul> <p>You can also find the object called <em>Subset_lt.RData</em>, which gives the ID of removals for each dataset (single/multiple removals) or subsets of single removals. </p> <p>The other RData resulted from the analysis. How to read their names: </p> <ul> <li>First part: <br> - <em>Buffer</em>: they link the attacks to removals or of control events. <br> - <em>Kernel</em>: they give the results of the kernel density estimation (z), according to the spatial (y) and temporal (x) coordinates, for each dataset.<br> - <em>Int</em>: they give, for each distance and time, the total amount of attack intensities before (INT_BEF) and after (INT_AFT) the combined locations and days of removals or control events. 1 unit of distance = 50 meters, 1 unit of time = 1 day.<br> - <em>Trend</em>: they give, for each distance and time, the trends in % of the attack intensities when comparing before and after, with their uncertainties and significance. Files with <em>spatshift</em> are designed for figures about spatial shift (unnested scales), contrary to files without <em>spatshift</em> (nested scales). </li> <li>Middle parts: (noted with an X after) <br> - <em>obs</em>: results from removals <br> - <em>jack</em>: results from jackknife samples of the removals <br> - <em>ctl</em>: results related to control sets <br> - <em>cor</em>: results corrected for livestock presence<br> - <em>raw</em>: results uncorrected for livestock presence<br> - <em>sim</em>: results for simulated attacks according to livestock presence </li> </ul> <p>Lengths of lists or sublists correspond to: </p> <p>- 20 elements: the two main datasets (single/multiple) and the 18 subsets of single removals. <br>- 100 elements: the 100 control sets. <br>- 1000 elements: the 1000 simulations of attacks (for the livestock presence correction). <br>- Other lengths and <em>jack</em> within the name: the jackknife samples. </p> <h1>Structure of the repository</h1> <h2><strong>Code 1</strong>: file <em>Buffer.R</em> </h2> <p>We keep only removals within geographic zones for the analysis (<em>Removal_analyzed_sf</em>), and sample their control events (100 simulations = control sets, <em>Removal_ctl_sf_lt</em>).</p> <p>We start by delimiting the spatio-temporal buffer for each row of the removal and control datasets. <br> - We identify the attacks from <em>Attack_sf.RData</em> within each buffer, thanks to the function <em>Buffer_fn</em>, giving the data frames <em>Buffer_X_df</em> (one row per attack) <br> - We select the pastures from <em>Pasture_sf.RData</em> within each buffer, thanks to the function <em>Buffer_pasture_fn</em>, giving the data frames <em>Buffer_X_sf</em> (one row per removal or control event) </p> <p>We calculate the spatial correction: <br> - We spatially slice each buffer into 200 rings with the function <em>Ring_fn</em>, giving the data frame <em>Ring_sf</em> (one row per ring) <br> - We add the total pastoral area of the ring of the attack ("SPATIAL_WEIGHT") with the function <em>Spatial_correction_fn</em>, for each attack of each buffer, within <em>Buffer_X_df</em> </p> <p>We calculate the pastoral correction: <br> - We create the pastoral matrix for each removal or control event with the function <em>Pastoral_matrix_fn</em>, giving a matrix of 200 rows (one for each ring) and 180 columns (one for each day, 90 days before the removal date and 90 day after the removal date), with the total pastoral area in use by sheep for each corresponding cell of the matrix (one element per removal, <em>Pastoral_X_mx_lt.RData</em>) <br> - We simulate, for each removal or control event, the random distribution of the attacks from <em>Buffer_X_df.RData</em> according to <em>Pastoral_X_mx_lt.RData</em> with the function <em>Buffer_sim_fn</em>. The process is done 1000 times (one element per simulation, <em>Buffer_X_sim_lt.RData</em>). </p> <h2>Code 2: file <em>Kernel.R</em> where we estimate the attack intensities </h2> <p>We classified the removals into 2 main datasets and 18 subsets, according to part 2.3.4 of the manuscript (<em>Subset_lt.RData</em>) (one element per set).<br>We compute the jackknife samples for each dataset or subset (<em>Removal_id_jack_lt</em>). <br>We perform the kernel estimations with the function <em>Kernel_fn</em> (<em>Kernel_X_lt</em>). <br>We sum the intensities of attacks before and after the removals or control events, with the function <em>Intensity_fn</em>, giving <em>Int_X_df_lt.RData</em>. </p> <h2>Code 3: file <em>Trend.R</em> where we calculate the trends of attack intensities after removals </h2> <p> We focus on the nested trends first: <br> - We calculate them (<em>Trend_X_df</em>) with function <em>Trend_fn </em><br> - We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (<em>Trend_X_comparison_df</em>) </p> <p>We focus on the spatial shifts (trends for each specific distance):<br> - We calculate them (<em>Trend_X_spatshift_df</em>) with function <em>Trend_fn</em> <br> - We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (<em>Trend_X_comparison_spatshift_df</em>) </p> <h2>- Code 4: file <em>Functions.R</em> where can be found all custom functions called in the other analysis codes </h2> <h2>- Code 5: file <em>Figures.R</em> produces part of the figures from the manuscript</h2> <p>Detailed comments are included in each code.</p> <h1>Support</h1> <p>If you have any question or request, do not hesitate to contact us at: oksana.grente@gmail.com</p> <h1>Authors and acknowledgment</h1> <p>Grente Oksana (CEFE, CNRS), Opitz Thomas (INRAE), Duchamp Christophe (OFB), Drouet-Hoguet Nolwenn (OFB), Chamaillé-Jammes Simon (CEFE, CNRS) and Gimenez Olivier (CEFE, CNRS). </p> <h1>License</h1> <p>GNU GENERAL PUBLIC LICENSE 3.0</p> <p> </p>
Data - Attack of the clones: population genetics reveals clonality of Colletotrichum lupini, the causal agent of lupin anthracnose
<p><em>Colletotrichum lupini</em>, causing lupin anthracnose, is one of the worst pathogens to lupin cultivation worldwide. Understanding its population structure and evolutionary potential is crucial to design successful disease management strategies. The objective of this study was to employ population genetics to investigate the genetic diversity, evolutionary dynamics and molecular basis of host-speciation of this notorious lupin pathogen. A collection of globally representative <em>C. lupini </em>isolates was genotyped through triple digest restriction-site associated DNA sequencing (3D-RADseq), resulting in a dataset of unparalleled resolution. Phylogenetic and structural analysis could distinguish four (I – IV) independent lineages. The strong population structure, low recombination rate and slow linkage decay strongly indicate that <em>C. lupini</em> reproduces clonally. Different morphologies and virulence patterns on white and Andean lupin were observed between and within clonal lineages. Lineage II isolates were shown to have a mini chromosome which was also partly present in lineage III and IV, but not in lineage I isolates. Variation in the presence of this mini-chromosome could indicate a function related to virulence or host-speciation. All four lineages were present in the South American Andes region, which is concluded to be the center of origin of this species. Only members of lineage II have been found outside South America since the 1990s, indicating it as the current pandemic population. As a seed-borne pathogen, <em>C. lupini</em> has mainly spread through infected but symptomless seeds, stressing the importance of phytosanitary measures to prevent future outbreaks of strains that are yet confined to South America.</p>
Severe environmental conditions create severe conflicts? A novel ecological pathway to extreme coyote attacks on humans
<p>1) Identifying the circumstances and causes of carnivore attacks on humans is important for prevention of future incidents as well as employing effective wildlife management strategies. Cape Breton Highlands National Park (CBHNP) in Nova Scotia has experienced multiple attacks by coyotes (Canis latrans) on humans, including a fatal attack on an adult in 2009.</p> <p>2) Here we use a combination of data on space use and diet collected from 2011–2013 to reveal that limited resources and a reliance on a large ungulate (moose, Alces americanus) as the mechanism leading to aggression by coyotes in CBHNP.</p> <p>3) Resident coyotes exhibited large home range sizes (mean=77.5 km2) indicative of limited resources and spatiotemporal avoidance of human activity. Carbon (d13C) and nitrogen (d15N) isotope values of sub-sampled coyote whiskers (n=32), which provide a longitudinal record of diet over the months before collection, revealed little intra- and inter-individual variation with nearly all individuals specializing on moose, a pattern that agrees with indices of natural resource availability. Specifically, stable isotope mixing models show that moose was the most important prey for most coyotes (25/32), representing between 41% and 78% of dietary inputs. Only four coyotes exhibited use of anthropogenic resources (food), and only one of seven coyotes involved in attacks on people had been consuming human foods before the attacks.</p> <p>4) Synthesis and Applications: We have described a unique ecological system in which a generalist carnivore has expanded its niche to specialize on a large prey species, with the unfortunate consequence of also expanding pathways to conflicts with people. Our results suggest extreme unprovoked predatory attacks by coyotes on people are likely to be quite rare and associated with unique ecological characteristics. Extreme management actions such as bounties are unnecessary, but managers may need to employ hazing or lethal removal earlier in the conflict process than under normal circumstances. Also, users of these areas should be made aware of the risks coyotes pose and encouraged to take precautions. </p>
A case report of an Eurasian Jay (Garrulus glandarius) attacking an incubating adult and depredating the eggs of the Japanese Tit (Parus minor)
<p>In May 2021, we opportunistically observed one Eurasian Jay (<em>Garrulus</em> <em>glandarius</em>) attacking an adult incubating Japanese Tit (<em>Parus</em> <em>minor</em>) and depredating nine tit eggs at a nestbox where a woodpecker had greatly enlarged the entrance. After the predation event, the Japanese tits abandoned the nest. We recommend that when using artificial nest boxes to protect hole-nesting birds, the appropriate entrance size should be proportional to the body size of the target species. This observation gives us a better understanding of the potential predators of secondary hole-nesting birds.</p>
Developmental changes in red-eyed treefrog embryo behavior increase escape-hatching success in wasp attacks
<p>The arboreal embryos of red-eyed treefrogs (<em>Agalychnis</em> <em>callidryas</em>) hatch prematurely to escape from egg-predators, and escape success increases with age. We assessed developmental changes in the behavior and hatching performance of embryos attacked by wasps (<em>Polybia</em> <em>rejecta</em>) and their contributions to improved embryo survival. We recorded videos of 4- and 5-day-old embryos exposed to wasp attacks and determined each embryo's fate. For a stratified random sample of embryos that escaped and died, we determined the occurrence, sequence, and timing of events during wasp-embryo interactions. We constructed path diagrams of event sequences, tested for age effects on transition probabilities, and measured durations of periods between key events. Overall escape success was 38% higher in older embryos. They were more likely to hatch pre-emptively than younger ones, thus less likely to experience direct attacks, suggesting that developmental gains in mechanosensory sensitivity may increase hatching responses to indirect cues. During direct attacks, embryos were equally likely to be captured by wasps at both ages and hatching speed was similar, suggesting no relevant difference in escape-hatching performance. After a wasp ruptured their egg capsule, older embryos were more likely to exit and did so much sooner; younger embryos remained in ruptured capsules for longer and were more likely to be attacked again. This developmental change in embryo behavior indicates decreased tolerance for egg-stage risk as the chance of tadpole survival increases, suggesting that ontogenetic adaptation to changing risk trade-offs contributes strongly to the developmental increase in escape success.<span></span></p>
Fig. 30 in Revision of Ceranisus and the related thrips-attacking entedonine genera (Hymenoptera: Eulophidae) of the world
Fig. 30. Entedonomphale nubilipennis, male antenna (paralectotype). Scale line = 0.1 mm.
Transfer-Path-Based Hardware-Reuse Strong PUF Achieving Modeling Attack Resilience With >200 Million Training CRPs [DATASET]
<p>This is the CRP dataset for the IEEE TIFS paper titled "Transfer-Path-Based Hardware-Reuse Strong PUF Achieving Modeling Attack Resilience With >200 Million Training CRPs". It contains 2 packages with details as below:</p> <p>Package 1: "CRPs_14k_2048TPs_bit5_bit6.zip"<br> Description: It consists of 2048*3 files, which contains all the CRPs extracted from 2048 TPs with DOA disable. Using these data, we can train each ANN model for each TP(i,j), and the corresponding prediction results are shown in Fig. 14 and Fig. 15 in the paper.</p> <p>- "tdc_stim_i_j.csv" is the 64-bit challenges of TP(i,j).<br> - "tdc_resp_F1_i_j_bit5.csv" is the responses of TP(i,j) with bit(5) of TDC used as PUF response<br> - "tdc_resp_F1_i_j_bit6.csv" is the responses of TP(i,j) with bit(6) of TDC used as PUF response</p> <p>Package 2: "CRPs_DOA_330818x1024_bit5_bit6.zip"<br> Description: It consists of 3 files. Using the dataset, we trained each ANN model for each bit in RSobf, and the prediction results of all the ANN models are shown in Fig.17. Notice that the length of RS is dependent on the specific challenge. Here, only those RS consisting of ≥1024 responses are selected and only the first 1024 bits in the selected RS are used for authentication accordingly.</p> <p>- "chal_330818.csv" is the 330818 64-bit challenges.<br> - "resp_330818x1024_bit5.csv" is the 330818 1024-bit obfuscated response streams (RSobf) extracted from 2048 TPs using bit(5) of TDC.<br> - "resp_330818x1024_bit6.csv" is the 330818 1024-bit RSobf extracted from 2048 TPs using bit(6) of TDC.<br> </p>
Molecular dynamics simulations of CD59 and CD59-inhibited Membrane Attack Complex
<p>Coarse-grain (CG) trajectories of CD59-C5b8 (last 1,500 ns):</p> <ul> <li>cd59-c5b8_1500ns_rep1.xtc</li> <li>cd59-c5b8_1500ns_rep2.xtc</li> <li>cd59-c5b8_1500ns_rep3.xtc</li> </ul> <p>PyLipID analysis results from CG CD59-C5b8 simulations:</p> <ul> <li>Interactions_CHOL.csv</li> <li>Interactions_DOPC.csv</li> </ul> <p>Atomistic CD59 simulations in DOPC membrane:</p> <ul> <li>cd59_at_rep1_light.trr</li> <li>cd59_at_rep2_light.trr</li> <li>cd59_at_rep3_light.trr</li> </ul> <p>CD59 Euler angles relative to the membrane: </p> <ul> <li>rep1.csv</li> <li>rep2.csv</li> <li>rep3.csv</li> </ul> <p>All xtc and trr files were down-sampled (frames removed) to decrease file size.</p>
Codes and dataset associated to the paper "Quantifying the impacts of the angle of attack on the morphology of atmospheric von Kármán vortex streets"
<p>Here you can find some codes and dataset used for the paper:</p> <p>"Quantifying the impacts of the angle of attack on the morphology of atmospheric von Kármán vortex streets":</p> <p>- WRF: namelist.wps, namelist.input and myoutfields.txt;</p> <p>- Python script: Rotation.py that allows to rotate the variables of interest in the WPS geo_em.do* files and Fig3.py that allows to reproduce the plot of the Figure 3 of the paper;</p> <p>- Dataset: km_profile.npy, PT_profile.npy, Wspd_profile.npy and z.npy, the four arrays required as input for the script Fig3.py. The z.npy array reports the height for the lower 28 eta-levels (up to about 2000 m) and in the other three arrays you can find the hourly mean values of potential temperature (PT_profile.npy), wind speed (WSPD_profile.npy) and eddy viscosity (km_profile.npy).</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.