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866 results for “attack”
Dataset for Sandboxing use case SUC2 related to cyber attacks affecting Wide Area Protection
<p><span>This dataset is related to the operation of the second KIOS CoE sandboxing use case (SUC2) which inclused 3 scenarios (S1-S3) which examins the behavious a WAP scheme of power grids in case of a short circuit fault and in case of two types of cyber attacks. The description of the architecture of the University of Cyprus/ KIOS CoE sandboxing environmnet used for extracting these datasets along with the full list of scenarios and their detailed implementation are described in the supporting documents.</span></p> <p><span>Brief description of each of the 3 scenarios of this SUC2 are provided below.</span></p> <p><span>The datasets for the first scenario (S1) of SUC2</span><span> examines the operation of a wide area protection scheme in a transmission line which receives data sent from PMUs at the two ends of the lines, when a short-circuit fault occurred in the range of the transmission line between buses 7 and 8 of the system. More details about the scenario SUC2/S1 related to this scenario's dataset can be found in Section </span><span>1.3.1</span><span> of the SUC2 supporting document. </span><span><span>The dataset includes electrical measurements of the current flow in line 7-8 (of the IEEE 9-bus system), in both magnitude and sinusoidal form</span><span>.</span><span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files, which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller as they were sent by the two PMUs, while the sine wave measurements were recorder through the OPAL-RT</span></span></p> <p><span>The datasets for second scenario (S2) of SUC2 investigates the operation of a wide area protection scheme which receives data sent from PMUs when a MITM FDI cyber-attack is conducted on the measurements of bus 7</span><span>, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the Typhoon controller via IEEE C37.118 protocol</span><span>. Section 1.3.2 of the SUC2 supporting document provides more details about the scenario related to this dataset. </span><span>This dataset includes electrical measurements of the current flow, in magnitude and sinusoidal format, of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of magnitude values were recorded by the Typhoon controller, while the data from the sinusoidal waveform were recorder by OPAL-RT. </span></p> <p><span>Thie dataset of the SUC2/S3 examines the operation of a wide area protection scheme which receives data sent from PMUs when a combined MITM with DoS cyber-attack is conducted, as actual attack, in the isolated communication network of the sandboxing environment, disrupting the C37.118 UDP communication exchanged between OPAL-RT 5707, where the digital twin of IEEE 9-bus system was implemented, and Typhoon controller. More details about this scenario associated to this dataset can be found in Section </span><span>1.3.3<span></span></span><span> of the supporting document of SUC2.</span></p> <p><span>This dataset includes electrical measurements of current’s flow magnitude of the transmission line between buses 7 and 8 of the <span>digital twin of the IEEE 9-bus system.</span> The dataset was recorded by the Typhoon controller, and it is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. In addition, the dataset includes network traffic packets captured as .pcapng<span> </span>and .csv files. <span> </span></span></p>
Cyber-attack scenarios for super-heaters system
<p>Simulated cyber-attacks for super-heaters system. For detailed description refer to pdf file. Dataset is in the form of tab separated txt files.</p> <p>In case of any questions please contact michal.syfert@pw.edu.pl or anna.sztyber@pw.edu.pl.</p> <p>Please cite: Sztyber-Betley, A.; Syfert, M.; Kościelny, J.M.; Górecka, Z. Controller Cyber-Attack Detection and Isolation. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2778. https://doi.org/10.3390/s23052778</p>
Cross Platform Dataset with Posts Surrounding U.S capitol attack
<p>This is a cross-platform dataset containing the posts around specific hashtags related to U.S. Capitol protests on January 6th 2021. </p>
Defeating Adversarial Attacks Againt Adversarial attacks in Network Security
<p>We investigate if the feature randomization approach to improve the robustness of forensic detectors to targeted attacks in network security, can be extended to detectors based on deep learning features. In particular, we study the transferability of adversarial examples targeting an original CNN image manipulation detector to other detectors that rely on a random subset of the features extracted from the flatten layer of the original network. The results we got by considering, two original network architectures and different classes of attacks, show that feature randomization helps to hinder attack transferability, even if, in some cases, simply changing the architecture of the detector, or even retraining the detector is enough to prevent the transferability of the attacks.</p>
HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
<p>Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets.</p> <p>To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a></p>
TCAB: Text Classification Attack Benchmark Dataset
<p>TCAB is a large collection of successful adversarial attacks on state-of-the-art text classification models trained on multiple sentiment and abuse domain datasets.</p> <p>The dataset is broken up into 2 files: <em>train.csv and</em> <em>val.csv</em>. The training set contains 1,448,751 instances (552,364 are "clean" unperturbed instances) and the validation set contains 482,914 instances (178,607 are "clean"). Each instance contains the following attributes:</p> <p><strong>scenario</strong>: Domain, either <em>abuse</em> or <em>sentiment</em>.</p> <p><strong>target_model_dataset</strong>: Dataset being attacked.</p> <p><strong>target_model_train_dataset</strong>: Dataset the target model trained on.</p> <p><strong>target_model</strong>: Type of victim model (e.g., <em>bert</em>, <em>roberta</em>, <em>xlnet</em>).</p> <p><strong>attack_toolchain</strong>: Open-source attack toolchain, either TextAttack or OpenAttack.</p> <p><strong>attack_name</strong>: Name of the attack method.</p> <p><strong>original_text</strong>: Original input text.</p> <p><strong>original_output</strong>: Prediction probabilities of the target model on the original text.</p> <p><strong>ground_truth</strong>: Encoded label for the original task of the domain dataset. 1 and 0 means toxic and toxic for abuse datasets, respectively. 1 and 0 means positive and negative sentiment for sentiment datasets. If there is a neutral sentiment, then 2, 1, 0 means positive, neutral, and negative sentiment.</p> <p><strong>status</strong>: Unperturbed example if "clean"; successful adversarial attack if "success".</p> <p><strong>perturbed_text</strong>: Text after it has been perturbed by an attack.</p> <p><strong>perturbed_output</strong>: Prediction probabilities of the target model on the perturbed text.</p> <p><strong>attack_time</strong>: Time taken to execute the attack.</p> <p><strong>num_queries</strong>: Number of queries performed while attacking.</p> <p><strong>frac_words_changed</strong>: Fraction of words changed due to an attack.</p> <p><strong>test_index</strong>: Index of each unique source example (original instance) (LEGACY - necessary for backwards compatibility).</p> <p><strong>original_text_identifier</strong>: Index of each unique source example (original instance).</p> <p><strong>unique_src_instance_identifier</strong>: Primary key to uniquely identify to every source instance; comprised of (<em>target_model_dataset</em>, <em>test_index</em>, <em>original_text_identifier</em>).</p> <p><strong>pk</strong>: Primary key to uniquely identify every attack instance; comprised of (<em>attack_name</em>, <em>attack_toolchain</em>, <em>original_text_identifier</em>, <em>scenario</em>, <em>target_model</em>, <em>target_model_dataset</em>, <em>test_index).</em></p>
Safety impact of DoS attacks on V2X-based collision warning
<p>The dataset represents Straight Crossing Path (SCP) intersection scenarios, where a Host Vehicle (HV) and a Remote Vehicle (RV) approach a right-angled intersection at different velocities and cross each other's paths simultaneously. By manipulating the starting positions, the driving scenarios were defined in such a way that the two vehicles collide in all cases.</p> <p>Scenarios were implemented with the following speed levels:</p> <table> <tbody> <tr> <td> <p><strong>Scenario</strong></p> </td> <td> <p><strong>RV speed [km/h]</strong></p> </td> <td> <p><strong>HV speed [km/h]</strong></p> </td> </tr> <tr> <td> <p>S1</p> </td> <td> <p>20</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>S2</p> </td> <td> <p>50</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S3</p> </td> <td> <p>20</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S4</p> </td> <td> <p>50</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S5</p> </td> <td> <p>20</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S6</p> </td> <td> <p>50</p> </td> <td> <p>130</p> </td> </tr> </tbody> </table> <p> </p> <p>We quantified the <a href="https://www.sciencedirect.com/science/article/pii/S2214209622000614" target="_blank" rel="noopener"><strong>safety risk (Safety Risk Index - SRI)</strong> </a>related to the specific V2X scenarios based on network performance metrics (End-to-End latency – E2E; Packet Delivery Ratio – PDR).</p> <p>In our dataset, we differentiated the strength of the attack based on the primary wireless communication parameters:</p> <p>· the attacker's data transmission rate (AR),</p> <p>· the attack packet length (APL).</p> <p>Based on the six driving scenarios (S1-S6) and the attack parameters (attack packet length, attack rate), 780 scenarios were simulated for a total of 15,600 unique test points (20 static spatial measurement point / scenario).</p>
Dataset for KIOS CoE Sandboxing use-case SUC4 corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme (IEC 61850 GOOSE)
<p><span>The datasets reflect on two main scenarios (S1-S2) related to SUC4 - corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme. </span><span>The first scenario explores the response of the coordinated overcurrent protection when circuit breakers (CBs) are healthy, under normal operation, i.e., SUC4/S1(without attack), and the under a FDI cyberattack on IEC 61850 - GOOSE communication protocol, i.e., SUC4/S1(with FDI attack). </span>Similarly, the second scenario investigates the response of the coordinated overcurrent protection when there a mechanical failure in the CB of the downstream feeder, under normal operation, i.e., SUC4/S2(without attack), and the under a message suppresion (MS) cyber-attack on GOOSE protocol, i.e., SUC4/S2(with MS attack). Details regarding the datasets captured during the execution of each scenario (with and without attacks), including electrical measurements and network traffic, are briefly rsummarized below, while the full details are provided in the supporting documents.</p> <ul> <li><span><strong>SUC4/S1(without attack) datasets/Normal operation (without cyber-attack on GOOSE) when CBs are healthy </strong>: This dataset is related to the operation of the sandboxing use case SUC4 described in this document, which examines operation of the protection scheme in a substation using overcurrent protective relays (IEDs) in the sandboxing environment, that communicate with each other via IEC6180/GOOSE protocol. Specifically, this dataset corresponds to the first scenario (S1) of SUC4, without any attack. More details about the scenario related to this dataset can be found in Section 1.3.1 of the SUC4 supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.</span></li> <li><span><strong>SUC4/S1(with FDI attack) datasets/FDI cyber-attack on GOOSE signals when CBs are healthy</strong>: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> <li><span><strong>SUC4/S2(without attack) datasets/ Normal operation (without attack on GOOSE) when CB presents a failure</strong>: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of<br>the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> <li><span><strong>SUC4/S2(with MS attack) datasets/MS cyber-attack on GOOSE signals when CB presents a failure</strong>: This dataset corresponds to the second scenario (S2) of SUC4, where an MS cyber-attack is conducted in the local network in order prevent critical benign messages, such inter-trip messages requesting backup protection, to reach their destination (back-up IED) when a CB failure occurs during a short-circuit event. As a result, the duration of a short-circuit is prolonged or the protection scheme is not able to clear the short-circuit event, which can cause catastrophic failures to power system. More details about the scenario related to<br>this dataset can be found in Section 1.3.2 of the support document. The dataset includes electrical measurements of the upstream and downstream feeders of<br>the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> </ul>
Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids
<p>These datasets <span> illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section </span><span>1.3 of the supporting document of SUC5 (</span><span>accompanied by an in-depth analysis of the processes and an impact assessment). A</span><span>ll data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals. </span></p> <ul> <li><span><span><strong>SUC5/S1 datasets/<span>MITM with FDI</span> cyber-attack</strong><span><strong> in an active distribution grid (grid-connected)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> <li><span><span><span><strong>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> </ul>
Dataset for Advanced Persistent Threat (APT) Attacks on Power Substation Networks via GOOSE Protocol Exploitation
<p>This dataset captures network traffic from a simulated Advanced Persistent Threat (APT) campaign targeting a power substation's communication network. The attacker maintains a prolonged presence within the network, conducting low-profile scans using Nmap to stealthily discover the network configuration. The focus is on the communication between the Remote Terminal Unit (RTU), the Programmable Logic Controller (PLC), and the Bay Protection Unit, all of which utilize the Generic Object Oriented Substation Event (GOOSE) protocol for critical operations.</p>
Dataset for DoS and DDoS Attacks on Digital Meter SICAM via GOOSE Protocol Flooding
<p>This dataset presents network traffic data from simulated Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks on a Digital Meter SICAM device using the GOOSE protocol. An unauthorized attacker floods the SICAM meter's communication by initially sending 100 GOOSE packets at 1 ms intervals, followed by an intensified attack of 500 GOOSE packets. These actions render the meter unreachable by the legitimate Control Station, disrupting normal operations and data retrieval processes.</p>
Data for paper "Parametric analyses of attack-fault trees"
<p>This is the dataset for paper "Parametric analyses of attack-fault trees" published in the proceedings of the 19th International Conference on Application of Concurrency to System Design (ACSD 2019).</p>
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 diversity, evolutionary dynamics and molecular basis of host interaction 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 and slow linkage decay strongly suggests that <em>C. lupini</em> reproduces clonally. Different morphologies and virulence patterns on white (<em>Lupinus albus</em>) and Andean lupin (<em>L. mutabilis</em>) were observed between and within clonal lineages. Isolates belonging to lineage II 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 role in host interaction. 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>
FIGURES 8 – 15 in A new phytophagous species of Eurytoma (Hymenoptera: Eurytomidae) attacking Werauhia gladioliflora (Bromeliales: Bromeliaceae)
FIGURES 8 – 15. Eurytoma werauhia, female, except as noted: 8, ventral mesosoma; 9, lateral metasoma (male); 10, dorsal pupa; 11, anteroventral head, larva; 12, lateral habitus, larva; 13, anterolateral habitus, larva; 14, procoxae; 15, fore wing.
FIGURES 16 – 21 in A new phytophagous species of Eurytoma (Hymenoptera: Eurytomidae) attacking Werauhia gladioliflora (Bromeliales: Bromeliaceae)
FIGURES 16 – 21. Werauhia gladioliflora: 16, flowering specimen, in situ; 17, infested inflorescence; 18, male of E. werauhia drowned in bractal mucilage; 19, dissected floral bud with E. werauhia larva; 20, floral buds with damage by E. werauhia: emergence holes and necrotic interiors; 21, dissected floral bud with E. werauhia pupa.
FIGURES 1 – 7 in A new phytophagous species of Eurytoma (Hymenoptera: Eurytomidae) attacking Werauhia gladioliflora (Bromeliales: Bromeliaceae)
FIGURES 1 – 7. Eurytoma werauhia, female, except as noted: 1, antenna; 2, antenna (male); 3 – 4, anterior, posterior head; 5 a, lateral metasoma; 5 b, lateral petiole; 6, lateral mesosoma; 7, propodeum.
Data used in Prefetch Side-Channel Attacks
<p>Machine-/System-/library-/version-specific data is generated as a part of the attack. The results from measurements cannot directly be applied to other systems/libraries/versions or machines. Therefore we have publish the source code to generate the dataset.</p>
Fig. 2 in Characterization of leaf-rollers attacking forest and fruit trees in Azerbaijan (Lepidoptera: Tortricidae)
Fig. 2: Distribution of Tortrix viridana, Archips rosanus and Archips xylosteanus in different regions of Azerbaijan.
Figures 16–18. Edessa leucogramma biology and natural history. 16 in First report of the stinkbug Edessa leucogramma (Perty) (Hemiptera: Heteroptera: Pentatomidae: Edessinae) attacking Handroanthus chrysanthus (Jacq.) S.O. Grose (Bignoniaceae), with descriptions of the adult and immatures and notes on associated fungi and protozoa
Figures 16–18. Edessa leucogramma biology and natural history. 16) Flagellate protozoa associated with E. leucogramma. a–c) Different types of flagellate protozoa found in the digestive tract (i.e., salivary glands and ventriculus) of E. leucogramma. Photographs at a magnification of 1000 times the actual size (1000×). 17) Illustration of the digestive system of E. leucogramma: (Amr.) rectal ampulla; (Ec.) esophagus; (Dgs.) salivary gland's duct; (Gacc.) accessory gland; (Ipos) hindgut; (Lan.) anterior lobule of the salivary gland; (Lpo.) posterior lobule of salivary gland; (Rec.) rectum; (Ven 1–4) ventriculus 1–4 (Tmal) Malpighian tubes (Amr) rectal ampulla. 18) Dead adult female of E. leucogramma, mummified presumably by the attack of an entomopathogenic fungus, insert (square, lower right) shows the sporangium and spores of the potential entomopathogenic fungus.
Figures 13–15. Edessa leucogramma biology and natural history. 13 in First report of the stinkbug Edessa leucogramma (Perty) (Hemiptera: Heteroptera: Pentatomidae: Edessinae) attacking Handroanthus chrysanthus (Jacq.) S.O. Grose (Bignoniaceae), with descriptions of the adult and immatures and notes on associated fungi and protozoa
Figures 13–15. Edessa leucogramma biology and natural history. 13) Reproductive system of the female of Edessa leucogramma. a) Photograph of female's reproductive system. b) Illustration of the female's reproductive system: (Be.) spermatheca; (CaG.) genital? chamber; (Espa.) spermathecal bulb; (Fl.) lateral filament; (Gop.) gonopore; (Lg.) ovarian ligament; (Oc.) common oviduct (Ola.) lateral oviduct; (Ov.) ovarium; (Ova.) ovarioles. c) Photograph of the spermatheca and associated parts. 14) Damage produced by the feeding of E. leucogramma on H. chrysanthus. 15) Dissection of the salivary glands of E. leucogramma (Organ where the live protozoa were found).
ScienceDex guides
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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.