Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

Fig. 2 in Severe attacks caused by Macrolenes dentipes (Olivier) on Feijoa, Acca sellowiana (O. Berg) Burret (Myrtaceae) in Italy (Coleoptera: Chrysomelidae)

Fig. 2 – Macrolenes dentipes (Olivier). Calabria, Borgia, 10.VI.2020. Percentage of leaf damage for each range.

opencc-by-4.0Dec 2022View details →

Fig. 1 in Severe attacks caused by Macrolenes dentipes (Olivier) on Feijoa, Acca sellowiana (O. Berg) Burret (Myrtaceae) in Italy (Coleoptera: Chrysomelidae)

Fig. 1 – Adults of Macrolenes dentipes (Olivier) on Feijoa with damaged leaves. Calabria, Borgia, 29.V.2021.

Fig. 3 in Biology and life history of Atanycolus cappaerti (Hymenoptera: Braconidae), a North American larval parasitoid attacking the invasive emerald ash borer (Coleoptera: Buprestidae)

Fig. 3. Immature stages of Atanycolus cappaerti. (A) Egg on Agrilus planipennis larva at 3× magnification; (B) 1st instar at 3× magnification; (C) 2nd instar at 3× magnification; (D) 3rd instar at 3× magnification; (E) 4th instar at 3× magnification; (F) 5th instar at 1.8× magnification; (G) 6th instar at 1.8× magnification; (H) pupal cocoon at 1.8× magnification; (I) eclosed adult.

Fig. 2 in Biology and life history of Atanycolus cappaerti (Hymenoptera: Braconidae), a North American larval parasitoid attacking the invasive emerald ash borer (Coleoptera: Buprestidae)

Fig. 2. Diapause behavior of Atanycolus cappaerti progeny when reared in normal rearing conditions (25 ± 2 °C, 65 ± 10% RH, and a photoperiod of 16:8 h L:D).

Fig. 1 in Biology and life history of Atanycolus cappaerti (Hymenoptera: Braconidae), a North American larval parasitoid attacking the invasive emerald ash borer (Coleoptera: Buprestidae)

Fig. 1. (A) Longevity and (B) realized fecundity and host utilization (parasitism) rate of adults of Atanycolus cappaerti when reared in single mating pairs (n = 16 for both sexes) and continually provided with emerald ash borer larvae on a weekly basis for their lifespan.

Fig. 2 in Tolerance of KS-4202 soybean to the attack of Bemisia tabaci biotype B (Hemiptera: Aleyrodidae)

Fig. 2. Comparison of the percentages of reducton in productvity between KS-4202 and Conquista for each pattern of Bemisia tabaci biotype B infestaton. The means of the columns labeled with the same letter for each pattern of infestaton do not differ according to Tukey's test (P> 0.05); ns = not significant. From the lef, the columns represent the treatments as follows: infested with no chemical control (F = 0.45; df = 3; P = 0.5507), infested and sprayed at 15 DAI (F = 17.65; df = 3; P = 0.0246), infested and sprayed at 30 DAI (F = 4.99; df = 3; P = 0.1116), infested and sprayed at 45 DAI (F = 20.21; df = 3; P = 0.0205), and infested and sprayed at 60 DAI (F = 4.53; df = 3; P= 0.1231). DAI = days afer infestaton.

Fig. 1 in Tolerance of KS-4202 soybean to the attack of Bemisia tabaci biotype B (Hemiptera: Aleyrodidae)

Fig. 1. Mean number of live Bemisia tabaci biotype B nymphs per cm2 for the KS-4202 and Conquista genotypes for each pattern of infestaton at 5 periods of evaluaton.

opencc-by-4.0Jul 2024View details →

Datasets for sandboxing use case SUC3 corresponding to cyber attacks affecting the differential protection scheme of a HV transformer

These datasets reflect two main scenarios (S1-S2) associated to the operation of a sandboxing use case SUC3 corresponding to cyber attacks affecting the differential protection scheme of a HV transformer. Details about are illustrated in Section 1.3 of the supporting document. These scenarios analyse the operation of the digital twin of the IEEE 9-bus system and the differential protection scheme under healthy conditions, cyber-attack on communication channels of IEC 61850 Sample Values (SVs) protocol, and a fault in HV side of a transformer in the power system. The scenarios are presented with selected time-series plots in Section 1.3, accompanied a detailed analysis of the processes included and an impact assessment. Thus, during execution of each scenario, data such as electrical measurements were captured and are collected in the form of the datasets presented here. Specifically,  <ul> <li>SUC3/S1 Differential protection operation during transformer fault corresponds to the dataset of first scenario (S1) of the third sandboxing use case (SUC3) of the KIOS CoE Sandboxing for cyber-physical analysis of EPES, which examines the operation of differential protection scheme (implemented in Typhoon controller) for a HV/MV transformer. The protection scheme receives data sent through IEC 61850 SVs from the two sides of the transformer. Specifically, this dataset corresponds to the first scenario (S1) of SUC3, where a short-circuit occurred on the HV side of a HV/MV transformer of the system. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. This dataset includes electrical measurements of the current flow, in RMS and sinusoidal format, from the HV and MV sides of HV/MV transformer of the digital twin of the IEEE 9-bus system. 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, while the sinusoidal measurements were recorder by OPAL-RT.</li> <li>SUC3/S2 MITM with FDI cyber-attack in the SVs of HV transformer side corresponds to the dataset of the second scanario (S2) of the third sandboxing use case (SUC3) of the KIOS CoE Sandboxing for cyber-physical analysis of EPES, which  examines a MITM with FDI cyber-attack is conducted on the measurements of the HV side of the transformer, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the differential protection scheme via IEC 61850 protocol. Section 1.3.1 of the supporting document provides more details about the scenario related to this dataset. This dataset includes electrical measurements of the current flow, in RMS and sinusoidal format, from the HV and MV sides of HV/MV transformer of the digital twin of the IEEE 9-bus system. 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, while the measurements from the sine waves were recorder by OPAL-RT.</li> </ul>

opencc-by-4.0Jul 2016View details →

Figure 3. A in Humpback whales interfering when mammal-eating killer whales attack other species: Mobbing behavior and interspecific altruism?

Figure 3. A mother humpback whale and newborn calf photographed off Baja California, Mexico, Oct 2009. When necessary, the mother will use her massive pectoral flippers to defend her small calf from attacking predators, especially killer whales. Photo: M. Lynn, NOAA, Southwest Fisheries Science Center.

opencc-by-4.0Jul 2016View details →

Figure 1 in Humpback whales interfering when mammal-eating killer whales attack other species: Mobbing behavior and interspecific altruism?

Figure 1. Locations and numbers of recorded interactions between humpback and killer whales described in Appendix S2 and summarized in Table 1; the number in each circle is the number of interactions from the general area.

opencc-by-4.0Nov 2018View details →

Preventing Shark Attacks

This data set is composed of five parts each having its proper origins, formats and rights. It is used to design a spatial application allowing a visitor who does not know the Reunion Island (i.e. administrative French territory located in the Indian Ocean) and who is sea’s user to locate areas authorized (i.e.  under governmental surveillance in order to reduce the shark risk) to enjoy its favorite water-based activity.

opencc-by-4.0Mar 2017View details →

Fig. 2 in First records of parasitoids attacking the Asian citrus psyllid in Ecuador

Fig. 2. Letamendi, Febres Cordero and Tarqui (triangles), Guayaquil city urban districts where Diaphorencyrtus aligarhensis was detected. The lower box represents where the city is located.

opencc-by-4.0Jun 2020View details →

Figs 1, 2 in Phenotypic matching in ovipositor size in the parasitoid Galeopsomyia sp. (Hymenoptera, Eulophidae) attacking different gall inducers

Figs 1, 2. Phenotic matching between the parasitoid Galeopsomyia sp. (Eulophidae) and its galling hosts, five species of Bruggmania (Cecidomyiidae, B. elongata, B. robusta, B. acaudata, and Bruggmania sp. 1 and sp. 2) in Guapira opposita (Nyctaginaceae): Fig. 1, gall thickness (dimension between the outer wall of the gall and the larval chamber); Fig. 2, female ovipositor length of parasitoid in the different hosts.

opencc-by-4.0Sep 2019View details →

Fig. 1 in First record of Empoasca kraemeri (Hemiptera: Cicadellidae) attacking sweet potato in Brazil

Fig. 1. Adults of Empoasca kraemeri (Hemiptera: Cicadellidae) on sweet potato leaves (A, B), immature (C) and resulting injuries; chlorotic spots (C, D) Diamantina, Minas Gerais State, Brazil, in 2017.

opencc-by-4.0Sep 2024View details →

GLAD42 - Global Locust Attack Dataset for 42 countries

This dataset is compiled by collecting Locust attack data of 42 countries from 1985 to 2020 from FAO and environemntal features data for the same countries and same time period from TerraClimate website. This dataset have Target variable Locustpresent which has two classes yes and no. Moreover independent features have coutries, regions, start year, start date, Precipitation, Soil moisture and maximum temperature. This dataset is useful for predicting locust attack across 42 countries

opencc-by-4.0Feb 2024View details →

NCSRD-DS-5GDDoS: 5G Radio and Core metrics containing sporadic DDoS attacks

NCSRD-DS-5GDDos v3.0 Dataset === NCSRD-DS-5GDDos is a comprehensive dataset recorded in a real-world 5G testbed that aligns with the 3GPP specifications. The dataset captures Distributed Denial of Service (DDoS) attacks initiated by malicious connected users (UEs).  The setup comprises of 3 cells with a total of 9 UEs connected to the same core network. The 5G network is implemented by the Amarisoft Callbox Mini solution (cell 2), and we further employ a second cell using the Amarisoft Classic (cell 1 & 3), that also hosts the 5G core. The setup utilizes a broad set of UE devices comprising a set of smart phones (Huawei P40), microcomputers (Raspberry Pi 4 - Waveshare 5G Hat M2), industrial 5G routers (Industrial Waveshare 5G Router), a WiFi-6 mobile hotspot (DWR-2101 5G Wi-Fi 6 Mobile Hotspot) and a CPE box (Waveshare 5G CPE Box). All UEs are being operated by subsidiary hosts which are responsible for the traffic generation, occurring from scheduled communications times. All identifiers are artificially generated and do not represent or based on personal data. We identify each UE through its ‘imeisv’ ID, that corresponds to the device in use, due to vendor implementation, that uses the same IMSI for all UEs. This dataset captures attack data from a total of 5 malicious User Equipment (UE) devices that initiated various flooding attacks on a 5G network. Each record includes key identifiers such as the IMEISV (International Mobile Equipment Identity Software Version number) and IP address of the attacking UE, along with the device type. The file "summary_report.csv" summarizes this information. The traffic types used in the attacks include syn flooding, UDP flooding, ICMP flooding, DNS flooding, and GTP-U flooding. The benign users stream YouTube and Skype traffic. The dataset is recorded through the use of a data collector that interfaces with the 5G network and gathers data regarding UEs, gNBs and the Core Network. The data are recorded in an InfluxdB and pre-processed into three separate tabular .csv files for more efficient processing: “amari_ue_data.csv”, “enb_counters.csv” and “mme_counters.csv”. In this version, we use an Amarisoft Classic (cells 1 & 3, Core Network) and an Amarisoft Mini (cell 2) (more information on the products can be found in https://www.amarisoft.com/). The ”amari_ue_data.csv” provides information on the UEs regarding identification (“imeisv”, “5g_tmsi”, “rnti”), IP addressing, bearer information, cell information (“tac”, “ran_plmn”), and cell information (“ul_bitrate”, “dl_bitrate”, “cell_id”, retransmissions per user per cell “ul_retx” as well as aggregated bit rates for each cell). The ”enb_counters.csv” focuses on cell-level information, providing downlink and uplink bitrates, usage ratio per user, cpu load of the gNB. We provide separate files of ”amari_ue_data.csv” and ”enb_counters.csv” generated from each gNB (Amarisoft Classic and Mini). The “mme_counters.csv” provides information on the Non-Access Stratum (NAS) of the 5G Network and focuses on session status reports (e.g., number of PDU session establishments, paging, context setup. This part gives an overview of the connection management throughout the recording session, and provides information on features suggested by 3GPP for abnormal user behavior. We also provide a separate pre-processed dataset, that merges the two "amari_ue_data_*.csv" file, including labeling of the malicious/benign samples, and may be more flexible for interested data scientists. Please refer to README.txt for the features included in each file. If you use this dataset, please also cite the following papers: M. Christopoulou, A. Garos, A. Vekraki, D. Santorinaios, I. Koufos, S. Karamitsiani, G. Xilouris, M.-A. Kourtis, G. Gardikis, and P. Trakadas, “User Terminals as Attackers: An Open Dataset Analysis of DDoS Attacks in 5G Networks,” in Proc. 2024 IEEE Conf. Standards Commun. Netw. (CSCN), 2024, pp. 301–307, doi: 10.1109/CSCN63874.2024.10849694. G. Xylouris, A. Vekraki, M. Christopoulou, M. A. Kourtis, E. K. Markakis and P. Trakadas, "Advancing Predictive Security for Consumer Applications in Beyond 5G/6G Networks With Annotated Datasets," in IEEE Transactions on Consumer Electronics, doi: 10.1109/TCE.2025.3567151.

Coordinated Reply Attacks in Influence Campaigns: Characterization and Detection

We release the datasets to replicate the results of `Coordinated Reply Attacks in Influence Operations: Characterization and Detection'. See https://github.com/osome-iu/io-coordinated-replies for details.

Dataset for Unauthorized Access Attacks on Digital Meter SICAM via TCP/IP Communication

This dataset captures network traffic involving unauthorized access attacks on a Digital Meter SICAM device within a controlled test environment. It includes both normal operations and simulated attacks over TCP/IP communication. The clean traffic records legitimate interactions between the Control Station and the SICAM meter, including successful logins, data retrievals, and routine logoffs at specified timestamps. The attack traffic documents an intruder's activities after infiltrating the network: conducting network scans with Nmap, executing dictionary and brute-force attacks using Hydra to discover passwords, and accessing measured values on the SICAM meter.

Dataset for Detecting False Data Injection Attacks in GOOSE Protocol Communication between RTU and Bay Protection Unit

This dataset focuses on the detection and prevention of False Data Injection (FDI) attacks targeting the communication between a Remote Terminal Unit (RTU) and a Bay Protection Unit in a power substation, utilizing the Generic Object Oriented Substation Event (GOOSE) protocol. It includes both clean traffic events and recorded instances of FDI attacks.

Psyllid Attacks in Guava

The image set of guava leaves was collected from an area affected by psyllid infestation. In total, 261 images were collected from 50 guava trees. The dataset includes both leaves with and without insect attacks, and these images were reviewed by experts for labeling confirmation. The image dimensions are 2250 x 4000 pixels, captured with a 48 Megapixel camera. This dataset includes image annotations in various formats, suitable for use with different versions of YOLO and other object detection models such as Detectron and its variants.