Dataset Search

Many Intrusion Detection Systems (IDS) has been proposed in the current decade. To evaluate the effectiveness of the IDS Canadian Institute of Cybersecurity presented a state of art dataset named CICIDS2017, consisting of latest threats and features. The dataset draws attention of many researchers as it represents threats which were not addressed by the older datasets. While undertaking an experimental research on CICIDS2017, it has been found that the dataset has few major shortcomings. These issues are sufficient enough to biased the detection engine of any typical IDS.

This dataset comprises synchronized multi-modal physiological recordings—functional Near-Infrared Spectroscopy (fNIRS), Electroencephalography (EEG), Electrocardiography (ECG), and Electromyography (EMG)—collected from 16 participants exposed to emotion-eliciting video stimuli. It includes raw signals, event markers, and Python scripts for data import and preprocessing. Special emphasis is placed on fNIRS, which, though less common in affective computing, provides valuable hemodynamic insights that complement electrical signals from EEG, ECG, and EMG.

The concept of “radio travelers” is not familiar to many. These people have all the qualities of travelers: the desire to visit interesting, sometimes hard-to-reach places with an unusual landscape, nature, mountains, sea. Secondly, and this is the main thing: having reached the goal, they continue traveling, but already on the radio amateur air! To do this, participants install antennas, connect amateur radio HF and VHF equipment to them and conduct two-way radio communications. There are quite a lot of people willing to conduct communications with such radio stations.

In order to reduce the dimensionality (reduce the number of features of the object of study), the article uses the methodology of “compendium–table–infographics”. Three projects related to the study of radio wave propagation in the Crimea and the Black Sea Fleet (1914, 1928 and 1954–1955), as well as studies of the physical parameters of fields and environments (hydroacoustic and magnetic fields, radar, thermal and laser characteristics of surface ships, seismic and seismoacoustic waves) are considered.

Sodium tube triode detectors designed by the American radio engineer Harold Donle are considered. An analysis of the operating principle of the sodium detector has been made. Donle's patent for the design of a sodium detector is presented. The first design of a sodium detector lamp, in which a container of liquid metal sodium was used as the anode, is presented in detail. The reasons for refusing further use of this detector lamp design are indicated. Attention is paid to the second improved version of the Sodion S-13 lamp detector using a small drop of sodium the size of a match head.

The paper proposes an analytical model for calculating the normalized equivalent isotropic radiated power, which allows analyzing the applicability of the asymptotic approximation proposed in the 3GPP TR 38.884 report as an acceptable method for reducing the measuring distance when determining the equivalent isotropic radiated power of 5G and higher communication system equipment in the FR2 frequency range.

The paper substantiates the method of orthogonalization of linearly independent functions based on the calculation of the orthogonality weight. The form of orthogonal functions does not differ from the form of the original functions. Synthesis of optimal receivers is carried out based on the criterion of minimum noise variance at the receiver output for a fixed reference value of the useful signal. In the case of non-white noise, op-timal receivers have a finite gain in the entire operating frequency band.

We propose a radar method for determining the roll angle of an aircraft using a passive beacon in the form of a radar polarization-anisotropic trihedral trihedral corner reflector (СR) with a triangular shape of reflecting edges and with horizontal intrinsic polarization. To determine the roll angle, the onboard radar uses probing pulsed radio signals with a linear plane of polarization coinciding with the transverse axis of the aircraft. Reception of reflected pulse radio signals from the passive beacon is carried out on board the aircraft in a circular polarization basis.

The article analyzes the methods for measuring the radio frequency parameters of 5G subscriber equipment in the millimeter wave range for the scenario of autonomous deployment (SA) of the 5G RAN radio access network and shows the features of these measurements associated with the need to carry out them using Over-the-Air methods. The issues of analyzing the parameters being tested, analyzing the methods of Over-the-Air measurements for testing 5G subscriber equipment and improving contactless testing methods are considered.

Success in creating the BINP SB RAS S-band klystron with an output power of 60 MW set the team a new task of creating a C-band klystron, since the global practice of developing modern charged particle accelerators tends to move towards higher frequen-cies. The designed C-band klystron has a twice higher operating frequency of 5712 MHz.

The article presents the results of a comprehensive theoretical and experimental study of ferromagnetic resonance and microwave magnetoelectric effect in multilayer ferrite-piezoelectric structures containing single-crystal iron-yttrium garnet of cut (111) in combination with piezoelectric materials. Mathematical modeling of physical processes occurring in ferrite was performed, which made it possible to reveal weak magnetic cubic anisotropy, which must be taken into account when developing electrically tunable microwave devices.

It is shown that in semiconductor structures with a low quantum dimension, it is possible to obtain the required potential relief for quasiparticles by changing the sizes of the structure elements. By using the example of a resonant-tunneling diode formed from planar nanowires of various cross-sections and lengths, it is demonstrated how such changes affect the potential for electrons and the electrical characteristics of the device. The considered approach is based on the effect of dimensional quantization of the energy of quasiparticles in solids.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that impairs a person's ability to concentrate, manage impulses, and maintain attention. ADHD can have a wide range of repercussions, including academic and professional difficulties as well as relationship and emotional issues. Individuals with ADHD may also have handwriting impairments, such as poor fine motor coordination, legibility, and writing speed. These writing difficulties may be related to dysgraphia, a specific writing impairment that affects people with ADHD.

The Aerial Image Segmentation Dataset (AISD) is a high-resolution semantic segmentation dataset designed specifically for extracting buildings and roads. The original image is sourced from the online remote sensing images provided by OpenStreetMap and manually annotated. In our experiment, we selected building data from the Potsdam and Tokyo regions. The original image size for Potsdam was 3296 × 3296 pixels, while for Tokyo it was 2500 × 2500 pixels. In this dataset, we cropped the target area image into a 512 × 512 size image.

This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.

This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.

This dataset collection supports the research presented in the manuscript titled “Privacy-preserving and Verifiable Federated Learning for Biometric Data in Edge Computing” (submitted to IEEE Transactions on Knowledge and Data Engineering). It includes three curated biometric datasets—SigD, BIDMC, and TBME—that are used to evaluate the BPVFL framework’s performance in privacy-preserving and verifiable federated learning scenarios.

This dataset supports the findings of the study evaluating a novel programmable system (MODD) for assessing and training cognitive-motor integration in young adults. The files include processed SPSS data (.sav format) for three MODD tasks—Go-NoGo (gng_wide.sav), Memory Task (mt_wide.sav), and X-Task (x_wide.sav)—alongside a consolidated cognitive test dataset (MODD_Cognitive data.sav) containing performance metrics across standardized tasks including Stroop, Sternberg, and Dual Task.

How We Built This: Data, Tools, and Trust

We used official data from UNdata (last accessed November 2024), focusing on threatened species by country and year. The information was grouped into three main biodiversity categories—Vertebrates, Invertebrates, and Plants.

Using Python and Pandas, we cleaned and filtered the dataset to remove duplicates and non-country entries. For each year between 2004 and 2023, we highlighted the top 25 countries with the highest number of threatened species per category. This made the data easier to visualize and understand.

Fair Use for Academic Research: If you use this dataset, please cite the following paper to ensure proper attribution

M. A. Onsu, P. Lohan, B. Kantarci, A. Syed, M. Andrews, S. Kennedy, "Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring," 30th IEEE Symposium on Computers and Communications (ISCC), July 2025, Bologna, Italy.

Preprint available here: https://arxiv.org/pdf/2502.11304

Adverse driving conditions like darkness, rain, and fog present significant challenges to professional drivers as well as to computer vision algorithms in autonomous vehicles. One potential solution is to use an on-board system for real-time image translation, transforming weather-affected images into clear ones.

This dataset contains 60,000 annotated records modeling UAV-based and IoT sensor-driven agriculture environments. Each record includes UAV imaging data (NDVI, NDRE, RGB damage score), IoT sensor values (NPK, pH, moisture, temperature, humidity), semantic labels (NDI, PDI), and metadata for energy consumption, latency, and service migration. It is designed for validating Digital Twin frameworks, semantic communication models, and Federated Deep Reinforcement Learning (FDRL) in precision farming.

This dataset contains 10,000 synthesized sequences (10 seconds each) of North Atlantic Right Whale vocalizations for acoustic event detection research. It features four vocalization types (upcalls, gunshots, screams, moancalls) with varying durations from 0.8-4.2 seconds. The data is stratified across four signal-to-noise ratio levels (-10 to 10 dB) and split into training (7,000), validation (1,500), and test (1,500) sets.