Dataset Search

The data of ROSMAP dataset have been preprocessed and dimensionally reduced in the original research, thus we did not perform further preprocessing on it. For SCZ dataset, we firstly removed features with more than 50% missing or 0 expression values for all omics sets. Log transformation was then utilized to normalize omics expression values, and the Z-score method was used to standardize all features of each sample in every omics sets. Only samples presented in both omics sets and label set were retained in the dataset of analysis.

This dataset supports the LookCursor AI project, which implements eye-tracking-based cursor control using OpenCV and Dlib. The primary file included is shape_predictor_68_face_landmarks.dat, a pre-trained model used to detect and map 68 facial landmarks essential for tracking eye movements. The dataset enables accurate facial feature detection, which is critical for cursor movement based on eye gaze. This resource is valuable for researchers working on assistive technology, human-computer interaction (HCI), and computer vision applications.

This dataset contains 70,176 records with data from 2021 - 01 - 11 onwards. It features eight columns including `date`, `pressure`, `humidity`, etc. The `date` is in datetime format, while others are of float type. These data likely relate to environmental factors and photovoltaic real - power, enabling studies on photovoltaic performance and its influencing elements.

These are three sets of simulated echo data generated by Matlab. The first set of echo data comes from a surface target with a size comparable to a 2-D MIMO array, but with a center point offset from the array. The second group contains three echo data from point targets. The first packet is a point target within the physical size of the array, the second packet is a point target outside the physical size of the array, and the third packet is the sum of the first two packets.

The aims of the experiment described were to analyse the effect of material differences and the highly curved surface of small cylinders on laser scanning data uncertainty, and to link the experimental results to theory.

To this end, seven cylinders with different surface properties were measured with a terrestrial laser scanner. The cylinders were chosen to represent materials common in the built and natural environments, in addition to having distinct reflectivity characteristics. 

 **PDBBindv2016**  | Binding Affinity Regression | Benchmark Evaluation (Effectiveness) | Each sample in the PDBBind v2016 dataset is a complex, but we extracted the sequence data with substantial information loss to yield a protein-ligand sequence pair. We maintained the same split setting used in a previous study, where the refined set (excluding the core set) is treated as  training (train.csv) and validation (valid.csv) sets, while the core set (complexes with the highest resolution) is treated as the test set (test.csv).

The AMD3IR dataset is a large-scale collection of Shortwave Infrared (SWIR) and Longwave Infrared (LWIR) images, designed to advance the ongoing research in the field of drone detection and tracking. It efficiently addresses key challenges such as detecting and distinguishing small airborne objects, differentiating drones from background clutter, and overcoming visibility limitations present in conventional imaging. The dataset comprises 20,865 SWIR images with 24,994 annotated drones and 8,696 LWIR images with 10,400 annotated drones, featuring various UAV models.

With the increasing uncertainties introduced by intermittent renewable energy sources, as a critical decision-making tool for power system operations, security-constrained unit commitment (SCUC) provides an efficient solution for economically and robustly responding to the changes in the power system operating state. In this study, a graph reinforcement learning (GRL)-based approach is proposed to address the day-ahead SCUC problem, incorporating alternating current (AC) power flow constraints.

This dataset presents approximately 200 hours of network traffic captures focused on online gaming and video streaming applications, addressing 5G-related challenges of high throughput and low latency. Collected in Ottawa, Canada, and globally from 11 mobile operators across nine countries, the data includes timestamps, packet lengths (bytes), protocols, and IP addresses. Supplementary information such as geolocation, content provider labels, and round-trip latency (ms) enhances usability.

This video shows the experiments of full power transfer of a new CP-TFQP magnetic topology. Power transfer is demonstrated at 50 kW, when the system is both fully aligned and misaligned. The proposed system operates at consistent efficiency, with a lower input dc bus range compared to traditional systems. The TFQP topology is the main contribution, and comprises four coils, built in only three layers.

A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. The data is collected on a Dallara AV-21 at a racing circuit in Indiana, in collaboration with the Indy Autonomous Challenge (IAC).

Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems.

CO2 Emissions Data Visualization Project – I Hug Trees

The I Hug Trees CO2 emissions project is a data-driven initiative that visualizes global carbon footprints using interactive treemaps and bar charts. The dataset, sourced from UN Data, contains CO2 emissions figures for the top 25 highest-emitting countries, extracted from a larger global dataset. This structured CSV dataset categorizes emissions by country, industry, and energy source, enabling comparative analysis and trend identification.

Code search is essential for code reuse, allowing developers to efficiently locate relevant code snippets. Traditional encoder-based models, however, face challenges with poor generalization and input length limitations. In contrast, decoder-only large language models (LLMs), with their larger size, extensive pre-training, and ability to handle longer inputs, present a promising solution to these issues. However, their effectiveness in code search has not been fully explored.

Dual active bridge (DAB) converter is an important converter for electric vehicles, energy storage systems. Data sets are introduced to control the DC-DC power control. Triple phase-shift of DAB is controlled by the range of zero voltage switching. Optimal switching to reduce high current stress DC-DC converter is In presented data sets are used with python program to execute the different stages of machine learning. Performance of all stages are well addressed each data output file.

Dual active bridge (DAB) converter is an important converter for electric vehicles, energy storage systems. Data sets are introduced to control the DC-DC power control. Triple phase-shift of DAB is controlled by the range of zero voltage switching. Optimal switching to reduce high current stress DC-DC converter is In presented data sets are used with python program to execute the different stages of machine learning. Performance of all stages are well addressed each data output file.

Community detection in complex networks is a crucial task that seeks to partition nodes into tightly connected subsets, known as communities. This process is important in fields like social network analysis, bioinformatics, information propagation, and recommendation systems. To evaluate the effectiveness of community detection algorithms, benchmark datasets such as LFR, Karate Club, and Dolphins are widely used. These datasets represent graphs with multiple nodes, each divided into communities with high internal connectivity.

Short-Term object-interaction Anticipation (STA) consists in detecting the location of the next-active objects, the noun and verb categories of the interaction, as well as the time to contact from the observation of egocentric video. 

This ability is fundamental for wearable assistants to understand user's goals and provide timely assistance, or to enable human-robot interaction. 

In this work, we present a method to improve the performance of STA predictions. Our contributions are two-fold:

EEG signals in the NMT-5k dataset were captured using a 21-channel setup following the 10/20 electrode placement system, maintaining a sampling rate of 200 Hz. Data collection was conducted using the KT88-2400 system from Contec Medical Systems.

High-power radio frequency converters (RFCs) need to deliver a wide-range power against variable load impedance. However, it is difficult to achieve a wide-range power regulation and zero voltage switching (ZVS) simultaneously. To address thi.s issue, this paper proposes a DC-link parallel AC-link series (DPAS) multiple power amplifier (MPA) architecture with outphasing modulation. The generalized mathematical model of the proposed topology is presented. The system’s soft-switching range and criterion are analyzed.

The accurate identification of miRNA-disease associations plays a crucial role in biomedical research and clinical applications. However, most research focuses on the existence of the association, without conducting further exploration. In this study, we propose a novel statistical meta-path contrastive learning-based approach (SMCLMDA), which aims to accurately identify the multidimensional relationships(up/down-regulation and causal/non-causal) between miRNAs and diseases.