Find research datasets worth reusing

opencc-by-nc-sa-4.0Feb 2023View details →

Signs of life detection behind obstacles using an UWB radar sensor

The "Signs of life detection behind obstacles using an UWB radar sensor" dataset was created by University of West Attica, by collecting data from 9 people using an UWB sensor, which was the X4M200 UWB radar sensor by Novelda, with and without the existence of a wall between the subject and the radar for the purposes of RESCUER project (https://rescuerproject.eu/ - a Horizon 2020 Research & Innovation Programme under Grant Agreement No.101021836). In all cases, the subject was lying down in the same stance for about 1 min and the radar was placed in four different positions: a) 0.2 m from the ground, b) 0.5 m from the ground, c) 1 m from the ground, d) 1 m from the ground and with a 45 degree angle. For comparison purposes, we also include in the dataset the case where no human is present within the radar's detection area. The subjects were placed in a distance between 0.5 and 5 metres from the radar and different stances were considered, while data were collected both for the amplitude (designated as abs) and the phase of the signal (designated as angle). This information is designated in each filename. For example, the filename "2 facing radar abs.csv", indicates that the subject is placed in 2 metres away from the radar, he/she is facing the radar and the file contains the signal's amplitude. The sampling rate of the radar was set to 17 samples per second and a distance step of about 0.05144 m was also considered.

opencc-by-4.0Feb 2023View details →

UAV-FD: a dataset for actuator fault detection in multirotor drones

This dataset collects real flight data from a hexarotor under the effects of a chipped blade. A conventional ArduPilot based controller is employed, where the ArduPilot firmware is customized to increase the signal logging rate of the IMU variables, thus capturing enough information at higher frequencies. Additional variables are available, including on-board measurements, commands, estimations, and parameters; in particular, the actual speed of each motor is measured as well. The purpose of the UAV-FD dataset is to accelerate the research on actuator fault diagnosis for multirotor vehicles.

opencc-by-4.0Jun 2022View details →

Machine vision for vial-positioning detections towards safe automation of material synthesis

This repository contains an DenseSSD's datasets that predicts vial-positioning detection using object detection techniques. DenseSSD can play vital roles in addressing these safety issues as well as can alert to user's messenger to notify and fix safety issues as soon as possible.

Animations and figures for Report - If light is stretched by gravitational waves, why can we use light as a ruler to detect them?

Package for .gifs and images for report, the two .gif animations are to better represent the images used to explain the entire process from section 3.

Structural similarities between SAM and ATP recognition motifs and detection of ATP binding in a SAM binding DNA methyltransferase

We have provided all the necessary files, outputs, as well as the ReadMe file for this work to be redone.

Neural-logic Multi-agent System for flood event detection

This repository contains the source code for the paper "Neural-logic Multi-agent System for flood event detection".  The code implements a multi-agent system (MAS) designed to detect flooding events.

opencc-by-nc-sa-4.0Mar 2023View details →

Honey Particle Detection Dataset

A synthetically generated dataset of honey samples observed by a digital microscope. The goal of this dataset is to train machine learning models to detect the pollen found in the images. This dataset contains 500 images with three different classes of particles commonly found in honey. The annotations are provided in YOLO format in a different directory. Each annotation is associated with the image by an unique ID. The images follow the naming convention "image_{ID}.png" and the annotations follow the convention "image_{ID}.txt". This dataset has been created by Sonicat Systems and is published under Creative Commons Attribution Non-Commercial Share Alike license.

Image dataset for training of an insect detection model for the Insect Detect DIY camera trap

This dataset contains images of an artifical flower platform with different insects sitting on it or flying above it. All images were automatically recorded with the <a href="https://maxsitt.github.io/insect-detect-docs/">Insect Detect DIY camera trap</a>, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (<a href="https://doi.org/10.1101/2023.12.05.570242">bioRxiv preprint</a>).<h2>Classes</h2>The following object classes were annotated in this dataset:<ul><li>wasp (mostly Vespula sp.)</li><li>hbee (Apis mellifera)</li><li>fly (mostly Brachycera)</li><li>hovfly (various Syrphidae, e.g. Episyrphus balteatus)</li><li>other (all Arthropods with insufficient occurences, e.g. various Hymenoptera, true bugs, beetles)</li><li>shadow (shadows of the recorded insects)</li></ul>View the <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/health">Health Check</a> for more info on class balance.<h2>Versions</h2><ul><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/4">v4 insect_detect_416_1class</a><ul><li>squashed to square (aspect ratio 1:1)</li><li>downscaled to 416x416 pixel</li><li>all classes merged into one class ("insect")</li></ul></li><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/5">v5 insect_detect_raw_4K</a><ul><li>original images in 4K resolution (3840x2160 pixel)</li></ul></li><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/7">v7 insect_detect_320_1class</a><ul><li>squashed to square (aspect ratio 1:1)</li><li>downscaled to 320x320 pixel</li><li>all classes merged into one class ("insect")</li></ul></li></ul><h2>Deployment</h2>You can use this dataset as starting point to train your own insect detection models. Check the <a href="https://maxsitt.github.io/insect-detect-docs/modeltraining/train_detection/">model training instructions</a> for more information.Open source Python scripts to deploy the trained models can be found at the <a href="https://github.com/maxsitt/insect-detect">insect-detect GitHub repo</a>.

Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization

Intersection Monitoring: A dataset for vehicle detection using an infrastructure camera including ground truth vehicle localization. This dataset contains images from a infrastructure camera monitoring an intersection and the ground-truth information for a vehicle crossing it from different directions. The data is aimed to develop and improve image-based vehicle detection algorithms. The dataset includes two different recordings with the same structure. For each of them, the sequence of images is provided in PNG format, along with the ground truth of the vehicle. The ground truth data was captured using a high-precision GNSS receiver installed on the test vehicle, fused with in-vehicle sensors using an Extended Kalman Filter (EKF) . Time considerations The camera and GNSS receiver clocks were synchronized before each test to have the same time base. Each test last about 140 seconds. Image files Image files can be found on the img/ folder within for each test. The camera was configured to record images at 25 fps: <ul> <li>Test 1: 3704 files</li> <li>Test 2: 3426 files</li> </ul> Vehicle localization ground truth The test vehicle is a prototype of Autonomous Vehicle developed by the <a href="https://autopia.car.upm-csic.es">AUTOPIA</a> research group at the <a href="https://car.upm-csic.es">Centre for Automation and Robotics</a> in Spain. Vehicle location mainly depends on a Trimble BX982 GNSS receiver using RTK inputs from a local station. However, the location algorithm applies an EKF for combining GNSS measurements with different onboard sensors providing yaw rate, longitudinal acceleration and speed, steering wheel position and speed, etc. For each test, the vehicle.csv file contains the vehicle information recorded at 20Hz. This file includes: <ul> <li>UTM Time: Time using the format HHMMSSss: <ul> <li>HH: Hours</li> <li>MM: Minutes</li> <li>SS: Seconds</li> <li>ss: Fraction of seconds</li> </ul> </li> <li>UTM East: East coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>UTMNorth: North coordinate of the GNSS antenna in the UTM frame, in meters.</li> <li>Orientation: Yaw angle of the vehicle, measured from the East axis (x-axis).</li> <li>Speed: Vehicle speed in m/s</li> <li>Acceleration: Vehicle acceleration in m/s^2</li> </ul> Camera info The file camera_parameters.json includes all the information about the intrinsic and extrinsic parameters for the camera. The configuration stored on this file appplies to all tests. The camera used is an AXIS M1125 with variable focal length. It was installed in a communication tower near the intersection. Vehicle info The file vehicle_parameters.json includes information about vehicle dimensions and antenna location. The GNSS antenna is installed near the rear axle of the vehicle, in the middle part of the vehicle. The configuration stored on this file appplies to all tests. Tools Some MATLAB tools can be found in <a href="https://github.com/autopia-car/datasets-tools-intersection-monitoring">https://github.com/autopia-car/datasets-tools-intersection-monitoring</a> Disclaimer That the dataset comes "AS IS", without express or implied warranty and/or any liability exceeding mandatory statutory obligations. This especially applies to any obligations of care or indemnification in connection with the dataset. The dataset was created for our research purposes only and no quality assessment was done for the usage in products of any kind. We can therefore not guarantee for the correctness, completeness or reliability of the provided data set.

GCMS data files for: Detection of dairy products from multiple taxa in Late Neolithic pottery from Poland: an integrated biomolecular approach

The detection of dairy processing is pivotal to our understanding of ancient subsistence strategies. This culinary process is linked to key arguments surrounding the evolution of lactase persistence in prehistory. Despite extensive evidence indicating the presence of dairy products in ceramics in the European Neolithic, questions remain about the nature and extent of milk (and lactose) processing and consumption. In order to investigate past patterns of dairy processing, here we analyse ancient proteins identified from Late Neolithic Funnel Beaker ceramics, scrutinising the principle that curd and whey proteins partition during the production of dairy foods from milk. Our results indicate the presence of casein-rich dairy products in these vessels suggesting the creation of curd-enriched products from raw milk. Moreover, this analysis reveals the use of multiple species for their dairy products in the Late Neolithic Funnel Beaker culture, adding to a growing body of evidence that multiple taxa were exploited for dairying in the Neolithic. Alongside palaeoproteomic analysis we also apply lipid residue analysis, with discrepancies in these two approaches suggesting that effects from isotope mixing may be underestimating the frequency of milk use in prehistoric pottery, highlighting the utility of a multi-stranded approach.

Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.1

This data is based on the paper: Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE 1.1 (unpublished).

Open-population SCR model to estimate spatiotemporal variation in individual birth locations, detection rates, and survival

This is an open population SCR model developed by R. Chandler and K. Engebretsen. The full model incorporates 4 spatial covariates in birth location density submodel, 3 location-specific, temporal covariates in the detection submodel, and 4 spatial covariates in the survival submodel. Formatted data is provided for the 2015 and 2016 fawning season in south Florida and the model can be fit using the script fitFawnModel.R.

Holothuria and sponge detected from an underwater image of the CCZ in Pacific Ocean

Holothuria and sponge detected (using an A.I.) from an underwater image of the CCZ in Pacific Ocean. The image was recorded from cruise SO268 in 2019.

Resource-Centric Goal Model Slicing for Detecting Feature Interactions

Supplementary materials of the paper entitled: “Resource-Centric Goal Model Slicing for Detecting Feature Interactions” file01 - The features related to the concept of Decline-Mutual-Exclusion. file02 - The features related to the concept of Decline-Produce-and-Use file03 - The features related to the concept of Decline-State-Changing file04 - The features related to the concept of Enhanced-State-Changing file05 - Zoom-features-2022

Data for: SNPs detected in pool-seq data from resistant and susceptible Cimex lectularius populations

In the last few years, the bed bug Cimex lectularius has been an increasing problem world-wide, mainly due to the development of insecticide resistance to pyrethroids. The characterization of resistance alleles is a prerequisite to improve surveillance and resistance management. To identify genomic variants associated with pyrethroid resistance in Cimex lectularius, we compared the genetic composition of two recent and resistant populations with that of two ancientsusceptible strains using a genome-wide pool-seq design. We identified a large 6 Mb "superlocus" showing particularly high genetic differentiation and association with the resistance phenotype. This superlocus contained several clustered resistance genes, andwas also characterized by a high density of structural variants (inversions, duplications). The possibility that this superlocus constitute a resistance "supergene" that evolved after the clustering of alleles adapted to insecticide and after reduction in recombination is discussed.

opencc-by-4.0Jun 2021View details →

Detection of Silver Nanoparticles in Seawater Using Surface-Enhanced Raman Scattering

Nanomaterials significantly contribute to the development of new solutions to improve consumer products properties. Silver nanoparticles (AgNPs) are one of the most used, and as human exposure to such NPs increases, there is a growing need for analytical methods to identify and quantify nanoparticles present in the environment. Here we designed a detection strategy for AgNPs in seawater using surface-enhanced Raman Scattering (SERS). Three commercial AgNPs coated with polyvinylpyrrolidone (PVP) were used to determine the relative impact of size (PVP-15nmAgNPs and PVP-100nmAgNPs) and aggregation degree (predefined Ag aggregates, PVP-50–80nmAgNPs) on the SERS-based detection method. The study of colloidal stability and dissolution of selected AgNPs into seawater was carried out by dynamic light scattering and UV-vis spectroscopy. We showed that PVP-15nmAgNPs and PVP-100nmAgNPs remained colloidally stable, while PVP-50–80nmAgNPs formed bigger aggregates. We demonstrated that the SERS-based method developed here have the capacity to detect and quantify single and aggregates of AgNPs in seawater. The size had almost no effect on the detection limit (2.15 ± 1.22 mg/L for PVP-15nmAgNPs vs. 1.51 ± 0.71 mg/L for PVP-100nmAgNPs), while aggregation caused an increase of 2.9-fold (6.08 ± 1.21 mg/L). Our results demonstrate the importance of understanding NPs transformation in seawater since this can influence the detection method performance.

Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps

Dataset analyzed in the study "Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps". Use of the data requires proper reference to [1]. Dataset contains Electromyographic data from forearm, recorded with an 8-channel sEMG Biometrics Ltd. device. The fields contained in the structure are those detailed in the following scheme:: <ul> <li>Group: 0 for healthy subjects; 1 for HOA patients</li> <li>Subject: subject ID;</li> <li>Grasp: grasp ID, according to Figure 1 [1];</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [2].</li> </ul> [1] Jarque-Bou, N.J.; Gracia-Ibáñez, V.; Roda-Sales, A.; Bayarri-Porcar, V.; Sancho-Bru, J.L.; Vergara, M. Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps. Sensors 2023, 23, 2413. https://doi.org/10.3390/s23052413 [2] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. J. NeuroEngineering Rehabil. (2018).

Does pre-sorting by colour using visible and high-energy violet light improve the detection of plant species in honey bee pollen baskets?

<div class="article-section__content en main"> Premise Pollen collected by honey bees from different plant species often differs in color, and this has been used as a basis for plant identification. The objective of this study was to develop a new, low-cost protocol to sort pollen pellets by color using high-energy violet light and visible light to determine whether pollen pellet color is associated with variations in plant species identity. Methods and Results We identified 35 distinct colors and found that 52% of pollen subsamples (n = 200) were dominated by a single taxon. Among these near-pure pellets, only one color consistently represented a single pollen taxon (Asteraceae: Cichorioideae). Across the spectrum of colors spanning yellows, oranges, and browns, similarly colored pollen pellets contained pollen from multiple plant families ranging from two to 13 families per color. Conclusions Sorting pollen pellets illuminated under high-energy violet light lit from four directions within a custom-made light box aided in distinguishing pellet composition, especially in pellets within the same color. </div>

Detecting Patterns In UML Diagrammatic Readings - An Experimental Study

This experiment was developed within the scope of the thesis "Constructing An Auditory Notation In Software Engineering - Understanding UML Models with voice and sound", with the main purpose of understanding if there any emerging patterns in how different people carry out diagrammatic readings of UML diagrams (Class and State Machine Diagrams, specifically). This is achieved by observing the most common actions amongst the participants. The transcriptions of this study are included here.

ECG and EEG stress features for: ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study

Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual cycle, women may exhibit different responses to stress from men. This, therefore, may have an impact on stress detection and classification accuracy of machine learning models that genders are not taken into account. However, this has never been investigated before. In addition, only a handful of stress detection devices are scientifically validated. To this end, this work proposes stress detection and multilevel stress classification models for unspecified and specified genders through ECG and EEG signals. Models for stress detection are achieved through developing and evaluating multiple individual classifiers. On the other hand, stacking technique is employed to obtain models for multilevel stress classification. ECG and EEG features extracted from 40 subjects (21 females and 19 males) were used to train and validate the models. In the low&high combined stress condition, RBF-SVM and kNN yielded the highest average classification accuracy for females (79.81%) and males (73.77%), respectively. Combining ECG and EEG, the average classification accuracy increased to at least 87.58% (male, high stress) and up to 92.70% (female, high stress). For multilevel stress classification from ECG and EEG, the accuracy for females was 62.60% and for males was 71.57%. This study shows that the difference in genders influences the classification performance for both the detection and multilevel classification of stress. The developed models can be used for both personal (through ECG) and clinical (through ECG and EEG) stress monitoring with and without taking genders into account.