Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms - SensitivityAnalysis

The dataset "Sensitivity Analysis" consists of image sequences (videos) for apple detection and tracking and its corresponding ground truth. The ground truth is presented in MOT format. This dataset is part of the paper: Villacrés, J., Viscaino, M., Delpiano, J., Vougioukas, S. & Cheein, F. A. (2022). Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms. Computers and Electronics in Agriculture. The article is currently accepted. For a better reference format, please refer to the journal's official website. If you have used the material presented in this data set, please cite the previous article. For more information regarding the dataset, please refer to the paper mentioned below.

dryad32/100

Data for: Ear pinnae in a neotropical katydid (Orthoptera: Tettigoniidae) function as ultrasound guides for bat detection

Early predator detection is a key component of the predator-prey arms race and has driven the evolution of multiple animal hearing systems. Katydids (Insecta) have sophisticated ears, each consisting of paired tympana on each foreleg that receive sound both externally, through the air, and internally via a narrowing ear canal running through the leg from an acoustic spiracle on the thorax. These ears are pressure-time difference receivers capable of sensitive and accurate directional hearing across a wide frequency range. Many katydid species have cuticular pinnae which form cavities around the outer tympanal surfaces, but their function is unknown. We investigated pinnal function in the katydid Copiphora gorgonensis by combining experimental biophysics and numerical modelling using 3D ear geometries. We found that the pinnae in C. gorgonensis do not assist in directional hearing for conspecific call frequencies, but instead act as ultrasound detectors. Pinnae induced large sound pressure gains (20–30 dB) that enhanced sound detection at high ultrasonic frequencies (>60 kHz), matching the echolocation range of co-occurring insectivorous gleaning bats. These findings were supported by behavioural and neural audiograms and pinnal cavity resonances from live specimens, and comparisons with the pinnal mechanics of sympatric katydid species, which together suggest that katydid pinnae primarily evolved for the enhanced detection of predatory bats.

opencc-zeroNov 2022View details →

Golden crucian carp detection dataset

Golden crucian carp detection dataset

Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: validation

Sanger sequencing and qPCR validation data for "Genome graphs detect human polymorphisms in active epigenomic states during influenza infection" manuscript.

JMLKelinci+: Detecting Semantic Bugs and Covering Branches with Valid Inputs using Coverage-Guided Fuzzing and Runtime Assertion Checking

Testing to detect semantic bugs is essential, especially for critical systems. Coverage-guided fuzzing and runtime assertion checking (RAC) are two well-known approaches for detecting semantic bugs. Coverage-guided fuzzing aims to generate inputs tests with high code coverage. However, while coverage-guided fuzzers are equipped with sanitizers that can detect a fixed set of semantic bugs, they can otherwise only detect bugs that lead to a crash. Thus, the first problem we address is how to help fuzzers detect previously unknown semantic bugs that do not lead to a crash. Moreover, a coverage-guided fuzzer may not necessarily cover all branches with valid inputs, although invalid inputs are useless for detecting semantic bugs.  So, the second problem is how to guide a fuzzer to cover all branches in a program using only valid inputs.  On the other hand, RAC monitors the expected behavior of a program dynamically and can only detect a semantic bug when a valid input test shows that the program does not satisfy its specification.   Thus, the third problem is how to provide high-quality input tests for a RAC that can trigger potential bugs. The combination of a coverage-guided fuzzer and RAC solves these problems and can cover branches with valid inputs and detect semantic bugs effectively. Our study uses RAC to guarantee that only valid inputs reach the program under test using the program's specified preconditions and it also uses RAC to detect semantic bugs using specified postconditions.  A prototype tool was developed for this study, named JMLKelinci+. Our results show that combining a coverage-guided fuzzer with RAC will lead to executing the program under test only with valid inputs and that this technique can effectively detect semantic bugs.  Also, this idea improves the feedback given to a coverage-guided fuzzer, enabling it to cover all branches faster in programs with non-trivial preconditions.

Why are design pattern detection tools created and how do users perceive them

Replication package containing the data obtained from the survey study about the design rationale behind the proposal of design pattern detection tools and the perception of DPD tools according to potential tool users. Contains a README file introducing each of the files in this replication package.

Global Atlas of Marine Heatwaves (MHWs) as detected from ESA CCI SST 1°x1° covering 1982-2021

Daily records of Marine Heatwaves (MHW) intensities and categories resulting from detection conducted on European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature (SST) satellite product, regridded to a 1°x1° regular grid, covering the period 01/01/1982-31/12/2021. The MHW detection has been carried out via Hobday's method (Hobday et al. 2016), with the following parameters: At a pixel-wise level a MHW event is detected when the SST value exceeds: - the 90th percentile threshold over climatology reference for 5 consecutive days at least, where - the climatology reference has been computed as the daily average over the whole period (1982-2021). The mhw field describes the intensity of anomaly [°C] of MHW events detected. The cat field gives information on the category of the events detected (1=moderate, 2=strong, 3=severe, 4=extreme). Definition of categories can be found in Hobday et al. (2018). The production of the dataset has been sustained with the support of the European Space Agency (ESA) "deteCtion and threAts of maRinE Heat waves" project (CAREHeat; grant number: 4000137121/21/I-DT) and of Copernicus Climate Change Service Quality Assessment of ECV Products (C3S_511; grant number: C3S_511_CNR) project. References: Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C., ... & Wernberg, T. (2016).  A hierarchical approach to defining marine heatwaves. Progress in Oceanography 141:227–238, https://doi.org/10.1016/j.pocean.2015.12.014. Hobday, A. J., Oliver, E. C., Gupta, A. S., Benthuysen, J. A., Burrows, M. T., Donat, M. G., ... & Smale, D. A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162-173, <a href="https://doi.org/10.5670/oceanog.2018.205">https://doi.org/10.5670/oceanog.2018.205</a>

Deep Learning Code Fragments for Code Clone Detection

Paper: Deep Learning Code Fragments for Code Clone Detection Authors: White, M., Tufano, M., Vendome, C. and Poshyvanyk, D. Conference: 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE)

Source data of scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection

The source data of the manuscript: scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.

Towards the Detection of Diffusion Model Deepfakes (Dataset)

This upload contains the dataset used in <a href="https://arxiv.org/abs/2210.14571">Towards the Detection of Diffusion Model Deepfakes</a>. Further details and instructions are provided in the <a href="https://github.com/jonasricker/diffusion-model-deepfake-detection">project's Github repository</a>.

Ground truth annotations for boiling bubble detection and measurement in microgravity

This is a dataset of ground truth annotations for benchmark data provided in A. Sielaff, D. Mangini, O. Kabov, M. Raza, A. Garivalis, M. Zupančič, S. Dehaeck, S. Evgenidis, C. Jacobs, D. Van Hoof, O. Oikonomidou, X. Zabulis, P. Karamaounas, A. Bender, F. Ronshin, M. Schinnerl, J. Sebilleau, C. Colin, P. Di Marco, T. Karapantsios, I. Golobič, A. Rednikov, P. Colinet, P. Stephan, L. Tadrist, The multiscale boiling investigation on-board the international space station: An overview, Applied Thermal Engineering 205 (2022) 117932. doi:10.1016/j.applthermaleng.2021.117932.   The annotations regard the 15 image sequences provided in the benchmark data and denoted as D1-D15. The annotators were asked to localize the contact points and points on the bubble boundary so an adequate contour identification is provided, according to the judgement of the expert. The annotators were two multiphase dynamics experts (RO, SE) and one image processing expert (ICS). The annotators used custom-made software to pinpoint samples upon contour locations in the images carefully, using magnification, undo, and editing facilities. The experts annotated the contact points and multiple points on the contour of the bubble until they were satisfied with the result. The annotations were collected for the first bubble of each sequence. For each bubble, 20 frames were sampled in chronological order and in equidistant temporal steps and annotated. All experts annotated data sets D1-D15. The rest were annotated by ICS after learning annotation insights from the multiphase dynamics experts. The format of the dataset is as follows.  A directory is dedicated to each bubble annotation. The directory name notes the number of the dataset and the annotator id. Each directory contains 20 text files and 20, corresponding, images. Each text file contains a list with the 2D coordinates of one bubble annotation. The first coordinate marks the left contact point and the last coordinate marks the right contact point. These coordinates refer to a corresponding image contained in the same directory. Text files and image files are corresponded through their file names, which contain the frame number. The frame number refers to the image sequence. Images are in lossless PNG format.

A Multimodal Dataset for Automatic Edge-AI Cough Detection

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge-AI fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events. Furthermore, several non-cough sounds (i.e. breathing, laughing, and throat clearing), background noises (i.e. music, traffic, bystander coughing) and motion scenarios (i.e. sitting, walking) mimicking daily life activities are also present, which the research community can use to accelerate ML algorithm development. For detailed information about using this dataset to train edge-AI models and example code, please refer to our public Git repository: https://github.com/esl-epfl/edge-ai-cough-count/

Pupillary dynamics reflect the impact of temporal expectation on detection strategy - data

This repository contains data from work that investigated how humans can extract hidden temporal cues from the occurrences of probabilistic targets and utilize them to inform target detection in a complex acoustic stream, using a combination of behavioral measures and pupillometry. It accompanies the article entitled: " Pupillary dynamics reflect the impact of temporal expectation on detection strategy" from the same authors. It contains: - preprocessed pupil data for all subjects - similarly matched behavior data for all subjects - custom code for both behavioral and pupil analysis

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

Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code

This package contains artifacts for 15 real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to v8 of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following: Original data: <ul> <li>For each project, its original artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> Fire Drill in issue-tracking data: <ul> <li>Ground truth for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> Fire Drill in source-code data: <ul> <li>Four technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>

Towards Fine-granularity Malicious Component Detection for Android Apps

These files are the experiment data of AMCDroid.         (1) is the dataset used in the experiment (we do not use the VirusShare dataset in 2019 since we failed to download it);         (2) is the malicious component detection result of the running example;         (3) to (7) are the experiment data in RQ1 to RQ5;         (8) is the malicious component detection result of Roaming Mantis; and         (9) is the malicious component detection result in internal validity. We will open source the code of AMCDroid after our paper is accepted.

Face detection dataset perturbed using Gimp's foggify filterwith turbulence=4.0 and opacity 100.

This dataset is a perturbed version of the dataset https://zenodo.org/record/3879989#.Y-HBCXbMI2w Please refer to https://zenodo.org/record/3879989#.Y-HBCXbMI2w  for eye coordinates and depth maps.

openother-ncFeb 2023View details →

Pan-Tumor T-Lymphocyte Detection Dataset

This is a pan-tumor immunohistochemistry dataset containing 2 mm2-sized regions of interest (ROIs) from 92 whole slide images stained for cluster of differentiation 3 (CD3, antibody clone SP7). The dataset covers four tumor indications: <ul> <li>32 head and neck squamous cell carcinoma (HNSCC) samples</li> <li>20 non-small cell lung cancer (NSCLC) samples</li> <li>20 triple-negative breast cancer (TNBC) samples</li> <li>20 gastric cancer (GC) samples</li> </ul> The samples were digitized at a resolution of 0.23 μm/px (40× objective lens) using the NanoZoomer 2.0-HT scanning system (Hamamatsu, Japan). All samples were prepared and digitized at Merck Healthcare KGaA. We provide bounding box annotations for three cell types: CD3+ immune cells, tumor cells, and other cells. For each tumor indication, five ROIs were manually annotated by three pathologists. For these ROIs, we provide all three annotations and the consensus of all raters. All other ROIs have been semi-automatically annotated using commercial image analysis software.

Detection and seismic study of gravity and Rossby mode pulsations in four contact binaries

MESA module and inlist files used in the paper “Detection and seismic study of gravity and Rossby mode pulsations in four contact binaries“

Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment

Supplemental Figures for Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment

Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment

Case Study Results for Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment