Find research datasets worth reusing

VirHunter: a deep learning-based method for detection of novel RNA viruses in plant sequencing data

This storage contains 2 archives: toy datasets to test the training of the VirHunter and weights of the  fully trained VirHunter models for 3 host species  (peach, grapevine, sugar beet) and for fragment sizes 500 and 1000.  . The toy dataset consists of 3 archived files: 'viruses.fasta', 'host.fasta', 'bacteria.fasta'. 'viruses.fasta' contains 10000 randomly selected plant viruses from the virus dataset described in the paper. 'host.fasta' consists of peach chromosome 2. 'bacteria.fasta' consists of 10 bacterial genomes selected randomly: GCF_000284415, GCF_000590555, GCF_001548055, GCF_002795265, GCF_003330825, GCF_003957805, GCF_005845345, GCF_009176625, GCF_010748935, GCF_014681765

Data and Material for 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'

Data and Material supporting the paper 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'.

opencc-by-4.0Dec 2021View details →

Human papillomavirus (HPV) detection in vaginal self-samples: evaluation of eNat® as an alternative suspension medium to ThinPrep®PreservCyt® for vaginal swabs

Dataset 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays Dataset 2: hrHPV viral load in cervical and vaginal self-samples Datakey 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays Datakey 2: hrHPV viral load in cervical and vaginal self-samples

opencc-by-4.0Jan 2022View details →

Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy

Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021).  The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a>

Outputs of the Jupyter Notebook - Tree crown detection using DeepForest

The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book. Contributions Notebook <ul> <li> Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a> </li> <li> Matt Allen (reviewer), Department of Geography - University of Cambridge, <a href="https://github.com/mja2106">@mja2106</a> </li> </ul> Modelling codebase <ul> <li> Ben Weinstein (maintainer & developer), University of Florida, <a href="https://github.com/bw4sz">@bw4sz</a> </li> <li> Henry Senyondo (support maintainer), University of Florida, <a href="https://github.com/henrykironde">@henrykironde</a> </li> <li> Ethan White (PI and author), University of Florida, <a href="https://github.com/ethanwhite">@weecology</a> </li> <li> Other contributors are listed in the <a href="https://github.com/weecology/DeepForest/graphs/contributors">GitHub repo</a> </li> </ul> Modelling publications <ul> <li> Ben G Weinstein, Sergio Marconi, Mélaine Aubry-Kientz, Gregoire Vincent, Henry Senyondo, and Ethan P White. Deepforest: a python package for rgb deep learning tree crown delineation. Methods in Ecology and Evolution, 11:1743–1751, 2020. URL: <a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472">https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472</a>, <a href="https://doi.org/https://doi.org/10.1111/2041-210X.13472">doi:https://doi.org/10.1111/2041-210X.13472</a>. </li> <li> Ben G Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, and Ethan White. Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks. Remote Sensing, 2019. URL: <a href="https://www.mdpi.com/2072-4292/11/11/1309">https://www.mdpi.com/2072-4292/11/11/1309</a>, <a href="https://doi.org/10.3390/rs11111309">doi:10.3390/rs11111309</a>. </li> <li> Ben G Weinstein, Sergio Marconi, Stephanie A Bohlman, Alina Zare, and Ethan P White. Cross-site learning in deep learning rgb tree crown detection. Ecological Informatics, 56:101061, 2020. URL: <a href="https://www.sciencedirect.com/science/article/pii/S157495412030011X">https://www.sciencedirect.com/science/article/pii/S157495412030011X</a>, <a href="https://doi.org/https://doi.org/10.1016/j.ecoinf.2020.101061">doi:https://doi.org/10.1016/j.ecoinf.2020.101061</a>. </li> </ul>

A Dataset for detecting Change Coupling and Structural Dependencies.

Introduction This repository hosts the results of runs evaluating the first developments of prototype tool for detecting Change Coupling and Structural Dependencies in the context of cyber-physical-systems(CPS). This tool analyzes the projects' code repository history and uses rule mining to detect logistical couplings, it also analyzes the source code to detect which of those changes have additional structural dependencies.    The two datasets are the result from the analysis of two popular GitHub CPS projects: <ul> <li>Eclipse Concierge (Java) — a small-footprint implementation of the OSGi Core Specification optimized for mobile and embedded devices.1</li> <li>PX4 — a flight control solution for drones, that also contains a Drone Middleware Platform, providing drivers and middleware to run drones 2</li> </ul> The datasets were generated by running a change coupling and structural dependencies analyzer, that is a prototype under development. The resulting raw data is available under the <code>project_results/Proj_Name</code> folder. Additionally, in the folder <code>notebooks </code>the user can find examples of how to easily query the functionality offered by the visualization and analytics libraries (in folder <code>analytics)</code>.   [1] https://github.com/eclipse/concierge [2] https://github.com/PX4/PX4-Autopilot

opencc-by-4.0Mar 2020View details →

Oral cancer speech corpus for paper "Detecting and analysing spontaneous oral cancer speech in the wild"

This is the oral cancer speech corpus used in the paper "Detecting and analysing spontaneous oral cancer speech in the wild". Description This dataset contains approximately 3 hours of oral cancer speech data collected from YouTube, including a file with additional metadata. We use this dataset to perform an oral cancer speech detection task in our paper. Funding This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No 766287. The Department of Head and Neck Oncology and surgery of the Netherlands Cancer Institute receives a research grant from Atos Medical (Horby, Sweden), which contributes to the existing infrastructure for quality of life research. Citation: If you use this dataset please cite: <pre><code>@misc{halpern2020detecting, title={Detecting and analysing spontaneous oral cancer speech in the wild}, author={Bence Mark Halpern and Rob van Son and Michiel van den Brekel and Odette Scharenborg}, year={2020}, eprint={2007.14205}, archivePrefix={arXiv}, primaryClass={eess.AS} }</code></pre>

opencc-by-4.0Dec 2021View details →

MOBDrone: a large-scale drone-view dataset for man overboard detection

Dataset The Man OverBoard Drone (MOBDrone) dataset is a large-scale collection of aerial footage images. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the mean sea level. Images are manually annotated with more than 180K bounding boxes localizing objects belonging to 5 categories --- person, boat, lifebuoy, surfboard, wood. More than 113K of these bounding boxes belong to the person category and localize people in the water simulating the need to be rescued. In this repository, we provide: <ul> <li> 66 Full HD video clips (total size: 5.5 GB)  </li> <li> 126,170 images extracted from the videos at a rate of 30 FPS (total size: 243 GB) </li> <li> 3 annotation files for the extracted images that follow the MS COCO data format (for more info see <a href="https://cocodataset.org/#format-data">https://cocodataset.org/#format-data</a>): <ul> <li> annotations_5_custom_classes.json: this file contains annotations concerning all five categories; please note that class ids do not correspond with the ones provided by the MS COCO standard since we account for two new classes not previously considered in the MS COCO dataset --- lifebuoy and wood </li> <li> annotations_3_coco_classes.json: this file contains annotations concerning the three classes also accounted by the MS COCO dataset --- person, boat, surfboard. Class ids correspond with the ones provided by the MS COCO standard. </li> <li> annotations_person_coco_classes.json: this file contains annotations concerning only the 'person' class. Class id corresponds to the one provided by the MS COCO standard. </li> </ul> </li> </ul> The MOBDrone dataset is intended as a test data benchmark. However, for researchers interested in using our data also for training purposes, we provide training and test splits: <ul> <li>Test set: All the images whose filename starts with "DJI_0804" (total: 37,604 images)</li> <li>Training set: All the images whose filename starts with "DJI_0915" (total: 88,568 images)</li> </ul> More details about data generation and the evaluation protocol can be found at our MOBDrone paper: <a href="https://arxiv.org/abs/2203.07973">https://arxiv.org/abs/2203.07973</a> The code to reproduce our results is available at this GitHub Repository: <a href="https://github.com/ciampluca/MOBDrone_eval">https://github.com/ciampluca/MOBDrone_eval</a> See also  <a href="http://aimh.isti.cnr.it/dataset/MOBDrone">http://aimh.isti.cnr.it/dataset/MOBDrone</a> Citing the MOBDrone The MOBDrone is released under a Creative Commons Attribution license, so please cite the MOBDrone if it is used in your work in any form. Published academic papers should use the academic paper citation for our MOBDrone paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people <blockquote> <pre>@inproceedings{MOBDrone2021, title={MOBDrone: a Drone Video Dataset for Man OverBoard Rescue}, author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko Passera and Fabrizio Falchi}, booktitle={ICIAP2021: 21th International Conference on Image Analysis and Processing}, year={2021} } </pre> </blockquote> and this Zenodo Dataset <blockquote> <pre>@dataset{donato_cafarelli_2022_5996890, author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko Passera and Fabrizio Falchi}, title = {{MOBDrone: a large-scale drone-view dataset for man overboard detection}}, month = feb, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.5996890}, url = {https://doi.org/10.5281/zenodo.5996890} }</pre> </blockquote> Personal works, such as machine learning projects/blog posts, should provide a URL to the MOBDrone Zenodo page (<a href="https://doi.org/10.5281/zenodo.5996890">https://doi.org/10.5281/zenodo.5996890</a>), though a reference to our MOBDrone paper would also be appreciated.   Contact Information If you would like further information about the MOBDrone or if you experience any issues downloading files, please contact us at <a href="mailto:mobdrone@isti.cnr.it">mobdrone[at]isti.cnr.it</a>   Acknowledgements This work was partially supported by NAUSICAA - "NAUtical Safety by means of Integrated Computer-Assistance Appliances 4.0" project funded by the Tuscany region (CUP D44E20003410009). The data collection was carried out with the collaboration of the Fly&Sense Service of the CNR of Pisa - for the flight operations of remotely piloted aerial systems - and of the Institute of Clinical Physiology (IFC) of the CNR - for the water immersion operations.

opencc-by-4.0Mar 2022View details →

Supporting Online Toxicity Detection with Knowledge Graphs: Data

This data repository contains the output files from the analysis of the paper "Supporting Online Toxicity Detection with Knowledge Graphs" presented at the International Conference on Web and Social Media 2022 (ICWSM-2022).   The data contains annotations of gender and sexual orientation entities provided by the Gender and Sexual Orientation Ontology (https://bioportal.bioontology.org/ontologies/GSSO). We analyse demographic group samples from the Civil Comments Identities dataset (https://www.tensorflow.org/datasets/catalog/civil_comments).

opencc-by-4.0Mar 2022View details →

Results of the 3D detection of cracks in tested disc-shaped specimens

3D images of fatigue crack obtained by laboratory tomography and synchrotron tomography within bi-disc specimens.

opencc-by-4.0May 2021View details →

HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic

Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets. To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a>

opencc-by-4.0Mar 2022View details →

Roundabout Aerial Images for Vehicle Detection

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047 This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.   <table align="center"> <tbody> <tr> <td> Roundabout (scenes) </td> <td> Frames </td> <td> Car </td> <td> Truck </td> <td> Cycle </td> <td> Bus </td> <td> Empty </td> </tr> <tr> <td> 1 (00001) </td> <td> 1,996 </td> <td> 34,558 </td> <td> 0 </td> <td> 4229 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 2 (00002) </td> <td> 514 </td> <td> 743 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 157 </td> </tr> <tr> <td> 3 (00003-00017) </td> <td> 1,795 </td> <td> 4822 </td> <td> 58 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 4 (00018-00033) </td> <td> 1,027 </td> <td> 6615 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 5 (00034-00049) </td> <td> 1,261 </td> <td> 2248 </td> <td> 0 </td> <td> 550 </td> <td> 0 </td> <td> 81 </td> </tr> <tr> <td> 6 (00050-00052) </td> <td> 5,501 </td> <td> 180,342 </td> <td> 1420 </td> <td> 120 </td> <td> 1376 </td> <td> 0 </td> </tr> <tr> <td> 7 (00053) </td> <td> 2,036 </td> <td> 5,789 </td> <td> 562 </td> <td> 0 </td> <td> 226 </td> <td> 92 </td> </tr> <tr> <td> 8 (00054) </td> <td> 1,344 </td> <td> 1,733 </td> <td> 222 </td> <td> 0 </td> <td> 150 </td> <td> 222 </td> </tr> <tr> <td> Total </td> <td> 15,474 </td> <td> 236,850 </td> <td> 2,262 </td> <td> 4,899 </td> <td> 1,752 </td> <td> 552 </td> </tr> <tr> <td> Data augmentation </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> </tr> <tr> <td> Total </td> <td> 61,896 </td> <td> 947,400 </td> <td> 9048 </td> <td> 19,596 </td> <td> 7,008 </td> <td> 2,208 </td> </tr> </tbody> </table>

opencc-by-4.0May 2022View details →

Artifacts for the ISSTA 2022 Paper: An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection

This repository contains the evaluation script and the corresponding data of the ISSTA'22 paper "An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection".

Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"

Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit" This package contains an anonymized packet of 802.11 probe requests captured in Lloret de Mar during the Indoor Positioning and Indoor Navigation 2021 conference. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).

POC detection training data and weights

The data and labels used to train the POC detection <a href="https://github.com/climate-processes/poc-detection">algorithm </a>used in support of this publication: https://doi.org/10.1029/2020GL092213. The associated model weights were also saved after training to aid reproducibility.

The benchmark datasets for Multi-class Change Detection (MCD)

Change detection (CD) provides a research basis for environmental monitoring, urban expansion and reconstruction as well as disaster assessment, by identifying the changes of ground objects in different time periods. Traditional CD focused on the binary change detection (BCD), focusing solely on the change and no-change regions. Due to the dynamic progress of earth observation satellite techniques, the spatial resolution of remote sensing images continues to increase, multi-class change detection (MCD) which can reflect more detailed land change has become a hot research direction in the field of CD. We have collected the current open source benchmark datasets in the MCD of remote sensing imagery , in order to facilitate the sharing of the latest research datasets in the MCD field. Users can access the relevant MCD datasets through the links in the files. Source: Q. Zhu, X. Guo, Ziqi Li, D. Li*, “A review of Multi-class Change Detection for Remote Sensing Imagery” Geo-spatial information science, 2022

Codes and dataset of the publication "Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe"

The folder contains the codes, input and output of the analysis carried out for supporting the publication of the paper: Tarpanelli A., Mondini A., Camici S.:Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe, Natural Hazards and Earth System Sciences, https://doi.org/10.5194/nhess-2022-63, 2022.   The codes should be run in order A1-A7 to generate all the figures of the paper. For details please send an email to: angelica.tarpanelli@irpi.cnr.it

opencc-by-4.0Nov 2021View details →

Summary Statistics from "Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2"

GWAMA summary statistics of PCSK9 levels using fixed-effect model. Genome-wide data is given for Europeans with statin adjustment and Europeans without statin treatment only (subset of the population). In addition, locus-wide data of the PCSK9 gene locus for African-Americans without statin treatment is listed. When using this data, please cite: Pott J, Gadin J, Theusch E, et al.. Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2. Hum Mol Genet. 2021 Sep 30:ddab279. doi: 10.1093/hmg/ddab279. PMID: 34590679 All txt files contain the following columns: <ul> <li>markername</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>ea (effect allele)</li> <li>oa (other allele)</li> <li>eaf (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (number of studies)</li> <li>beta (effect estimate)</li> <li>se (standard error)</li> <li>p (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>phenotype (phenotyp setting)</li> </ul>

opencc-by-4.0Aug 2022View details →

Disturbances in vegetation detected with BFAST in the Purapel fluvial catchment

This dataset contains the results (69 TIFF files) of seasonal disturbances detected in vegetation in the Purapel catchment (southern Chile) for the period from 2002 to 2019. These disturbances were obtained by applying the Breaks for Additive Season and Trend (BFAST, Verbesselt et al., 2010) algorithm to 745 Landsat 5, 7 and 8 imagery. We used Collection 2 Level 2 surface reflectance products and applied the CFMask algorithm (Foga et al., 2017) for cloud masking before utilizing the BFAST algorithm. The BFAST algorithm detects changes in the NDVI time series of each pixel. To determine which event was considered a disturbance, we used the same intensity thresholds as in Cabezas and Fassnacht (2018). We then filtered the results to keep just the disturbances with areas greater than 1 hectare, eliminating noisy data. Except for 2 big wildfires (2015 and 2017) it was assumed that all of the disturbances were clear cuts, since forestry is the main productive activity in the region. This was confirmed by validating the data with 35 manually drawn polygons that were randomly distributed across the catchment. Then, we performed an accuracy assessment, obtaining a confusion matrix with a balanced accuracy of 0,86 and a F1 score of 0,69. Each TIFF file is a binary grid with “zeros” representing no disturbance and “ones” representing a disturbance in the season that the name of the file indicates. A GIF file is also included, which contains the time series of the disturbances for easier graphical purposes. References J. Cabezas and F. E. Fassnacht. Reconstructing the Vegetation Disturbance History of a Biodiversity Hotspot in Central Chile Using Landsat, Bfast and Landtrendr. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 7636–7639. IEEE, 7 2018. ISBN 978-1-5386-7150-4. doi: 10.1109/IGARSS.2018.8518863. S. Foga, P. L. Scaramuzza, S. Guo, Z. Zhu, R. D. Dilley, T. Beckmann, G. L. Schmidt, J. L. Dwyer, M. Joseph Hughes, and B. Laue. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194:379–390, 6 2017. ISSN 00344257. doi: 10.1016/j.rse.2017.03.026. J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1):106–115, 1 2010. ISSN 00344257. doi: 10.1016/ j.rse.2009.08.014. URL http://linkinghub.elsevier.com/retrieve/pii/S003442570900265X.