Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
169
datasets available to search
ShareScore release 0.9.0
Dataset results
169 results for “timing of detection”
Real-time black ice detection using YOLOX on drone
<p><strong>Detailed Info:</strong> https://github.com/hsh060824/blackice-drone-dataset</p> <p> </p> <p><strong>Dataset Type</strong>: Object Detection Dataset (with bounding boxes)</p> <p> </p> <p><strong>Overview</strong></p> <p>Road safety during winter months remains a critical concern due to the elusive nature of black ice, a thin layer of ice that forms on road surfaces, making it challenging for drivers to identify and navigate safely. In an effort to address this issue, our research team at Cheongshim International Academy (CSIA) has conducted extensive studies on real-time black ice detection utilizing YOLOX, a state-of-the-art object detection algorithm, deployed on drones. As a significant contribution to the research community, we are pleased to share our meticulously curated image dataset, which encapsulates diverse scenarios and conditions representative of real-world black ice occurrences.</p> <p> </p> <p><strong>Background</strong></p> <p>Black ice poses a significant threat to road safety, especially during winter, as it is often challenging for drivers to detect, leading to increased risks of accidents and hazardous road conditions. Our dataset aims to fill the gap in existing resources by providing a comprehensive collection of images showcasing various instances of black ice under different environmental conditions. The dataset covers diverse scenarios, including different lighting conditions, road surfaces, and black ice formations, making it a valuable resource for developing and testing robust black ice detection models.</p> <p> </p> <p><strong>Significances of the Dataset</strong></p> <p>The significance of this dataset lies in its potential to advance the development of effective black ice detection algorithms. By sharing our dataset with the research community, we aim to facilitate the creation of more accurate and reliable models for real-time detection of black ice using drone technology. The dataset includes annotations in COCO format, providing detailed information about the location and characteristics of black ice instances in each image.</p> <p> </p> <p><strong>Categorization</strong></p> <p>In our pursuit of advancing the field of computer vision and contributing to ongoing research endeavors, we proudly introduce three distinct image datasets meticulously curated by our research team. These datasets, categorized as "White," "Black," and "Outdoors (OD)," cater to unique scenarios and are designed to fuel the development of specialized models addressing specific challenges in visual recognition.</p> <p> </p> <p><strong>White Dataset: </strong></p> <ul> <li><strong>Composition:</strong> This dataset comprises 413 images, each meticulously annotated with an average of 1.1 annotations per image, depicting the unique optical characteristics of black ice.</li> <li><strong>Properties:</strong> The average proportion of instance pixel area is 3.16%, emphasizing the subtlety of the black ice formations. The average image brightness is measured at 149.358.</li> <li><strong>Capture Environment:</strong> The images were taken in controlled indoor laboratory conditions, ensuring consistency and repeatability.</li> <li><strong>Creation Method:</strong> The dataset was generated by cooling asphalt samples in a freezer to temperatures ranging from -4°C to -20°C. Subsequently, 4°C water was sprayed onto the sample surfaces, creating black ice. The dataset captures the optical properties of black ice, showcasing its interaction with light.</li> <li><strong>Significance: </strong>Valuable for highlighting the optical characteristics of black ice, enhancing model accuracy in well-lit scenarios.</li> </ul> <p> </p> <p><strong>Black Dataset: </strong></p> <ul> <li><strong>Composition:</strong> This dataset comprises 814 images, with a detailed annotation structure averaging 3.5 annotations per image, showcasing the challenges of recognition in low-light conditions.</li> <li><strong>Properties:</strong> The average proportion of instance pixel area is notably higher at 12.37%, reflecting the complex and varied formations of black ice. The average image brightness is measured at 123.028.</li> <li><strong>Capture Environment:</strong> Similar to the White Dataset, images were captured in a controlled indoor laboratory environment. Asphalt pelt was placed under the black iced asphalt pieces to replicate realistic scenarios.</li> <li><strong>Creation Method:</strong> The dataset creation involved the same process of cooling asphalt samples, followed by spraying water to create black ice. To simulate real-world conditions, asphalt pelt was used as a background, and various shapes of black ice were randomly placed in each image.</li> <li><strong>Significance:</strong> Realistic emulation of black ice using backgrounds made up of asphalt pelts, providing essential drark images for robust model training.</li> </ul> <p> </p> <p><strong>Outdoor (OD) Dataset</strong></p> <ul> <li><strong>Composition:</strong> This dataset is the most extensive, consisting of 1624 images, with an average of 1.5 annotations per image, capturing the challenges of recognizing black ice in outdoor winter conditions.</li> <li><strong>Properties:</strong> The average proportion of instance pixel area is 12.34%, mirroring the complexity of real-world outdoor scenarios. The average image brightness is significantly lower at 56.575.</li> <li><strong>Capture Environment:</strong> Unlike the indoor datasets, the OD dataset was captured outdoors in winter conditions where black ice naturally forms.</li> <li><strong>Creation Method:</strong> Black ice was created on the asphalt road of Cheongsim International High School by spraying +4°C water onto the surface. DJI Tello's built-in camera was used for capturing images from various angles, simulating drone-like perspectives. This dataset is designed to closely replicate real-world scenarios, providing a valuable resource for training models for outdoor applications.</li> <li><strong>Significance: </strong>Represents real-world outdoor scenarios, offering a unique perspective for developing models capable of handling diverse and challenging conditions.</li> </ul> <p> </p> <p><strong>Cameras: </strong></p> <ul> <li>iPhone SE2 (Apple, California)</li> <li>iPhone SE3 (Apple, California)</li> <li>iPhone 12 (Apple, California)</li> <li>iPhone 14 Pro (Apple, California)</li> <li>Q9 (LG Electronics, Seoul, Korea)</li> <li>V30 (LG Electronics, Seoul, Korea)</li> <li>Tello (DJI, ShenZhen, China)</li> </ul>
Simulated NGS datasets for real-time detection of novel pathogens
<p>Datasets based on the <a href="https://doi.org/10.5281/zenodo.3678563">bacterial</a> and <a href="https://doi.org/10.5281/zenodo.4312525">viral</a> simulated NGS datasets. Fastq files correspond to tests sets of those datasets. Basecall files were generated based on the fastq files with an 8nt simulated barcode between the mates of a read pair. The "rn" datasets containg random length subreads (25-250bp) of the original validation and training reads.</p> <p>The Nanopore datasets were resimulated with <a href="https://github.com/liyu95/DeepSimulator">DeepSimulator 1.5</a> (Li et al., 2020) based on the original datasets (i.e. using the same species composition as the original data). The test Nanopore dataset contains full reads (target average length: 8kb) and the training and validation datasets - 250bp subreads.</p>
Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches (Replication Package Part 3: OpenSSL dataset)
<h1><strong>The Replication Package of</strong></h1> <h1><strong>"Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches"</strong></h1> <h3><strong>Part 3 (OPENSSL Dataset)</strong></h3> <div> <div>This repository includes:</div> <ol> <li><em><strong>Code.zip</strong></em> that contains the codes to replicate some parts of this study:<br>a. <em>1_generate_datasets</em> implements our methodology to generate the datasets.<br>b. <em>2_run_models</em> runs the ML models during the evaluation.<br>c. <em>3_result_replication </em>generates charts presented in the paper from the ML evaluation results.</li> <li><em><strong>Datasets.zip</strong></em> that contain 2 folders:<br>a. <em>original</em> datasets: 1 from <a href="https://github.com/CGCL-codes/VulDeePecker" target="_blank" rel="noopener">NVD Vuldeepecker</a> and 3 extracted from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a>.<br> <div> <div>b. <em>OPENSSL</em> datasets: train, validation, test sets for each time of observation extracted using our methodology from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a> dataset for project <em>openssl</em>.</div> </div> </li> <li><em><strong>Pretrained-models.zip</strong></em> that we generated during our evaluation (3 test results for each time point in the timeline [2013-2019]).</li> <li><em><strong>Results.zip</strong></em> of our evaluation, the folder <em>ALL</em> contains the overall results and other folders are results by model.</li> </ol> <p><strong>UPDATED version 5<br></strong>- added a GLOBAL_README.md which contains the 3 stages and how they are connected to each other<br>- updated LineVul.ipynb: import AdamW from torch.optim instead of transformers<br>- updated README.md in Code2Vec with the prerequisites of Java to run gradlew for astmine</p> <p><strong>UPDATED version 6<br></strong>- updated CodeBert.ipynb: import AdamW from torch.optim instead of transformers</p> <p>Documentations</p> <ol> <li><em><strong>INSTALL.pdf </strong></em>: how to install the codes</li> <li><em><strong>README.pdf</strong></em>: readme file</li> <li><em><strong>REQUIREMENTS.pdf</strong></em>: hardware and software requirements</li> <li><em><strong>STATUS.pdf</strong></em> : status for artifact submission</li> <li><em><strong>LICENSE.pdf</strong></em>: the license of this artifact</li> <li><em><strong>PAPER.pdf</strong></em>: the camera-ready version of the paper</li> </ol> </div> <div> <div>Please refer to the following repositories for the other datasets and pre-trained models:</div> <div>- Part 1 NVD Vuldeeepecker : <a href="https://doi.org/10.5281/zenodo.8207883" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8207883</a></div> - Part 2 LINUX : <a href="https://doi.org/10.5281/zenodo.10960662" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10960662</a><br> <div>- Part 4 POPPLER : <a href="https://doi.org/10.5281/zenodo.14713143">https://doi.org/10.5281/zenodo.14713143</a></div> <div> </div> <div>This work was partly funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647) and the Horizon Europe Program Sec4AI4Sec (Grant n. 101120393), by the Italian Ministry of University and Research (MUR) under the P.N.R.R. – NextGenerationEU grant n.\ PE00000014 (SERICS subproject COVERT), and by the Dutch Research Council (NWO) under the grant NWA.1215.18.006 (Theseus) and grant KIC1.VE01.20.004 (HEWSTI). </div> </div>
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
<h2><strong>CESNET-TimeSeries24: The dataset for network traffic forecasting and anomaly detection</strong></h2> <p>The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods.<br><br>Last but not least, the CESNET-TimeSeries24 dataset provides traffic time series on institutional and IP subnet levels to cover all possible anomaly detection or forecasting scopes. Overall, the time series dataset was created from the 66 billion IP flows that contain 4 trillion packets that carry approximately 3.7 petabytes of data. The CESNET-TimeSeries24 dataset is a complex real-world dataset that will finally bring insights into the evaluation of forecasting models in real-world environments.<br><br></p> <p>Please cite the usage of our dataset as:</p> <blockquote> <p>Koumar, J., Hynek, K., Čejka, T. <em>et al.</em> CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting. <em>Sci Data</em> <strong>12</strong>, 338 (2025). https://doi.org/10.1038/s41597-025-04603-x<br><br>@Article{cesnettimeseries24,<br> author={Koumar, Josef and Hynek, Karel and {\v{C}}ejka, Tom{\'a}{\v{s}} and {\v{S}}i{\v{s}}ka, Pavel},<br> title={CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting},<br> journal={Scientific Data},<br> year={2025},<br> month={Feb},<br> day={26},<br> volume={12},<br> number={1},<br> pages={338},<br> issn={2052-4463},<br> doi={10.1038/s41597-025-04603-x},<br> url={https://doi.org/10.1038/s41597-025-04603-x}<br>}<br><br></p> </blockquote> <p> </p> <h3>Time series</h3> <p>We create evenly spaced time series for each IP address by aggregating IP flow records into time series datapoints. The created datapoints represent the behavior of IP addresses within a defined time window of 10 minutes. The vector of time-series metrics v_{ip, i} describes the IP address ip in the i-th time window. Thus, IP flows for vector v_{ip, i} are captured in time windows starting at t_i and ending at t_{i+1}. The time series are built from these datapoints. </p> <p>Datapoints created by the aggregation of IP flows contain the following time-series metrics:</p> <ul> <li><strong><em>Simple volumetric metrics:</em></strong> the number of IP flows, the number of packets, and the transmitted data size (i.e. number of bytes)</li> <li><strong><em>Unique volumetric metrics:</em></strong> the number of unique destination IP addresses, the number of unique destination Autonomous System Numbers (ASNs), and the number of unique destination transport layer ports. The aggregation of \textit{Unique volumetric metrics} is memory intensive since all unique values must be stored in an array. We used a server with 41 GB of RAM, which was enough for 10-minute aggregation on the ISP network. </li> <li><strong><em>Ratios metrics:</em></strong> the ratio of UDP/TCP packets, the ratio of UDP/TCP transmitted data size, the direction ratio of packets, and the direction ratio of transmitted data size</li> <li><em><strong>Average metrics:</strong></em> the average flow duration, and the average Time To Live (TTL)</li> </ul> <p> </p> <p><strong>Multiple time aggregation: </strong> The original datapoints in the dataset are aggregated by 10 minutes of network traffic. The size of the aggregation interval influences anomaly detection procedures, mainly the training speed of the detection model. However, the 10-minute intervals can be too short for longitudinal anomaly detection methods. Therefore, we added two more aggregation intervals to the datasets--1 hour and 1 day.</p> <p><strong>Time series of institutions:</strong> We identify 283 institutions inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution's data. </p> <p><strong>Time series of institutional subnets:</strong> We identify 548 institution subnets inside the CESNET3 network. These time series aggregated per each institution ID provide a view of the institution subnet's data. </p> <p> </p> <h3>Data Records</h3> <p>The file hierarchy is described below:</p> <blockquote> <p>cesnet-timeseries24/</p> <p> |- institution_subnets/</p> <p> | |- agg_10_minutes/<id_institution>.csv</p> <p> | |- agg_1_hour/<id_institution>.csv</p> <p> | |- agg_1_day/<id_institution>.csv</p> <p> | |- identifiers.csv</p> <p> |- institutions/</p> <p> | |- agg_10_minutes/<id_institution_subnet>.csv</p> <p> | |- agg_1_hour/<id_institution_subnet>.csv</p> <p> | |- agg_1_day/<id_institution_subnet>.csv</p> <p> | |- identifiers.csv</p> <p> |- ip_addresses_full/</p> <p> | |- agg_10_minutes/<id_ip_folder>/<id_ip>.csv</p> <p> | |- agg_1_hour/<id_ip_folder>/<id_ip>.csv</p> <p> | |- agg_1_day/<id_ip_folder>/<id_ip>.csv</p> <p> | |- identifiers.csv</p> <p> |- ip_addresses_sample/</p> <p> | |- agg_10_minutes/<id_ip>.csv</p> <p> | |- agg_1_hour/<id_ip>.csv</p> <p> | |- agg_1_day/<id_ip>.csv</p> <p> | |- identifiers.csv</p> <p> |- times/</p> <p> | |- times_10_minutes.csv</p> <p> | |- times_1_hour.csv</p> <p> | |- times_1_day.csv</p> <p> |- ids_relationship.csv<br> |- weekends_and_holidays.csv</p> </blockquote> <p>The following list describes time series data fields in CSV files:</p> <ul> <li><strong>id_time: </strong>Unique identifier for each aggregation interval within the time series, used to segment the dataset into specific time periods for analysis.</li> <li><strong>n_flows: </strong>Total number of flows observed in the aggregation interval, indicating the volume of distinct sessions or connections for the IP address.</li> <li><strong>n_packets: </strong>Total number of packets transmitted during the aggregation interval, reflecting the packet-level traffic volume for the IP address.</li> <li><strong>n_bytes: </strong>Total number of bytes transmitted during the aggregation interval, representing the data volume for the IP address.</li> <li><strong>n_dest_ip: </strong>Number of unique destination IP addresses contacted by the IP address during the aggregation interval, showing the diversity of endpoints reached.</li> <li><strong>n_dest_asn: </strong>Number of unique destination Autonomous System Numbers (ASNs) contacted by the IP address during the aggregation interval, indicating the diversity of networks reached.</li> <li><strong>n_dest_port: </strong>Number of unique destination transport layer ports contacted by the IP address during the aggregation interval, representing the variety of services accessed.</li> <li><strong>tcp_udp_ratio_packets: </strong>Ratio of packets sent using TCP versus UDP by the IP address during the aggregation interval, providing insight into the transport protocol usage pattern. This metric belongs to the interval <0, 1> where 1 is when all packets are sent over TCP, and 0 is when all packets are sent over UDP.</li> <li><strong>tcp_udp_ratio_bytes:</strong> Ratio of bytes sent using TCP versus UDP by the IP address during the aggregation interval, highlighting the data volume distribution between protocols. This metric belongs to the interval <0, 1> with same rule as <em>tcp_udp_ratio_packets</em>.</li> <li><strong>dir_ratio_packets: </strong>Ratio of packet directions (inbound versus outbound) for the IP address during the aggregation interval, indicating the balance of traffic flow directions. This metric belongs to the interval <0, 1>, where 1 is when all packets are sent in the outgoing direction from the monitored IP address, and 0 is when all packets are sent in the incoming direction to the monitored IP address.</li> <li><strong>dir_ratio_bytes: </strong>Ratio of byte directions (inbound versus outbound) for the IP address during the aggregation interval, showing the data volume distribution in traffic flows. This metric belongs to the interval <0, 1> with the same rule as <em>dir_ratio_packets</em>.</li> <li><strong>avg_duration: </strong>Average duration of IP flows for the IP address during the aggregation interval, measuring the typical session length.</li> <li><strong>avg_ttl: </strong>Average Time To Live (TTL) of IP flows for the IP address during the aggregation interval, providing insight into the lifespan of packets.</li> </ul> <p>Moreover, the time series created by re-aggregation contains following time series metrics instead of <strong>n_dest_ip</strong>, <strong>n_dest_asn</strong>, and <strong>n_dest_port</strong>:</p> <ul> <li><strong>sum_n_dest_ip: </strong>Sum of numbers of unique destination IP addresses.</li> <li><strong>avg_n_dest_ip: </strong>The average number of unique destination IP addresses.</li> <li><strong>std_n_dest_ip: </strong>Standard deviation of numbers of unique destination IP addresses.</li> <li><strong>sum_n_dest_asn: </strong>Sum of numbers of unique destination ASNs.</li> <li><strong>avg_n_dest_asn: </strong>The average number of unique destination ASNs.</li> <li><strong>std_n_dest_asn: </strong>Standard deviation of numbers of unique destination ASNs)</li> <li><strong>sum_n_dest_port: </strong>Sum of numbers of unique destination transport layer ports.</li> <li><strong>avg_n_dest_port: </strong> The average number of unique destination transport layer ports.</li> <li><strong>std_n_dest_port: </strong>Standard deviation of numbers of unique destination transport layer ports.</li> </ul> <p> </p> <p>Moreover, files <em>identifiers.csv</em> in each dataset type contain IDs of time series that are present in the dataset. Furthermore, the <em>ids_relationship.csv</em> file contains a relationship between IP addresses, Institutions, and institution subnets. The <em>weekends_and_holidays.csv</em> contains information about the non-working days in the Czech Republic.</p>
Detection of ultra-weak laser pulses by free-running single-photon detectors: modeling dead time and dark counts effects
<p>In quantum communication systems, the precise estimation of the detector´s response to the incoming light is necessary to avoid security breaches. The typical working regime uses a free-running single-photon avalanche diode in combination with attenuated laser pulses at telecom wavelength for encoding information. We demonstrate the validity of an analytical model for this regime which considers the effects of dark counts and dead time on the measured count rate. For the purpose of gaining a better understanding of these effects, the photon detections were separated from the dark counts via a software-induced gating mechanism. The model was verified by experimental data for mean photon numbers covering three orders of magnitude as well as for laser repetition frequencies below and above the inverse dead time. Consequently, our model would be of interest for predicting the detector response not only in the field of quantum communications, but also in any other quantum physics experiment where high detection rates are needed.</p>
Testing strengths, limitations and biases of current Pulsar Timing Arrays detection analyses on realistic data
<p>In this project, we carried out an extensive investigation of the performance of current Pulsar Timing Arrays (PTA) analyses on simulated PTA datasets where we modeled the gravitational waves (GW) signal as the incoherent superposition of sinusoidal signals from a cosmic population of super-massive black hole binaries (SMBHBs). Here we publish the dataset referred to in the paper as the <em><strong>SMBHB_set</strong></em>: 100 realisations of PTA datasets with 25 pulsars and a GWB signal of nominal amplitude 2.4e-15. For each realisation, listed from 1001 to 1100, this repository contains the pulsars .par and .tim files, the mcmc chain (sampling over 66 parameters: 60 for pulsars intrinsic noise parameters and log amplitude and slope of a common red noise process), and the SMBHB details for that specific realisation of the GWB. The columns of the <em>MBHB_list.dat</em> files contain (from left to right): log10 MBH chirp mass (in solar masses in the source frame), mass ratio, source redshift, log10 of GW fundamental mode (n=2) in the observer frame, phi and theta angles in the sky, source inclination angle, source polarisation angle, source initial orbital phase, source initial direction of periastron and source initial eccentricity.</p> <p>If you make use of any of this data, please cite:<br><br></p> <div>@article{ refId0,</div> <div> author = {{Valtolina, Serena} and {Shaifullah, Golam} and {Samajdar, Anuradha} and {Sesana, Alberto}},</div> <div> title = {Testing strengths, limitations, and biases of current pulsar timing arrays’ detection analyses on realistic data},</div> <div> DOI= "10.1051/0004-6361/202348084",</div> <div> url= "https://doi.org/10.1051/0004-6361/202348084",</div> <div> journal = {A&A},</div> <div> year = 2024,</div> <div> volume = 683,</div> <div> pages = "A201",</div> <div>}</div>
Time-lapse of AARTFAAC detections of radio meteors in Perseids 2020
<p>LOFAR AARTFAAC images (integrated over all observing bands) during the Perseids meteor shower in the night 2020 August 12 --13. The large-scale diffuse emission is the Galactic plane. The brightest radio sources Cassiopeia~A and Cygnus~A have been subtracted, sometimes leaving some artefacts. In red, trajectories as computed from the CAMS BeNeLux optical observations are overlaid.</p> <p>Observing frequencies were in the range 30 to 60 MHz.</p>
Supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks"
<p>This archive contains supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks".</p> <p>In particular, the following data is provided:</p> <ul> <li>dataset of labeled simulated TDR signals (1 million samples)</li> <li>dataset of labeled experimental signals (16 samples)</li> <li>trained convolutional neural network model</li> </ul>
Time-Resolved Photoionization Detection of a Single Er3+ Ion in Silicon
<p>The data used for generating the plots in the manuscript and supporting information.</p>
Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems
<p><strong>Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems</strong></p> <p>This is the replication package for the paper, ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems, which has been accepted at the International Conference on Software Architecture (ICSA), 2021. A preprint of the paper is included in this replication package (paper.pdf).</p> <p>This artifact is archived on Zenodo with the following DOI: <a href="https://doi.org/10.5281/zenodo.5834633">https://doi.org/10.5281/zenodo.5834633</a></p> <p>The study associated with this artifact was carried out by the following investigators:</p> <ul> <li><a href="http://christimperley.co.uk">Christopher S. Timperley</a> (Carnegie Mellon University)</li> <li><a href="https://tobiasduerschmid.github.io">Tobias Dürschmid</a> (Carnegie Mellon University)</li> <li><a href="https://www.cs.cmu.edu/~schmerl">Bradley Schmerl</a> (Carnegie Mellon University)</li> <li><a href="http://www.cs.cmu.edu/~garlan">David Garlan</a> (Carnegie Mellon University)</li> <li><a href="https://clairelegoues.com">Claire Le Goues</a> (Carnegie Mellon University)</li> </ul> <p>If you have any questions regarding the research or the replication package, you should contact Christopher, Tobias, or Bradley.</p> <p><strong>Abstract</strong></p> <p>Robot systems are growing in importance and complexity. Ecosystems for robot software, such as the Robot Operating System (ROS), provide libraries of reusable software components that can be configured and composed into larger systems. To support compositionality, ROS uses late binding and architecture configuration via “launch files” that describe how to initialize the components in a system. However, late binding often leads to systems failing silently due to misconfiguration, for example by misrouting or dropping messages entirely.</p> <p>In this paper we present ROSDiscover, which statically recovers the run-time architecture of ROS systems to find such architecture misconfiguration bugs. First, ROSDiscover constructs component level architectural models (ports, parameters) from source code. Second, architecture configuration files are analyzed to compose the system from these component models and derive the connections in the system. Finally, the reconstructed architecture is checked against architectural rules described in first-order logic to identify potential misconfigurations.</p> <p>We present an evaluation of ROSDiscover on real world, off-the-shelf robotic systems, measuring the accuracy, effectiveness, and practicality of our approach. To that end, we collected the first data set of architecture configuration bugs in ROS from popular open-source systems and measure how effective our approach is for detecting configuration bugs in that set.</p>
A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus
<p>Raw data associated to the study entitled:</p> <p><em>A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus</em><strong> </strong></p> <p><em>- </em>Metadata file</p> <p>- Computational data</p> <p>- Extraction data</p> <p>- Fluorescence detection</p> <p>- Fluorescence imaging</p> <p>- Labbooks</p> <p>- Surface characterization</p>
Fig. 1 in Detecting co-infections of Echinococcus multilocularis and Echinococcus canadensis in coyotes and red foxes in Alberta, Canada using real-time PCR
Fig. 1. Standard curve for qPCR assays to detect E. canadensis and E. multilocularis using Cox143 and Nad234 primers/probes, respectively.
ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs
<p> ODDS Smart Building Depth Dataset</p> <p>#Introduction:</p> <p>The goal of this dataset is to facilitate research focusing on recognizing objects in smart buildings using the depth sensor mounted at the ceiling. This dataset contains annotations of depth images for eight frequently seen object classes. The classes are: person, backpack, laptop, gun, phone, umbrella, cup, and box.</p> <p><br> #Data Collection:</p> <p>We collected data from two settings. We had Kinect mounted at a 9.3 feet ceiling near to a 6 feet wide door. We also used a tripod with a horizontal extender holding the kinect at a similar height looking downwards. We asked about 20 volunteers to enter and exit a number of times each in different directions (3 times walking straight, 3 times walking towards left side, 3 times walking towards right side) holding objects in many different ways and poses underneath the Kinect. Each subject was using his/her own backpack, purse, laptop, etc. As a result, we considered varieties within the same object, e.g., for laptops, we considered Macbooks, HP laptops, Lenovo laptops of different years and models, and for backpacks, we considered backpacks, side bags, and purse of women. We asked the subjects to walk while holding it in many ways, e.g., for laptop, the laptop was fully open, partially closed, and fully closed while carried. Also, people hold laptops in front and side of their bodies, and underneath their elbow. The subjects carried their backpacks in their back, in their side at different levels from foot to shoulder. We wanted to collect data with real guns. However, bringing real guns to the office is prohibited. So, we obtained a few nerf guns and the subjects were carrying these guns pointing it to front, side, up, and down while walking.</p> <p><br> #Annotated Data Description:</p> <p>The Annotated dataset is created following the structure of Pascal VOC devkit, so that the data preparation becomes simple and it can be used quickly with different with object detection libraries that are friendly to Pascal VOC style annotations (e.g. Faster-RCNN, YOLO, SSD). The annotated data consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the eight classes present in the image. Multiple objects from multiple classes may be present in the same image. The dataset has 3 main directories:</p> <p>1)DepthImages: Contains all the images of training set and validation set. </p> <p>2)Annotations: Contains one xml file per image file, (e.g., 1.xml for image file 1.png). The xml file includes the bounding box annotations for all objects in the corresponding image. </p> <p>3)ImagesSets: Contains two text files training_samples.txt and testing_samples.txt. The training_samples.txt file has the name of images used in training and the testing_samples.txt has the name of images used for testing. (We randomly choose 80%, 20% split)</p> <p><br> #UnAnnotated Data Description:</p> <p>The un-annotated data consists of several set of depth images. No ground-truth annotation is available for these images yet. These un-annotated sets contain several challenging scenarios and no data has been collected from this office during annotated dataset construction. Hence, it will provide a way to test generalization performance of the algorithm.</p> <p><br> #Citation:</p> <p>If you use ODDS Smart Building dataset in your work, please cite the following reference in any publications:<br> @inproceedings{mithun2018odds,<br> title={ODDS: Real-Time Object Detection using Depth Sensors on Embedded GPUs},<br> author={Niluthpol Chowdhury Mithun and Sirajum Munir and Karen Guo and Charles Shelton},<br> booktitle={ ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN)},<br> year={2018},<br> }<br> </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.
<p>The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection. </p>
BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 1: Time vs frequency analysis of 10 Hz SSVEP response
<p>The stability of the SSVEP signal was examined by using wavelet analysis (Wu and Yao 2008). Since there is a trade-off between time and frequency resolution in wavelet analysis, examining the stability of SSVEP with wavelet analysis is getting harder in systems where the visual stimulus frequencies are close to each other, as shown in Figure 1. </p>
Detection of a Timing Channel in an UPPAAL Model of a Cyber-Manufacturing System
<p>Model of a cyber-manufacturing system for the UPPAAL model checker, including a mitigation of a timing channel.</p>
Fig. 3 in Comparison of the modified agglutination test and real-time PCR for detection of Toxoplasma gondii exposure in feral cats from Phillip Island, Australia, and risk factors associated with infection
Fig. 3. Predicted lines of fit for the multivariable logistic regression model plotted as probability of Toxoplasma gondii qPCR positivity in feral cats on Phillip Island (Victoria) versus body weight for each season. Dashed lines show 95% confidence intervals.
Fig. 1 in Comparison of the modified agglutination test and real-time PCR for detection of Toxoplasma gondii exposure in feral cats from Phillip Island, Australia, and risk factors associated with infection
Fig. 1. Location and Toxoplasma gondii infection status, as detected by real-time PCR (qPCR), of feral cats trapped on Phillip Island (Victoria) from July 2016 to December 2017. Map shows the distribution of different location types (Park, Agricultural, Residential) used in multivariable regression analysis. A circular spread of points around a location marked with 'x' indicates multiple animals were sampled at the same site (i.e. same GPS coordinates). Red = T. gondii qPCR positive, white = T. gondii qPCR negative. Map created using Quantum GIS, version 3.8. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in A novel quantitative real-time PCR diagnostic assay for fecal and nasal swab detection of an otariid lungworm, Parafilaroides decorus
Fig. 2. Standard curve based on sensitivity data. A 95% confidence interval for the linear regression model is shaded in grey.
Fig. 1. Repeat family selection for the P. decorus diagnostic assay. A in A novel quantitative real-time PCR diagnostic assay for fecal and nasal swab detection of an otariid lungworm, Parafilaroides decorus
Fig. 1. Repeat family selection for the P. decorus diagnostic assay. A. Number of sequencing reads for P. decorus compared to outgroup species reads for each repeat family (1–104) on a log scale. Arrows indicate repeat families with no reads from the outgroup species. Plot was made using Tableau Software, 2019. B. Within a cluster, reads with similar sequences are closer together. Edges connect a read with its closest match (creating a pair) and the length of this edge represents the amount of overlap between the reads. The mean edge width provides context for the lengths in the cluster, so in a cluster with a larger mean edge width the edges are actually longer than edges in a cluster with a smaller mean edge width. Reads therefore may be distant because of sequence divergence, or in the case of a long repeat (more than 150 base pairs), because of a lack of overlap between reads. However, because there will likely be continuous reads covering different regions of the repeat, these longer repeats should still appear as a tight, though possibly larger, cluster. Read dots that stray from the central cluster most likely represent sequence divergence. Higher density therefore indicates lower sequence divergence.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.