Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Characterization of photodiodes for detection of variations in part-to-part gap and weld penetration depth during remote laser welding of copper-to-steel battery tab connectors

In this folder, .csv files and Matlab scripts for processing data from experimental campaign that has been reported in the following paper: Chianese, G., Franciosa, P., Nolte, J., Ceglarek, D., and Patalano, S., “Characterization of photodiodes for detection of variations in part-to-part gap and weld penetration depth during remote laser welding of copper-to-steel battery tab connectors.” Journal of Manufacturing Science and Engineering. In-press. As the experimental campaign is made up of two phases, files are divided in two folders: (i)     Phase I - Weld penetration depth; and, (ii)    Phase II - Part-to-part gap. In each of these two folders, there are: (i)    a Matlab live script that, once the code is run, allows the user to select the signals (please, select all of them); (ii)     functions to run the code; (iii)    Matlab variable files; (iii)    a folder with .csv files that contain signals recorded during experimental campaign; (iv)      an Excel file reporting experimental setup and process parameters; (v)    a PDF file with the live script and results of the data processing for the readers that cannot run Matlab codes.

opencc-by-4.0Oct 2021View details →

Physical Interaction Detection

This folder contains data collected from a robot physically interacting with a human or the environment. The data has been used for the development, training, and validation of methods for detecting discrete changes in either human intent or environmental contact.   Each folder contains a description of the test conditions under which the data was collected, and an explanation of the data format.

opencc-zeroMay 2024View details →

EverWatch airborne bird imagery dataset for detection and species classification of Everglades wading birds

Training and evaluation data to support the development of wading bird species detection models in the Everglades. <div> <h1>Dataset contents</h1> <ul> <li>Training data: 4438 images with 45069 annotations</li> <li>Validation data: 493 images with 5422 annotations</li> <li>Test data: 196 images with 4113 annotations</li> </ul> </div> <div> <h1>Dataset structure</h1> All data is combined in everwatch_dataset.zip. This archive contains image crops (stored as PNG files) and their associated annotations in CSV format (stored in train_annotations.csv, validation_annotations.csv, and test_annotations.csv) and COCO format (stored in train_annotations.json, validation_annotations.json, and test_annotations.json). The CSV files with the annotations contain the following columns.</div> <ul> <li>image_path - basename of the image</li> <li>label - Species label</li> <li>xmin, xmax, ymin, ymax - coordinates for the ground truth bounding box on the image</li> </ul> COCO formatted data follows COCO conventions so labels are in “categories” and “category_id”, image paths are in “file_name”, and bounding boxes are stored as “bbox” in the default “ltwh” format [left, top, width, height]. Data on the annotation type for each image is stored in annotation_types.csv, which contains the following columns: <ul> <li>image_path - basename of the image</li> <li>dataset - which data split the image is part of (train, validation, or test)</li> <li>annotation type - approach used for annotating the data (crop_level or colony_level) </li> </ul> <div> <h2>Labels</h2> <ul> <li>'Great Egret'</li> <li>'Great Blue Heron'</li> <li>'White Ibis'</li> <li>'Roseate Spoonbill'</li> <li>'Wood Stork'</li> <li>'Snowy Egret'</li> <li>'Anhinga'</li> <li>'Unknown' (test only)</li> </ul> <h1>Annotation methods</h1> To provide data for training and testing computer vision models we used a pragmatic combination of labels from a variety of different approaches. Seven species were identified when labeling: White Ibis (Eudocimus albus), Great Egret (Ardea alba), Great Blue Heron (Ardea herodias), Snowy Egret (Egretta thula), Wood Stork (Mycteria americana), Roseate Spoonbill (Platalea ajaja), and Anhinga (Anhinga anhinga). Anhinga have not traditionally been included in airborne counts because they are difficult to see from the air, but were included in our labeling as a nuisance class because they were occasionally detected by the models and classified as Great Blue Herons. <h2>Crop level annotations</h2> Our primary approach labeled birds in 3145 1500×1500 pixels crops from surveys conducted in 2020 and 2021. Crops were automatically extracted from the orthomosaics for each survey. Labeling was conducted using a private Zooniverse project and every bird in each crop was annotated as a point location (the center of the bird) with a species label. The point-based labels were converted into bounding boxes for model training and evaluation by centering a bounding box on each point using a 50x50 pixel box for larger species (Great Egret, Great Blue Heron, Wood Stork, Roseate Spoonbill, Anhinga) and a 36x36 pixel for smaller species (White Ibis, Snowy Egret). We combined these point-based data with explicit bounding box-based labels created using a combination of QGIS and LabelStudio. We selected imagery for this labeling to help address small sample sizes for some species in the initial training datasets. Imagery was selected using a combination of expert field knowledge of where less common species were nesting and automated detection of likely rare species. For automated detection we ran a preliminary model based on the crop data over large amounts of survey imagery and identified crops that the model thought included less common species. These crops were then provided to annotators for labeling. This type of “active learning”, where an existing  model is used to identify images for labeling is increasingly recognized as a valuable approach for rapidly producing improved machine learning models (Kellenberger et al. 2019, 2021, Norouzzadeh et al. 2021). In combination these crop-based annotations included 2948 of 1500x1500m crops, including 41173 birds, that were used for model training. The remaining 197 crops, including 4113 birds, were reserved for model evaluation. These annotations are indicated by 8 digit numeric file names. <h2>Colony level annotations</h2> We combined the crop-based labels with full colony counts from 2022 conducted as part of reporting maximum counts. This labeling was conducted using Photoshop to label every bird in an entire colony as a point location with a species label, but due to the scale of labeling the point locations are not placed carefully on the center of the bird. To convert these point locations into bounding boxes needed for model training and evaluation, we used the bird detector module in the DeepForest Python package (Weinstein et al. 2022) to predict the location of birds within the image. We then associated the species label from the photoshop points with the overlapping bounding box prediction. For the minority of boxes that did not overlap a predicted bird, we drew a 1m buffer around the point.  We used data from 13 full colony counts, totaling 9318 birds, which were cropped into 1983 1500x1500 pixel crops. Full colony counts did not overlap with existing training and testing data from the 1500×1500 pixels crops because they were from different years. These annotations are indicated by variable length file names that start with a site name, followed by a date, followed by a number. <h1>Difficult to distinguish species</h1> Model training for most object detection algorithms requires that all birds in an image are labeled. Therefore, for all labeling approaches, the annotator’s best estimate of the species label was included even in cases where the species identity could not be conclusively determined from the imagery. The most common difficulty for annotators in distinguishing between species is White Ibis and Snowy Egrets. These birds both have white feathers, are of similar size, and appear similar in airborne imagery. As a result, annotators often use information beyond what is visibly apparent from individual birds to distinguish between these species, including what is known from the field about which species are present in the associated colonies and patterns in how the species aggregate in space. <h1>Cleaning of evalutation dataset</h1> Test data for evaluating the performance of the model was based exclusively on the crop-based labels. To ensure that the test data was as accurate as possible, all labels in the test set were checked and adjusted if necessary by the lead of the labeling effort (Lindsey Garner). This included both checking the species identification, adjusting any bounding boxes that did not properly contain the associated bird, and adding any labels that were missing entirely. To allow for understanding when it was not possible for a human to label a bird confidently to species, the labels for birds in the test set that could not be identified by the labeling lead were changed to indicate that the species was unknown (n = 94 birds out of 4113 test labels). </div>

opencc-by-4.0Nov 2018View details →

Research data supporting "Detection of microRNA biomarkers via inhibition of DNA-mediated liposome fusion"

Raw research data supporting Jumeaux, C. et al., Nanoscale (2018), DOI: 10.1039/C8NA00331A .

Dataset of automated earthquake detections in the Hikurangi Margin (New Zealand) during the 2014 Slow Slip Event

The dataset provided in this repository contains: - Yarce_REST_events.dat: Contains information of 854 events, with event id, origin time (epoch time), latitude, longitude, depth, number of P arrivals, number of S arrivals, total number of arrivals, local magnitude. - Yarce_REST_arrivals.data: Contains the arrival data information of the 854 events in Yarce_REST_events_v2.csv: event id, station, phase (P or S), arrival time.

Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks"

Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks" Jeonghwan Cheon, Seungdae Baek, and Se-Bum Paik* *Contact: sbpaik@kaist.ac.kr   To run demo codes for "<a href="https://github.com/vsnnlab/Invariance">Invariance of Object Detection in Untrained Deep Neural Networks</a>", please download files below.   1. Image.zip - Object dataset (Foldername: selectivity_var): This set was used to find units that selectively respond to a specific object class. It contains nine object classes (bed, chair, desk, dresser, nightstand, monitor, sofa, table, toilet) and 200 images are prepared to an object class. Each image has different object identities, which means it rendered from different object 3D models (Princeton ModelNet, a 3D CAD model dataset for computer vision and cognitive science [https://modelnet.cs.princeton.edu/]). To render image of object dataset, horizontal viewpoint variation angle was randomly set between -30° and +30°. In object dataset, brightness and contrast of images are statistically comparable across the object class. - Viewpoint dataset for invariance test (Folder name: invariance_test): This set was used to test the viewpoint invariant characteristic of object selective units. This dataset consists of 13 subsets which has different viewpoints from -180° to +180° in linear scale step. It contains 200 different object identities in an object class, which are the same as those used in the object dataset. - Viewpoint dataset for finding invariant unit (Folder name: invariance_unit): This set was used to find object selective units that specifically or invariantly responded to object images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-60°, -30°, 0°, 30°, 60°) with 50 object identities which were not used to find object selective unit - SVM dataset (Folder name: SVM_var): This set was used to train and test SVM which performs object detection task. It contains 60 different object identities in an object class, which were not used to find object selective unit. Specifically, it consists of 18 subsets which has different viewpoint variation range from 0° to 180°. For example, subset with 180° viewpoint variation range contains images which shows different viewpoints of objects within range of -90° and +90°.

opencc-by-4.0Oct 2022View details →

Datacubes for Landslide Detection with SAR Imagery

Four analysis-ready datacubes with multiple layers of SAR time-series data and interferometric products (coherence, InSAR) and auxiliary topographic information (elevation, slope, aspect, curvature) for multiple landslide events along with the corresponding landslide labels.   <table> <tbody> <tr> <td>Area</td> <td>Cause</td> <td>Date of event</td> <td>Dimensions</td> <td>Label Type</td> <td>Label Source</td> </tr> <tr> <td>Hokkaido, Japan</td> <td>Earthquake</td> <td>06-Sep-18</td> <td>{'y': 5382, 'x': 4398, 'timestep': 26, 'timepair': 16}</td> <td>Segmentation</td> <td><a href="https://zenodo.org/record/2577300">link</a></td> </tr> <tr> <td>Puerto Rico</td> <td>Hurricane Maria</td> <td>16-09-2017 - 02-10-2017</td> <td>{'y': 7134, 'x': 19251, 'timestep': 6, 'timepair': 4}</td> <td>Landslide heads</td> <td><a href="https://www.geosociety.org/gsatoday/science/G383A/GSATG383A.pdf">link</a></td> </tr> <tr> <td>Mt Talakmau, Indonesia</td> <td>Earthquake</td> <td>25-Feb-22</td> <td>{'y': 4313, 'x': 3006, 'timestep': 20, 'timepair': 12}</td> <td>Segmentation</td> <td><a href="https://www.unosat.org/products/3064">link</a></td> </tr> <tr> <td>Kaikoura, New Zealand</td> <td>Earthquake</td> <td>14-Nov-16</td> <td>{'y': 16973, 'x': 15930, 'timestep': 5, 'timepair': 9}</td> <td>Landslide centroids</td> <td><a href="https://data.gns.cri.nz/landslides/wms.html">link</a></td> </tr> </tbody> </table>   Acknowledgements This work has been enabled by the Frontier Development Lab Program (FDL). FDL is a collaboration between SETI Institute and Trillium Technologies Inc., in partnership with the Department of Energy (DOE) , National Aeronautics and Space Administration (NASA), and U.S. Geological Survey (USGS).The material is based upon work supported by NASA under award No(s) NNX14AT27A.

Detectability and impact of repetitive surveys on threatened West African crocodylians: Data M1

West African crocodylians are among the most threatened and least studied crocodylian species globally. Assessing population status and establishing a basis for population monitoring is the highest priority action for this region. Monitoring of crocodiles is influenced by many factors that affect detectability, including environmental variables and individual or population-level wariness. We investigated how these factors affect detectability and counts of the Critically Endangered Mecistops cataphractus and the newly recognized Crocodylus suchus. We implemented 195 repetitive surveys at 38 sites across Côte d'Ivoire between 2014 and 2019. We used an occupancy-based approach and a count-based GLMM analysis to determine the effect of environmental and anthropogenic variables on detection, and modeled crocodile wariness over repetitive surveys. Despite their rarity and level of threat, detection probability of both species was relatively high (0.75 for M. cataphractus and 0.81 for C. suchus), but a minimum of two surveys was required to infer absence of either species with 90% confidence. We found that detection of M. cataphractus was significantly negatively influenced by fishing net encounter rate, while high temperature for the previous 48h of the day of the survey increased C. suchus detection. Precipitation and aquatic vegetation had significant negative and positive influence, respectively, on M. cataphractus counts and showed the opposite effect for C. suchus counts. We also found that fishing encounter rate had a significant negative effect on C. suchus counts. Interestingly, survey repetition did not generally affect wariness for either species, though there was some indication that at least C. suchus was more wary by the fourth replicate. These results are informative for designing future survey and monitoring protocols for these threatened crocodylians in West Africa, and for other endangered crocodylians globally.

Fig. 4 in The detection of an alien pest, the cotton leaf roller Haritalodes decorata (Lepidoptera: Crambidae), on the Black Sea Coast of Russia

Fig. 4. The localities on the territory of the Russian Black Sea Cost where Haritalodes

Fig. 2 in The detection of an alien pest, the cotton leaf roller Haritalodes decorata (Lepidoptera: Crambidae), on the Black Sea Coast of Russia

Fig. 2. Maximum likelihood COI tree illustrating intraspecific divergence of Haritalodes

Fig. 3 in The detection of an alien pest, the cotton leaf roller Haritalodes decorata (Lepidoptera: Crambidae), on the Black Sea Coast of Russia

Fig. 3. Biology of Haritalodes derogata in Sochi, Yana Fabritsiusa str., 22–27.VIII

Figure Data "Homodyne Detection of Non-Gaussian Quantum Steering"

1) Data points + code required to recreate the first 12 figures in the manuscript "Homodyne Detection of Non-Gaussian Quantum Steering". 2) Simulated homodyne detector data used in the final section of the paper.

DrawMon: A Distributed System for Detection of Atypical Sketch Content in Concurrent Pictionary Games

Dataset for training CanvasNet, a model for detecting atypical sketch instances. CanvasNet processes the rendered image as input and outputs a list of atypical activities (if any) along with associated meta-information (atypical content category, 2-D spatial location). For more details, visit our page https://drawm0n.github.io/.

Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR

Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics' Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1400 kg/h. When emission rate estimates were generated using wind from High-Resolution Rapid Refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger's stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), accuracy of individual emission rate estimates produced using HRRR wind ranged from -64.1% to 87.0%. Across all controlled releases 38.1% of estimates had error within +/- 20%, and 87.3% of measurements were within a factor of two (-50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h) emission estimates are biased high; however, when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications which must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models, or to audit reported emission levels from oil and gas systems.

opencc-by-4.0Dec 2021View details →

Predictive modelling of brain metastasis risk and non-invasive biomarker detection using DNA methylation signatures

Methylated cell-free DNA was sequenced for 123 BM plasma and compared to plasma methylomes from 107 gliomas, central nervous system (CNS) lymphomas (CNSL), and non-CNS tumor controls. Plasma methylome-based classifiers of BM from other entities were built in fifty 80% discovery set iterations of 92/123 BM samples. External publicly-available tissue methylation data on 442 LUAD, 85 BM, and 146 glioma/CNSL/control samples were acquired for validation and the remaining 31/123 BM plasma samples were used for additional validation.

Dataset for: Designing a surveillance program for early detection of alien plants and insects in Norway

Naturalized species of alien plants and animals comprise < 3% of biodiversity recorded in Norway but have had major impacts on natural ecosystems through displacement of native species. Encroachment of alien species has been especially problematic for coastal sites close to transport facilities and urban areas with high-density housing. The goal of our field project was to design and test a surveillance program for early detection of alien species of vascular plants and terrestrial insects at the first phase of establishment in natural areas. In our 3-year project (2018–2020), we sampled 60 study plots in three counties in the Oslofjord region of southern Norway. Study plots (6.25 ha) were selected by two criteria: manual selection based on expert opinion (27 plots) or by random selection based on weights from a hotspot analysis of occurrence of alien species (33 plots). Vascular plants were surveyed by two experienced botanists who found a total of 239 alien species of vascular plants in 95 rounds of surveys. Insects and other invertebrates were captured with a single Malaise trap per site, with 3-4 rounds of repeated sampling. We used DNA-metabarcoding to identify invertebrates based on DNA extractions from crushed insects or from the preservative media. Over 3,500 invertebrate taxa were detected in 255 rounds of sampling. We recorded 20 alien species of known risk and 115 species that were new to Norway, including several 'doorknocker' species identified by previous risk assessments. We modeled the probabilities of occupancy (y) and detection (p) with occupancy models with repeated visits by multiple observers (vascular plants) or multiple rounds of sampling (insects). The two probabilities covaried with risk category for alien organisms and both were low for species categorized as no known or low risk (range = 0.052 to 0.326) but were higher for species categorized as severe risk (range = 0.318 to 0.651). Selecting sites at random or manually did not improve the probability of finding novel alien species, but occupancy had a weak positive relationship with housing density for some categories of alien plants and insects. We used our empirical estimates to test alternative sampling designs that would minimize the combined variance of occupancy and detection (A-optimality criterion). Sampling designs with 8–10 visits per site were best for surveillance of new alien species if the probabilities of occupancy and detection were both low, and provided low conditional probabilities of site occupancy (psi-hat(cond) £ 0.032) and a high probabilities of cumulative detection (p-hat(star) ³ 0.943). Our field results demonstrate that early detection is feasible as a key component of a national surveillance program based on early detection and rapid response (EDRR).

Binaural detection thresholds and audio quality of speech and music signals in complex acoustic environments

Every-day acoustical environments are often complex, typically comprising one attended target sound in the presence of interfering sounds (e.g., disturbing conversations) and reverberation. Here we assessed binaural detection thresholds and (supra-threshold) binaural audio quality ratings of four distortions types: spectral ripples, non-linear saturation, intensity and spatial modifications applied to speech, guitar, and noise targets in such complex acoustic environments (CAEs). The target and (up to) two masker sounds were either co-located as if contained in a common audio stream, or were spatially separated as if originating from different sound sources. The amount of reverberation was systematically varied. Masker and reverberation had a significant effect on the distortion-detection thresholds of speech signals. Quality ratings were affected by reverberation, whereas the effect of maskers depended on the distortion. The results suggest that detection thresholds and quality ratings for distorted speech in anechoic conditions are also valid for rooms with mild reverberation, but not for moderate reverberation. Furthermore, for spectral ripples, a significant relationship between the listeners’ individual detection thresholds and quality ratings was found. The current results provide baseline data for detection thresholds and audio quality ratings of different distortions of a target sound in CAEs, supporting the future development of binaural auditory models.

NGS data from: Detection of unintended on-target effects in CRISPR genome editing by DNA donors carrying diagnostic mutations

We developed a method to detect copy number variation and loss of heterozygosity in genome-edited cell lines by only sequencing the edited site. We introduced different substitutions on both chromosomes of diploid H9 human embryonic stem cells using CRISPR/Cas9 and a mixture of homology-directed repair donor DNAs carrying different substitutions. Here, we deposit the associated sequencing data (BAM-files) generated by extracting genomic DNA from genome-edited H9 cells, PCR amplifying the region of interest and sequencing on a MiSeq platform. We sequenced the edited site of cell bulks to investigate the editing efficiencies of individual donors from the donor mixtures. For proof of concept, we isolated over 850 cellular clones from the edited cell bulks and sequenced the edited site and heterozygous positions flanking the edited site to determine the number of alleles at the edited site and detect copy-neutral loss of heterozygosity. Additionally, we sequenced the DNA donors to assess, the distribution of individual donors in the mixtures.

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

The dataset "Case Study" 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.