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opencc-by-4.0May 2024View details →

The supporting data and code for research "Quantification of Entanglement and Coherence with Purity Detection"

The supporting data and code for research "Quantification of Entanglement and Coherence with Purity Detection"

opencc-by-4.0May 2024View details →

QSage: Structural and Semantic Metric Analysis for Quantum Code Smell Detection

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opencc-by-sa-4.0Jun 2024View details →

Data for "Chiral topological light for detection of robust enantiosensitive observables"

Data for "Chiral topological light for detection of robust enantiosensitive observables" This repository contains the data files and Jupyter notebook to reproduce the numerical results from the paper "Chiral topological light for detection of enantiosensitive observables", N. Mayer, D. Ayuso, P. Decleva, M. Khokhlova, E. Pisanty, M. Ivanov and O. Smirnova, Nature Photonics (2024), <a href="https://doi.org/10.1038/s41566-024-01499-8">doi:10.1038/s41566-024-01499-8</a>, <a href="https://arxiv.org/abs/2303.10932">arXiv:2303.10932</a> as well as the results contained in its Supplementary Files.  The Python code used to calculate the data files is available from the authors upon reasonable request. Please refer to the Methods section of the paper for a detailed description of the numerical approach behind the numerical simulations. Note that the figures in the paper are assembled in a separate software from the plots reproduced here, and that we include here only the results for the theoretical simulations of the molecular response. The repository contains the following files: <ul> <li>atomic_units.py: Python file containing useful definitions of quantities in atomic units.</li> <li>readme.txt: file containing a detailed description of the files contained in files.zip</li> <li>Figures_CTL.ipynb: Jupyter notebook to load the files in files.zip and plot the images of the paper.</li> <li>files.zip: zip file containing the data files.</li> </ul> The copyright of this collection rests with the authors (2024). It is made available under the Creative Commons Attribution-ShareAlike 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>) license. For any academic use that results in a publication, please cite the main paper in addition to this deposit.

Supplement - DEM of Difference Geomorphic Change Detection

Using the 2013 OSMP lidar DEM and the newly created site DEMs, DEMs of difference (DODs) were calculated for each Site to show how the landscape has changed over a period of time (James et al. 2012). Because of differences in datums, datasets created here were offset from the 2013 dataset. As such, we selected five points on DODs in areas thought to be stable (e.g., bedrock outcrops), avoiding edges of rasters, as these values are not an accurate representation of the datum shift. The five points were then averaged and DODs shifted accordingly such that DODs could be interpreted as deposition (positive values), no change, or erosion (negative values). Artifacts within the Structure from Motion data made some absolute change values unreasonable. Therefore, to aid in visual interpretation of erosion and deposition, we chose to use relative change by normalizing the ranges of DOD values with the equation: “((DOD dataset – minimum value)/ (maximum value- minimum value)) - 0.5”. These values were interpreted from -0.5 (erosion) to 0 (no change) to 0.5 (deposition). DODs can be found in this repository.

Datasets from: "Deep learning for detecting and characterizing oil and gas well pads in satellite imagery"

This repository contains the following:  - <code>training</code> folder: Datasets used to train well pad and storage tank models. Note that <code>annotations_image</code> for each example are with respect to a image downloaded from the Google Earth satellite basemap at zoom level 1600 and EPSG:3857, with sizes 640x640 px and 512x512 px for well pads and storage tanks respectively (see also the <code>image_extent </code>column). <code>annotations_latlon</code> also provides the annotations in coordinate space. We also note that we are unable to redistribute the satellite imagery used to train the models in this study due to data licensing. Samples of satellite images may be made available upon request to the corresponding author. - <code>deployment</code> folder: Deployment detections for well pads and storage tanks across the entire Permian and Denver basins. Datasets contain confidence scores (<code>bbox_score</code>) and coordinate locations (<code>geometry</code>) for each detection, as well as a well pad identifier (<code>wp_id</code>) that indicates which well pad a storage tank belongs to.

Supplementary file 3; Photographs illustrating different ways in which signs of livestock, humans or wild mammals were detected and recorded as either direct sighting, tracks, spoor or other signs:

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Supplementary File 8; Dot plots summarising the total frequencies of direct observations, tracks, spoor, and other signs of all wild animals detected in radial and transect surveys over the course of the study:

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opencc-by-4.0Aug 2023View details →

Datasets for "Detecting rhythmic spiking through the power spectra of point process model residuals"

This repository contains synthetic and empirical datasets that were analyzed in the preprint "Detecting rhythmic spiking through the power spectra of point process model residuals." Relative to the previous version (1.0.0), this new version (1.0.1) only entails changes in the naming of some folders, and the text of an explanatory text file.  The changes reflect the reordering of the figures in the newest version of the manuscript.

openmit-licenseJun 2024View details →

Formation, Structure, and Detectability of the Geminids Meteoroid Stream (Data)

Data generated and analyzed in the Cukier and Szalay (2023) paper "Formation, Structure, and Detectability of the Geminids Meteoroid Stream" (DOI: 10.3847/PSJ/acd538)

opencc-by-4.0Dec 2023View details →

Dataset and code for 'Signal to Noise Ratio and Spectral Sampling Constraints on Olivine Detection and Compositional Determination in the Intermediate Infrared Region'

Feature fitting code and spectra of olivine samples used in paper "Signal to Noise Ratio and Spectral Sampling Constraints on Olivine Detection and Compositional Determination in the Intermediate Infrared Region: Applications in Planetary Sciences"

Dataset for Detecting Vehicle in Pedestrian Areas version 2

The Car_Truck_Bus_Pedestrian-Area_v2.zip is an updated collection featuring a greater number of images, specifically 7808 images, each in very high resolution in .jpg format. The total file size is now 290 MB. It continues to serve the purpose of detecting vehicles in pedestrian areas within urban environments and is captured at a resolution of 1280×720 (HD). Each image is approximately 58 KB in size. The image data was collected from 100 strategically placed public CCTVs in Medellín, Colombia, under various light and weather conditions, recorded every 60 seconds between 8:00 a.m. and 3:00 p.m. For each image, it is necessary to identify cars, buses, trucks, and pedestrians in particular zones, differentiated from the background. This enhanced dataset aims to train and evaluate machine learning models for urban traffic management and pedestrian safety applications. Data in this document is published under the Creative Commons Attribution 4.0 International License.

SD4EO - Physically Based Rendering images of human settlements for solar panel detection

This dataset contains part of the results of the SD4EO project, including images corresponding to the use case of solar panel detection on human settlements. The dataset contains images that have been simulated corresponding to low altitude aerial images. Each image in the dataset includes pixel-level labels for each element present in the image, ensuring perfect accuracy due to the synthetic nature of the images. This eliminates common errors in manual or semi-automatic labeling processes. Image labeling (Open Street Map color code) <div> <table> <tbody> <tr> <td> Element </td> <td> R </td> <td> G </td> <td> B </td> </tr> <tr> <td> Road </td> <td> 255 </td> <td> 255 </td> <td> 255 </td> </tr> <tr> <td> Building </td> <td> 196 </td> <td> 182 </td> <td> 171 </td> </tr> <tr> <td> Highway </td> <td> 232 </td> <td> 146 </td> <td> 162 </td> </tr> <tr> <td> Industrial zone </td> <td> 234 </td> <td> 203 </td> <td> 228 </td> </tr> <tr> <td> Parking </td> <td> 238 </td> <td> 238 </td> <td> 238 </td> </tr> <tr> <td> Railway </td> <td> 112 </td> <td> 112 </td> <td> 112 </td> </tr> <tr> <td> Residential zone </td> <td> 224 </td> <td> 223 </td> <td> 223 </td> </tr> <tr> <td> Vegetation </td> <td> 174 </td> <td> 223 </td> <td> 163 </td> </tr> <tr> <td> Water </td> <td> 170 </td> <td> 211 </td> <td> 223 </td> </tr> </tbody> </table> </div> For solar panels masks white color is used to mark the pixels corresponding to solar panels. Image name convention The name convention follows the next schema of fields, separated by the character “_” -          ID number -          Sub ID number -          Meters per pixel resolution -          Type of image: color, labels, solarpanels Creation and funding All the images have been generated using a tool developed in Unity. This tool will be soon available to enable the generation of new datasets. This research work has been funded by the European Space Agency (ESA) under the FutureEO program and the SD4EO project (Contract No.: 4000142334/23/I-DT), supervised by the ESA Φ-lab. License and attribution This dataset is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0). When using the images from this dataset, please attribute them as follows: "Synthetic images created by the research group ARTEC - IRTIC - University of Valencia".

opencc-zeroJul 2024View details →

dryad32/100

buzzfindr: Automating the detection of feeding buzzes in bat echolocation recordings

Quantification of bat communities and habitat heavily rely on non-invasive acoustic bat surveys the scope of which has greatly amplified with advances in remote monitoring technologies. Despite the unprecedented amount of acoustic data being collected, analysis of these data is often limited to simple species classification which provides little information on habitat function. Feeding buzzes, the rapid sequences of echolocation pulses emitted by bats during the terminal phase of prey capture, have historically been used to evaluate foraging habitat quality. Automated identification of feeding buzzes in recordings could benefit conservation by helping identify critical foraging habitat. I tested if detection of feeding buzzes in recordings could be automated with bat recordings from Ontario, Canada. Data were obtained using three different recording devices. The signal detection method involved sequentially scanning narrow frequency bands with the "Bioacoustics" R package signal detection algorithm, and extracting temporal and signal strength parameters from detections. Buzzes were best characterized by the standard deviation of the time between consecutive pulses, the average pulse duration, and the average pulse signal-to-noise ratio. Classification accuracy was highest with artificial neural networks and random forest algorithms. I compared each model's receiver operating characteristic curves and random forest provided better control over the false-positive rate so it was retained as the final model. When tested on a new dataset, buzzfindr's overall accuracy was 93.4% (95% CI: 91.5% - 94.9%). Overall accuracy was not affected by recording device type or species frequency group. Automated detection of feeding buzzes will facilitate their integration in the analytical workflow of acoustic bat studies to improve inferences on habitat use and quality.

Flood Detection with Sentinel-1 Satellite Based on Samarinda Januari 2020 Flood

This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.

Remote Sensing DInSAR Method for Land Subsidence Detection in Semarang, Central Java, Indonesia

This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.

MADDD-seq, a novel massively parallel sequencing tool for simultaneous detection of DNA damage and mutations

The file "data.tar" contains the output of the MADDD-seq pipepline. There is one sub-folder per sample. For each sample, the most important files are: <ul> <li>max_variants_2.adduct.gtf : A GTF file with the location (and details) about each adduct called by the pipepline</li> <li>max_variants_2.DSC.vcf.gz : A VCF (Variant Call File) with information about mutations called.</li> <li>coverage.rds : pre-computed coverage information in binary format to be loaded in R.</li> </ul> To analyze this data, use the following R files: adducts.R, mutations.R and jason-function-2022-04.R   The file "kallisto-h5.tar"  contains the output of running Kallisto on the regular RNAseq data (for expression level analysis). To analyze this data, use the following R files: Yeast-MNNG-MGT.Rmd and myDESeq2.R   The source code of the R files will need to be modified to point at the location of files on the computer being used. These modifications are pointed by comments in the code and are located towards the start of each file.

opencc-by-sa-4.0Jul 2024View details →

IMAD-DS: A Dataset for Industrial Multi-Sensor Anomaly Detection Under Domain Shift Conditions

IMAD-DS is a dataset developed for multi-rate multi-sensor anomaly detection (AD) in industrial environments, that considers varying operational and environmental conditions known as domain shifts. Dataset Overview: This dataset includes data from two scaled industrial machines: a robotic arm and a brushless motor. It includes both normal and abnormal data recorded under various operating conditions to account for domain shifts. These shifts are categorized into: Robotic Arm: The robotic arm is a scaled version of a robotic arm used to move silicon wafers in a factory. Anomalies are created by removing bolts at the nodes of the arm, resulting in an imbalance in the machine. Brushless Motor: The brushless motor is a scaled representation of an industrial brushless motor. Two anomalies are introduced: first, a magnet is moved closer to the motor load, causing oscillations by interacting with two symmetrical magnets on the load; second, a belt that rotates in unison with the motor shaft is tightened, creating mechanical stress. The following domain shifts are included in the dataset: Operational Domain Shifts: Variations caused by changes in machine conditions (e.g., load changes for the robotic arm and speed changes for the brushless motor). Environmental Domain Shifts: Variations due to changes in background noise levels. Combinations of operating and environmental conditions divide each machine's dataset into two subsets: the source domain and the target domain. The source domain has a large number of training examples. The target domain, instead, has limited training data. This discrepancy highlights a common issue in the industry where sufficient training data is often unavailable for the target domain, as machine data is collected under controlled environments that do not fully represent the deployment environments.   Data Collection and Processing: Data is collected using the STEVAL-STWINBX1 IoT Sensor Industrial Node. The sensor used to record the dataset are the following. ·        Analog Microphone (16 kHz) ·        3-axis Accelerometer (6.7 kHz) ·        3-axis Gyroscope (6.7 kHz) Recordings are conducted in an anechoic chamber to control acoustic conditions precisely Data Format: Files are already divided into train and test sets. Inside each folder, each sensor's data is stored in a separate '.parquet' file. Sensor files related to the same segment of machine data share a unique ID. The mapping of each machine data segment to the sensor files is given in .csv files inside the train and test folders. Those .csv files also contain metadata denoting the operational and environmental conditions of a specific segment.

dryad32/100

Iowa herptile detection histories and landcover metrics

Predictions of species occurrence allow land managers to focus conservation efforts on locations where species are most likely to occur. Such analyses are rare for herpetofauna compared to other taxa, despite increasing evidence that herptile populations are declining because of land cover change and habitat fragmentation. Our objective was to create predictions of occupancy and colonization probabilities for 15 herptiles of greatest conservation need in Iowa. From 2006–2014, we surveyed 295 properties throughout Iowa for herptile presence using timed visual-encounter surveys, coverboards, and aquatic traps. Data were analyzed using robust design occupancy modeling with landscape-level covariates. Occupancy ranged from 0.01 (95% CI = -0.01, 0.03) for prairie ringneck snake (Diadophis punctatus arnyi) to 0.90 (95% CI = 0.898, 0.904) for northern leopard frog (Lithobates pipiens). Occupancy for most species correlated to landscape features at the 1-km scale. General patterns of species' occupancy included the negative effects of agricultural features and the positive effects of water features on turtles and frogs. Colonization probabilities ranged from 0.007 (95% CI = 0.006, 0.008) for spiny softshell turtle (Apalone spinifera) to 0.82 (95% CI = 0.62, 1.0) for western fox snake (Pantherophis ramspotti). Colonization probabilities for most species were best explained by the effects of water and grassland landscape features. Predictive models had strong support (AUC > 0.70) for six out of 15 species (40%), including all three turtles studied. Our results provide estimates of occupancy and colonization probabilities and spatial predictions of occurrence for herptiles of greatest conservation need across the state of Iowa.

opencc-zeroJul 2024View details →

3D tumor reconstruction using self-supervised registration and cell-detection-based area delineation

Breast cancer tissue sequentially sectioned for 3D alignment. <ul> <li>ndpi files contain renamed (all) and re-oriented whole slide images (IU, rotated by 180°) originally scanned</li> <li>Downsampled zip folder contains overview images of the individual WSIs</li> </ul>

Dynamic web page change content detection

This dataset contains 4 parts. "SimilarWeb dataset with screenshots" is created by scraping web elements, their CSS, and corresponding screenshots in three different time intervals for around 100 web pages. Based on this data, the "SimilarWeb dataset with SSIM column" is created with the target column containing the structural similarity index measure (SSIM) of the captured screenshots. This part of the dataset is used to train machine learning regression models. To evaluate approach, "Accessible web pages dataset" and "General use web pages dataset" parts of the dataset are used.