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Leech-derived iDNA complements traditional surveying methods, enhancing species detections for rapid biodiversity sampling in the tropics
<p>Deforestation, exploitation, and other drivers of biodiversity loss in Madagascar leave its highly endangered and predominantly endemic wildlife at risk of extinction. Decreasing biodiversity threatens to compromise ecosystem functions and vital services provided to people. New, economical, and diverse methods of biodiversity monitoring can help to establish reliable baseline and long-term records of species richness. Metabarcoding with invertebrate-derived DNA (iDNA) has emerged as a promising new biosurveillance tool. An unexpected wet forest fragment tucked in the dry cliffs of Madagascar's southcentral plateau, the Ivohibory Protected Area (IPA), hosts a unique mosaic of species diversity, featuring both dry and wet forest species. Recently elevated to protected status, the IPA has been surveyed for flora and fauna with a range of inventory methods over three years and six expeditions (2016, 2017, & 2019). We collected 1,451 leeches over 12 days from the IPA to supplement known species richness and to compare results against current records. With iDNA, we pooled tissues, and isolated, and amplified bloodmeal DNA with five sets of primers. We detected 20 species of which four are species of frogs previously undetected and three of which are previously unknown to exist in this region. iDNA surveys can provide complementary data to traditional surveying methods like camera traps, line transects, and bioacoustic methods.</p>
Long-Tailed Anomaly Detection (LTAD) Dataset
<p><strong>Introduction</strong></p> <p>Anomaly detection (AD) aims to identify defective images and localize their defects (if any). Ideally, AD models should be able to: detect defects over many image classes; not rely on hard-coded class names that can be uninformative or inconsistent across datasets; learn without anomaly supervision; and be robust to the long-tailed distributions of real-world applications. To address these challenges, we formulate the problem of long-tailed AD by introducing several datasets with different levels of class imbalance for performance evaluation.</p> <p>To encourage more follow up works on long-tailed AD, we are publicly releasing the dataset split used in our paper (“Long-Tailed Anomaly Detection with Learnable Class Names” by Chih-Hui Ho, Kuan-Chuan Peng, and Nuno Vasconcelos, CVPR 2024).</p> <p>Files in the unzipped folder:</p> <p>1. ./README.md: This Markdown file</p> <p>2. ./dataset_split: Folder contains long-tail splits from three datasets. See below for details.</p> <p><strong> </strong></p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~16MB</li> <li>Three datasets are used in this project, including [MVTec](https://www.mvtec.com/company/research/datasets/mvtec-ad), [VisA](https://github.com/amazon-science/spot-diff) and [DAGM](https://www.kaggle.com/datasets/mhskjelvareid/dagm-2007-competition-dataset-optical-inspection). Please download the datasets from their original repositories.</li> <li>The dataset split provided in this folder is organized as follows:<br>```<br>dataset_split<br>|---dagm_lt<br>|---mvtec_lt<br>|---visa_lt<br>|-----|-- exp<br>|-----|-----|----- 100<br>|-----|-----|----- |-----test.json<br>|-----|-----|----- |-----train.json<br>|-----|-----|----- 200<br>|-----|-- step<br>|-----|-- ...<br>```</li> <li>Each long-tailed dataset split contains a subfolder ``imbalance_type/imbalance_factor", where imbalance type can be [exponential (exp), step, reverse exponential (exp_reverse), reverse step (step_reverse)]. The definition of imbalance type and imbalance factor can be found in our paper. Each subfolder contains two json files, one for training and the other for testing.</li> <li>Each entry in the json file contains the meta information of an image and is similar to<br>```<br>{"filename": "candle/test/bad/000.JPG", "label": 1, "label_name": "defective", "clsname": "candle", "maskname": "candle/ground_truth/bad/000.png"}<br>```<br>- filename: location of the input image in the dataset<br>- label: indicates whether the input image is normal (labeled as 0) or defective (labeled as 1)<br>- label name: can be "good" or "defective"<br>- clsname: class name of the input image<br>- maskname (optional): location of the binary image that indicates the defect region. This is only available for test.json, because there is no defect image during training.</li> </ul> <p><strong>Citation</strong></p> <p>If you use the LTAD dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{Ho_2024_CVPR, author = {Ho, Chih-Hui and Peng, Kuan-Chuan and Vasconcelos, Nuno}, title = {Long-Tailed Anomaly Detection with Learnable Class Names}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024} } </code></pre> <p><strong>License</strong></p> <p>The LTAD dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>. For the images in the MVTec, VisA, and DAGM datasets, please refer to their websites for their copyright and license terms.</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023-2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Data required for "Low mutation rate of spontaneous mutants enables detection of causative genes by comparing whole genome sequences"
<p>In the early 1900s,mutation breeding to select varieties with desirable traits using spontaneous mutation was actively conducted around the world, including Japan. In rice, the number of fixed mutations per generation was estimated to be 1.38-2.25. Although this low mutation rate was a major problem for breeding in those days, in the modern era with the development of NGS technology, it was conversely considered to be an advantage for efficient gene identification. In this paper, we proposed an in silico approach using next-generation sequencing (NGS) to compare the whole genome sequence of a spontaneous mutant with that of a closely related strain with a nearly identical genome, to find polymorphisms that differ between them, and to identify the causal gene by predicting the functional variation of the gene caused by the polymorphism. Using this approach, we found four causal genes for the dwarf mutation, the round shape grain mutation and the awnless mutation. Three of these genes were the same as those previously reported, but one was a novel gene involved in awn formation. The novel gene was isolated from Bozu-Aikoku, a mutant of Aikoku with the awnless trait, in which nine polymorphisms were predicted to alter gene function by their whole-genome comparison. Based on the information on gene function and tissue-specific expression patterns of these candidate genes, Os03g0115700/LOC_Os03g02460, annotated as a shortchain dehydrogenase/reductase SDR family protein, is most likely to be involved in the awnless mutation. Indeed, complementation tests by transformation showed that it is involved in awn formation. Thus, this method is an effective way to accelerate genome breeding of various crop species by enabling the identification of useful genes that can be used for crop breeding with minimal effort for NGS analysis.</p>
Street Scene Video Anomaly Detection Dataset
<p><strong><span>Introduction</span></strong></p> <p><span>The Street Scene dataset consists of 46 training video sequences and 35 testing video sequences taken from a static USB camera looking down on a scene of a two-lane street with bike lanes and pedestrian sidewalks.<span> </span>Videos were collected from the camera at various times during two consecutive summers.<span> </span>All of the videos were taken during the daytime.<span> </span>The dataset is challenging because of the variety of activities taking place such as cars driving, turning, stopping and parking; pedestrians walking, jogging and pushing strollers; and bikers riding in bike lanes. In addition, the videos contain changing shadows, and moving background such as a flag and trees blowing in the wind.</span></p> <p><span>There are a total of 202,545 color video frames (56,135 for training and 146,410 for testing) each of size 1280 x 720 pixels. The frames were extracted from the original videos at 15 frames per second.</span></p> <p><span>The 35 testing sequences have a total of 205 anomalous events consisting of 17 different anomaly types. A complete list of anomaly types and the number of each in the test set can be found in our paper.</span></p> <p><span>Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. Track lengths vary from tens of frames to 5200 which is the length of the longest testing sequence. A single frame can have more than one anomaly labeled.</span></p> <p><span>NOTE: This version of the dataset differs slightly with the original made available in 2020.<span> </span>Some anomalies were found in a few of the normal training sequences.<span> </span>These training frames were deleted from the dataset.<span> </span>Specifically, the following frames were removed:</span></p> <p><span>Train026: frames 1-184 (car taking a u-turn)</span></p> <p><span>Train027: frames 1-229 (jay walkers)</span></p> <p><span>Train031: frames 1-299 (jay walkers, illegally parked car)</span></p> <p><strong><span>At a Glance</span></strong></p> <ul> <li><span>The size of the unzipped dataset is ~46GB</span></li> <li><span>The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity) along with ground truth annotations, and a README.md file describing the data organization and ground truth annotation format.</span></li> <li><span>The zip file contains a Train directory, a Test directory and a README.md file.</span></li> </ul> <p><strong><span>Other Resources</span></strong></p> <p><span>None</span></p> <p><strong><span>Citation</span></strong></p> <p><span>If you use the Street Scene dataset in your research, please cite our contribution:</span></p> <pre><code>@inproceedings{ramachandra2020street, title={Street Scene: A new dataset and evaluation protocol for video anomaly detection}, author={Ramachandra, Bharathkumar and Jones, Michael}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={2569--2578}, year={2020} } </code></pre> <p><strong><span>License</span></strong></p> <p><span>The Street Scene dataset is released under </span><a href="https://creativecommons.org/licenses/by-sa/4.0/"><span>CC-BY-SA-4.0 license</span></a><span>.</span></p> <p><span>All data:</span></p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0 </code></pre>
Forest disturbance detection by using remote sensing and artificial intelligence in Africa
<p>The dataset arises from the "Forest Disturbance Detection Using Remote Sensing and Artificial Intelligence in Africa" (EO4Forest) project, a collaboration funded by the European Space Agency and conducted by Wrocław University of Environmental and Life Sciences (Poland) and Lagos State University (Nigeria). Designed for forest monitoring in the Ogun and Lagos States, the dataset includes detailed land cover classification maps for the years 2015, 2019, 2022, and 2023, all at a 20-meter spatial resolution to ensure accurate representation of land cover. A legend file accompanies the maps, clarifying six defined land cover classes: water, urbanized areas, soil, cropland, grasslands, and forest.</p> <p>The dataset includes over 112,000 training and 11,900 validation samples, which are essential for the accurate development and evaluation of the Random Forest classification models used. Special emphasis was placed on the forest class, ensuring a diverse representation of forest types, including tropical humid forests, mangroves, and dry woodlands.</p> <p>In addition to land cover maps, the dataset also contains forest gain and loss maps, with a particular focus on recent updates for selected subareas in 2022 and 2023, available in the NTR_ForestUpdate folder.</p> <p>Based on optical satellite imagery from Sentinel-2 and Landsat-8, the dataset leverages spectral indices and the Random Forest algorithm to classify land cover types. It provides valuable insights for environmental research related to deforestation, reforestation, and afforestation. Forest change maps are included to highlight areas of forest loss and gain, capturing the dynamic shifts in Nigeria's forest cover and offering detailed geographic context.</p> <p>The methodology, including data acquisition, preprocessing, feature extraction (spectral index calculation), classification, and accuracy assessment, is fully documented in Python scripts available in the associated GitHub repository. This ensures transparency and reproducibility, offering users both the processed outputs and the tools necessary for custom analyses and advanced forest monitoring.</p>
Bow echo detection and segmentation
<p>This is a code and data repository for a CNN-based bow echo detector meant for use on NEXRAD composite reflectivity mosaics discussed in:<br>"A derecho climatology (2004-2021) in the United States based on machine learning identification of bow echoes" by Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui</p> <p><br>The repository contains the following items:</p> <p>initial_training_dataset.zip -- The dataset used to train the initial version of the segmentation model. It contains 500 positive samples with hand-drawn masks and 3,350 negative samples with no masks. The samples are stored as .png files with pixel values between 0-255 linearly mapped from dBZ values between 0-50. The negative samples are stored as single-channel images while the positive samples are RGB images with the reflectivity data stored in the blue channel and the corresponding masks stored in the red channel.</p> <p>pseudo_labeled_training_dataset.zip -- The larger training set used for the final version of the CNN with 1,199 masked positive cases and 1,978 negative cases. These are also stored as .png files. Here, the masks have been produced by a CNN and reviewed for accuracy by a human.</p> <p>testing_dataset.zip -- 217 plots of testing cases. These are radar images of MCS's from 2010 that may or may not contain bow echoes.</p> <p>test_labels.csv -- A collection of human and CNN labels for the 217 test cases.</p> <p>conus_bow_echoes.zip -- NetCDF files containing CNN segmentation results for reflectivity mosaics from 2004-2021. Bow echo masks are stored as signed integers. 0 = No bow echo, -1 = bow echo not associated with a tracked MCS, >0 bow echo associated with an MCS where each unique MCS in a year is assigned a unique integer based on PyFLEXTRKR.</p> <p>June_2010.mp4 -- An animation of composite reflectivity (coloring), MCS tracks (gray shading), and bow echoes (black contours) for the month of June 2010.</p> <p>project_code.zip -- The collection of Python scripts used to construct, train, and apply the CNN.</p> <p>cnn.h5 -- The trained Unet 3+ model. The model performs required input clipping and scaling internally and can operate directly on reflectivity data.</p> <p>sample_mosaic.npz -- Input file for 'inference_demo.py'. Contains a single composite reflectivity mosaic</p> <p>sample_output.png -- The plot produced by "inference_demo.py".</p> <p>inference_demo.py -- A Python script that demonstrates how to apply the bow echo segmentation CNN to reflectivity data, including post-processing steps.</p>
SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge (WildSVDD Track)
<p>For more information about SVDD Challenge 2024, please refer to https://challenge.singfake.org/.<br><br>WildSVDD track dataset is an extension of <a href="https://singfake.org/">SingFake</a> dataset. </p>
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
<p>HDF5 data files for 1249 supraglacial lakes detected in ICESat-2 ATL03 data over Central West Greenland (melt seasons 2019 and 2020) and the Amery Ice Shelf Catchment (melt seasons 2018-19 and 2020-21). Each HDF5 data file is associated with a .jpg "quicklook" file of the same name, showing ATL03 photon elevations with the estimated along-track fits to the lake surface and lakebed and the resulting maximum lake depth, along with the corresponding ICESat-2 ground track over cloud-free concurrent satellite imagery.</p> <p>The data files are structured as following: </p> <div> <div> <div> <div> <div> <pre>group: depth_data/ - dataset: bathymetry_confidence - dataset: lakebed_fit_elevation_meters - dataset: lat - dataset: lon - dataset: surface_fit_elevation_meters - dataset: water_depth_meters - dataset: x_along_track_meters group: fluid_bathymetry_peaks/ - dataset: elevation_meters - dataset: peak_prominence - dataset: x_along_track_meters group: mframe_data/ - dataset: delta_time - dataset: density_ratio_1 - dataset: density_ratio_2 - dataset: density_ratio_3 - dataset: density_ratio_4 - dataset: major_frame_id - dataset: passes_bathymetry_check - dataset: passes_flatness_check - dataset: photon_density_peak_elevation - dataset: q_1_number_peaks - dataset: q_2_prominece - dataset: q_3_elev_spread - dataset: q_4_alignment - dataset: q_s - dataset: x_along_track_meters_end - dataset: x_along_track_meters_start group: photon_data/ - dataset: afterpulse_probability - dataset: fluid_signal_confidence - dataset: geoid_elevation_meters - dataset: lat - dataset: lon - dataset: photon_elevation_above_geoid_meters - dataset: pulse_saturation_level - dataset: x_along_track_meters group: properties/ - dataset: beam_number - dataset: beam_strength - dataset: cycle_number - dataset: granule_id - dataset: gtx - dataset: ice_sheet - dataset: lake_quality - dataset: lat - dataset: lon - dataset: melt_season - dataset: rgt - dataset: sc_orient - dataset: surface_elevation - dataset: time_utc </pre> </div> </div> </div> </div> </div>
Fecal standing crop with real time correction using scat detection dogs to estimate population density
<p>Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Fecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find fecal samples in the field and to estimate the probability of which feces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten fecal samples of gray brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 x 1200 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental fecal samples to determine the FSC parameters that were subsequently used with naturally occurring fecal samples (also dog-detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 23% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.54 individuals km-2 (SD = 2.21, Dry) and 5.52 indiv. km-2 (SD = 3.71, Wet).</p> <p><em>Synthesis and applications:</em> Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability, and climate in fecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.</p>
Self-Learning Vehicle Detection Dataset for Urban Environments
<p>This dataset was collected as part of a research study aimed at enhancing vehicle detection algorithms through a self-learning approach tailored for urban environments. The primary objective was to minimize dependency on extensive manual labeling and improve adaptability and effectiveness in dynamic urban conditions. The study utilized urban camera infrastructures to gather real-time traffic data, focusing on a diverse range of vehicle types.</p> <p>The dataset includes images captured from traffic cameras situated at the intersection of Calle de Alcalá and Calle de Velázquez in Madrid, Spain, operated by the Madrid City Council. Data collection spanned from November 30, 2023, to December 6, 2023, covering daytime traffic between 8:30 hours and 18:00 hours. A total of 770 images were captured at approximately 5-minute intervals.</p> <p>This dataset specifically targets five vehicle types: buses, cars, motorcycles, trucks, and vans, chosen to encompass a wide range of vehicle sizes, shapes, and functionalities commonly encountered in city traffic. A subset of 134 images was manually labeled, into sets for training, validation (fine-tuning phase), and validation (self-training phase). The remaining 653 images were labeled automatically via the self-learning process proposed in the research.</p>
Data and code for: Does the definition of a novel environment affect the ability to detect cryptic genetic variation?
<p>Anthropogenic change exposes populations to environments that have been rare or entirely absent from their evolutionary past. Such novel environments are hypothesised to release cryptic genetic variation, a hidden store of variance that can fuel evolution. However, support for this hypothesis is mixed. One possible reason is a lack of clarity in what is meant by 'novel environment', an umbrella term encompassing conditions with potentially contrasting effects on the exposure or concealment of cryptic variation. Here, we use a meta-analysis approach to investigate changes in the total genetic variance of multivariate traits in ancestral versus novel environments. To determine whether the definition of a novel environment could explain the mixed support for a release of cryptic genetic variation, we compared absolute novel environments, those not represented in a population's evolutionary past, to extreme novel environments, those involving frequency or magnitude changes to environments present in a population's ancestry. Despite sufficient statistical power, we detected no broadscale pattern of increased genetic variance in novel environments, finding the type of novel environment did not explain any significant variation in effect sizes. When effect sizes were partitioned by experimental design, we found increased genetic variation in studies based on broad-sense measures of variance, and decreased variation in narrow-sense studies, in support of previous research. Therefore, the source of genetic variance, not the definition of a novel environment, was key to understanding environment-dependant genetic variation, highlighting non-additive genetic variance as an important component of cryptic genetic variation and avenue for future research.</p>
Fig. 1 in First molecular detection of Francisella tularensis in turtle (Testudo graeca) and ticks (Hyalomma aegyptium) in Northwest of Iran
Fig. 1. The schematic map of the studied areas, West Azerbaijan (Oshnavieh), Iran.
An Exploratory Evaluation of Continuous Feedback to Enhance Machine Learning Code Smell Detection
<p><em>Context</em>: Code smells are symptoms of bad design choices implemented on the source code. To manage and enhance software quality, it is important to be aware of code smells and refactor them whenever possible. As a result, several code smell detection tools and techniques have been proposed over the years. These tools and techniques present different strategies to detect code smells. More recently, machine learning algorithms have also been proposed to support code smell detection. However, we lack empirical evidence on how expert feedback could improve detection of these machine learning based techniques. <em>Objective:</em> This paper aims to propose and evaluate a machine-learning based strategy to improve detection of code smells by means of continuous feedback provided by the system expert.<em> Method: </em>To evaluate the strategy, we follow an experimental design to compare results of the detection before and after the feedback, both when feedback is provided at once and continuously. We focus on four code smells - God Class, Long Method, Feature Envy, and Refused Bequest - detected in twenty Java systems by using five code smell detection tools. We also extracted class- and method-level metrics from the systems for training the machine learning algorithms. <em>Results:</em> We observed that continuous feedback improves the performance of code smell detection. For the detection of God Class, a code smell with a detection performance initially good, we achieved an average improvement of 0.13 in terms of F1. For Refused Bequest, another class-level code smell, we achieved an average improvement of 0.58 in terms of F1 after all interactions of the strategy. For the method-level code smells, Long Method and Feature Envy, we achieved an average improvement of 0.66 and 0.72 in terms of F1, respectively.<em> Conclusions: </em>Our promising results are a stepping stone towards the development of tools relying on continuous feedback for machine learning detection of code smells.</p>
COVID-19 Fake News Detection Dataset
<p>Please note that this data set was originally shared by Patwa et al. (2021) on GitHub. </p> <p><strong>Reference </strong></p> <p>Patwa, P., Sharma, S. Pykl, S., Guptha, V., Kumari, G., Akhtar, M. S., Ekbal, A., Das A. & Chakraborty, T. (2021). Fighting an Infodemic: COVID-19 Fake News Dataset. Combating Online Hostile Posts in Regional Languages during Emergency Situation, Cham, Springer International Publishing. https://doi.org/10.1007/978-3-030-73696-5_3</p>
GPR dataset: pulsed radar and SFCW data for rebar detection (experimental data)
<p>GPR data corresponding to the article "Rebar detection: Comparison of stepped frequency continuous wave and pulsed GPR" presented in the 7th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures (SMAR 2024). </p> <p><span>This work presents a comparison in rebar mapping between two GPR systems with different modulation techniques: Stepped frequency continuous wave (SFCW) and pulsed radar. The SFCW system used has a frequency range of 400-6000 MHz, while the pulsed system used a ground-coupled central frequency antenna of 2.3 GHz. Measurements were conducted on laboratory specimens, with rebar diameters ranging from 8 to 32 mm. Three different specimens were used, with one for calibration and other two to analyze both the horizontal and vertical resolutions of the frequency antennas.</span></p>
Surpassing Threshold Barriers: Evaluating the Efficacy of Nature-Inspired Algorithms in Detecting Applied Refactorings
<p>This page is provided as supplementary material for the article: Surpassing Threshold Barriers: Evaluating the Efficacy of Nature-Inspired Algorithms in Detecting Applied Refactorings. More specifically, we provide the results, the refactorings detected by each tool, as a downloadable JSON file. </p> <p>Results.json: You can access the results comparing ACA with RefDetect, RefactoringMiner, NSGA-II, and the Greedy algorithm.</p> <ul> <li>Overview of JSON Template: Each entry in this file provides details about commits, including their SHA-1 hash, GitHub URL, and an exhaustive list of refactorings identified in that specific commit. Refactorings are defined by their type (e.g., 'RenameClass') and are accompanied by detailed descriptions of the refactoring. Additionally, the entry specifies the tools that detected the refactoring, such as 'ACA' and 'RefactoringMiner.' The validation field indicates whether the refactoring was confirmed, using 'TP' for true positive and 'FP' for false positive. Optional comment information may be also included.</li> </ul> <p>Times.xlsx: The Excel file containing the time taken by each tool to identify refactoring in each commit.</p> <p>Results.xlsx: The Excel file contains precision, recall, and F-score metrics for all identified refactoring types.</p>
SOMD - SOftware Mention Detection
<p>The dataset contains the training and test data for the SOftware Mention Detection challenge. The data is derived from the SoMeSci Knowledge Graph of software mentions.</p> <ul> <li>Subtask 1 deals with the recognition of software mentions and the classification of mention (e.g. Usage, Creation,...) and software types (e.g. Application, PlugIn,...) at the same time </li> <li>Subtask 2 requires the recognition of additional meta data of software mentions (e.g. Version, Developer, URL,...)</li> <li>Subtask 3 deals with extracting the relations between the different entities of interest (e.g. Version_of, License_of,...)</li> </ul> <p>A detailed description of the dataset including the creation and a baseline for the different subtasks can be found in the following article </p> <p>D. Schindler, F. Bensmann, S. Dietze, and F. Krüger, “SoMeSci—A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles,” in Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), Virtual Event, QLD, Australia: Association for Computing Machinery, Nov. 2021. doi: <a href="https://doi.org/10.1145/3459637.3482017">10.1145/3459637.3482017</a>.</p>
Fig. 1 in First report of successful Naegleria detection from environmental resources of some selected areas of Rawlakot, Azad Jammu and Kashmir, Pakistan
Fig. 1. Sampling sites in the targeted area of Rawlakot, Azad Jammu and Kashmir, Pakistan
Himax Dataset for Object Detection of Bottles and Tin-Cans
<p>Object detection Dataset was collected with a Himax HM01B0 greyscale camera. The datasets contain QVGA images of Bottles and Tin-Cans and their respective labels. The labels follow the PascalVOC format specification. The dataset also include tfrecod files for ease of use with tensorflow. This dataset was used in our paper Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones</p>
Raw data from "AC transport detection of magnetic transitions in small and granular samples"
<p>Raw data supporting the submitted manuscript "AC transport detection of magnetic transitions in small and granular samples". Additional information avaliable upon request.</p>
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.