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Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests
<p><strong>Data description</strong></p> <p>These datasets were generated for the Geostory "Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests" in the context of the Open Earth Monitor Cyberinfrastructure project.</p> <p>We used open source high-resolution Sentinel-1 satellite data to develop a wall-to-wall map of forest disturbances in the four-year period between the start of 2020 and end of 2023 in Estonia. First results are presented. The methodology is based on RADD-alerts developed for the pan-tropics (Reiche et al. 2021). Three years (2017-2019) of imagery was used as a historical period, and detections were generated for ~4 years (2020-2023). Winter images from November through March were not included as frozen conditions can introduce false detections. This will be addressed in the next version. Disclaimer: Disturbance maps have not been validated.</p> <p>Two additional layers are provided for visualization: a forest baseline layer (<em>forestcover</em>), masking out non-forest disturbance detections, was derived from Copernicus 10m 2018 forest cover density and GLAD 30m 2019 tree removal datasets, and a protected areas layer (<em>natura</em>), which displays the extent of Natura 2000 coverage in Estonia.</p> <p>'.SLD' files are provided for visualization (note: the <em>disturbance</em> .SLC file must be adjusted to contain appropriate time reference fields).</p> <p><strong>Naming Convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For instance:</p> <ul> <li>disturbance_radd_c_10m_s_20200101_20200131_eu_epsg.3035_v20240222.tif</li> </ul> <p>with the following fields:</p> <ul> <li>Generic variable name: <strong>disturbance</strong></li> <li>Variable procedure combination i.e. method standard: <strong>radd</strong></li> <li>Position in the probability distribution / variable type: <strong>c</strong></li> <li>Spatial support: <strong>10m</strong></li> <li>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): <strong>s</strong></li> <li>Time reference begin time (YYYYMMDD): <strong>20200101</strong></li> <li>Time reference end time: <strong>20200131</strong></li> <li>Bounding box (2 letters max): <strong>eu </strong></li> <li>EPSG code: <strong>epsg.3035</strong></li> <li>Version code i.e. creation date: <strong>v20240222</strong></li> </ul> <p><strong>Source Data</strong></p> <p>Disturbance maps:</p> <p>Contains modified Copernicus Sentinel data [2017-2023] and Generated using European Union's EEA-10 Copernicus DEM; https://doi.org/10.5270/ESA-c5d3d65</p> <p>Forest baseline:</p> <p>Generated using European Union's Copernicus Land Monitoring Service information; https://doi.org/10.2909/486f77da-d605-423e-93a9-680760ab6791 and GLAD tree removal; https://doi.org/10.1016/j.rse.2023.113797</p> <p>Natura 2000: </p> <p>Generated using European Environmental Agency's Natura 2000 layers; https://sdi.eea.europa.eu/data/dae737fd-7ee1-4b0a-9eb7-1954eec00c65</p>
Real-time deformability cytometry reference data
<p>This dataset consists of four exemplary real-time fluorescence and deformability cytometry measurements. The HDF5-files can be opened with dclab [1] or Shape-Out [2].</p> <p><strong>calibration_beads.rtdc</strong><br> The calibartion beads (8 Peaks, PolyAN) consist of eight bead populations with different mixtures of fluorophores.</p> <p><br> <strong>CD34_HSPC.rtdc</strong><br> Hematopoietic stem and progenitor cells (HSPCs) were obtained using apheresis. The cells were tagged with a fluorescently labeled antibody that binds to the CD34 transmembrane protein. CD34-positive HSPCs are gated with `fl3_max > 90`. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> <strong>leukocytes.rtdc</strong><br> The leukocyte population (white blood cells) of this blood sample can be visualized by setting `aspect < 2` and `area_ratio < 1.05`. For more information, see e.g. [4].</p> <p><br> <strong>reticulocytes.rtdc</strong><br> Blood contains mostly red blood cells (RBCs) and about 1% reticulocytes (which develop into mature RBCs). Reticulocytes contain ribosomal RNA which was stained with Syto13 for this measurement. Set `area_ratio < 1.05` to remove aggregates. Data were used in [3].</p> <p><br> [1] <a href="https://github.com/ZellMechanik-Dresden/dclab">https://github.com/ZellMechanik-Dresden/dclab</a></p> <p>[2] <a href="https://github.com/ZellMechanik-Dresden/ShapeOut">https://github.com/ZellMechanik-Dresden/ShapeOut</a></p> <p>[3] Rosendahl et al., "Real-time fluorescence and deformability cytometry". Nature Methods, 15(5):355–358, 2018. doi:<a href="https://dx.doi.org/10.1038/nmeth.4639">10.1038/nmeth.4639</a>.</p> <p>[4] Toepfner et al., "Detection of human disease conditions by single-cell morpho-rheological phenotyping of whole blood". eLife, 7:e29213, 2017. doi:<a href="https://dx.doi.org/10.1101/145078">10.1101/145078</a>.</p> <p><br> SHA256 sums:<br> 08c2ef13eed903ef0f9e451727ab8484df09b5d3b39227dab726e0164dcbe244 calibration_beads.rtdc<br> 663b44a9db88d85996500045489e37a317cf115719223a531d617f8e3d450e79 CD34_HSPC.rtdc<br> 68bd538b42ffb990f1db52d5f3b21f37c9aff31208ab284f3910fd6872c40fdb leukocytes.rtdc<br> 5c323ea75bf7eeb2a28d922730772d50270dd872d6957e60d6062663f3628fb3 reticulocytes.rtdc</p>
Hand-selective visual regions represent how to grasp 3D tools for use: brain decoding during real actions
Open the record for dataset details and reuse information.
Large-scale Ridesharing DARP Instances Based on Real Travel Demand
<p>This repository presents a set of large-scale Dial-a-Ride Problem (DARP) instances. The instances were created as a standardized set of ridesharing DARP problems for the purpose of benchmarking and comparing different solution methods.</p><p>The instances are based on real demand and realistic travel time data from 3 different US cities, Chicago, New York City and Washington, DC. The instances consist of real travel requests from the selected period, positions of vehicles with their capacities and realistic shortest travel times between all pairs of locations in each city.</p><p>The instances and results of two solution methods, the Insertion Heuristic, and the optimal Vehicle-group Assignment method, can be found in the dataset. The dataset and methodology used to create it are described in the paper <a href="https://arxiv.org/abs/2305.18859">Large-scale Ridesharing DARP Instances Based on Real Travel Demand</a>.</p>
Simulated metagenomes with quality and abundance distributions derived from real samples
<p>Species abundances and quality values were derived from the following list of samples:</p> <pre><code>SAMEA2466896 SAMEA2466916 SAMEA2466952 SAMEA2466953 SAMEA2466965 SAMEA2466996 SAMEA2467015 SAMEA2467039 SAMEA2621010 SAMEA2621033 SAMEA2621107 SAMEA2621155 SAMEA2621229 SAMEA2621247 SAMEA2621300 SAMEA2622357 </code></pre> <p>Reference abundances (.abund files) were generated using <a href="https://github.com/motu-tool/mOTUs_v2">mOTUs profiler</a>.<br> Metagenomes were simulated with <a href="https://sourceforge.net/projects/cmessi/">cMESSi</a> using <a href="http://progenomes.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> for species and the aforementioned abundances. In cases where a <em>ref_mOTU_v2</em> corresponded to more than one genome, the abundance of said <em>ref_mOTU</em> was distributed equally over all genomes.<br> GFF location files were produced using location information generated by cMESSi.<br> Two variants of truth values were obtained by intersecting coordinates of simulated reads with coordinates of <a href="http://eggnogdb.embl.de">eggNOG</a> orthologous groups (OG at NOG level) as predicted by <a href="https://github.com/jhcepas/eggnog-mapper">eggNOG-mapper</a>.</p> <ol> <li>.cog-simulated files contain the NOG distribution that was effectively simulated, <em>i.e.</em> a count of the number of reads overlapping with genes annotated with each NOG. A read overlapping multiple genes is considered for each gene. If a gene possesses multiple NOG annotations, each annotation gets assigned the total number of overlapping reads. Longer genes will (in expectation) generate more reads, all else being equal.</li> <li>.cog-distribution file contains the expected distribution for every NOG on all samples. The number of genes annotated with each NOG is multiplied by the abundance of the corresponding species. Length of the gene is not taken into account.</li> </ol> <p>If you use this dataset, please cite: <a href="https://www.biorxiv.org/node/111718.full">NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language</a></p>
Synthetic and real EEG datasets for closed-loop neuroscience
<p>The dataset is made primarily for the task of real-time low latency filtering of the EEG data in the closed loop neuroscience experiments and for EEG forecasting task. The dataset consists of a real data and 5 options of the synthetic data of varying difficulty.</p><p>The real dataset consists of 25 people involved into the P4 alpha neurofeedback training. Its total size is about 16.3 hours. A more detailed instruction for this file is provided in the file Real dataset instructions.txt.</p><p>Synthetic data is generated in 5 different ways: sine wave with white noise, sine wave with pink noise, narrow-band filtered pink noise sample with pink noise, state-space model with white noise and state-space model with pink noise. Each of these datasets has about 34.5 hours of data. It is generated similarly to (Wodeyar et al, 2021). A more detailed instruction for the synthetic dataset can be found in the file Synthetic datasets instructions.txt.<br> </p><p>In LowLatencyEEGFiltering.zip one can find a code for the models used in our paper for low-latency filtering with this data.</p><p>NOTE: Code is also published in the following GitHub repository: https://github.com/ivsemenkov/LowLatencyEEGFiltering</p><p> </p><p>If you use our data or code please cite: https://www.doi.org/10.1088/1741-2552/acf7f3</p>
Virtually Real short film "Sonnenwende" by Timo von Gunten, versions with real and virtual backgrounds, 8min35sec, german, ProRes 1998 × 1080
<p>Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison.</p> <p>These 2 QuickTime files represents both the "real" and "virtual" version of the short Film "Sonnenwende" (2019) by Timo von Gunten. The real version was shot on location using real backgrounds, the virtual version was shot in the studio in front of a green-screen using virtual backgrounds that were added in post production. </p> <p>Running time: 8min35sec. This is the highest quality version available. </p> <p>Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.</p>
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>
High-Voltage Disconnector State Identification: synthetic and real images of substation disconnectors
<p>This dataset contains the training and test images used in the work detailed in the article: Barpp Gomes, V., Marchesi, B., Gruber, Y.A. <em>et al.</em> Exploring Synthetic Data for Training Deep Learning Models for High-Voltage Disconnector State Identification. <em>J Control Autom Electr Syst</em> (2025). <a href="https://doi.org/10.1007/s40313-025-01204-2">https://doi.org/10.1007/s40313-025-01204-2</a></p> <p>Contais about 940,000 synthetic (CGI-rendered) and 60,000 real (camera-captured) samples of four types of substation disconnectors, on both open and closed states:</p> <ul> <li>230 kV center break (type 1, as indicated in the article);</li> <li>230 kV center break (type 2);</li> <li>230 kV double side break</li> <li>525 kV horizontal semi-pantograph</li> </ul> <p>Each zip file contains images of one type of substation disconnector. Images are sized 320x128 and are organized in folders, as follows:</p> <ul> <li>00_train_synth: Synthetic training images.</li> <li>01_train_real: A small set of real training images, as indicated in the article.</li> <li>02_test_real_normal1: One set of real test images.</li> <li>03_test_real_normal2: Another set of real test images, from a different time period.</li> <li>04_test_real_maneuvers: A special set of real test images in which the switches have been operated (are in different states).</li> </ul>
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
<h2>General</h2> <p>For more details and the most up-to-date information please consult our project page: <a href="https://kainmueller-lab.github.io/fisbe" target="_blank" rel="noopener">https://kainmueller-lab.github.io/fisbe</a>.</p> <h2>Summary</h2> <ul> <li>A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains <ul> <li>30 completely labeled (segmented) images</li> <li>71 partly labeled images</li> <li>altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)</li> </ul> </li> <li>To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects</li> <li>A set of metrics and a novel ranking score for respective meaningful method benchmarking</li> <li>An evaluation of three baseline methods in terms of the above metrics and score</li> </ul> <h2>Abstract</h2> <p>Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.</p> <h2>Dataset documentation:</h2> <p>We provide a detailed documentation of our dataset, following the <a href="https://arxiv.org/abs/1803.09010" target="_blank" rel="noopener">Datasheet for Datasets</a> questionnaire:</p> <p><em>>> <a href="https://kainmueller-lab.github.io/fisbe/datasheet" target="_blank" rel="noopener">FISBe Datasheet</a></em></p> <p>Our dataset originates from the <a href="https://www.janelia.org/project-team/flylight" target="_blank" rel="noopener">FlyLight project</a>, where the authors released a large image collection of nervous systems of ~74,000 flies, <a href="https://gen1mcfo.janelia.org/cgi-bin/gen1mcfo.cgi" target="_blank" rel="noopener">available for download</a> under CC BY 4.0 license.</p> <h2>Files</h2> <ul> <li>fisbe_v1.0_{completely,partly}.zip <ul> <li>contains the image and ground truth segmentation data; there is one <em>zarr</em> file per sample, see below for more information on how to access <em>zarr</em> files.</li> </ul> </li> <li>fisbe_v1.0_mips.zip <ul> <li>maximum intensity projections of all samples, for convenience.</li> </ul> </li> <li>sample_list_per_split.txt <ul> <li>a simple list of all samples and the subset they are in, for convenience.</li> </ul> </li> <li>view_data.py <ul> <li>a simple python script to visualize samples, see below for more information on how to use it.</li> </ul> </li> <li>dim_neurons_val_and_test_sets.json <ul> <li>a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.</li> </ul> </li> <li>Readme.md <ul> <li>general information</li> </ul> </li> </ul> <h2>How to work with the image files</h2> <p>Each sample consists of a single 3d MCFO image of neurons of the fruit fly.<br>For each image, we provide a pixel-wise instance segmentation for all separable neurons.<br>Each sample is stored as a separate <em>zarr</em> file (<a href="https://zarr.readthedocs.io" target="_blank" rel="noopener">zarr</a> is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").<br>The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.<br>The segmentation mask for each neuron is stored in a separate channel.<br>The order of dimensions is CZYX.</p> <p>We recommend to work in a virtual environment, e.g., by using conda:</p> <p><code>conda create -y -n flylight-env -c conda-forge python=3.9</code><br><code>conda activate flylight-env</code></p> <h3>How to open <em>zarr</em> files</h3> <ol> <li>Install the python zarr package: <pre><code>pip install zarr</code></pre> </li> <li>Opened a zarr file with:<br> <p><code>import zarr</code><br><code>raw = zarr.open(<path_to_zarr>, mode='r', path="volumes/raw")</code><br><code>seg = zarr.open(<path_to_zarr>, mode='r', path="volumes/gt_instances")</code></p> <p><code># optional:</code><br><code>import numpy as np</code><br><code>raw_np = np.array(raw)</code></p> </li> </ol> <p>Zarr arrays are read lazily on-demand.<br>Many functions that expect numpy arrays also work with zarr arrays.<br>Optionally, the arrays can also explicitly be converted to numpy arrays.</p> <h3>How to view <em>zarr</em> image files</h3> <p>We recommend to use <a href="https://napari.org" target="_blank" rel="noopener">napari</a> to view the image data.</p> <ol> <li>Install napari: <pre><code>pip install "napari[all]"</code></pre> </li> <li>Save the following Python script: <br> <p><code>import zarr, sys, napari</code></p> <p><code>raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")</code><br><code>gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")</code></p> <p><code>viewer = napari.Viewer(ndisplay=3)</code><br><code>for idx, gt in enumerate(gts):</code><br><code> viewer.add_labels(</code><br><code> gt, rendering='translucent', blending='additive', name=f'gt_{idx}')</code><br><code>viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')</code><br><code>viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')</code><br><code>viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')</code><br><code>napari.run()</code></p> </li> <li>Execute: <pre><code>python view_data.py <path-to-file>/R9F03-20181030_62_B5.zarr</code></pre> </li> </ol> <h2>Metrics</h2> <ul> <li>S: Average of avF1 and C</li> <li>avF1: Average F1 Score</li> <li>C: Average ground truth coverage</li> <li>clDice_TP: Average true positives clDice</li> <li>FS: Number of false splits</li> <li>FM: Number of false merges</li> <li>tp: Relative number of true positives</li> </ul> <p>For more information on our selected metrics and formal definitions please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>Baseline</h2> <p>To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely <a href="https://github.com/Kainmueller-Lab/PatchPerPix" target="_blank" rel="noopener">PatchPerPix (ppp)</a>, <a href="https://github.com/google/ffn" target="_blank" rel="noopener">Flood Filling Networks (FFN)</a> and a non-learnt application-specific <a href="https://www.biorxiv.org/content/10.1101/2020.06.07.138941v1" target="_blank" rel="noopener">color clustering from Duan et al.</a>.<br>For detailed information on the methods and the quantitative results please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>License</h2> <p>The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>.</p> <h2>Citation</h2> <p>If you use <em>FISBe</em> in your research, please use the following BibTeX entry: </p> <pre><code>@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }</code></pre> <h2>Acknowledgments</h2> <p>We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable<br>discussions.<br>P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.<br>This work was co-funded by Helmholtz Imaging.</p> <h2>Changelog</h2> <p>There have been no changes to the dataset so far.<br>All future change will be listed <a href="https://kainmueller-lab.github.io/fisbe/changelog" target="_blank" rel="noopener">on the changelog page</a>.</p> <h2>Contributing</h2> <p>If you would like to contribute, have encountered any issues or have any suggestions, please <a href="https://github.com/Kainmueller-Lab/fisbe/issues" target="_blank" rel="noopener">open an issue</a> for the FISBe dataset in the accompanying github repository.</p> <p>All contributions are welcome!</p>
PM_033513_P_Vila_Real_Mateus
<u>File Name</u>: PM_033513_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). A lagoa. Uma escultura. Uma mulher nua. Joã Cutileiro. 1981. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The pond A sculpture A nude woman JoãCutileiro 1981 <br><u>Keywords</u>: Cultural heritage, Europe, Mateus, Monuments, Norte, Portugal, Public garden/park, Sculpture, Techniques, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033512_P_Vila_Real_Mateus
<u>File Name</u>: PM_033512_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). A lagoa. Uma escultura. Uma mulher nua. Joã Cutileiro. 1981. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The pond A sculpture A nude woman JoãCutileiro 1981 <br><u>Keywords</u>: Cultural heritage, Europe, Mateus, Norte, Portugal, Public garden/park, Sculpture, Techniques, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033504_P_Vila_Real_Mateus
<u>File Name</u>: PM_033504_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). A capela. A fachada. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The chapel The facade Baroque 18th century <br><u>Keywords</u>: Cultural heritage, Europe, Mateus, Monuments, Norte, Palace, Portugal, Public garden/park, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033503_P_Vila_Real_Mateus
<u>File Name</u>: PM_033503_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Os jardins. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The gardens Baroque 18th century <br><u>Keywords</u>: Cultural heritage, Europe, Mateus, Monuments, Norte, Palace, Portugal, Public garden/park, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033502_P_Vila_Real_Mateus
<u>File Name</u>: PM_033502_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Os jardins. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The gardens Baroque 18th century <br><u>Keywords</u>: Cultural heritage, Europe, Mateus, Monuments, Norte, Palace, Portugal, Public garden/park, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033497_P_Vila_Real_Mateus
<u>File Name</u>: PM_033497_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Os jardins. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The gardens Baroque 18th century <br><u>Keywords</u>: Cultural heritage, Europe, Mateus, Norte, Portugal, Public garden/park, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033514_P_Vila_Real_Mateus
<u>File Name</u>: PM_033514_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Exterior. A fachada. Detalhe. A parte superior. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) Exterior The facade Detail The upperpart Baroque 18th century <br><u>Keywords</u>: Baroque, Cultural heritage, Europe, Mateus, Monuments, Norte, Palace, Portugal, Styles, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033495_P_Vila_Real_Mateus
<u>File Name</u>: PM_033495_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Os jardins. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The gardens Baroque 18th century <br><u>Keywords</u>: Baroque, Cultural heritage, Europe, Mateus, Norte, Portugal, Public garden/park, Styles, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033490_P_Vila_Real_Mateus
<u>File Name</u>: PM_033490_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Os jardins. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The gardens Baroque 18th century <br><u>Keywords</u>: Cultural heritage, Europe, Mateus, Norte, Portugal, Public garden/park, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_033492_P_Vila_Real_Mateus
<u>File Name</u>: PM_033492_P_Vila_Real_Mateus.jpg <br><u>Sublocation</u>: Casa de Mateus (Palácio de Mateus) <br><u>Location</u>: Mateus <br><u>Province</u>: Norte, Vila Real <br><u>Country</u>: Portugal <br><u>Header</u>: Palácio (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus). Os jardins. Barroco. Século 18. <br><u>Description</u>: Palace (Palácio de Mateus ou Solar de Mateus ou Casa de Mateus) The gardens Baroque 18th century <br><u>Keywords</u>: Baroque, Cultural heritage, Europe, Mateus, Norte, Portugal, Public garden/park, Styles, Thematic, Vila Real <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
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.