Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
369
datasets available to search
ShareScore release 0.7.1
Dataset results
369 results for “Benchmark Dataset”
Dataset: Environmental benchmarks for European Cement Industry
<p>This dataset contains the information relative to the article "Environemntal benchmarks for European cement industry".</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.spc.2024.01.020" target="_blank" rel="noopener">Reference paper</a></p> <p><a href="https://www.researchgate.net/publication/377796848_Environmental_benchmarks_for_the_European_cement_industry" target="_blank" rel="noopener">ResearchGate link</a></p>
A Benchmark dataset on Semantic Change in Scholarly Publications on Disability
<p>This is a benchmark dataset for semantic shift detection in disability-related corpora, including collected title and abstract text from PubMed and ArXiv, annotation sets based on domain experts and LLMs, and extracted KGs (Wikidata entity claims). The corpus from PubMed covers the period from the 1900s to 2023, while the corpus from ArXiv covers the period from the 1990s to 2023. The corpus was filtered based on 16 disability-related target words. In the annotation sets, '1' indicates that a semantic shift occurred for a target word, while '0' indicates the opposite. In particular, the LLM-based annotation sets include their generated text, and we used the Llama2 and GPT-4 models. '7b' refers to the parameter size of the Llama2 model. Graph_data.zip contains Wikidata entity claims.</p>
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>
A living catalogue of artificial intelligence datasets and benchmarks for medical decision making
<p>We provide a comprehensive curated catalogue of <strong>artificial intelligence datasets</strong> and <strong>benchmarks for medical decision making</strong>. At the time of first release (April 2021), the dataset contains more than 400 biomedical and clinical datasets of which 252 are publicly available or available upon request.</p> <p>The dataset was compiled based on a systematic literature review covering both biomedical and computer science literature and grey literature data sources. All datasets were manually systematized and annotated for meta-information, such as:</p> <ul> <li>Availability and licensing information</li> <li>Type of source data</li> <li>Links to source publications, main references or dataset repositories</li> </ul> <p>Benchmark dataset were additionally annotated for the following information:</p> <ul> <li>Associated task</li> <li>Performance metrics commonly used for evaluation</li> <li>Clinical relevance</li> <li>The availability of data splits</li> </ul> <p>In addition to the versioned TSV file on Zenodo, the dataset can also be explored live via <a href="https://docs.google.com/spreadsheets/d/1QjUxxnZ3tuyW5dj6nkt_o5yJcWUZec4ttfJxO8Zlty4/edit?usp=sharing">this Google Spreadsheet</a>. The dataset is intended as a living, extendable resource. Edit suggestions and additions are encouraged and can be submitted via the comment function of the Google sheet.</p> <p> </p> <p><strong>File descriptions</strong></p> <p><em>annotated-datasets.tsv</em> -- contains the annotated datasets</p> <p><em>arXiv-literature-export.tsv</em> -- contains the original literature record export from arXiv</p> <p><em>pubmed-literature-export.tsv</em> -- contains the original literature record export from PubMed</p> <p><em>README.md</em> -- contains a detailed description of all annotation fields</p>
Benchmark dataset for preprint: "EDEN: A high-performance, general-purpose, NeuroML-based neural simulator"
<p>The benchmark files and scripts to reproduce the figures of the preprint "EDEN: A high-performance, general-purpose, NeuroML-based neural simulator" ( https://arxiv.org/abs/2106.06752 )</p> <p>The benchmarks require a computer running Linux with Docker installed.</p> <p>Unpack the paper_experiments.zip file and follow the instructions in the README.md file to run the benchmarks and reproduce the figures.</p> <p> </p>
MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.
<p>This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as:</p> <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> <p>From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.</p>
SenTopX: A Benchmark Twitter Dataset for User Sentiment on Various Topics
<p>This is a longitudinal Twitter dataset of 143K users during the period 2017-2021. The following is the detail of all the files:</p> <ul> <li><a href="11243662" target="_blank" rel="noopener noreferrer">SenTopX_userIDs.txt</a>: contains user IDs of 143K Twitter users.</li> <li><a href="../api/records/11243662/draft/files/userIDs_tweetIDs.zip/content" target="_blank" rel="noopener noreferrer">userIDs_tweetIDs.zip</a>: contains Tweet IDs of users, the name of the file is the user ID and the file contains the list of all the tweet IDs.</li> <li><a href="../api/records/11243662/draft/files/users_16_perspective_toxicity_scores.csv/content" target="_blank" rel="noopener noreferrer">users_16_perspective_toxicity_scores.csv</a> contains user IDs and 16 median Perspective API scores, the vector is shared as mean, median, and Gini Index of scores calculated over all tweets of a user.</li> <li><a href="../api/records/11243662/draft/files/LDAvis_top30_words_for_extracted_topics.csv/content" target="_blank" rel="noopener noreferrer">LDAvis_top30_words_for_extracted_topics.csv</a> contains the top 30 most relevant words extracted from each topic extracted by tweet-level topic modeling using the BERTweet topic model.</li> <li><a href="../api/records/11243662/draft/files/topic_modelling_statistics_per_user.csv/content" target="_blank" rel="noopener noreferrer">topic_modelling_statistics_per_user.csv</a> contains important and relevant statistics related to topic modeling results: <ul> <li> <p>1. user: This column represents the identifier for the user. Each row in the CSV corresponds to a specific user, and this column helps to track and differentiate between the users.</p> <p>2. avg_topic_probability: This column contains the average probability of the topics for each user calculated across all of the tweets in order to compare users in a meaningful way. It represents the average likelihood that a particular user discusses various topics over the observed period.</p> <p>3. maximum_topic_avg: This column holds the value of the highest average probability among all topics for each user. It indicates the topic that the user most frequently discusses, on average.</p> <p>4. index_max_avg_topic_probability_200: This column specifies the index or identifier of the topic with the highest average probability out of 200 possible topics. It shows which topic (out of 200) the user discusses the most.</p> <p>5. global_avg: This column includes the global average probability of topics across all users. It provides a baseline or overall average topic probability that can be used for comparative purposes.</p> <p>6. max_global_avg: This column contains the maximum global average probability across all topics for all users. It identifies the most discussed topic across the entire user base.</p> <p>7. index_max_global_avg: This column shows the index or identifier of the topic with the highest global average probability. It indicates which topic (out of 200) is the most popular across all users.</p> <p>8. entropy_200_topic: This column represents the entropy of the topics for each user, calculated over 200 topics. Entropy measures the diversity or unpredictability in the user's discussion of topics, with higher entropy indicating more varied topic discussion.</p> <p>In summary, these columns are used to analyze the topic engagement and preferences of users on a platform, highlighting the most frequently discussed topics, the variability in topic discussions, and how individual user behavior compares to overall trends.</p> </li> </ul> </li> </ul>
TCM: Benchmark Datasets for Predictive Maintenance in Steel Manufacturing
<h1>Anomaly-TCM</h1> <p>Predictive Maintenance (PdM) is a strategy that uses advanced data analytics to predict equipment failures and maintain industrial machinery in good condition. Its goals are to minimize downtime, reduce operational costs, and ensure product quality. PdM methods are applicable across various industries, including steel manufacturing.</p> <p>In steel production, cold rolling is a critical process that reduces the thickness of hot-rolled steel. Developing PdM methods for tandem cold mills (TCM) can significantly improve production efficiency. However, researchers often rely on real manufacturing data, which is typically unavailable, unlabeled, and noisy, making it difficult to validate and compare methods.</p> <p>To overcome this, we created synthetic datasets for the cold rolling process to identify anomalies based on physical principles. These datasets were generated using a mathematical model of a 5-stand TCM, calculating key process parameters like rolling force, torque, speed, tension, gap, thickness reduction, and motor power. We introduced anomalies related to specific failures in the process.</p> <p>We produced six diverse datasets, each with varying complexity, to enable benchmarking of machine learning-based PdM methods for the cold rolling process. Four different types of anomalies were introduced, which are related to a physics-based deviations in the process:</p> <ol> <li>Anomaly in reduction scheme</li> <li>Anomaly in work roll (increased work roll friction)</li> <li>Anomaly in bearing (increased motor torque)</li> <li>Anomaly in electric motor (decrease efficiency)</li> </ol> <p> The details of the datasets are provided below.</p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>Observations</strong></td> <td><strong>Anomalies</strong></td> <td><strong>Share of Anomalies</strong></td> <td><strong>Features</strong></td> <td><strong>Anomaly Types</strong></td> <td><strong>Products</strong></td> <td><strong>Data Drift</strong></td> </tr> <tr> <td>tcm5_dataset_1</td> <td>20009</td> <td>1045</td> <td>5.2%</td> <td>51</td> <td>1</td> <td>4</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_2</td> <td>20001</td> <td>1035</td> <td>5.2%</td> <td>51</td> <td>1</td> <td>20</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_3</td> <td>20003</td> <td>981</td> <td>4.9%</td> <td>51</td> <td>4 (16)</td> <td>4</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_4</td> <td>20001</td> <td>925</td> <td>4.6%</td> <td>51</td> <td>4 (16)</td> <td>20</td> <td>FALSE</td> </tr> <tr> <td>tcm5_dataset_5</td> <td>20005</td> <td>1031</td> <td>5.2%</td> <td>51</td> <td>4 (16)</td> <td>5</td> <td>TRUE</td> </tr> <tr> <td>tcm5_dataset_6</td> <td>20008</td> <td>954</td> <td>4.8%</td> <td>51</td> <td>4 (16)</td> <td>25</td> <td>TRUE</td> </tr> </tbody> </table> <p> </p> <p>Each dataset is generated as a data stream, meaning the observations follow a chronological order, represented by increasing work roll mileage (which is reset after a predefined threshold). The table below provides details about the features and labels present in the datasets. Several features are recorded for each rolling stand, totaling 51 features. Apart from the anomaly related to reduction, the other anomalies are specific to individual stands, resulting in 16 anomaly labels in total.</p> <table> <tbody> <tr> <td><strong>Feature</strong></td> <td><strong>Suffixes</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>thickness_entry</td> <td>-</td> <td>mm</td> <td>steel entry thickness</td> </tr> <tr> <td>thickness_exit</td> <td>-</td> <td>mm</td> <td>steel exit thickness</td> </tr> <tr> <td>width</td> <td>-</td> <td>mm</td> <td>steel width</td> </tr> <tr> <td>ys_entry</td> <td>-</td> <td>MPa</td> <td>steel entry yield strength</td> </tr> <tr> <td>ys_exit</td> <td>-</td> <td>MPa</td> <td>steel exit yield strength</td> </tr> <tr> <td>work_roll_diam</td> <td>1 to 5</td> <td>mm</td> <td>work roll diamaeter (stands 1 to 5)</td> </tr> <tr> <td>work_roll_mileage</td> <td>1 to 5</td> <td>km</td> <td>work roll mileage (stands 1 to 5)</td> </tr> <tr> <td>reduction</td> <td>1 to 5</td> <td>-</td> <td>thickness reduction (stands 1 to 5)</td> </tr> <tr> <td>tension</td> <td>0 to 5</td> <td>N</td> <td>interstand tension (0 is tension before stand 1, 1-5 refer to tension after stands 1-5)</td> </tr> <tr> <td>roll_speed</td> <td>1 to 5</td> <td>NaN</td> <td>linear work roll speed (stands 1 to 5)</td> </tr> <tr> <td>force</td> <td>1 to 5</td> <td>N</td> <td>rolling force (stands 1 to 5)</td> </tr> <tr> <td>torque</td> <td>1 to 5</td> <td>Nm</td> <td>rolling torque (stands 1 to 5)</td> </tr> <tr> <td>gap</td> <td>1 to 5</td> <td>mm</td> <td>stand gap (stands 1 to 5)</td> </tr> <tr> <td>motor_power</td> <td>1 to 5</td> <td>kW</td> <td>electric motor power (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_Reduction</td> <td>-</td> <td>-</td> <td>(label) anomaly in reduction scheme</td> </tr> <tr> <td>Anomaly_Electric</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in electric motor (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_Bearing</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in stand bearing (stands 1 to 5)</td> </tr> <tr> <td>Anomaly_WorkRoll</td> <td>1 to 5</td> <td>-</td> <td>(label) anomaly in work roll friction (stands 1 to 5)</td> </tr> </tbody> </table>
A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods
<p><strong>Description</strong><br> This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. <br> The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. <br> The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. <br> In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. <br> We also include sample codes of baseline models for benchmarking of more elaborated models.</p> <p><strong>Data usage</strong><br> The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper:<br> <em>Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494</em></p> <p>Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data.</p> <p><strong>Sample code</strong><br> As part of the data release, we are also including the sample code written in Python 3. <br> The preprocessed data used in the scripts are also provided. <br> The code can be used to reproduce the results presented in this work and as a starting point for future studies. <br> Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. <br> All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. </p> <p><strong>Units</strong><br> All time stamps are in UTC (YYYY-MM-DD HH:MM:SS).<br> All irradiance and weather data are in SI units.<br> Sky image features are derived from 8-bit RGB (256 color levels) data.<br> Satellite images are derived from 8-bit gray-scale (256 color levels) data.</p> <p><strong>Missing data</strong><br> The string "NAN" indicates missing data</p> <p><strong>File formats</strong><br> All time series data files as in CSV (comma separated values)<br> Images are given in tar.bz2 files</p> <p><strong>Files </strong></p> <ul> <li><em>Folsom_irradiance.csv</em> Primary One-minute GHI, DNI, and DHI data.</li> <li><em>Folsom_weather.csv </em> Primary One-minute weather data.</li> <li><em>Folsom_sky_images_{YEAR}.tar.bz2</em> Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.</li> <li><em>Folsom_NAM_lat{LAT}_lon{LON}.csv </em> Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. </li> <li><em>Folsom_sky_image_features.csv </em> Secondary Features derived from the sky images.</li> <li><em>Folsom_satellite.csv </em> Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. </li> <li><em>Irradiance_features_{horizon}.csv</em> Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). </li> <li><em>Sky_image_features_intra-hour.csv</em> Secondary Sky image features for the intra-hour forecasting issuing times. </li> <li><em>Sat_image_features_intra-day.csv</em> Secondary Satellite image features for the intra-day forecasting issuing times. </li> <li><em>NAM_nearest_node_day-ahead.csv </em> Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.</li> <li><em>Target_{horizon}.csv</em> Secondary Target data for the different forecasting horizons.</li> <li>F<em>orecast_{horizon}.py </em> Code Python script used to create the forecasts for the different horizons. </li> <li><em>Postprocess.py</em> Code Python script used to compute the error metric for all the forecasts.</li> </ul> <p> </p>
SCG Dataset from Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
<p><strong>Abstract:</strong> Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.</p>
ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-Based Type Inference
<ul> <li>The dataset is gathered on Sep. 17th 2020 from GitHub.</li> <li>It has <em>clean</em> and <em>complete</em> versions (from v0.7): <ul> <li>The clean version has 5.1K <strong>type-checked </strong>Python repositories and 1.2M type annotations.</li> <li>The complete version has 5.2K Python repositories and 3.3M type annotations.</li> </ul> </li> <li>The dataset's source files are type-checked using <a href="https://mypy.readthedocs.io/">mypy</a> (clean version).</li> <li>The dataset is also de-duplicated using the <a href="https://github.com/saltudelft/CD4Py">CD4Py</a> tool.</li> <li>Check out the <strong>README.MD</strong> file for the description of the dataset.</li> <li>Notable changes to each version of the dataset are documented in <strong>CHANGELOG.md</strong>.</li> <li>The dataset's scripts and utilities are available on <a href="https://github.com/saltudelft/many-types-4-py-dataset">its GitHub repository</a>.</li> </ul>
Simulation dataset to benchmark 3D force inference methods
<p>Dataset of 47 artificial images (.tif) and corresponding segmentation masks (.tif), generated from simulations of foam-like cell structures (early embryos) of various cell numbers (2 to 11), cell sizes and interfacial tensions.<br> The ground truth simulation tensions and pressures to be inferred are provided as Numpy arrays (.npy).</p> <p>This dataset was used to benchmark a method to infer cellular forces in 3D from microscopy images of multicellular contours, that is available on <a href="https://github.com/VirtualEmbryo/foambryo">https://github.com/VirtualEmbryo/foambryo</a>.<br> Non-manifold multimaterial meshes corresponding to artificial microscopy images are also provided as binary files (.rec) and may be opened with our delaunay-watershed Python code, available on <a href="https://github.com/VirtualEmbryo/delaunay-watershed">https://github.com/VirtualEmbryo/delaunay-watershed</a>.</p> <p><strong>Credits, contact, citations</strong><br> If you use this dataset, please cite the published version of the following preprint: <br> <em>Ichbiah, S., Delbary, F., McDougall, A., Dumollard, R., & Turlier, H. (2023). Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy. bioRxiv, 2023-04. </em><a href="https://doi.org/10.1101/2023.04.12.536641">https://doi.org/10.1101/2023.04.12.536641</a><br> <br> We hope that this dataset may be useful to benchmark future 3D force inference methods.<br> If you have any question on this dataset, please contact <a href="mailto:herve.turlier@college-de-france.fr?subject=%5BZenodo%5D%203D%20tension%20inference%20benchmark%20dataset">Hervé Turlier</a>.</p> <p><strong>License</strong><br> Copyright (c) 2023 Turlier Lab - <a href="https://www.turlierlab.com/">https://www.turlierlab.com/</a><br> This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
A harmonized Landsat Sentinel-2 (HLS) dataset for benchmarking time series reconstruction methods of vegetation indices
<p>Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed to fill the gaps present in the images, and some works also presented performance comparisons between them (Roerink et al., 2000; Moreno-Martínez et al., 2020; Siabi et al., 2022). Because of the lack of a ground truth for the reconstructed images, the performance evaluation requires the creation of datasets where artificial gaps are introduced in a reference image, such that metrics like the root mean square error (RMSE) can be computed comparing the reconstructed images with the reference one. Different approaches have been used to create the reference images and the artificial gaps, but in most cases, the artificial gaps are introduced using arbitrary patterns and/or the reference image is produced artificially and not using real satellite images (e.g. Kandasamy et al., 2013; Liu et al., 2017; Julien & Sobrino, 2018). In addition, to the best of our knowledge, few of them are openly available and directly accessible allowing for fully reproducible research.</p> <p>We provide here a benchmark dataset for time series reconstruction method based on the<strong> <a href="https://hls.gsfc.nasa.gov/">harmonized Landsat Sentinel-2 (HLS)</a> </strong>collection where the artificial gaps are introduced with a realistic spatio-temporal distribution. In particular, we selected six tiles that we considered representative for most of the main climate classes (e.g. equatorial, arid, warm temperature, boreal and polar), as depicted in the preview.</p> <p>Specifically, following the <strong><a href="https://hls.gsfc.nasa.gov/products-description/tiling-system/">relative tiling system</a></strong> shown above, we downloaded the Red, NIR and F-mask bands from both the HLSL30 and HLSS30 collections for the tiles 19FCV, 22LEH, 32QPK, 31UFS, 45WFV and 49MWM. From the Red and NIR band we derived the NDVI as:</p> <p><span class="math-tex">\(NDVI = {NIR - Red \over NIR + Red}\)</span></p> <p>only for clear-sky on lend pixels (F-mask bits 1, 3, 4 and 5 equal zero), setting as not a number the remaining pixels. The images are then aggregated on a 16 days base, averaging the available values for each pixel in each temporal range. The so obtained data, are considered from us as the reference data for the benchmarking, and stored following the file naming convention</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI.tif</em></p> <p>where <em>TILE_NAME</em> is one between the above specified ones, <em>YYYY</em> is the corresponding year (spanning from 2015 to 2022) and <em>DDD</em> is the day of the year from which the corresponding 16 days range starts. Finally, for each tile, we have a time series composed of <strong>184</strong> images (23 images for 8 years) that can be easily manipulated, for example using the <strong><a href="https://github.com/scikit-map/scikit-map/tree/master">Scikit-Map library</a></strong> in Python.</p> <p>Starting from those data, for each image we considered the mask of currently present gaps, we randomly rotated it by 90, 180 or 270 degrees and we added artificial gaps in the pixels of the rotated mask. Doing so, we believe that the spatio-temporal distribution will be still realistic, providing a solid benchmark for gap-filling methods that work on time series, on spatial pattern or combination of the both.</p> <p>The data including the artificial gaps are stored with the naming structure</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI_art_gaps.tif</em></p> <p>following the previously mentioned convention. The performance metrics, such as RMSE or normalized RMSE (NRMSE), can be computed by applying a reconstruction method on the images with artificial gaps, and then comparing the reconstructed time series with the reference one only on the artificially created gaps locations. </p> <p>This dataset was used to compare the performance of some gap-filling methods and we provide a <strong><a href="https://github.com/OpenGeoHub/EO-benchmark/blob/main/gap_filling_methods/gap_filling_comparison.ipynb">Jupyter notebook</a></strong> that shows how to access and use the data. The files are provided in GeoTIFF format and projected in the coordinate reference system WGS 84 / UTM zone 19N (EPSG:32619). </p> <p>If you succeed to produce higher accuracy or develop a new algorithm for gap filling, please contact authors or post on our GitHub repository. May the force be with you!</p> <p>References:</p> <ol> <li> <p>Julien, Y., & Sobrino, J. A. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección, (51), 19-31. <a href="https://doi.org/10.4995/raet.2018.9749">https://doi.org/10.4995/raet.2018.9749</a> </p> </li> <li> <p>Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6), 4055-4071. <a href="https://doi.org/10.5194/bg-10-4055-2013">https://doi.org/10.5194/bg-10-4055-2013</a> </p> </li> <li> <p>Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179. <a href="https://doi.org/10.1016/j.rse.2016.11.023">https://doi.org/10.1016/j.rse.2016.11.023</a> </p> </li> <li> <p>Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901.<a href="https://doi.org/10.1016/j.rse.2020.111901"> https://doi.org/10.1016/j.rse.2020.111901</a> </p> </li> <li> <p>Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. <a href="https://doi.org/10.1080/014311600209814">https://doi.org/10.1080/014311600209814</a></p> </li> <li> <p>Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2022). Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images. Computers and Electronics in Agriculture, 193, 106619.<a href="https://doi.org/10.1016/j.compag.2021.106619"> https://doi.org/10.1016/j.compag.2021.106619</a></p> </li> </ol>
LakeBeD-US: Ecology Edition - a benchmark dataset of lake water quality time series and vertical profiles
LakeBeD-US: Ecology Edition is a harmonized lake water quality dataset containing time series and vertical profiles of 21 lakes in the United States monitored by long-term monitoring institutions. These institutions include the North Temperate Lakes Long-Term Ecological Research program (NTL-LTER), Niwot Ridge Long-Term Ecological Research program (NWT-LTER), National Ecological Observatory Network (NEON), and the Carey Lab at Virginia Tech as part of the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) site in collaboration with the Western Virginia Water Authority. The data include depth-discrete observations of 17 water quality variables including temperature, dissolved oxygen, chemical properties, Secchi depth, and more. Observations are divided into data collected by automated sensors at a relatively high temporal frequency and manually sampled data at a relatively low temporal frequency. All data were collected in situ. The data are available as Apache Parquet files, and the included R scripts give guidance on how to utilize and query the dataset in R. LakeBeD-US: Ecology Edition is an ecological science-oriented companion to LakeBeD-US: Computer Science Edition. The Computer Science Edition is available on the Hugging Face Hub.
Reference Dataset for Benchmarking Organ Doses Derived from Monte Carlo Simulations of CT Exams
<p>This reference dataset contains CT scanner x-ray source characteristics, filtration profile, de-identified patient image data and size characteristics, voxelized patient models, exam characteristics, x-ray tube current data, and organ dose results in tabular form from Monte Carlo (MC) simulations of abdominal/pelvis CT exams of pregnant patients. This dataset can be used for benchmarking MC simulation codes for CT dosimetry.</p>
A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey
<p>Open access to curated datasets positively impacts on scientific research of machine learning and deep learning techniques. It is a fact that benchmarks and public datasets prepared for data science assist researchers interested in evaluating, testing, and building new data-driven methodologies for specific domain areas.</p> <p>In geosciences, there has been a remarkable growth of public datasets arranged to address machine learning challenges related to the oil and gas industry, particularly for reserves exploration and data interpretation. </p> <p>For these reasons, we present the Taranaki dataset, which is a collection of seismic horizons interpreted for a seismic stratigraphic interpretation study in the Taranaki Basin, offshore New Zealand. This data comprises fourteen seismic horizons that mark stratigraphic discordances in the Tui-3D seismic dataset. We annotated five seismic horizons on 33 inline sections and nine horizons on 19 crossline sections.</p> <p>Besides, we present the results of a series of experiments that compare a method of interpolation and a method of deep learning for seismic segmentation. The deep learning experiments evaluated the result of different image tile sizes to train the model, which is presented separately in this dataset. </p> <p>Finally, we evaluated both methodologies to interpret the horizons of this dataset in selected seismic sections. Also, we assessed the absolute error of each method with the ground truth interpretations proposed in this dataset.</p>
Datasets for benchmarking and ML modelling
<p><em><span>hydrogen-harm</span></em><span> data set of crystalline hydrogen configurations: energies at VMC and LRDMS level; purpose: benchmark for MLP; developed in the group of Michele Casula (CNRS) </span></p> <p><span><em>prot-hex</em> data set for protonated water hexamer: trajectories from classical molecular dynamics with nuclear forces at VMC level of theory; purpose: ML modelling; developed in the group of Michele Casula (CNRS)</span></p> <p><span><em>intexcit</em> data sets for a set of organic molecular complexes in lowest excited states: dispersion interaction energies, interaction energies, components of SAPT interaction energies at the CAS wavefunction level; purpose: benchmarking <em>ab initio</em> methods and density functional dispersion correction modelling; developed by Kasia Pernal (TUL) and Michal Hapka (University of Warsaw) </span></p>
Statistical Process Control Benchmark Dataset
<p>Datasets to the planned publication "Generalized Statistical Process Control via 1D-ResNet Pretraining" by Tobias Schulze, Louis Huebser, Sebastian Beckschulte and Robert H. Schmitt (Chair for Intelligence in Quality Sensing, Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University)</p> <p>Data for benchmarking SPC against other process monitoring methods. The data consist of a one-dimensional timeseries of floats (x.csv). Addititionally information whether the data are within the specifications are provided as another time series (y.csv). The data are generated by solving an optimization problem for each time to generate a mixture distribution of different probability distributions. Then for each timestep one record is sampled. Inputs for the optimization problem are the given probability distributions, the lower and upper limit of the tolerance interval as well as the desired median of the data. Additionally weights of the different probability distributions can be given as boundary condions for the different time steps. Metadata generated from the solving are stored in k_matrix.csv (wheights at each time step) and distribs (probability distribution objects according to https://doi.org/10.5281/zenodo.8249487). The data consists of phases with data from a stable mixture distribution and phases with data from a mixture distribution that do not fulfill the stability criteria.</p> <p>The train data were used to train the G-SPC model. The test data were used for benchmarking purposes</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.</p>
L'Alpe d'Huez: a dataset to benchmark topographic map generalisation
<p>This dataset derives from the one used in a past EuroSDR benchmark (http://dx.doi.org/10.1016/j.compenvurbsys.2009.06.002), and should be used as a benchmark for topographic map generalisation techniques. It contains several topographic layers as shapefiles (roads, buildings, rivers, forests, contour lines...), a style description to display the data at the 1:50k scale, and a table of the generalisation constraints that should be respected in this 1:50k scale map.</p> <p>The initial data can considered as detailed for maps at the 1:15k scale. The projection of the data is "Lambert II Etendu", EPSG:27572.</p> <p>The area is 11*11 km large.</p>
HornMT – Machine Translation Benchmark Dataset for Languages in the Horn of Africa
<p>The <strong>HornMT</strong> repository contains data and the associated metadata for the project <a href="https://lesan.ai/benchmark">Machine Translation Benchmark Dataset for Languages in the Horn of Africa</a>. It is a multi-way parallel corpus that will serve as a benchmark to accelerate progress in machine translation research and production systems for languages in the Horn of Africa.</p> <p>Supported Languages</p> <table> <tbody> <tr> <td> <p>Language</p> </td> <td> <p>ISO 639-3 code</p> </td> </tr> </tbody> <tbody> <tr> <td> <p>Afar</p> </td> <td> <p>aaf</p> </td> </tr> <tr> <td> <p>Amharic</p> </td> <td> <p>amh</p> </td> </tr> <tr> <td> <p>English</p> </td> <td> <p>eng</p> </td> </tr> <tr> <td> <p>Oromo</p> </td> <td> <p>orm</p> </td> </tr> <tr> <td> <p>Somali</p> </td> <td> <p>som</p> </td> </tr> <tr> <td> <p>Tigrinya</p> </td> <td> <p>tir</p> </td> </tr> </tbody> </table> <p><strong> </strong></p> <p>data/ contains one text file per language and each file contains news snippets in the same order for each language.</p> <p>data<br> ├── aar.txt<br> ├── amh.txt<br> ├── eng.txt<br> ├── orm.txt<br> ├── som.txt<br> └── tir.txt</p> <p>metadata.tsv contains tab separated data describing each news snippet. The metadata contains the following fields.</p> <ul> <li> <p><strong>Scope</strong> - describes whether the news is global or local. It takes two values: Global news and Local news.</p> </li> <li> <p><strong>Category</strong> - News category covering the following 12 topics</p> <ul> <li> <p>Art and Culture</p> </li> <li> <p>Business and Economy</p> </li> <li> <p>Conflicts and Attacks</p> </li> <li> <p>Disaster and Accidents</p> </li> <li> <p>Entertainment</p> </li> <li> <p>Environment</p> </li> <li> <p>Health</p> </li> <li> <p>International Relations</p> </li> <li> <p>Law and Crime</p> </li> <li> <p>Politics</p> </li> <li> <p>Science and Technology</p> </li> <li> <p>Sport</p> </li> </ul> </li> <li> <p><strong>Source</strong> - List of one or more URLs from which the news content is extracted or based on.</p> </li> <li> <p><strong>Domain</strong> - TLD corresponding to the URL(s) in Source.</p> </li> <li> <p><strong>Date</strong> - The publication date of the source article. The format is yyyy-mm-dd.</p> </li> </ul> <p>Other formats</p> <p>All the data and associated metadata together in one file is also available in other file formats.</p> <p><strong>HornMT.xlsx</strong> - data and associated metadata in xlsx format.</p> <p><strong>HornMT.json</strong> - data and associated metadata in json format.</p> <p>Below is an example row.</p> <pre><code class="language-javascript">{ "data":{ "eng":"The World Meteorological Organisation reports that the ozone layer is damaged to its worst extent ever in the Arctic.", "aaf":"Baad Metrolojih Eglali Areketekeh Addal Ozonih qelu faxe waktik lafetle calat biyakisem xayose.", "amh":"የአለም የአየር ንብረት ድርጅት በአርክቲክ አካባቢ ያለው የኦዞን ምንጣፍ ከፍተኛ ጉዳት እንደደረሰበት አስታወቀ፡፡", "orm":"Dhaabbanni Meetiroolojii Addunyaa baqqaanni oozonii Arkiitik keessatti gara sadarkaa isa hamaa haga ammaatti akka miidhame gabaase.", "som":"Ururka Saadaasha Hawada Adduunka ayaa ku warramaya in lakabka ozoneka ee Ka koreeya dhulka baraflayda uu waxyeelladii abid ugu darnaa soo gaadhay.", "tir":"ውድብ ሜትሮሎጂ ዓለም ኣብ ኣርክቲክ ዝርከብ ናሕሲ ኦዞን ኣዝዩ ብዝኸፍአ ደረጃ ከምዝተጎድአ ሓቢሩ፡፡" }, "metadata":{ "scope":"Global", "category":"Science and Technology", "source":"https://www.independent.co.uk/environment/climate-change/ozone-layer-damaged-by-unusually-harsh-winter-2263653.html", "domain":"www.independent.co.uk", "date":"2011-04-05" } }</code></pre> <p><strong>Team</strong></p> <p>Afar</p> <ul> <li> <p>Mohammed Deresa</p> </li> <li> <p>Yasin Nur</p> </li> </ul> <p>Amharic</p> <ul> <li> <p>Tigist Taye</p> </li> <li> <p>Selamawit Hailemariam</p> </li> <li> <p>Wako Tilahun</p> </li> </ul> <p>Oromo</p> <ul> <li> <p>Gemechis Melkamu</p> </li> <li> <p>Galata Girmaye</p> </li> </ul> <p>Somali</p> <ul> <li> <p>Abdiselam Mohamed</p> </li> <li> <p>Beshir Abdi</p> </li> </ul> <p>Tigrinya</p> <ul> <li> <p>Berhanu Abadi Weldegiorgis</p> </li> <li> <p>Michael Minassie</p> </li> <li> <p>Nureddin Mohammedshiek</p> </li> </ul> <p><strong>Project Leaders</strong></p> <ul> <li> <p>Asmelash Teka Hadgu <a href="mailto:asme@lesan.ai">asme@lesan.ai</a></p> </li> <li> <p>Gebrekirstos G. Gebremeskel <a href="mailto:gebrekirstos.gebremeskel@ru.nl">gebrekirstos.gebremeskel@ru.nl</a></p> </li> <li> <p>Abel Aregawi <a href="mailto:abel@lesan.ai">abel@lesan.ai</a></p> </li> </ul> <p><strong>License</strong></p> <p>Shield: <a href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></p> <p>This work is licensed under a<br> <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</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.