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709
datasets available to search
ShareScore release 0.9.0
Dataset results
709 results for “Coverage”
Gaia benchmark stars at HERMES spectral resolution and wavelength coverage
<p>These data were prepared by P. Jofre, University of Cambridge.</p>
Full-coverage atmospheric CO2 reconstruction data in China from 2010 to 2019
<p>This monthly atmospheric CO2 dataset was reconstructed from GOSAT XCO2 retrievals using a spatiotemporal kriging method. We employed this dataset to investiage the spatiotemporal variation in atmospheric CO2 and its influencing factors. Please the publication as below:</p><p>--Chen, X., He, Q., Ye, T., Liang, Y., & Li, Y. (2023). Decoding spatiotemporal dynamics in atmospheric CO2 in Chinese cities: Insights from satellite remote sensing and geographically and temporally weighted regression analysis. <i>Science of The Total Environment</i>, 167917.</p><p> </p><p>We also share other atmopsheric reconstruction datasets:</p><p>For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p><p>For full-coverage, 1-km, CO2 data in China, please go to <a href="https://zenodo.org/doi/10.5281/zenodo.10022904">10.5281/zenodo.10022904. </a>This dataset was reconstructed based on OCO-2 XCO2 retrievals and machine learning algorithm.</p><p>For full-covereage, 1-km, PM2.5 data in China, please go to <a href="https://zenodo.org/doi/10.5281/zenodo.8437234">10.5281/zenodo.8437234 or </a><a href="https://zenodo.org/record/8347128">10.5281/zenodo.8347128.</a></p>
Caribbean Regional Annual Hard Coral Coverage (%) with Regional Annual Surface Ocean Hydrogen Ion Activity (10^-9 mol/kg)
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East Asian Sea Regional Annual Hard Coral Coverage (%) with Regional Annual Surface Ocean Hydrogen Ion Activity (10^-9 mol/kg)
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Gulf of Oman Regional Annual Hard Coral Coverage (%) with Regional Annual Surface Ocean Hydrogen Ion Activity (10^-9 mol/kg)
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Pacific Regional Annual Hard Coral Coverage (%) with Regional Annual Surface Ocean Hydrogen Ion Activity (10^-9 mol/kg)
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South Asia Regional Annual Hard Coral Coverage (%) with Regional Annual Surface Ocean Hydrogen Ion Activity (10^-9 mol/kg)
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NANCY SNS JU Project - 5G Coverage Expansion Dataset 1
<p>The dataset is the experimental output of a 5G New Radio (NR) coverage expansion use case in the context of the NANCY project (<a href="https://nancy-project.eu/" rel="nofollow">https://nancy-project.eu/</a>). Two experimental scenarios were carried out, namely a) a scenario where a user equipment (UE) is directly connected to a Base Station (BS) through a 5G NR link, and b) a scenario where an intermediate node is employed, which acts as a relay between the base station and the UE. To this end, two 5G BSs were deployed, using Ettus Research USRP B210 devices. Also, two 5G modules equipped with custom SIM cards were used as end devices that connect to the BSs. From a software perspective, the srsRAN was used to deploy the USRP-based BSs, while the Open5GS software was used for Core Network functionality. Furthermore, iPerf3 and VLC Media Player were utilized to generate network traffic between the BS and the end device.</p> <p>The dataset’s potential applications are wide; for instance, it can be used to provide insights into how the different video streaming resolutions affect the network load. The data collected from the video streaming sessions was critical in evaluating the network's ability to handle video content of varying quality. In this respect, higher video resolutions resulted in more data-intensive streaming and higher requirements in terms of network capacity Furthermore, the dataset can be leveraged by Machine Learning (ML) and Artificial Intelligence (AI) algorithms for proactively orchestrating the network resources to maximize the users’ quality of experience and quality of service.</p> <p>The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456.</p>
Data Supporting High-Level to Low-Level Requirements Coverage Reviewing with Large Language Models
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Studying the Impact of Early Test Termination Due to Assertion Failure on Code Coverage and Spectrum-based Fault Localization
<p>A dataset for early test termination</p>
Data from: Assessing urban-scale spatiotemporal heterogeneous metro station coverage using multi-source mobility data
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Concentrations and Yields of Total Hg and MeHg in Large Boreal Rivers Linked to Water and Wetland Coverage in the Watersheds
<p>Large rivers are major contributors of mercury (Hg) exports to the ocean, as they integrate processes of loading and loss occurring at the watershed level. Within a watershed, stream-scale studies have revealed that specific landscape features, such as wetlands or lakes, are hotspots for Hg and methylmercury (MeHg) loading, sinks and transformation, but we still do not know how these landscape features operate at the whole network scale and over large geographic gradients. In this study, we evaluate how landscape metrics (vegetation types, wetland and lake cover, climate, hydrology) are related to riverine concentrations and watershed yields of Hg and MeHg in 18 large boreal rivers draining watersheds that range from 44 km<sup>2</sup> to 209 453 km<sup>2</sup>, distributed along a 650 km latitudinal transect in the James Bay region of Québec. Our results reinforce the role of wetlands as sources of MeHg, but further show that surface coverage of water in the watershed is the major driver of both Hg and MeHg concentrations and exports to the coast at the whole network scale. Our findings also demonstrate that seasonality modulates the relationship between landscape features and the various Hg forms. Based on hydrometric data, we additionally estimate annual exports for the whole Eastern James Bay to 441 kg Hg and 15 kg MeHg, for an average landscape yield of 1.24 g Hg km<sup>-2</sup> y<sup>-1</sup> and 0.041 g MeHg km<sup>-2</sup> y<sup>-1</sup>. Our study provides tools to broadly predict riverine Hg concentrations and exports with only a few easily accessible landscape metrics.</p>
GPCE_2022_Submission_17-Continuous_T-Wise_Coverage
<p>The results of evaluating continuous t-wise coverage.</p>
SMP coverage and traffic prediction in the municipality of Chapadinha in Maranhão
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low coverage simluated reads for InfoGenomeR test
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PROBLEMS THE COVERAGE PROCESSES OF ELECTION CAMPANY IN PARTY PUBLICATIONS
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Chinese newspaper coverage of human gene patents
<p>Workbook for the content analysis of Chinese newspaper coverage of human gene patents</p>
Supplementary material 2 from: Ahmed M, Back MA, Prior T, Karssen G, Lawson R, Adams I, Sapp M (2019) Metabarcoding of soil nematodes: the importance of taxonomic coverage and availability of reference sequences in choosing suitable marker(s). Metabarcoding and Metagenomics 3: e36408. https://doi.org/10.3897/mbmg.3.36408
: Data type: source code
Supplementary material 1 from: Ahmed M, Back MA, Prior T, Karssen G, Lawson R, Adams I, Sapp M (2019) Metabarcoding of soil nematodes: the importance of taxonomic coverage and availability of reference sequences in choosing suitable marker(s). Metabarcoding and Metagenomics 3: e36408. https://doi.org/10.3897/mbmg.3.36408
: Data type: species data
Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Identification and Coverage Estimation from Ground-level Imagery
<h1>🌿Species Identification and Coverage Estimation from Ground-level Imagery for Vegetation Monitoring 📷</h1> <p>This repository contains the data and code used in <a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.70024"><strong>Müller, Puliti, and Breidenbach (2025)</strong></a> to train and apply deep learning models for <strong>species coverage estimation</strong> using ground-level imagery.</p> <p>It includes:<br>✅ A <strong>YOLOv8 object detection </strong>model for detecting frames in images and one <strong>species instance segmentation</strong> model for species identification and identifying and segmenting species.</p> <p>✅ Method to parse instance segmentation masks to <strong>species-specific coverage estimates</strong> in images.</p> <p>✅ The <strong>data </strong>used to train and evaluate the models</p> <p> </p> <h2>🚀 Workflow Overview</h2> <p>This demo provides a step-by-step approach for training and applying the models:</p> <p>1️⃣ <strong>Training</strong>:</p> <ul> <li>Train two models using labeled images: <ul> <li><strong>Frame Object Detection</strong> (dataset: <code>Frame_data</code>)</li> <li><strong>Species Instance Segmentation</strong> (dataset: <code>Species_segmentation_data</code>)</li> </ul> </li> </ul> <p>2️⃣ <strong>Confidence Optimization</strong>:</p> <ul> <li>Optimize the confidence threshold based on downstream <strong>cover estimation</strong> performance.</li> </ul> <p>3️⃣ <strong>Inference</strong>:</p> <ul> <li>Predict on test images (<code>Species_cover_data_test</code>).</li> </ul> <p>4️⃣ <strong>Evaluation</strong>:</p> <ul> <li>Compare predictions with <strong>field estimates</strong> (<code>Field_data_NFI</code>).</li> </ul> <p>📌 The code has been tested on <strong>Windows</strong> with <strong>Python 3.10</strong>.</p> <p> </p> <h2>🛠 How to Run the Demo</h2> <p>Follow these steps to set up and run <code>demo.ipynb</code>:</p> <div> <div> <div> </div> </div> <div> <blockquote> <p># Create a new environment<br>conda create -n VegCover python=3.10</p> <p># Activate the environment<br>conda activate VegCover</p> <p># Install dependencies<br>pip install -r requirements.txt</p> <p># Install Jupyter Lab<br>pip install jupyterlab</p> <p># Open the demo notebook<br>jupyter-lab</p> </blockquote> </div> </div> <h2> </h2> <h2>📖 How to Cite</h2> <p>If you use this work, please cite:</p> <p><strong>Müller, P., Puliti, S., & Breidenbach, J. (2025).</strong> Towards Enhancing Field-Based Vegetation Monitoring: A Deep Learning Approach for Species Coverage Estimation from Ground-Level Imagery. <em>Methods in Ecology and Evolution.</em></p> <h2> </h2> <h2>📜 License</h2> <p>This project is licensed under the <strong>GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later)</strong>.</p> <p>🔹 <strong>Key points of this license:</strong></p> <ul> <li>You are free to <strong>use, modify, and distribute</strong> the software.</li> <li>If you modify and deploy this software (even as a web service), you <strong>must share your modifications</strong> under the same AGPL-3.0-or-later license.</li> <li>This ensures that improvements remain open-source and benefit the community.</li> </ul> <p>📖 Full license text: <a href="https://www.gnu.org/licenses/agpl-3.0.en.html">GNU AGPL v3.0</a></p> <div> <pre> </pre> </div>
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.