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.
709
datasets available to search
ShareScore release 0.9.0
Dataset results
709 results for “Coverage”
Spatial association and modelling of vaccination coverage in Thailand, 2021 - 2022
<p><span>Information on COVID-19 vaccine services from MOPH immunization Center <span>(</span>MOPH IC<span>) <span>(</span></span><span>Department of Disease Control, <span>2023)</span></span> Information on COVID-<span>19 </span>patients (per <span>1</span>,<span>000 </span>population), COVID-<span>19 </span>deaths (per <span>100</span>,<span>000 </span>population), people with chronic diseases (%), pregnant women (per <span>1</span>,<span>000 </span>population), medical personnel (per <span>1</span>,<span>000 </span>population), hospitals (per <span>100</span>,<span>000 </span>population), subdistrict health promotion hospitals (per <span>100</span>,<span>000 </span>population), and village health volunteers (per <span>1</span>,<span>000 </span>population) from the Ministry of Public Health. <span>(Department of Disease Control, 2023; Department Of Health Service Support, 2023; HDC, 2023; Ministry of Public Health, 2022)</span> Information on population density (sq. km.), proportion of population in municipal areas (%), proportion of elderly people (%), proportion of working age (%), business establishments (per <span>1</span>,<span>000 </span>population), average monthly household income (baht), proportion of population that has a mobile phone (%), and proportion of population </span><span>internet access</span><span> (%) from the National Statistical Office. <span>(National Statistical Office, 2023)</span> Information on nighttime light from The Earth Observation Group <span>(EOG., 2023)</span> Information on public transport vehicles (per <span>1</span>,<span>000 </span>population) and private vehicles (per <span>1</span>,<span>000 </span>population) from the Ministry of Transport. <span>(Department of Land Transport, 2023)</span> Information on the proportion of treatment rights (%) includes universal coverage scheme rights, social security scheme (SSS), and government rights (OFC) from the National Health Security Office. <span>(National Health Security Office, 2023)</span></span></p> <p> </p> <p><span><span>a_pop : population density (sq. km.) in 2021</span></span></p> <p><span><span>a_pop65 : population density (sq. km.) in 2022</span></span></p> <p><span><span>urban% : proportion of population in municipal areas (%) in 2021</span></span></p> <p><span><span>Urban%65 : proportion of population in municipal areas (%) in 2022</span></span></p> <p><span><span>Older_21 : proportion of elderly people (%) in 2021</span></span></p> <p><span><span>Older_22 : proportion of elderly people (%) in 2022</span></span></p> <p><span><span>7CD_21 : people with chronic diseases (%) in 2021</span></span></p> <p><span><span>7CD_22 : people with chronic diseases (%) in 2022</span></span></p> <p><span><span>Preg_21 : pregnant women (per <span>1</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>Preg_22 : pregnant women (per <span>1</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>Work_21 : proportion of working age (%) in 2021</span></span></p> <p><span><span>Work_22 : proportion of working age (%) in 2022</span></span></p> <p><span><span>NTL64 : nighttime light in 2021</span></span></p> <p><span><span>NTL65 : nighttime light in 2022</span></span></p> <p><span><span>BSN_21 : business establishments (per <span>1</span>,<span>000 </span>population) in 2021 </span></span></p> <p><span><span>BSN_22 : business establishments (per <span>1</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>P-car_21 : public transport vehicles (per <span>1</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>P-car_22 : public transport vehicles (per <span>1</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>I-car_21 : private vehicles (per <span>1</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>I-car_22 : private vehicles (per <span>1</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>Phone_21 : population that has a mobile phone (%) in 2021</span></span></p> <p><span><span>Phone_22 : population that has a mobile phone (%) in 2022</span></span></p> <p><span><span>Internet_21 : proportion of population <span>internet access</span> (%) in 2021</span></span></p> <p><span><span>Intermet_22 : proportion of population <span>internet access</span> (%) in 2022</span></span></p> <p><span><span>Income : average monthly household income (baht)</span></span></p> <p><span><span>PH-P_21 : medical personnel (per <span>1</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>PH-P_22 : medical personnel (per <span>1</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>VHV_21 : village health volunteers (per <span>1</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>VHV_22 : village health volunteers (per <span>1</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>Hos-P_21 : hospitals (per <span>100</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>Hos-P_22 : hospitals (per <span>100</span>,<span>000 </span>population) in 2022<br></span></span></p> <p><span><span>Local-p_21 : subdistrict health promotion hospitals (per <span>100</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>Local-p_22 : subdistrict health promotion hospitals (per <span>100</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>UC_21 : universal coverage scheme rights (%)</span></span></p> <p><span><span>UC_22 : universal coverage scheme rights (%)</span></span></p> <p><span><span>SSS_21 : social security scheme (%)</span></span></p> <p><span><span>SSS_22 : social security scheme (%)</span></span></p> <p><span><span>OFC_21 : government rights (%)</span></span></p> <p><span><span>OFC_22 : government rights (%)</span></span></p> <p><span><span>Covid-p_21 : COVID-<span>19 </span>patients (per <span>1</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>Covid-p_22 : COVID-<span>19 </span>patients (per <span>1</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>Dcovid-p_21 : COVID-<span>19 </span>deaths (per <span>100</span>,<span>000 </span>population) in 2021</span></span></p> <p><span><span>Dcovid-p_22 : COVID-<span>19 </span>deaths (per <span>100</span>,<span>000 </span>population) in 2022</span></span></p> <p><span><span>Vcovid21_1 : COVID-19 vaccine coverage 1 dose (%) in 2021</span></span></p> <p><span><span>Vcovid21_2 : COVID-19 vaccine coverage 2 dose (%) in 2021</span></span></p> <p><span><span>Vcovid21_3 : COVID-19 vaccine coverage 3 dose (%) in 2021</span></span></p> <p><span><span>Vcovid22_1 : COVID-19 vaccine coverage 1 dose (%) in 2022</span></span></p> <p><span><span>Vcovid22_2 : COVID-19 vaccine coverage 2 dose (%) in 2022</span></span></p> <p><span><span>Vcovid22_3 : COVID-19 vaccine coverage 3 dose (%) in 2022</span></span></p>
Tidy coverage data for all 9943 CrossRef members
<p>Included in this deposit are the scripts and data collected on the coverage of metadata by the CrossRef members. Collection date is March 27, 2018. The data is available in long, <a href="http://vita.had.co.nz/papers/tidy-data.pdf">tidy</a>, format. Each row depicts the coverage of one specific aspect, indicating which member and how many DOIs that member deposited. This allows for easy parsing in for example ggplot2 or dplyr.</p>
Simulated paired-end reads for "Swimming downstream" workflow - uniform coverage (10-12)
<p>Simulated paired-end reads for "Swimming downstream" workflow</p>
Simulated paired-end reads for "Swimming downstream" workflow - uniform coverage (7-9)
<p>Simulated paired-end reads for "Swimming downstream" workflow</p>
VCF files for 100 high coverage baboon genomes from the Southwest National Primate Research Center
<p>Catalog of SNP and small indel variation in 100 high coverage (>20X) whole genome sequences from baboons (genus <em>Papio</em>) at the Southwest National Primate Research Center (SNPRC). These files use the Panu_2.0 baboon reference genome. See "Analysis of 100 high coverage genomes from a pedigreed captive baboon colony" by Robinson et al. 2019 for further details.</p>
Synthetic dataset used in "The maximum weighted submatrix coverage problem: A CP approach"
<p>Synthetic dataset used in "The maximum weighted submatrix coverage problem: A CP approach".</p> <p>Includes both the generated datasets as a zip archive and the python script used to generate them.</p> <p>Each instance is composed of two files in the form</p> <ul> <li>XxY_K_O_0xN_AxB_Smatrix.tsv being the matrix to use. Each row on a separate line, with tab-separated cells.</li> <li>XxY_K_O_0xN_AxB_Ssolution.txt giving the implanted solution. One submatrix per line. Then two JSON arrays follow, separated by a tabulation. The first is the list of rows selected in the submatrix, the second the columns.</li> </ul> <p>With:</p> <ul> <li>X and Y the size of the matrix</li> <li>K the number of submatrices in the implanted solution</li> <li>O the (minimum) overlap percentage of each submatrix</li> <li>N the sigma used for the background noise</li> <li>A and B the size of the implanted submatrices (subject to noise)</li> </ul> <p> </p>
"The maximum weighted submatrix coverage problem: A CP approach": experiment results
<p>This is the result of the experiment from the paper "The maximum weighted submatrix coverage problem: A CP approach".</p> <p>The original analysis was lost, and so the extraction of the result has been re-done. It should not differ from the original results.</p> <p> </p> <p>The interesting files are the following:</p> <p>- all.json contains a JSON list. Each element of the list contains the result of running all the methods on a specific matrix. Each entry is in the form {"name": "file of the matrix", "k": number of searched submatrices, "run": {"method": ["time", "objective value"]}}. CONTAINS ONLY RESULTS FOR THE SYNTH DATASET</p> <p>- best.json contains the best result obtained for each matrix. It is extracted from all.json.CONTAINS ONLY RESULTS FOR THE SYNTH DATASET</p> <p>- The other zips contains the results for the real dataset.</p>
Fig. 3 Base pair coverage across the 1296 in ELAV Intron 8: a single-copy sequence marker for shallow to deep phylogeny in Eupulmonata Hasprunar & Huber, 1990 and Hygrophila Férussac, 1822 (Gastropoda: Mollusca)
Fig. 3 Base pair coverage across the 1296 aligned sites in the ELAVI8 MSA (see ELAVI8_panpul.fas in the Supporting Information). The y-axis represents the percentage of sites that are represented at that location across all specimens
Benchmarking datasets used in the manuscript "HyLight: Strain aware assembly of low coverage metagenomes"
Open the record for dataset details and reuse information.
Metrics: BOLDS data coverage
Includes species-level taxa.
Metrics: Data Hubs data coverage - family level
NCBI, GGBN, BHL, BOLDS For family-level taxa.
Metrics: Data Hubs data coverage - genus level
NCBI, GGBN, BHL, BOLDS For genus-level taxa.
Metrics: NCBI data coverage
Open the record for dataset details and reuse information.
Metrics: iNaturalist data coverage
For species, genus and family level taxa.
Metrics: BHL data coverage
Open the record for dataset details and reuse information.
Metrics: GGBN data coverage
Open the record for dataset details and reuse information.
Safe trajectories from local information for coverage control in non-convex environments
<h2>Description</h2> <p>This dataset contains 3-channels grid-based representations of local information individually retrieved by robots in a team, tasked with a coverage control operation. Data collection was performed running 50 episodes of a coverage control mission with a team of 16 robots controlled by a theoretically proven safe expert controller.</p> <h3>Features</h3> <p>Features encode local information in a 3-channels <em>64 x 64</em> image, corresponding to the discretized sensing region of the robot. The first channel encodes the local likelihood density, the second one the position of team-mates, and the third channel contains the position of obstacles and boundaries. </p> <p><em>imgs{i}.npy</em> files contain data collected over each episode in the form of a [<em>S, N, C, W,</em> W] <em>numpy </em>array, where S is the number of steps of that episode, N is the number of robots, C = 3 is the number of channels, and W = 64 is the size of the image. </p> <h3>Labels</h3> <p>Labels contain the 2D velocity calculated by the expert controller, which is theoretically proven to guarantee collision avoidance. </p> <p><em>vels{i}.npy </em>files contain data collected over each episode in the form of a [<em>S, N, 2</em>] <em>numpy</em> array, associated to the corresponding feature.</p> <p> </p> <h2>Training and Testing</h2> <p>Code for training and testing a CNN-based model mapping local information to 2D velocity is available at <a href="https://github.com/ARSControl/cnn_coverage.git">https://github.com/ARSControl/cnn_coverage.git</a>.</p> <p> </p> <h2>Contact Information</h2> <p>If you are interested in any further information, please contact <a href="mailto:mattia.catellani@unimore.it">mattia.catellani@unimore.it</a>.</p> <p> </p>
Multi-UAV Uniform Sweep Coverage in Unknown Environments: A Mergeable Nervous System (MNS)-Based Random Exploration
<p><strong>Abstract:</strong><br>This paper investigates the problem of multi-UAV uniform sweep coverage, where a homogeneous swarm of UAVs must collectively and evenly visit every portion of an unknown environment for a sampling task without having access to their own location and orientation. Random walk-based exploration strategies are practical for such a coverage scenario as they do not rely on localization and are easily implementable in robot swarms. We demonstrate that the Mergeable Nervous System (MNS) framework, which enables a robot swarm to self-organize into a hierarchical ad-hoc communication network using local communication, is a promising control approach for random exploration in unknown environments by UAV swarms. To this end, we propose an MNS-based random walk approach where UAVs self-organize into a line formation using the MNS framework and then follow a random walk strategy to cover the environment while maintaining the formation. Through simulations, we test the efficiency of our approach against several decentralized random walk-based strategies as benchmarks. Our results show that the MNS-based random walk outperforms the benchmarks in terms of the time required to achieve full coverage and the coverage uniformity at that time, assessed across both the entire environment and within local regions.</p>
Metrics: GBIF data coverage
<p>Includes species, genus and family level taxa.</p>
Review Results of Randomly Selected Paper on the Coverage of Enrichment Results Documentation
<p>This is part of the PhD thesis "Enhancing Analysis and Interpretation Workflows for Transcriptome Data with an Interactive R/Bioconductor Toolkit".</p> <p>These are the detailed results reporting the individual information documented during the literature review. The file contains a sheet for each of the three literature searches and an additional sheet providing detailed documentation on the collected parameters.</p> <p>The selection process of the paper can be found in the linked repository. </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.