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.
22
datasets available to search
ShareScore release 0.7.1
Dataset results
22 results for “heatmap”
Heatmatrix and Heatmap Layers and Alternatives used in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)
<p>This dataset contains the <strong>visualisations </strong>of the solutions of Conflict Detection and Resolution (CD&R) use case.</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside, one can find:</p> <p>-One archive, "Heatmatrix.zip" , containing the heatmatrix creating using the solutions dataset.</p> <p>-One archive, "Heatmaps_Layer_Alternatives.zip", containing all the layers created and used to created the heatmaps, the heatmaps, and alternative heatmaps (with other candidate solutions).</p> <p>Those layers and heatmaps are used to develop other visualisation used in the validation.</p>
Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
<p>This repository provides the data used for the experiments of the paper "Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning" by Hazem Fahmy, Fabrizio Pastore, Mojtaba Bagherzadeh, and Lionel Briand appearing in IEEE Transactions on Reliability (doi: 10.1109/TR.2021.3074750)</p> <p>Deep neural networks (DNNs) are increasingly important in safety-critical systems, for example in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components.</p> <p>We observe three major challenges with existing practices regarding DNNs in safety-critical systems: (1) scenarios that are underrepresented in the test set may lead to serious safety violation risks, but may, however, remain unnoticed; (2) char- acterizing such high-risk scenarios is critical for safety analysis; (3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine.</p> <p>To address these problems in the context of DNNs analyzing images, we propose HUDD, an approach that automatically supports the identification of root causes for DNN errors. HUDD identifies root causes by applying a clustering algorithm to heatmaps capturing the relevance of every DNN neuron on the DNN outcome. Also, HUDD retrains DNNs with images that are automatically selected based on their relatedness to the identified image clusters.</p> <p>We evaluated HUDD with DNNs from the automotive domain. HUDD was able to identify all the distinct root causes of DNN errors, thus supporting safety analysis. Also, our retraining approach has shown to be more effective at improving DNN accuracy than existing approaches.</p> <p> </p>
Raw data and heatmaps of VLP deposition modeling
<p>Supplementary Information and Raw Data for <a href="https://www.plus.ac.at/biowissenschaften/der-fachbereich/arbeitsgruppen/duschl/members/martin-himly/list-of-publications/">Hofstätter N., Hofer S., Duschl A., and Himly M.<br> Children’s privilege in COVID-19: The protective role of the juvenile lung morphometry and ventilatory pattern on airborne SARS-CoV-2 transmission and severe pulmonary disease (2021). <em>Biomedicines </em>9(10):1414.</a> DOI: <a href="https://doi.org/10.3390/biomedicines9101414">https://doi.org/10.3390/biomedicines9101414</a></p> <p>1. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon nose breathing</p> <p>2. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon mouth breathing</p> <p>3. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon nose breathing</p> <p>4. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon mouth breathing</p> <p>5. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon nose breathing</p> <p>6. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon mouth breathing</p> <p>7. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon nose breathing</p> <p>8. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon mouth breathing</p>
Heatmaps of quantitative and qualitative phenotypes of zebrafish pronephroi upon compound exposure
<p>Heatmaps of quantitative and qualitative phenotypes of embryonic zebrafish pronephroi after exposure to compounds from the Prestwick library.</p> <p>For further details please see:</p> <p><em>Westhoff JH, Steenbergen PJ, Thomas LSV, Heigwer J, Bruckner T, Cooper L, Tönshoff B, Hoffmann GF and Gehrig J (2020) In vivo High-Content Screening in Zebrafish for Developmental Nephrotoxicity of Approved Drugs. Front. Cell Dev. Biol. 8:583. doi: 10.3389/fcell.2020.00583</em></p> <p>The images represent full resolution versions of the thumbnails presented in: </p> <ol> <li>Supplementary Figure 3 | Fully annotated heat map of quantitative features.</li> <li>Supplementary Figure 4 | Fully annotated heat map of qualitative features.</li> </ol> <p> </p> <p> </p>
Heatmaps of orthology and protein domain preservation in RNA Processing complexes throughout the fungal kingdom
<p>An analysis of the presence/absence of orthologues for Fungal RNA Processing protein complexes, and the presence/absence of the known PFAM protein domains within each protein within these complexes in the organism's proteome. </p> <p>Each image represents one RNA Processing protein complex.</p> <p>Orthology (far left panel in each image) is relative to Yeast, and taken from a query against the EnsEMBL orthology database (black = no orthologue; red = orthologue). <br> <br> Each orthologue was then queried for its PFAM domains, and the non-redundant set of PFAM domains representing each set of orthologous proteins, spanning all species, was then scanned against the complete proteome of each species. The resulting heatmap indicates the presence or absence of that PFAM domain anywhere in the proteome of that species. (black = absent; red = 1 copy; grey->blue = more than one copy)</p>
Land surface temperature (heatmaps) derived from earth observation data to assess thermal behaviour of 3 European cities: Milano, Logroño and Athens.
<p>Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the <strong>land surface temperature (heatmaps)</strong> of three European cities: Milan, Logroño and Athens. TECNALIA is the responsible partner for these datasets.</p> <p>There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health. </p> <p>The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan, Logroño and Athens were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows.</p> <p>- 2013-2017<br>- 2014-2018<br>- 2015-2019<br>- 2016-2020<br>- 2017-2021<br>- 2018-2022<br>- 2019-2023</p> <p>The input data used in this dataset come from Landsat 8 downloaded from <a href="https://earthexplorer.usgs.gov/">Earth Explorer (usgs.gov)</a>.</p> <p>The format of this dataset is organized in two ZIP format files:</p> <p>- LANDSAT_8_L2SP_000000-milan_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-logrono_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-athens_LST_peak.zip</p> <p>Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST.</p> <p>The values of the images are in degree Celsius and nodata value is -9999.</p> <p> </p>
Fig. 5. Heatmap with a in Fig. 2. 60 in Identification and Distribution of Wedge Clams (Donacidae: Bivalvia) in Thailand by Geometric Morphometric and Molecular Analysis.
Fig. 5. Heatmap with a dendogram showing dietary plant abundance at the genus level for adult, subadult, and juvenile Asian elephants. Gradient heatmap shows the 20 most abundant genera.
Fig. 4. Heatmap with a in Fig. 2. 60 in Identification and Distribution of Wedge Clams (Donacidae: Bivalvia) in Thailand by Geometric Morphometric and Molecular Analysis.
Fig. 4. Heatmap with a dendogram showing dietary plant abundance at the genus level for male and female Asian elephants. Gradient heatmap shows the 20 most abundant genera.
AIS heatmap: North Sea and Dutch Inland Waterways for the months January, April, July, October in 2019
<p>This dataset contains information on vessel movements in the North Sea and Dutch Inland Waterways for the months January, April, July, and October in 2019. It provides a heatmap representation of vessel traffic density during these specific months, which can be useful for various maritime and environmental analyses.</p> <p>1. File Formats</p> <p>The dataset is provided in the following file formats:</p> <ul> <li>NetCDF : The primary data files are available in netcdf format. For each grid cell the variables sog (Speed Over Ground) and count (Number of AIS messages) are available</li> <li>GeoTIFF (Georeferenced Tagged Image File Format): Heatmap images are provided in GeoTIFF format, suitable for geographic visualization.</li> </ul> <p>The dataset is split into tiles. Each tile conforms to the <a href="https://wiki.openstreetmap.org/wiki/Tiles">OSM tiling</a> naming scheme.</p> <p>2. Variables </p> <p>The dataset includes the following key variables:</p> <ul> <li><strong>Speed Over Ground (SOG)</strong>: The average vessel's speed over the ground for all the messages.</li> <li><strong>Count</strong>: The number of AIS messages received in this location</li> </ul> <p>3. Data Collection Method </p> <p>The AIS data used in this dataset was collected from AIS transponders on vessels operating in the North Sea and Dutch Inland Waterways. These transponders transmit information such as vessel position, speed, and identification. The dataset aggregates this information to create heatmap images for analysis. We did this on all the messages. Some ships emit more messages than others. Ships emit messages at higher frequency when sailing than when stationary. </p> <p>4. Source of Original Data</p> <p>The original AIS data used to create this dataset was sourced from the AIS archive from Rijkswaterstaat. This dataset was analysed for the purpose of a <a href="https://ais-scrolly.netlify.app/">storymap</a>.</p> <p> </p> <p> </p>
Heatmap input data
<p>Dataset can be visualized by using the destair_heatmap.R script (https://github.com/destairdenbi/tools/tree/master/destair_heatmap).</p>
Interactive Heatmap of all Disease Phenolog Behavioural Phenotypes
<p><span><span>An interactive clustermap of behavioural tracking data for a panel of 25 <em>C. elegans</em> disease model phenologs (associated with the linked published paper). This is a static html file that can be opened in a browser and zoomed in for a detailed inspect of how the various strains differ from the control (N2). Mousing over the heatmap shows the name of features at each position so that a more intutive conclusion of the data, i.e., 'Strain A is slow' or 'Strain B is more curved', can be reached.</span></span><span></span></p>
Figure 6. Heatmap showing the top 200 in Sleeping with the enemy: unravelling the symbiotic relationships between the scale worm Neopolynoe chondrocladiae (Annelida: Polynoidae) and its carnivorous sponge hosts
Figure 6. Heatmap showing the top 200 most abundant ASVs for each sample. The colour range (0 to 4) represents the log10 transformation of the rarefied counts.
Interactive heatmaps for metagenome assembled genome (MAG) metagenomic potential and metaproteomic peptide recruitment
<p>Interactive heatmaps for supplementary figure 1 and supplementary figure 4 from publication to be submitted titled "<strong>Microbial genome-resolved metaproteomic analyses frame intertwined carbon and nitrogen cycles in river hyporheic sediments". </strong></p>
"Empirical study on Visual Attention Characteristics of basketball players of different levels during free-throw shooting"AOI and Heatmap
<p>“罚球投篮中不同水平篮球运动员视觉注意力特征的实证研究”AOI与热图</p>
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects. in A global approach for natural history museum collections
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects.
Heatmap of p-values obtained by the non-parametric Wilcoxon Post-hoc Test for Experiment 1; Maximum penalty applied
<p>Heatmaps of the p-values obtained by the non-parametric Wilcoxon Post-hoc Test. </p> <p>Used for all of the following data measures across all 10 conditions. </p> <p>The maximum penalty applied for Experiment 1. </p>
Heatmap of p-values obtained by the non-parametric Wilcoxon Post-hoc Test for Experiment 1; Mean CI applied
<p>Heatmaps of the p-values obtained by the non-parametric Wilcoxon Post-hoc Test. </p> <p>Used for all of the following data measures across all 10 conditions. </p> <p>Mean condition imputation was applied to all of the failed trials for all data measures. </p>
Heatmap of p-values obtained by the non-parametric Wilcoxon Post-hoc Test for Experiment 2; Mean CI applied
<p>Heatmaps of the p-values obtained by the non-parametric Wilcoxon Post-hoc Test. </p> <p>Used for all of the following data measures across all 13 conditions. </p> <p>Mean condition imputation was applied across all of the failed trials. </p>
Heatmap of p-values obtained by the non-parametric Wilcoxon Post-hoc Test for Experiment 2; Maximum penalty applied
<p>Heatmaps of the p-values obtained by the non-parametric Wilcoxon Post-hoc Test. </p> <p>Used for all of the following data measures across all 13 conditions. </p> <p>The maximum penalty applied for Experiment 2. </p>
From approximation error to optimality gap - full set of heatmaps
Open the record for dataset details and reuse information.
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.