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.
428
datasets available to search
ShareScore release 0.9.0
Dataset results
428 results for “image map”
FCM16: a forest cover map of China in 2020 created entirely from GF-1 WFV images
<p>The dataset is defined in Albers projection with a spatial resolution of 16 m. It is in a GeoTIFF format and the a pixel value indicate whether it is forest cover or not. Value 0 means non-forest cover and value 1 means forest cover. The spatial extent of this dataset includes mainland China and neighboring regions.</p> <p>Reference:</p> <p>Article: GF-1 WFV satellite images based forest cover mapping in China supported by open land use/cover datasets<br>Journal: Scientific data</p>
Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells
<p>Raw data for our publication "Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells", including imaging files (Paravision), ASCII, MATLAB and EC Lab data files organised corresponding to each figure in the paper and supplementary information.</p>
Image Supplement to Hounshell, "Lines, Points, Waves, Streams. Mapping Ethnographic Types in Swiss Hausforschung, 1880s–1950s"
<p><strong>General description</strong>: The images in this repository are provided as a color supplement to the figures printed in black and white or otherwise mentioned in the following article: Eric Hounshell, “Lines, Points, Waves, Streams. Mapping Ethnographic Types in Swiss <em>Hausforschung</em>, 1880s–1950s,” <em>Historische Anthropologie</em> 31:1 (2023), 130-164 [DOI forthcoming]. All images are credited to Jakob Hunziker (1827-1901).</p> <p><strong>Image References</strong>:</p> <p>Fig. 1: Die Verbreitung der Hausformen in der Schweiz, in: Jakob Hunziker, Das Schweizerhaus nach seinen landschaftlichen Formen und seiner geschichtlichen Entwicklung, Aarau 1914, volume 8.</p> <p>Fig. 2: Untitled, in: Folder AG54.198, Jakob Hunziker Nachlass, Staatsarchiv Aargau.</p> <p>Appendix 1: Bedachung, in: Folder AG54.198, Jakob Hunziker Nachlass, Staatsarchiv Aargau.</p> <p>Appendix 2: Untitled, in: Folder AG54.198, Jakob Hunziker Nachlass, Staatsarchiv Aargau.</p> <p>Appendix 3: Untitled, in: Folder AG54.198, Jakob Hunziker Nachlass, Staatsarchiv Aargau.</p>
Data From: B1 Field inhomogeneity correction for qDESS T2 mapping: application to rapid bilateral knee imaging
<p><strong>Purpose</strong>: T2 mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of between-knee asymmetry in OA onset and progression. The quantitative double-echo in steady-state (qDESS) can provide fast simultaneous bilateral knee T2 and high-resolution morphometry for cartilage and meniscus.<br> The qDESS uses an analytical signal model to compute T2 relaxometry maps, which require knowledge of the flip angle (FA). <br> In the presence of B1 inhomogeneities, inconsistencies between the nominal and actual FA can affect the accuracy of T2 measurements.<br> We propose a pixel-wise B1 correction method for qDESS T2 mapping exploiting an auxiliary B1 map to compute the actual FA used in the model.<br> <br> <strong>Methods</strong>: The technique was validated in a phantom and in vivo with simultaneous bilateral knee imaging. <br> T2 measurements of femoral cartilage (FC) of both knees of six healthy participants were repeated longitudinally to investigate the association between T2 variation and B1.</p> <p><strong>Results</strong>: The results showed that applying the B1 correction mitigated T2 variations that were driven by B1 inhomogeneities. <br> Specifically, T2 left-right symmetry increased following the B1 correction (rc = 0.74 > rc = 0.69). Without the B1 correction, T2 values showed a linear dependence with B1. The linear coefficient decreased using the B1 correction (from 24.3 <span class="math-tex">\(\pm\)</span> 1.6 ms to 4.1 <span class="math-tex">\(\pm\)</span> 1.8) and the correlation was not statistically significant after the application of the Bonferroni correction (p-value > 0.01).<br> <br> <strong>Conclusion</strong>: the study showed that B1 correction could mitigate variations driven by the sensitivity of the qDESS T2 mapping method to B1, therefore increasing the sensitivity to detect real biological changes. The proposed method may improve the robustness of bilateral qDESS T2 mapping, allowing for an accurate and more efficient evaluation of OA pathways and pathophysiology through longitudinal and cross-sectional studies.</p> <p><strong>This repository contains the raw data for reproducing the results reported in the paper. The data are provided in NIFTI format. The repository also contains the MALTAB scripts used to compute B<sub>1</sub> maps from the NIFTI data.</strong></p>
Image bank to use with the Resource Flow Mapping tool when implementing the Clean and Green framework
<p>This image bank consists of a set of 116 images that can be used together with the Resource Flow mapping tool and other sensitization tools when implementing the Clean and Green framework as part of rural sanitation programs. The image bank consists of a wide range of images including everything from images depicting the use of various types of ecological sanitation toilets to the use of excreta-derived products in agriculture. All the images have a white background, and hence can be used in a variety of media and contexts. Each image has a descriptive file name. The images can be downloaded individually, or collectively by downloading the "Images.zip" file. The image bank was developed as part of the Clean and Green framework project at the Stockholm Environment Institute, in collaboration with WaterAid Burkina Faso. Learn more: https://www.sei.org/projects-and-tools/projects/clean-and-green/ </p> <p> </p>
Satellite images and road-reference data for AI-based road mapping in Equatorial Asia
<p><span>1. </span><span>INTRODUCTION</span></p> <p><span>For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image 'tiles' and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea).</span><span> </span></p> <p><span>2. </span><span>FURTHER INFORMATION</span></p> <p><span>The following is a summary of our data. Fuller details on these data and their underlying methodology are given in the corresponding article, under consideration by the journal Remote Sensing as of September 2023: </span></p> <p><span>Sloan, S., Talkhani, R.R., Huang, T., Engert, J., Laurance, W.F. (2023) Mapping remote roads using artificial intelligence and satellite imagery. Under consideration by Remote Sensing.</span></p> <p><span>Correspondence regarding these data can be directed to:</span></p> <p><span>Sean Sloan</span></p> <p>Department of Geography, Vancouver Island University, Nanaimo, B.C, Canada</p> <p><span><a href="mailto:sean.sloan@viu.ca"><span>sean.sloan@viu.ca</span></a></span>; </p> <p><span>Tao (Kevin) Huang</span></p> <p>College of Science and Engineering, James Cook University, Cairns, Queensland 4878, Australia</p> <p><a href="mailto:tao.huang1@jcu.edu.au">tao.huang1@jcu.edu.au</a><span> </span></p>
Satellite images and road-reference data for AI-based road mapping in Equatorial Asia
Open the record for dataset details and reuse information.
Mapping built infrastructure in semi-arid systems using data integration and open-source approaches for image classification
Open the record for dataset details and reuse information.
Images from Newspaper Navigator predicted as maps, with human corrected labels
<p>The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/). </p> <blockquote> <p>[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in <em>Chronicling America</em>. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the <a href="https://labs.loc.gov/work/experiments/beyond-words/">Beyond Words</a> crowdsourcing project.</p> <p>source:<a href="https://news-navigator.labs.loc.gov/"> https://news-navigator.labs.loc.gov/</a></p> </blockquote> <p>One of these categories is 'maps'. In the original training data for Newspaper Navigator, there were relatively few labelled examples of maps. The predictions for maps have an <a href="https://github.com/LibraryOfCongress/newspaper-navigator">Average Precision of 69.5%, and 34 images in the validation data</a>.</p> <p>This dataset contains a sample of these images which have been predicted as 'maps'. It also includes additional labels which indicate whether the predicted map image is a 'map' or 'not a map'. </p> <p>The data is organised as follows:</p> <ul> <li>The images themselves can be found in 'newspaper_maps.zip' </li> <li>`2020_30_10_13_19_228_sample.json` contains metadata about each image drawn from the Newspaper Navigator Dataset.</li> <li>map_labels.csv contains the labels for the images as a CSV file </li> </ul>
Supplementary Dataset for Detecting structured sources in noisy images via Minkowski maps
<p>Supplementary Dataset of the publication "<a href="https://dx.doi.org/10.1209/0295-5075/128/60001">Detecting structured sources in noisy images via Minkowski maps</a>" by the same authors:</p> <p>M. A. Klatt, K. Mecke. <em>EPL (Europhysics Letters)</em> <strong>128</strong>:60001 (2019)<br> <a href="https://dx.doi.org/10.1209/0295-5075/128/60001">https://dx.doi.org/10.1209/0295-5075/128/60001</a></p> <p>The dataset contains all simulated data and parameters of the figures and table in the main text and supplementary material.</p> <p>The corresponding code is available at the GitHub repository:<br> <a href="https://github.com/michael-klatt/minkmaps">https://github.com/michael-klatt/minkmaps</a></p>
Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study
Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decreased ADCs on fDMs were analyzed using repeated measures analysis of variance (ANOVA). Changes in tumor size were used as covariate to examine the ability of ADCs and fDM parameters to predict treatment response. Results: Repeated measures ANOVA revealed that the percentage of voxels with increased ADCs on fDMs (p = 0.002) as well as the mean ADC increase (p = 0.011) were significantly higher in good responders with a large reduction in tumor size on CT. Conclusion: Our results indicate that the percentage of voxels with significantly increased ADCs on fDMs seems to be a promising biomarker for early prediction of treatment response in patients with non-small cell lung carcinoma. Contrary to averaged values, this approach allows the spatial heterogeneity of treatment response to be resolved.
Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing - Image and In SItu Sequencing data
<p>Image and ISS data for Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing reveals a biologically relevant zonation of the desmoplastic rim.</p><p>Image data consists of:</p><ul><li>Nucler stain (DAPI)</li><li>Masks for liver, rim and tumor regions</li><li>H&E images of parallel tissue sections</li></ul><p>Gene and cluster marker data is collected in the <i>markers.h5ad</i> file which can be read using AnnData (<a href="https://anndata.readthedocs.io/en/latest/">https://anndata.readthedocs.io/en/latest/)</a>. </p><p> </p>
Data for MAPS: Pathologist-level Cell Type Annotation from Tissue Images through Machine Learning
<p>Extracted datasets and processed images used to generate figures in manuscript <i>"MAPS: Pathologist-level Cell Type Annotation from Tissue Images through Machine Learning".</i></p>
Mapping Enzyme Activity in Living Systems by Real-Time Mid-Infrared Photothermal Imaging of Nitrile Chameleons
Open the record for dataset details and reuse information.
HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images
<p>These two zipped folders contain all data necessary to train, validate and reproduce results from the paper.</p> <p>Unzipping the files will create 6 folders. Data from folders with the same name across both zips should be combined into one folder. The 'annotations' folder contains all ground truth annotations for training all three deep learning models. The 'datasets' folder contains images for training the nuclei localisation and cell classification models. The 'embeddings' folder contains cell embedding vectors and nuclei coordinates from two slides used to create nodes to train the graph tissue classification model. The 'graph_splits' folder contains regions defining the validation and test splits for the graph model. The 'slides' folder contains a sample region of a whole slide image as a .tiff file for running the inference demo. The 'trained_models' folder contains trained weights for each of the three models.</p> <p>Further instructions for dataset use and creation of custom datasets are available in the GitHub readme: https://github.com/Nellaker-group/happy.</p>
Image distortion data from "An open-source MRI compatible frame for multimodal presurgical mapping in macaque and capuchin monkeys"
Open the record for dataset details and reuse information.
Microstructure images, ilastik labels and probability maps.
<p>Microstructure images of several Al alloys reinforced with quasicrystals. Images were obtained using a Zeiss Axio Imager.A1m with AxioCam ERc 5s. Original images were enhanced using FIJI and analysed using ilastik. Included is the ilastik training set, labels used and probability maps.</p>
Mapping Areas Invaded by Pinus sp. from Geographic Object-Based Image Analysis (GEOBIA) Applied on RPAS (Drone) Color Images
<p><strong>Abstract: </strong>Invasive alien species reduce biodiversity. In southern Brazil, the genus <em>Pinus</em> is considered invasive, and its dispersal by humans has resulted in this species reaching ecosystems that are more sensitive and less suitable for cultivation, as it was the case for the restingas on the island of Santa Catarina. Invasion control requires persistent efforts to identify and treat each new invasion case as a priority. In this study, areas invaded by <em>Pinus</em> sp. in restingas were mapped using images taken by a remotely piloted aircraft system (RPAS, or drone) to identify the invasion areas in great detail, enabling management to be planned for the most recently invaded areas, where management is simpler, more effective, and less costly. Geographic object-based image analysis (GEOBIA) was applied on images taken from a conventional RGB camera embedded in a RPAS, which resulted in a global accuracy of 89.56%, a mean kappa index of 0.86, and an F-score of 0.90 for the <em>Pinus</em> sp. Processing was conducted with open source software to reduce operational costs.</p>
Magnetic susceptibility-weighted imaging mapped the migratory route of a paragonimus in brain
<p>A 53-year-old man with a recent history of left temporal lobe hemorrhage presented with weakness of left upper limb for 4 days. Paragonimus eggs were detected in patient's sputum. Work up revealed paragonimiasis, a lung fluke worm infection endemic to East Africa, West Africa and South America which rarely involves in brain. Characteristic tunnel sign was visualized on brain MRI. Notably, susceptibility-weighted imaging revealed a migratory pattern of the parasite from the left temporal lobe to the right parietal lobe and resultant patchy hemorrhage. Treatment with praziquantel resulted in favorable outcome without residual deficits.</p>
Cross-correlation coefficient maps generated in the paper "Correlation of Venusian Mesoscale Cloud Morphology Between Images Acquired at Various Wavelengths" by Narita et al. published in Journal of Geophysical Research - Planets
<p>This data archive contains the cross-correlation coefficient maps. Unzipping the compressed file, the following directories corresponding to different wavelength pairs appear. </p> <p> IR1_IR2/ : 0.9 micron & 2.02 micron<br> UVI283_UVI365/ : 283 nm & 365 nm<br> IR2_UVI365/ : 2.02 micron &. 365 nm<br> IR2_UVI283/ : 2.02 micron & 283 nm<br> IR2_LIR/ : 2.02 micron & 10 micron</p> <p>If usual unzip tools do not work, the use of 7zip is recommended:<br> https://www.7-zip.org/download.html</p> <p>Each directory contains CSV files for the longitude-latitude distribution of the correlation coefficient. The 2880 longitude grids cover the longitude range of 0 - 360 degrees, and the 1440 latitude grids cover the latitude range of -90 - +90 degrees, with a pixel resolution of 0.125 degree/pixel. Invalid regions are filled with the value of 1.1.</p> <p>Each filename is composed of the date, the instrument (wavelength), and the time. For example, for the file "20160720_ir2_150821_hp6_IR1_150209_hp6_sb24.csv":</p> <p> 20160720 : July 20, 2016<br> ir2 : 2.02 micron filter of IR2 camera<br> 150821 : IR2 exposure at 15:08:21<br> hp6: High-pass filtering size is 6 deg x 6 deg<br> IR1 : 0.9 micron filter of IR1 camera<br> 150209 : IR1 exposure at 15:02:09<br> sb24 : Sliding box size is 24 deg x 24 deg</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.