Find research datasets worth reusing

Electronic structure orientation as a map of in-plane antiferroelectricity in beta'-In2Se3

Matlab data files and metadata files for experimental data as described in the paper titled "Electronic structure orientation as a map of in-plane antiferroelectricity in beta'-In2Se3".

opencc-zeroMay 2024View details →

Submitted Completed pointings to the Gravitational Wave Treasure Map for event MS181101ab

Attached in a .json file is the completed pointing information for 25 observation(s) for the EM counterpart search associated with the gravitational wave event MS181101ab. These observations were taken on the DECam, ZTF, and DLT40 instruments.

Map of Saek villages

Map of Saek villages and GPS data. Base map: ESRI World Imagery (https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer).

Data for "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024)

This archive contains the data and scripts needed to reproduce the analysis in "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024). Contents data/: Input data and posterior distributions of different model fits datasets/: Observational datasets figures/: Folder to save figures in fluxes/: Saved lightcurves for auxiliary plotting purposes archive_paper_plotter.ipynb: Example script to plot fitted eclipse maps eclipse_pixel_sampling.py: Script to fit eclipse map and test k-fold CV score paper_eclipse_suite.py: Script to use simulated or observational data to fit an eclipse map

opencc-by-4.0Jan 2024View details →

OpenEarthMap Land Cover Mapping Few-Shot Learning Challenge

<pre>***The challenge is over, please use this verion for post-challenge research. This version contains all the data and files, except the labels of the query-set of the testset that are withheld and researchers can submit the predictions at the challenge leaderboard for evaluation***</pre> Overview This challenge is co-organized with the <a href="https://sites.google.com/view/l3divu2024/overview" target="_blank" rel="noopener">L3D-IVU 2024 CVPR</a> workshop. The challenge is an extension of the <a href="https://open-earth-map.org/" target="_blank" rel="noopener">OpenEarthMap </a>benchmark dataset for a generalized few-shot semantic segmentation (GFSS) task. The challenge aims to evaluate and benchmark learning methods for few-shot semantic segmentation on the OpenEarthMap dataset to promote research on geoinformatics for social good. The motivation is to enable researchers to develop few-shot learning algorithms for high-resolution RS image semantic segmentation. Page <a href="https://cliffbb.github.io/OEM-Fewshot-Challenge/" target="_blank" rel="noopener">https://cliffbb.github.io/OEM-Fewshot-Challenge/</a> Baseline The baseline model for the challenge is available <a href="https://github.com/cliffbb/OEM-Fewshot-Challenge" target="_blank" rel="noopener">here</a>. Leaderboard The <a href="https://codalab.lisn.upsaclay.fr/competitions/19210" target="_blank" rel="noopener">challenge leaderboard </a>is opened for post-challenge research to enable researchers to evaluate the predictions on the query-set of the testset. Description The dataset has been designed for remote sensing few-shot learning, particularly, for GFSS tasks in land cover mapping. The dataset consists of only 408 samples from the original <a href="https://open-earth-map.org/" target="_blank" rel="noopener">OpenEarthMap</a> dataset for RS image semantic segmentation. It extends the original 8 semantic classes of the OpenEarthmap benchmark to 15 classes, which is split into 7:4:4 for train_base_class, val_novel_class, and test_novel_class disjointed sets, respectively (i.e., train_base_class ∩ val_novel_class ∩ test_novel_class = ∅). The 408 samples are also split into 258 as `trainset`, 50 as `valset`, and 100 as `testset`. The `trainset` is for pre-training a backbone network. It contains only the images and labels of the train_base_class split. Both the `valset` and the `testset` consist of a support set and a query set for a 5-shot  with 4 novel classes  and 7 base classes  GFSS task. The `valset` and the `testset` contain the images and labels of the val_novel_class and the test_novel_class splits, respectively. The challenge is in two phases: development phase and evaluation phase. The `valset` is for the development phase and the `testsets` is for the evaluation phase. Both `valset` and `testset`  have 20 image-label pair examples, 5-set examples for each of the 4 novel classes in the support set. The `valset` and the `testset` contain an additional 30 images and 80 images, respectively, in the query set, which are to be predicted using the 20 labelled images in their support set. The labels for each image in the support sets do not contain any of the train_base_class split. Also, in each 5-set examples, the labels contain only one novel class (i.e., one novel class per 5-set examples). However, in the `valset`, the labels for the images in the query set contain both train_base_class and val_novel_class; and in the `testset, the labels for the images in the query set contain both  train_base_class and test_novel_class `. Note that both the support set and query set in the `valset` are different from the ones in the `testset`. <pre> File Structure and Content (All files are in `.tif` format): ----------------------------------------------------------- 1. **trainset.zip**: - Contains `images` and `labels` folders - `images` folder: 258 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m. - `labels` folder: 258 segmentation masks of the images in the `images` folder. 2. **valset.zip**: - Contains `images` and `labels` folders - `images` folder: 50 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m. - `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for the 30 ``query set`` images in the `images` folder are withheld. 3. **testset.zip**: - Contains `images` and `labels` folders - `images` folder: 100 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m. - `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for the 80 ``query set`` images in the `images` folder are withheld. 4. **train.txt**: - Contains a list of file names in the `trainset.zip`. 3. **val.json** and **test.json**: - Contains a list of file names the in the `valset.zip` and `testset.zip`, respectively. Below is the structure of the `val.json` and `test.json` files. - fnames = { {"support_set": {8: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"], 9: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"], 10: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"], 11: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"]}, {"query_set": ["filename_1.tif", "filename_2.tif", "filename_3.tif", ... ...., "filename_n.tif"]} } Land Cover Mapping Classes Strucure: ------------------------------------ 1. **The `trainset`: classId2className = {     # ***Base classes***     1: 'tree',     2: 'rangeland',     3: 'bareland',     4: 'agric land type 1',     5: 'road type 1',     6: 'sea, lake, & pond',     7: 'building type 1' } 2. **The `valset` and `testset`: classId2className = {     # ***Base classes***     1: 'tree',     2: 'rangeland',     3: 'bareland',     4: 'agric land type 1',     5: 'road type 1',     6: 'sea, lake, & pond',     7: 'building type 1' # ***Novel classes***                     8: '',                     9: '',                     10: '',                     11: '' } - The class names for the ***Novel classes*** depends on the data set. For the `valset`, the class names can be updated as: { 8: 'road type 2',                     9: 'river',                     10: 'boat & ship',                     11: 'agric land type 2' } For the `testset`, the class names can be updated as: { 8: 'vehicle & cargo-trailer',                     9: 'parking space',                     10: 'sports field',                     11: 'building type 2' } License</pre> See <a href="../records/7223446" target="_blank" rel="noopener">OpenEarthMap</a>

opencc-by-4.0Nov 2023View details →

Supplementary Material - Systematic Mapping Study - Information and Software Technology

Supplementary material - Article title: "Constructing the graphical structure of expert-based Bayesian networks in the context of software engineering: A systematic mapping study" - Information and Software Technology. This file contains the detailed procedure employed in the systematic mapping.

dryad36/100

A global map of species at risk of extinction due to natural hazards

An often-overlooked question of the biodiversity crisis is how natural hazards contribute to species extinction risk. To address this issue, we explored how four natural hazards: earthquakes, hurricanes, tsunamis, and volcanoes, overlapped with the distribution ranges of amphibians, birds, mammals, and reptiles that have either narrow distributions or populations with few mature individuals. To assess which species are at risk from these natural hazards, we combined the frequency and magnitude of each natural hazard to estimate a probability of impact. We considered species at risk if they overlapped with regions where any of the four natural hazards historically occurred (n = 3,722). Those species with at least a quarter of their range subjected to a high probability of impact were considered at high risk (n = 2,001) of extinction due to natural hazards. In total, 834 reptiles, 617 amphibians, 302 birds, and 248 mammals were at high risk and they were mainly distributed on islands and in the tropics. Hurricanes (n = 983) and earthquakes (n = 868) affected the most species, while tsunamis (n = 272), and volcanoes (n = 171) affected considerably fewer. The region with the highest number of species at high risk was the Pacific Ring of Fire, especially due to volcanoes, earthquakes and tsunamis, while hurricane-related high-risk species were concentrated in the Caribbean Sea, Gulf of Mexico, and northwestern Pacific Ocean. Our study provides important information regarding the species at risk due to natural hazards and can help guide conservation attention and efforts to safeguard their survival.

opencc-zeroJun 2024View details →

White matter hyperintensity maps in aging and neurodegenerative diseases

<div>The files contain voxel-wise white matter hyperintensity (WMH) maps for 11 different neurodegenerative disease cohorts from the Canadian Consortium on Neurodegeneration in Aging (CCNA) COMPASS-ND dataset in the MNI-ICBM152-2009c space.</div> <div> </div> <div>For more information regarding the participants and method details, see: </div> <div>Dadar, M., Mahmoud, S., Zhernovaia, M., Camicioli, R., Maranzano, J., Duchesne, S., & CCNA Group. (2022). White matter hyperintensity distribution differences in aging and neurodegenerative disease cohorts. NeuroImage: Clinical, 36, 103204.</div>

The Execution Perspective in Software Architecture Descriptions: A Systematic Mapping

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2018View details →

MAP 1 in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature

MAP 1. Topographic base map for the Philippine Archipelago.

opencc-by-4.0Oct 2013View details →

Data from: Maranville JC, Baxter SS, Witonsky DB, Chase MA, Di Rienzo A. (2013) Genetic Mapping with Multiple Levels of Phenotypic Information Reveals Determinants of Lymphocyte Glucocorticoid Sensitivity.

Genotype data for 88 African American indviduals in binary PLINK format published in  Maranville JC, Baxter SS, Witonsky DB, Chase MA, Di Rienzo A. (2013) Genetic Mapping with Multiple Levels of Phenotypic Information Reveals Determinants of Lymphocyte Glucocorticoid Sensitivity. Am J Hum Genet. 93(4):735-743

All-sky three-dimensional dust density and extinction Maps of the Milky Way out to 2.8 kpc

Three-dimensional dust density maps are crucial for understanding the structure of the interstellar medium of the Milky Way and the processes that shape it. However, constructing these maps requires large datasets and the methods used to analyse them are computationally expensive and difficult to scale up. As a result it is has only recently become possible to map kiloparsec-scale regions of our Galaxy at parsec-scale grid sampling. We present all-sky three-dimensional dust density and extinction maps of the Milky Way out to 2.8~kpc in distance from the Sun using the fast and scalable Gaussian Process algorithm Dustribution. The sampling of the three-dimensional map is $l,b,d = 1^{\circ} \times1^{\circ} \times 1.7$~pc. The input extinction and distance catalogue contains 120 million stars with photometry and astrometry from Gaia DR2, 2MASS and AllWISE. This combines the strengths of optical and infrared data to probe deeper into the dusty regions of the Milky Way. We compare our maps with other published 3D dust maps. All maps quantitatively agree at the $0.001$~mag~pc$^{-1}$ scale with many qualitatively similar features, although each map also has its own features. We recover Galactic features previously identified in the literature. Moreover, we also see a large under-density that may correspond to an inter-arm or -spur gap towards the Galactic Centre.   Here we provide the full dataset generated from Dustribution covering the full sky out to 2.8 kpc. We provide the 3D dust extinction and density files as well the l,b,d coordinate grid that the data is presented in.    The data in each file is as follows: <ul> <li>lbdGrid_l_bounds.pkl.npy: The boundaries of the grid cells of the merged map in l (degrees). Numpy array of size 881.</li> <li>lbdGrid_b_bounds.pkl.npy: The boundaries of the grid cells of the merged map in b (degrees). Numpy array of size 273.</li> <li>lbdGrid_d_bounds.pkl.npy: The boundaries of the grid cells of the merged map in d (pc). Numpy array of size 1644.</li> <li>Density_Median_lbd.pkl.npy: The median of 100 density samples of the merged map (mag pc^-1). Treat this as the estimate of the density. Numpy array of size (880, 272, 1644)</li> <li>Density_16P_lbd.pkl.npy: The 16th percentile of the density of the merged map (mag pc^-1). Treat median-16p as the estimate of the lower uncertainty on the density. Numpy array of size (880, 272, 1644)</li> <li>Density_84P_lbd.pkl.npy: The 84th percentile of the density of the merged map (mag pc^-1). Treat 84p-median as the estimate of the upper uncertainty on the density. Numpy array of size (880, 272, 1644) </li> <li>Ext_Median_lbd.pkl.npy: The median of the integral of 100 sample maps at each cell in the merged map (mag). Treat this as the estimate of the extinction in each cell. Numpy array of size (880, 272, 1644). </li> <li>Ext_16P_lbd.pkl.npy: The 16th percentile of the integral of 100 sample maps at each cell in the merged map (mag). Treat median-16p as the estimate of the lower uncertainty on the extinction in each cell. Numpy array of size (880, 272, 1644)</li> <li>Ext_84P_lbd.pkl.npy: The 84th percentile of the integral of 100 sample maps at each cell in the merged map (mag). Treat 84p-median as the estimate of the upper uncertainty on the extinction in each cell. Numpy array of size (880, 272, 1644)</li> <li>read_and_plot.ipynb: a short jupyter notebook demonstrating how to read in the data files, with some quick examples of plotting them.</li> </ul>

opencc-by-4.0Mar 2015View details →

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

The dataset is the vegetation type map for India as per <a href="https://www.sciencedirect.com/science/article/pii/S0303243415000574?via%3Dihub">Roy et 2015 "New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities". </a> The dataset consistes of two files- (1) a raster GIS file at 60m spatial resolution  (EPSG 32643 WGS 84/ UTM Zone 34) in which each pixel value means a vegetation class as defined and mapped in Roy et al., 2015 and (2) a csv file which contains information matching the pixel value with the vegetation type. For all additional information, please refer to the pper reviewed publication.

A collection of map-based applications created to communicate sustainable mobility topics

<h2>Source of the sample map-based applications</h2> <div> <table> <tbody> <tr> <td>ID</td> <td>Published year</td> <td>Link</td> </tr> <tr> <td>R1</td> <td>2021</td> <td>https://futuretransport-news.com/voi-launches-impact-dashboard-for-moresustainable-transport-choices/ </td> </tr> <tr> <td>R2</td> <td>2022</td> <td>https://trafficinfratech.com/ritm3-effective-solution-for-sustainable-traffic-management</td> </tr> <tr> <td>R3</td> <td>2022</td> <td>https://staex.io/smart-city-staex-regioit </td> </tr> <tr> <td>R4</td> <td>2022</td> <td>https://www.paircity.com/home   </td> </tr> <tr> <td>R5</td> <td>2019</td> <td>https://www.geodan.com/knowledge-and-innovation/managing-urban-processesintelligently-with-the-amsterdam-smart-city-dashboard/</td> </tr> <tr> <td>R6</td> <td>2018</td> <td>https://www.greenappsandweb.com/en/android-en/rewarding-sustainable-mobilitysolutions/</td> </tr> <tr> <td>R7</td> <td>2021</td> <td>https://unhabitat.org/sites/default/files/2021/08/sump-guidelineseng.pdf   </td> </tr> <tr> <td>R8</td> <td>2020</td> <td>https://kidsgogreen.eu/en/   </td> </tr> <tr> <td>R9</td> <td>2023</td> <td>https://datatopics.worldbank.org/sdgatlas/goal-9-industryinnovation-and-infrastructure/?lang=en</td> </tr> <tr> <td>R10</td> <td>2020</td> <td>https://www.edinburgh.gov.uk/downloads/file/29320/city-mobility-plan-2021-2030     </td> </tr> <tr> <td>R11</td> <td>2021</td> <td>https://plan4better.de/en/tutorials/isochrone/   </td> </tr> <tr> <td>R12</td> <td>2022</td> <td>https://www.eiturbanmobility.eu/wpcontent/uploads/2022/11/EIT-UrbanMobilityNext915−min−City144dpi.pdf </td> </tr> <tr> <td>R13</td> <td>2023</td> <td>https://www.nature.com/articles/s41598-023-32326-9 </td> </tr> <tr> <td>R14</td> <td>2022</td> <td>https://mobilitylab.hel.fi/app/uploads/2022/09/Digital-Twin-for-Mobilty.-Working-paper-version-9-September-2022.pdf </td> </tr> <tr> <td>R15</td> <td>2020</td> <td>https://xyzt.ai/2020/12/01/become-a-traffic-data-rockstar/ </td> </tr> <tr> <td>R16</td> <td>2022</td> <td>https://www.argaleo.com/en/oplossingen/fietsbeleid/ </td> </tr> </tbody> </table> </div> <div> </div>

openmit-licenseJun 2024View details →

Supplement - Geologic Map

For lithology, we used the geologic maps of Colton (1978) and Kellogg et al. (2008) digitizing for the study region where necessary; GIS version found here in Shapefile format.

opencc-by-4.0Feb 2024View details →

ESCO-DigComp Mapping

This dataset maps the competences of the European Digital Competence Framework (<a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128415">DigComp</a>) to the skills descriptors in <a href="https://esco.ec.europa.eu/en/about-esco/what-esco">ESCO</a>, the European classification of skills, qualifications and occupations, which is the target classification used in <a href="https://www.cedefop.europa.eu/en/tools/skills-online-vacancies">Skills-OVATE</a>, a database of European online job advertisements (OJA). This experimental mapping allows to reconcile the “demand side” of digital skills sought by employers with the “supply side” of education and training for digital skills, through the lens of DigComp, which is used in many EU digital skills initiatives at international, national and regional levels.

opencc-by-4.0Dec 2022View details →

Map 1 in Fisheries at the heart of a development issue in Mauritania: Small coastal pelagics between market logic and nutritional rationality

Map 1: Geographical areas of small pelagics fishing and landings

BIG-MAP ELN

Demonstration of the OpenSemanticLab capabilities for the documentation of battery testing demonstrated during the BIG-MAP data workshop 2024 in Grindelwald

Figure 20. Distribution maps for Paraoxypilus species, A. P. verreauxii, B. P in Review of the genera Glabromantis, Paraoxypilus, and Phthersigena (Mantodea: Nanomantidae: Fulciniinae)

Figure 20. Distribution maps for Paraoxypilus species, A. P. verreauxii, B. P. kimberleyensis.

Figure 19. Distribution maps for Paraoxypilus species, A. P. tasmaniensis, B. P in Review of the genera Glabromantis, Paraoxypilus, and Phthersigena (Mantodea: Nanomantidae: Fulciniinae)

Figure 19. Distribution maps for Paraoxypilus species, A. P. tasmaniensis, B. P. armatus.