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.
13,117
datasets available to search
ShareScore release 0.9.0
Dataset results
13,117 results for “Maps”
Swift/BAT-GUANO upper limits fits map for S231206ca
Open the record for dataset details and reuse information.
Recording provenance of workflow runs with RO-Crate (RO-Crate and mapping)
<p>RO-Crate for the manuscript that describes Workflow Run Crate, includes mapping to PROV using SKOS/SSSOM.</p>
Data and figures of SuperDARN convection maps
<p>This dataset includes SuperDARN convection maps for the analysis of the 2016-03-11 event. The convection maps are generated through the following steps:</p> <p>In the first step, we process SuperDARN RAWACF files from all available radars in the Northern Hemisphere using the make_fit routine. The resulting fitacf files contain key physical properties of SuperDARN backscatter, including power, velocity, spectral width, elevation, and associated errors. To enhance the quality of the fitacf files, we employ the fit_speck_removal routine, which effectively eliminates noise and despecks the files. </p> <p>In the second step, we generate grid files from the fitacf files. These grid files comprise geo-magnetically located line-of-sight velocity vectors. The make_grid routine is employed with default options to create grid files for all available radars in the northern hemisphere during the specified event intervals. Subsequently, we merge these individual grid files into a consolidated file using the combine_grid routine. </p> <p>In the third step, we utilize the MAP POTENTIAL packet to generate the convection map from the grid file. The map_grd routine is used to reformat the grid file into a cnvmap format file, using the default option of AACGM-v2. The map_addimf routine is used to add solar wind OMIN data to the map file. Additionally, the map_addmodel routine calculates the TS18 statistical model (Thomas and Shepherd, 2018), incorporating it into the convection map file with default options. Finally, the map_fit routine, with default options, performs spherical harmonic fitting to generate the final convection map file.</p> <p>Reference:<br>Thomas, E. G., and S. G. Shepherd (2018), Statistical patterns of ionospheric convection derived from mid-latitude, high-latitude, and polar SuperDARN HF radar observations, J. Geophys. Res. Space Physics, 123, 3196-3216, doi:10.1002/2018JA025280. </p>
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
VCF file of whole genome sequencing data of 163 rats mapped jointly to mRatBN7.2
<p>We analyzed whole genome sequencing data of 163 rats. These data were first mapped to mRatBN7.2, followed by variant calling using deepvariant and joint analysis using GLNexus. Sites that are likely called due to base-level errors in mRatBN7.2 are removed. </p>
Geo-stratigraphic map of Tsiolkovskiy crater (Moon, Far side)
<p>Geo-stratigraphic map of the lunar far side Tsiolkovskiy crater: GIS project and map sheet.</p>
Swift/BAT-GUANO upper limits fits map for S231224e
Open the record for dataset details and reuse information.
The Map Of The World Tablet - RapidCompact
Based on "The Map of the World tablet" (https://skfb.ly/6WWzY) by The British Museum is licensed under CC Attribution-NonCommercial-ShareAlike (http://creativecommons.org/licenses/by-nc-sa/4.0/). I used [rapidcompact.com](http://rapidcompact.com) optimize this excellent 3D scan, choosing default settings and a 3mb target filesize. Color texture was then rebaked separately with xNormal. Object info > Clay tablet; map of the world. > > Production date: 6thC BC (approx) > > Museum number: 92687 > > © The Trustees of the British Museum. Shared under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) licence. > > See: https://www.britishmuseum.org/collection/object/W_1882-0714-509 Source: Objaverse 1.0 / Sketchfab
Scroll Map
Ancient scroll Source: Objaverse 1.0 / Sketchfab
OSID: UAV 3D Mapping Test 0517
Pix4D DJI Mavic Pro Site:Shanghai Source: Objaverse 1.0 / Sketchfab
Egypt Map
**# Egypt map** Egypt map low poly style Source: Objaverse 1.0 / Sketchfab
USGS Topographic Map
USGS Topographic map of Tillamook, Oregon USA. Map obtaining from USGS mapping service located at https://ngmdb.usgs.gov/topoview/viewer/ Source: Objaverse 1.0 / Sketchfab
treasure map
treasure map created in 3ds map, edited in zbrush Source: Objaverse 1.0 / Sketchfab
82 Block--63k or 50k scale Maps of South Asia
<p>6 maps of the Survey of India covering block 82.</p>
63 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>98 new map files to add to the set of 63k/50k Maps of South Asia, Block 63. </p>
54 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>98 new map files to add to the set of 63k/50k Maps of South Asia, Block 54.</p>
44 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>98 new map files to add to the set of 63k/50k Maps of South Asia, Block 44.</p>
57 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>97 new map files to add to the set of 63k/50k Maps of South Asia, Block 57.</p>
64 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>98 new map files to add to ythe set of 63k/50k Maps of South Asia, Block 64.</p>
annual maps of gloabl forest management types from 2001-2020
<p>This dataset provided the annual global fine composition of forests from a management perspective. Based on multi-data sources such as MOD13Q1, human footprint, and DEM, we extracted variables such as vegetation growth characteristics, local texture information, human activities, and terrain property. Using machine learning and change detection methods, annual global maps of forest management types with a spatial resolution of 250 m were generated for the detailed composition of forests from 2001 to 2020 from a forest management perspective. Forest management types were defined into six categories: natural regeneration forests (both unmanaged and managed), plantation forests (rotation >15 years and ≤15 years), oil palm plantations, and agroforestry. Point-scale validation results indicated an overall accuracy ranging from 75.55% to 96.26%. The annual forest management type data holds significant importance in understanding the fine composition of forests.</p> <div>1:unmanaged naturally regenerated forest</div> <div>2:managed naturally regenerated forest</div> <div>3:planted forest with rotation >15 years </div> <div>4:planted forest with rotation≤15 years</div> <div>5:oil palm plantation</div> <div>6:agroforestry</div> <div>7:Others</div> <p>A research article about this dataset, please cite: <span>Hongtao Xu,</span><span>Bin He, </span><span>Lanlan Guo, </span><span>Xing Yan, </span><span>Jinwei Dong, </span><span>Wenping Yuan, </span><span>Xingming Hao, </span><span>Aifeng Lv, </span><span>Xiangqi He, </span><span>Tiewei Li. </span>Changes in the Fine Composition of Global Forests from 2001 to 2020.<span><em> J Remote Sens. </em></span>2024;4:0119.<span>DOI:<a href="https://doi.org/10.34133/remotesensing.0119">10.34133/remotesensing.0119</a></span></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.