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.
92
datasets available to search
ShareScore release 0.9.0
Dataset results
92 results for “Globe”
Future hourly wet-bulb globe temperature dataset for 842 cities in Japan
We provide projected hourly wet-bulb globe temperature (WBGT) data for 842 cities in Japan for April to October in the period 2030-2100. The projection is generated by applying prediction models built by learning the relationship between historical hourly WBGT and the daily weather indices using a machine learning method called eXtreme Gradient Boosting, to a future climate scenario data for Japan area called NIES2020. Projection data for the historical period 1980-2014 are also available.
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2018: Globe
<p>Consolidated epoch 2018 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include </p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2017: Globe
<p>Consolidated epoch 2017 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe
<p>Near real time epoch 2019 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Fibrinogen-like globe domain of human Tenascin-C (hFBG-C); A Target Enabling Package
<p>Chronic activation of the innate immune system by the damage-associated molecular pattern FBG-C (C-terminal fibrinogen-like globe domain of Tenascin-C) contributes to a variety of inflammatory diseases including arthritis, systemic sclerosis, and cancer. This TEP summarizes the first reported efforts to develop small-molecule FBG-C binders, with the aim to disrupt FBG-C-mediated pro-inflammatory protein-protein interactions (PPIs). We present the soluble expression of disulphide-containing human FBG-C (hFBG-C) in <em>E. coli</em>, the novel structure of hFBG-C, and preliminary chemical matter against hFBG-C derived from a crystallographic fragment screen. Finally, we introduce two robustly validated cellular assays, in either immortalized monocytes or primary human macrophages, which provide a route to development of small molecules which inhibit hFBG-C-activated inflammation.</p>
Wetbulb Globe Temperature
<p>This dataset contains simplified WetBulb Globe Temperature (WBGT) at hourly frequency spanning from 1979 to 2022. The variables utilized for WBGT calculation include dry-bulb temperature, humidity, and surface pressure obtained from the ERA5 reanalysis. First, an isobaric wet-bulb temperature (Tw) is computed using these variables. Then the simplified WBGT is determined through the formula WBGT*= 0.7Tw+0.3Td. More details are described in "Dawei Li*, Jiacan Yuan*, and Robert E. Kopp (2020): Escalating global exposure to compound heat-humidity extremes with warming. <em>Environmental Research Letters</em>. DOI:10.1088/1748-9326/ab7d04". Please cite this article when using this dataset.</p> <p>WBGT-ERA5-v2.0 is an updated version for WBGT-ERA5-v1.1. In the version of WBGT-ERA5-v1.1, an assumption was made that RH = q/qs, where saturation specific humidity (qs) was considered equal to the saturation mixing ratio rs. In the version of WBGT-ERA5-v1.2, we calculate RH and qs exactly following their original definitions: RH = e/es (where e is vapor pressure and es is saturated vapor pressure), and qs = rs/(1+rs). The updates will slightly improve the precision of the wet-bulb temperature estimation under high-temperature condition </p>
Music Informatics for Radio Across the GlobE (MIRAGE) MetaCorpus (v0.2)
<h1>Overview</h1> <p>Welcome to the <strong><em>Music Informatics for Radio Across the GlobE</em></strong> (<em><strong>MIRAGE</strong></em>) <strong><em>MetaCorpus</em></strong>. The current (v0.2) development release consists of metadata (e.g., artist name, track title) and musicological features (e.g., instrument list, voice type, tempo) for 1 million events streaming on 10,000 internet radio stations across the globe, with 100 events from each station. </p> <p>Users who wish to access, interact with, and/or export metadata from the MIRAGE-MetaCorpus may also visit the MIRAGE online dashboard at the following url:</p> <ul> <li><a href="https://pearl-laboratory.github.io/mirage-mc/" target="_blank" rel="noopener">https://pearl-laboratory.github.io/mirage-mc/</a></li> </ul> <h1>Attribution</h1> <p>The current MIRAGE-MetaCorpus is available under a CC4 license. Users may cite the dataset here:</p> <blockquote> <p>Sears, David R.W. “Music Informatics for Radio Across the Globe (MIRAGE) Metacorpus -- 2024”. Zenodo, July 19, 2024. <a href="https://doi.org/10.5281/zenodo.12786202" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12786202</a>.</p> </blockquote> <p>Users accessing the MIRAGE-MetaCorpus using the online dashboard should also cite the following ISMIR paper:</p> <blockquote> <p>Ngan V.T. Nguyen, Elizabeth A.M. Acosta, Tommy Dang, and David R.W. Sears. "Exploring Internet Radio Across the Globe with the MIRAGE Online Dashboard," in <em>Proceedings of the 25th International Society for Music Information Retrieval Conference </em>(San Francisco, CA, 2024). </p> </blockquote> <h1>Data Sources</h1> <p>This repository of the MIRAGE-MetaCorpus contains 81 metadata variables from the following open-access sources:</p> <ul> <li>Radio Garden (RG) -- <a href="https://radio.garden" target="_blank" rel="noopener">https://radio.garden</a></li> <li>Natural Earth map data set (NE) -- <a href="https://www.naturalearthdata.com/" target="_blank" rel="noopener">https://www.naturalearthdata.com/</a></li> <li>Internet Radio Station Stream Encoder (SE)</li> <li>Annotator Review (AR)</li> <li>Monitoring/Matching Algorithm (MA)</li> <li>WikiData (WD) -- <a href="https://www.wikidata.org" target="_blank" rel="noopener">https://www.wikidata.org</a></li> <li>MusicBrainz (MB) -- <a href="https://musicbrainz.org/" target="_blank" rel="noopener">https://musicbrainz.org/</a></li> </ul> <p>Each event also includes attribution metadata from the following commercial sources:</p> <ul> <li>Spotify (SP) -- <a href="https://open.spotify.com/" target="_blank" rel="noopener">https://open.spotify.com/</a> <ul> <li>Note that users may examine an additional 19 metadata variables on the MIRAGE online dashboard that were obtained from the Spotify API.</li> </ul> </li> <li>Musixmatch (MX) -- <a href="https://www.musixmatch.com/" target="_blank" rel="noopener">https://www.musixmatch.com/</a></li> <li>YouTube (YT) -- <a href="https://www.youtube.com/" target="_blank" rel="noopener">https://www.youtube.com/</a></li> <li>Genius (GE) -- <a href="https://genius.com/" target="_blank" rel="noopener">https://genius.com/</a></li> <li>AZlyrics (AZ) -- <a href="https://www.azlyrics.com/" target="_blank" rel="noopener">https://www.azlyrics.com/</a></li> </ul> <h1>Data Sets</h1> <p>The metadata reflect information about each event's location (e.g., city, country), station (name, format, url), event (id, local time at station, etc.), artist (name, voice type, etc.), and track (e.g., title, year of release, etc.). For that reason, the MIRAGE-MetaCorpus includes the following datasets:</p> <ul> <li>MIRAGE.csv -- the complete metacorpus (1 million)</li> <li>events.csv -- all event-level metadata (1 million)</li> <li>tracks.csv -- all track-level metadata (414,886)</li> <li>artists.csv -- all artist-level metadata (259,783)</li> <li>stations.csv -- all station-level metadata (10,000)</li> <li>locations.csv -- all location-level metadata (4,324)</li> </ul> <p>A subset of the MIRAGE-MetaCorpus is also available for events with metadata from online music libraries that reliably matched the event's description in the radio station's stream encoder:</p> <ul> <li>MIRAGE_reliable.csv (473,850)</li> <li>events_reliable.csv (473,850)</li> <li>tracks_reliable.csv (204,969)</li> <li>artists_reliable.csv (80,005)</li> <li>stations_reliable.csv (9,284)</li> <li>locations_reliable.csv (4,142)</li> </ul> <h1>Contact</h1> <p>If you are a copyright owner for any of the metadata that appears in the MIRAGE-MetaCorpus and would like us to remove your metadata, please contact the developer team at the following email address: <a href="mailto:miragedashboard@gmail.com" target="_blank" rel="noopener">miragedashboard@gmail.com</a> </p>
Datasets for figures in Implementation and evaluation of Wet Bulb Globe Temperature within non-urban environments in the Community Land Model version 5
<p>The files contain 4 scripts and 6 netcdf files. </p> <p>"laborCap_200400.ncl" uses "Lancet_LRF.nc" to create Figure 1.</p> <p>Script "world_plot_ensemble_Avg.I2000.csh", drives a NCL script, "plot_modern.I2000.WBGT.ncl" to make figures 3 and 4, using the netcdf files, "I2000_PR_22_x1_60_5.exceed.WBGT.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BG_R.20yrs.75_99.nc," "I2000_PR_22_x1_60_5.exceed.WBGT_BC_R.20yrs.75_99.nc," and "I2000_PR_22_x1_60_5.exceed.WBGT_AC_R.20yrs.75_99.nc."</p> <p>"heatmap.wbgt.v4.org.ncl" uses netcdf "I2000_PR_23_Chicago_x1_60_1.11-17.Chicago.allvars.nc" to create figures 5-7. </p>
NASA GLOBE Cloud GAZE Test Dataset
<p>NASA GLOBE Community science project Leveraging Online and User Data through GLOBE And Zooniverse Engagement, or CLOUD GAZE is a NASA funded pilot project aimed to help NASA better understand the effect clouds are having on Earth’s climate. The CLOUD GAZE project is a collaboration between two giants of citizen science:<a href="https://www.globe.gov/"> The GLOBE Program</a> and the<a href="https://www.zooniverse.org/"> Zooniverse</a> online platform and is funded through NASA’s Citizen Science for Earth Systems Program. The CLOUD GAZE citizen science project characterizes cloud properties from sky photographs sent in through GLOBE Clouds ground observations. The GLOBE Clouds/CLOUD GAZE team at NASA Langley Research Center extracts cloud properties from sky photographs submitted to the GLOBE Program using the Zooniverse online platform.</p> <p>The team produces datasets from three sources: ground-cloud observations from The GLOBE Program collocated with NASA/NOAA satellite data and the CLOUD GAZE cloud cover and cloud type characterizations. The datasets are for cloud type worldwide investigations and serve as training sets for machine learning. This data is provided as CSV files. </p> <p><a href="https://www.globe.gov/documents/16792331/0/Summary+Data+Variables+CLOUD+GAZE_2.0.docx/388b8c8f-e869-148f-31c2-78f2d005f38d?t=1654531372682">NASA GLOBE CLOUD GAZE Data Description</a></p> <p>The data obtained from the Zooniverse, NASA Langley Research Center (NASA LaRC), and The GLOBE Program are free of charge for use in research, publications, and commercial applications. When data from The Zooniverse, The GLOBE Program, and NASA LaRC are used in a publication, we request this acknowledgment be included, "These data were obtained from the Zooniverse online platform, the GLOBE Program and NASA Langley Research Center." Please include such statements, either where the use of the data or other resource is described, or within the Acknowledgements section of the publication.</p>
Dataset for Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe
<p>This dataset is used in the following study: "Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe."</p> <p>Dataset includes:</p> <ul> <li>Dry and wet spells for the Northern and the Southern Hemisphere respectively.</li> <li>The output of the statistical model for each season (MJJASO/NDJFMA) and for each spell type (dry/wet) for both the hemispheres (NH/SH).</li> </ul> <p>File naming used belongs to mainly two categories: one for naming spell file, and second for naming the output file from the statistical model. An example from each category is explained below. The rest of the files follow the same naming style.</p> <ol> <li><em>Spell file</em>;<strong> NH_1.0mm_dry_spells_all_months_gap_1_days_no_spell2_check.nc</strong>: Northern Hemisphere 1.0mm threshold dry spell for all months with a gap of 1 day</li> <li><em>Statisical model output file</em>; <strong>NH_weibull_MJJASO_drythresh_1_min_spell_count_40_time_steps_min_spell_length_5D_1D_N_1980_2016.nc</strong>: Northern Hemisphere Weibull model output for MJJASO using dry threshold of 1.0 mm with a minimum spell count of 40 time-steps and a minimum spell length of 5 days for the period 1980-2016</li> </ol> <p>This dataset alone is sufficient for reproducing the analysis presented in the study. Open source tools like Python, R, etc can be used to read '.nc' file type. Additional code help in the form of Jupyter notebooks reproducing figures made from this dataset can be viewed <a href="https://github.com/avatar101/RRWPS-extremes">here on GitHub.</a></p>
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
Electron Accepting Capacities of a wide variety of peat materials from around the Globe similarly explain CO2 and CH4 production
<p>In peat soils the availability of terminal electron acceptors (TEAs), both inorganic and organic, largely determines the ratio of carbon dioxide to methane formation under waterlogged, anoxic conditions. The redox properties of peat organic matter and their relationship with anoxic carbon mineralization are yet only investigated for a limited amount of peat and reference materials, although electron accepting capacities of organic matter (EACOM) largely predominate over canonical inorganic TEAs in peatlands. To address this knowledge gap, we incubated 60 peat samples from four different depths of 15 sites located in five major peatland regions (including Germany, Sweden, Russia, France and Chile) distributed around the globe covering a variety of both bog and fen type samples and characterized their capacities to serve as electron acceptors for anaerobic carbon dioxide production.<br> The dataset consists of a wide variety of recorded and calculated variables for a 56-day incubation of those samples. These variables include the formation and rates of methane, carbon dioxide, electron acceptor capacities and electron donator capacities at two different times, data on stable isotopes in delta notation (such as nitrogen, carbon and sulfur), molar element ratios for carbon/nitrogen, carbon/sulfur and nitrogen/phosphorus and elemental contents like silicon, phosphorus, sulfur, calcium and iron as well as specific fourier transformed infrared spectroscopy ratios regarding the ratios of polysaccharides and aromatic structures. The dataset was created mostly in 2019, with some additional measurements carried out in 2020 and 2021. </p>
Supplementary material for "Oikopleura dioica, the cosmopolitan appendicularian hides multiple cryptic species around the globe"
<p>18S and ITS sequences used to estimate phylogenetic trees for "Oikopleura dioica, the cosmopolitan appendicularian hides multiple cryptic species around the globe." To identify 18S rDNA genes from genome sequences, we searched genome sequences for the best-hit matches to the Rfam models for the eukaryotic small subunit (SSU; RF01960) and large subunit (LSU; RF02543) using cmsearch of the Infernal package (v.1.1.4). The SSU and LSU models from Rfam were downloaded in April 2021. The internal transcribed spacer (ITS) regions were obtained by extracting the region between SSU and LSU.<strong> </strong>Multiple sequence alignments for the 18S and ITS regions were created with MUSCLE (v5) within Seaview (v.3.2).</p>
Globe At Night - Community Health
Results of the community health analysis of the Globe At Night project (January 2016 - December 2018)
Data from: Early life microbial succession in the gut follows common patterns in humans across the globe
Open the record for dataset details and reuse information.
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2015: Globe
<p>Base epoch 2015 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Braille globe
A beautiful handmade Braille globe by the 'godfather of blind children', Frank Tunley. Mr Tunley dedicated his life to improving the lives of visually impaired children and adults through the production of braille globes and maps as well as models, toys, doll houses and games. Read more: http://blogs.slq.qld.gov.au/jol/2014/01/14/new-accession-narbethong-house-photographs/ Source: Objaverse 1.0 / Sketchfab
terrestrial globe
As one of my other uploads I created this litte globe for the room of the evil queen from the snow white fairy tale project. It is used in a VR-experience my colleagues and I created. If you download my work and use it in one of your projects - please let me know and mention me in your credits. I want to know in what amazing projects this globe may be used. Source: Objaverse 1.0 / Sketchfab
Earth globe
Johann Gabriel Doppelmayr (1677–1750) 1728, Norymberga Jagiellonian University Museum Collegium Maius Inventory number: 4119; 119/V https://muzea.malopolska.pl/en/objects-list/2727 Source: Objaverse 1.0 / Sketchfab
Christmas Globe
I wanted to make something for christmas, but It turned out to be a busy month. I started this project off early in the month, but I havn't been able to finish it untill now. I wanted to do PBR-materials on it, but that would've taken too long since I'm a beginner with PBR painting. Hope you enjoy it anyways! Merry christmas, everyone!! Don't be afraid to give me feedback on my work! It's one of the best ways to get better! Source: Objaverse 1.0 / Sketchfab
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.