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.
3,937
datasets available to search
ShareScore release 0.9.0
Dataset results
3,937 results for “number”
The number of realizations of all Laman graphs with at most 12 vertices
<p>This data set consists of files for all Laman graphs (minimally rigid graphs) with at most 12 vertices and files for their Laman numbers (number of complex relaizations).</p> <p>The data is computed by a combinatorial algorithm of Capco, Gallet, Grasegger, Koutschan, Lubbes and Schicho (see <a href="https://doi.org/10.1137/17M1118312">10.1137/17M1118312</a> for a description and <a href="https://doi.org/10.5281/zenodo.1245506">10.5281/zenodo.1245506</a> for an implementation).</p>
Dataset for "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements"
<p>This repository contains the source data for the manuscript "<strong>Method to retrieve cloud condensation nuclei number concentrations using lidar measurements</strong>" published in <em>Atmospheric Measurement Techniques</em>. In situ measured data from five filed campaigns and corresponding theoretical simulated CCN number concentrations, lidar extinction and backscatter are included.</p>
Dataset for Variation in number agreement in Finnish dialects
<p>This material contains the dataset and the scripts from the <a href="https://version.helsinki.fi/gramadapt/verbinlukukongruenssi2021">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius & Viljami Haakana 2021. <a href="https://researchportal.helsinki.fi/files/164044959/SinnemakiHaakana_2021_Kongruenssi.pdf">Variationistinen korpustutkimus predikaatin differentiaalisesta lukukongruenssista ja substantiiviluokasta suomen murteissa</a>. In Leena Maria Heikkola, Geda Paulsen, Katarzyna Wojciechowicz & Jutta Rosenberg (eds.), <a href="https://www.doria.fi/handle/10024/181090"><em>Språkets funktion: Juhlakirja Urpo Nikanteen 60-vuotispäivän kunniaksi - Festskrift till Urpo Nikanne på 60-årsdagen - Festschrift for Urpo Nikanne in honor of his 60th birthday</em></a>, 83–112. Åbo: Åbo Akademi Förlag.</p>
Number of BEV (a battery electric vehicle) and PHEV (a plug-in hybrid electric vehicle) vehicles for each Country (2019)
<p>According to the Global E.V. Outlook 2020, China ranks first in vehicles in operation with electric or hybrid engines. In second place in the U.S. and third place in Norway.</p>
Aggregated number of officially recorded COVID-19 deaths in 2020 in Poland by county (powiat) with indication of sources
<p>This is a complimentary dataset for the article "Deaths during the first year of the COVID‑19 pandemic: insights from regional patterns in Germany and Poland" by Myck, Oczkowska, Garten, Krol, Brandt in BMC Public Health (DOI: 10.1186/s12889-022-14909-9).</p>
Dataset for the Underestimation of the Number of Didden Objects
<p>Data analysis of 5 experiments in the study of "Underestimation of the Number of Hidden Objects".</p>
Ultrafine particle number concentration observations perpendicular to a main street in Berlin, Germany, in summer 2017
<p>The data set was recorded in summer 2017 in Berlin, Germany. It was the basis of the paper:</p> <p>Fritz, S., Schubert, S., and Schneider, C. (2021). Measurements of spatial variability of sub-micron particle number concentrations perpendicular to a main road in a built-up area. metz. 30, 315–331. doi:10.1127/metz/2021/1058</p> <p>The measurement setup, study site, and devices used are described more detailed in the paper.</p> <p>The data sets uploaded include observations of particle number concentrations, wind direction and wind speed at ground level as well as traffic count data. The time stamp is in local time for Berlin/Germany.</p> <p>The data sets:</p> <p>coordinates.csv contains the coordinates of the measurement points used in file obs_pnc_wind. The column ‘distance’ links these two files. The values in the column ‘distance’ are the distance from the curb of the main road. The distance ‘-10’ represents the measurement point on the central strip of the main road. The two columns ‘latitude’ and ‘longitude’ provide the coordinates of the measurement points.</p> <p>obs_pnc_wind contains the observation data for particle number concentration (PNC), wind speed, and wind direction. PNC was recorded in #/cm³ with a TSI 3007 CPC at a 1-second resolution. Wind speed `ws’ [m/s] was recorded with a KESTREL 5000 with a 2-second resolution. Wind direction ‘wdir’ [°] was manually recorded as the most prominent wind direction within the 3-minute measurements per measurement point in angles of 10°. Wind direction was changed to NA (wdir_comment > 0), if it could not be determined either due to too calm wind (wdir_comment = 1) or with continuously changing wind direction (wdir_comment = 2). Along the measurement route, sometimes additional sources occurred and were recorded in the variable ‘source_type’ at the time they passed by the measurement device. Variable ‘source_comment’ provides additional information about ‘source_type’ where necessary.</p> <p>traffic_count contains the vehicles counted on the main street (Straße des 17. Juni) for each of the 72 runs. It was calculated as [vehicles/hour] based on 5-minute traffic counts. During the traffic counts, we differentiated between smaller (light duty) vehicles (<3.5 t, cars, vans, motorcycles, scooters) and bigger (heavy duty) vehicles (> 3.5 t, trucks and busses).</p>
Global Biodiversity Information Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130
<p>The Global Biodiversity Information Facility (GBIF) indexes thousands of biodiversity datasets from Natural History Collections, citizen science initiatives (e.g., iNaturalist, eBird), and other sources. As part of the index process, GBIF associates at least two identifiers with indexed records: a record id (aka gbifID) and a dataset id (aka dataset key). These ids are central to do lookup, reference data, and package interpreted data products.</p> <p>This publication contains an exhaustive list of GBIF IDs and ids associated by their data providers as derived from:</p> <p>GBIF.org (01 March 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.pk3trq</p> <p>The resource (size: ~260GB) provided by GBIF had content id hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 and was used to generate the resource included in this publication using</p> <pre><code class="language-bash">preston cat 'zip:hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97!/0015281-230224095556074.csv'\ | cut -f 1,2,3,37,38,39\ | gzip\ > gbifid.tsv.gz </code></pre> <p>with the content id of gbifid.tsv.gz (size: ~35GB) being hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8 .</p> <p>the first 10 lines of gbifid.tsv.gz as extracted via</p> <pre><code>preston cat --remote https://zenodo.org/record/7789866/files,https://linker.bio hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8\ | gunzip\ | head</code></pre> <p>are:</p> <pre><code>gbifID datasetKey occurrenceID institutionCode collectionCode catalogNumber 2997162320 c71c8000-9fc7-422c-804a-ce6abe751771 3399442 CEPEC CEPEC CEPEC00109669 2997162309 c71c8000-9fc7-422c-804a-ce6abe751771 2733085 CEPEC CEPEC CEPEC00000818 2997162317 c71c8000-9fc7-422c-804a-ce6abe751771 2733086 CEPEC CEPEC CEPEC00000888 2997162313 c71c8000-9fc7-422c-804a-ce6abe751771 3399443 CEPEC CEPEC CEPEC00109744 2997162306 c71c8000-9fc7-422c-804a-ce6abe751771 2733087 CEPEC CEPEC CEPEC00000889 2997162316 c71c8000-9fc7-422c-804a-ce6abe751771 3399440 CEPEC CEPEC CEPEC00109605 2997162324 c71c8000-9fc7-422c-804a-ce6abe751771 2733088 CEPEC CEPEC CEPEC00000890 2997162308 c71c8000-9fc7-422c-804a-ce6abe751771 3399441 CEPEC CEPEC CEPEC00109615 2997162303 c71c8000-9fc7-422c-804a-ce6abe751771 2733089 CEPEC CEPEC CEPEC00000891</code></pre> <p>Note that at time of writing, the html resource associated with the occurrence id 2997162320, and data set key c71c8000-9fc7-422c-804a-ce6abe751771 (extracted from of the first data row example above) are available via:</p> <p>https://gbif.org/occurrence/2997162320</p> <p>and</p> <p>https://gbif.org/dataset/c71c8000-9fc7-422c-804a-ce6abe751771</p> <p>respectively.</p> <p>This resource was initially created to help integrate with Bionomia (https://bionomia.net) to help associate people identifiers provided by bionomia to their original records via their GBIF ids. Bionomia re-uses GBIF records ids as a way to define links between records and the people (e.g., curators, collectors, identifiers) that worked on them. </p> <p>In other words, this resource provides a versioned translation table from the GBIF data universe (as defined by GBIF record ids, and dataset keys) to the data collections that exist (and evolve) independent of it. </p> <p>Note that the resource identified by hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 was not included in this publication it was too big (260GB) to fit. You may be able to retrieve the resource from its original location at https://api.gbif.org/v1/occurrence/download/request/0015281-230224095556074.zip .</p>
Venus visco-elastic Love numbers
<p>Supporting software and data for the manuscript <strong>Constraining the Venus interior structure with future VERITAS measurements of the gravitational atmospheric loading </strong></p> <p>https://doi.org/10.3847/PSJ/acc73c</p> <p> </p> <p>See README.pdf for additional details. </p>
EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study
<p>Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords.</p> <p>This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal <em>Acta Psychologica</em> (https://doi.org/10.1016/j.actpsy.2023.103942)</p>
Difference and number of works published over the years grouped by the objective of the generative process
<p>Difference and number of works published over the years grouped by the objective of the generative process. Part of the study "What do we mean by GenAI?"</p>
Number of works grouped by AI technique employed
<p>Number of works grouped by AI technique employed. Part of the study "What do we mean by GenAI?"</p>
Number of works grouped by objective, domain and AI technique employed
<p>Number of works grouped by objective, domain and AI technique employed. Part of the study "What do we mean by GenAI?"</p>
Number of works published over the last five years
<p>Number of works published over the last five years. Part of the study "What do we mean by GenAI?"</p>
Number of works grouped by domain of application
<p>Number of works grouped by domain of application. Part of the study "What do we mean by GenAI?"</p>
Number of works published over the years grouped by generated content type
<p>Number of works published over the years grouped by generated content type. Part of the study "What do we mean by GenAI?"</p>
Hardware-biased quantum random numbers
<p>Bitstring from a hardware-biased quantum random number generator. The data is used in the numerical experiments for the publication "On the effects of biased quantum random numbers on the initialization of artificial neural networks" (<a href="http://arxiv.org/abs/2108.13329">arXiv:2108.13329</a>).</p> <p> </p>
CSM01 Seasonal Summary of Numbers of Small Mammals on 14 LTER Traplines in Prairie Habitats at Konza Prairie (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/88/8. The abstract below was extracted from the Level 0 data package and is included for context: Data set contains seasonal summaries (spring and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each grassland trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during two 4-day trapping periods in spring (late February to early April) and autumn (early October to mid-November) for each of 14 permanent traplines established on seven fire-grazing treatments (two traplines per treatment). These seven fire-grazing treatments include three sites that are grazed by bison (1 unburned, 1 annual burn and 1 4-year burn) and four sites that are not grazed by bison (1 unburned, 1 annual burn and 2 4-year burn).
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/339/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/26/11. The abstract below was extracted from the Level 0 data package and is included for context: Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/325/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed
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.