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.
1,291
datasets available to search
ShareScore release 0.9.0
Dataset results
1,291 results for “gap”
FIGURE 6 in Leuconidae Sars, 1878 (Crustacea: Cumacea) from the southwestern Atlantic, filling the distribution gap along the Brazilian coast
FIGURE 6. Leucon (Crymoleucon) pseudograndidentatus sp. nov. Paratype (lost in the fire): mature female, 5 mm, #HAB3 B08, MNRJ 28943. Habitus (pseudorostrum broken), antenna 1–2, mandible, maxilla 1–2 and maxillipeds.
FIGURE 11 in Leuconidae Sars, 1878 (Crustacea: Cumacea) from the southwestern Atlantic, filling the distribution gap along the Brazilian coast
FIGURE 11. Eudorella helenae sp. nov. Holotype: habitus without appendages and carapace with zoom; mature female, 2.8 mm, #SANSED6 A03-R2 (500), MNRJ 30572.
FIGURE 14 in Leuconidae Sars, 1878 (Crustacea: Cumacea) from the southwestern Atlantic, filling the distribution gap along the Brazilian coast
FIGURE 14. Gyneleucon tripedium sp. nov. Carapace, antenna 1–2 (antenna 2 exceptionally folded forward), mandible, maxilla 1–2and maxilliped 2–3: mature male, 1.9 mm, #AMB14 F3, MNRJ 28968.
FIGURE 13 in Leuconidae Sars, 1878 (Crustacea: Cumacea) from the southwestern Atlantic, filling the distribution gap along the Brazilian coast
FIGURE 13. Eudorella helenae sp. nov. Paratype (lost in the fire): mature female, 3.1 mm, HAB11 A3, MNRJ 28957. Habitus, pereopods and uropod (peduncle of uropod, dorsodistal seta broken, dashed line).
FIGURE 10 in Leuconidae Sars, 1878 (Crustacea: Cumacea) from the southwestern Atlantic, filling the distribution gap along the Brazilian coast
FIGURE 10. Leucon (Macrauloleucon) watlingi sp. nov. Paratype (lost in the fire): mature female, 4.9 mm, #HAB7 H6, MNRJ 28904. Pereopod 1 and uropod. Paratype: mature female, 4.2 mm, #SANSED1 B09-R1(0–2), MNRJ 30552. Pereopod 2–5.
FIGURE 9 in Leuconidae Sars, 1878 (Crustacea: Cumacea) from the southwestern Atlantic, filling the distribution gap along the Brazilian coast
FIGURE 9. Leucon (Macrauloleucon) watlingi sp. nov. Paratype (Lost in the fire): mature female, 4.9 mm, #HAB7 H6, MNRJ 28904. Habitus, antenna 1–2, mandible, maxilla 1–2 and maxillipeds.
LGHAP v2: Global daily 1-km gap-free AOD grids (2010)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2017)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2020)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2001)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2013)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2015)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
Primitive groups of degree 4096 to 8191 for PrimGrp in GAP
<p>This database contains the primitive groups of degree 4096 to 8191. It is intended to be used in GAP via the PrimGrp library (https://gap-packages.github.io/primgrp/).</p> <p>The groups were originally computed by Benjamin Mark Stratford and reported in Chapter 3 of the following PhD thesis:</p> <p>Benjamin Mark Stratford,<br>Topics in computational group theory relating to classifications of permutation groups. <br>PhD thesis, University of Warwick, 2022.<br>(http://webcat.warwick.ac.uk/record=b3821786)</p> <p>Additional information such as the order, simplicity, solubility, O'Nan-Scott type, collected suborbits, transitivity, and socle type have also been computed for the convenience of the user, and to facilitate identification of an arbitrary primitive group. The primitive identification number of each group has been assigned according to the ordering in which the data was originally provided. Along with the generators of the group, this information is stored as a GAP record object.</p> <p>In order to use this data, download the file "ExtendedPrimitiveGroupsData.tar" into the "data" directory of the PrimGrp library. Untar the file in this directory. This should result in a subdirectory called "ExtendedPrimitiveGroupsData" which contains files of the format "PrimitiveGroups_N_ID.g.gz" where N is the degree of the permutation group and ID is its primitive identification. Do not decompress the files of the format "PrimitiveGroups_N_ID.g.gz". Do not move them into the "data" directory, but rather leave them in the "ExtendedPrimitiveGroupsData" subdirectory.</p> <p>Note that the extended primitive groups data is reasonably large (~1GB) and ensure you have sufficient space available on your computer.</p> <p>PrimGrp version 4.0.0 or later is required. For more information about how to use the primitive groups library in GAP see the PrimGrp documentation (https://gap-packages.github.io/primgrp/)</p>
Observation of low-energy sub-gap states in a supramolecular electron spin lattice on superconducting Pb(111)
<p>Data set of the manuscript "Gate-tunable topological superconductivity in a supramolecular electron spin lattice"</p>
The Gap that Matilda will bridge: a look at the Colombian case
<p><span>Objective: To determine gender gaps in Emeritus researchers in Colombia.<span> </span></span></p> <p><span>Methodology: Oaxaca-Blinder-Kitakagwa decomposition model, correcting the sample selection bias with the inclusion of Mills' inverse ratio (Heckman's Lambda) through an ordered <em>probit</em> model. Data: Information available in the ScienTI Platform – Colombia during the period 2015-2021 </span></p>
Critical Gaps in Medical Research Reporting by Online News Media: Dataset
<p>Dataset for <strong>Critical Gaps in Medical Research Reporting by Online News Media</strong></p>
Observation of carrier cooling in direct band gap hexagonal silicon-germanium
Open the record for dataset details and reuse information.
Results of the community survey associated with manuscript "Knowledge gaps in quantifying the climate change response of biological storage of carbon in the ocean" by Henson et al.
<p><span><span>Full anonymised results of the community survey are available as part of the Supporting Information (Data Set S1)</span></span> for manuscript "Knowledge gaps in quantifying the climate change response of biological storage of carbon in the ocean" by Stephanie Henson, Chelsey A. Baker, Paul Halloran, Abigail McQuatters-Gollop, Stuart Painter, Alban Planchat, Alessandro Tagliabue</p> <p><strong> </strong></p>
Band gap formation in commensurate twisted bilayer graphene/hBN moiré lattices
Open the record for dataset details and reuse information.
Data for: Identifying the knowledge and capacity gaps in Southeast Asian insect conservation
<p>Datasets used in the paper "Identifying the knowledge and capacity gaps in Southeast Asian insect conservation".</p> <p>Curated datasets are provided in <em>Curated_Datasets.zip</em> and consists of (1) Insect occurrence, (2) primary literature, (3) authorship, (4) funding, and (5) Twitter data. Refer to <em>README_curated_datasets.txt</em> for more information on each dataset and their term definitions.</p> <p>Raw insect occurrence data from GBIF are provided in <em>GBIF_Raw_Insect_Occurrences.zip</em>. Refer to <em>README_GBIF_Raw_Insect_Occurrences.txt</em> for more information on each dataset and their term definitions.</p> <p>Data and models used in the authors' spaCy classifier analysis are provided in <em>spaCy_Data.zip</em> and <em>spaCy_Model_Best.zip</em>. Refer to <em>README_spacy.txt</em> for more information on each file and term definitions of the datasets used.</p> <p>Accompanying codes used to produce the figures and conduct the analyses are available in Github (https://github.com/teelabntu/SEA_Insect_Conservation).</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.