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.
18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “GREAT project”
Projected Snow Cover Reductions and Mid-latitude Cyclone Responses in the North American Great Plains, 1986 - 2005
Extratropical cyclones are responsible for major weather events and trends in the mid-latitudes and preferentially develop in regions of enhanced cyclogenesis and proceed along climatological storm tracks. It has been shown that terrestrial snow cover exerts considerable influence on atmospheric baroclinicity which is largely responsible for the aforementioned cyclogeneses and storm tracks. Research about the effect which terrestrial snow cover exerts on cyclones' intensities, trajectories, and precipitation characteristics is limited but indicates a robust relationship with these factors. Many examinations of climate model projections have generally shown a poleward shift in storm tracks by the late 21st century though none have determined the degree to which the coincident poleward shift in snow extent is responsible. A method of imposing 10th, 50th, and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 models of the Coupled Model Intercomparison Project Phase Five (CMIP5) is used to alter 20 historical cold season cyclones which tracked over or adjacent to the North American Great Plains. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) are initialized at 0 to 4 days prior to cyclogenesis. Including control and sensitivity testing wherein snow is unaltered or removed entirely, each cyclone case is simulated 25 times for a total of 500 simulations.
GEM-Hydro gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides gridded model simulations in NetCDF format over the Lake Erie using the GEM-Hydro model done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The data are produced with SPS (GEM-Surf + SVS, the surface component of GEM-Hydro) open-loop runs with the SVS calibrated parameters obtained during GRIP-E project. For more information on the model and on calibration methodology, see GEM-Hydro section in Mai et al. 2020 (in prep.).</p> <p>The original model outputs had all variables accumulated for each day. During post-processing all variables have been de-accumulated by subtracting the accumulation of the previous hour from the accumulation of the current hour. Two variables (ALAT and O1) are also only valid over the land tile of each grid cell. Two additional variables (ALAT_full and O1_full) valid now over the whole grid cell have been added for convenience of the users.</p> <p><strong>Domain boundaries (WGS84 system): </strong><br> - lon_min = -85.5, lon_max = -77.94<br> - lat_min = 40.3, lat_max = 44.26</p> <p><strong>Resolution of model variables provided:</strong><br> - spatial: ~10km x 10km <br> - temporal: hourly </p> <p><strong>Simulation period:</strong><br> - 01 Jan 2011 - 31 Dec 2014 <br> - 01 Jan 2010 - 31 Dec 2010 (warm-up)</p> <p><strong>Meteorological input data:</strong><br> - RDRS-v1; see Mai et al. 2020 (in prep)</p> <p><strong>Variables available:</strong><br> float <strong>PR_0</strong>(time, rlat, rlon) ;<br> PR_0:units = "m" ;<br> PR_0:long_name = "Quantity of precipitation (valid over whole grid cell)" ;<br> float <strong>AHFL_0</strong>(time, rlat, rlon) ;<br> AHFL_0:units = "mm" ;<br> AHFL_0:long_name = "Surface evaporation (valid over whole grid cell)" ;<br> float <strong>TRAF_60268832</strong>(time, rlat, rlon) ;<br> TRAF_60268832:units = "mm" ;<br> TRAF_60268832:long_name = "Surface runoff (valid over whole grid cell)" ;<br> float <strong>ALAT_0</strong>(time, rlat, rlon) ;<br> ALAT_0:units = "mm" ;<br> ALAT_0:long_name = "Accumulation of total soil lateral flow (valid over land tile of grid cell)" ;<br> float <strong>ALAT_0_full</strong>(time, rlat, rlon) ;<br> ALAT_0_full:units = "mm" ;<br> ALAT_0_full:long_name = "Accumulation of total soil lateral flow (valid over whole grid cell)" ;<br> float <strong>O1_0</strong>(time, rlat, rlon) ;<br> O1_0:units = "mm" ;<br> O1_0:long_name = "Accumulation of base drainage (valid over land tile of grid cell)" ;<br> float <strong>O1_0_full</strong>(time, rlat, rlon) ;<br> O1_0_full:units = "mm" ;<br> O1_0_full:long_name = "Accumulation of base drainage (valid over whole grid cell)" ;<br> float <strong>WT_59868832</strong>(time, rlat, rlon) ;<br> WT_59868832:units = "1" ;<br> WT_59868832:long_name = "Fraction of grid cell covered with land" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. </p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based bias adjusted potential evapotranspiration
<p>Potential evapotranspiration calculated from the UKCP18 RCM ensemble using the Penman-Monteith method as implemented by Robinson et al. (2017) and bias adjusted using Lange et al. (2019). This dataset was used for analysis of future drought characteristics in Reyniers et al. (2022). Details on the bias adjustment of this potential evapotranspiration dataset, as well as bias-adjusted precipitation and temperature from the same climate model ensemble, can be found in Reyniers et al. (2025).</p> <p>---</p> <p>Reyniers, N., Osborn, T. J., Addor, N., and Darch, G.: Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index, Hydrol. Earth Syst. Sci., 27, 1151–1171, https://doi.org/10.5194/hess-27-1151-2023, 2023.</p> <p>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025. </p> <p>Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J., Rudd, A. C. (2017). Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. HESS, <em>21</em>(2), 1189-1224.</p> <p>Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). <em>GMD,</em> <em>12</em>(7), 3055-3070.</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based SPI and SPEI data
<p>Standardised Precipitation Index (SPI; McKee et al., 1993) and Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2009) computed from UKCP18 Strand 3 simulations (Met Office Hadley Centre, 2018).</p> <p>This data was produced for the study by Reyniers et al. (<em>in prep</em>) analysing (diferences in) drought projections using these indicators. The methodology used to produce this data can be found there if/when the paper is accepted, however do not hesitate to reach out with any further questions. Please note the RCM data was bias adjusted prior to SPI and SPEI computation. There is one file per ensemble member containing the full simulated period on a monthly time step, using aggregation periods of 1, 3, 6, 12, 24 and 36 months for the computation of SP(E)I.</p> <p><strong>References</strong></p> <p>McKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, pp. 179–183, Boston, 1993</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. Centre for Environmental Data Analysis, <em>date of citation</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Reyniers, N., Osborn, T. J., Addor, N., Darch, G.: Projected changes in droughts and extreme droughts in Great<br> Britain are strongly influenced by the choice of drought index. Hydrology and Earth System Sciences, <em>in prep. for HESS</em></p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2009.</p>
Data from: the great tit HapMap project: a continental-scale analysis of genomic variation in a songbird
<p>A major aim of evolutionary biology is to understand why patterns of genomic diversity vary within taxa and space. Large-scale genomic studies of widespread species are useful for studying how environment and demography shape patterns of genomic divergence. Here, we describe one of the most geographically comprehensive surveys of genomic variation in a wild vertebrate to date; the great tit (<em>Parus major</em>) HapMap project. We screened <em>ca</em> 500,000 SNP markers across 647 individuals from 29 populations, spanning ~30 degrees of latitude and 40 degrees of longitude - almost the entire geographic range of the European subspecies. Genome-wide variation was consistent with a recent colonisation across Europe from a South-East European refugiam, with bottlenecks and reduced genetic diversity in island populations. Differentiation across the genome was highly heterogeneous, with clear "islands of differentiation", even among populations with very low levels of genome-wide differentiation. Low local recombination rates were a strong predictor of high local genomic differentiation (F<sub>ST</sub>), especially in island and peripheral mainland populations, suggesting that the interplay between genetic drift and recombination causes highly heterogeneous differentiation landscapes. We also detected genomic outlier regions that were confined to one or more peripheral great tit populations, probably as a result of recent directional selection at the species' range edges. Haplotype-based measures of selection were related to recombination rate, albeit less strongly, and highlighted population-specific sweeps that likely resulted from positive selection. Our study highlights how comprehensive screens of genomic variation in wild organisms can provide unique insights into spatio-temporal evolutionary dynamics.</p>
Data from: the great tit HapMap project: a continental-scale analysis of genomic variation in a songbird
Open the record for dataset details and reuse information.
Watershed shapes for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides the shapefiles of the 46 calibration and 7 validation watersheds used within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E).</p> <p>The watersheds draining towards the following WSC and USGS gauge stations have been used for:</p> <p><strong>1a. calibration of objective 1</strong> (low-human impact)<br> (28 stations in total; 13 of them in both objective 1 and 2)<br> 02GA010 02GA018 02GA038 02GA047 02GB007<br> 02GC002 02GC010 02GC018 02GD004 02GE007<br> 02GG002 02GG003 02GG006 02GG009 02GG013<br> 04159492 04159900 04160600 04161820 04164000<br> 04165500 04166100 04177000 04196800 04197100<br> 04207200 04208504 04213000</p> <p><strong>1b. calibration of objective 2</strong> (most-downstream gauges closest to Lake Erie)<br> (31 stations in total; 13 of them in both objective 1 and 2)<br> 02GB001 02GB007 02GC002 02GC007 02GC018<br> 02GC026 02GE007 02GG003 02GG009 02GG013<br> 04159900 04160600 04165500 04166500 04174500<br> 04176500 04177000 04193500 04195820 04198000<br> 04199000 04199500 04200500 04208504 04209000<br> 04212100 04213000 04213500 04214500 04215000<br> 04215500</p> <p><strong>2. (spatial) validation</strong><br> (7 stations in total)<br> 02GE003 04167000 04168000 04185000 04195500<br> 04201500 04208000</p> <p>===============================================================</p> <p>These data have been derived under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program.</p>
mHM_UFZ gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie
<p>This dataset provides gridded model simulations in netcdf format over the Lake Erie using the mHM model (Samaniego, et al., 2010, Kumar et al, 2013), done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E).</p> <p>Model code is available under: <a href="https://git.ufz.de/mhm/mhm">https://git.ufz.de/mhm/mhm</a>, revision number: 8271b54</p> <p>Domain boundaries (WGS84 system): lon_min = -86.0, lon_max = -78.0, lat_min = 40.0, lat_max = 45.0</p> <p>Model variables are simulated at spatial grid of 0.125deg x 0.125deg, daily time step. </p> <p>Simulation period: 01 Jan 2011 - 31 Dec 2014 (with 1year -2010- warm-up)</p> <p>Meteorological input data, see Mai et al. 2020 (in prep)</p> <p>Following model variables are provided for two mHM_parameter.nml realizations (*obj1 and *obj2, as defined in Mai et al. 2020 in prep). </p> <p>double <strong>snowpack</strong>(time, northing, easting) ;<br> snowpack:long_name = "depth of snowpack" ;<br> snowpack:unit = "mm" ;<br> double <strong>SM_Lall</strong>(time, northing, easting) ;<br> SM_Lall:long_name = "average soil moisture over all layers" ;<br> SM_Lall:unit = "mm mm-1" ;<br> double <strong>unsatSTW</strong>(time, northing, easting) ;<br> unsatSTW:long_name = "reservoir of unsaturated zone" ;<br> unsatSTW:unit = "mm" ;<br> double <strong>satSTW</strong>(time, northing, easting) ;<br> satSTW:long_name = "water level in groundwater reservoir" ;<br> satSTW:unit = "mm" ;<br> double <strong>aET</strong>(time, northing, easting) ;<br> aET:long_name = "actual Evapotranspiration" ;<br> aET:unit = "mm d-1" ;<br> double <strong>Q</strong>(time, northing, easting) ;<br> Q:long_name = "total runoff generated by every cell" ;<br> Q:unit = "mm d-1" ;<br> double <strong>QD</strong>(time, northing, easting) ;<br> QD:long_name = "direct runoff generated by every cell (runoffSeal)" ;<br> QD:unit = "mm d-1" ;<br> double <strong>QIf</strong>(time, northing, easting) ;<br> QIf:long_name = "fast interflow generated by every cell (fastRunoff)" ;<br> QIf:unit = "mm d-1" ;<br> double <strong>QIs</strong>(time, northing, easting) ;<br> QIs:long_name = "slow interflow generated by every cell (slowRunoff)" ;<br> QIs:unit = "mm d-1" ;<br> double <strong>QB</strong>(time, northing, easting) ;<br> QB:long_name = "baseflow generated by every cell" ;<br> QB:unit = "mm d-1" ;<br> double <strong>recharge</strong>(time, northing, easting) ;<br> recharge:long_name = "groundwater recharge" ;<br> recharge:unit = "mm d-1" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. This work has received funding from the Initiative and Networking Fund of the Helmholtz Association through the project Advanced Earth System Modelling Capacity (ESM) (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/www.esm-project.net">www.esm-project.net</a>).</p>
FIGURE 1 in A new species of Amphimedon (Porifera, Demospongiae, Haplosclerida, Niphatidae) from the CapricornBunker Group of Islands, Great Barrier Reef, Australia: target species for the ' sponge genome project'
FIGURE 1. Amphimedon queenslandica sp.nov. A, Holotype QMG315611 in aquarium (scale 1 cm) (photo Nelson Lauzon). B, Peripheral skeletal reticulation (scale 1 mm). C, Closer view of the subectosomal skeleton (scale 1 mm). D, Paratype QMG322874 in aquarium (scale 1 cm) (photo Simon Walker). E, Thicker fibres with embedded spicules forming a denser surface skeleton, supported beneath by the more cavernous subectosomal reticulation (scale 500 μm). F, Network of smaller secondary (uni and paucispicular) tracts overlaying the thicker primary (multi and paucispicular) fibres (scale 500 μm). G, Uni and paucispicular secondary skeletal tracts interconnecting the larger primary fibres (scale 100 μm). H, Isodictyal reticulation of smaller secondary (uni and paucispicular) tracts (scale 50 μm). I, Oxeas embedded in spongin binding the nodes of smaller secondary tracts (scale 50 μm). J, Oxea megascleres (scale 50 μm).
Validation Data used for manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model"
<p>those are the processed data that used for model-data comparison in the manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model", including Lake Surface Temperature and Lake Surface Ice Cover from Great Lakes Surface Environmental Analysis (GLSEA), Surface Air temperature and Precipitation from Climatic Research Unit (CRU). </p>
Few juveniles or males were collected. Only four males from groups 7, 8, 9, and 11, all in clade D, were included in the dataset. The male in Fig. 13E–H conforms to the general morphological description of males in Lobocriconema with an undifferentiated labial region, the absence of a stylet, a degenerate pharyngeal region, a FIGURE 7. SEM images of specimens representing clades D (A–H) and B (I). NID numbers are associated with unique specimens, all are females except image C. A) Lobocriconema sp., face view with conspicuous labial disc surrounded by irregular labial structure, Nine-Mile Prairie, Nebraska, NID 4533. B) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Big Thicket National Preserve, Texas, NID 4560. C) Lobocriconema sp., juvenile, head with visible submedian lobes, body scales with fine terminal projections, Spring Creek Prairie, Nebraska, NID 4514. D) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Nine-Mile Prairie, Nebraska, NID 4527 E) Lobocriconema sp., cephalic profile with protruding stylet, Nine-Mile Prairie, Nebraska, NID 4529. F) Lobocriconema sp., head profile lacking submedian lobes, Tunica Hills, Louisiana, NID 4574. G) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4533. H) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4526. I) Lobocriconema sp., face view lacking submedian lobes, Great Smoky Mountains National Park, Purchase Knob, NID 4570. in Species discovery and diversity in Lobocriconema (Criconematidae: Nematoda) and related plant-parasitic nematodes from North American ecoregions
Few juveniles or males were collected. Only four males from groups 7, 8, 9, and 11, all in clade D, were included in the dataset. The male in Fig. 13E–H conforms to the general morphological description of males in Lobocriconema with an undifferentiated labial region, the absence of a stylet, a degenerate pharyngeal region, a FIGURE 7. SEM images of specimens representing clades D (A–H) and B (I). NID numbers are associated with unique specimens, all are females except image C. A) Lobocriconema sp., face view with conspicuous labial disc surrounded by irregular labial structure, Nine-Mile Prairie, Nebraska, NID 4533. B) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Big Thicket National Preserve, Texas, NID 4560. C) Lobocriconema sp., juvenile, head with visible submedian lobes, body scales with fine terminal projections, Spring Creek Prairie, Nebraska, NID 4514. D) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Nine-Mile Prairie, Nebraska, NID 4527 E) Lobocriconema sp., cephalic profile with protruding stylet, Nine-Mile Prairie, Nebraska, NID 4529. F) Lobocriconema sp., head profile lacking submedian lobes, Tunica Hills, Louisiana, NID 4574. G) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4533. H) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4526. I) Lobocriconema sp., face view lacking submedian lobes, Great Smoky Mountains National Park, Purchase Knob, NID 4570.
Figure 2 in Diversity of Chironomidae (Diptera) breeding in the Great Stour, Kent: baseline results from the Westgate Parks non-biting midge project
Figure 2. (a) Genus percent abundance per site, and (b) genus dominance plot per site in the Great Stour in Kent, UK.
Figure 3 in Diversity of Chironomidae (Diptera) breeding in the Great Stour, Kent: baseline results from the Westgate Parks non-biting midge project
Figure 3. (a) Dendrogram showing the Bray-Curtis dissimilarities among sites, and (b) Principal Components Analysis (PCA) and biplot of Chironomidae genera for sites in the Great Stour in Kent, UK.
Great Beginnings for Healthy Native Smiles: An Early Childhood Caries Prevention Project
ClinicalTrials.gov study NCT04556175. IPD Sharing: NO. Countries: 1. Publications: 0.
The Getting Real About The Talk (GReAT) Project - A Qualitative, Patient-Centered Evaluation of the Factors for Successfully Having 'The Talk' and Implementation for Attending and Trainee Physicians
ClinicalTrials.gov study NCT04078932. IPD Sharing: NO. Countries: 0. Publications: 7.
Addressing Cardiometabolic Health In Populations Through Early Prevention in the Great Lakes Region Project 1 - Epidemiology (ACHIEVE P1-EPI)
ClinicalTrials.gov study NCT06593496. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Inter-regional Project of the Great Western Exploration Approach for Exome Molecular Causes Severe Intellectual Disability Isolated or Syndromic
ClinicalTrials.gov study NCT02136849. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Figure 1 in Diversity of Chironomidae (Diptera) breeding in the Great Stour, Kent: baseline results from the Westgate Parks non-biting midge project
Figure 1. Location of the Great Stour in Kent, UK, and six sampling sites of Chironomidae genera in Westgate Parks and nearby areas. (a) Map of the UK with Kent county highlighted. (b) Kent county showing the Great Stour divided into the Upper Great Stour, the East Stour and the main Great Stour with the sampling locations in the city of Canterbury. (c) Sampling locations, including Rheims Way (Site 1), Westgate Gardens (Site 2), Westgate Towers (Site 3), Bingley Island (Site 4; a side stream of the Great Stour), Horton (Site 5; a site 3 km upstream from Westgate Parks) and Kingsmead Field (Site 6 a site 1 km downstream from Westgate Parks).
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.