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4,276 results for “Estimation”
Toward Estimating the Rank Correlation between the Test Collection Results and the True System Performance
<p>This archive contains the simulated collections and the estimated correlation coefficients. For the full code and description, please refer to https://github.com/julian-urbano/sigir2016-correlation</p>
Comparison results from 'A global comparison of integrated water vapour estimates from WMO radiosondes, AERONET sun photometers and GPS for the 17 year period from 1997 to 2013' by Wilson, Hansen, Bingley and Milton
<p>This dataset contains the raw comparison results from the paper <em>A global comparison of integrated water vapour estimates from WMO radiosondes, AERONET sun photometers and GPS for the 17 year period from 1997 to 2013</em> by Wilson, Hansen, Bingley and Milton.</p> <p>The three files contain comparisons between:</p> <p>a) AERONET and Radiosonde (Validation_AERONET-Radiosonde.csv)</p> <p>b) AERONET and GPS (Validation_AERONET.csv)</p> <p>c) Radiosonde and GPS (Validation_Radiosonde.csv)</p>
Level A Pan Europe Solar Index for estimation of Potential evaporation February
Solar Index for estimation of Potential evaporation February. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation August
Solar Index for estimation of Potential evaporation August. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation March
Solar Index for estimation of Potential evaporation March. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module)
Level A Pan Europe Solar Index for estimation of Potential evaporation July
Solar Index for estimation of Potential evaporation July. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation Apr
Solar Index for estimation of Potential evaporation April. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation November
Solar Index for estimation of Potential evaporation November. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation September
Solar Index for estimation of Potential evaporation September. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation May
Solar Index for estimation of Potential evaporation May. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation June
Solar Index for estimation of Potential evaporation June. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation October
Solar Index for estimation of Potential evaporation October. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation January
Solar Index for estimation of Potential evaporation January. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Level A Pan Europe Solar Index for estimation of Potential evaporation December
Solar Index for estimation of Potential evaporation December. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module).
Probabilistic state-level estimates of US coastal storm property damages from climate change
<p>Probabilistic estimates of property damage (2010 USD) by damage mechanism, sea level measure, region, and time period. Values are derived from <em>Estimates of US coastal damages by local sea level </em>(doi: https://doi.org/10.5281/zenodo.820149) using the code at https://github.com/ClimateImpactLab/acp-impacts</p> <p>Direct (surge + wind) and business-interruption storm damage estimates as well as estimated property below sea level are projected by estimating damages as a function of local sea level (LSL). These damage functions are then applied to probabilistic estimates of local sea level from Kopp et al (2014) using a Monte Carlo simulation. Percentiles of the resulting distributions are presented in the included files. Estimates of property below sea level are calculated for both mean sea level (MSL) as well as mean higher high water (MHHW).</p> <p>In each period, estimates of the cumulative value of inundated property, or property below sea level, are removed from the exposure data set when computing future storm damages; therefore, projections of future storm damages differ between the two measures of sea level.</p>
Estimates of US coastal damages by local sea level
<p>Estimates of coastal property damage and business interruption from storms and estimates of property below sea level by region and exposure category. Regions are groups of US counties designated by state and <em>coastalflag</em>, which indicates whether a region includes counties sharing a US coastline (coastalflag=1) or only inland counties (coastalflag=0). Estimates are produced using the RMS Model.</p> <p>These values are used as inputs by <em>Probabilistic state-level estimates of US coastal storm property damages from climate change</em> (doi: https://dx.doi.org/10.5281/zenodo.820086) using the code at https://github.com/ClimateImpactLab/acp-impacts</p> <p>Hurricane damage estimates include estimates using historical hurricane activity as well as estimates including projected changes in hurricane frequency and intensity under RCP 4.5 and 8.5.</p>
Genome-wide estimation of linkage disequilibrium-independent SNPs in Drosophila melanogaster (Sussex LHM).
<p>Uses R to create SNP density across each chromosome arm. Uses Plink 1.9 to select independent SNPs with step sizes corresponding to chromosome density. Output data is combined_chromosomes_lhm_indep.txt a list of SNP IDs.<br> </p>
Daily European biospheric methane emissions estimated with the ecosystem model JSBACH-HIMMELI.
<p>Daily estimates of European biospheric methane emissions from JSBACH-HIMMELI model from year 1990 to year 2023. JSBACH-HIMMELI is an ecosystem process model based on JSBACH land surface model, YASSO soil carbon model and HIMMELI methane emission model. The gridded fluxes are available with a resolution of 0.1x0.1 degrees and in units of mol m-2 s-1 (m-2 refers to grid cell area). The gridded flux file contains a variable for methane fluxes, including a sum of methane fluxes from peatlands, inundated lands and mineral soils. More information of the model set-up is documented in Petrescu, A. M. R., et al., The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019, Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, 2023, Tyystjärvi, V., 2024. Future methane fluxes of peatlands are controlled by management practices and fluctuations in hydrological conditions due to climatic variability. EGUsphere 1–37. https://doi.org/10.5194/egusphere-2023-3037 and Raivonen, M. et al., 2017. HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands. Geoscientific Model Development 10, 4665–4691. <a href="https://doi.org/10.5194/gmd-10-4665-2017">https://doi.org/10.5194/gmd-10-4665-2017</a></p>
MIrreM Public Database on Irregular Migration Stock Estimates
<p>The <em>MIrreM </em><em>Public Database on Irregular Migration Stock Estimates</em> (the Database) provides an inventory and critical appraisal of country-level estimates of irregular migration stocks in 13 European countries, the United States and Canada for the period 2008 to 2023. It is a deliverable of the MIrreM project, which is a follow-up to Clandestino. Clandestino covered the period 2000-2008.</p> <p>Users of the Database are advised to consult the following <strong>companion documents:</strong></p> <ul> <li>The README File (version 3), which can be accessed alongside the Database, and contains contextual and technical information about the Database. </li> <li>Discussion of the context, the underlying concepts, and the methodology used in the data collection and quality assessment: Vargas-Silva, C., Leerkes A., Kierans, D., Siruno, L. and Kraler, A. (2025). Tools for collecting information on irregular migration estimates and indicators. <em>Open Research Europe 5:176. <a href="https://doi.org/10.12688/openreseurope.20695.1" target="_blank" rel="noopener noreferrer">https://doi.org/10.12688/openreseurope.20695.1</a></em></li> <li>Analysis of the stock estimates: Kierans, D. and Vargas-Silva, C. (2024). <em>The Irregular Migrant Population of Europe</em>. MIrreM Working Paper No. 11. Krems: University for Continuing Education Krems (Danube University Krems). <a href="https://doi.org/10.5281/zenodo.13857073">https://doi.org/10.5281/zenodo.13857073 </a></li> </ul> <p>Furthermore, users of the Database are notified of a ‘sister’ database of the MIrreM project, which captures and assesses estimates and indicators of irregular migration <em>flows</em> over the same period, the <em>MIrreM </em><em>Public Database on Irregular Migration Flow Estimates and Indicators </em>and accompanying analysis:</p> <ul> <li>Siruno, L., Leerkes, A., Badre, A., Bircan, T., Brunovská, E., Cacciapaglia, M., Carvalho, J., Cassain, L., Cyrus, N., Desmond, A., Fihel, A., Finotelli, C., Ghio, D., Hendow, M., Heylin, R., Jauhiainen, J.S., Jovanovic, K., Kierans, D., Mohan, S.S., Nikolova, M., Oruc, N., Ramos, M.P.G., Rössl, L., Sağiroğlu, A.Z., Santos, S., Schütze, T., & Sohst, R.R. (2024) <em>MIrreM Public Database on Irregular Migration Flow Estimates and Indicators.</em> Krems: University for Continuing Education Krems (Danube University Krems). <a href="https://doi.org/10.5281/zenodo.10813413">https://doi.org/10.5281/zenodo.10813413</a>. </li> <li>Siruno, L., Leerkes, A., Hendow, M. & Brunovksá, E. (2024) <em>Working Paper on Irregular Migration Flows</em>. MIrreM Working Paper No. 9. Krems: University for Continuing Education Krems (Danube University Krems). <a href="../records/10702228">https://zenodo.org/records/10702228</a></li> </ul>
Human Capital-weighted population estimates for 185 countries from 1970 to 2100
<p>We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population's productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970-2020 and 2020-2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.</p>
ScienceDex guides
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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.