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19 results for “2000-2015”
Hydrodynamic and morphological information, and the absolute variations of the vulnerability indices for the period 2000-2015 of the Spanish Iberia Peninsula estuaries.
<p>The dataset included in this repository was obtained during the project entitled 'Sensibilidad física y biotic de los estuarios peninsulares al cambio global (SENSES)' funded by 'Fundación Biodiversidad', PRCV00487. The data were used in the research article 'Sensitivity of Iberian estuaries to changes in sea water temperature, salinity, river-flow, mean sea level, and tidal amplitudes' submitted to <em>Estuarine, Coastal and Shelf Science</em>.</p> <p>Brief description of dataset:</p> <p>For each estuary, the following parameters were calculated</p> <ul> <li>Fachade: the location of the estuary</li> <li>Area (km<sup>2</sup>) </li> <li>D (m): water depth at the mouth of the estuary in 2000 and 2015</li> <li>Tidal Prim (m<sup>3</sup>)</li> <li>Q<sub><em>f</em></sub> (m<sup>3</sup>/s): river flow in 2000 and 2015</li> <li><em>a </em>(m): tidal amplitude of the free surface elevation in 2000 and 2015</li> <li>∆<em>U</em> (m/s): absolute variation of the tidal current amplitude between 2000 and 2015</li> <li>∆<em>E</em> (W/m<sup>2</sup>): absolute variation of the tidal energy flux propagation index between 2000 and 2015</li> <li>∆<em>Ri </em>: absolute variation of the bulk Richardson number index between 2000 and 2015</li> <li>∆<em>SI</em>: absolute variation of the salinity intrusion index between 2000 and 2015</li> </ul> <p>A wide description of the parameters can be found in Serrano, M. A. et al (submitted to <em>Estuarine, Coastal and Shelf Science</em>)</p> <p>Contact person: mserranog@ugr.es</p>
NH and ME Landsat chlorophyll-a retrieval algorithms and in situ measurements 2000 (Landsat 7), 2013-2015 (Landsat 8)
Predicting algal blooms has become a priority for scientists, municipalities, businesses, and citizens. Remote sensing offers solutions to the spatial and temporal challenges facing existing lake research and monitoring programs that rely primarily on high-investment, in situ measurements. Techniques to remotely measure chlorophyll-a (chl-a) as a proxy for algal biomass have been limited to specific large water bodies in particular seasons and narrow chl-a ranges. Thus, a first step toward prediction of algal blooms is generating regionally robust algorithms using in situ and remote sensing data. This study explores the relationship between in-lake measured chl-a data from Maine and New Hampshire lakes and remotely-sensed chl-a retrieval algorithm outputs. Landsat 8 images were obtained and then processed after required atmospheric and radiometric corrections. Six previously developed algorithms were tested on a regional scale on eleven scenes from 2013-2015 covering 192 lakes. Additionally, data related to one Landsat 7 scene (2000) are included in this data set. Boucher, J, K.C. Weathers, H. Norouzi, B. Steele. In Press. Assessing the effectiveness of Landsat 8 chlorophyll-a retrieval algorithms for regional freshwater monitoring. Ecological Applications.
High-resolution wind power generation time series for Germany in the period 2000-2015
<p>High-resolution wind power generation time series for Germany in the period 2000-2015. A paper describing the applied methodology can be found in this repository as well.</p> <p>The final temporal resolution is hourly and the spatial resolution NUTS 3. The data is stored in csv and hdf5 files.</p> <p>More information on the data set, e.g. missing time stamps and versioning, can be found in readme.txt.</p>
Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM) simulations focused on Eight Mile Lake, Alaska and Imnavait Creek, Alaska [2000-2015]
<p>This set of files store model simulations using the biosphere model Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM), developed to simulate biophysical and biogeochemical interactions between the soil, vegetation and atmosphere. To improve predictions of net carbon releases from thawing permafrost, we tested the sensitivity of a suite of model parameters. We analyzed the responses of ecosystem carbon balances to permafrost thaw by running site-level simulations at two long-term tundra ecological monitoring sites in Alaska: Eight Mile Lake (EML) and Imnavait Creek watershed (IMN). These sites are characterized by similar tussock tundra vegetation but differing soil drainage conditions and climate, IMN consists of well-drained soils, and EML has historically well-drained soils, however permafrost thaw has altered drainage conditions to wetter soils. Simulations were conducted at a 1km resolution, over a 1,000 km2 area (10x10 km square) centered on two long term ecological research sites in Alaska: Eight Mile Lake located in Interior Alaska (63.8900° N, 149.2535° W), and Imnavait creek watershed located on the northern foothills of the Brooks range (68°37′ N, 149°18′ W).</p> <p>Historical simulations are spanning the 2000 to 2015, and forced using climate simulations from the Climate Research Unit, time series 4.0. We ran 1,000 site level simulations for each model variable. The variables that are produced are gross primary productivity (GPP, in gC.m-2.m-1), net ecosystem exchange (NEE, gC.m-2.m-1), ecosystem respiration (RECO, gC/m2/m-1), active layer thickness (ALT, m), soil temperature (TLAYER,°C) at 5, 10, 40 cm depths, soil moisture (LWCLAYER, m-3/m-3) at 5, 10 cm depths, and snow depth (SNOWDEPTH, m), evapotransipiration(EET, mm/m2/time), potential evapotransipiration (PET, mm/m2/time), leaf area index (LAI, m2/m2), organic layer thickness (OLT, m). The data are stored as compiled csv files, with time as the index, and each model sample output stored in the columns. In addition, there is a postprocessing python script to demonstrate the step and workflow used to generate the individual csv files post processed from the raw model outputs stored as netcdfs.</p>
Data from: Use of influenza antivirals in patients hospitalized in Hong Kong, 2000-2015
Objectives: We aimed to describe patterns in the usage of antivirals to treat influenza virus infection in hospitals in Hong Kong from 2000 through 2015. Methods: We analyzed centralized electronic health records that included dispensation information and diagnosis codes. Information collected on admissions included patient age, sex, admission year and month, and medications dispensed, and were matched with the first 15 discharge diagnosis codes. We divided monthly admission episodes by relevant population denominators to obtain admission rates, and stratified analyses by drug type, age group, and diagnosis codes. Results: Amantadine was used for influenza treatment in the early 2000s but changed with recommendations to avoid its use in 2006, and is now mainly used to treat Parkinson's disease. Oseltamivir usage increased substantially in 2009 and is now commonly used, with almost 40,000 hospitalizations treated with oseltamivir in the years 2012 through 2015, 66% of which was in persons ≥65 years of age. During the entire study period, of the 98,253 admission episodes in which oseltamivir was dispensed, 40,698 (41%) included a diagnosis code for influenza, and 80,283 (82%) included any diagnosis code for respiratory illness. Conclusions: The amount of oseltamivir used from 2012-15 was comparable to a separate ecological estimate of around 13,000 influenza-associated hospitalizations per year on average. We did not have access to individual patient laboratory testing data.
Mediterannean Sea-Temperature and Salinity Annual Climatology for six periods (1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2015)
<p>Mediterranean Sea Annual Climatology for six periods (1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2015) using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 30 standards depths from 5-4000m. The observations are from the period 1950-2015.</p>
Mediterannean Sea-Temperature and Salinity Seasonal Climatology for six periods (1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2015)
<p>Mediterranean Sea Seasonal Climatology for six periods (1950-1959, 1960-1969, 1970-1979, 1980-1989, 1990-1999, 2000-2015) using the SeaDataNet V2 aggregated dataset and the DIVA software tool 4.6.11. The Climatology covers the geographical region W6.25-E36.5, N30-N40 and is computed on 31 standards depths from 0-4000m. The observations are from the period 1950-2015.</p>
Data from: Use of influenza antivirals in patients hospitalized in Hong Kong, 2000-2015
Open the record for dataset details and reuse information.
Demographic characteristics, site and phylogenetic distribution of dogs with appendicular osteosarcoma: 744 dogs (2000-2015)
Open the record for dataset details and reuse information.
Monthly inundated fraction over China for 2000-2015 from GIEMS-2
<p>The data set is released to support the paper: Xi, Y. et al. Trade-off between forestation and wetland conservation in China. Nat. Commun. (2021) (In revision). The NetCDF file provides the monthly inundated fraction over China for 2000-2015 at 0.5º × 0.5º spatial resolution, which is extracted from the initial GIEMS-2 data set (Prigent et al., 2020). When referring to the data set, the paper should be cited: Prigent, C., Jimenez, C. & Bousquet, P. Satellite-Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS-2). J. Geophys. Res.-Atmos. 125, e2019JD030711, doi:10.1029/2019JD030711 (2020).</p>
Historical reconstruction of background air pollution over France for 2000-2015
<p>This datasets covers 16-year of air pollution concentrations and air quality indicators over France. Using a kriging<br> method that combines background measurements of air quality and modeling with the Chemistry Transport Model CHIMERE,<br> hourly concentrations of NO2, O3, PM10 and PM2.5 are produced with a spatial resolution about 4 kilometers. Regulatory<br> indicators (annual average, SOMO35, AOT40 etc…) are also calculated from these data.</p> <p>Regulatory indicators are given at the regional, departmental and municipal levels as csv files for the different years. They are also provided at the model grid resolution on a netcdf format. </p>
MODIS NDVI Data, Smoothed and Gap-filled, for the Conterminous US: 2000-2015
This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, smoothed and gap-filled, for the conterminous US for the period 2000-01-01 through 2015-12-31. The data were generated using the NASA Stennis Time Series Product Tool (TSPT) to generate NDVI data streams from the Terra satellite (MODIS MOD13Q1 product) and Aqua satellite (MODIS MYD13Q1 product) instruments. TSPT produces NDVI data that are less affected by clouds and bad pixels.
ABoVE: Lichen Forage Cover over Fortymile Caribou Range, Alaska and Yukon, 2000-2015
This dataset provides modeled estimates of lichen ground cover at 30 m resolution across the Fortymile study area in interior eastern Alaska, U.S., and the Yukon Territory, Canada, for the nominal year 2015. The mapped lichens are important winter forage for the nine resident caribou (Rangifer tarandus) herds in the region. A random forest modeling approach with vegetation inputs and environmental and spectral predictors was used to estimate lichen cover for 2015. Input data for the model were aggregated from historical in-situ vegetation plots, visual aerial surveys, and recent unmanned aerial system (UAS) imagery to align with 30 m resolution Landsat pixels over the 583,200 km2 study area. The model was also used to estimate lichen cover for the year 2000 by applying the trained model to historical Landsat imagery. An estimate of lichen volume in 2015, based on a published algorithm, is also provided. In addition, site-level presence-absence maps at <1 m resolution and site-level lichen cover maps at both 2 m and 30 resolution are provided. Site-level data were derived from high-resolution RGB imagery collected in summer 2017 from UASs at 22 forested and alpine sites across interior Alaska and western Yukon. Due to the use of two unique UAS imagers at 7 sites, there are 29 data collections across the 22 sites. Each UAS data collection is associated with three data files. These landscape-scale maps could be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management, and conservation.
ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015
This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data.
ABoVE: Dall Sheep Lamb Recruitment and Climate Data, Alaska and NW Canada, 2000-2015
This dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep. For this study, the mountain ranges were divided into 24 mountain units due to differing climate gradients. Estimated covariate environmental and climate data used to examine the relationship between environmental conditions and Dall sheep population performance (per mountain unit) are also provided and include precipitation, temperature, snow cover, elevation, and distance to the center of the range.
High Mountain Asia 4-km Dynamically Downscaled Meteorological Data, 2000-2015 V001
This High Mountain Asia (HMA) data set contains simulated meteorological data for the Indus Basin from 2000 through 2015, at three horizontal resolutions – 36 km, 12 km, and 4 km – and 9 pressure levels spanning 1000 hPa – 200 hPa. The data were produced by using the Advanced Research Weather Research & Forecasting (ARW-WRF) model to dynamically downscale Climate Forecast System Reanalysis (CFSR) data into three nested domains with increasing horizontal resolution.
Database - Some indicators of PISA (2000-2015), by countries
<p>Database - Some indicators of PISA (2000-2015), by countries</p>
Database - Some indicators of PISA (2000-2015), by countries
<p>Database - Some indicators of PISA (2000-2015), by countries</p>
Database - Some Indicators of PISA (2000-2015), by countries
<p>Database - Some Indicators of PISA (2000-2015), by countries</p>
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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.