976 resultados para Dataset


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This dataset provides raw data of chemical analyses made during studies on seasonal variations of some major ions in the stream water of the River Duddon in Cumbria. Measurements of sodium, calcium, potassium, magnesium and chloride ions and pH were taken at 5 stations in the River Duddon between January 1970 and August 1974.

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This dataset provides raw data of chemical analyses made during studies on seasonal variations of some major ions in the stream water of the upper basin of the River Duddon in Cumbria. Measurements of sodium, calcium, potassium, magnesium and chloride ions and pH were taken at 26 stations in the River Duddon basin between 1972 and 1974.

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This dataset provides raw data of chemical analyses made during studies on seasonal variations of some major ions in the stream water of the catchment of Lake Windermere in Cumbria. Measurements of sodium, calcium, potassium, magnesium, chloride ions and pH were taken at 37 stations in the catchment between 1975 and 1978.

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This dataset provides raw data of chemical analyses made during studies on seasonal variations of treated sewage effluent from Grasmere Treatment Unit in Cumbria. Measurements of sodium, calcium, potassium, magnesium and chloride ions were taken between 1974 and 1976.

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This dataset provides raw data of chemical analyses made during studies on seasonal variations of 25 frequently sampled tarns in Cumbria. Measurements of sodium, calcium, potassium, magnesium, pH, chloride ions, alkalinity, sulphite, strong acids and nitrate were taken between 1954 and 1956 and between 1974-1976.

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This dataset provides raw data of chemical analyses made during studies on seasonal variations of 182 tarns in the English Lake District, Cumbria. Measurements of sodium, calcium, potassium, magnesium, pH, chloride ions, alkalinity, sulphite, strong acids and nitrate were taken between 1953 and 1978.

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Many visual datasets are traditionally used to analyze the performance of different learning techniques. The evaluation is usually done within each dataset, therefore it is questionable if such results are a reliable indicator of true generalization ability. We propose here an algorithm to exploit the existing data resources when learning on a new multiclass problem. Our main idea is to identify an image representation that decomposes orthogonally into two subspaces: a part specific to each dataset, and a part generic to, and therefore shared between, all the considered source sets. This allows us to use the generic representation as un-biased reference knowledge for a novel classification task. By casting the method in the multi-view setting, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance. © 2013 Springer-Verlag.

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The large uncertainties in estimates of cropland area in China may have significant implications for major cross-cutting themes of global environmental change-food production and trade, water resources, and the carbon and nitrogen cycles. Many earlier studies have indicated significant under-reporting of cropland area in China from official agricultural census statistics datasets. Space-borne remote sensing analyses provide an alternative and independent approach for estimating cropland area in China. In this study, we report estimates of cropland area from the National Land Cover Dataset (NLCD-96) at the 1:100,000 scale, which was generated by a multi-year National Land Cover Project in China through visual interpretation and digitization of Landsat TM images acquired mostly in 1995 and 1996. We compared the NLCD-96 dataset to another land cover dataset at I-km spatial resolution (the IGBP DIScover dataset version 2.0), which was generated from monthly Advanced Very High Resolution Radiometer (AVHRR)-derived Normalized Difference Vegetation Index (NDVI) from April, 1992 to March, 1993. The data comparison highlighted the limitation and uncertainty of cropland area estimates from the DIScover dataset. (C) 2003 Elsevier Science B.V. All rights reserved.

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In this paper, the buildingEXODUS (V1.1) evacuation model is described and discussed and attempts at qualitative and quantitative model validation are presented. The data set used for the validation is the Tsukuba pavilion evacuation data. This data set is of particular interest as the evacuation was influenced by external conditions, namely inclement weather. As part of the validation exercise, the sensitivity of the buildingEXODUS predictions to a range of variables and conditions is examined, including; exit flow capacity, occupant response times and the impact of external conditions on the developing evacuation. The buildingEXODUS evacuation model was found to be able to produce good qualitative and quantitative agreement with the experimental data.

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Assessing the skill of biogeochemical models to hindcast past variability is challenging, yet vital in order to assess their ability to predict biogeochemical change. However, the validation of decadal variability is limited by the sparsity of consistent, long-term biological datasets. The Phytoplankton Colour Index (PCI) product from the Continuous Plankton Recorder survey, which has been sampling the North Atlantic since 1948, is an example of such a dataset. Converting the PCI to chlorophyll values using SeaWiFS data allows a direct comparison with model output. Here we validate decadal variability in chlorophyll from the GFDL TOPAZ model. The model demonstrates skill at reproducing interannual variability, but cannot simulate the regime shifts evident in the PCI data. Comparison of the model output, data and climate indices highlights under-represented processes that it may be necessary to include in future biogeochemical models in order to accurately simulate decadal variability in ocean ecosystems.

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During the 1980s, a rapid increase in the Phytoplankton Colour Index (PCI), a semiquantitative visual estimate of algal biomass, was observed in the North Sea as part of a regionwide regime shift. Two new data sets created from the relationship between the PCI and SeaWiFS chlorophyll a (Chl a) quantify differences in the previous and current regimes for both the anthropogenically affected coastal North Sea and the comparatively unaffected open North Sea. The new regime maintains a 13% higher Chl a concentration in the open North Sea and a 21% higher concentration in coastal North Sea waters. However, the current regime has lower total nitrogen and total phosphorus concentrations than the previous regime, although the molar N: P ratio in coastal waters is now well above the Redfield ratio and continually increasing. Besides becoming warmer, North Sea waters are also becoming clearer (i.e., less turbid), thereby allowing the normally light-limited coastal phytoplankton to more effectively utilize lower concentrations of nutrients. Linear regression analyses indicate that winter Secchi depth and sea surface temperature are the most important predictors of coastal Chl a, while Atlantic inflow is the best predictor of open Chl a; nutrient concentrations are not a significant predictor in either model. Thus, despite decreasing nutrient concentrations, Chl a continues to increase, suggesting that climatic variability and water transparency may be more important than nutrient concentrations to phytoplankton production at the scale of this study.

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Sentinel-3A is scheduled for launch in Oct. 2015, with Sentinel-3B to follow 18 months later. Together these missions are to take oceanographic remote-sensing into a new operational realm. To achieve this a large number of processing, calibration and validation tasks have to be applied to their data in order to assess for quality, absolute bias, short-term changes and long-term drifts. ESA has funded the Sentinel-3 Mission Performance Centre (S3MPC) to carry out this evaluation on behalf of ESA and EUMETSAT. The S3MPC is run by a consortium led by ACRI [1] and this paper describes the work on the calibration/validation (cal/val) of the Surface Topography Mission (STM), which is co-ordinated by CLS and PML.