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FEM simulation by Ansys
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Data in summary: 1- Building total B side: This is metered data from one of two mains busbars that supplies all none-emergency services and HVAC equipment 2- Building total A side: This is metered data from the second of two mains busbars that supplies all emergency services including fire safety, comm rooms, emergency lighting and public announcement. It also is connected to a PV array with peak electrical supply of around 33kWe. 3- Half hourly building demand and deferrable load breakdowns: This is processed data that includes building total and HH instances of deferrable loads for all sub-categories of loads considered in this work. It also includes HH instances of PV generation, and outside air temperature. 4- Early morning ramp rates following plant start-up: This is a file containing the difference between two instantaneous recordings of total building electricity consumption that shows the continuous fluctuation in total electricity demand in the building. 5- CO2-raw (Typical office): This files contains actual CO2 data in an office that represents typical space occupant density in the case study building. 6- CO2-raw (worst case): This files contains actual CO2 data in a teaching space that represents the highest observed space occupant density in the case study building. 7- Warming and cooling rates in the worst case zones: This file include actual data describing the operational temperature in the worst case zones most prone to overheating in summer and excessive heat loss in winter.
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The dataset includes 2,016 impact echo signals from eight identical laboratory-made concrete specimens. This dataset is annotated in two classes: sound concrete (Class S) and defected concrete (Class D).
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The objective of this dataset was to present the forage biomass production over time in different pasture management systems. We selected two farms located in the Western region of São Paulo State, Brazil. Pasture field data collection was carried out in two farms during three dates (June and November 2018 and March 2019) over two seasons (wet and dry). Samples were regularly taken through time to monitor forage biomass. These fields represent a wide variety of pasture management, as follow: Farm 1 (Santa Clara): i) traditional, low forage productivity, cattle rotation; ii) traditional, intermediate forage productivity, fertilized, cattle rotation; iii) intensified pasture, high forage productivity, reformed, cattle rotation. Farm 2 (Poderosa): i) traditional degraded*, recently reformed with millet + grass, cattle rotation; ii) traditional, low forage productivity, signs of degradation, fertilized, cattle rotation. *degraded was based on visual analysis of pasture area with sparse grass and exposed soil in some areas. With the support of NDVI images from the MODIS sensor, sample pixels were used to allocate the sample points. The areas of these pixels were divided into nine sampling points and in each of these points, the forage biomass was collected. Soil analyses were also carried out in two seasons (June 2018 and March 2019). The data files were organized in three folders. Each folder represents one field campaign. These folders have a shapefile of all the fields, the same file in kml extension (to open on Google Earth) and a zip file with photography of each field during the field campaign. The attribute table of the shapefile has a description of the fields and biomass. Excel files show the same information of the attribute table and a description of the items. A figure with the template of the biomass collection scheme is also available. Soil analyses are in the folders 'June 2018' and 'March 2019'. A more detailed description and discussion about these data and their association with soil chemical analysis were described in a scientific report (available by request). The biomass collection allowed the analysis of the forage production and better diagnoses about resource utilization strategies over the different pasture systems. This work was funded by the São Paulo Research Foundation (process numbers 2018/10770-1, 2017/06037-4, 2016/08741-8, 2017/08970-0, 2018/11052-5 and 2014/26767-9) as part of the Global Sustainable Bioenergy Initiative.
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Data for Analysis of Nano-Silica and Xanthan Gum as a High-Temperature Thixotropic Agent for Oil-Well Cement
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Supplementary materials corresponding to the identically named paper including R scripts, derived data sets, and the full statistical test results.
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The raw sequencing data obtained from hamsters treated with different interventions including 1) standard diet (control); (2) standard diet and monosodium glutamate (MSG) in drinking water (MSG); (3) high-fat and high-fructose diets (HFF), and (4) MSG+HFF.
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These datasets involve 1) ambient air quality testing, 2) spontaneous combustion fire frequency record, 3) temperature anomalies detected by Landsat, 4) photos of mine waste heap as well as affected environment, 5) estimation of remedial cost, 6) VDO of gas emission from a crack on top of the mine waste heap, and 7) XRD analysis of coal-mine waste
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The PICO Statements dataset is a collection of 130 abstracts from Randomized Clinical Trials and Controlled Trials, manually annotated by medical practitioners, to identify sentences that not only contain all four PICO elements but also answer clinically stated questions. These sentences are referred to as PICO Statements. In Evidence-Based Medicine (EBM), the PICO framework is used by medical practitioners to narrow the search space and enable faster decision-making towards treatment procedures. The framework is named after the four elements that comprise it, Population, Intervention, Comparator and Outcome. Previous datasets focus on identifying either whole sentence to a single PICO element or, more recently, the sequence of tokens in the sentence that describe each element. Similar to previous research, we consider Intervention and Comparator as one element in our annotation scheme. For each sentence, we binary annotate the existence of each PICO element individually and if the sentence is a PICO Statement. The dataset is offered, in an abstract per file manner, in two formats: 1) XML format, for sentence classification. The XML format present each abstract, along with its title, annotated on a sentence level, with all four annotations present for each sentence in a binary format. The XML Schema (.xsd) files are also available in the miscellaneous folder. 2) pseudo-IOB format, for PICO entity prediction. The pseudo-IOB format, presents each abstract, along with its title, annotated on a token level, with the same binary annotations repeating for each token in the sentence. The binary annotations in the pseudo-IOB format are corresponding to the PICO elements in the following order: Population, Intervention/Comparator, Outcome, PICO Statement. In both annotation schemes contain the same abstracts and the file names are corresponding to the PubMedIDs of the publications from which the abstracts originate.
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Geometric and energetic features of halogenated rotamers of the following backbone structures, C-C, N-N, P-P, O-O, S-S, N-P, O-S, C-N, C-P, C-O, C-S, N-O, N-S, P-O and P-S from quantum chemical calculations are presented. The data set is considered to be comprehensive combinations of non-metal elements in the form abcx-ydef whereby a,b,c,d,e,f are halogen (fluorine to iodine), hydrogen or a lone pair and x,y are carbon, nitrogen, phosphorus, oxygen and sulfur. Preliminary work on all possible halogenation of methane, ammonia, phosphine, water and hydrogen sulfide are also included.
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