This is a guide that describes how to interact with pop ups and the attribute tables in web maps where that functionality is available. Not all widgets or functionality is available in every web map.

We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25-49 days old) were used to train and test the algorithm and, unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains the random forest model classified behaviours with very good accuracy (92%), specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%).

Residential Property Attribute data provides the most current building attributes available for residential properties as captured within Landgate's Valuation Database. Attribute information is captured as part of the Valuation process and is maintained via a range of sources including building and sub division approval notifications. This data set should not be confused with Sales Evidence data which is based on property attributes as at the time of last sale. This dataset has been spatially enabled by linking cadastral land parcel polygons, sourced from Landgatge's Spatial Cadastral Database (SCDB), to the Residential Property Attribute data sourced from the Valuation database. Customers wishing to access this data set should contact Landgate on +61 (0)8 9273 7683 or email businesssolutions@landgate.wa.gov.au © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions. Changes will be applied to this dataset resulting from the implementation of the Community Titles Act 2018 please refer to the Data Dictionary below.

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.pdf file. Visit https://dataone.org/datasets/doi%3A10.6067%3AXCV82N51GF_meta%24v%3D1336050512504 for complete metadata about this dataset.

This dataset contains all of the attribute data. This includes RXNORM provided attributes, such as normalized 11-digit National Drug Codes (NDCs), UNII codes, and human or veterinary usage markers, and source-provided attributes, such as labeler, definition, and imprint information. Each attribute has an 'Attribute Name' (ATN) and 'Attribute Value' (ATV) combination. For example, NDCs have an ATN of 'NDC' and an ATV of the actual NDC value.

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition.; It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level.

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition. It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level.

This dataset provides information on the enforcement of pollution source inspections and regulations by the Taipei City Environmental Protection Bureau.

This data include the associations between features extracted from multimodal user data and the four-dimensional user attributes, the four-dimensional user attribute classification framework, and the multimodal user data utilized for user attribute inference.

This database (2004) is the only harmonized soil database for Europe, extending also to Eurasia. It contains a soil geographical database SGDBE (polygons) to which a number of essential soil attributes are attached, and an associate database PTRDB, with attributes which values have been derived through pedotransfer rules. Also part of the database is the Soil Profile Analytical Database, that contains measured and estimated soil profiles for Europe.

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy …Show full descriptionThe multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition.; It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level.

Including dependent variables: likes, comments, collects, shares; Independent variables: perception of advertising disclosure, proportion of negative reviews, proportion of women, proportion of Gen Z audience, proportion of middle age audience, proportion of middle-aged and elderly audience; And control variables: release days, video duration, price

Explanation of the abbreviated variable names in the attribute table headings and a description of the variables.

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition.;\r It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level.

The data in this map service is updated every weekend.Note: This data includes all activities regardless of whether there is a spatial feature attached.Note: This is a large dataset. Metadata and Downloads are available at: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FACTS+common+attributesTo download FACTS activities layers, search for the activity types you want, such as timber harvest or hazardous fuels treatments. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. This feature class contains the FACTS attributes most commonly needed to describe FACTS activities.

Data, code, and materials for the manuscript "Introspective access to value-based multi-attribute choice processes" by Adam Morris, Ryan Carlson, Hedy Kober, and Molly Crockett.

Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Current building datasets suffer from incomplete coverage of building multi-attributes. This paper presents the first national-scale Multi-Attribute Building dataset (CMAB) with artificial intelligence, covering 3,667 spatial cities, 31 million buildings, and 23.6 billion m² of rooftops with an F1-Score of 89.93% in OCRNet-based extraction, totaling 363 billion m³ of building stock. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating morphology, location, and function features. Using multi-source data, including billions of remote sensing images and 60 million street view images (SVIs), we generated rooftop, height, structure, function, style, age, and quality attributes for each building with machine learning and large multimodal models. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation, mostly above 80%. Our dataset and results are crucial for global SDGs and urban planning.Data records: A building dataset with a total rooftop area of 23.6 billion square meters in 3,667 natural cities in China, including the attribute of building rooftop, height, structure, function, age, style and quality, as well as the code files used to calculate these data. The deep learning models used are OCRNet, XGBoost, fine-tuned CLIP and Yolo-v8.Supplementary note: The architectural structure, style, and quality are affected by the temporal and spatial distribution of street views in China. Regarding the recognition of building colors, we found that the existing CLIP series model can not accurately judge the composition and proportion of building colors, and then it will be accurately calculated and supplemented by semantic segmentation and image processing. Please contact zhangyec23@mails.tsinghua.edu.cn or ylong@tsinghua.edu.cn if you have any technical problems.Reference Format: Zhang, Y., Zhao, H. & Long, Y. CMAB: A Multi-Attribute Building Dataset of China. Sci Data 12, 430 (2025). https://doi.org/10.1038/s41597-025-04730-5.

The quality of the patterns which are the results of data mining is depends upon the quality ofdata supplied to it. Most of the real time databases which are the sources for data mining posses thedeficiency in terms of completeness, correctness and consistency. Improving the quality of data in termsof completeness is a challenging task. Many methods were proposed for imputing the missing values forhomogenous attributes. This paper proposes a mixed kernel function, which imputes the missing valuesfor the mixed attributes (the independent attributes are heterogeneous). The mixed kernel function is anintegrated unit which adopts the right method to impute the value for right attribute. For the categoricalattribute, our kernel function first assigns the mode value and the iteration continues till the right (mostprobable) value gets converged and for the discrete attribute the mean value gets assigned and theiteration continues till the most probable value is reached. The mixed kernel function is tested with asample database; it proves that it is performing well in terms of accuracy and iterations compared tolinear kernel function.

Serial number, area code, area name, location code, location name, location number, Longitude, Latitude