This dataset contains a well-structured collection of data suitable for building and analyzing Bayesian Networks. The data is organized in an Excel file format, making it easy to manipulate, visualize, and use with various statistical software and programming languages. Node Information: Each column represents a variable (node) in the Bayesian Network. Edge Information: The relationships (edges) between the variables are implicitly defined by the data. Observations: Each row in the dataset corresponds to an observation or a data point, providing the values for each variable. Potential Applications:

Predictive Modeling: Use the dataset to build predictive models that can estimate the probability of certain outcomes based on observed data. Decision Support: Develop decision support systems that can suggest optimal actions based on probabilistic reasoning. Educational Purposes: Ideal for students and educators to understand and demonstrate the principles of Bayesian Networks. Columns:

Healthcare: Predict the likelihood of a patient developing a certain condition based on their medical history and symptoms. Finance: Model the probability of credit default based on financial indicators and borrower characteristics. Marketing: Determine the likelihood of a customer purchasing a product based on their browsing and purchasing history. Acknowledgements: This dataset was compiled and organized to support the research and application of Bayesian Networks. We encourage users to explore, analyze, and share their findings.

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Description and PurposeThese data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2022):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethodsThe survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and LimitationsThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. This data is the weighted data provided by the ETC Institute, which is used in the final published PDF report.The 2022 Annual Community Survey report is available on data.tempe.gov. The individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Wydale HolmesContact E-Mail (author): wydale_holmes@tempe.govContact (maintainer): Wydale HolmesContact E-Mail (maintainer): wydale_holmes@tempe.govData Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset consists of an excel spreadsheet and shapefile representing the locations of simulation nodes used in the AWRA-R model. Some of the nodes correspond to gauging station locations or dam locations whereas other locations represent river confluences or catchment outlets which have no gauging. These are marked as "Dummy". ## Purpose Locations are used as pour points in oder to define reach areas for river system modelling. ## Dataset History Subset of data for the Hunter that was extracted from the Bureau of Meteorology's hydstra system and includes all gauges where data has been received from the lead water agency of each jurisdiction. Simulation nodes were added in locations in which the model will provide simulated streamflow. There are 3 files that have been extracted from the Hydstra database to aid in identifying sites in each bioregion and the type of data collected from each on. These data were used to determine the simulation node locations where model outputs were generated. The 3 files contained within the source dataset used for this determination are: Site - lists all sites available in Hydstra from data providers. The data provider is listed in the #Station as _xxx. For example, sites in NSW are _77, QLD are _66. Some sites do not have locational information and will not be able to be plotted. Period - the period table lists all the variables that are recorded at each site and the period of record. Variable - the variable table shows variable codes and names which can be linked to the period table. Relevant location information and other data were extracted to construct the spreadsheet and shapefile within this dataset. ## Dataset Citation Bioregional Assessment Programme (XXXX) HUN AWRA-R simulation nodes v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/fda20928-d486-49d2-b362-e860c1918b06. ## Dataset Ancestors * Derived From National Surface Water sites Hydstra

This digital dataset was created as part of a U.S. Geological Survey study in cooperation with the Santa Barbara County Water Agency to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Cuyama Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Cuyama Valley 3-D hydrogeologic framework models (3DHFM), that define the elevation, thickness, extent, and lithology-based texture variations of three hydrogeologic units in the Cuyama Valley, CA, groundwater basin. A USGS report that described the construction of 3-D geologic framework and textural models for Cuyama Valley groundwater basin was published in 2013 (Sweetkind and others, 2013). This data release formalizes the input geologic data and model outputs as a digital dataset. The Cuyama Valley 3DHFM incorporates as input data stratigraphic and lithologic information derived from water, monitoring, and oil and gas wells, as well as data from geologic maps and interpreted structure contour maps. Input surface and subsurface data have been reduced to points that define the top elevation and textural or grain-size characteristics of each hydrogeologic units at x,y locations; these point data sets serve as digital input to the framework models. The _location of wells used sources of subsurface stratigraphic and lithologic information are provided as separate point feature classes in a geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also provided in a Microsoft Excel spreadsheet that includes separate TABs for well _location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole. Two types of geologic frameworks were constructed: (1) a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the three basin-fill hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on kriging-based interpolation of classed downhole lithologic data. Each of the frameworks is stored within a second geospatial database as an array of polygonal cells or cell centroids: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a point feature class which contains a mesh of cell centroids that represent model cells that have multiple attributes including x,y _location, elevation, and thickness of each hydrogeologic unit. Computed textural information for each of the three basin-fill hydrogeologic units are stored in separate feature classes of polygonal cells where a single textural variable “percent coarse grained” is an attribute at each x,y _location. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units, and a Data Dictionary that duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles and borehole data are provided in Microsoft Excel spreadsheet. The elevation, thickness, and textural model of each hydrogeologic unit are also released as raster files.

These data include the individual responses for the City of Tempe Annual Business Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Business Survey results are used as indicators for city performance measures. The performance measures with indicators from the Business Survey include the following (as of 2023):1. Financial Stability and Vitality5.01 Quality of Business ServicesThe _location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the _location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.Additional InformationSource: Business SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData DictionaryMethods:The survey is mailed to a random sample of businesses in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used.To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city.Processing and Limitations:The _location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the _location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city.The data are used by the ETC Institute in the final published PDF report.

The U.S. Geological Survey (USGS) entered into cooperative agreements with the Santa Barbara County Water Agency and Vandenberg Space Force Base to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the San Antonio Creek Valley watershed (SACVW). As part of this study, the USGS developed a digital three-dimensional hydrogeologic framework model (HFM). This dataset contains a geospatial database related to the digital HFM, individual geographic information system (GIS) shapefiles from the geodatabase, and borehole data used to support development of the HFM in a Microsoft Excel workbook (*.xlsx extension). The geospatial database contains the following data elements: (1) a boundary polygon that defines the HFM extent; (2) line features that define the location of faults used in the HFM; (3) a point dataset defining location of boreholes used in HFM construction; and (4) a polygon feature class containing interpolated elevations and thicknesses of hydrogeologic units as a cellular array. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units, and a Data Dictionary. Spatial data are also presented as shapefiles and borehole data are provided in Mircosoft Excel spreadsheet.

This dataset contains all match results and bets for all tennis tournaments. ATP Men´s Tour from 2000 till 2023. WTA Women´s Tour from 2007 till 2023.

Original Data

Data Dictionary

Key to results data:

ATP = Tournament number (men) WTA = Tournament number (women) Location = Venue of tournament Tournament = Name of tounament (including sponsor if relevant) Data = Date of match (note: prior to 2003 the date shown for all matches played in a single tournament is the start date) Series = Name of ATP tennis series (Grand Slam, Masters, International or International Gold) Tier = Tier (tournament ranking) of WTA tennis series. Court = Type of court (outdoors or indoors) Surface = Type of surface (clay, hard, carpet or grass) Round = Round of match Best of = Maximum number of sets playable in match Winner = Match winner Loser = Match loser WRank = ATP Entry ranking of the match winner as of the start of the tournament LRank = ATP Entry ranking of the match loser as of the start of the tournament WPts = ATP Entry points of the match winner as of the start of the tournament LPts = ATP Entry points of the match loser as of the start of the tournament W1 = Number of games won in 1st set by match winner L1 = Number of games won in 1st set by match loser W2 = Number of games won in 2nd set by match winner L2 = Number of games won in 2nd set by match loser W3 = Number of games won in 3rd set by match winner L3 = Number of games won in 3rd set by match loser W4 = Number of games won in 4th set by match winner L4 = Number of games won in 4th set by match loser W5 = Number of games won in 5th set by match winner L5 = Number of games won in 5th set by match loser Wsets = Number of sets won by match winner Lsets = Number of sets won by match loser Comment = Comment on the match (Completed, won through retirement of loser, or via Walkover)

Key to match betting odds data:

B365W = Bet365 odds of match winner B365L = Bet365 odds of match loser B&WW = Bet&Win odds of match winner B&WL = Bet&Win odds of match loser CBW = Centrebet odds of match winner CBL = Centrebet odds of match loser EXW = Expekt odds of match winner EXL = Expekt odds of match loser LBW = Ladbrokes odds of match winner LBL = Ladbrokes odds of match loser GBW = Gamebookers odds of match winner GBL = Gamebookers odds of match loser IWW = Interwetten odds of match winner IWL = Interwetten odds of match loser PSW = Pinnacles Sports odds of match winner PSL = Pinnacles Sports odds of match loser SBW = Sportingbet odds of match winner SBL = Sportingbet odds of match loser SJW = Stan James odds of match winner SJL = Stan James odds of match loser UBW = Unibet odds of match winner UBL = Unibet odds of match loser

MaxW= Maximum odds of match winner (as shown by Oddsportal.com) MaxL= Maximum odds of match loser (as shown by Oddsportal.com) AvgW= Average odds of match winner (as shown by Oddsportal.com) AvgL= Average odds of match loser (as shown by Oddsportal.com)

Acknowlegement

Foto von Matthias David auf Unsplash

Tennis-Data would like to acknowledge the following sources which are currently utilised in the compilation of Tennis-Data's results and odds files.

Results: Xscores - http://www.xscores.com/ ATPtennis.com - http://www.atptennis.com/ ATP Tour Rankings and Results Page - http://www.stevegtennis.com/ Livescore - http://www.livescore.net/

Rankings: ATPtennis.com - http://www.atptennis.com/ ATP Tour Rankings and Results Page - http://www.stevegtennis.com/ WTA TOur Rankings - http://www.sonyericssonwtatour.com

Betting odds for matches generally represent the most recent before play starts, as reported by oddsportal.com and the individual bookmakers.