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.

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 dataset was generated from soybean (Glycine max) field trials conducted at the West Tennessee Research and Education Center in Jackson, TN and at the Research and Education Center at Milan in Milan, TN as well as from molecular marker screening conducted at the West Tennessee Research and Education Center in Jackson, TN. Table 3 includes measured data for height, yield, and seed size, and rating data for lodging and seed quality for JTN-5110, 5601T, and select other released germplasm lines and cultivars tested in replicated breeder yield trials in Jackson and Milan, TN from 2010-2016, excluding 2014. This data may be useful in measuring yield gain in future releases of soybean germplasm or cultivars with broad resistance to soybean cyst nematode (SCN; Heterodera glycines). This data should not be used to measure yield gain for elite high-yielding cultivars that do not have broad cyst nematode resistance. Table 5 includes rating data for JTN-5110 and soybeans with established SCN resistance from simple sequence repeat (SSR) markers: Satt309 and Sat_168, associated with rhg1 on chromosome 18; Sat_162, associated with Rhg4 on chromosome 8; and Satt574, associated with cqSCN-005 on chromosome 17. This data may be useful in understanding the role of these molecular regions in SCN resistance for JTN-5110 and parent line Anand. This data should not be used to draw broad conclusions about cyst nematode resistance, in general. Table 7 includes rating data for JTN-5110 and check cultivars from frogeye leafspot (caused by Cercospora sojina) field disease screenings conducted in Milan, TN from 2010-2012. This data may be useful in measuring changes in frogeye leafspot incidence and severity in West Tennessee. This data should not be used to draw broad conclusions or represent different geographic areas. Resources in this dataset:Resource Title: Data dictionary. File Name: data dictionary.csvResource Description: A data dictionary defining the fields in Tables 3, 5, and 7Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 3 - JTN-5110 compared to 5601T. File Name: Table 3 - JTN-5110 compared to 5601T.csvResource Description: Breeder yield trial data from Jackson and Milan, TN from 2010-2016, excluding 2014Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 5 - compiled marker data. File Name: Table 5 - compiled marker data.csvResource Description: Genetic marker data for SSR markers associated with soybean cyst nematode resistance. Screening conducted in Jackson, TN from 2005-2020.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Table 7 - frogeye leafspot evaluation. File Name: Table 7 - frogeye leafspot evaluation.csvResource Description: Data from frogeye leafspot field screening conducted in Milan, TN from 2010-2012.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

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.

Two files are generated daily: 1. Kita_Facilities.csv 2. Kita_Facilities_Performance.csv The file Kita_Einrichtung.csv provides exactly one line with information for each daycare center and is sorted ascendingly according to the first column KITAEinrichtung_EinrNr. The file Kita_Einrichtung_Leistung.csv contains all services offered by the daycare centres and is also sorted ascendingly according to the first column KITAEinrichtung_EinrNr. format The files have been created under the UTF-8 codepage. The end of the line is marked with Carriage Return + Line Feed. The columns are separated by the separator ^ (=hat). Data structure The first line of the files is optimized with the content sep=^ for editing with the program Microsoft Excel. If Excel is not used, this line should be ignored. However, Excel considers all csv files as ANSI-encoded and does not automatically convert to UTF-8, so that, for example, the umlauts are not displayed correctly. Therefore, the use of the excel-internal text conversion wizard is explicitly recommended at this point for the import of the data to Excel, since all necessary settings for an error-free import can be made there. The second line of the files contains the column headings. From the third line to the end of the file, the exported records of the nursery database are located. Column definition of the Kita_Setup.csv file 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. Column definition of the Kita_Setup_Performance.csv file 1. 2.