Data Dictionary template for Tempe Open Data.

The Pesticide Data Program (PDP) is a national pesticide residue database program. Through cooperation with State agriculture departments and other Federal agencies, PDP manages the collection, analysis, data entry, and reporting of pesticide residues on agricultural commodities in the U.S. food supply, with an emphasis on those commodities highly consumed by infants and children.This dataset provides information on where each tested sample was collected, where the product originated from, what type of product it was, and what residues were found on the product, for calendar years 1992 through 2023. The data can measure residues of individual compounds and classes of compounds, as well as provide information about the geographic distribution of the origin of samples, from growers, packers and distributors. The dataset also includes information on where the samples were taken, what laboratory was used to test them, and all testing procedures (by sample, so can be linked to the compound that is identified). The dataset also contains a reference variable for each compound that denotes the limit of detection for a pesticide/commodity pair (LOD variable). The metadata also includes EPA tolerance levels or action levels for each pesticide/commodity pair. The dataset will be updated on a continual basis, with a new resource data file added annually after the PDP calendar-year survey data is released.Resources in this dataset:Resource Title: CSV Data Dictionary for PDP.File Name: PDP_DataDictionary.csv. Resource Description: Machine-readable Comma Separated Values (CSV) format data dictionary for PDP Database Zip files. Defines variables for the sample identity and analytical results data tables/files. The ## characters in the Table and Text Data File name refer to the 2-digit year for the PDP survey, like 97 for 1997 or 01 for 2001. For details on table linking, see PDF. Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excelResource Title: Data dictionary for Pesticide Data Program. File Name: PDP DataDictionary.pdf. Resource Description: Data dictionary for PDP Database Zip files. Resource Software Recommended: Adobe Acrobat, url: https://www.adobe.comResource Title: 2023 PDP Database Zip File. File Name: 2023PDPDatabase.zipResource Title: 2022 PDP Database Zip File. File Name: 2022PDPDatabase.zipResource Title: 2021 PDP Database Zip File. File Name: 2021PDPDatabase.zipResource Title: 2020 PDP Database Zip File. File Name: 2020PDPDatabase.zipResource Title: 2019 PDP Database Zip File. File Name: 2019PDPDatabase.zipResource Title: 2018 PDP Database Zip File. File Name: 2018PDPDatabase.zipResource Title: 2017 PDP Database Zip File. File Name: 2017PDPDatabase.zipResource Title: 2016 PDP Database Zip File. File Name: 2016PDPDatabase.zipResource Title: 2015 PDP Database Zip File. File Name: 2015PDPDatabase.zipResource Title: 2014 PDP Database Zip File. File Name: 2014PDPDatabase.zipResource Title: 2013 PDP Database Zip File. File Name: 2013PDPDatabase.zipResource Title: 2012 PDP Database Zip File. File Name: 2012PDPDatabase.zipResource Title: 2011 PDP Database Zip File. File Name: 2011PDPDatabase.zipResource Title: 2010 PDP Database Zip File. File Name: 2010PDPDatabase.zipResource Title: 2009 PDP Database Zip File. File Name: 2009PDPDatabase.zipResource Title: 2008 PDP Database Zip File. File Name: 2008PDPDatabase.zipResource Title: 2007 PDP Database Zip File. File Name: 2007PDPDatabase.zipResource Title: 2006 PDP Database Zip File. File Name: 2006PDPDatabase.zipResource Title: 2005 PDP Database Zip File. File Name: 2005PDPDatabase.zipResource Title: 2004 PDP Database Zip File. File Name: 2004PDPDatabase.zipResource Title: 2003 PDP Database Zip File. File Name: 2003PDPDatabase.zipResource Title: 2002 PDP Database Zip File. File Name: 2002PDPDatabase.zipResource Title: 2001 PDP Database Zip File. File Name: 2001PDPDatabase.zipResource Title: 2000 PDP Database Zip File. File Name: 2000PDPDatabase.zipResource Title: 1999 PDP Database Zip File. File Name: 1999PDPDatabase.zipResource Title: 1998 PDP Database Zip File. File Name: 1998PDPDatabase.zipResource Title: 1997 PDP Database Zip File. File Name: 1997PDPDatabase.zipResource Title: 1996 PDP Database Zip File. File Name: 1996PDPDatabase.zipResource Title: 1995 PDP Database Zip File. File Name: 1995PDPDatabase.zipResource Title: 1994 PDP Database Zip File. File Name: 1994PDPDatabase.zipResource Title: 1993 PDP Database Zip File. File Name: 1993PDPDatabase.zipResource Title: 1992 PDP Database Zip File. File Name: 1992PDPDatabase.zip

An excel template with data elements and conventions corresponding to the openLCA unit process data model. Includes LCA Commons data and metadata guidelines and definitions Resources in this dataset: Resource Title: READ ME - data dictionary. File Name: lcaCommonsSubmissionGuidelines_FINAL_2014-09-22.pdf Resource Title: US Federal LCA Commons Life Cycle Inventory Unit Process Template. File Name: FedLCA_LCI_template_blank EK 7-30-2015.xlsxResource Description: Instructions: This template should be used for life cycle inventory (LCI) unit process development and is associated with an openLCA plugin to import these data into an openLCA database. See www.openLCA.org to download the latest release of openLCA for free, and to access available plugins.

Overview: The Lower Nooksack Water Budget Project involved assembling a wide range of existing data related to WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. This Data Management Plan provides an overview of the data sets, formats and collaboration environment that was used to develop the project. Use of a plan during development of the technical work products provided a forum for the data development and management to be conducted with transparent methods and processes. At project completion, the Data Management Plan provides an accessible archive of the data resources used and supporting information on the data storage, intended access, sharing and re-use guidelines.

One goal of the Lower Nooksack Water Budget project is to make this “usable technical information” as accessible as possible across technical, policy and general public users. The project data, analyses and documents will be made available through the WRIA 1 Watershed Management Project website http://wria1project.org. This information is intended for use by the WRIA 1 Joint Board and partners working to achieve the adopted goals and priorities of the WRIA 1 Watershed Management Plan.

Model outputs for the Lower Nooksack Water Budget are summarized by sub-watersheds (drainages) and point locations (nodes). In general, due to changes in land use over time and changes to available streamflow and climate data, the water budget for any watershed needs to be updated periodically. Further detailed information about data sources is provided in review packets developed for specific technical components including climate, streamflow and groundwater level, soils and land cover, and water use.

Purpose: This project involves assembling a wide range of existing data related to the WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. Data will be used as input to various hydrologic, climatic and geomorphic components of the Topnet-Water Management (WM) model, but will also be available to support other modeling efforts in WRIA 1. Much of the data used as input to the Topnet model is publicly available and maintained by others, (i.e., USGS DEMs and streamflow data, SSURGO soils data, University of Washington gridded meteorological data). Pre-processing is performed to convert these existing data into a format that can be used as input to the Topnet model. Post-processing of Topnet model ASCII-text file outputs is subsequently combined with spatial data to generate GIS data that can be used to create maps and illustrations of the spatial distribution of water information. Other products generated during this project will include documentation of methods, input by WRIA 1 Joint Board Staff Team during review and comment periods, communication tools developed for public engagement and public comment on the project.

In order to maintain an organized system of developing and distributing data, Lower Nooksack Water Budget project collaborators should be familiar with standards for data management described in this document, and the following issues related to generating and distributing data: 1. Standards for metadata and data formats 2. Plans for short-term storage and data management (i.e., file formats, local storage and back up procedures and security) 3. Legal and ethical issues (i.e., intellectual property, confidentiality of study participants) 4. Access policies and provisions (i.e., how the data will be made available to others, any restrictions needed) 5. Provisions for long-term archiving and preservation (i.e., establishment of a new data archive or utilization of an existing archive) 6. Assigned data management responsibilities (i.e., persons responsible for ensuring data Management, monitoring compliance with the Data Management Plan)

This resource is a subset of the LNWB Ch03 Data Processes Collection Resource.

Context

This dataset was built as a supplementary to "[European Soccer Database][1]". It includes data dictionary, extraction of detailed match information previously contains in XML columns.

Content

• PositionReference.csv: A reference of position x, y and map them to actual position in a play court.
• DataDictionary.xlsx: Data dictionary for all XML columns in "Match" data table.
• card_detail.csv: Detailed XML information extracted form "card" column in "Match" data table.
• corner_detail.csv: Detailed XML information extracted form "corner" column in "Match" data table.
• cross_detail.csv: Detailed XML information extracted form "cross" column in "Match" data table.
• foulcommit_detail.csv: Detailed XML information extracted form "foulcommit" column in "Match" data table.
• goal_detail.csv: Detailed XML information extracted form "goal" column in "Match" data table.
• possession_detail.csv: Detailed XML information extracted form "possession" column in "Match" data table.
• shotoff_detail.csv: Detailed XML information extracted form "shotoffl" column in "Match" data table.
• shoton_detail.csv: Detailed XML information extracted form "shoton" column in "Match" data table.

Acknowledgements

Original data comes from [European Soccer Database][1] by Hugo Mathien. I personally thank him for all his efforts.

Inspiration

Since this is a open dataset with no specific goals / objectives, I would like to explore the following aspects by data analytics / data mining:

1. Team statistics Including overall team ranking, team points, winning possibility, team lineup, etc. Mostly descriptive analysis.
2. Team Transferring Track and study team players transferring in the market. Study team's strength and weakness, construct models to suggest best fit players to the team.
3. Player Statistics Summarize player's performance (goal, assist, cross, corner, pass, block, etc). Identify key factors of players by position. Based on these factors, evaluate player's characteristics.
4. Player Evolution Construct model to predict player's rating of future.
5. New Player's Template Identify template and model player for young players cater to their positions and characteristics.
6. Market Value Prediction Predict player's market value based on player's capacity and performance.
7. The Winning Eleven Given a season / league / other criteria, propose the best 11 players as a team based on their capacity and performance.

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

This content has been updated - view the USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes, Release 1.1 - December 2015 at https://doi.org/10.15482/USDA.ADC/1324677 for version 1.1 data, or visit the USDA Special Interest Database on Flavonoids dataset at https://doi.org/10.15482/USDA.ADC/1178142 for links to the most current data.

This database was developed with support from the Office of Dietary Supplements, National Institutes of Health for flavonoid intake studies. The database is a useful tool for flavonoid intake and health outcome studies for any population globally. It contains data for 29 individual flavonoid compounds in six subclasses of flavonoids for every food in a subset of 2,926 food items which provide the basis for the Food and Nutrient Database for Dietary Studies (FNDDS 4.1). Proanthocyanidins data are not included at the present time. For flavonoid intake data for the U.S. population based on NHANES 2007-08, please refer to the Food Surveys Research Group website. Resources in this dataset:Resource Title: FDB-EXP.accdb. File Name: FDB-EXP.zipResource Description: (Local copy of the Access Database file - 10/26/2016)

This file contains USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes imported into a MS Access database. It includes relationships between files. You need MS Access 2007 to use this file. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference.Resource Title: READ ME - USDA’s Expanded Flavonoid Database for the Assessment of Dietary Intakes Documentation and User Guide. File Name: FDB-EXP.pdfResource Description: (Local copy of the PDF file - 10/26/2016)

Information regarding documentation, development of the database, limitations, format, and references.Resource Title: Data Dictionary. File Name: FDB-EXP-DD_2.pdf

This is a list containing sense IDs from Open Slovene WordNet 1.0 (OSWN; http://hdl.handle.net/11356/1888) and the Digital Dictionary Database of Slovene (DDDS) developed by the Centre for Language Resources and Technologies of the University of Ljubljana.

The file consists of four columns containing the following data:

• synset ID from the Open Slovene WordNet;
• sense ID from the Digital Dictionary Database of Slovene;
• lexical unit ID from the Digital Dictionary Database of Slovene;
• form of the lexical unit from the Digital Dictionary Database of Slovene.

The list allows the end user to access OSWN data through the DDDS API (documented at https://wiki.cjvt.si/books/digital-dictionary-database/chapter/rest-api), namely which senses and lexical units from DDDS are assigned to a certain synset ID in OSWN.

The ESS-DIVE location metadata reporting format provides instructions and templates for reporting a minimum set of metadata for discrete point locations in geographic space represented by x, y, and z coordinates. This format was created based on a need for earth and environmental science researchers to more consistently provide metadata about locations where they conduct studies. To create the format, we incorporated elements from ESS-DIVE’s community reporting formats as well as 12 additional data standards or other data resources (e.g., databases, data systems, or repositories). In the template, we ask researchers to indicate unique locations using Location IDs and indicate hierarchies of locations through parent location IDs. We also provide additional optional fields for researchers to indicate how they measured the point location and the date and time that the location was first used as a research siteThis dataset contains support documentation for the reporting format (README.md and instructions.md), a terminology guide (guide.md), a crosswalk indicating how this reporting format relates to existing standards and data resources (Location_metadata_crosswalk.csv), a data dictionary (dd.csv), file-level metadata (flmd.csv), and the location metadata templates in both CSV (Location_metadata_template.csv) and Excel formats (Location_metadata_template.xlsx).

Feedstock readiness level evaluations are performed for a specific feedstock-conversion process combination and for a particular region. FSRL evaluations complement evaluations of Fuel Readiness Level (FRL) and environmental progress. The table in this dataset collates the results of the FSRL evaluations listed under the Farm2Fly Ag Data Commons datasets to enable users to quickly identify, review, and compare available evaluations. Evaluation scores are explained in the FSRL Checklist and Template available on the NAL Ag Data Commons - scores range from 1 to 9, with higher values indicating greater maturity of the feedstock in each area of assessment (production, market development, policy evaluation and compliance, and linkage to conversion efficiency). The overall score reflects the lowest maturity area within the four assessment areas. Summary data files of the compiled evaluations will be added to the repository on a quarterly basis, and are cumulative (the last quarter will contain the compiled evaluation results from the entire year). To access the newest evaluations that are not yet included in the most recent compilation, visit the Farm 2 Fly program page to view all datasets. The date of update/submission is indicated in the title of the file. Resources in this dataset:Resource Title: FSRL Evaluations Summary Table_Q2_2017. File Name: FSRL Evaluations Summary Table_Q2_2017.xlsxResource Description: As of June 2017: This document summarizes all available databases on the Farm2Fly repository at the Ag Data Commons to enable users to find, identify, and compare among evaluations. Contains graph and grid-enabled data for data visualization. Categories for comparison include Feedstock, Process, and Region, crossed with Ratings for Production, Market, Policy, Linkage, and Overall Score. Ratings are based on the Feedstock Readiness Level scale (1-9), with higher values indicating greater maturity of the process. The Overall Score reflects the least mature of the four assessment areas within a given evaluationResource Title: FSRL Summary Table Data Dictionary. File Name: Data Dictionary Summary Table FSRL 2.csvResource Description: Data dictionary describing the format and entry of data for the FSRL evaluations summary table.Resource Title: FSRL Evaluations Summary Table_Q2_2017.csv. File Name: FSRL Evaluations Summary Table_Q2_2017.csvResource Description: As of June 2017: This document summarizes all available databases on the Farm2Fly repository at the Ag Data Commons to enable users to find, identify, and compare among evaluations. Contains graph and grid-enabled data for data visualization. Categories for comparison include Feedstock, Process, and Region, crossed with Ratings for Production, Market, Policy, Linkage, and Overall Score. Ratings are based on the Feedstock Readiness Level scale (1-9), with higher values indicating greater maturity of the process. The Overall Score reflects the least mature of the four assessment areas within a given evaluation

We study the behaviour and cognition of wild apes and other species (elephants, corvids, dogs). Our video archive is called the Great Ape Dictionary, you can find out more here www.greatapedictionary.com or about our lab group here www.wildminds.ac.uk We consider these videos to be a data ark that we would like to make as accessible as possible. While we are unable to make the original video files open access at the present time you can search this database to explore what is available, and then request access for collaborations of different kinds by contacting us directly or through our website. We label all videos in the Great Ape Dictionary video archive with basic meta-data on the location, date, duration, individuals present, and behaviour present. Version 1.0.0 contains current data from the Budongo East African chimpanzee population (n=13806 videos). These datasets are being updated regularly and new data will be incorporated here with versioning. As well as the database there is a second read.me file which contains the ethograms used for each variable coded, and a short summary of other datasets that are in preparation for subsequent version(s). If you are interested in these data please contact us. Please note that not all variables are labeled for all videos, the detailed Ethogram categories are only available for a subset of data. All videos are labelled with up to 5 Contexts (at least one, rarely 5). If you are interested in finding a good example video for a particular behaviour, search for 'Library' = Y, this indicates that this clip contains a very clear example of the behaviour.

The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS (Natural Resources Conservation Service). The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings. The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses. SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database. A complete SSURGO dataset consists of:

GIS data (as ESRI® Shapefiles) attribute data (dbf files - a multitude of separate tables) database template (MS Access format - this helps with understanding the structure and linkages of the various tables) metadata

Resources in this dataset:Resource Title: SSURGO Metadata - Tables and Columns Report. File Name: SSURGO_Metadata_-_Tables_and_Columns.pdfResource Description: This report contains a complete listing of all columns in each database table. Please see SSURGO Metadata - Table Column Descriptions Report for more detailed descriptions of each column.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Metadata - Table Column Descriptions Report. File Name: SSURGO_Metadata_-_Table_Column_Descriptions.pdfResource Description: This report contains the descriptions of all columns in each database table. Please see SSURGO Metadata - Tables and Columns Report for a complete listing of all columns in each database table.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Data Dictionary. File Name: SSURGO 2.3.2 Data Dictionary.csvResource Description: CSV version of the data dictionary

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are 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.

Hunter Asset Database v2.4 supersedes previous versions of the Hunter Asset database. In this V2.4 database:

(1) Updated M2 test results for 86 assets from the external review

(2) Updated asset names for two assets (AID: 8642 and 8643) required from the external review

(3) Created Draft Water Dependent Asset Register file using the template V5

This dataset contains the Asset database (.mdb), a Geodatabase version for GIS mapping purposes (.gdb), the draft Water Dependent Asset Register spreadsheet, a data dictionary document, and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below.

The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separate file geodatabase joined by AID/ElementID.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. A report on the WAIT process for the Hunter is included in the zip file as part of this dataset.

Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Hunter subregion are found in the "AssetList" table of the database.

Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of " HUN_asset_database_doc_20151120.doc", located in this filet.

The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Detailed information describing the database structure and content can be found in the document " HUN_asset_database_doc_20151120.doc" located in this file.

Some of the source data used in the compilation of this dataset is restricted.

Dataset History

OBJECTID VersionID Notes Date_

1 1 Initial database. 29/08/2014

3 1.1 Update the classification for seven identical assets from Gloucester subregion 16/09/2014

4 1.2 Added in NSW GDEs from Hunter - Central Rivers GDE mapping from NSW DPI (50 635 polygons). 28/01/2015

5 1.3 New AIDs assiged to NSW GDE assets (Existing AID + 20000) to avoid duplication of AIDs assigned in other databases. 12/02/2015

6 1.4 "(1) Add 20 additional datasets required by HUN assessment project team after HUN community workshop

       (2) Turn off previous GW point assets (AIDs from 7717-7810 inclusive) 

       (3) Turn off new GW point asset (AID: 0)

       (4) Assets (AIDs: 8023-8026) are duplicated to 4 assets (AID: 4747,4745,4744,4743 respectively) in NAM subregion . Their AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using   

         values from that NAM assets.

      (5) Asset (AID 8595) is duplicated to 1 asset ( AID 57) in GLO subregion . Its AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that GLO assets.

      (6) 39 assets (AID from 2969 to 5040) are from NAM Asset database and their attributes were updated to use the latest attributes from NAM asset database 

     (7)The databases, especially spatial  database, were changed such as duplicated attributes fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to 

        the spatial data"  16/06/2015

7 2 "(1) Updated 131 new GW point assets with previous AID and some of them may include different element number due to the change of 77 FTypes requested by Hunter assessment project team

      (2) Added 104 EPBC assets, which were assessed and excluded by ERIN

      (3) Merged 30 Darling Hardyhead assets to one (asset AID 60140) and deleted another 29 

      (4) Turned off 5 assets from community workshop (60358 - 60362) as they are duplicated to 5 assets from 104 EPBC excluded assets

     (5) Updated M2 test results

     (6) Asset Names (AID: 4743 and 4747) were changed as requested by Hunter assessment project team (4 lower cases to 4 upper case only). Those two assets are from Namoi asset database and their asset names 

       may not match with original names in Namoi asset database.

     (7)One NSW WSP asset (AID: 60814) was added in as requested by Hunter assessment project team. The process method (without considering 1:M relation) for this asset is not robust and is different to other NSW 

      WSP assets. It should NOT use for other subregions. 

     (8) Queries of Find_All_Used_Assets and Find_All_WD_Assets in the asset database can be used to extract all used assts and all water dependant assts" 20/07/2015

8 2.1 "(1) There are following six assets (in Hun subregion), which is same as 6 assets in GIP subregion. Their AID, Asset Name, Group, SubGroup, Depth, Source and ListDate are using values from GIP assets. You will

         not see AIDs from AID_from_HUN in whole HUN asset datable and spreadsheet anymore and you only can see AIDs from AID_from_GIP ( Actually (a) AID 11636 is GIP got from MBC (B) only AID, Asset Name 

         and ListDate are different and changed)

      (2) For BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx, (a) Extracted long ( >255 characters) WD rationale for 19 assets (AIDs:  

         8682,9065,9073,9087,9088,9100,9102,9103,60000,60001,60792,60793,60801,60713,60739,60751,60764,60774,60812 ) in tab "Water-dependent asset register" and 37 assets (AIDs: 

         5040,8651,8677,8682,8650,8686,8687,8718,8762,9094,9065,9067,9073,9077,9081,9086,9087,9088,9100,9102,9103,60000,60001,60739,60742,60751,60713,60764,60771,

         60774,60792,60793,60798,60801,60809,60811,60812) in tab "Asset list" in 1.30 Excel file (b) recreated draft BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx 

      (3) Modified queries (Find_All_Asset_List and Find_Waterdependent_asset_register) for (2)(a)"  27/08/2015

9 2.2 "(1) Updated M2 results from the internal review for 386 Sociocultural assets

      (2)Updated the class to Ecological/Vegetation/Habitat (potential species distribution) for assets/elements from sources of WAIT_ALA_ERIN, NSW_TSEC, NSW_DPI_Fisheries_DarlingHardyhead"  8/09/2015

10 2.3 "(1) Updated M2 results from the internal review

           \* Changed "Assessment team do not say No" to "All economic assets are by definition water dependent"

          \* Changed "Assessment team say No" : to "These are water dependent, but excluded by the project team based on intersection with the PAE is negligible"

          \* Changed "Rivertyles" to "RiverStyles""  22/09/2015

11 2.4 "(1) Updated M2 test results for 86 assets from the external review

      (2) Updated asset names for two assets (AID: 8642 and 8643) required from the external review

      (3) Created Draft Water Dependent Asset Register file using the template V5"  20/11/2015

Dataset Citation

Bioregional Assessment Programme (2015) Asset database for the Hunter subregion on 20 November 2015. Bioregional Assessment Derived Dataset. Viewed 07 June 2018, http://data.bioregionalassessments.gov.au/dataset/0bbcd7f6-2d09-418c-9549-8cbd9520ce18.

Dataset Ancestors

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Travelling Stock Route Conservation Values

• Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129

• Derived From NSW Wetlands

• Derived From Climate Change Corridors Coastal North East NSW

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From Climate Change Corridors for Nandewar and New England Tablelands

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Asset database for the Hunter subregion on 27 August 2015

• Derived From [Birds Australia - Important

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F4cd683ac2eb5f1ac7c70102c62e3f660%2F_b8f65cae-7e1d-4f8c-a291-6556c8ad2295.jpeg?generation=1758107205268513&alt=media" alt="">

The database currently contains data from January 1950 to April 2025, as entered by NOAA's National Weather Service (NWS). Bulk data are available in comma-separated files (CSV). These files can be viewed in Excel and other spreadsheet applications.

https://www.ncdc.noaa.gov/stormevents/ftp.jsp

Disclaimer

Storm Data is an official publication of the National Oceanic and Atmospheric Administration (NOAA) which documents the occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce. In addition, it is a partial record of other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occurs in connection with another event. Some information appearing in Storm Data may be provided by or gathered from sources outside the National Weather Service (NWS), such as the media, law enforcement and/or other government agencies, private companies, individuals, etc. An effort is made to use the best available information but because of time and resource constraints, information from these sources may be unverified by the NWS. Therefore, when using information from Storm Data, customers should be cautious as the NWS does not guarantee the accuracy or validity of the information. Further, when it is apparent information appearing in Storm Data originated from a source outside the NWS (frequently credit is provided), Storm Data customers requiring additional information should contact that source directly. In most cases, NWS employees will not have the knowledge to respond to such requests. In cases of legal proceedings, Federal regulations generally prohibit NWS employees from appearing as witnesses in litigation not involving the United States.

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Data Dictionary

Please see Data Dictionary

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This database, structured as a text-file with one line per structural entry per concept, associates symbolic structures with the names of lexical concepts such as scientist, art, war, politics, gun, etc.

Each line specifies an "open-form" structure which uses an open-ended set of predicates, and one one or canonical rewritings of this structure using a small, closed set of predicates (essentially semantic primitives).

For each open-form and closed-form structure, the symbol marked with an asterix denotes the entity that the structure is about. The same structure that relates scientist to science may be associated with "scientist" and with "science" but the asterix will be on a different symbol in each case. During analogically structure-mapping, a symbol marked with an asterix can only be mapped to another symbol that is so marked; this prevents a structure about art or magic, say, being mapped to a structure about scientists, say; rather, scientists should be mapped to artists or magicians while science is mapped to art or magic.

For each open-form and closed-form structure we also provide an abstraction form in which non-predicate entities are replaced with numeric variables. Two different structures, for scientist and artist say, will be superficially different but they may have the same abstraction form, which means they can be structure-mapped consistently. Abstract forms can be used as keys in a hash map that maps to all of the specific structures that instantiate those abstractions. In this way, retrieving possible analogues becomes very efficient.

Each abstraction form has a number that indicates the number of specific forms it is associated with. If this number is 1, it means that the form is unique; if greater than 1, it indicates there are other, different specific forms that have the same abstract form (and can thus be retrieved as a potential analogue).

This digital data release contains gridded elevation surfaces for twenty-six (26) subsurface horizons, a grid of the estimated thickness of strata eroded during the Cenozoic, and fault traces at the level of the Precambrian surface from a previously published 3D geologic model of the Anadarko Basin Province (Higley and others, 2014). In the original release of the 3D model, elevation surfaces were exported to a Zmap interchange file format, potentially limiting access to the data for users without access to specialized software. In this digital data release, elevation surfaces are provided in more readily accessible formats and modeled horizons are given more thorough stratigraphic descriptions than provided in the original model documentation. Within the AnadarkoBasin_Higley geodatabase, the GeologicMap feature dataset contains a line feature class (ContactsAndFaults) containing fault traces at the level of the Precambrian surface, a polyline representing the approximate Anadarko Basin boundary, and model area boundary digitized from the original publication; a polygon feature dataset (MapUnitPolys) with the approximate Anadarko Basin boundary and the model area boundary; and raster datasets for the 26 subsurface horizons and a single thickness grid representing the estimated eroded thickness of strata. Nonspatial tables define the data sources used (DataSources), define terms used in the dataset (Glossary), and provide a description of the modeled surfaces (DescriptionOfMapUnits) that provides the user with far greater stratigraphic detail than the original publication. Separate file folders contain the vector data in shapefile format, the raster data in ASCII and GeoTiff file formats, and the tables as comma-separated values file format. In addition, a tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables (EntityAndAttributes). Elevation surfaces exported from the 3D model in Zmap interchange file format and additional datasets are available through the original publication (Higley and others, 2014: https://pubs.usgs.gov/dds/dds-069/dds-069-ee/).

SDR 2.0 Cotton File: Cumulative List of Variables in the Surveys of the SDR Database is a comprehensive data dictionary, in Microsoft Excel format. Its main purpose is to facilitate the overview of 88118 variables (i.e. variable names, values, and labels) available in the original (source) data files that we retrieved automatically for harmonization purposes in the SDR Project. Information in the Cotton File comes from 215 source data files that comprise ca. 3500 national surveys administered between 1966 and 2017 in 169 countries or territories, as part of 23 international survey projects. The COTTON FILE SDR2 is a product of the project Survey Data Recycling: New Analytic Framework, Integrated Database, and Tools for Cross-national Social, Behavioral and Economic Research, financed by the US National Science Foundation (PTE Federal award 1738502). We thank the Ohio State University and the Institute of Philosophy and Sociology, Polish Academy of Sciences, for organizational support.

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Switched source data from collecting records from each state independently to using the WestDAAT dataset v1.0 - Initial public release Description In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, as well as geospatial data defining the areas in which water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. In addition, while all states make their water rights publicly available, each provides their records in unique formats, meaning that file types, field availability, and terms vary from state to state. This leads to additional challenges to managing resources which crossmore » state lines, or conducting consistent multi-state water analyses. For the first version of HarDWR, we collected the water rights databases from 11 Western States of the United States. In order to preform regional analyses with the collected data, the raw records had to be harmonized into one single format. The Water Data Exchange (WaDE) is a program dedicated to the sharing of water-related data for the Western U.S. in a singular consistent format. Created by the Western States Water Council (WSWC) to facilitate the collection and dissemination of water data among WSWC's member states and the public, WaDE provides an important service for those interested in water resource planning and management in their focus region. Of the services which WaDE provides, the one of the most interesting is the WestDAAT dataset, which is a collection of water rights data provided by the 18 WSWC member states that have been standardized into a single format, much like we had done on a more limited scale with HarDWR v1. For this version of HarDWR we decided to use WestDAAT, specifically a snapshot created in Feburary 2024, as our water rights source data. A full explanation of the benefits gained from this switch can be found in the description of the updated Harmonized Water Rights Records v2.0, but in short it has allowed us to focus more of our efforts on answering research questions and gaining a more realistic understanding of how water rights are allocated. For more information on how the data for WestDAAT was collected, please see the WaDE data summary. Terms of Use While WaDE works directly with the state agencies to collect and standardize the water rights records, the ultimate authority for the water rights data remains the individual states. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. As several of the states update their water rights databases daily, the information provided here is not the latest possible, and should not be used for legal purposes. WestDAAT itself has irregular updates. Additional questions about the data the source states provided should be directed to the respective state agencies (see methods.csv and organization.csv files described below). In addition, although data was presented here was not collected directly from the states, several states requested specifically worked disclaimers when sharing their data. These disclaimers are included here as an acknowledgement from where the water rights data is primarily sourced. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." File Descriptions The unmodified February, 2024 WestDAAT snapshot is composed of nine files. Below is a brief description of each file, as well as how they were utilized for HarDWR. WaDEDataDictionaryTerms.xlsx: As the file's name implies, this is a data dictionary for all of the below named files. This file describes the column names for each of the following files, with the exception of citation.txt which does not have any columns. The descriptions for each file are divided by tab,with the same name as their associated file, within this document. allocationamount.csv: The "main" file of the group, it contains the water right records for each state. Of particular note, each water right is broken down into one or more water allocations. Allocations may be withdrawn from one or more locations, or even multiple allocations associated with a particular location. This is a more subtle and realistic representation of how water is used than what was available in the first version of HarDWR. For the records from some states, this can mean that multiple allocations listed under a single right will appear as rows within this file. citation.txt: A combination of contact information for WaDE personnel, disclaimer about how the data should be used, and guidelines for citing WestDAAT. methods.csv: A file describing the source and method by which WaDE collected water rights data from each state. organization.csv: A file listing the water rights authoritative agencies for each state. sites.csv: This file provides the geographic, and other descriptors, of the physical location of allocations, called 'sites'. To reiterate, it is possible for one allocation to be associated with multiple sites, as well as one site to be associated with multiple allocations. The two descriptors which we were most interested in where the site's coordinates, as well as whether the site was classified as a Point of Diversion (POD) or a Place of Use (POU). As a general rule, PODs are geographic points, while POUs are areas typically represented as property boundaries or irregularly shaped polygons. sites_pouGeometry.csv: For those allocations with a POU site, this file contains the defining points for the associated polygons. variables.csv: A file describing the units in which an allocation's water amount is reported within WestDAAT. This information is essentially a repeat of the 'AllocationFlow_CFS' and 'AllocationVolume_AF' columns within allocationamount.csv, at least for our purposes. watersources: This file describes the source of water from which each site extracts from. For our purposes, this table was used to determine whether the water came from Surface Water, Groundwater, or Unspecified Water.« less

The Data Package includes a set of csv files of input meteorological parameters for locations of 17 meteorological stations within the East River watershed. These meteorological datasets were downloaded from the (1) PRISM database--monthly precipitation, air temperature (minimum, mean, and maximum), vapor pressure deficit (minimum and maximum), and dewpoint temperature, and (2) NCEP/NCAR Reanalysis database – wind database. The datasets were used for calculations of the Potential Evapotranspiration (ETo), Actual Evapotranspiration (ET), Standard Precipitation Index (SPI) , Standard Evapotranspiration-Precipitation Index (SPEI) for the period from 1966 to 2021. The main research questions addressed are: the evaluation of the long-term temporal trends of climatic parameters, hierarchical clustering, and areal mapping/zonation of the East River watershed. Calculations were conducted in the Rstudio environment. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. The input datasets were downloaded from (a) PRISM database (the Northwest Alliance for Computational Science and Engineering at the Oregon State University), and (b) NCEP/NCAR Reanalysis database.

The raw databases from the Maternal and Child Protection (MCP) component of the DREES Social Assistance Survey are available here. These are data aggregated at departmental level, as reported by the departmental councils during the various collections. They concern the part relating to the activity of PMI services: consultation points, personnel, actions in favour of children, prenatal and postnatal actions in favour of (future) mothers and actions in the field of family planning and education. For data relating to the activity of the services, this dataset includes: - For the years 2007 to 2015, the data of each year and the associated code dictionaries are grouped in a single file in .zip format. - As of 2016, one file per year, in .zip format, includes the database and the associated code dictionary. For personnel data, this dataset contains a single file containing data from the years 2007 to 2022. In addition, the data transmitted by the departments of the departmental councils may be missing or partial. Some of them may have been estimated or possibly corrected. These revised national and departmental series on PMI staff and service activities are available here .