This template covers section 2.5 Resource Fields: Entity and Attribute Information of the Data Discovery Form cited in the Open Data DC Handbook (2022). It completes documentation elements that are required for publication. Each field column (attribute) in the dataset needs a description clarifying the contents of the column. Data originators are encouraged to enter the code values (domains) of the column to help end-users translate the contents of the column where needed, especially when lookup tables do not exist.

Data Dictionary template for Tempe Open Data.

🇺🇸 미국

As part of Monash University (Helix) Health Research Data Governance strategy a working group was established in 2019 to develop a data dictionary template for use in Health Research.This is the Word document that can be output from the excel version of the template (which is the master) It contains all the metadata (characteristics) that should be included in a health research data dictionary in a standardised format.Instructions for use are contained in the PDF.

This is a data dictionary example we will use in the MVP presentation. It can be deleted after 13/9/18.

Data dictionary and brochure for REAP (Resilient Economic Agricultural Practices). https://data.nal.usda.gov/node/5594

Data Entry Template 2017 includes

Excel templates for Experiment description worksheets, Site characterization worksheets, Management worksheets, Measurement worksheets where experimental unit data are reported, and Information that may be useful to the user, including drop down lists of treatment specific information and ranges of expected values. General and introductory instructions, as well as a Data Validation check are also included.A data dictionary typically provides a detailed description for each element or variable in a dataset or data model. Data dictionaries are used to document important and useful information such as a descriptive name, the data type, allowed values, units, and text description.Dataset citation: (dataset) USDA Agricultural Research Service. (2017). REAP (Resilient Economic Agricultural Practices). Agricultural Research Service. https://doi.org/10.15482/USDA.ADC/1372394.

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.pdfResource 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.

This CSV Template for Altmetric Customers - Publications is part of the CSV Primer Toolkit - a resource to help you decide whether to use CSV files as an integration method of your data within the Altmetric Explorer. Please do download to access the data dictionary as well.

The ESS-DIVE reporting format for file-level metadata (FLMD) provides granular information at the data file level to describe the contents, scope, and structure of the data file to enable comparison of data files within a data package. The FLMD are fully consistent with and augment the metadata collected at the data package level. We developed the FLMD template based on a review of a small number of existing FLMD in use at other agencies and repositories with valuable input from the Environmental Systems Science (ESS) Community. Also included is a template for a CSV Data Dictionary where users can provide file-level information about the contents of a CSV data file (e.g., define column names, provide units). Files are in .csv, .xlsx, and .md. Templates are in both .csv and .xlsx (open with e.g. Microsoft Excel, LibreOffice, or Google Sheets). Open the .md files by downloading and using a text editor (e.g. Notepad or TextEdit). Though we provide Excel templates for the file-level metadata reporting format, our instructions encourage users to 'Save the FLMD template as a CSV following the CSV Reporting Format guidance'. In addition, we developed the ESS-DIVE File Level Metadata Extractor which is a lightweight python script that can extract some FLMD fields following the recommended FLMD format and structure.

Local Law 87 energy audits from 2019-2024. This dataset includes detailed building-level energy audit results, collected using the U.S. Department of Energy's Asset Score Audit Template (Audit Template) tool.

The structure of the LL87 dataset has been updated due to the transition to data collection via Audit Template. To access Audit Template, visit: https://buildingenergyscore.energy.gov.

Field definitions and column names have been expanded and standardized relative the previous dataset (2012-2018). The total number of fields has increased from approximately 1,000 to 2,000 due to greater system-level detail captured by the new reporting tool, including multiple HVAC systems, energy conservation measures, and tenant space characteristics. While a small number of fields remain unchanged, many new fields have been introduced, and several fields from the previous dataset are no longer used or have been replaced. Users should refer to the updated “Column Information” tab in the data dictionary for full details.

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 site This 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).

Building a comprehensive data inventory as required by section 6.3 of the Directive on Open Government: “Establishing and maintaining comprehensive inventories of data and information resources of business value held by the department to determine their eligibility and priority, and to plan for their effective release.” Creating a data inventory is among the first steps in identifying federal data that is eligible for release. Departmental data inventories has been published on the Open Government portal, Open.Canada.ca, so that Canadians can see what federal data is collected and have the opportunity to indicate what data is of most interest to them, helping departments to prioritize data releases based on both external demand and internal capacity. The objective of the inventory is to provide a landscape of all federal data. While it is recognized that not all data is eligible for release due to the nature of the content, departments are responsible for identifying and including all datasets of business values as part of the inventory exercise with the exception of datasets whose title contains information that should not be released to be released to the public due to security or privacy concerns. These titles have been excluded from the inventory. Departments were provided with an open data inventory template with standardized elements to populate, and upload in the metadata catalogue, the Open Government Registry. These elements are described in the data dictionary file. Departments are responsible for maintaining up-to-date data inventories that reflect significant additions to their data holdings. For purposes of this open data inventory exercise, a dataset is defined as: “An organized collection of data used to carry out the business of a department or agency, that can be understood alone or in conjunction with other datasets”. Please note that the Open Data Inventory is no longer being maintained by Government of Canada organizations and is therefore not being updated. However, we will continue to provide access to the dataset for review and analysis.

These applications were developed, with support from the U.S. National Science Foundation, over more than a decade at The Evergreen State College (TESC) to address the needs of forest canopy researchers.

The tools include:

Databank Database Generator: Graphical tool to generate ecological databases from standard and custom database templates. Generated database includes data entry forms, data dictionary, and Ecological Metadata Language (EML) documents.

CanopyView: An interactive visualization tool designed to view tree structure, canopy coverage, and other data stored in Databank-generated databases.

This publication also includes images that were stored in a web content management system operated as part of the project website. (Image annotations from that system have not been recovered).

This corpus contains data files that were generated as part of the NOVIC paper (see above). This includes the complete Object Noun Dictionary, the exact templates used for the multiset prompt templating strategy, and a large dataset of 1.8M LLM-generated and templated captions assorted by target noun. The captions were generated based on all of the target nouns in the Object Noun Dictionary.

The data is directly available at the following links:

Object Noun Dictionary (JSON) Multiset prompt templates LLM-generated captions dataset

Refer to the NOVIC code and Object Noun Dictionary code for examples of how the data can be used, as well as regenerated.

The Slovenian-Japanese online dictionary for Slovenian speaking learners of Japanese was compiled by extracting and converting the Japanese-Slovenian dictionary jaSlo 3.1 (http://hdl.handle.net/11356/1050) into a preliminary Slovene-Japanese dictionary, automatically and then manually cleaning duplicates and inappropriate entries, labelling Slovene headwords with MULTEXT-East part-of-speech and difficulty levels according to the CEFR scale as available in the Core Vocabulary of Slovene (http://hdl.handle.net/11356/1697). The entries were manually edited via Lexonomy (https://www.lexonomy.eu/).

Senses of polysemous words and corresponding translation equivalents were manually glossed with semantic hints, in part also with examples, extracted from the Japanese-Slovene parallel corpus jaSlo (https://nl.ijs.si/jaslo/index-en.html#parallel) and manually adapted for the learner's dictionary. Japanese translational equivalents from different registers were tagged according to their level of politeness and with notes on usage restrictions aimed at dictionary users who are learning Japanese as a foreign language.

The sloJa dictionary is available in TEI Lex0 encoding (https://dariah-eric.github.io/lexicalresources/pages/TEILex0/TEILex0.html) and in an XML encoding derived from the basic template used by Lexonomy.

The Plan ID Crosswalk PUF (CW-PUF) is one of the files that make up the Marketplace PUF. The purpose of the CW-PUF is to map QHPs and SADPs offered through the Marketplaces in 2014 to plans that will be offered through the Marketplaces in 2015. These data either originate from the Plan Crosswalk template (i.e., template field), an Excel-based form used by issuers to describe their plans in the QHP application process, or were generated by CCIIO for use in data processing (i.e., system-generated).This data dictionary describes the variables contained in the CW-PUF. Each record relates to a mapping between a plan offered in 2014 and a plan offered in 2015 at the county or county-zip code level.

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

This dataset is a compilation of Aqueous Spring and Wells Chemistry observations compiled by the Pennsylvania Department of Conservation and Natural Resources - Bureau of Topographic and Geologic Survey, published as a Web feature service, a Web map service, and ESRI service and an Excel spreadsheet for the National Geothermal Data System. The spreadsheet information about the template, notes related to revisions of the template, Resource provider information, the data, a field list (data mapping view), An Analyte Dictionary and a worksheet with vocabularies for use in populating the spreadsheet (data valid terms).

Methodology

This Well Completion Report dataset represents an index of records from the California Department of Water Resources' (DWR) Online System for Well Completion Reports (OSWCR). This dataset is for informational purposes only. All attribute values should be verified by reviewing the original Well Completion Report. Known issues include: - Missing and duplicate records - Missing values (either missing on original Well Completion Report, or not key entered into database) - Incorrect values (e.g. incorrect Latitude, Longitude, Record Type, Planned Use, Total Completed Depth) - Limited spatial resolution: The majority of well completion reports have been spatially registered to the center of the 1x1 mile Public Land Survey System section that the well is located in.

Usage

Date data was updated: 7/5/2022

OSWCR.csv: Records from the California Department of Water Resourcesí Online System of Well Completion Reports.

OSWCR_DataDictionary.csv: Data dictionary for OSWCR.csv

WCRLinks.csv: Table of links to Well Completion Report PDFs. This table is related to OSWCR via the WCRNumber field.

WCRLinks_DataDictionary.csv: Data dictionary for WCRLinks.csv

WellNumbers.csv: Table of state and local well numbers that are stored in OSWCR

GeologicLog_FreeForm.csv: OSWCR provides three different methods to enter lithologic information. The ìFree Formî method allows users to enter any material description they wish for each depth interval.

GeologicLog_QuickPick.csv: OSWCR provides three different methods to enter lithologic information. The ìQuick Pickî method provides a set of standard values for material type, color, and texture, but also allows the user to enter any additional descriptive information.

GeologicLog_USCS.csv: OSWCR provides three different methods to enter lithologic information. The ìUSCS/ASTM D2488î method provides standard values for soil classification, with along with soil color and other descriptive information.

GeologicLog_GeneralizedLithology: This table provides generalized lithology descriptions and texture classifications that have been interpreted from well completion reports by various programs. This table is under development.

CasingData.csv: Casing data that have been entered into OSWCR including the weld type, casing material type, and casing specifications.

AnnularMaterial.csv: Annular fill data that have been entered into OSWCR including the Fill Type and Fill Type Details.

BoreholeInformation.csv: Diameter of the boreholes and the depth range over which that diameter applies

Submittal_Template_For_Suggested_Corrections.xlsx: Template for submitting suggested Well Completion Report database corrections.

Well owner information has been redacted from the attached tables and from the PDFs that these tables link to.

The attached data and data structure are subject to change, and are for informational purposes only.

All values listed in these tables should be verified by reviewing the original Well Completion Report.

Known issues include:

- Missing values (either missing on original Well Completion Report, or not key entered into database)

- Incorrect values (e.g. wrong Latitude, Longitude, Record Type, Planned Use, Total Completed Depth)

The California Department of Water Resources welcomes feedback to improve the accuracy of the Well Completion Report database.

Please email suggested corrections to benjamin.brezing@water.ca.gov using the attached Submittal_Template_For_Suggested_Corrections.xlsx file.