Excel spreadsheet containing the full schema for the North Coast Address Management Solution (NAMS_V2.5)

This dataset contains the ontology schema.org

The ckanext-search-schema extension enhances CKAN by allowing administrators control over the search engine schema used by the platform. This configuration enables customization of how datasets and resources are indexed and searched, potentially improving search relevance and performance. It leverages SOLR's capabilities for defining field types. Key Features: Search Schema Management: Facilitates the management of the search engine schema definitions within CKAN, providing a mechanism to tailor the data indexing process. SOLR Field Type Integration: Utilizes SOLR 8 classes to define data types, enabling precise control over how different data fields are indexed and queried, as referenced in the linked Apache SOLR documentation. Customizable Data Indexing: By modifying schema configurations, administrators can optimize data indexing processes to better match their specific data and user search requirements. Technical Integration: The extension integrates with CKAN by adding 'search-schema' to the ckan.plugins setting in the CKAN configuration file. This allows the extension to hook into CKAN's core functionality and modify the Solr schema creation and management. The exact mechanisms of integration (e.g. plugins or alterations to middleware) are unspecified in the provided documentation. Benefits & Impact: By implementing the ckanext-search-schema extension, CKAN administrators gain the ability to customize their CKAN search functionality that will meet unique requirements. This can lead to improved search accuracy, efficiency, and overall user experience, where specific and custom data indexing models can be established.

jinaai_jina-embeddings-v2-base-code-7232024-77ap-webapp Dataset

  Dataset Description

The dataset "Database schema for a data management system" is a generated dataset designed to support the development of domain specific embedding models for retrieval tasks.

  Associated Model

This dataset was used to train the jinaai_jina-embeddings-v2-base-code-7232024-77ap-webapp model.

  How to Use

To use this dataset for model training or evaluation, you can load it… See the full description on the dataset page: https://huggingface.co/datasets/fine-tuned/jinaai_jina-embeddings-v2-base-code-7232024-77ap-webapp.

The ckanext-validation-schema-generator extension for CKAN provides functionality to automatically generate table schemas for resources, specifically targeting tabular data formats. It aims to streamline the process of defining and applying metadata schemas to resources within a CKAN instance, ensuring data consistency and facilitating validation. The extension relies on the CKAN datastore and provides API endpoints to initiate, monitor, and manage the schema generation process. Key Features: Automatic Schema Generation: Automatically generates table schemas based on the content of tabular data resources stored in the CKAN datastore. This removes the need for manual schema creation. Configurable Schema Field Names: Allows specifying custom field names in both datasets and resources to store the generated schema, providing flexibility in how schema information is managed. Default values are available for both resource and package (dataset) configurations. Schema Edition Option: Allows administrator or maintainer to edit the generated data schema before applying it to dataset or resource. API Key Support for Remote Resources: Enables the inclusion of API keys in requests for remote or private resources, ensuring secure access during schema generation. The API key can be configured via a setting. Asynchronous Job Management: Utilizes CKAN's job queue to handle schema generation in the background, preventing delays during user interactions. This allows larger or more complex resources to be processed without blocking the user interface. Comprehensive API Endpoints: Provides dedicated API endpoints (vsg_generate, vsg_status, vsg_update, vsg_apply) to trigger schema generation, retrieve status updates, update task data, and apply the generated schema to resources or datasets. Configurable Job Timeout: Permits configuration of the maximum time allowed for schema generation, preventing jobs from running indefinitely. Technical Integration: The extension integrates with CKAN by adding a plugin that provides new API actions. These actions interact with CKAN's task management system to queue and track schema generation jobs. The extension also utilizes CKAN's configuration system, allowing administrators to customize settings such as API keys, field names, and timeouts within the CKAN configuration file (e.g., ckan.ini). The generated schema is stored as metadata associated with the resource or dataset, depending on the apply_for parameter used with the vsg_apply API action. Benefits & Impact: By automating schema generation, ckanext-validation-schema-generator reduces the manual effort required to manage metadata for tabular data resources. It facilitates data validation and ensures consistency across datasets. The ability to edit generated schemas before applying them grants flexibility and helps tailor the definitions to specific needs.

CMRS provides improved workload management and processes related to fulfilling requests for military Records.

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 Lower Nooksack Water Budget (LNWB) Collection Resource.

This is a data schema created using Agri-food Data Canada's Semantic Engine . Data schemas describe data that are collected on an ongoing basis from research centres, and provide add-on documentation that enhances the value of raw data. This schema is used to organize and manage data related to health events for dairy cattle within the DairyComp Herd Management Software. It structures how health events are recorded in the DairyComp Herd Management Software, and enables the integration of health event records with other data such as milk production, breeding records, and preventive treatments. Data described in this schema is sample data, to request access to the related data, please visit the Ontario Dairy Research Centre Data Portal.

We share an XML Schema for describing data collected from a laboratory experiment in an organized way that attempts to communicate scientific intent and the phased process of data creation. It was developed as part of laboratory information management system (LIMS) prototyping effort by a collaboration at NIST between the NEXUS Microscopy facility (managed within the Materials Science and Engineering Division) and the Material Measurement Lab's Office of Data and Informatics. When a facility user reserves and then operates one of the facility microscopes, the LIMS system automatically gathers metadata from the reservation calendar, the files created by the instrument, and potentially other sources and organizes them into an XML document describing what was done. The system uses the NIST Configurable Data Curation System (CDCS) to display the document as a scientist-oriented summary of the microscopy experiment. The schema is fairly general and free of specifics tied to microscopy (apart perhaps from a reference to samples). This schema is expected to be a useful prototype not only across LIMS efforts within NIST but possibly more broadly across the global microscopy research community. The XML schema is fully documented internally, including definitions of all elements and types.

The Custom Schema extension for CKAN enables administrators to extend the default dataset schema with custom metadata fields. This allows users to provide more detailed information about datasets than is possible with the standard CKAN fields. By enabling the inclusion of specific, user-defined fields, the extension aims to improve data discoverability and accuracy. Key Features: Customizable Metadata Fields: Allows administrators to add new, custom metadata fields to the dataset schema, tailored to specific needs and data types. Dataset Edit Integration: The added custom fields are seamlessly integrated into the dataset edit form, providing a user-friendly interface for data entry and management. Mono-Repo Structure: Operates from a mono-repo structure where each branch could potentially contain schemas specific to a customer's unique needs. Schema Definition: Leverages the ckanext-scheming extension for defining and managing the custom schema, providing a structured approach to schema modification. Technical Integration: The Custom Schema extension integrates with CKAN by adding new plugins and configuring the existing dataset edit page. It requires the ckanext-scheming extension to be installed and enabled. To ensure proper functionality, custom_schema must be placed before scheming_datasets in the ckan.plugins line of the CKAN configuration file. Benefits & Impact: Implementing the Custom Schema extension can significantly improve the quality and discoverability of datasets within a CKAN instance. By allowing users to add tailored metadata, organizations can capture more specific information about their data, facilitating more precise searches and analyses. Ultimately, this leads to enhanced data governance and more effective utilization of data resources.

This schema defines a data model for collections that aggregates and organizes related digital objects or resources within the Human Reference Atlas ecosystem. The schema enables systematic organization and discovery of related resources while maintaining detailed provenance and membership information for comprehensive data management. This schema is described in detail in the HRA KG paper.

Bibliography:

• Bueckle, Andreas, Bruce W. Herr II, Josef Hardi, Ellen M. Quardokus, Mark A. Musen, and Katy Börner. 2025. “Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data.” bioRxiv. https://doi.org/10.1101/2024.12.22.630006.

The NFDI4Health Task Force COVID-19 Metadata Schema (Metadata Schema) contains a list of properties describing a resource being registered in the Study Hub of the NFDI4Health Task Force COVID-19 (Study Hub).

Ported from https://data.sfgov.org/City-Management-and-Ethics/Case-Data-from-San-Francisco-311-SF311-/vw6y-z8j6 to conform with the 311 schema

The ckanext_ubdcschema extension for CKAN appears to provide functionality related to data schema management, especially within the context of the University of British Columbia Data Club (ubdc). While detailed documentation is not provided in the readme, it seems to introduce a specific schema or set of schemas tailored for use within the UBDC environment, making it easier to manage and validate datasets according to predefined standards. This extension likely aims to enhance data consistency and interoperability by enforcing schema compliance across datasets. Key Features: * UBDC Schema Integration: Integrates a specific data schema (or schemas) designed for the University of British Columbia Data Club. This likely includes predefined fields, data types, and validation rules. * Schema Validation: Potentially implements validation mechanisms to ensure that datasets conform to the defined UBDC schema, aiding in data quality control. * Metadata Enhancement: May offer tools to assist users in populating metadata fields according to the UBDC schema, leading to improved data discoverability and usability. * Data Modeling: Provides a framework or structures to model the datasets. Use Cases: 1. UBDC Data Management: Allows the University of British Columbia Data Club to manage and standardize its datasets, simplifying data sharing and analysis within the organization. 2. Schema Compliance: Facilitates compliance with specific data schema requirements applicable to data published within the UBDC ecosystem. 3. Data Discovery: Provides structured and uniform metadata for the datasets for the relevant parties or internal teams. Technical Integration: Due to lack of documentation, it can only be assumed that the ckanext_ubdcschema extension integrates with CKAN likely through plugins and extensions which introduces schema-related customizations and validation features. Configuration steps are likely required involving defining and loading the UBDC schema, configuring data validation rules, and setting up necessary permissions within the CKAN configuration file. Benefits & Impact: The ckanext_ubdcschema extension can improve data quality and consistency within a CKAN instance managed by the UBDC. This standardization enables more effective data sharing, discoverability, and interoperability of datasets within the UBDC and potentially with external partners. Its main goal is to streamline data management processes and adhere to the standards set by the UBDC.

This resource contains slides for the AGU Fall Meeting 2023 presentation (#IN23A-07) in San Francisco on Dec 12. Session: IN23A: Advancing Open Science: Emerging Techniques in Knowledge Management and Discovery II Oral

Effective response to global crises relies on universal access to scientific data and models, understanding their attributes, and representing their interconnectivity to facilitate collaborative research and decision making. In the age of distributed data, geospatial researchers frequently invest significant time searching for, accessing, and working to understand scientific data. This often leads to the recreation of existing datasets as well as challenges in determining methods for accessing, using, and ultimately establishing connections between resources. In recent years, following FAIR and CARE principles, there is an emerging practice to leverage structured and robust metadata to accelerate the discovery of web-based scientific resources and products. This practice assists users in not only discovery, but also in understanding the context, quality, and provenance of data, as well as the rights and responsibilities of data owners and consumers. It also empowers organizations to leverage their data more effectively and derive meaningful insights from them. Doing so, however, can be difficult, especially when diverse resources needed for scientific applications may be spread across multiple repositories or locations. We present a solution for leveraging the Schema.org vocabulary along with various web encodings such as the Resource Description Framework (RDF) with JSON-LD to create an actionable, curated catalog of scientific resources ranging from spatio-temporal data to software source code. We explore how resources of various types and common scientific formats, such as multidimensional, software containers, source code, and spatial features, which are stored across various repositories and distributed cloud storage, can be described and cataloged. Recognizing the impracticality of manually cataloging metadata, we have developed generic capabilities to automatically extract metadata for such resources, while empowering scientists to provide additional context. By incorporating comprehensive metadata, the exploration of diverse data relationships can be realized to gain insight into gaps and opportunities to improve the connectivity between science communities.

The Water Supply and Management Schema (SAGE) is a collective planning document, for a coherent hydrographic perimetre, setting general objectives for the use, development, quantitative and qualitative protection of the water resource. It must be compatible with the Schema Water Supply and Management Managers (SDAGE).The perimetre and the time frame in which it is developed are determined by the SDAGE. In default, it shall be stopped by the prefets of the department, the case being decided on the proposal of the territorial collectivites of interest. The SAGE is established by a Commission Locale de l’Eau (CLE) representing the various actors in the territory, subject to a public investigation and is approved by the Prefet. He has a legal framework: the Regulation and its cartographic documents are enforceable against third parties and decisions in the field of water must be compatible or made compatible with the plan for the supply and sustainable management of the water resource. Planning documents (scheme of territorial coherence, local planning plan and municipal map) must be compatible with the objectives of protection defined by the SAGE. The schema departemental of the carrieres must be compatible with the provisions of the SAGE. The reference texts are Articles L.212-3 to L.212-11 of the Environmental Code and circular DE/SDATDCP/BDCP/No. 10 of 21 April 2008.

The bcgov-schema extension provides custom schemas specifically for use within the BC Data Catalogue. These schemas define the structure and validation rules for metadata associated with datasets and other resources within the CKAN instance, ensuring data consistency and quality across the platform. It enhances the data governance capabilities of CKAN, supporting a standardized approach to metadata management. Key Features: Custom Schema Definitions: Provides pre-defined schemas tailored to the BC Data Catalogue, facilitating standardized metadata entry and validation. Consistent Data Governance: Ensures data consistency and quality by enforcing structured metadata requirements across the platform. Integration with Other Extensions: Designed to work in conjunction with ckanext-scheming, ckanext-repeating and ckanext-composite. This allows developers to combine this extension's schemas with other extensions for even more functionality. Technical Integration: Based on the Readme, the bcgov-schema extension appears to integrate with CKAN through the use of other extensions which are ckanext-scheming, ckanext-repeating and ckanext-composite. The existing extensions provide means to use schemas to validate metadata and handle repeating fields. Benefits & Impact: By implementing the bcgov-schema extension, organizations can enforce standardized metadata practices, which enhances discoverability, improves data quality, and promotes interoperability. The schemas pre-packaged within this extension allow for consistency across the BC Data Catalogue.

🇫🇷 프랑스

This is a data schema created using Agri-food Data Canada's Semantic Engine . Data schemas describe data that are collected on an ongoing basis from research centres, and provide add-on documentation that enhances the value of raw data. This schema is applicable to all datasets collecting data from records of once-in-life events entered on DairyComp Herd Management Software. Once-in-life events refer to significant, unique occurrences that typically happen only once in an animal’s lifetime, such as the initial calving, first breeding, or major health interventions. Once-in-life events are integrated with other data collected in the DairyComp Herd Management Software, such as routine health checks, milk production records, and breeding information. Data described in this schema is sample data, to request access to the related data, please visit the Ontario Dairy Research Centre Data Portal.

This is a relational database schema for a sales and order management system, designed to track customers, employees, products, orders, and payments. Below is a detailed breakdown of each table and their relationships:

1. productlines Table (Product Categories)

• Represents different product categories.
• Primary Key: productLine
• Attributes:
  • textDescription: A short description of the product line.
  • htmlDescription: A detailed HTML-based description.
  • image: Associated image (if applicable).
• Relationships:
  • One-to-Many with products: Each product belongs to one productLine.

2. products Table (Product Information)

• Stores details of individual products.
• Primary Key: productCode
• Attributes:
  • productName: Name of the product.
  • productLine: Foreign key linking to productlines.
  • productScale, productVendor, productDescription: Additional product details.
  • quantityInStock: Number of units available.
  • buyPrice: Cost price per unit.
  • MSRP: Manufacturer's Suggested Retail Price.
• Relationships:
  • Many-to-One with productlines (each product belongs to one category).
  • One-to-Many with orderdetails (a product can be part of many orders).

3. orderdetails Table (Line Items in an Order)

• Stores details of each product within an order.
• Composite Primary Key: (orderNumber, productCode)
• Attributes:
  • quantityOrdered: Number of units in the order.
  • priceEach: Price per unit.
  • orderLineNumber: The sequence number in the order.
• Relationships:
  • Many-to-One with orders (each order has multiple products).
  • Many-to-One with products (each product can appear in multiple orders).

4. orders Table (Customer Orders)

• Represents customer orders.
• Primary Key: orderNumber
• Attributes:
  • orderDate: Date when the order was placed.
  • requiredDate: Expected delivery date.
  • shippedDate: Actual shipping date (can be NULL if not shipped).
  • status: Order status (e.g., "Shipped", "In Process", "Cancelled").
  • comments: Additional remarks about the order.
  • customerNumber: Foreign key linking to customers.
• Relationships:
  • One-to-Many with orderdetails (an order contains multiple products).
  • Many-to-One with customers (each order is placed by one customer).

5. customers Table (Customer Details)

• Stores customer information.
• Primary Key: customerNumber
• Attributes:
  • customerName: Name of the customer.
  • contactLastName, contactFirstName: Contact person.
  • phone: Contact number.
  • addressLine1, addressLine2, city, state, postalCode, country: Address details.
  • salesRepEmployeeNumber: Foreign key linking to employees, representing the sales representative.
  • creditLimit: Maximum credit limit assigned to the customer.
• Relationships:
  • One-to-Many with orders (a customer can place multiple orders).
  • One-to-Many with payments (a customer can make multiple payments).
  • Many-to-One with employees (each customer has a sales representative).

6. payments Table (Customer Payments)

• Stores payment transactions.
• Composite Primary Key: (customerNumber, checkNumber)
• Attributes:
  • paymentDate: Date of payment.
  • amount: Payment amount.
• Relationships:
  • Many-to-One with customers (each payment is linked to a customer).

7. employees Table (Employee Information)

• Stores details of employees, including reporting hierarchy.
• Primary Key: employeeNumber
• Attributes:
  • lastName, firstName: Employee's name.
  • extension, email: Contact details.
  • officeCode: Foreign key linking to offices, representing the employee's office.
  • reportsTo: References another employeeNumber, establishing a hierarchy.
  • jobTitle: Employee’s role (e.g., "Sales Rep", "Manager").
• Relationships:
  • Many-to-One with offices (each employee works in one office).
  • One-to-Many with employees (self-referential, representing reporting structure).
  • One-to-Many with customers (each employee manages multiple customers).

8. offices Table (Office Locations)

• Represents company office locations.
• Primary Key: officeCode
• Attributes:
  • city, state, country: Location details.
  • phone: Office contact number.
  • addressLine1, addressLine2, postalCode, territory: Address details.
• Relationships:
  • One-to-Many with employees (each office has multiple employees).

Conclusion

This schema provides a well-structured design for managing a sales and order system, covering: ✅ Product inventory
✅ Order and payment tracking
✅ Customer and employee management
✅ Office locations and hierarchical reporting