Base guidelines for data to be published on the site.

The arrayexpress extension for CKAN facilitates the import of data from the ArrayExpress database into a CKAN instance. This extension is designed to streamline the process of integrating ArrayExpress experiment data, a valuable resource for genomics and transcriptomics research, directly into a CKAN-based data portal. Due to limited documentation, specific functionalities are inferred to enhance data accessibility and promote efficient management of ArrayExpress datasets within CKAN. Key Features: ArrayExpress Data Import: Enables the import of experiment data from the ArrayExpress database into CKAN, providing access to valuable genomics and transcriptomics datasets. Dataset Metadata Creation: Automatically generates CKAN dataset metadata based on ArrayExpress data, reducing manual data entry and ensuring consistency. (inferred functionality) Streamlined Data Integration: Simplifies the integration process of ArrayExpress resources into CKAN, improving access to experiment-related information. (inferred functionality) Use Cases: Genomics Data Portals: Organizations managing data portals for genomics or transcriptomics research can use this extension to incorporate ArrayExpress data, increasing the breadth of available data and improving user access. Research Institutions: Research institutions can simplify data imports to share their ArrayExpress datasets with collaborators, ensuring data consistency and adherence to metadata standards. Technical Integration: The ArrayExpress extension integrates with CKAN by adding functionality to import and handle ArrayExpress data. While the exact integration points (plugins, API endpoints) aren't detailed in the provided documentation, the extension would likely use CKAN's plugin architecture to add data import capabilities, and the metadata schema may need to be adapted for compatibility (inferred integration). Benefits & Impact: By using the arrayexpress extension, organizations can improve the accessibility of ArrayExpress data within CKAN. It reduces the manual effort required to integrate experiment data and helps in maintaining a consistent and comprehensive data catalog for genomics and transcriptomics research (inferred integration).

The datacitation extension for CKAN aims to facilitate proper data citation practices within the CKAN data catalog ecosystem. By providing tools and features to create and manage citations for datasets, the extension promotes discoverability and acknowledgment of data sources, enhancing the reproducibility and transparency of research and analysis based on these datasets. The available information is limited, but based on the name, the extension likely focuses on generating, displaying, and potentially exporting citation information. Key Features (Assumed based on Extension Name): * Dataset Citation Generation: Likely provides functionality to automatically generate citation strings for datasets based on metadata fields, adhering to common citation formats (e.g., APA, MLA, Chicago). * Citation Metadata Management: Potentially offers tools to manage citation-related metadata within datasets, such as author names, publication dates, and version numbers, which are essential elements for creating accurate citations. * Citation Display on Dataset Pages: It's reasonable to expect that the extension displays the generated citation information prominently on the dataset's display page, facilitating easy access for users. * Citation Export Options: May provide options to export citations in various formats (e.g., BibTeX, RIS) to integrate with reference management software popular among researchers. * Citation Style Customization: Possibly provides configuration options to customize the citation style used for generation, accommodating different disciplinary requirements. Use Cases (Inferred): 1. Research Data Repositories: Data repositories can utilize datacitation to ensure that researchers cite datasets correctly, which is crucial for tracking the impact of data and recognizing the contributions of data creators. 2. Government Data Portals: Government agencies can implement the extension to promote the proper use and attribution of open government datasets, fostering transparency and accountability. Technical Integration: Due to limited information, the integration details are speculative. However, it can be assumed that the datacitation extension likely integrates with CKAN by: * Adding a new plugin or module to CKAN that handles citation generation and display. * Extending the CKAN dataset schema to include citation-related metadata fields. * Potentially providing API endpoints for programmatic access to citation information. Benefits & Impact: The anticipated benefits of the datacitation extension include: * Improved data discoverability and reusability through proper citation practices. * Enhanced research reproducibility and transparency by ensuring that data sources are properly acknowledged. * Increased recognition of data creators and contributors. * Simplified citation management for users of CKAN-based data catalogs. Disclaimer: The above information is largely based on assumptions derived from the extension's name and common data citation practices. The actual features and capabilities of the datacitation extension may vary due to the unavailability of a README file.

This is not a real dataset that is stored here, but just a trial to find out how to properly use CKAN.

Sample dataset

For the Leiden Longevity Study, long-lived siblings of European descent were recruited together with their offspring and the partners of the offspring between 2002 and 2006. Families were recruited if at least two long-lived siblings were alive and fulfilled the age criterion of 89 years or older for males and 91 years or older for females, representing less than 0.5% of the Dutch population in 2001 (Schoenmaker et al 2006, doi: 10.1038/sj.ejhg.5201508; Westendorp et al 2009, doi: 10.1111/j.1532-5415.2009.02381.x). In total 944 long-lived siblings were included with a mean age of 94 years (range 89-104), 1671 offspring (61 years, 34-81) and 744 partners (60 years, 30-79). It is family based study consists of 1671 offspring of 421 nonagenarians sibling pairs of Dutch descent, and their 744 partners. The dataset of the LLS IOP1 Nonagenarian Siblings consists of 944 long-lived siblings from 421 families of whom we have biosamples, biomarkers, and questionnaire data. We followed these nonagenarian participants for their vital status till 2020.

Description of a dataset

again test sample

This is a description of the dataset in the source language

This dataset provides comprehensive input data for simulating urban energy performance in the town of Kernante (Saint-Nazaire, Loire-Atlantique, France) using the Ktirio Urban Building (kub) application. It is part of the Ktirio project, which assesses and optimizes urban energy systems using real-world data and numerical simulations. The dataset supports both Level-of-Detail 0 and 1 (LoD-0 and LoD-1) modeling and includes: Building meshes (LoD-0 and LoD-1) GIS and location metadata Time-series weather data Heating system configurations Simulation settings These inputs can be used to run simulations evaluating energy usage, heating strategies, and sensitivity to climate conditions.

test description

The rdf extension for CKAN was designed to add RDF (Resource Description Framework) capabilities to CKAN. Its primary function was to enable the export and management of dataset metadata in RDF format, facilitating interoperability with semantic web technologies and making CKAN datasets discoverable by RDF-aware applications and search engines. However, this extension is now deprecated and unmaintained. Users requiring similar functionality are advised to use ckanext-dcat instead. Key Features: RDF Export: Offered capabilities to export CKAN dataset metadata as RDF. The original format supported and specific RDF vocabularies used (e.g., DCAT, Dublin Core) are unknown. Semantic Web Integration: Aimed to enhance CKAN's integration with the Semantic Web by providing a structured, machine-readable representation of dataset metadata. The full degree of integration and specific functionalities were unavailable. Metadata Interoperability: Meant to improve metadata interoperability by allowing CKAN datasets to be understood and processed by applications that consume RDF. Use Cases: Semantic Data Portals: Used to turn a CKAN instance into a semantic data portal, making it easier for external applications to discover and consume dataset metadata. Linked Data Integration: Facilitated the integration of CKAN datasets into linked data initiatives. Technical Integration: Details of how this extension integrated with CKAN's architecture (e.g., plugins, API modifications) are unconfirmed due to deprecation. The exact integration method should have involved mapping CKAN's internal metadata schema to RDF vocabularies, but detailed steps cannot be described since the extension is unsupported. Benefits & Impact: When active, the 'rdf' extension broadened the reach of CKAN datasets by making their metadata available in a standard, machine-readable format. This in turn improved interoperability and discoverability, and enabled more effective use of CKAN as part of semantic web infrastructure. However, given the deprecated status, these benefits are no longer available, and users need to migrate to ckanext-dcat or other alternatives.

Data collection within the ABOARD Cohort started in 2022 and is ongoing. Participants receive annual online questionnaires on (mental) health, quality of life, and use of healthcare resources. In addition, medical data is collected from participants that visited a physician because of (self-reported) memory problems.

another test

The Victorian Government Open Data Directory uses CKAN to surface thousands of datasets from across the Victorian Government. This API is based on version 2.7.3 of the CKAN API documentation and …Show full descriptionThe Victorian Government Open Data Directory uses CKAN to surface thousands of datasets from across the Victorian Government. This API is based on version 2.7.3 of the CKAN API documentation and provides access to CKAN functionality for the purpose of integrating with other CKAN instances. It can also be used as a data source for other applications to search and download the datasets provided by Data.Vic. This API is the updated version that implements the Whole of Victorian Government API Design Standards for RESTful APIs developed by the Victorian Government.

This dataset represents regions, which are part of the national field level structure to support chapters. The Regions role is administrative as well as to provide oversight and program technical support to the chapters. This Region shapefile reflects the region boundaries with the associated attribute information. Red Cross Geography Model: Counties make up chapters, chapters make up regions and regions make up divisions. There are five exceptions to the Red Cross Geography model: Middlesex County, MA, Los Angeles, Kern, Riverside and San Bernardino Counties in California which are covered by more than one chapter. (many to one). In the case of these five counties, the geometry was dissolved from zip codes.

uploading and testing stuffs

This multi-center research project focuses on assessing and understanding the contribution of hemodynamic changes to vascular cognitive impairment (VCI).

Clinical data of the Personalized Parkinson Project, including motor scores, cognitive and neuropsychiatric assessments, autonomic symptoms, sleep and quality of life.