Sample dataset

Data.gov.ie is built using CKAN v2.2 (link is external), which provides a powerful API that allows developers to retrieve datasets, groups or other CKAN objects and search for datasets. There is full documentation (link is external) available for the CKAN API online. Example API Calls Get JSON-formatted lists of a site’s datasets or other CKAN objects: data.gov.ie/api/3/action/package_list data.gov.ie/api/3/action/tag_list Get a full JSON representation of a dataset, resource or other object: data.gov.ie/api/3/action/package_show?id=the-walled-towns-of-ireland data.gov.ie/api/3/action/tag_show?id=marine Search for packages or resources matching a query: data.gov.ie/api/3/action/package_search?q=museum data.gov.ie/api/3/action/resource_search?query=name:The%20Walled%20Towns%20of%20Ireland More information at https://data.gov.ie/developers

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).

Suomen ympäristökeskuksen (Syke) laatima valtakunnallinen valuma-aluejako koostuu viidestä hierarkiatasosta, jotka kattavat uomien ja järvien valuma-alueiden lisäksi myös merialueet. Tarkimmalla tasolla valuma-aluejaossa on yli 40 000 valuma-aluetta. Valtakunnallinen valuma-aluejako on osa Vesistöjen perustietovarantoa. Valuma-aluejako on tuotettu mallintamalla hyödyntäen Syken laatimaa virtaussuuntamallia, joka perustuu Maanmittauslaitoksen 10 m:n korkeusmalliin. Virtaussuuntamallin virtausreittien tarkkuutta on parannettu kovertamalla siihen uomaverkostoa ja vesialueita sekä yleisten teiden tierummut. Hierarkkisen perustan valtakunnalliselle valuma-aluejaolle luo Syken Ranta10-rantaviiva-aineiston uomaverkosto ja järvet. Valtakunnallinen valuma-aluejako on luotu palvelemaan hyvin laajasti vesivarojen käyttöä ja hoitoa, vesiensuojelua ja vesientutkimusta sekä vesivaroihin liittyvää kansainvälistä ja kansallista raportointia ja tietojärjestelmätyötä. VALUMA-ALUEJAON HIERARKIATASOT Valtakunnallinen valuma-aluejako koostuu viidestä hierarkiatasosta, joita kuvaamaan on kehitetty laajennettavissa oleva hierarkkinen tunnusjärjestelmä. Uomien ja järvien valuma-alueille on määritetty purkupisteet. TASO1 jakautuu viiteen valuma-alueeseen sen mukaan, mihin mereen pintavesi lopulta virtaa. Erityistapauksena myös Laatokka muodostaa oman osa-alueensa, vaikka se lopulta laskeekin Itämereen. TASO2 kuvaa päävesistöalueita ja rannikkoalueita (yli 80 kpl). Yksittäisen päävesistöalueen muodostaa vähintään 200 km2:n kokoinen yhden tai useamman laskujoen kautta mereen purkautuvien sisävesien muodostama kokonaisuus. Rannikkoalueet kattavat myös merialueet merisaarineen. Päävesistöalueiden ja rannikkoalueiden nimet ja numerointi noudattelevat aiempaa valuma-aluejakoa. TASO3 sisältää yli 200 osa-aluetta, jotka on muodostettu hyödyntämällä soveltuvilta osin Valuma-aluejako1990:n I jakovaiheen osa-alueita ja merenhoidon aluejakoa sekä HELCOM:n allasjakoa. TASO4 on valuma-aluejaon tarkin valtakunnallisesti yhtenäiseen uomahierarkiaan perustuva taso. Se muodostuu yli 20 000 osa-alueesta, jotka on mallinnettu Ranta10:n järville ja uomille/uomajatkumoille. Uomajatkumolla tarkoitetaan peräkkäisten uomien ja alle 50 ha järvien muodostamia uoma-järviketjuja. Yli 50 ha:n kokoiset järvet ja yli 100 metrin pituiset uomat/uomajatkumot omaavat oman valuma-alueen. Merialueilla apuna osa-alueiden muodostamisessa ovat olleet Vesistöjen perustietovarannon meriperusyksiköiden lisäksi osin myös VHS-rannikkovesimuodostumat. Tällä tasolla valuma-alueiden koon alarajaksi on määritetty 50 ha eli sitä pienemmät valuma-alueet on yhdistetty aina alapuoliseen valuma-alueeseen. TASO5 on valtakunnallisen valuma-aluejaon tiheäseulaisin taso, jossa valuma-alueita on jaettu edelleen pienempiin osiin myös vesienhoidollisin perustein. Taso 5 koostuu uomille, järville ja merialueille sekä niiden osille mallinnetuista valuma-alueista (yht. yli 40 000 osa-aluetta). Perustan Taso5:n valuma-aluerajauksille luovat pintavesien osia kuvaavat vesistöjen perusyksiköt. Järvien valuma-alueet (yli 12 000 kpl) on määritetty yli 1 ha:n järviperusyksiköille uomaverkoston varrella sekä yli 50 ha:n järviperusyksiköille uomaverkoston ulkopuolella. Uomien valuma-alueet (yli 28 000 kpl) on määritetty yli 100 m pituisille uomaperusyksiköille. Meri- ja rannikkoalueiden osa-alueet (yli 400 kpl) on määritetty meriperusyksiköiden avulla huomioiden rannikon välialueet ja merisaarten vedenjakajat. VALUMA-ALUEIDEN TIEDONHALLINTA Perustan valtakunnalliselle valuma-aluejaolle ja sen tiedonhallinnalle luo Vesistöjen perustietovaranto, joka koostuu uomia, järviä ja merialueita kuvaavista paikkatietoaineistoista sekä niihin liitettävissä olevasta tietovarastosta (VesiPetoDW). Valuma-aluejaon hierarkiatasoihin ja niiden osa-alueisiin liittyvä tietovarasto on tallennettu Vesistöjen perustietovarannon VesiPetoDW-tietokantaan. Vesistöjen perustietovarannon tauluista löytyvät valmiiksi lasketut fysiografiset ja hierarkkiset tiedot on linkitettävissä paikkatietotuotteeseen tunnusten avulla. Myöhemmin tiedot ovat saatavilla myös API-rajapinnan kautta. PUUTTEET JA EPÄTARKKUUDET VALUMA-ALUEJAOSSA Virtaussuuntamallin laskentaan ja valuma-aluejaon mallintamiseen käytetyissä lähtöaineistoissa voi esiintyä paikoin puutteita, epätarkkuuksia tai virheitä, jotka saattavat vaikuttaa valuma-alueiden rajaustuloksiin sekä niiden ominaisuustietojen laskentaan. Erityisesti Suomen rajojen ulkopuolisilla vesistöalueiden osa-alueilla valuma-alueiden rajaukset eivät ole luotettavia lähtöaineiston epätarkkuudesta johtuen. Samoin valuma-alueille lasketut tilastotiedot ovat puutteellisia ulkomaille sijoittuvien osa-alueiden osalta. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0). Aineistosta on julkaistu INSPIRE-tietotuote. LISÄTIETOJA Valuma-aluejaon viitedokumentti https://geoportal.ymparisto.fi/meta/julkinen/dokumentit/Valumaaluejako.pdf Vesistöjen perustietovaranto https://ckan.ymparisto.fi/dataset/vesistojen-perustietovaranto Vesistöjen perusyksiköt https://ckan.ymparisto.fi/dataset/vesistojen-perusyksikot Virtaussuuntamalli10m https://ckan.ymparisto.fi/dataset/virtaussuuntamalli-10-m Ranta10 https://ckan.ymparisto.fi/dataset/ranta10-rantaviiva-1-10-000 Tämä valtakunnallinen valuma-aluejako tulee korvaamaan 1990-luvulla käyttöönotetun valuma-aluejaon. https://ckan.ymparisto.fi/dataset/valuma-aluejako-1990 THE FINNISH RIVER BASIN SYSTEM The Finnish River basin system made by the Finnish Environment Institute (Syke) consists of five hierarchical levels, which cover not only the catchment areas of river segments and lakes, but also sea areas. At the most precise level, there are more than 40,000 catchment areas in the river basin system. The river basin system is part of the Water database. The river basin system has been produced by modeling using the flow direction model prepared by Syke, which is based on the 10 m digital elevation model (DEM) of the National Land Survey of Finland. The accuracy of the flow paths of the flow direction model has been improved by burning in the river network and lakes as well as road culverts of public roads. The river network and lakes of Syke’s Ranta10 shoreline data set creates the hierarchical basis for the river basin system. The Finnish River basin system has been created to serve very broadly the use and management of water resources, water protection and water research, as well as international and national reporting and information systems related to water resources. THE HIERARCHY LEVELS OF THE RIVER BASIN SYSTEM The Finnish River basin system consists of five hierarchy levels, including expandable hierarchical identification system. Discharge points have been determined for the catchment areas of rivers and lakes. LEVEL 1 is divided into five catchment areas, depending on which sea the surface water eventually flows into. As a special case, Lake Ladoga also forms its own sub-region, even if it eventually flows into the Baltic Sea. LEVEL 2 describes main watershed areas and coastal areas (more than 80 areas). An individual main river basin is formed by inland waters discharging into the sea through one or more tributaries with a size of at least 200 km2. Coastal areas also cover sea areas with sea islands. The names and numbering of the main watershed areas and coastal areas follow the previous river basin system. LEVEL 3 contains more than 200 sub-areas, which have been formed by utilizing, where applicable, the sub-areas of the 1st level of the previous river basin system, as well as the marine management areas and HELCOM's basin division. LEVEL 4 is the most accurate level of the river basin system based on a nationally uniform river network hierarchy. It consists of more than 20,000 sub-catchments modeled on Ranta10's lakes and river segments. Lakes over 50 ha in size and “river segment continuations” over 100 meters long have their own catchment area. In marine areas, in addition to the marine basic units of the Water database, WFD coastal water bodies have also been used to help in the formation of sub-regions. At this level, the lower limit of the size of the catchment areas has been determined to be 50 ha, i.e. catchment areas smaller than that are always connected to the catchment area below. In LEVEL 5 catchment areas have been further divided into smaller parts also based on WFD water bodies. Level 5 consists of catchment areas modeled for river segments, lakes and sea areas and their parts (over 40,000 parts in total). The basis for Level 5 catchment area delineations are the basic units of the Water database. The catchment areas of the lakes (more than 12,000) have been determined for basic lake units of more than 1 ha along the river network and for basic lake units of more than 50 ha outside the river network. The catchment areas of the river segments (more than 28,000 pieces) have been determined for river basic units longer than 100 m. Sub-areas of sea and coastal areas (more than 400 units) have been defined using marine basic units, taking into account the intermediate areas of the coast and watersheds of sea islands. INFORMATION MANAGEMENT OF CATCHMENTS The basis for the river basin system and its data management is the creation of the Water database of water basic units, which consists of spatial data materials describing river segments, lakes and marine areas, as well as a data warehouse that can be connected to them (VesiPetoDW). The pre-calculated physiographical and hierarchical data found in the tables of the Water database can be linked to the spatial data product using unique Ids. Later, the data will also be available via the API interface. DEFICIENCIES AND INACCURACIES IN THE RIVER BASIN SYSTEM The source materials used for the calculation of the flow direction model and the modeling of the catchment area

This data package contains Go source code and documentation for a CKAN API integration tool. Key components include: go.mod and go.sum: Go module configuration and dependencies test.md: Test documentation or scripts pkg/ckanapi/: Core CKAN API implementation with packages for organization, resource, user, and package management pkg/ckanbot/: Bot logic implementation pkg/ckanbot_descriptor/: Descriptor tool components including AppHubAI API integration and utility functions cmd/ckanbot_descriptor/: Command-line executable for the descriptor tool The archive also includes an AI-generated image description depicting a computer case with labeled hardware components like GPUs, CPU, power supply, and water cooling system. This image appears to be unrelated to the Go codebase but is included as part of the package contents. |----ckanbot | go.mod | test.md | go.sum |----pkg | |----ckanap` | | | |organization.go | | | |ckanapi.go | | | |resource.go | | | |user.go | | | |package.go | | |----ckanbot | | | |ckanbot.go | | |----ckanbot_descriptor | | | |----apphubai_api | | | | |apphubai.go | | | |descriptor.go | |----cmd | | |----ckanbot_descriptor | | | |ckanbot_descriptor | | | |main.go

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.

The Victorian Government's open data portal, data.vic, uses CKAN to surface thousands of data sets and metadata records from across the Victorian Government.\r \r 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.\r

Multiple European city cases for the Urban Building (KUB) application. Each city is provided as a ZIP bundle containing all the data needed to run a KUB simulation end-to-end (GIS, mesh, weather, FMU building models, metadata, and configuration files). Note: The FMU files and associated metadata (description files) are downloaded or referenced automatically via Feel++ Remote-Data URIs when you run a simulation. You do not need to manually fetch or unpack FMUs—the containerized KUB workflow takes care of it. Overview Dataset name: ktirio-cases Number of city cases: 9 Purpose: Provide ready-to-use input bundles for urban-scale building energy and comfort simulations with Ktirio Urban Building (KUB). CKAN landing page: https://ckan.hidalgo2.eu/dataset/ktirio-cases Each ZIP archive includes: GIS metadata A JSON (or shapefile) mapping each building’s polygon to its OpenStreetMap (OSM) ID and other attributes. LoD-0 mesh An MSH file (mesh_0_Lod0.msh or similarly named) representing the terrain and building footprints at Level-of-Detail 0. Weather CSV A time-series file (e.g. weather.csv or weatherPoznan.csv) containing hourly (or sub-hourly) meteorological data. Typical columns include: time, temperature_2m, surface_pressure, relative_humidity_2m, wind_speed_10m, wind_direction_10m, direct_radiation, diffuse_radiation, cloud_cover, … FMU building models & descriptions A set of LoD-0 FMU files (e.g. Building_001.fmu, Building_002.fmu, …), each representing a single building’s thermal model. Corresponding metadata description files (XML or JSON) listing the outputs, variable names, and building element IDs. Configuration files A “simulator” config (e.g. idealHeater.cfg or BoilerHeaters.cfg) that lists the FMU URIs (remote or local) and FMU metadata URIs. A “city” config (e.g. strasbourg.cfg, poznan.cfg) that points to the mesh, GIS JSON, and weather CSV, and defines simulation parameters (cem.instance.*, postprocess.*, etc.). City Bundles Included athens.zip – Athens, Greece erlangen.zip – Erlangen, Germany gyor.zip – Győr, Hungary luxembourg.zip – Luxembourg, Luxembourg madrid.zip – Madrid, Spain

again test sample

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.

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.

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.

This repository contains the dataset used in the paper "Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset" by Dr. Samaneh Zolfaghari, Teodor Stoev, and Prof. Dr. Kristina Yordanova. If you use the dataset, please cite the paper using the Bibtex below @ARTICLE{10409517, author={Zolfaghari, Samaneh and Stoev, Teodor and Yordanova, Kristina}, journal={IEEE Access}, title={Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset}, year={2024}, volume={}, number={}, pages={1-1}, doi={10.1109/ACCESS.2024.3356352}} as well as the original KTA dataset paper "Kitchen task assessment dataset for measuring errors due to cognitive impairments" by Yordanova, Kristina and Hein, Albert and Kirste, Thomas @inproceedings{yordanova2020kitchen, title={Kitchen task assessment dataset for measuring errors due to cognitive impairments}, author={Yordanova, Kristina and Hein, Albert and Kirste, Thomas}, booktitle={2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)}, pages={1--6}, year={2020}, organization={IEEE} } Description of the files All the archive files containing our data are in the folder data which contains two other folders all_actions (containing the experimental data and the labels we used for the evaluation of the classifier when trained with all action classes in the KTA dataset), and most_common_actions (containing the experimental data and labels we used to evaluate the classifier on the 6 most common actions).

The California School Campus Database (CSCD) is now available for all public schools and colleges/universities in California.CSCD is a GIS data set that contains detailed outlines of the lands used by public schools for educational purposes. It includes campus boundaries of schools with kindergarten through 12th grade instruction, as well as colleges, universities, and public community colleges. Each is accurately mapped at the assessor parcel level. CSCD is the first statewide database of this information and is available for use without restriction.PURPOSEWhile data is available from the California Department of Education (CDE) at a point level, the data is simplified and often inaccurate.CSCD defines the entire school campus of all public schools to allow spatial analysis, including the full extent of lands used for public education in California. CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes.The lands in CSCD are defined by the parcels owned, rented, leased, or used by a public California school district for the primary purpose of educating youth. CSCD provides vetted polygons representing each public school in the state.Data is also provided for community colleges and university lands as of the 2018 release.CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes. It should not be used as the basis for official regulatory, legal, or other such governmental actions unless reviewed by the user and deemed appropriate for their use. See the user manual for more information.Link to California School Campus Database.

Similar Datasets: Recommends datasets similar to the currently viewed dataset, assisting users in discovering related data resources. Personalized Recommendations: Offers personalized dataset recommendations to users, tailoring the discovery process to individual interests and needs. Technical Integration: The extension requires the installation of Virtuoso v6, FedX v2 (if using multiple endpoints), LODStats, and Redland rdflib. It relies on database modifications via a provided SQL file (db.sql) and configuration settings added to the CKAN configuration file. It also requires setting up CRON jobs to execute commands periodically. The extension might also require a Cloudmade API key for certain functionalities. Benefits & Impact: By implementing the Semantic extension, CKAN-based applications gain improved data discoverability through semantic enrichment. The enhanced search capabilities allow users to find datasets more efficiently and effectively. The enriched dataset pages and recommendations features foster exploration of related datasets, contributing to better utilization of available data resources. The integration of SPARQL unlocks advanced data querying options, broadening the utility of CKAN.

Cells are the building blocks of life, from single-celled microbes through to multi-cellular organisms. To understand a multitude of biological processes we need to understand how cells behave, how they interact with each other and how they respond to their environment. The use of new methodologies is changing the way we study cells allowing us to study them on minute scales and in unprecedented detail. These same methods are allowing researchers to begin to sample the vast diversity of microbes that dominate natural environments. The aim of this special issue is to bring together research and perspectives on the application of new approaches to understand the biological properties of cells, including how they interact with other biological entities

This API returns the metadata of a dataset (package) and its resources

Local jurisdctions in the San Diego region based on the MUNICIPAL_BOUNDARIES layer in the Regional GIS Data Warehouse.

msl datasets for https://github.com/Yasas1994/vcat