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 .hidden { display: none }

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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

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.

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

This is a description of the dataset in the source language

This is a description of the dataset in the source language

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.

test description

This is a plain text description of this demo dataset

The UCC-SMART Study is a single-centre prospective cohort study, ongoing in both inclusion (600 new patients included per year) and follow-up, in which patient care and scientific research concerning cardiovascular risk factors and disease are integrated.

Plant research produces data in a profusion of types and scales, and in ever-increasing volume. What are the challenges and opportunities presented by data management in contemporary plant science? And how can researchers make efficient and fruitful use of data management tools and strategies?

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

Description of a dataset

Modified in some way for CPAL specific purposes

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

Documents from the study conducted by the Consumer Finance Research Group operating out of CHRR at Ohio State University.