The ckanext-govdatade extension tailors CKAN for use with the GovData.de platform, likely providing specific functionalities and configurations needed to interact with the German open government data portal. This extension is designed to streamline the integration of CKAN installations with GovData.de, accommodating the unique requirements and standards of the platform. It appears to focus on harvesting data and includes specific dependencies to enable this functionality. Key Features: GovData.de Compatibility: Provides specific adjustments and configurations required for CKAN to operate seamlessly within the GovData.de ecosystem. This includes adaptations for metadata standards, API interactions, and other platform-specific needs. Harvester Integration: Implements a data harvester, enabling CKAN to retrieve metadata and datasets from GovData.de. This simplifies the process of aggregating data from the German open data portal into CKAN. Group Import Feature Reliance: Depends on a forked version of ckanext-harvest to ensure necessary group import feature is supported. Use Cases: Government Agencies (Germany): A German government entity that intends to harvest datasets from the main GovData.de portal into their local CKAN deployment. Open Data Aggregators (Germany): This enables organizations (non-profit or commercial) focusing on collecting, aggregating, and providing open data within Germany. Technical Integration: The extension integrates with CKAN via plugins that need to be specified in the CKAN configuration file. A specific step involved in setting up the extension's logging functionality requires providing the CKAN process with write access to a log file. It relies on a customized version of ckanext-harvest, emphasizing the importance of this dependency for full functionality. Benefits & Impact: By tailoring CKAN to GovData.de, this extension promotes adherence to specific regulations and standards and facilitates better data sharing, aggregation, and management for German open data initiatives. This ultimately reduces the effort for institutions interacting with the GovData.de platform.

Q: What was the average temperature for the month? A: Colors show the average monthly temperature across the contiguous United States. White and very light areas had average temperatures near 50°F. Blue areas on the map were cooler than 50°F; the darker the blue, the cooler the average temperature. Orange to red areas were warmer than 50°F; the darker the shade, the warmer the monthly average temperature. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments collect the highest and lowest temperature of the day at each station over the entire month, and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly average of daily mean temperatures, then plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: Shades of blue show areas that had monthly average temperatures below 50°F. The darker the shade of blue, the lower the average temperature. Areas shown in shades of orange and red had average temperatures above 50°F. The darker the shade of orange or red, the higher the average temperature. White or very light colors show areas where the average temperature was near 50°F. Q: Why do these data matter? A: The 5x5km NClimGrid data allow scientists to report on recent temperature conditions and track long-term trends at a variety of spatial scales. The gridded cells are used to create statewide, regional and national snapshots of climate conditions. Energy companies use this information to estimate demand for heating and air conditioning. Agricultural businesses also use these data to optimize timing of planting, harvesting, and putting livestock to pasture. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Average Temperature References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions) NCEI Monthly National Analysis) Climate at a Glance - Data Information) NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-us-monthly-averageThis upload includes two additional files:* Temperature - US Monthly Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots.* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

Dataset Card for MDK

This dataset was created as part of the Bertelsmann Foundation's Musterdatenkatalog (MDK) project. The MDK provides an overview of Open Data in municipalities in Germany. It is intended to help municipalities in Germany, as well as data analysts and journalists, to get an overview of the topics and the extent to which cities have already published data sets.

  Dataset Description







  Dataset Summary

The dataset is an annotated corpus of… See the full description on the dataset page: https://huggingface.co/datasets/and-effect/mdk_gov_data_titles_clf.

Data.nasa.gov: A catalog of publicly available NASA datasets What is Data.nasa.gov? Data.nasa.gov is NASA’s publicly available metadata repository, hosting diverse datasets related to science, space exploration, aeronautics, and more. Making NASA’s metadata publicly accessible, in compliance with the OPEN Government Data Act, fosters transparency, collaboration, and scientific advancement. Most dataset pages only house metadata for each dataset. Typically, actual data is hosted on other NASA archive sites but now data.nasa.gov will have that metadata and the links to data that exists in other locations.

Q: Where was the monthly temperature warmer or cooler than usual? A: Colors show where average monthly temperature was above or below its 1991-2020 average. Blue areas experienced cooler-than-usual temperatures while areas shown in red were warmer than usual. The darker the color, the larger the difference from the long-term average temperature. Q: Where do these measurements come from? A: Weather stations on every continent record temperatures over land, and ocean surface temperatures come from measurements made by ships and buoys. NOAA scientists merge the readings from land and ocean into a single dataset. To calculate difference-from-average temperatures—also called temperature anomalies—scientists calculate the average monthly temperature across hundreds of small regions, and then subtract each region’s 1991-2020 average for the same month. If the result is a positive number, the region was warmer than the long-term average. A negative result from the subtraction means the region was cooler than usual. To generate the source images, visualizers apply a mathematical filter to the results to produce a map that has smooth color transitions and no gaps. Q: What do the colors mean? A: Shades of red show where average monthly temperature was warmer than the 1991-2020 average for the same month. Shades of blue show where the monthly average was cooler than the long-term average. The darker the color, the larger the difference from average temperature. White and very light areas were close to their long-term average temperature. Gray areas near the North and South Poles show where no data are available. Q: Why do these data matter? A: Over time, these data give us a planet-wide picture of how climate varies over months and years and changes over decades. Each month, some areas are cooler than the long-term average and some areas are warmer. Though we don’t see an increase in temperature at every location every month, the long-term trend shows a growing portion of Earth’s surface is warmer than it was during the base period. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Environmental Visualization Laboratory (NNVL) produces the source images for the Difference from Average Temperature – Monthly maps. To produce our images, we run a set of scripts that access the source images, re-project them into desired projections at various sizes, and output them with a custom color bar. Additional information Source images available through NOAA's Environmental Visualization Lab (NNVL) are interpolated from data originally provided by the National Center for Environmental Information (NCEI) - Weather and Climate. NNVL images are based on NOAA Merged Land Ocean Global Surface Temperature Analysis data (NOAAGlobalTemp, formerly known as MLOST). References NCEI Monthly Global Analysis NOAA View Temperature Anomaly Merged Land Ocean Global Surface Temperature Analysis Global Surface Temperature Anomalies Climate at a Glance - Data Information Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a...This upload includes two additional files:* Temperature - Global Monthly, Difference from Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-global-monthly-difference-a...)* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

Metadaten werden bei GovData in CKAN verwaltet. Sie können ohne Anmeldung über die Schnittstelle ausgelesen werden. Die Metadaten Struktur DCAT-AP.de ist hier beschrieben: https://www.dcat-ap.de/

Ein Beispielabruf zum Suchwort Kindergarten lautet z.B.: https://ckan.govdata.de/api/3/action/dcat_catalog_search?q=kindergarten&format=rdf

Der Endpunkt ist in den Formaten RDF, Turtle und JSON-LD verfügbar. Die Links finden Sie in den Informationen zu den Datendateien.

Die Original-Metadaten der zuliefernden Stellen können zusätzlich auch über den entsprechenden Endpunkt eines Apache Jena Fuseki Triple Stores mittels SPARQL-Abfragen durchsucht und kombiniert werden. Im Vergleich zur CKAN-API werden über den TripleStore auch zusätzliche von den Datenbereitstellern übertragene Metadaten abgebildet.

Bebauungspläne von Ernst

Statistik der Bewerbungen und Geförderten Frauen und Männer in der Künstler/Innenförderung pro Jahr.

The topic is the data and map basis for the location and names of the sources within the meaning of the Saxon Water Act. For development projects and other uses in the area of springs, the legal provisions must be observed and the Lower Water Authority must be involved.

This dataset can be used in accordance with the terms of use Data License Germany - Attribution - Version 2.0 (http://www.govdata.de/dl-de/by-2-0). No liability is assumed for the accuracy of the data, in particular the state capital Dresden assumes no liability for third-party results collected or calculated using this data.

The German Earthquake Catalogue is based on a database providing information on the seismicity in Germany an adjacent areas. It contains locations of seismic events since year 800 where their epicentre determinations are based on historical sources as well as on measurements at seismometer stations since the start of instrumental seismological recording in the 20th century. Today, digital data acquisition at seismometer stations of the German regional seismic network (GRSN), the seismic GERES array, and the Gräfenberg array (GRF) takes place. All events with a local magnitude ML 2.0 and higher are listed. The GML file together with a Readme.txt file are provided in ZIP format (GERSEIS-INSPIRE.zip). The Readme.text file (German/English) contains detailed information on the GML file content. Data transformation was proceeded by using the INSPIRE Solution Pack for FME according to the INSPIRE requirements. Due to the continuous processing of the seismic events, the INSPIRE dataset is updated annually.

Q: Was the month drier or wetter than usual? A: Colors show where and by how much monthly precipitation totals differed from average precipitation for the same month from 1991-2020. Green areas were wetter than the 30-year average for the month and brown areas were drier. White and very light areas had monthly precipitation totals close to the long-term average. Q: Where do these measurements come from? A: Daily measurements of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gather the data and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly total and plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). To calculate the percent of average precipitation values shown on these maps—also called precipitation anomalies—NCEI scientists take the total precipitation in each 5x5 km grid box for a single month and year, and divide it by its 1991-2020 average for the same month. Multiplying that number by 100 yields a percent of average precipitation. If the result is greater than 100%, the region was wetter than average. Less than 100% means the region was drier than usual. Q: What do the colors mean? A: Shades of brown show places where total precipitation was below the long-term average for the month. Areas shown in shades of green had more liquid water from rain and/or snow than they averaged from 1991 to 2020. The darker the shade of brown or green, the larger the difference from the average precipitation. White and very light areas show where precipitation totals were the same as or very close to the long-term average. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation. Q: Why do these data matter? A: Comparing an area’s recent precipitation to its long-term average can tell how wet or how dry the area is compared to usual. Knowing if an area is much drier or much wetter than usual can encourage people to pay close attention to on-the-ground conditions that affect daily life and decisions. People check maps like this to judge crop progress; monitor reservoir levels; consider if lawns and landscaping need water; and to understand the possibilities of flooding. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on climate data (NClimGrid) produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Total Precipitation NClimGrid Precipitation Normals References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions NCEI Monthly National Analysis Climate at a Glance - Data Information NCEI Climate Monitoring - All ProductsSource: https://www.climate.gov/maps-data/

Analysis of ‘Ernährungs- und Sportverhalten’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.govdata.de/web/guest/daten/-/details/01e46cac-d98e-44a6-89a7-3b88b2f58539 on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Die Daten beschreiben Verhaltensweisen bzgl. Ernährung und Sport, die im Rahmen des NRW-Gesundheitssurveys erhoben wurden. Der NRW-Gesundheitssurvey ist eine jährlich durchgeführte, repräsentative telefonische Befragung der Bevölkerung in Nordrhein-Westfalen. Es wird sowohl der Anteil der Bevölkerung mit sportlicher Betätigung nach Umfang/Woche, Alter, Sozialstatus und Geschlecht, sowie der Body Mass Index (BMI) der erwachsenen Bevölkerung aufgeführt. Auf der Homepage des Landeszentrums Gesundheit Nordrhein-Westfalen finden Sie weitere Darstellungen zu den Indikatoren:

Sportliche Betätigung, Alter, Sozialstatus, Geschlecht, Survey

Body Mass Index, Alter, Sozialstatus, Geschlecht, Survey

Zur Erläuterung und Vereinfachung der Dateninterpretation finden Sie zudem als Worddokument die Beschreibung der Indikatoren. Zudem finden Sie hier weitere Informationen zum Themenbereich Gesundheitsrelevante Verhaltensweisen.

--- Original source retains full ownership of the source dataset ---

Метаданните се управляват от GovData в CKAN. Те могат да бъдат прочетени чрез интерфейса, без да влизат в системата. Структурата на метаданните DCAT-AP.de е описана тук: https://www.dcat-ap.de/

Пример за извличане на информация за думата „детска градина“ е например: https://ckan.govdata.de/api/3/action/dcat_catalog_search?q=kindergarten&format=rdf

Крайната точка е достъпна във форматите RDF, Turtle и JSON-LD. Връзките могат да бъдат намерени в информацията за файловете с данни.

Оригиналните метаданни на резултатите също могат да бъдат търсени и комбинирани чрез съответната крайна точка на Apache Jena Fuseki Triple Store с помощта на SPARQL заявки. В сравнение с CKAN API, допълнителните метаданни, предавани от доставчиците на данни, също се показват чрез Threestore.

Q: What are the chances that total precipitation will be below, near, or above average over the next three months? A: Colors show where total precipitation has an increased chance of being higher or lower than usual during the next three months. The darker the shading, the greater the chance for the indicated condition. White areas have equal chances for precipitation totals that are below, near, or above the long-term average (median) for the next three months. Q: How do experts develop these forecasts? A: Climate scientists base future climate outlooks on current patterns in the ocean and atmosphere. They examine projections from climate and weather models and consider recent trends. They also check historical records to see how much precipitation fell when patterns were similar in the past. Q: Why do these data matter? A: Water managers, farmers, and forestry officials have an intense interest in precipitation outlooks. They use them to help make decisions about water resources, irrigation, and fire-fighting resources. Flood forecasters also use these outlooks. They want to know as early as possible if an area is likely to receive more precipitation than usual. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Climate Prediction Center (CPC) produces the source images for monthly temperature outlooks. To produce our images, we run a set of scripts that access mapping layers from CPC, re-project them into desired projections at various sizes, and output them with a custom color bar. References One-Month to Three-Month Climate Outlooks. http://www.cpc.ncep.noaa.gov/products/forecasts/ Source: https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-three-month-outlookThis upload includes two additional files:* Precipitation - Three-Month Outlook _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-three-month-outlook)* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

Analysis of ‘Wohnbevölkerung in Wesel nach Stadtteilen und Wohnplätzen (HWI + NWI) zum 30.06.’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.govdata.de/web/guest/daten/-/details/wohnbevolkerung-in-wesel-nach-stadtteilen-und-wohnplatzen-hwi-nwi-zum-30-0659982 on 29 August 2021.

--- Dataset description provided by original source is as follows ---

letzter Halbjahresbestand

--- Original source retains full ownership of the source dataset ---

Q: How are conditions related to drought likely to change? A: Colors on each map show experts' estimates of how conditions related to drought are likely to change by the end of the next month. Areas where experts expect below-average precipitation or above-average temperatures (which increase evaporation) may see drought develop, persist, or worsen. Areas with forecasts for above-average precipitation may see drought conditions improve or end. Q: What information do experts use to produce drought outlooks? A: Starting with knowledge of current drought status, experts examine weather forecasts and climate outlooks to check the chances for below-, near-, or above-average precipitation and temperature during the next month. The experts also consider seasonal patterns of precipitation (for example, if a region usually has a wet summer), if an El Niño or La Niña event is occurring, and if an active tropical storm season is expected. They compare this range of factors with forecasts to determine if conditions are likely to become drier or wetter. Based on this input, experts make their best estimates of how conditions will change, and assign regions into outlook categories on the map. Q: What do the colors mean? A: Colors show experts' assessments of where and how conditions related to drought are likely to change by the end of next month. Gold areas are not currently experiencing drought, but they are likely to become drier through the next month. Dark brown areas show where drought conditions currently exist and are likely to persist or worsen. Tan areas are currently experiencing drought; they are likely to see improvement, but not an end, to drought conditions. Green shows where drought conditions are likely to end. Q: Why do these data matter? A: Drought outlooks can help decision makers plan ahead. For instance, if drought is likely to develop in a region, farmers and ranchers may decide to delay planting, plant a drought-resistant crop, or stockpile food and water for livestock. Forestry officials may choose to hire additional firefighters and secure equipment to be ready to fight wildfires. Water managers may consider instituting water restrictions. If drought is likely to improve or end, ranchers may choose to begin using stockpiled resources, and farmers may choose crops that require adequate moisture. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Climate Prediction Center (CPC) produces the source images for the Monthly Drought Outlook. To produce our images, we run a set of scripts that access map layers from CPC, and re-project them into desired projections at various sizes. Q: Data Format Description A: The CPC Drought Outlook zip files contain shapefiles for use in GIS software, as well as KML files appropriate for software like Google Earth. The original Drought Outlook data files used to make this Data Snapshot can be found at this data directory, as well as this web page. References Expert Assessment - Monthly Drought Outlook Summary Expert Assessments - Monthly Drought Outlook Discussion NWS Drought Fact Sheet Source: https://www.climate.gov/maps-data/data-snapshots/data-source/drought-monthly-outlook This upload includes two additional files:* Drought - Monthly Outlook _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots.* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

Q: What are the chances that total precipitation will be below, near, or above average next month? A: Colors show where total precipitation has an increased chance of being higher or lower than usual during the next month. The darker the shading, the greater the chance for the indicated condition. White areas have equal chances for precipitation totals that are below, near, or above the long-term average (median) for the month. Q: How do experts develop these forecasts? A: Climate scientists base future climate outlooks on current patterns in the ocean and atmosphere. They examine projections from climate and weather models and consider recent trends. They also check historical records to see how much precipitation fell when patterns were similar in the past. Q: What do the colors mean? A: Colors on the map show experts’ level of confidence in their forecasts for above or below median precipitation totals. Each location on the map has some chance to receive precipitation that ranks in the bottom, middle, or top third of records from the previous three decades. White areas have equal chances for each condition. Colors show where the odds for one of the three conditions are higher than for the other two. A common mistake is to interpret these maps as predictions of precipitation amounts. However, dark green areas are not predicted to receive more precipitation than light green areas. The dark green areas simply have a higher likelihood of receiving above median amounts of rain than the light green areas do. Similarly, dark brown areas are not predicted to receive less rain than light brown areas. Keep in mind that outlooks show the most likely condition for each region, not the only possible outcome. Q: Why do these data matter? A: Water managers, farmers, and forestry officials have an intense interest in precipitation outlooks. They use them to help make decisions about water resources, irrigation, and fire-fighting resources. Flood forecasters also use these outlooks. They want to know as early as possible if an area is likely to receive more precipitation than usual. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's Climate Prediction Center (CPC) produces the source images for monthly precipitation outlooks. To produce our images, we run a set of scripts that access map layers from CPC, re-project them into desired projections at various sizes, and output them with a custom color bar. Additional information CPC issues monthly outlooks one-half month before the beginning of the month of interest. On the day before the new month begins, experts update the outlook for the upcoming month. Each monthly outlook in Data Snapshots shows the date the outlook was issued. Outlooks that include Alaska are available: while displaying an outlook of interest, click the Download button, select Full Resolution Assets, and then click OK References One-Month to Three-Month Climate Outlooks. http://www.cpc.ncep.noaa.gov/products/forecasts/ Current Outlook Discussion http://www.cpc.ncep.noaa.gov/products/predictions/long_range/fxus07.html Source: https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-outlookThis upload includes two additional files:* Precipitation - Monthly Outlook _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-outlook)* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

Analysis of ‘Zugriffsstatistiken von kreis-kleve.de für März 2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.govdata.de/web/guest/daten/-/details/zugriffsstatistiken-von-kreis-kleve-de-fur-marz-2019553af on 30 September 2021.

--- Dataset description provided by original source is as follows ---

CSV-Datei mit den Zugriffszahlen pro Tag im jeweiligem Monat.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Messergebnisse zur Radioaktivität in: Rohmilch aus dem Tank (03.11.2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.govdata.de/web/guest/daten/-/details/d6b6a4ca-c93d-4734-b848-fd4c766ec0d0 on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Messdaten zur Überwachung der Radioaktivität in der Umwelt, in Lebens- und Futtermitteln

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Messergebnisse zur Radioaktivität in: Oberflächenwasser (31.10.2014)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.govdata.de/web/guest/daten/-/details/388e1234-eaad-46c5-ac28-bd9078ec9479 on 05 November 2021.

--- Dataset description provided by original source is as follows ---

Messdaten zur Überwachung der Radioaktivität in der Umwelt, in Lebens- und Futtermitteln

--- Original source retains full ownership of the source dataset ---