The 'GISCO NUTS 2021' data set represents the NUTS 2021 regulation and statistical regions by means of multipart polygon, polyline and point topology. The NUTS geographical information is completed by attribute tables and a set of cartographic help lines to better visualize multipart polygonal regions. The NUTS nomenclature is a hierarchical classification of statistical regions defined by Eurostat. The NUTS classification subdivides the EU economic territory into 3 statistical levels. The NUTS 2021 classification has been established through the Commission Delegated Regulation 2019/1755, which entered into force on 8th August 2019 and applies from 1st January 2021. A non official NUTS-like classification has been defined for the EFTA countries and the candidate countries. At present, six scale ranges (100K, 1M, 3M, 10M and 20M, 60M) are maintained in the GISCO geodatabase. The polygon and boundary classes delineate the regions, while the points provide an anchor for each region. Associated tables contain basic information such as the name of the region. The public data set will be available at 1M, 3M, 10M, 20M, 60M, while the full data set at 100K is restricted. The data set covers EU Member States, EFTA countries, EU candidate countries and the UK. Following the departure of the UK from the European Union, the UK is no longer flagged as an EU Member State but retains its place in the NUTS and statistical regions data set. This dataset (NUTS_2021) is derived from the EuroBoundary Map 2020 (EBM2020) from Eurogeographics as well as GISCO NUTS 2016 (from Türkiye). The list of NUTS2021 codes including changes with respect to NUTS2016 is available on https://ec.europa.eu/eurostat/documents/345175/629341/NUTS2021.xlsx. The public metadata for NUTS 2021 released by Eurostat is available here: https://gisco-services.ec.europa.eu/distribution/v2/nuts/nuts-2021-metadata.xml. This revision (May 2021) includes minor changes in the dataset such as (see https://gisco-services.ec.europa.eu/distribution/v2/nuts/nuts-2021-release-notes.txt): * 2020-10-05 Point snapping is disabled in all datasets, number of decimals increased for 01M datasets. * 2020-11-18 Inclusion of Jan Mayen and Svalbard in to Norways Statistical Regions. Amendment to Serbia NUTS BN line status. * 2020-12-05 Fixed broken utf-8 encoding. * 2021-03-15 Added LAU 2011,2012,2013,2014,2015,2020 * 2021-04-26 Fixed country labels 2001, 2006 (incorrect Kosovo coordinates) IMPORTANT NOTE: Additional information, including the conditions of use and acknowledgement notice is included in the document provided with the dataset "GISCO NUTS 2021 Additional Information.pdf". Public access to this data set is restricted due to intellectual property rights. It shall only be used internally by the EEA, its ETCs and subcontractors working on behalf of the EEA. This metadata has been slightly adapted from the original metadata information provided by Eurostat (European Commission) and is to be used only for internal EEA purposes. An introduction to the NUTS classification is available here: http://ec.europa.eu/eurostat/web/nuts/overview.
• Aus dem Datenbestand schmett_p (Schmetterlinge Punktdaten) wurde die vorliegende Rasterdarstellung, bezogen auf Messtischblatt-Quadranten (MTBQ) und ab dem Jahr 1850, abgeleitet. schmett_p: • Fundpunkte von Schmetterlinge • im vorliegenden Auszug aus MultiBaseCS befinden sich Daten folgender Herkunft (detaillierte Aufstellung siehe Feld „HERKUNFT“) : o Daten aus dem FFH-Monitoring des LUNG o Daten aus den FFH-Verbreitungskartierungen des LUNG o Daten aus den Großschutzgebieten o Daten aus Gutachten und Diplomarbeiten o Daten von ehrenamtlichen Kartierern o Daten aus Zufallsbeobachtungen o weitere Daten verschiedener Herkunft • Für das LINFOS wurden die Daten im Shape-Format aus MultiBaseCS exportiert, LINFOS-konform aufbereitet und in den Metadaten beschrieben. • Es handelt sich nicht um eine systematische, vollständige Untersuchung der gesamten Landesfläche. Vielmehr wurden Daten aus verschiedenen Projekten und ehrenamtlicher Tätigkeit zusammengetragen. Für Bereiche ohne Fundpunkte kann daher nicht automatisch von einem fehlenden Vorkommen der Art ausgegangen werden. Bei Vorliegen entsprechender Lebensräume bzw. Habitatstrukturen müssen im Rahmen von Genehmigungen und Zulassungen Untersuchungen zum möglichen Vorkommen der Art(en) durchgeführt werden.
The CRM-geothermal database was created within the Horizon Europe CRM-geothermal project (Grant Agreement No. 101058163) to support the assessment of geothermal systems as sources of both renewable energy and critical raw materials (CRMs). The primary purpose of data collection was to compile, harmonise, and make openly available geoscientific and geochemical data relevant to the occurrence, enrichment, and potential co-production of CRMs from geothermal environments in Europe and East Africa. The database integrates legacy data compiled from peer-reviewed literature, national geological and geothermal databases, and previous European research projects (notably REFLECT), together with new data generated by project partners through field sampling and laboratory analyses. Sampling campaigns targeted geothermal wells and surface manifestations in selected regions, including Türkiye, the East African Rift (Kenya, Tanzania, Malawi), Cornwall (UK), and Iceland. Laboratory analyses include major ion chemistry, trace and critical element concentrations, mineralogical composition, and gas data, determined using methods such as ICP-MS, XRF, and XRD. All records were harmonised using a unified metadata schema, standardised units, and consistent reporting formats. Quality control involved automated validation routines and manual expert review. Each record includes spatial coordinates, sampling context, analytical method, references, and a quality flag indicating data origin and traceability. The database is provided as a structured Excel file and contains interconnected datasets on geothermal wells, fluids, rocks, gases, and mineral precipitates. In total, the dataset comprises 9,773 records covering a wide range of geological settings, from volcanic and metamorphic systems to sedimentary basins. The CRM-geothermal database is FAIR-aligned, openly available, and intended for reuse in geothermal research, resource assessment, and studies on the sustainable co-production of geothermal energy and critical raw materials. Method: The CRM-geothermal database was compiled using a combined approach integrating literature-based data collection, database harmonisation, and new data generation through field sampling and laboratory analysis. Legacy data were collected from peer-reviewed scientific publications, national geological and geothermal databases, technical reports, and previous European research projects, with a particular emphasis on the REFLECT project. Relevant parameters were manually extracted, digitised where necessary, and cross-checked against original sources to ensure consistency and traceability. New data were generated within the CRM-geothermal project through targeted sampling campaigns at selected geothermal sites in Europe and Eastern Africa. Samples of geothermal fluids, rocks, gases, and mineral precipitates were collected from wells and surface manifestations following standard geochemical sampling protocols. Laboratory analyses were performed by project partner institutions using established analytical techniques, including inductively coupled plasma mass spectrometry (ICP-MS) for trace and critical elements, X-ray fluorescence (XRF) for bulk chemical composition, and X-ray diffraction (XRD) for mineralogical characterisation. Gas compositions were determined using gas chromatography and noble gas mass spectrometry where applicable. Detection limits and analytical uncertainties follow laboratory-specific standards and are documented where available. All data were harmonised using a unified metadata schema. Units, parameter names, and reporting formats were standardised, and spatial information was converted to WGS 84 decimal degrees. Quality control was applied through automated validation scripts checking metadata completeness, coordinate validity, and numerical plausibility, followed by manual expert review to ensure scientific coherence and correct sample attribution. The final dataset was organised into interconnected thematic tables (wells, fluids, rocks, gases, and scales) and exported as a structured Excel file for dissemination. Each record includes references, analytical method information, and a quality flag indicating data origin and traceability. Technical Info: The CRM-geothermal data publication is provided as a structured multi-sheet Excel (XLSX) file representing a curated snapshot of the CRM-geothermal database at the time of publication. The dataset was generated through controlled export workflows following data validation and harmonisation. The Excel file contains separate worksheets for thematic data tables (wells, fluids, rocks, gases, and mineral precipitates). Each worksheet preserves unique identifiers, standardised metadata fields, and cross-references between related records, allowing the dataset to be used independently of any external system or software platform.
Abweichend von konventionellen Standardverfahren zur Regelung von Wärmeerzeuger sollen in dem Projekt eine Vielzahl von internen und externen Eingangsgrößen zur Regelung verwendet werden. Hierbei handelt es sich unter anderem um Wetterdaten, Strompreise, Stromerzeugung und Korrelationsgrößen für die Belegung. In die Entscheidungsfindung zur Regelung fließen nicht nur aktuelle Zustandsgrößen ein, sondern auch zukünftige Werte. Es erfolgt somit eine prädiktive und vorausschauende Regelung anstelle einer reaktiven Standardregelung. Für solch eine Optimierungsaufgabe mit einer Vielzahl an Eingangsgrößen und einem längeren Betrachtungshorizont eigenen sich Methoden des maschinellen Lernens wie z.B. dem Reinforcement Learning. Der Vorteil dieser Methode ist eine bedarfsgenaue und wirtschaftliche Wärmebereitstellung. Mit Hilfe von Simulationsmodellen kann eine Vielzahl von Szenarien nachgebildet und als Trainingsdaten verwendet werden. Ziel des Forschungsprojektes ist es, die Optimierung von sektorübergreifenden Energiesystemen zu automatisieren und mit Hilfe von maschinellem Lernen und Metadaten die Anlagenparameter kontinuierlich anzupassen. Durch Transfer Learning können Messdaten anderer Gebäude als Trainingsdaten verwendet werden. Der Heizenergiebedarf wird mit Hilfe von neuronalen Netzen prognostiziert. Die entwickelten Prognose- und Regelungsmodelle werden in Kombination mit der Kommunikationsschnittstelle bei mehreren Gebäuden angewendet. Hierdurch sollen Generalisierungsmöglichkeiten gefunden werden, durch die das Trainieren bei neuen Gebäuden schneller erfolgen kann. Das Alleinstellungsmerkmal der Projektidee ist die Symbiose der Algorithmenentwicklung und ihre unmittelbare Validierung im Praxiskontext. Das Hermann-Rietschel-Institut hat als Arbeitsschwerpunkt die Entwicklung des Heizlastprognosemodells und dem Regelungsalgorithmus für die Wärmeerzeuger.
This metadata refers to the geospatial dataset representing the status of the EEA Industrial Reporting database as of 20 February 2026 (version 16). The release and emissions data cover the period 2007-2024 as result of the data reported under the E-PRTR facilities, 2017-2024 for IED installations and WI/co-WIs, and 2016-2024 for LCPs. These data are reported to EEA under Industrial Emissions Directive (IED) 2010/75/EU Commission Implementing Decision 2018/1135 and the European Pollutant Release and Transfer Register (E-PRTR) Regulation (EC) No 166/2006 Commission Implementing Decision 2019/1741. The dataset brings together data formerly reported separately under E-PRTR Regulation Art.7 and under IED Art.72. Additional reporting requirements under the IED are also included.
This dataset contains in-situ measurements of ship-generated wave heights and currents collected during 14 campaigns from 1998 to 2022 in German coastal waterways. It includes 81,092 filtered datapoints (from an initial 97,877) across 46 measurement stations in 28 cross-sections, with 23 unique locations, some of which were repeated after a certain time. Each wave event is linked to the ship and nautical parameters responsible for its generation. A more detailed metadata description for each campaign is attached to the dataset. Citation for this data set: Seemann, A.; Melling, G. (2024): Ship Wave Measurements in German Coastal Waterways from 1998 to 2022 [Data set], DOI: https://doi.org/10.48437/42c292-ebac3d Data Descriptor Paper: Seemann, A., Melling, G. Measurement of ship-generated waves in German coastal waterways from 1998–2022. Sci Data 12, 54 (2025). https://doi.org/10.1038/s41597-024-04299-5
Dieser Dienst enthält öffentlich verfügbare Bodendaten der Abteilung 6 - Geologie und Boden des LfU-SH. Informationen zu den einzelnen Karten können jeweils den zugehörigen Metadaten zu den einzelnen Datensätzen entnommen werden. Der Dienst wird regelmäßig um neu publizierte Daten erweitert.
This metadata refers to the geospatial dataset representing the status of the EEA Industrial Reporting database as of 20 February 2026 (version 16). The release and emissions data cover the period 2007-2024 as result of the data reported under the E-PRTR facilities, 2017-2024 for IED installations and WI/co-WIs, and 2016-2024 for LCPs. These data are reported to EEA under Industrial Emissions Directive (IED) 2010/75/EU Commission Implementing Decision 2018/1135 and the European Pollutant Release and Transfer Register (E-PRTR) Regulation (EC) No 166/2006 Commission Implementing Decision 2019/1741. The dataset brings together data formerly reported separately under E-PRTR Regulation Art.7 and under IED Art.72. Additional reporting requirements under the IED are also included.
Die Elementkarte stellt die räumliche Verteilung der klassifizierten Gehalte des 50. Perzentils von Blei (in mg/kg) innerhalb der 184 geochemischen Gesteinseinheiten in Bayern dar. In die Auswertung gehen dabei nur die Daten der ersten (von maximal drei) Lithologien einer geochemischen Gesteinseinheit ein. Für Informationen im Hinblick auf die Auswertung der Daten sowie auf die kartenmäßige Darstellung wird auf die Metadaten der digitalen Lithogeochemischen Karte 1:25 000 von Bayern (dLGK25) verwiesen.
Die Elementkarte stellt die räumliche Verteilung der klassifizierten Gehalte des 50. Perzentils von Cadmium (in mg/kg) innerhalb der 184 geochemischen Gesteinseinheiten in Bayern dar. In die Auswertung gehen dabei nur die Daten der ersten (von maximal drei) Lithologien einer geochemischen Gesteinseinheit ein. Für Informationen im Hinblick auf die Auswertung der Daten sowie auf die kartenmäßige Darstellung wird auf die Metadaten der digitalen Lithogeochemischen Karte 1:25 000 von Bayern (dLGK25) verwiesen.
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