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Physical oceanography during ALKOR cruise AL601

Conductivity-temperature-depth profiles were measured using a ADM-CTD SN MOCNET during RV ALKOR cruise AL601. The CTD was equipped with duplicate sensors for temperature, conductivity and oxygen. The oxygen sensor (galvanic oxygen micro-sensor (AMT)) was exchanged in the beginning of the year. The sensors are used throughout the year and no post-cruise calibration is applied. All other sensors of the CTD are calibrated irregularly. This dataset presents conductivity–temperature–depth (CTD) profiles collected during the research cruise. The data were processed using a custom Python workflow designed to summarize, standardize, and prepare CTD measurements for publication. Raw sensor output files (.TOB format) were parsed using a dedicated reader that extracts header metadata and harmonizes variable naming conventions across all profiles. Quality control procedures included the removal of non-physical values, treatment of missing or malformed entries, and consistency checks across key variables such as pressure, temperature, conductivity, and derived parameters (e.g., salinity and oxygen). Oxygen values were scaled, and salinity values were corrected according to the respective CTD calibration. Sensor channels were standardized and renamed to ensure compatibility with common data standards. Geographic coordinates were converted from degrees and minutes to decimal degrees to improve geospatial usability. Pressure was linearly interpolated to a uniform 1 dbar grid, and all depth-dependent parameters were interpolated accordingly. Maximum recorded pressure was cross-checked against local bathymetry (elevation); in cases of mismatch, profiles were truncated at the maximum depth of the corresponding location. The workflow further includes visual quality control of oxygen saturation, station locations, and vertical profiles of temperature, salinity, and oxygen.

Tägliche Raster der mittleren Bodenfeuchte unter Winterweizen für Deutschland

Die täglichen Raster der Bodenfeuchte werden für 10 cm Schichten bis zu einer Tiefe von 2 Meter und für vordefiniert Schichtdicken von 0-30, 0-60 und 0-90 cm für drei unterschiedliche landwirtschaftliche Kulturarten mit dem Modell AMBAV 2.0 V1.5 berechnet. Die für die Berechnung nötigen meteorologischen Eingabefelder müssen in stündlicher Auflösung vorliegen und werden von interpolierten Wetterstationsdaten abgeleitet. Desweiteren wird das Modell mit Bodeninformationen aus den Bodenleitprofilen der nutzungsdifferenzierten Bodenübersichtskarte (BÜK 1000 N) der Bundesanstalt für Geowissenschaften und Rohstoffe (BGR) parametrisiert. Bei gleicher Bodenart unterscheiden sich die Böden je nach Nutzung. Es wird zwischen Wald-, Grünland- und Ackerbaunutzung unterschieden. Außerdem ist die Gründigkeit der Böden, sowie der Skelettanteil (Grobboden > 2 mm) in den verschiedenen Bodenschichten berücksichtigt. Die Daten haben eine räumliche Auflösung von 1 x 1 km und decken ganz Deutschland ab. Daten außerhalb von Deutschland oder in Siedlungsgebieten mit versiegelten Flächen haben eine Fehlkennung -9999. Alle Angaben zum Raster sind in den Metadaten des netcdf Files hinterlegt.

INSPIRE Gesundheit Lärm im UIS Baden-Württemberg

Das Thema Lärm umfasst einerseits Daten zu den Lärmschutzbereichen an Flugplätzen und andererseits zur Umgebungslärmkartierung 2017. Betroffen sind bei der Lärmkartierung Hauptverkehrsstraßen und nicht-bundeseigene Haupteisenbahnstrecken außerhalb der Ballungsräume und der Großflughafen Stuttgart. Die berechneten Schallpegel sind zu Pegelklassen in 5 dB(A)-Abstufung zusammengefasst. Als Messwert (Measure) wird die untere Grenze der Pegelklasse angegeben, da eine Klassifizierung (bspw. "55 - 60 dB(A)") im bisherigen INSPIRE-Datenmodell nicht vorgesehen ist. Die folgenden Originaldatensätze (und Metadaten) aus dem UIS Baden-Württemberg sind enthalten: - Lärmschutzbereich an Flugplätzen (https://registry.gdi-de.org/id/de.bw.lubw.mdk/f794e024-3e5b-49f5-9d83-077f65297dd5) - Lärmkartierung 2017: Straßenverkehrslärm 24 Stunden (LDEN) (https://registry.gdi-de.org/id/de.bw.lubw.mdk/811bcf23-22ba-4094-a106-38d5b60c48de) - Lärmkartierung 2017: Straßenverkehrslärm Nacht (LNight) (https://registry.gdi-de.org/id/de.bw.lubw.mdk/94a4ea4d-15c4-48c7-8572-d2c66b9ba242) - Lärmkartierung 2017: Schienenverkehrslärm 24 Stunden (LDEN) (https://registry.gdi-de.org/id/de.bw.lubw.mdk/b80d861e-e30b-4283-9e9a-f495597992c0) - Lärmkartierung 2017: Schienenverkehrslärm Nacht (LNight) (https://registry.gdi-de.org/id/de.bw.lubw.mdk/1e41418d-edc3-4e4e-b7a1-39df028d5b6b) - Lärmkartierung 2017: Flugverkehrslärm 24 Stunden (LDEN) (https://registry.gdi-de.org/id/de.bw.lubw.mdk/a57a5acb-0473-4693-82de-7f0454d37f91) - Lärmkartierung 2017: Flugverkehrslärm Nacht (LNight) (https://registry.gdi-de.org/id/de.bw.lubw.mdk/6d554b2c-c9e4-4b08-98d9-9ce5738b2e12) | Prüfung: Konformität zu INSPIRE Durchführungsbestimmung | Dateninhalt (Bild): Konformität zu INSPIRE Durchführungsbestimmung

Environmental parameters, including dissolved organic carbon and molecular indices derived from ultra-high-resolution mass spectrometry (FT-ICR-MS) and infrared spectroscopy (ATR-FTIR)

We studied dissolved organic matter (DOM) dynamics in the sea surface microlayer (SML) during a multidisciplinary mesocosm study at the Sea sURface Facility (SURF) of the Institute for Chemistry and Biology of the Marine Environment (ICBM) in Wilhelmshaven, Germany (53.5148 °N, 8.1463 °E). The study was conducted from 18 May to 16 June 2023 as part of the BASS research unit (Biogeochemical processes and Air-sea exchange in the Sea-Surface microlayer). This dataset contains environmental data, including dissolved organic carbon (DOC), dissolved organic nitrogen (DON) and DOM molecular indices (MLBwL, Ibio, Iphoto, IDEG) calculated from ultrahigh-resolution mass spectrometry data (Fourier-transform ion cyclotron resonance mass spectrometer, FT-ICR-MS). Furthermore, we present attenuated total reflectance Fourier Transform Infrared (ATR-FTIR) data from representative samples for each bloom phase. General metadata from the multidisciplinary mesocosm study, including temperature, salinity and chlorophyll a, are provided in Bibi et al. on PANGAEA at the following link: doi:10.1594/PANGAEA.984101.

Dissolved organic matter composition in the sea surface microlayer during a Mesocosm study in Wilhelmshaven, Germany (FT-ICR-MS and ATR-FTIR), spring 2023

We studied dissolved organic matter (DOM) dynamics in the sea surface microlayer (SML) during a mesocosm study at the Sea sURface Facility (SURF) of the Institute for Chemistry and Biology of the Marine Environment (ICBM) in Wilhelmshaven, Germany (53.5148 °N, 8.1463 °E). The study was conducted from 18 May to 16 June 2023 as part of the multidisciplinary BASS research unit (Biogeochemical processes and Air-sea exchange in the Sea-Surface microlayer). SURF was filled with pretreated natural seawater from the nearby Jade Bay (53° 28' 42'' N, 8° 12' 15'' E) to replicate natural conditions. We selected this approach to examine the regrowth of surviving phytoplankton cells after the initial water treatments, simulating a native microbial community starting with almost no bioproduction or pre-existing bioproduction products. To induce and maintain the phytoplankton bloom, inorganic nitrogen, phosphorus, and silicate were added on May 26, May 31, and June 01, 2023. By that, we induced an algal bloom of Emiliania huxleyi and Cylindrotheca closterium. Water samples were collected using a glass plate for the SML and a tube at 40 cm depth for the underlying water (ULW). This dataset contains DOM molecular data from ultrahigh-resolution mass spectrometry (Fourier-transform ion cyclotron resonance mass spectrometer, FT-ICR-MS), molecular indices calculated from FT-ICR-MS data (Ibio, Iphoto, IDEG) and environmental data, including dissolved organic carbon (DOC) and dissolved organic nitrogen (DON). Furthermore, it contains attenuated total reflectance Fourier Transform Infrared (ATR-FTIR) data from representative samples for each bloom phase. By combining molecular analyses with nutrient and bloom-phase data, we highlight the in situ production of carbohydrate-like and laminarin-derived DOM as a significant contributor to SML composition. General metadata from the multidisciplinary mesocosm study, including temperature, salinity and chlorophyll a, are provided in Bibi et al. on PANGAEA at the following link: doi:10.1594/PANGAEA.984101.

GISCO - Nomenclature of Territorial Units for Statistics 2021 (NUTS 2021), May 2021

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.

Resistivity models inverted from Time-Domain Controlled-Source Electromagnetic (TD-CSEM) data from the southeastern North Sea acquired during expedition AL611

Surface-towed time-domain controlled-source electromagnetic (TD-CSEM) data were acquired during expedition AL611 in the southeastern North Sea to map subseafloor electrical resistivity. The survey used a 100 m horizontal electric-dipole transmitter and two inline electric-field receivers towed at ~250 m and ~500 m offsets (SWAN system, Pestoressa et al., 2023). Transients (2 s period, 50% duty) were processed for timing alignment, transient selection and filtering, DC-offset removal, stacking, and logarithmic gating. Two-dimensional resistivity models were obtained with an extended time-domain implementation of MARE2DEM (Haroon et al., 2018; Key, 2016); parameter details are documented in the metadata. The dataset includes processed TD-CSEM inputs and inversion results both as SEGY and XYZ files with embedded positions.

Schutzgebiete_INS - Naturschutzgebiete

Der Kartendienst stellt die für INSPIRE gemeldete Schutzgebietsdaten des Saarlandes dar.:Naturschutzgebiete im Saarland: Bei dieser Schutzgebietskategorie handelt es sich um Gebiete, in denen ein besonderer Schutz von Natur und Landschaft in ihrer Ganzheit oder in einzelnen Teilen erforderlich ist. Schutzgebiete dieser Kategorie sind die am strengsten geschützten Gebiete. Insbesondere werden die wild lebenden Pflanzen oder Tiere, Biotope oder bestimmte Lebensgemeinschaften zu ihrem Erhalt, ihrer Entwicklung, aufgrund ihres seltenen Vorkommens oder auch aus wissenschaftlichen Gründen unter den gesetzlichen Schutz gestellt. Aktuell sind 121 Naturschutzgebiete unter Schutz gestellt. Für welche Gebiete welche Erfassungsschärfe vorliegt, kann aus dem Attribut-Feld „Erfassungsmaßstab“ abgelesen werden. Die Bestandskarte der Naturschutzgebiete im Saarland gibt Auskunft über die aktuell ausgewiesenen Naturschutzgebiete. Sachdaten/Attributinformationen: NAME:Name des Naturschutzgebietes AUSWEISUNG:Datum der Ausweisung AMT_J_S:im Amtsblatt veröffentlicht-Jahr_Seite GEB_ID_SL:Gebietsnummer Saarland für Bund/Länder-Datenaustausch INFO:Link zu den dazugehörigen Metadaten NR_BER_SAM:Nummer der bereinigten Sammlung des NSG VO_QUELLE:Verordnung über das Naturschutzgebiet ERFASSUNG: Erfassungsgrundlage RECHTSGR:Rechtsgrundlage SHAPE_AREA:vom System berechnete Flächen SHAPE_LEN:vom System berechneter Umring FLAECHE_HA:amtliche Flächengröße in HA gemäß Verordnung

Waterbase - Water Quality ICM, 2024

Waterbase is the generic name given to the EEA's databases on the status and quality of Europe's rivers, lakes, groundwater bodies and transitional, coastal and marine waters, on the quantity of Europe's water resources, and on the emissions to surface waters from point and diffuse sources of pollution. The dataset contains time series of nutrients, organic matter, hazardous substances, pesticides and other chemicals in rivers, lakes, groundwater, transitional, coastal and marine waters. A list of spatial object identifiers with selected attributes, reported through WFD and WISE Spatial data reporting, is added to dataset as spatial reference. The data has been compiled and processed by EEA. Please refer to the metadata for additional information. *** The dataset is split into two parts: Part 1: DisaggregatedData; Part 2: AggregatedData, AggregatedDataByWaterBody, SpatialObject_DerivedData. *** Data is reported by EEA member countries as individual samples from monitoring sites in the DisaggregatedData table or as annual aggregates of samples from monitoring sites in the AggregatedData table. Therefore data found in one table is not found in the other, and visa versa. Data in the the AggregatedDataByWaterBody is mostly historical. For an alternative option how to access the Waterbase data without downloading the full dataset, please see the 'Discodata user guide' in the Documents section.

Measurements of the marine carbon system from different approaches, discrete sampling and sensors during July 2023 in a Mesocosm Facility, Wilhelmshaven, Germany

The file contains data from the Marine Carbon System. It gathered parameters from the inorganic carbon and incorporate the organic alkalinity as a main contributor to the sea surface microlayer (SML) compared to the Underlaying Water (ULW). Data was collected during Mesocosm Study from 18-May to 17-July 2024 in the Sea sURface Facility (SURF), Institute for Chemistry and Biology of the Marine Environment, Wilhelmshaven, Germany. Discrete samples to measure Dissolved Inorganic Carbon (DIC), Total Alkalinity (TA) and Organic Alkalinity (OA) were collected. For SML data, DIC, TA and OA was collected every third day following the glass‑plate technique (Harvey and Burzell, 1972). The ULW data, DIC, TA and OA were collected every day using a suction system to collect the sample from 0.4 m depth. Discrete samples were transported to the laboratory for further analysis; DIC was determined coulometrically (CM5017, UIC, IL, USA), and TA concentration was directly measured by high-precision closed-cell potentiometric titration (916 Ti-Touch, Metrohm, Switzerland). OA concentration was determined directly after TA was measured, using the same sample (from which all carbonate species had been purged), denoted as back titration. The dataset includes quality flags 0-4 with flags 1 and 2 are ready for use. See metadata for more information.

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