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Waterbase - Biology, 2024

Waterbase serves as the EEA’s central database for managing and disseminating data regarding the status and quality of Europe's rivers, lakes, groundwater bodies, transitional, coastal, and marine waters. It also includes information on the quantity of Europe’s water resources and the emissions from point and diffuse sources of pollution into surface waters. Specifically, Waterbase - Biology focuses on biology data from rivers, lakes, transitional and coastal waters collected annually through the Water Information System for Europe (WISE) – State of Environment (SoE) reporting framework. The data are expected to be collected within monitoring programs defined under the Water Framework Directive (WFD) and used in the classification of the ecological status or potential of rivers, lakes, transitional and coastal water bodies. These datasets provide harmonised, quality-assured biological monitoring data reported by EEA member and cooperating countries, as Ecological Quality Ratios (EQRs) from all surface water categories (rivers, lakes, transitional and coastal waters).

MIN4EU LGRB-BW: near-surface mineral raw material occurrences - harmonized dataset

Since 1999, the Geologic Survey of Baden-Württemberg publishes a statewide geological map series 1 : 50 000 "Karte der mineralischen Rohstoffe 1 : 50 000 (KMR 50)". On it, the distribution of near-surface mineral raw material prospects and occurrences (mainly) and deposits (subordinate) is shown. This continuously completed and updated map currently covers around 60% of the federal state. It is the base for the regional associations in the task of mineral planning. The prospects and occurrences are classified according to different raw material groups (e.g. raw material for crushed stone (limestone, igneous rocks, metamorphic rocks, sand and gravel), raw materials for cement, dimension stone, high purity limestone, gypsum ...). Their spatial delineation is based on various group-specific criteria such as minimum workable thickness, minimum resources, ratio overburden/workable thickness, and so on. It is assumed that they contain deposits as a whole or in parts. In the vast majority of cases, the data is not sufficient for the immediate planning of mining projects, but it does facilitate the selection of exploration areas. The name of each area (e.g. L 6926-3) consists of three parts. L = roman rnumeral fo 50, 6926 = sheet number of the topographic map 1 : 50 000, 3 = number of the area/mineral occurrence shown on this sheet. Co-occurring land-use conflicts, e.g. water protection areas and nature conservation areas, forestry and agriculture, are not taken into account in the processing of KMR 50. Their assessment is the task of land use planning, the licensing authorities and the companies interested in mining. The data is stored in the statewide raw material area database "olan-db" of the LGRB.

ADCP current measurements (600 kHz) on RV HEINCKE during RV HEINCKE cruise HE667

Ocean velocities were collected by a Teledyne RDI 600 kHz Workhorse Mariner ADCP that was mounted on RV HEINCKE during RV HEINCKE cruise HE667. The transducer was located at 4 m below the water line. The instrument was operated in single-ping, broadband mode with bin size of 1 m and a blanking distance of 1 m. The velocity of the ship was calculated from position fixes obtained by the Global Positioning System (GPS) received directly from RV HEINCKE. Heading, Pitch and Roll were obtained both from the MRU of RV HEINCKE and the internal ADCP gyro. Heading as well as pitch and roll data from ADCP's internal gyrocompass and the navigation and motion data were used by the data acquisition software ViSea DAS (AquaVision®) internally to convert ADCP velocities into earth coordinates. Accuracy of the ADCP velocities mainly depends on the quality of the position fixes and internal ADCP heading data. Further errors stem from a misalignment of the transducer with RV HEINCKE's centerline. ADCP data is provided at minutely sample rate. Raw data or secondly binned data are available on request.

Master tracks in different resolutions of ALKOR cruise AL644, Kiel - Kiel, 2025-11-17 - 2025-11-28

Raw data acquired by position sensors on board RV Alkor during expedition AL644 were processed to receive a validated master track which can be used as reference of further expedition data. During AL644 data from the Seapath 330 system, the Furuno GP-170 and the Furuno GP-150 GPS receivers were used to calculate the mastertrack. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.

Water column, solid phase and porewater data in the Kiel Bight, SW Baltic Sea from 2016 to 2025

During the research cruises BE03/2016 (08.03.2016), BE10/2016 (19.10.2016), BE10/2018 (23.10.2018), BE03/2019 (15.03.2019), L23-13 (13.09.2023 - 15.09.2023), Sagitta24-1 (16.09.2024), Sagitta24-2 (23.09.2024), Hai24VE2 (24.09.2024), L25-2b (09.02.2025 - 17.02.2025) and EMB374 (04.09.2025 - 13.09.2025), CTDs were deployed and sediment corers were retrieved at 99 stations in Kiel Bight in the southwestern Baltic Sea. Water column oxygen concentrations were determined using oxygen sensors attached to the CTD framework. At selected water depths, water samples were collected with Niskin bottles for the analysis of nitrate concentrations using an autoanalyzer. Short sediment cores (<50cm) were recovered using a Multicorer (MUC), Minicorer (MIC) or Rumohrlot (RL). Bottom waters were sampled from the supernatant water in the sediment cores. Solid phase sediment samples were analyzed for total organic carbon using an element analyzer. Porewater was extracted from the sediment cores using rhizones and analyzed for total alkalinity (titration), ammonium (photometer), sulfate (ion chromatography), hydrogen sulfide (photometer), dissolved iron (ICP-OES) and dissolved manganese (ICP-OES). The collected data will be used to (i) determine the spatial and temporal variability of hydrogen sulfide in bottom waters of the Kiel Bight, (ii) identify the controlling factors governing the accumulation of hydrogen sulfide at the seafloor, and (iii) establish an early warning system of sulfidic seafloor conditions for regional stakeholders in the Baltic Sea.

Geochemical parameters in peat depth profiles from ombrotrophic bogs in North and Central Europe. Pürgschachen Moor, Austria

This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).

pondscape

<p>The database of the PONDSCAPE project (Towards a sustainable management of pond diversity at the landscape level) comprises taxon occurrence data of eight different organism groups (bacteria, phytoplankton, diatoms, cladoceran, macro-invertebrates (mollusks, heteropterans and coleopterans), macrophytes, amphibians and fish) and data on physical, chemical and morphometric variables of 125 farmland ponds covering five biogeographic regions in Belgium/Luxembourg</p>

Historical mapping of canals and ditches and the Danube surface water area in the Greater Donaumoos Region over the last 235 years

This dataset focuses on the historical mapping of the Greater Donaumoos fen region using old maps spanning the last 235 years. The main observations include the georeferencing of these historical maps and the subsequent vectorisation of the anthropogenic ditches and the Danube's surface area. The data collection encompasses maps spanning multiple centuries, providing temporal coverage that highlights landscape changes over significant historical periods. The data was collected to enhance archaeological, historical, and ecological research, offering insights into past landscapes and their transformations over time. The method involved digitising old maps and applying geospatial techniques to align them accurately with current geographical coordinates (Schmidt et al., 2024). This process was essential to create vector data representing the historical state of the ditches and the Danube river in this region. The purpose of this data collection is to provide a valuable resource for researchers studying historical land use, environmental changes, and regional development. The georeferencing and vectorisation processes were conducted using QGIS, ensuring precise alignment and accurate representation of historical features. The data generated from this project is crucial for understanding how the Greater Donaumoos fen region has evolved, offering a foundational dataset for further interdisciplinary studies.

Monitoring of CO2 emissions from passenger cars Regulation (EU) 2019/631

The Regulation (EU) No 2019/631 requires Countries to record information for each new passenger car registered in its territory. Every year, each Member State shall submit to the Commission all the information related to their new registrations. In particular, the following details are required for each new passenger car registered: manufacturer name, type approval number, type, variant, version, make and commercial name, specific emissions of CO2 (NEDC and WLTP protocols), masses of the vehicle, wheel base, track width, engine capacity and power, fuel type and mode, eco-innovations and electricity consumption. Data for EU-27 and UK are reported in the main database.

Geochemical parameters in peat depth profiles from ombrotrophic bogs in North and Central Europe. Fochteloër Veen, the Netherlands

This dataset contains geochemical variables measured in six depth profiles from ombrotrophic peatlands in North and Central Europe. Peat cores were taken during the spring and summer of 2022 from Amtsvenn (AV1), Germany; Drebbersches Moor (DM1), Germany; Fochteloër Veen (FV1), the Netherlands; Bagno Kusowo (KR1), Poland; Pichlmaier Moor (PI1), Austria and Pürgschachen Moor (PM1), Austria. The cores AV1, DM1 and KR1 were taken using a Wardenaar sampler (Royal Eijkelkamp, Giesbeek, the Netherlands) and had diameter of 10 cm. The cores FV1, PM1 and PI1 had an 8 cm diameter and were obtained using an Instorf sampler (Royal Eijkelkamp, Giesbeek, the Netherlands). The cores FV1, DM1 and KR1 were 100 cm, core AV1 was 95 cm, core PI1 was 85 cm and core PM1 was 200 cm. The cores were subsampeled in 1 cm (AV1, DM1, KR1, FV1) and 2 cm (PI1, PM1) sections. The subsamples were milled after freeze drying in a ballmill using tungen carbide accesoires. X-Ray Fluorescence (WD-XRF; ZSX Primus II, Rigaku, Tokyo, Japan) was used to determine Al (μg g-1), As (μg g-1), Ba (μg g-1), Br (μg g-1), Ca (g g-1), Cl (μg g-1), Cr (μg g-1), Cu (μg g-1), Fe (g g-1), K (g g-1), Mg (μg g-1), Mn (μg g-1), Na (μg g-1), P (μg g-1), Pb (μg g-1), Rb (μg g-1), S (μg g-1), Si (μg g-1), Sr (μg g-1), Ti (μg g-1) and Zn (μg g-1). These data were processed and calibrated using the iloekxrf package (Teickner & Knorr, 2024) in R. C, N and their stable isotopes were determined using an elemental analyser linked to an isotope ratio mass spectrometer (EA-3000, Eurovector, Pavia, Italy & Nu Horizon, Nu Instruments, Wrexham, UK). C and N were given in units g g-1 and stable isotopes were given as δ13C and δ15N for stable isotopes of C and N, respectively. Raw data C, N and stable isotope data were calibrated with certified standard and blank effects were corrected with the ilokeirms package (Teickner & Knorr, 2024). Using Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) (Agilent Cary 670 FTIR spectromter, Agilent Technologies, Santa Clara, Ca, USA) humification indices (HI) were determined. Spectra were recorded from 600 cm-1 to 4000 cm-1 with a resolution of 2 cm-1 and baselines corrected with the ir package (Teickner, 2025) to estimate relative peack heights. The HI (no unit) for each sample was calculated by taking the ratio of intensities at 1630 cm-1 to the intensities at 1090 cm-1. Bulk densities (g cm-3) were estimated from FT-MIR data (Teickner et al., in preparation).

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