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).
High resolution radar data (lmax) of Flechtdorf
This metadata refers to the geospatial dataset representing the status of the EEA Industrial Reporting database as of 15 December 2025 (version 15). 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.
Estuaries and coasts are characterized by ecological dynamics that bridge the boundary between habitats, such as fresh and marine water bodies or the open sea and the land. Because of this, these ecosystems harbor ecosystem functions that shaped human history. At the same time, they display distinct dynamics on large and small temporal and spatial scales, impeding their study. Within the framework of the OTC-Genomics project, we compiled a data set describing the community composition as well as abiotic state of an estuary and the coastal region close to it with unprecedented spatio-temporal resolution. We sampled fifteen locations in a weekly to twice weekly rhythm for a year across the Warnow river estuary and the Baltic Sea coast. From those samples, we measured temperature, salinity, and the concentrations of Chlorophyll a, phosphate, nitrate, and nitrite (physico-chemical data); we sequenced the 16S and 18S rRNA gene to explore taxonomic community composition (sequencing data and bioinformatic processing workflow); we quantified cell abundances via flow cytometry (flow cytometry data); and we measured organic trace substances in the water (organic pollutants data). Processed data products are further available on figshare.
Raw physical oceanography data was acquired by a ship-based Seabird SBE911plus CTD-Rosette system onboard RV HEINCKE . The CTD was equipped with duplicate sensors for temperature (SBE3plus) and conductivity (SBE4) as well as one sensor for oxygen (SBE43). Additional sensors such as a WET Labs C-Star transmissometer, a WET Labs ECO-AFL fluorometer (FLRTD) and an altimeter (Teledyne Benthos PSA-916) were mounted to the CTD. The data was recorded using pre-cruise calibration coefficients. No correction, post-cruise calibration or quality control was applied. Processed profile data are available via the link below.
This dataset comprises key carbonate chemistry parameters measured and calculated in incubation experiments under different experimental conditions. pH, water temperature, and salinity were measured with a WTW multimeter (MultiLine® Multi 3630 IDS). Total alkalinity was determined by open-cell titration with an 888 Titrando (Metrohm). Saturation state of calcite and aragonite were calculated using phreeqpython, a Python wrapper of the PhreeqC engine (Vitens 2021) with pH, water temperature, total alkalinity, and major ions as major input, and phreeqc.dat as database for the thermodynamic data (Parkhurst and Appelo 2013). As the original Elbe water was supersaturated with carbon dioxide (CO2) with respect to the atmosphere, its partial pressure of CO2 (pCO2) level decreased during the incubation period with open flasks, which caused an adjustment of calcite saturation state (ΩC) for ambient air conditions. To adapt for the impact of pCO2 variations during the experiment, saturation state of calcite and aragonite was calculated assuming an equilibrium with an atmospheric pCO2 of 415 ppm (normalized ΩC and normalized aragonite sautration state ΩA). Since ion concentrations were measured for only a small number of samples, the ion concentrations of the remaining samples were reconstructed using stoichiometry based on the initial solution composition and total alkalinity. The concentrations of conservative ions (Na+, K+, Cl-, SO42-) were assumed remain constant, while ions related to carbonate precipitation (Ca2+, Mg2+) were calculated based on changes in measured alkalinity (see Figure 5 of the associated paper). Detailed analysis and calculation procedures are described in the Method section of the associated paper.
With the introduction of mobile mapping technologies, geomonitoring has become increasingly efficient and automated. The integration of Simultaneous Localization and Mapping (SLAM) and robotics has effectively addressed the challenges posed by many mapping or monitoring technologies, such as GNSS and unmanned aerial vehicles, which fail to work in underground environments. However, the complexity of underground environments, the high cost of research in this area, and the limited availability of experimental sites have hindered the progress of relevant research in the field of SLAM-based underground geomonitoring. In response, we present SubSurfaceGeoRobo, a dataset specifically focused on underground environments with unique characteristics of subsurface settings, such as extremely narrow passages, high humidity, standing water, reflective surfaces, uneven illumination, dusty conditions, complex geometry, and texture less areas. This aims to provide researchers with a free platform to develop, test, and train their methods, ultimately promoting the advancement of SLAM, navigation, and SLAM-based geomonitoring in underground environments. SubSurfaceGeoRobo was collected in September 2024 in the Freiberg silver mine in Germany using an unmanned ground vehicle equipped with a multi-sensor system, including radars, 3D LiDAR, depth and RGB cameras, IMU, and 2D laser scanners. Data from all sensors are stored as bag files, allowing researchers to replay the collected data and export it into the desired format according to their needs. To ensure the accuracy and usability of the dataset, as well as the effective fusion of sensors, all sensors have been jointly calibrated. The calibration methods and results are included as part of this dataset. Finally, a 3D point cloud ground truth with an accuracy of less than 2 mm, captured using a RIEGL scanner, is provided as a reference standard.
High resolution radar data (lmax) of Isen
High resolution radar data (lmax) of Boostedt
High resolution radar data (lmax) of Rostock
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