The DANUBIUS-RI Data Portal provides a set of services to the research and academic Community.

The overall goal of the DANUBIUS-RI Data Centre is to provide scientiststhe user community with free and open access to all DANUBIUS-RI data, together with access to innovative and high quality data products and tools for quality assurance, data analysis and research.


Observation Node

The data collected from the Supersites and the Observation Node will comprise satellite, airborne, drone and in-situ sensing capabilities. Routine data acquisition from satellite platforms will benefit from validation activities from the deployment of standardized sensors from fixed, buoy and occasionally man and unmanned airborne platforms. Airborne, drone and in-situ sensors may also be deployed to fill the gaps in EO capability, as a result of limitations in spatial, temporal and radiometric resolution and overall sensing capability.

Analysis Node

The Analysis Node will facilitate the implementation of common methods and procedures in the fields of hydrology, chemistry, biology, ecotoxicology, geo-hydromorphology, geology and hygiene within DANUBIUS-RI. For variables that are common to all Supersites, the Analysis Node will ensure that samples are collected, stored, processed and analyzed according to the DANUBIUS Commons by all institutions associated with the Supersites.

Modelling Node

The Modelling Node will take advantage of the data availability provided within the Supersites, for calibration and validation activities and feeding the models for a better development and refinement. The Modelling Node will provide modelling outputs as digital data for a broad use among Nodes and Supersites. It will also facilitate the access to interchangeable re-usable tools for each Supersite, integrating the fundamental processes characterizing each River – Sea (RS) system, on different spatial and temporal scales and providing services built around the specific requirements and conditions of each.

Impact Node

The Impact Node will facilitate scientific knowledge development on the interface between natural and social sciences; the development of methodologies and tools that will help to solve problems in highly complex dynamic RS systems; the transfer of scientific output and practical tools derived from DANUBIUS-RI. It will provide assessments (e.g. facilitating of the assessment of the potential social impact of knowledge developed by DANUBIUS-RI.

...and data

Remote sensed data are acquired information about the Earth’s surface or atmosphere without actually being in contact with it. Remote sensing data provides essential information that helps in monitoring various applications such as change detection and land cover classification. Remote sensing is a key technique used to obtain information related to the Earth’s resources and environment. Some types of satellite imagery data can be easily accessed online through various applications. Remotely sensed satellite images and derived datasets have a variety of spectral, spatial and temporal resolutions.

In-situ data is data measured at specific known locations, but obviously the measurements are made only where the instruments are present. Most of the in situ oceanic and coastal data distributed by CMEMS, EMODnet and SeaDataNet are in netCDF-CF format. A common tool for visualizing oceanic data is ODV (Ocean Data View). Most observation systems store data and metadata in a relational database. Some data is distributed in ASCII (asc, dat, grd , tab) .csv or .txt, tab separated format. This is a common format for discrete and continuous (e.g. buoy) data.

In situ data is commonly distributed in ASCII .csv format. The OGS standard SOS (Sensor Observation Service) is getting more and more important. Most observation systems are using a relational database. In contrast to remote sensing data, the difficulty of in-situ data is based on the diversity of:

  • Parameters: physical, biological, chemical
  • Platforms: stationary, mobile, above water, on or near the surface, underwater or at the bottom, autonomous
  • Technique of sampling
  • Processing of data: traceability of the data processing code(program) used by the programmer
  • Sampling frequency: discrete or continuous

Laboratory data is collected from an experimental study that involves taking measurements which can be manipulated by human intervention (temperature, velocity, mass). In a laboratory experiment the researcher manipulates the variables under consideration and tries to determine how the manipulation influences the other variables.

In most of the experiments the raw data, produced directly from an instrument, is subject to one or several forms of post-processing, to yield derived data that identify new information or properties. Some portion of this derived data is then selected for consumption as the result of the experiment in a subsequent analysis activity. Laboratory data used for research publication needs to be made available in standard forms and there are a number of de facto standard file formats accepted (such as ASCII). Laboratory data needs to observe good practice guidelines and the EU legislation/regulations and describe the way an experiment was carried out to enable a specific form of conclusion to be drawn (e.g. the number of replicates required), to provide some level of evidence regarding the way the experiment was conducted, the appropriate protocol or practice used.

Modeling outputs are computational data generated by models, intended as methods for reproducing the reality, schematizing its major features and simplifying the main driving processes with an acceptable loss in representativeness. Models are used to study processes that cannot be directly measured, as an interpolator of the data in space and time or to determine “what if” scenarios: for example, the effect of sea level rise on RS systems due to climate change. For computational data the quality of input data and the availability of complete data sets in terms of space and time are crucial to have good representation of real processes (calibration/validation) and as consequence reliable outputs for scenarios. All model outputs can be reproduced, given information about the model type, setup and input data, and computing time availability. Some model outputs do not require long time preservation, particularly if they are not produced in operational way. Since computational time is sometimes highly expensive, some kind of model outputs should be preserved, given the major effort in reproducing them (time, facilities and maintenance costs).

All data will be curated using standard DANUBIUS-RI accepted services, making sure that all data is discoverable. All final level datasets will be shared through the DANUBIUS-RI data portal. DANUBIUS-RI believes in the concept of FAIR data (Findable, Accessible, Interoperable and Re-usable) and will work towards offering the DANUBIUS-RI users,  data with implemented FAIR principles. DANUBIUS-RI supports openness and sharing of data and will therefore stimulate the exchange of good practices in data access and sharing by coordinate with existing European initiatives.

DANUBIUS-RI data will be collected from ten current Supersites: Danube DeltaElbe-North SeaEbro-Llobregat Deltaic SystemNestosPo delta and North Adriatic LagoonsThames EstuaryGuadalquivir EstuaryTay CatchmentRhine - Meuse DeltaMiddle Rhine as well as from the Observation, Analysis and Modeling Nodes.


Current DANUBIUS-RI components