To increase the expertise of the widening country partner and all VIDIS partners covering the scientific fields necessary for VIDIS implementation, in particular, atmospheric science and air pollution assessment, artificial intelligence and machine learning. Knowledge, methods, and approaches will be shared by training with special emphasis on early stage researchers.

  1. Scientific training, early researchers.
  2. Training in project development, proposal writing, scientific project management
  3. Short term visits and staff exchange;
  4. External courses participation

Logistics of this task will be supported from the WP4 Management and communication by the event coordinator.


The following training areas will be considered:

Monitoring and measurements and sensor network scientific issues:

  • Gaining scientific excellence in utilization, calibration, deployment of low cost sensor nodes for measuring various air pollutants, in particular ambient particles, and in data stream capturing, storage and analysis, including performance assessments
  • Development of laboratory calibration methods, including control test atmosphere, chamber studies
  • Obtaining Machine learning and artificial intelligence methods for calibration of sensor devices scientific excellence exposure estimation using various methods (spatial interpolation, regression, optimization, data fusion, etc…)
  • Data assimilation techniques and other big data methods to support development of spatial maps of air quality including exposure
  • Specific issues related to data collected from mobile monitoring devices, in-motion data collection
  • Share concepts, ideas, methods, routines, and scripts for integration QA/QC of data collected by different sources, agencies, instruments, etc., and discuss the development of a reliable multi-dimensional database
  • Experiencing the using of sensor platform for ROS, including their applications and performance assessment
  • Experiencing modelling approaches for determination of source contribution to observed levels training young researchers in experimental atmospheric sciences, and developing practical skills in process modelling and statistical interpretation of Big Data streams
  • Sharing data and practicing data science tools, in part through short courses
  • Present different modelling schemes (spatial interpolation, regression, dispersion, optimization, etc.) to present information on air quality, and train how to select suitable candidates for different applications
  • Use of data with personal information from AirHeritage and other projects, including privacy and GDPR related issues
  • Application of different air pollution exposure models
  • Operation of personal devices and of Wireless Distributed Environmental Sensory Networks, WDESN, for measuring personal exposure, and application of GIS for reconstruction of probable/possible personal routes when data are missing
  • Presentation of machine learning tools and data fusion techniques for analysis of data streams from

Research management skills and general skills:

  • training young researcher in the above by working on their data using various tools and methods and common visualization and reporting standards
  • train researchers from Widening country in the project proposal writing and completing project proposal on the example of above topics

T 2.1 Training of ESR and PhD students/Postdoctoral research staff from Widening partner institution

T2.2 Training in project development, proposal writing, scientific project management

T 2.3 Short term staff visits

T 2.4 Summer school organization

WP number Deliverable Number  Deliverable Title Due Date (in months) Lead beneficiary  Type Dissemination level
WP2 D2.1 Activity report year 1 and implementation plan for year 2 with ESR in summer schools and 2nd WeBIOPATR conference  14  1 – VINCA  Report  Public
 D2.2 Activity report year 2, implementation plan for year 2 with ESR in summer schools and 2nd WeBIOPATR conference.  26 1 – VINCA
D2.3 Summary activity report 36 2 – NILU

WP2 Leader

Dr Alena Bartonova ORCID logoRG logo
Senior Scientist