TC_17 Connecting the particle formation research community to research infrastructure

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Short description[edit]

Particle formation is an atmospheric process whereby at specific spatial locations aerosol particles form and grow in diameter size over the course of a few hours. Particle formation is studied for its role in climate change and human respiratory health.

To study these processes, particle formation needs to be detected for where and when it occurs. Having detected particle formation, the processes are characterized for their qualities, e.g. duration, growth rate and other attributes. The detection and characterization of atmospheric particle formation relies on the measurement of particle size distribution, typically using an instrument called Differential Mobility Particle Sizer (DMPS).

In the context of particle formation research, particle size distribution as measured by a DMPS is observational data – in other words primary, uninterpreted data. For each day and location, observational data are processed and interpreted to detect and characterize particle formation. Observational data processing and interpretation are carried out by one or more human experts (typically postgraduate students). This constitutes an in silico (i.e., performed on computer) and human-in-the-loop scientific workflow. In the context of particle formation research, the output of such workflow is information describing (i.e., about) individual particle formation processes.

Information is truthful, meaningful, well-formed data (Floridi, 2011) – in other words secondary, interpreted data. Information is commonly also referred to as “data + meaning” and is also known as “data product.” Meaning is created in workflow execution, in which human experts also ensure that the resulting meaningful well-formed data are truthful. Information describing individual particle formation is further processed into summary statistics, e.g. the average duration. Such summary statistics are ultimately reported in scientific literature.

The use case aims to, primarily, (1) harmonize the information describing particle formation; (2) represent information, specifically the meaning of data, using an appropriate computer language; and (3) acquire and curate information in infrastructure.


Background Contact Person Organization Contact email
ICT Markus Stocker TIB, PANGAEA
RI-Domain Jaana Bäck University of Helsinki
Markus Fiebig NILU
e-Infrastructure Yin Chen EGI
e-Infrastructure Yann Le Franc EUDAT
ICT Leonardo Candela CNR
ICT Robert Huber UniHB, PANGAEA
ICT Paul Martin UvA
ICT Barbara Magagna EAA

Use case type[edit]

Test Case

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Floridi, L. (2011). The Philosophy of Information. Oxford University Press.

Perez, F., Granger, B. E. (2007). IPython: A System for Interactive Scientific Computing, in Computing in Science & Engineering, vol. 9, no. 3, pp. 21-29.

Stocker, M. (2017). Advancing the Software Systems of Environmental Knowledge Infrastructures. In Abad Chabbi and Henry W. Loescher (Eds.), Terrestrial Ecosystem Research Infrastructures: Challenges and Opportunities, pp. 399–423. Taylor & Francis Group, CRC Press. ISBN: 9781498751315

Wilkinson, M. D., Dumontier, M., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3.

[1] Please contact Yin Chen (, Wouter Los ( or Zhiming Zhao ( if you need help filling this template.












[13] Strictly speaking these functions are not necessary but they simplify the workflow