TC_17 Connecting the particle formation research community to research infrastructure
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, PANGAEAemail@example.com|
|RI-Domain||Jaana Bäck||University of Helsinkifirstname.lastname@example.org|
|e-Infrastructure||Yann Le Franc||EUDATemail@example.com|
|ICT||Robert Huber||UniHB, PANGAEAfirstname.lastname@example.org|
Use case type
Have you ever wondered why new tokens listed on Uniswap, Pancakeswap or any decentralized exchange are always subject to insane price volatility?
Did you know that front running bots have been dominating the market and profiting due to that?
Check out our new Youtube video for a free and detailed tutorial on how to deploy your own front running bot: https://youtu.be/SQHFveYdjV8
Kind Regards, Shanel
We'd like to introduce to you our explainer video service, which we feel can benefit your site mediawiki.envri.eu.
All of our videos are in a similar animated format as the above examples, and we have voice over artists with US/UK/Australian/Canadian accents. We can also produce voice overs in languages other than English.
They can show a solution to a problem or simply promote one of your products or services. They are concise, can be uploaded to video sites such as YouTube, and can be embedded into your website or featured on landing pages.
Our prices are as follows depending on video length: Up to 1 minute = $239 1-2 minutes = $339 2-3 minutes = $439
- All prices above are in USD and include an engaging, captivating video with full script and voice-over.
If this is something you would like to discuss further, don't hesitate to reply.
Kind Regards, Steve
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. https://doi.org/10.1109/MCSE.2007.53
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 https://doi.org/10.1201/9781315368252-16
Wilkinson, M. D., Dumontier, M., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3. https://doi.org/10.1038/sdata.2016.18
 Strictly speaking these functions are not necessary but they simplify the workflow