Here we do data analysis for citizen science projects with the open data https://www.inaturalist.org/projects/city-nature-challenge-2019 The project is in progress and discussions between @Liubov Tupikina, Muki Haklay, CRI.
Preliminary notebook for analysis are "citizen_science_analysis"
RQ1: Can we identify macroscopic laws of citizen science projects? The main goal of the project to identify short-term and long-term dynamics in citizen science projects. (general RQ)
RQ2: How citizen science communities are growing in time, what drives their growth if not preferential attachment or exogenous factors?
RQ3: If we are looking at City Nature Challenge data - we have now data for San Francisco, LA, and London, which now have 4 or 3 years time series. How the power law like distributions of human activities are emerging? It's interesting that it is similar for SF and LA and the pattern that they have in year 4, London has in year 3.
RQ4: (a) Are there differences in data collection patterns in each year and between years? Are there also emerging pattern over time in a location? (b) Are there also emerging pattern over time in a location?
RQ5: To what degree the heavy contributors influence the total pattern and distribution of data? Is there any observed impact from major contributors (e.g. someone who is heavy contributors dropping off because they can't keep up with the competition). There is also the question about repeat recruitment - are the pattern of one timers vs people who come back stay the same from year to year? Are there any patterns or places that are behaving differently in terms of patterns of contribution?
Some of the problems for citizen science community are: heterogeneity of collected data, missing data, non-controlled growth of community of observers.
The project is now discussed together with CorrelAid X Paris x CRI https://github.com/correlaid-paris/
Contact person: Liubov Tupikina (CRI), Muki Haklay, Correlaid.
Welcome to join!