analyzing the role of citizen science in modern research
TRANSCRIPT
Analyzing the role of citizen science in modern
research Luigi Ceccaroni, Jaume Piera
Eurecat; 1000001 Labs; CSIC
June 15th, 2015
• Index
• Citizen science and GEOSS
• EO technologies
• Knowledge-management methodologies
• Methodologies for the participation of citizens
• Challenges
• Main active players in citizen science
• European citizen observatories:
• Citclops, CITI-SENSE, WeSenseIt, COBWEB
• Citizen-science associations:
• ECSA, CSA, ACSA
• Observation and identification portals:
• iNaturalist, eBird, iSpot
• (Key) vehicles to respond to European policy priorities?
• (Key) players actively contributing to GEOSS?
• Impact of citizen science on policy functions:
1. Identification of new policy issues 2. Data gathering (relation to GEOSS) 3. Generation of citizens/society participation 4. Awareness raising 5. Lobbying about environmental interests 6. Support of environmental interest-groups position 7. Policy implementation (e.g., monitoring) (relat. to GEOSS) 8. Policy compliance 9. Policy enforcement 10. Policy review/revision/evaluation
• Contribution of citizen science to data gathering, monitoring, GEOSS
• Innovative and smart EO technologies
• New mobile apps and sensor platforms to capture environmental descriptors • E.g., water color, transparency, fluorescence, environmental
light, odor, PM10, PM2.5, NO2, O3, UV, wind speed, water level, snow depth, cloud cover, rainfall, species/biodiversity
• E.g., low-cost, Arduino-based sensors, acoustic rain gauge, irrigation probe
• Contribution of citizen science to data gathering, monitoring, GEOSS
• Innovative and smart EO technologies
• Comparison of satellite data (e.g., reflectance from MERIS) to citizen-taken data (e.g., pictures)
• Extension of existing standards
• Context awareness; in-situ intelligence: • E.g., embedded agents using local knowledge to
autonomously adapt the sensor network to changing requirements
• Contribution of citizen science to data gathering, monitoring, GEOSS
• Innovative and smart knowledge-management methodologies
• Ability to deal with uncertainty
• Ability to deal with device variability
• Use of existing data repositories (with associated problems) (e.g., picture repositories)
• Combination of traditional sensing and citizen-based sensing
• Contribution of citizen science to data gathering, monitoring, GEOSS
• Innovative and smart knowledge-management methodologies
• Incorporation of heterogeneous data into environmental models: new prediction, forecasts, recommendations • E.g., water color, flood extent, water quality, air quality
• Design and use of dynamic heterogeneous monitoring networks (including social and physical sensors)
• Contribution of citizen science to data gathering, monitoring, GEOSS
• Innovative and smart knowledge-management methodologies
• New sensor classification from a variety of viewpoints • Remote sensor: citizen not involved
• Social sensor: citizen communicates without intention (or not!) (e.g., Twitter-mining oriented to emergencies)
• Integration with a variety of social media
• User profiling, reputation systems
• Contribution of citizen science to data gathering, monitoring, GEOSS
• Innovative and smart methodologies for the participation of citizens
• Experiments in recruitment of participating people (e.g., existing citizen-science communities, schools)
• Gamification
• Our personal identities firmly connected to our profiles on social networks: peer pressure
• Risk: smart sensing felt as social engineering
• Challenges/conclusions
• Common data model
• GEOSS Architecture Implementation Pilot
• Creation of communication channels with ESA and other agencies modeling the same data
• Integration of citizen science with all steps of knowledge management:
• Monitoring, data, indicators, assessments, understanding, action
• Challenges/conclusions
• Prolonging battery longevity
• Trust
• Heterogeneity of sensing modalities (low and high fidelity, new and pre-existing)
• Overcoming the misconception that citizen-based data corrupt/jeopardize high-fidelity data: curation (and PR) needed
• Accuracy/uncertainty of citizen-based data varies over time/space/situations
• Challenges/conclusions
• Noise in social-network mining
• Filtering of data coming from social sensors:
• Language identification
• Domain
• Geolocation
• User profiling/modeling
• Semantics
• Artificial-bots identification
• Disambiguation using context
• Challenges/conclusions
• Hidden cost of sensors
• Not as simple hardware but as “data in a server”: construction, deployment, maintenance
• From single observatories to networks of observatories
• Dealing with cultural/linguistic variability
Analyzing the role of citizen science in modern
research Luigi Ceccaroni, Jaume Piera
Eurecat; 1000001 Labs; CSIC