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

luigi.ceccaroni@ce.eurecat.org