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The ‘Wikipedia problem’
• We know little about the people that collect it,
their skills, knowledge or patterns of data
collection
• Perceptions of loose coordination and no top-
down quality assurance processes
Hunter, J., Alabri, A. and Ingen, C., 2013. Assessing the quality and trustworthiness of citizen
science data. Concurrency and Computation: Practice and Experience, 25(4), pp.454-466.
• Data quality concerns linked
to professional standing and
roles – science version of “the
cult of the amateur”
• Special concerns when citizen
science linked to activism
• Basic unfamiliarity with
crowdsourcing mechanisms
Underlying concerns
• There are over 50
papers that are
exploring the reliability
of citizen science in
collecting data
• Most show that data is
of good quality and can
be used for many
purposes
Can volunteers collect data?
Quality: scarcity & abundance
• Scarcity • Abundance
Participating in Big Garden
Birdwatch (source: RSPB)
(cc) source: UCL Faculty of
Mathematical and Physical Sciences
Quality: scarcity & abundance
• Scarcity
– Investment in training
– Maximising output from
each transaction
– Top-down procedures to
ensure ‘once & good’ –
optimisation
– Standard equipment and
software
• Abundance
– assumption of variable skills
and training
– Ensuring microtasks are
enjoyable and rewarding
– Multiplicity of procedures
and interactions to ensure
engagement
– Multiplicity of equipment
with limited information
about characteristics
• Crowdsourcing - the number of people that edited the
information
• Social - gatekeepers and moderators
• Geographic - broader geographic knowledge
• Domain knowledge - the knowledge domain of the
information
• Instrumental observation – technology based
calibration
• Process oriented – following a procedure
Quality Assurance
Haklay, M., 2017. Volunteered geographic information, quality assurance. in D. Richardson, N. Castree, M. Goodchild, W. Liu, A. Kobayashi, & R. Marston (eds.) The International Encyclopedia of
Geography: People, the Earth, Environment, and Technology. Hoboken, NJ: Wiley/AAG
• Using collective
wisdom, or
aggregating individual
responses
• Each piece of data is
being evidenced by
multiple
observers/analysers
Crowdsourcing
• Social quality assurance use a
hierarchy of participants, with
those with known expertise
checking and assisting other
participants
• iSopt was designed as a social
network that support this
Social
Silvertown, J., Harvey, M., Greenwood, R., Dodd, M., Rosewell, J., Rebelo, T., Ansine, J. and McConway, K., 2015.
Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys, (480), p.125.
• Using
spatial rules
about what
is expected
Geographical
• In many scientific domains, we
can use knowledge from what
we already know about
geographical, temporal, and
other characteristics
Domain knowledge
Flockhart, D.T., Wassenaar, L.I., Martin, T.G., Hobson, K.A., Wunder, M.B. and Norris, D.R., 2013. Tracking multi-generational
colonization of the breeding grounds by monarch butterflies in eastern North America. Proceedings of the Royal Society of London B:
Biological Sciences, 280(1768), p.20131087.
• Instrumental
observation
provide us with an
evidence from a
sensor about the
measurement
Instrumental observation
Jo Somerfield
• Ensuring that
participants follow
an exact protocol
that ensure
standardise data
collection
Process oriented
CoCoRHaS
• When projects organisers are
asked, they describe multiple
methods (notice that these can
be grouped in the above
groups)
• Many times methods are
combined (75% of the time)
Combination of methods
Wiggins, A., Newman, G., Stevenson, R.D. and Crowston, K., 2011, December. Mechanisms for data quality and validation in
citizen science. In e-Science Workshops (eScienceW), 2011 IEEE Seventh International Conference on (pp. 14-19). IEEE.

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The 'Wikipedia problem' - Assessing quality of citizen science data

  • 1. The ‘Wikipedia problem’ • We know little about the people that collect it, their skills, knowledge or patterns of data collection • Perceptions of loose coordination and no top- down quality assurance processes Hunter, J., Alabri, A. and Ingen, C., 2013. Assessing the quality and trustworthiness of citizen science data. Concurrency and Computation: Practice and Experience, 25(4), pp.454-466.
  • 2. • Data quality concerns linked to professional standing and roles – science version of “the cult of the amateur” • Special concerns when citizen science linked to activism • Basic unfamiliarity with crowdsourcing mechanisms Underlying concerns
  • 3. • There are over 50 papers that are exploring the reliability of citizen science in collecting data • Most show that data is of good quality and can be used for many purposes Can volunteers collect data?
  • 4. Quality: scarcity & abundance • Scarcity • Abundance Participating in Big Garden Birdwatch (source: RSPB) (cc) source: UCL Faculty of Mathematical and Physical Sciences
  • 5. Quality: scarcity & abundance • Scarcity – Investment in training – Maximising output from each transaction – Top-down procedures to ensure ‘once & good’ – optimisation – Standard equipment and software • Abundance – assumption of variable skills and training – Ensuring microtasks are enjoyable and rewarding – Multiplicity of procedures and interactions to ensure engagement – Multiplicity of equipment with limited information about characteristics
  • 6. • Crowdsourcing - the number of people that edited the information • Social - gatekeepers and moderators • Geographic - broader geographic knowledge • Domain knowledge - the knowledge domain of the information • Instrumental observation – technology based calibration • Process oriented – following a procedure Quality Assurance Haklay, M., 2017. Volunteered geographic information, quality assurance. in D. Richardson, N. Castree, M. Goodchild, W. Liu, A. Kobayashi, & R. Marston (eds.) The International Encyclopedia of Geography: People, the Earth, Environment, and Technology. Hoboken, NJ: Wiley/AAG
  • 7. • Using collective wisdom, or aggregating individual responses • Each piece of data is being evidenced by multiple observers/analysers Crowdsourcing
  • 8. • Social quality assurance use a hierarchy of participants, with those with known expertise checking and assisting other participants • iSopt was designed as a social network that support this Social Silvertown, J., Harvey, M., Greenwood, R., Dodd, M., Rosewell, J., Rebelo, T., Ansine, J. and McConway, K., 2015. Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys, (480), p.125.
  • 9. • Using spatial rules about what is expected Geographical
  • 10. • In many scientific domains, we can use knowledge from what we already know about geographical, temporal, and other characteristics Domain knowledge Flockhart, D.T., Wassenaar, L.I., Martin, T.G., Hobson, K.A., Wunder, M.B. and Norris, D.R., 2013. Tracking multi-generational colonization of the breeding grounds by monarch butterflies in eastern North America. Proceedings of the Royal Society of London B: Biological Sciences, 280(1768), p.20131087.
  • 11. • Instrumental observation provide us with an evidence from a sensor about the measurement Instrumental observation Jo Somerfield
  • 12. • Ensuring that participants follow an exact protocol that ensure standardise data collection Process oriented CoCoRHaS
  • 13. • When projects organisers are asked, they describe multiple methods (notice that these can be grouped in the above groups) • Many times methods are combined (75% of the time) Combination of methods Wiggins, A., Newman, G., Stevenson, R.D. and Crowston, K., 2011, December. Mechanisms for data quality and validation in citizen science. In e-Science Workshops (eScienceW), 2011 IEEE Seventh International Conference on (pp. 14-19). IEEE.