Citizen science, training, data quality and interoperability More and more people are interested in participating in citizen-science projects, and the technology is becoming more accessible. Data quality is essential for citizen-science projects. Without quantified-quality data, the results of citizen science projects cannot be trusted. There are several challenges to ensuring data quality in citizen-science projects, such as participant motivation and training, data-entry errors, and environmental factors. These challenges can be addressed by using innovative technologies, such as artificial intelligence, and by developing better training methods. Mobile devices are becoming increasingly powerful and sophisticated, and they are making it easier for participants to collect data anywhere and anytime. Artificial intelligence is being used to develop new tools that can automatically analyse data and identify patterns. This makes it easier to identify and correct data errors. Online communities are providing a space for citizen scientists to connect with each other, share data, and learn from each other. This is helping to improve the quality of data collected by citizen-science projects. Citizen-science projects are increasingly aware of the importance of data ethics. This is leading to the development of new standards and guidelines for collecting and using citizen science data.

1. Is citizen scientists' training relevant in
achieving data quality and
interoperability?
“Interoperability: the role of best practices and standards”
online, 13 September, 2023
Luigi Ceccaroni
Earthwatch

2. Index
2
● Introduction
● The role of citizen scientists' training in achieving data
quality and interoperability
● Challenges and limitations of citizen scientists' training
● Conclusion
Luigi speaking

3. Introduction
3

4. Citizen science
Citizen science is work undertaken by civic educators and scientists together with
citizen communities to advance science, foster a broad scientific mentality, and
encourage democratic engagement, which empowers people in society to join
the debate about complex modern problems.
Definition adapted from
Ceccaroni, L., Bowser, A., & Brenton, P. (2017). Civic Education and Citizen Science: Definitions, Categories, Knowledge
Representation. In Analyzing the Role of Citizen Science in Modern Research (pp. 1-23). Hershey, PA: IGI Global.
DOI:10.4018/978-1-5225-0962-2.ch001. 2017-01
4
Luigi speaking

5. Citizen science often involves people
in data collection
5
Luigi speaking

6. Data quality
6
Data quality is the degree to which data meet the
requirements for their intended use. It is a measure of how
well-suited a data set is to serve its specific purpose.
Measures of data quality are based on characteristics
such as accuracy, completeness, consistency, validity,
uniqueness, and timeliness.
Luigi speaking

7. Interoperability
7
Interoperability refers to the ability of different systems to
communicate and exchange data.
Luigi speaking

8. The importance of data quality in citizen science
8
Data quality is important for citizen-science projects because
it ensures that the data collected by participants are
accurate, complete, and consistent. This is important for
the project's credibility and for the reliability of the results.
Luigi speaking

9. The importance of interoperability in citizen science
9
Interoperability is important for citizen science projects
because it allows the data collected by participants to be
shared with other scientists and organisations. This can
help to improve the understanding of the data and make
them more useful for decision-making.
Luigi speaking

10. Things that can be done to improve data quality and
interoperability in citizen-science projects
10
● Training participants on how to collect and manage data
effectively.
● Using standardised data-formats and protocols to ensure
that the data can be easily shared and reused.
● Providing clear and concise documentation about the
data, including their purpose, format, and limitations.
● Encouraging participants to participate in quality-control
activities, such as reviewing and correcting data.
Luigi speaking

11. The role of citizen scientists' training in
achieving data quality and
interoperability
11

12. Different aspects of citizen scientists' training
12
● Project overview
● Data collection: This covers the specific tasks that participants
will be asked to do, as well as the data-quality standards that
need to be met.
● Data analysis: This teaches participants how to analyse the
data they collect and how to interpret the results.
● Communication
● Collaboration
● Reflection
Luigi speaking

13. How can training help to improve data quality and
interoperability?
13
● Teaching participants about data-quality standards: This will help participants to understand
what is expected of them and collect data that are accurate, complete, and consistent.
● Providing participants with the tools and resources they need: This includes providing them
with training materials, data-entry tools, and access to data-quality experts.
● Encouraging participants to participate in quality-control activities: This could involve
reviewing data, identifying errors, and suggesting improvements.
● Creating a culture of data quality: This means emphasising the importance of data quality to
the project and to the participants.
● Using standardised data formats and protocols: This will make it easier for participants to
share data with each other and with other scientists.
● Providing clear and concise documentation about the data: This will help participants to
understand the data and to use them effectively.
Luigi speaking

14. Examples of successful citizen-science projects that
have used training to improve data quality and
interoperability
14
FreshWater Watch: This project collects data on the quality of freshwater ecosystems. The project has strict
data-quality standards and uses standardised data formats to ensure that the data can be easily shared and
analysed. Participants are trained on how to collect data on water quality, and their data are carefully reviewed
by experts. This has helped the project to make significant contributions to the understanding of freshwater
ecosystems and to inform decision-making about water quality, especially in relation to SDG 6 in Africa.
● The project provides training materials and resources to participants. These materials include videos,
tutorials, and fact sheets that teach participants about water quality and how to collect data.
● The project offers online and in-person training workshops. These workshops provide participants with the
opportunity to learn from experts and to get hands-on experience collecting data.
● The project has a team of data-quality reviewers. These reviewers check the data collected by participants
for accuracy and completeness.
● The project has a system for reporting and resolving data-quality issues. This system allows volunteers to
report any problems with their data, and it ensures that these problems are addressed quickly.
https://www.freshwaterwatch.org/
Luigi speaking

15. Examples of successful citizen-science projects that
have used training to improve data quality and
interoperability
15
eBird: This project collects data on bird sightings around the
world. The project uses a standardised data format and provides
clear and concise documentation about the data. Participants are
trained on how to identify birds and how to enter data into the
eBird database. This has made it easy for researchers to use the
data to study bird migration patterns and other aspects of bird
behaviour.
https://ebird.org/
Luigi speaking

16. Examples of successful citizen-science projects that
have used training to improve data quality and
interoperability
16
Galaxy Zoo: This project classifies galaxies. The project
uses a web-based interface that makes it easy for
participants to participate. Participants are trained on how to
identify different types of galaxies. The data collected by
participants has been used to study the evolution of galaxies
and to make new discoveries about the universe.
https://www.zooniverse.org/projects/zookeeper/galaxy-zoo/
Luigi speaking

17. Challenges and limitations of citizen
scientists' training
17

18. Challenges and limitations of citizen scientists'
training
18
• Cost: Training can be expensive, especially if it requires travel or the hiring of
experts.
• Time: Training can take time, which may not be available to participants or project
leaders.
• Accessibility: Training may not be accessible to all participants, such as those with
disabilities or limited internet access.
• Language: Training may not be available in all languages, which can limit the
participation of participants from some countries.
• Retention: Participants may not retain the information they learn in training,
especially if it is not relevant to their interests or needs.
• Motivation: Participants may not be motivated to participate in training, especially
if it is not seen as relevant or interesting.
• Evaluation: It can be difficult to evaluate the effectiveness of training, as there is
no one-size-fits-all approach.
Luigi speaking

19. How these challenges can be addressed
19
• Cost: Use online training modules, offer shorter training sessions, and partner with
organisations that can provide training at a reduced cost.
• Time: Offer training at convenient times and locations, and provide asynchronous
training options.
• Accessibility: Make training materials available in multiple languages and formats,
and provide accommodations for people with disabilities.
• Language: Translate training materials into multiple languages.
• Retention: Make training interactive and engaging, and provide opportunities for
practice and feedback.
• Motivation: Make training relevant to participants' interests and needs, and
provide opportunities for recognition and rewards.
• Evaluation: Collect feedback from participants and track the quality of the data
they collect.
Luigi speaking

20. Conclusion
20

21. Summary
21
• Citizen-science projects rely on participants to collect data.
• Data quality and interoperability are essential for citizen-science
projects.
• Training can help to improve data quality and interoperability.
• There are challenges and limitations to citizen scientists' training.
• These challenges can be addressed by
• using online training modules
• offering shorter training sessions
• making training relevant to volunteers
• using interactive methods
• providing ongoing support
• evaluating training
Luigi speaking

22. The future of citizen science and data quality
22
• More and more people are interested in participating in citizen-science projects, and the technology is
becoming more accessible.
• Data quality is essential for citizen-science projects. Without quantified-quality data, the results of citizen
science projects cannot be trusted.
• There are several challenges to ensuring data quality in citizen-science projects, such as participant
motivation and training, data-entry errors, and environmental factors.
• These challenges can be addressed by using innovative technologies, such as artificial intelligence, and by
developing better training methods.
• Mobile devices are becoming increasingly powerful and sophisticated, and they are making it easier for
participants to collect data anywhere and anytime.
• Artificial intelligence is being used to develop new tools that can automatically analyse data and identify
patterns. This makes it easier to identify and correct data errors.
• Online communities are providing a space for citizen scientists to connect with each other, share data, and
learn from each other. This is helping to improve the quality of data collected by citizen-science projects.
• Citizen-science projects are increasingly aware of the importance of data ethics. This is leading to the
development of new standards and guidelines for collecting and using citizen science data.
Luigi speaking

23. 23
Luigi Ceccaroni
lceccaroni@earthwatch.org.uk
This project has received funding from the European Union’s
Horizon 2020 research and innovation programme.
This presentation was created entirely by LaMDA without significant human input.