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Nick Taylor,
Mel Woods,
Andy Cobley,
Laura Underwood,
Gerid Hager,
Dr Saskia Coulson,
Inian Moorthy,
Linda See,
Dilek Fraisl,
Margaret Gold,
Joan Masó,
Valantis Tsiakos,
Uta Wehn,
and
Ester Prat
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is for anyone interested in citizen science and citizen observatories, and learning about how to design a citizen science project. You can use the hashtag #CitizenScienceMOOC to talk about this course on social media.
Topics Covered
Read more
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is for anyone interested in citizen science and citizen observatories, and learning about how to design a citizen science project. You can use the hashtag #CitizenScienceMOOC to talk about this course on social media.
Topics Covered
Read more
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is for anyone interested in citizen science and citizen observatories, and learning about how to design a citizen science project. You can use the hashtag #CitizenScienceMOOC to talk about this course on social media.
Topics Covered
- Understanding the issue or problem: exploring environmental issues and deciding on a research focus.
- Creating a community: finding the people who are brought together by a shared concern and positively nurturing the sharing of ideas and experiences.
- Deciding what data to collect: using the research question(s) to select what information will be gathered.
- Capturing or generating the data: collecting the information, keeping motivated and engaged.
- Analysing the data: interpreting the data, being able to spot trends and anomalies.
- Disseminating results: using the findings from the data to communicate with others about the environmental concern.
- Change-making / planning action: using the findings to lobby for change, or plan an intervention or action to inform others about the environmental concern.
Register for this course and see more details by visiting:
OpenCourser.com/course/lxua2g/citizen
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Reviews summary
Empowering citizen science for community impact
According to learners, this course offers a highly practical and inspiring guide to launching citizen science projects. Students praise its clear, step-by-step approach to topics like identifying environmental issues, collecting and analyzing data, and crucially, making a real difference. Many found the emphasis on creating and engaging a community particularly valuable. While exceptionally well-suited for beginners and hobbyists, some with prior scientific background felt the course could benefit from greater depth in advanced data analysis and dissemination strategies. Overall, it's considered an empowering foundation for those passionate about local change.
Highlights the vital role of community in successful citizen science initiatives.
"The focus on community engagement and data interpretation was especially valuable. The discussions were lively..."
"I particularly enjoyed the emphasis on 'creating a community' and building relationships, which is often overlooked."
"This course is a solid primer. It lays out the process clearly and emphasizes the importance of community."
Excellently suited for those new to citizen science or environmental activism.
"Absolutely brilliant! As a hobbyist interested in local conservation, this course gave me the perfect framework."
"It's a great course for beginners and those looking to apply their interest."
"I found it better for absolute beginners without prior research experience."
Provides clear, actionable steps for designing and executing citizen science projects.
"This course was incredibly inspiring and practical! I learned so much about how to actually plan and execute a citizen science project from scratch."
"The step-by-step approach to 'planning action' and 'change-making' was exactly what I needed."
"I loved the practical advice on 'deciding what data to collect' and the encouragement to actually start small projects."
Some specific topics could be expanded, and external links may need updating.
"I found the section on 'Disseminating results' a bit light – it felt like an overview rather than a deep dive..."
"My only minor critique is that some of the links to external resources were outdated..."
"There wasn't much about funding or long-term sustainability of projects, which would have been useful..."
May lack the detailed analysis or case studies expected by those with prior scientific background.
"I found it felt a bit too theoretical for me in parts, especially regarding data analysis. I was hoping for more hands-on examples..."
"Disappointed with the depth... I was looking for more advanced discussions or case studies on complex projects."
"It's too high-level for someone already familiar with research methodologies."
Activities
Coming soon
We're preparing activities for
Citizen Science Projects: How to Make a Difference.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Citizen Science Projects: How to Make a Difference will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/lxua2g/citizen
Reading list
We haven't picked any books for this reading list yet.
This handbook provides practical guidance for researchers and practitioners on how to design, implement, and evaluate Citizen Science projects.
Provides a practical guide to using Citizen Science for environmental management, covering topics such as data collection, analysis, and interpretation.
This report by the National Research Council provides a comprehensive overview of public participation in scientific research, including Citizen Science.
Explores the innovative aspects of Citizen Science, focusing on its impact on open science, society, and policy.
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Explores big data analytics techniques and their applications across various fields. It covers data collection methods for handling large and complex datasets, as well as advanced analytics methodologies such as machine learning and predictive modeling. Researchers interested in leveraging big data for insights and decision-making will find this book valuable.
Presents a comprehensive overview of survey research methods, including sampling design, questionnaire construction, data collection modes, and analysis techniques. It provides insights into the challenges and complexities of survey research and offers practical guidance for conducting successful surveys.
This practical guide to data collection methods helps researchers design and implement effective studies, encompassing a wide range of techniques, including surveys, interviews, observations, and archival research. It provides clear and detailed instructions for each method and emphasizes the importance of reliability and validity.
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Focuses on qualitative interviewing techniques, providing a step-by-step guide to conducting effective interviews. It covers ethical considerations, interview design, data analysis, and common challenges researchers may encounter. It offers a valuable resource for those interested in qualitative data collection methods.
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This comprehensive textbook provides a detailed overview of the principles and practices of data collection in the social sciences, covering topics such as research design, sampling, data quality, and ethical considerations. It offers a strong foundation for understanding the entire data collection process.
This comprehensive encyclopedia provides an extensive overview of social science research methods, including data collection techniques such as surveys, interviews, observations, and experiments. It covers both quantitative and qualitative approaches and serves as a valuable reference for researchers seeking a deeper understanding of various data collection methodologies.
This introductory text provides a clear and accessible overview of data collection and analysis principles. It covers fundamental concepts such as data types, sampling techniques, data cleaning, and statistical analysis, making it suitable for beginners in data science and related fields.
Provides an introduction to multivariate analysis methods, such as principal component analysis, cluster analysis, and discriminant analysis, using the R programming language. It explains the underlying concepts and provides practical examples and exercises, making it suitable for researchers interested in applying multivariate techniques to their data.
Offers a beginner-friendly introduction to data science, including data collection techniques. It provides a broad overview of data analysis concepts and machine learning algorithms, catering to individuals with little or no prior knowledge in these areas.
Focuses on longitudinal data analysis, which involves collecting and analyzing data over time. It covers techniques such as generalized linear mixed models and structural equation modeling, providing a valuable resource for researchers working with longitudinal datasets.
This textbook offers a comprehensive introduction to survey research, covering the entire process from research design to data analysis. It provides theoretical foundations, practical guidance, and real-world examples, helping students understand the strengths and limitations of survey methods.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Provides a comprehensive overview of big data analytics, covering topics such as data management, data mining, and data visualization. It valuable resource for students and practitioners who want to gain a better understanding of big data analytics.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Provides a comprehensive overview of machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. Written by leading experts in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
A classic text in the field of statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lxua2g/citizen
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Time
12 hours
Via
FutureLearn
Institutions
University of Dundee
WeObserve
Instructors
Nick Taylor
Mel Woods
Andy Cobley
Laura Underwood
Gerid Hager
Dr Saskia Coulson
Inian Moorthy
Linda See
Dilek Fraisl
Margaret Gold
Joan Masó
Valantis Tsiakos
Uta Wehn
Ester Prat
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