Skip to main content

Authors: Carina Veeckman [VUB], Floor Keersmaekers [VUB], Karel Verbrugge [VUB], Eline Livémont [VUB]

Veeckman, C., Keersmaekers, F., Verbrugge, K., & Livémont, E. (2022). D3.3 Citizen Science Starters Kit (Online Citizen Science Training Materials) (Version 2). Zenodo.

Crucial design factors for citizen science

Module 3 describes some crucial design factors for conducting successful citizen science research. These design factors relate to specific processes and mechanisms that can either drive or hinder the success of citizen science. The definition of what ‘successful’ citizen science is will vary from context to context. Success might be defined by the amount of gathered data and the number of research publications, or by the established social impact. Success is thus context-specific and will be in line with the objectives and goals of the research project.

The following design factors are described in this module: (1) A communication and feedback culture, (2) Motivational strategies to participate, (3) Mechanisms for ensuring data quality, and (4) Usage of citizen science platforms for data management.

Goal: At the end of this module, you are able to understand crucial design factors and have some general guidance at hand for starting your citizen science research project.

A communication and feedback culture

A crucial design factor in citizen science is the set-up and maintenance of a communication and feedback culture, both internally and externally. Communication is a vital aspect of citizen science, and it is a necessary part in every step of the research process. Communication activities will be needed for recruiting and engaging citizen scientists, increasing the visibility of your research project, informing about the project’s results and outcomes, etc. It takes good practice to communicate effectively, and you may not underestimate the amount of time that you will spend communicating with your target audiences. Ideally, your research project will have a community manager, a science communicator and a science trainer who can look after these activities:

  • The community manager is the main point of contact for your citizen scientists if they have any questions. In some research projects, it will be necessary to have a forum or a central support service. The community manager is proactive in sharing information and news, and in finding the right answer to questions from citizens. It is not necessary to be available 24 hours per day, but desirable to provide an answer within a respectable amount of time (within 1 to 3 days). Furthermore, the community manager can also motivate participating citizens to help each other out. As such, participating citizens can also become ambassadors for your research project.
  • The science communicator ensures that scientific content is easily understandable and accessible to a broad audience. The science communicator proofreads the materials and checks if it adheres to inclusive communication principles.
  • The science trainer makes sure that citizen scientists are properly trained for collecting or analysing data by providing manuals or support on the ground.

There are many factors in place that affect the success of communication activities in a citizen science project. Overall, it is recommended that a communication plan for your research project is developed in advance. A communication plan is a detailed description of all communication steps by which you plan to engage your target audiences. It lists the steps in chronological order, and links them with the relevant target audiences, the tools and channels, and the aims you hope to achieve. A communication plan should be written in the planning phase of your research project, and adjusted throughout the entire project. It is important to allocate a budget for this, as it will help you to set priorities; do you plan to evenly spread your resources over the lifetime of the project, or do you plan some peaks in your communication? The communication plan also allows you to evaluate how successful the activities have been at particular moments in the research project.

The factors listed below need to be considered for a successful communication strategy:

  • Identify your target audiences: When identifying your target audiences, you can categorize them into primary, secondary and intermediary target audiences. The primary target audience will be the group of citizens who feel highly engaged with your research project, and who are the most affected by the research aim. This group will contribute the most when it comes to collecting or analysing data. A secondary target audience is a group who is aware of, but not directly involved in, the project. A secondary target audience might become a primary target audience, for instance a government authority that becomes interested in your research. Lastly, an intermediary target audience is a network, an organisation or a person that might connect you to others, a teacher forum if you like to engage youngsters for instance.
  • Get to know your target audiences: The better you understand your target audiences, the more effective and personal you can make your communication. In listing your target audiences, it is also good to have specific details about them. What is the size of the group? What is the average age? What is the gender distribution? And what is their level of education regarding the research topic? What are their motivations to join your research project? Not understanding your target audiences, and not knowing what stimulates them to be part of citizen science, is one of the biggest pitfalls. In the planning and design phase of your research, you can look into already established research studies to see if you can find any interesting information related to your target audience. You can also decide to develop a short intake survey once citizens subscribe to your research. In this way, you can log their former experiences, knowledge and motivations, and employ the right strategies and tools to recruit them.
  • Use a diverse mix of communication channels and tools: You can use a wide variety of channels and tools for supporting your communication activities, either digitally, on paper or face-to-face. When identifying and describing your target audience, you can also match it with the most efficient communication channels and tools. Choosing the right medium ensures that your message arrives to your intended audience, and increases the chances that your audience reads, hears, or sees your messages. Depending on the purpose of your message, you might choose a different medium of communication. For instance, if you want to inform and train citizens, you might choose physical workshops so you can provide the opportunity for asking questions. On the other hand, if you would like to inform about an urgent issue related to data collection, you might choose social media or personal e-mails. Try to be creative in the mix of media that you choose for your communication activities. Presentations with lengthy explanations should be balanced with playful, social events. Furthermore, using a varied mix of media will also affect the diversity of your project participants. Launching an open call via social or mass media will allow you to reach a huge number of potential scientists. If you combine this approach with more targeted communication, such as collaborating with intermediary organisations, then you will be able to reach out to more specific profiles.
  • Use of language: The language, specifically the tone of voice and its terminology, matter greatly when communicating with your target audiences. Getting the ‘wrong’ language might exclude citizens from the communication processes. Therefore, it is crucial that you reflect on how inclusive the language used is. For instance, is the language adapted to audiences of different cultural and literal backgrounds? Are gender differences taken into account? And are you using understandable language? Which terminology are you using for describing participating citizens (cf. Module 1)? Talking with participating citizens can help you to understand how they feel affected by it, and might enable you to co-create a more inclusive and understandable language.
  • Open communication: Citizen science is a two-way communication process between researchers, citizens and other stakeholders (e.g. policymakers, interest groups, etc.) involved in the research project. When planning your research project, you have to reflect on how you can stay in touch with participating citizens, but also how they can connect with you and other members of the community. You can question the preference of communication channels and evaluate what type of information they like to receive. During the executing phase, it is of critical importance that (personal) feedback is provided as it gives recognition for the citizens’ contributions. If feedback cannot be provided immediately, then you can send a message that the collected data was successfully received and that the data will be validated within a certain period of time. Drop-out can occur at this stage due to a lack of openness about the results. After completing the task, participating citizens are eager to know more about the results. Therefore, it is recommended that once a task is completed you are open about the further steps in the research project, and you already provide some first insights through simple visualizations or statistics (e.g. the number of contributions, explanations about the analysis methods, insights into citizens’ profiles, etc.). During the final stage of the research project, the final results are shared with the target audiences. Again, it is recommended that a two-way dialogue is stimulated and that the research results are not just presented during an event or published online through a downloadable report. Interactive workshops can ensure the sustainability of the results and can provide space for mutual learning related to (policy) recommendations or future research trajectories.

A practical guide to communication and engagement in citizen science:

It takes practice to stay open, accessible and inviting through communication. This practical guidebook equips you with a few tricks of the trade. The first part of the book focuses on the building blocks of a good communication plan. A communication plan reflects upon the project objective(s), the level of engagement, the target audience and its motivations and, finally, the evaluation of success.

The second part of the book focuses on tactics and tools that you can use for the engagement strategy. An engagement strategy helps you to reflect upon the expectations, motivations and behavioural aspects of your target audience to keep them on board in the long term. Six tactics and tools are provided, such as storytelling, gamification, and usage of social media, to support either initial or continued participation.

The third and fourth part of the book provide practical tips and tricks, as well as a template to start drafting your own communication and engagement plan. The guide was published in 2019 by Scivil, the Knowledge Center on Citizen Science in Flanders, and in collaboration with SMIT, EOS Wetenschap and Tales and Talks. The content of the guide is based on studies of citizen participation and the real-life experience of science communicators. This guide is for anyone who finds themselves communicating and engaging with citizen scientists.

You can download the guide here in English and in Dutch

Further reading:

  • The Scottish National Standards for Community Engagement might provide a useful reference point for ensuring high-quality and effective engagement processes. There are ten standards in total for setting up successful engagement with stakeholders, focusing on support, planning, methods, working together, improvement, etc. Indicators are provided for each standard, which can be incorporated into your communication and engagement plan.
  • If you are interested in learning more about inclusive language, you can check out the book ‘Inclusive communication’ by Hannan Challouki. Furthermore, the organisation ‘Wablieft’, centre for clear language, helps you with writing accessible texts (for vulnerable groups). They organise workshops and also offer paid proofreading and editing services.
  • This article by de Vries et al. (2019) performed a literature review on citizen scientists’ preferences for communication of scientific outputs.

Share your thoughts and opinions – Time for reflection:

  • Would you consider using mass media to promote your research (project)? Do you have any doubts or concerns?
  • Do you have the necessary capacities in your team to support communication activities? How are roles divided?
  • What are your experiences and tips for using social media for communicating about your research (project)?

Motivational strategies for participation

In addition to a communication plan, it is also effective to have an engagement plan in place. Engagement stands for the active involvement of citizens in your research activities and will be defined by the chosen level of participation). Therefore, an engagement strategy will focus on the identification and monitoring of motivations which support or prevent citizen scientists from taking part in your research project. In line with citizens’ expectations, the engagement strategy will also propose (new) tactics and tools to secure continued participation in the long term. During the implementation phase, the communication and engagement plans will be closely interconnected with each other. For instance, if monitoring tools reveal that participation rates are dropping, then new communication activities can be planned that stress particular motivations to take part in the project.

Motivations to take part in citizen science research can be very diverse, but are mostly intrinsically driven. When citizens are intrinsically motivated, they engage in the research project because it is personally rewarding, or they find enjoyment in the process itself. If they were participating out of a reward or social status gain, then citizens would be more driven by external drivers which are not always directly related to the project. In most cases, citizens are motivated to take part because they like to contribute to science, or because they have an interest in the particular research topic. In citizen health science, the motivation is strongly linked to a personal interest in contributing towards a treatment or cure.

Case study: Reasons for monitoring air quality

As part of the development process for its engagement strategy, the hackAIR project surveyed 370 potential citizen scientists. An online questionnaire gauged motivations for, and barriers to, air quality monitoring and measurement in the neighbourhood. The leading motivations were: general curiosity about the measurement results (56%), concern about the local air quality caused by the perception of living in an area with poor air quality (43%) and personal health problems (30%). These reasons were used as triggers during opportunities to communicate later in the project.

Motivations can also change over time. At the beginning of the project, citizens are mostly driven by the desire to learn, general curiosity and out of interest. Over time, these motivations rather shift towards scientific learning, social connections, and feeling appreciated. Be aware that not everyone will stay till the end of the project. The drop-out rate is usually the highest at the time of initial participation, or just after it. This is mostly due to the usage of jargon, or a non-user-friendly application or data protocol. The drop-out after a longer term of participation will be mostly due to a lack of openness about the scientific process, lack of feedback about the results, and a lack of recognition and appreciation.

Case study: Engagement metrics of the Eye for Diabetes project

‘The Eye for Diabetes’ project motivated citizens through Zooniverse to train an algorithm for identifying symptoms of diabetic retinopathy. Citizens were invited to annotate retinal images for symptoms of the disease. During the lifetime of the project (January 2019-June 2020), a total of 3,950 citizens registered to take part in the project. Halfway through the project, the number of contributions increased due to making the platform more accessible to international users by adding an English version. Peaks in contributions could also be correlated with organised events, and the start of the COVID-19 pandemic. The statistics suggest that half of the classifications were performed by non-registered users. On average, one citizen scientist watched and labelled 24 retina images, while the top contributor labelled 1,000 images. This shows that only a small percentage of the participating citizens were hardworking and loyal to the project, while the majority of contributions were made by a larger group of citizens who passed by unplanned.

Further reading:

  • Did you already hear about the 90-9-1 principle in internet culture? This rule states that in online websites, 90% of the participants only consume content, 9% change or update content, and 1% adds content.
  • The motivations of citizen scientists are usually investigated through social science research, by organizing surveys or in-depth interviews. If you would like to collect information about the motivations of the citizen scientists in your research (project), you can use the following questionnaire by Levontin. The questionnaire consists of 18 categories, with 58 items in total. Depending on the scope of your research (project), you can select the most appropriate items.
  • For monitoring the participation rates in your online research (project), you can rely on several engagement metrics. Aristeidou et al. (2017) propose looking into the activity ratio (the number of days a participating citizen was active and contributed versus the days she/ he/they remained in the project), the activity duration (the number of days a participating citizen is linked to the project versus the total number of days) and the lurking ratio (the number of days a participating citizen was browsing content on the citizen science platform, but not contributing). Based on these metrics, you can categorize your participating citizens into different engagement profiles (e.g. hard-working volunteers, loyal volunteers, lurkers, etc.).
  • This toolkit of provides further information about how you can build a community. Tips are provided for knowing, engaging and nurturing the community.
  • The BiodivERsA project provides an interesting handbook on stakeholder engagement in research projects. It includes practical guidance for better planning and engaging with non-academic stakeholders, including policymakers.

Share your thoughts and opinions – Time for reflection:

  • Do you have any tips on how you can manage the expectations of participating citizens in your project? Citizens may expect to see rapid change, while in reality this might not be the outcome of the research (project).
  • What are potential drivers and barriers for participating in your research (project)? Are there clearly stated benefits for participating citizens?
  • How can you ensure continued participation in your research (project)? Which tactics and tools could help?

Mechanisms for ensuring data quality

Regarding the quality of the citizen science data, there are certain questions and doubts that can arise. Are citizen scientists able to gather reliable data? Can they intentionally or unintentionally influence the results? And are you measuring what you intended to measure in a correct way?

Different stakeholders might have different expectations about the data. As a researcher, you are looking for scientific accuracy in the data for achieving your analytical objectives. If policymakers are involved in the research project, they might have other expectations and needs regarding the data. For them, the data traceability might be most important, as they do not want to run the risk of inconsistency in information acquisition and processing. For the participating citizens, in turn, it should also be ensured that the data protocol is easy enough to follow so that they are not deterred from the project. A more rigid protocol can result in higher quality data, while a more flexible protocol can give more freedom to participating citizens – but with a higher risk of low data quality. An agreement should thus be sought among all stakeholders involved on the definition of (acceptable) data quality.

Strictly speaking, data quality is referring to the correctness, accuracy and completeness of the data6. However, it is recommended that a more holistic approach is taken, and that aspects of data contextualisation (communicating about the context in which data and information were created), data reuse (clarifying data ownership and using open standards) and data interoperability (ensuring unproblematic reuse) are also looked into. These three factors all have an influence on the data accuracy of your project.

Overall, it must be stressed that issues related to data quality are not unique to citizen science. In more conventional science methods, the replicability and reliability of the research results can also be a hurdle. Furthermore, studies also show that the quality of the data in citizen science research is more likely to be determined by the study design, the methodological approach and communication skills, rather than the citizen engagement approach per se.

In order to ensure the data quality in your research (project), there are several mechanisms that you can set up during or after the generation of data:

  • Pre-test your data protocol: Before launching your citizen science campaign, it is advised that you thoroughly test your protocol. As such, you can identify errors in measurements and ameliorate the design. It also helps you to spot the types of errors participating citizens can make, and maybe even to investigate ways you can rate or reward a good quality contribution of a citizen. It is best to display examples or errors anonymously without embarrassing anyone.
  • Training of participating citizens: First of all, training can help to teach citizens how to collect, process or analyse the data. Clear step-by-step descriptions will help them to improve their scientific literacy and to perform the task in a good way. Training can be organised face-to-face or through online tools and platforms, e.g. manuals, FAQ, tutorial videos, etc.
  • Data validation: Data validation mechanisms ensure that the data meet certain criteria and can therefore be used or analysed. For instance, validation checks in surveys that ensure that you write the data or a postcode in the correct format.
  • Data verification: Next, the data submitted by citizen scientists can be checked and verified in collaboration with more experienced citizens, or by researchers. This can be done for the whole dataset, or only for randomly chosen samples of the dataset. On the Doedat platform (DoIt) of the Meise Botanical gardens, the scientists are verifying the data themselves. They still consider this way of working to be more efficient than when they have to make the observations without the help of citizens. Nowadays, you can also use software-based systems (based on artificial intelligence) that automatically identify outliers.
  • The law of large numbers: Ensuring a large number of samples or observations, or involving citizens in a measurement on multiple occasions can ensure better data quality. You collect a larger amount of data on which you can make statistical corrections. With a large amount of data, you can also have duplicates, which can help you check the accuracy of the results. For instance, the Curious Noses project involved a large number of citizens in the measurement of NO2 . All participating citizens received two measuring tubes, which were installed at the same time. If the two measurements did not match, then the complete sample was excluded from the database and regarded as not reliable.
  • Systematically divide an area into segments or keep track of the sample frequency: If applicable, it is also recommended that the periods and locations of observations are selected carefully. In order to make valid statements, it is best to systematically cover different types of areas in all seasons. For instance, Spinicornis maps the distribution of woodlice by dividing the Belgian territory into segments. They organise multiple field trips to each of those segments in order to systematically cover all seasons.

Further reading:

  • The data charter for citizen science (available in Dutch and English) can help you with further questions on data quality. This data charter is published by Scivil, the Flemish Knowledge Centre on Citizen Science, in collaboration with Digitaal Vlaanderen.
  • You can read more about the usage of citizen science data for environmental monitoring in policymaking in this best practices report of the European Commission. It lists opportunities, challenges and potential benefits for policy uptake.
  • This article by Fritz et al. (2019) presents a roadmap about how citizen science data can be used as an alternative source for measuring the United Nations Sustainable Development Goals.
  • This study by Lovell et al. (2009) illustrates the effectiveness of participating citizens for sampling terrestrial savanna invertebrates in comparison to professional researchers. The results of the study show that there was little difference between the two samples, and that appropriate training helped to improve the validity of the data.
  • In this article by Freitag et al. (2016), additional mechanisms and strategies are described for ensuring good data quality.

Share your thoughts and opinions – Time for reflection:

  • Which data quality issues would you anticipate for your project?
  • To what extent are you having concerns or distrust towards the usage of citizen-generated data in your research?
  • How would you deal with participating citizens that have a particular agenda and who might cause significant bias in the data?
  • Would you prefer to set up a peer review (i.e. by expert citizens) or an expert review of the data? What are potential challenges and benefits?

Citizen science tools and platforms for data management

Nowadays, digital tools and platforms offer great support for the collection, analysis and visualization of citizen science data. When you start to plan your research project, you need to reflect on whether you need any technological support and, if so, which type of platform or tool is best suited. The technological requirements will be greatly determined by your budget, your data protocol  and the project goals. In determining these requirements, you also have to account for the sustainability aspects of your data: in which format will you publish the data, and for how long can the information remain available? Make sure that you also know how to deal with certain technological challenges67, e.g. are citizens able to collect data in areas that are out of connection, is the platform easy to use, and how & by whom will it be maintained in the long run?

The success of your research project will be determined by all these technical choices. It has been proven that data management via digital citizen science platforms can ease the interaction and communication between researchers and citizens, and can be cost-effective and time-efficient68. It can also be a motivational trigger for citizens to use and discover new technologies. However, digital technology is not always necessarily the best option. Sometimes, using pen and paper might be the best solution, when automated observations pose privacy concerns for example.

Case study:

Muide Meulestede Morgen (Muide Meulestede Tomorrow in Ghent, Belgium) Measuring instruments do not have to be high-tech. You can just as easily collect traffic data using only pen and the Muidepoort. paper. This method of pegging was applied in the ‘Muide Muilestede Morgen’ project. This urban renewal project has an eye for sustainable mobility. A number of residents raised the issue of the excess amount of through traffic at More information:

Sensors in citizen science

You can use advanced, measurement technology such as sensors. There are hundreds of different types available, and it is often difficult to see the wood for the trees. There are sensors in all price ranges, from simple devices costing a few euros to professional set-ups costing thousands of euros. In order to solve your scientific problem, it is best to first ask yourself what a sensor must be able to measure – and with what degree of precision and accuracy. At the request of the ‘Agentschap Binnenlands Bestuur’ (Agency for Internal Affairs) of the Flemish government, a market analysis was performed by the consultancy company PwC on the available sensors that can be used for citizen science. Based on the research domain and the challenge, a list of sensors has been made available for consultation, together with a how-to guide. In line with open science principles, it is recommended to go with open hardware, low tech, or do-it-yourself solutions from this list.

Existing online platforms and applications

You can also decide to make use of online platforms and applications. Citizen science platforms are web-based infrastructures with one single entrance point69. These platforms offer an overview and search function of active citizen science projects, often in combination with guidance and support materials. They can be categorized into (non-) commercial platforms, and platforms for specific projects or specific topics, either nationally or globally bounded.

Citizen science platforms have the advantage that most of them already have an established community base, and that they are well managed by the initiators. Online platforms like Zooniverse or ‘DoeDat’ (DoIt), offer a wide range of activities that can be performed by the participating citizens, often along with some community features (e.g. comment section, personal track records, blogposts, etc.). You can upload your dataset or raw data on these platforms and ask citizens to analyse those data. This often involves annotating images, making classifications or transcribing texts. With online platforms like these, you make use of the software behind the platforms. You offer your information or data, and you remain the owner of the analysed data afterwards. The results of the analyses of the citizen scientists are delivered in an open format (e.g. CSV sheet). Of course, you can also develop your own platform or tool for data collection, analysis or visualisation. This could be based on open-source code, such as OpenStreetMap, or on request by a private company. Make sure that you have sufficient budget available for supporting these activities, and that they are pretested for their user-friendliness.

The following existing citizen science platforms can be used for data collection, analysis or visualisation; they are all entry-level:

  • The Zooniverse is an international platform for the annotation and transcription of datasets, and includes more than one million interested citizen scientists worldwide. If you would like to run your project on Zooniverse, you will have to apply to the platform. In the project builder section, you can upload your datasets and choose the tasks you want the volunteers to do.
  • DoeDat (DoIt) is the online crowd sourcing platform of the Meise Botanical Gardens, on which citizens can digitise their herbaria. The purpose of this crowdsourcing platform is to help the Meise Botanical Gardens to digitize their collections and to give citizens the chance to play an active part in this process. You can also post your project on the ‘DoeDat’ platform if it fits within the themes of the platform. For instance, Luca Schools of Arts has a project ‘Flemish and Dutch flower still lifes from the 16th and 17th century’. The participating citizens are invited to describe the images as accurately as possible.
  • iNaturalist is an international application and online community through which citizens help to identify plants and animals. You download a mobile application, take a picture via the application, and you upload it to the community. The Dutch variant of this platform is by Natuurpunt, Natagora and Stichting Natuurinformatie. Here, you also download a mobile application to upload your observation. Automatic image recognition helps to identify the species or animal.
  • MijnTuinlab (MyGardenLab) is a Flemish platform which collects citizen science projects that can be run in your own garden. It is an initiative of Natuurpunt, Kenniscentrum tuin+ (Erasmushogeschool) and KU Leuven. Examples of projects are FlowerPower, Spin-City, Weekly Bird Count, etc.
  • Vele Handen’ (Many Hands) engages citizen scientists in the transcription of historical, often handwritten, documents. ‘VeleHanden’ is a crowdsourcing platform of Picturae. Picturae is a Dutch enterprise active in digitizing and opening up heritage collections for museums, archives and libraries at home and abroad.
  • The BiodivERsA citizen science toolkit lists more useful tools and applications for biodiversity researchers.

Lastly, the following platforms can be used for listing and promoting your research (project) to citizens:

  • SciStarter is an international platform which disseminates your project to a community of citizen scientists. SciStarter allows citizen scientists to track and earn credit for their contributions to science projects. They also offer some training modules.
  • The Dutch-speaking platform for citizen science is “Iedereen Wetenschapper” (Everyone’s a Scientist). You can submit your project to the website www.iedereenwetenschapper. be. If your project satisfies the conditions, they will send out a standard questionnaire and the editor at Everyone’s a Scientist will post your project to the platform. Your project is published on the platform for free, and will be mentioned in the monthly newsletter and on the Everyone’s a Scientist social networking site. They also offer paid services for editing texts, helping with recruitment strategies, etc. The platform does not offer any collection or analysis options.
  • Lastly, you can also promote your project on the Eu-Citizen. Science platform.

Tips for building your own application or platform:
If you want to build an application or platform yourself, you should not underestimate the costs. Several online tools allow you to make a rough calculation of development costs for mobile apps: App Development Cost, Buildfire, Digitalya. You can build a citizen science application using these online tools; some of them are free while others are paid:

Share your thoughts and opinions – Time for reflection:

  • Do you have any tips about using (low-cost) sensors for environmental monitoring? How do you balance the trade-off between data accuracy and the cost of the sensor?
  • What are your experiences related to citizen science platforms for networking purposes?
  • Can you recommend any other platform or mobile application for citizen-generated data collection or analysis?



User Type
  • Researcher/research institution
  • Teacher/school
Resource type
  • Case studies
  • Getting started
  • Step by step guides
Research Field