Implementing Citizen Science within Open Science: Identifying Extra-Academic Skills, Collaborations, Rewards and Recognitions in the Context of a University

Research Highlights

A local citizen science case study shows how the principles and guidelines of the new TU Delft Open Science Programme can be applied to achieve high quality citizen science.
Successful citizen science practices require institutional support in skills, rewards, and collaborations outside the “standard” researcher toolbox.
Offering tools, training, and facilitation for these extra-academic citizen science elements to researchers is needed to implement effective citizen science within the TU Delft Open Science program

Summary

How can a university effectively support and promote citizen science initiatives in an open science framework? Delft University of Technology (TU Delft) has taken steps in this direction through its open science program which includes citizen science projects. However, executing citizen science projects is not a simple task. Those who organize and oversee these projects need a solid grasp of the subject matter, practical knowledge of citizen science, and an understanding of open science principles. It is therefore essential to identify crucial tools and support mechanisms necessary for TU Delft researchers to excel in citizen science and open science.
To address these challenges, TU Delft conducted a case study using a local hydrology citizen science project called, “Delft Measures Rain.” The aim was to implement citizen science methods and TU Delft’s Open Science principles to shed light on the essential skills, collaborations, rewards, and recognitions required for successful citizen science projects. This research highlights how researchers, citizens, government officials, and non-governmental organisations (NGOs) can all gain from engaging in participatory research that incorporates citizen science and open science principles.
The paper outlines 10 key elements that cover tools, support structures, and infrastructures that universities can offer their researchers to encourage and facilitate the successful, legally compliant, and open practice of citizen science. The case study demonstrates that, with the right combination of academic and practical knowledge, tools, collaborations,rewards, and recognition, citizen science can fulfil its promises and significantly contribute to the success of open science.

Introduction

Open Science (hereafter: ‘OS’) aims to bring about socio-cultural and technological change to the scientific research process based on openness, reproducibility, and connectivity. OS is already a requirement in many leading universities in Europe and is becoming a norm worldwide (Morais et al., 2021). OS aims to change the way research is designed, performed, captured, and assessed by opening up the entire research process (Vicente-Saez & Martinez-Fuentes, 2018). It does not have a strict definition, but is defined by Vicente-Saez & Martinez-Fuentes (2018) as “transparent and accessible knowledge that is shared and developed through collaborative networks,” while Gomez-Diaz & Recio (2020) define OS as “the political and legal framework where research outputs are shared and disseminated to be visible, accessible, and reusable.” (p.15)
An argument for more openness of scientific research processes and results is, among others, that it brings an opportunity to accelerate innovation. Furthermore, it is the responsible thing to do, since most research is publicly funded, and transparency is considered to increase the reproducibility and reliability of the results (Fecher & Friesike, 2014; Laine, 2017). The current draft of the UNESCO Recommendation on Open Science includes the guiding principles for OS to provide a framework for enabling conditions and practices within which OS values are endorsed, and the ideals of open science are made a reality. These ideals are: (a) transparency, scrutiny, critique, and reproducibility; (b) equality of opportunities; (c)
responsibility, respect, and accountability; (d) collaboration, participation, and inclusion; (e) flexibility; and (f) sustainability (UNESCO, 2021).
OS is creating new forms of scientific interaction that generate new and unique opportunities. This has strong impacts on core academic processes like research, education, and innovation (NPOS, 2017). It is, for instance, easier to replicate an experiment if the relevant data sets are digitally available to any person who wishes to corroborate a researcher’s findings. Furthermore, OS increases the opportunity for citizens or lay-people to interact with or influence science. Citizen Science (hereafter: ‘CS’), is a form of research where non-professional scientists are actively involved in one or more parts of a scientific research effort, which can vary from participating in data collection to being part of shaping the research question, analysing data, and publishing results (Hecker et al., 2018; NPOS, 2020). It is often presented as an ‘enabler’ of openness in science (Suman & Pierce, 2018), and it is therefore often recommended to consider CS and OS jointly (DITOs consortium,
2017; Wehn et al., 2020).
The Citizen Science & Open Science Community of Practice (CoP), in the context of the UNESCO Recommendation on Open Science, identified the development of OS together with CS as mutually beneficial. Developing them together could be an important window of opportunity for laying the foundations of science in the future (Wehn et al., 2020). This CoP argued that there are shared characteristics between both CS and OS and recommended acknowledging CS as an important pillar of OS. This recommendation on OS has recently been adopted by UNESCO (International Science Council, 2021). For CS
to play an important role in OS, universities should take part in CS activities (Hecker et al., 2018). University engagement in CS, especially when the goal is to implement it within OS guiding principles, faces several institutional and practical challenges that transcend individual projects (DITOs consortium, 2017). While there are numerous individual scientists
already running CS projects (see, for instance, eu-citizen.science (2024) or Zooniverse (2024)), often in a university setting, universities don’t yet often consistently facilitate or support by their researchers to enable and implement CS research practices. To realise excellence in such CS projects, the support, guidance, and oversight of a range of institutional stakeholders is required (LERU, 2016).
CS is connected to all aspects of OS and is recognised as an important aspect of OS in general (NPOS, 2020; Wehn et al., 2020; Kunst et al., 2021). However, CS is more than making methods, data, and results openly available. It invites everybody (e.g., society, citizens) to participate in the process of practicing science. Effectively and correctly involving citizens in this process requires additions and changes to conventional research processes and OS guidelines, such as accessible
data platforms or forms of public engagement. Since these extra tasks are usually not part of the standard development in a research career, researchers may lack relevant knowledge and skills in these areas, and support by their universities on these tasks may be lacking. It is worthwhile to ask the question: if we take time constraints into account, should we want and/or
expect scientists to execute all the aspects of a CS project by themselves? Is that even feasible? A lack of literature and practical knowledge on this matter was also encountered by the Delft University of Technology (TU Delft) when setting
up their OS programme in 2020. Working in a new and undiscovered territory, the positioning and implications of CS within the OS framework of TU Delft are explored in this paper.
Currently, many tools and information sources to make scientists more familiar and knowledgeable about general OS practices are being developed at TU Delft. Also (inter)nationally, there are increasing developments in OS guidelines and
tools (Science Europe, 2015; Open Science EU, 2017; NPOS, 2018; European Commission, 2021). Since CS is recognised as an integral part of OS, it is vital that these tools and sources are also applicable to CS. Unfortunately, this is not always taken into regard when developing these guidelines. With this paper, we aim to test to what extent the OS guidelines fit with CS
goals to make a start in finding good practices for universities to facilitate CS from an OS perspective. We aim to build on first guidelines and recommendations by LERU and the CS & OS CoP, and go a step further towards defining the practical mechanisms, tools, and services that universities should offer to their scientific staff.
Our main goal is to determine what a university should do to enable and support its research staff in good CS practices, especially within an OS context. We explore this via a citizen science case study that put CS from an OS perspective
into practice. Effectively, we aim to answer two questions: 1) what types of skills, collaborations, rewards, and recognitions should be supported by universities to make sure their researchers can practice good and sound CS; and 2) what is
needed from such a CS project to deliver on OS goals? We start by setting the scene explaining how the TU Delft OS Programme is built, and how CS is part of this programme. Subsequently, we give insight into the CS case study Delft
Measures Rain that we used to implement and reflect on the TU Delft OS Programme. Then, we explain how we executed the reflection on the OS guiding principles in relation to CS, and what the results of that reflection are.

Setting the Scene: Citizen Science within the TU Delft Open Science Programme

The TU Open Science Programme

TU Delft has a long history of engagement with OS. With its ‘TU Delft Strategic Plan Open Science 2020–2024 – Research and Education in the Open Era’ (Haslinger, 2019), TU Delft wishes to take OS to the next level: a situation in which OS is the default way of practising research and education, where the “information era” becomes the “open era.” The programme consists of six interrelated projects:

1. Open Education: the research increases open educational resources
2. Open Access: the results are accessible to everyone (not behind a paywall)
3. Open Publishing: the results are published in an open access peer-reviewed journal or publishing platform
4. FAIR Data: all data are Findable, Accessible, Interoperable, and Reusable
5. FAIR Software: the researcher uses or delivers FAIR and open software
6. Open Hardware: the researcher uses or delivers FAIR and open hardware

CS was added later as a 7th project after an additional exploration of the subject (Kunst et al., 2021). The projects aim at creating and disseminating various types of tools and resources for the benefit of TU Delft researchers, teachers, and students, as well as the public (Haslinger, 2019).
They range from educational materials and software to a publishing platform. All outputs of the programme will be as ‘FAIR’ as possible (OpenAire, 2016). In addition to these six projects, the programme includes three important cross-cutting themes as preconditions for the successful implementation of each project. These themes are: 1) ensuring appropriate
rewards and recognition, 2) facilitating fruitful collaboration with third parties, and 3) gathering relevant skills for open science.

Operationalisation of CS within the TU Delft Open Science Program
CS is, like OS, an emerging movement within science, and there is no common, agreed-upon definition. However, the European Citizen Science Association (ECSA) has proposed 10 principles for Citizen Science and added characteristics that attempt to represent a wide range of interpretations in an inclusive way (ECSA, 2015). This loose definition or description allows the inclusion of different types of participatory projects and programmes, where context-specific criteria can be set for each project (ECSA, 2020). Like the approach taken by ECSA, the goal of the TU Delft OS Programme was not to give an enclosed definition of CS or even to define a universal set of rules for exclusion or inclusion, for fear of limiting the advancement of the field. Instead, the CS exploration at TU Delft developed a working definition specifically for the TU Delft OS programme, based on the ECSA 10 principles (Kunst et al., 2021) (Fig. 1 ).

 A citizen science project: “Delft Measures Rain”

The 2020 project, Delft Measures Rain (DMR) was developed by the Citizen Science platform WaterLab in collaboration with TU Delft scientist Dr. Ir. Marie-Claire ten Veldhuis and external partners, including Smartphones4Water (S4W), the municipality of Delft, and several internaldepartments within TU Delft. WaterLab is an external consortium consisting of TU Delft Science Centre (science museum), IHE Delft, and Pulsaqua, a small consultancy company. WaterLab has plenty of practical experience in citizen science projects concerning water quality and quantity, using the distinct expertise from all the four partners (WaterLab 2023). Likewise, S4W has already had experience with developing and coordinating citizen science projects. Citizens of the city of Delft were encouraged to participate and work together with scientists and students from the TU Delft Water Management department to investigate rainfall patterns within the city.
The DMR project answered two main research questions: 1) What is the quality of the data collected from the citizen rain gauges? and 2) How is rainfall spatially distributed over the city of Delft? In total, 95 participants across Delft participated in the project, submitting a total of 1991 measurements between July 17 and September 14, 2020. Each participant received a kit with a manual on how to build their own rain gauge and instructions for taking and submitting their measurements.
The data collection method was based on the methodology developed and internationally applied by S4W, with rain gauges made from accessible materials like (used) soda bottles and concrete (Smartphones4Water, 2020). The rain gauge was analogue and contained no electronic sensor, and measurements were submitted manually through an online data collection form. Data submissions were validated with an additional check based on a submitted picture for each measurement. The results would immediately be visible on an online, open data map, making it possible for citizen scientists
to keep track of their own submitted data and that of others. Subsequently, the data and particularly data-quality were analysed by an undergraduate student under the supervision of researchers at the Water Management department. Results showed that there is some evaporation loss and are small measurement errors when the daily rainfall is below a threshold
of 4 mm (Timori 2020). However, because the goal was to focus on weather events that cause larger rainfall amounts, which are above this 4 mm threshold, this was not an issue in answering the 2nd research question (for full results and data quality analysis, please see Timori 2020). Updates and news were shared with the citizen scientists every two weeks via newsletters and when the project finished, the results were shared via infographics and a webinar.

Methodology

To determine what a university can do to enable successful CS practices within an OS context for its research staff and students, we followed a three-step process. We applied that process to the CS case study DMR within the TU Delft OS Programme. We recognise the importance of evaluating the experience of the citizen scientists in the context of this case study, and these evaluations have been made within the DMR project. However, for this paper we specifically focus on the role of the university n supporting researchers to execute these projects. We therefore focus our analysis on the perspective of the university and researcher, and not on the citizen experience. 

First, we needed to establish how the case study DMR performed according to the OS working definition of CS (as stated in Fig. 1). We reflected on the case study according to the following questions, where in principle the answer should hold a ‘yes’ and a deliberation on how this was executed for this project, and what it achieved:

1) Did either citizens and/or scientists define the problem?
2) Did the project generate new knowledge or understanding, while using the scientific method?
3) Was there a clear attribution of roles between the participants?
4) Did all participants in the project benefit from participation, and were they acknowledged for their efforts?
Note: With benefit, we indicate a broad range of gains or experiences that are regarded as beneficial by the particular actor.
Some examples: for scientists this could be the dataset or experience in the field, for NGO’s this could be connections to a new network or target group, for citizens this could be gained knowledge, finding a new interest, or feeling empowered in their ability to act.
5) Did all participants benefit from the results of the project?
6) Were communication and tools tailor-made to the project?
7)Was the project evaluated for its scientific output and data quality, the participant experience, and, when
relevant, wider societal or policy impact?

Second, during and after the DMR project took place we evaluated in what manner the project implements the OS guiding principles. While setting up and executing DMR, we listed the deliverables that were achieved for each of the seven TU Delft OS projects. We also listed the deliverables that were missing to fully commit to these OS requirements. However, as said,
CS goes further then only OS, and so we listed deliverables that go beyond OS needs, but that can be essential for CS implementation.

Finally, each deliverable was then analysed to identify what ensured a successful creation and execution of these deliverables. An analysis was done by the authors of this paper, with their own perception of and experience with the project, by distinguishing: 

1)The roles of different actors:
- Scientist
- Student
- WaterLab as assisting organisation
- Citizens
- Partners
- Other
2) Skills required for the deliverables, distinguishing between skills that are already part of the general academic skill set that is used in research projects, indicated with [R], and skills that we define as extra-academic, indicated with [E]
3) Collaborations playing a role in creating deliverables
4) Rewards and recognitions needed to motivate different actors

Results

Requirements of a CS project within OS

In Table 1, the results for how the DMR project performed with regards to the TU Delft CS definition are demonstrated. The project applies to all CS requirements as stated in the TU Delft working definition and gives a description for each requirement.
Table 1 This table shows the qualitative check performed to define whether the DMR project applied to all the requirements for a CS project withing the TU Delft OS program.

Deliverables of DMR according to the TU Delft OS pillars

We list and structure the deliverables of DMR according to each TU Delft OS project in the table below (table 2). The table also demonstrates deliverables or items that were still missing in the project and that will need adjustment to enable full OS guiding principles to be implemented.

Contributions to deliver results

The deliverables listed in Table 2 were analysed according to the actors that ensured or created them, the skills these actors needed for that, the established collaborations, and finally the rewards and recognitions that motivated the achievement of these deliverables. These are demonstrated in Figure 2.
First, we identified who acted and contributed to create each deliverable of DMR. For this case study, researchers from TU Delft formulated the research question, decided on the general methodology and performed the data analysis. The extra-academic skills and elements were primarily executed by WaterLab.
Within the framework of DMR, the main role of the citizens has been to contribute to the data collection and recording, but we invited citizens to take part in more than that: five of the participants have provided input about their experiences and directly contributed to this paper as authors. Furthermore, the researchers got unexpected insights into the increased
interest and awareness of their research topics with the participants, which indirectly increases the relevance of urban rainfall research.

Ultimately, we collaborated with multiple parties from different parts of society: governmental (municipality of Delft), an NGO (S4W) and different departments within the university (communications, the library, and alumni relations).

Discussion

Context and limitations

Through analysis of the DMR project, we have identified multiple extra-academic elements that are essential to implement good and open CS practices, but which are not yet covered by general TU Delft OS practices and developments. However, the elements that contributed to effective implementation of the CS case study project within the TU Delft OS Programme
should be seen in that perspective: the context of DMR is not generally applicable to all CS projects, and the way the TU Delft organises its OS programme might not be (fully) comparable to other universities. Additionally, the academic roles citizens can take (as in this case, collaborate to write a paper) are not the only roles a citizen can or should play in CS as there are many more options (Franzoni, Poetz & Sauermann, 2021). Possibly, when citizens take up other or more roles, the needed skills and associated tools might change. Nevertheless, this case study can feature as a practical example of how CS can be facilitated within an OS context by universities. It is important to note here that this paper still primarily focuses on the overall success of the project from a university and OS perspective. The needs and successes as interpreted by the participating citizens do not necessarily fully overlap with these goals.

It is important to mention that we have only looked at the needed skills and infrastructure, but not the amount of time needed to acquire those skills or build these infrastructures. The time needed for that can vary between projects and is an important factor to take into consideration when planning a project. To add to that, we would also like to emphasize that
funding is an important aspect, and that budget should be dedicated to training and acquiring new skills required for CS projects, engaging citizens, communication, and making the project and materials look attractive regardless of who
takes this role. We defined ‘finding funding’ as one of the skills needed specifically for CS, due to the special needs CS has, which are often not yet covered within regular research funds. This has already been emphasised by LERU (2016),
who state that adequate funding for community management, platform development and other non-research functions characteristic of citizen science should be ensured, next to funding to promote the use of open science practices in citizen science projects, by requiring open access publication, open data standards, and the use of open source software.
Additionally, more research is needed to assess how this context functions for other CS projects and in the context of different university guidelines and support systems, to compare strategies. Additionally, it would be valuable to
research and compare how universities already practice and implement CS, as was already started by the European Time4CS project (Time4CS 2023).

University support for extra-academic skills

With regard to an extension of the standard research skills towards extra-academic, it is important that a CS-project team should be able to decide whether the research question can be answered satisfactorily using a CS methodology. That requires understanding of the possibilities, benefits, and drawbacks of CS. If CS indeed fits the research question, the scientific methodology often needs to be adapted to be used and applied by non-experts, without losing scientific rigor. Additionally, data collection by citizens requires specialised infrastructure and software that is publicly accessible, usable, GDPR-proof (General Data Protection Regulation), and user-friendly. Relevant skills for doing CS research include knowledge and skills in engaging and motivating participants, science communication and education, and know-how in visual design and relevant software (see Table 2 for more elaboration). Many (international) projects have addressed ways for researchers to learn those skills (Pettibone et al., 2016, WeObserve 2018ab, Goudeseune et al. 2020, SciStarter 2023, UCL 2023, Veeckman et al., 2023). However, research and toolboxes on how to build lasting and sustainable support by universities servicing their researchers to learn those skills, or support them in executing those extra-academic aspects, are less common. Fortunately, the TIME4CS projects released in 2023 a toolset on institutional embedding of CS in universities, based on the experience of certain frontrunners throughout Europe, including TU Delft (Mondardini & Roffler 2021, Herrera & Haklay 2021, 2022).

We believe that a dedicated unit within a university can organise teams of people who could provide guidance and expertise along the research project, with training, support, or executing some of the tasks within a CS project where and when necessary. Additionally, we endorse the recommendations of Wehn et al., (2020), aimed at maximising effective contribution of CS to OS, to “1) [draw] on the vast practical experience within its communities, . . . 2) [foster] greater and enhanced cooperation, synergies, and crosspollination of practitioners among and between Citizen Science and Open Science communities, and 3) “[ensure] global access to supporting infrastructures, including technical infrastructures, and community networks.” (Wehn et al., 2020, p. 1). As different skills, deliverables, and parties are required in CS, this requires different rewards and recognition mechanisms than standard research projects. This is currently recognised in the move of NL universities towards a new Rewards and Recognitions model by enforcing a national programme for it, that also focuses on more reward for creating societal impact, and practicing OS (VSNU et al., 2019, Erkennen & Waarderen 2023). It has already been translated to a TU Delft-specific context (TU Delft Recognition & Rewards programme 2021).

When realising that the DMR project needed partners that have skills, connections, or tools that the scientists and WaterLab did not have to successfully implement the project, we looked for third-party collaborations. As can be seen in Figure 2, third-party collaborations improved both the quality and reach of the DMR project. Salmon et al. 2021, demonstrate in their research the facilitative role of so-called enablers, who are often implicitly involved in citizen science projects as a mechanism to allow scientists to interact with citizens or members of a specific community. Furthermore, The Citizen Science Global Partnership was created in 2017 because of the realisation that the potential of citizen science requires coordination, mobilisation, and partnerships across geographies, sectors, and research domains. For many CS projects, such collaborations will provide value and may even increase the long-term sustainability of the project (Salmon et al., 2021). Within the TU Delft OS Programme, third party collaborations are usually interpreted as collaborating with businesses, start-ups, or other (profit-oriented) industry parties (Haslinger et al., 2019), meaning that help with collaboration with other groups than these are not included in the standard university support services and need to be developed.

Ethical and legal challenges

While in this paper we focused on the practical challenges that occur when implementing CS within OS at a university, we would like to stress that there are also ethical and legal challenges to face (DITOs consortium, 2017). It is important
that a researcher or project manager keeps the ethical and legal aspects of collaborations with other groups from outside the university, such as citizens, NGOs, or governments at heart. It is also important to notice that ‘third party’ in this
case does not refer to a hierarchy, but to refer to collaboration with groups other than university-to-university collaborations.
Collaborations, data collection, and data analysis with other parties in general can have implications regarding GDPR-regulations, data-ownership, or intellectual property rights. As was the case for the DMR project, especially in the
form of the EU privacy regulations described in the GDPR and the strict limits on sharing data, even among collaborative partners. As queried by Suman and Pierce (2018), the data processing requirements under the GDPR represent a possible hindrance to the advancement of CS and OS, as they can make sharing data and information among collaborating partners more complicated or cumbersome. This can create an undesirable disincentive to engage the public in research and broadly share research data, resulting in a hindrance to the progress of OS and CS. This dilemma has also been explored during a workshop organised by the SensJus project at the Brocher Foundation, Geneva, in October 2021, where it was concluded that the implications of the GDPR for the active sharing of (environmental) health data within the framework of citizen science and how data processing requirements under the GDPR may affect the advancement of citizen science 
for (environmental) health research, from a theoretical and empirical perspective, has been scarcely researched (SensJus, 2022).
We want to emphasize that this should not discourage researchers to engage with CS. However, it is important that universities support their researchers on these aspects as well, as they would for other research collaborations,
especially since the needed infrastructure and support for CS differs from the needs of non-CS research projects. At this moment, the need for and content of legal and ethical protocols, guidelines, tools, training, and/or support for CS
applications are being explored by the OS and CS teams at TU Delft. 

The way forward

Universities can contribute to practicing sound CS by providing professional infrastructure, knowledge and skills, ethical and legal background, educational facilities for citizen scientists, sustainable teaching, and funding, all tailored to CS projects (Hecker et al., 2018). As Hecker et al. (2018) mention, universities might be able to play a role to support researchers, especially to help them receive funding for acquiring these additional skills. Although here are many challenges in moving to an OS environment for universities, the most difficult change needed seems to be a cultural change to seeing CS not as an exception, but one of many variations in academic research methods. A programme of change management needs to accompany and support any move to OS to decide which mix of policies, measures, support systems, and engagements best supports their missions and implementation strategies (Ignat & Ayris, 2020). By adopting and supporting CS and implementing OS guiding principles while doing so, universities in turn gain breadth and strength in research, which consolidates their position and recognition in society, brings new resources, and increases public trust in universities (Hecker et al., 2018; Wehn et al., 2020; Kunst et al., 2021, Hall et al, 2022).

Taking the results of our study, we would therefore like to encourage the TU Delft and other universities to further develop support, infrastructure, and tools for scientists and research departments when it comes to good CS and OS practices. From this case study, we can deduce what training, support (e.g., infrastructure or tools) and facilities are needed to stimulate and support researchers within the university to perform effective and open CS:

1) Sufficient (additional) time and budget to implement these elements in the citizen science project, or support in finding additional budget suitable for CS projects;
2) Training and/or support in science communication and education;
3) Training and/or support in interacting with (larger groups of) citizens before, during and after the research project;
4) Support and public channels for citizen recruitment, engagement, and general outreach;
5) New, open, and GDPR-safe infrastructures for data-collection, visualisation, and open data sharing with citizens;
6) Training in adjusting research questions, methodology, and materials to CS reproducible;
7) Training in data-validation methods for data collected with a CS methodology;
8) Support in logistics (e.g., assembling and sending out research kits);
9) Support in finding suitable 3rd party collaborations, especially outside the university environment;
10) Training and/or support in correctly rewarding and recognising the efforts of the citizen scientists.

Conclusion

We used the CS project, Delft Measures Rain as a case study to reflect on TU Delft OS Programme and the UNESCO OS guiding principles to establish what is needed to practice CS successfully and in accordance with the OS goals and guidelines of TU Delft. We established that DMR reflected the TU Delft CS definition and requirements. We identified which (extra) academic skills, rewards, recognitions, and collaborations were needed to make the CS project deliver on both the CS and OS goals. As a case study, the results are limited to the context of this specific university’s OS and CS guidelines, and comparison with other similar projects in different contexts will prove valuable.

We show how a good understanding of the communicational, educational, and other requirements for CS, as well as a supporting toolkit and infrastructure can aid researchers in setting up and executing CS projects. Additional rewards and recognitions on top of the standard academic rewards (such as a published article in a peer reviewed journal) are needed to motivate researchers to invest their time and efforts. It is needed to provide them with the appropriate amount of time and budget to make the project work, while at the same time acknowledging the efforts and input from the participating citizens. Based on our findings, we provide a list of 10 recommendations for universities as to how they can support practicing good, legal, and open CS by their researchers. We recommend universities that are interested in developing their CS and OS programme to start working on providing and developing the supporting tools, infrastructures, rewards, and recognitions to embrace CS as a valuable asset to their research and public engagement programmes.