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Over the past decade, citizen science has experienced growth and popularity as a scientific practice and as a new form of stakeholder engagement and public participation in science or in the generation of new knowledge. One of the key requirements for realising the potential of citizen science is evidence and demonstration of its impact and value. Yet the actual changes resulting from citizen science interventions are often assumed, ignored or speculated about. Based on a systematic review of 77 publications, combined with empirical insights from 10 past and ongoing projects in the field of citizen science, this paper presents guidelines for a consolidated Citizen Science Impact Assessment framework to help overcome the dispersion of approaches in assessing citizen science impacts; this comprehensive framework enhances the ease and consistency with which impacts can be captured, as well as the comparability of evolving results across projects. Our review is framed according to five distinct, yet interlinked, impact domains (society, economy, environment, science and technology, and governance). Existing citizen science impact assessment approaches provide assessment guidelines unevenly across the five impact domains, and with only a small number providing concrete indicator-level conceptualisations. The analysis of the results generates a number of salient insights which we combine in a set of guiding principles for a consolidated impact assessment framework for citizen science initiatives. These guiding principles pertain to the purpose of citizen science impact assessments, the conceptualisation of data collection methods and information sources, the distinction between relative versus absolute impact, the comparison of impact assessment results across citizen science projects, and the incremental refinement of the organising framework over time.

The European-Commission-funded project 'Citclops' (Citizens' observatory for coast and ocean optical monitoring) developed methods, tools and sensors, which can be used by citizens to monitor natural waters, with a strong focus on long-term data series related to environmental sciences. The new sensors, based on optical technologies, respond to a number of scientific, technical and societal objectives, ranging from more precise monitoring of key environmental descriptors of the aquatic environment (water colour, transparency and fluorescence) to an improved management of data collected with citizen participation. The sensors were tested, calibrated, integrated on several platforms, scientifically validated and demonstrated in the field. The new methods and tools were tested in a citizen-science context. The general conclusion is that citizens are valuable contributors in quality and quantity to the objective of collecting, integrating and analysing fragmented and diverse environmental data. An integration of these data into data-analysis tools has a large potential to support authoritative monitoring and decision-making. In this paper, the project's objectives, results, technical achievements and lessons learned are presented.

In May 2024, a workshop was held as part of the European Citizen Science Association (ECSA) 2024 conference in Vienna to explore the creation of transnational communities to upscale citizen science, to address global issues as outlined by the European Union Horizon Missions and their objectives. This report summarises the discussions and issues that were raised during the workshop, from the point of view of a range of different actors in the citizen science discipline. This includes success stories of projects that have upscaled to an international scope, their methodologies for doing so, and the challenges citizen science initiatives face when attempting to contribute to global-scale challenges. The examples shared demonstrate a range of approaches when upscaling citizen science projects, giving rise to discussions regarding project management, shared learning and practice, citizen science tools and resources, scalability and context, and data interoperability. The success and challenges revealed will provide a clear roadmap for current and future citizen science practitioners, especially those with the ambition of upscaling their efforts to tackle challenges at national, regional, or global levels.

Marine processes are observed with sensors from both the ground and space over large spatio-temporal scales. Citizen-based contributions can fill observational gaps and increase environmental stewardship amongst the public. For this purpose, tools and methods for citizen science need to (1) complement existing datasets; and (2) be affordable, while appealing to different user and developer groups. In this article, tools and methods developed in the 7th Framework Programme of European Union (EU FP 7) funded project Citclops (citizens’ observatories for coast and ocean optical monitoring) are reviewed. Tools range from a stand-alone smartphone app to devices with Arduino and 3-D printing, and hence are attractive to a diversity of users; from the general public to more specified maker- and open labware movements. Standardization to common water quality parameters and methods allows long-term storage in regular marine data repositories, such as SeaDataNet and EMODnet, thereby providing open data access. Due to the given intercomparability to existing remote sensing datasets, these tools are ready to complement the marine datapool. In the future, such combined satellite and citizen observations may set measurements by the engaged public in a larger context and hence increase their individual meaning. In a wider sense, a synoptic use can support research, management authorities, and societies at large.

Herpes Zoster (shingles) is an infection that occurs when varicella-zoster virus reactivates from the latent state. Incidence and severity of Herpes Zoster disease increase with age. Antiviral drugs are the elective treatment; however, prevention of disease reactivation through effective and safe vaccines is available in Italy out-of-pocket from age 65 onwards. The Romagna Local Health Authority (northern Italy) administered catch-up vaccinations in March–May 2022 for immunizations not performed during the COVID-19 pandemic. In this study, adherence rates to the catch-up campaign and recall activities adopted in two centers were investigated. The uptakes for only the catch-up vaccinations were 11.4% and 12.4%. Having suffered from Herpes Zoster or having family members who suffered from it would not seem to be drivers of increased uptake. Although sending text-messages to all involved patients was the main motivation for vaccine uptake (85.7–95.1%), word of mouth and web/news advertising also contributed to adoption in Center No. 2. In both centers, the need for greater synergy between public health departments and general practitioners to engage their patients emerged, as did the need for additional recall measures. Studying the main drivers of vaccine hesitancy, especially at the local level, can help in targeting campaigns and catch-up activities in order to achieve widespread acceptance.

Members of the public are making substantial contributions to science as citizen scientists, and advances in technologies have enabled citizens to make even more substantial contributions. Technologies that allow computers and machines to function in an intelligent manner, often referred to as artificial intelligence (AI), are now being applied in citizen science. Discussions about guidelines, responsibilities, and ethics of AI usage are already happening outside the field of citizen science. We suggest such considerations should also be explored carefully in the context of citizen science applications. To start the conversation, we offer the citizen science community an essay to introduce the state-of-play for AI in citizen science and its potential uses in the future. We begin by presenting a systematic overview of AI technologies currently being applied, highlighting exemplary projects for each technology type described. We then discuss how AI is likely to be increasingly utilised in citizen science into the future, and, through scenarios, we explore both future opportunities and potential risks. Lastly, we conclude by providing recommendations that warrant consideration by the citizen science community, such as developing a data stewardship plan to inform citizens in advance of plans and expected outcomes of using data for AI training, or adopting good practice around anonymity. Our intent is for this essay to lead to further critical discussions among citizen science practitioners, which is needed for responsible, ethical, and useful use of AI in citizen science.

The United Nations’ Sustainable Development Goals (SDGs) are an important global framework which provides a shared vision for a more sustainable future for all people and the planet. In the last five years, citizen science as a discipline has paid increasing attention to the SDGs and the contributions that citizen science could make towards their achievement. This paper presents a collection of 51 questions and a corresponding set of answers which can be used by citizen science projects to self-assess their impact towards the SDGs. The questions and answers were originally derived from the official UN description of the SDG targets and indicators and were refined through a workshop and series of nine interviews with citizen science project coordinators. The outcomes of the workshop and interviews reveal the challenges of assessing impacts towards the SDGs in a way which is relevant to the majority of citizen science projects. In many cases, the wording of the SDGs had to be altered to make sense in the context of citizen science. The final set of questions and answers are structured to reflect two pathways of impact: citizen science contributing to the (official) monitoring of the SDGs, and citizen science contributing to the direct achievement of the SDGs.

Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user’s ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.

In this article, key points are discussed concerning knowledge representation for clinical decision support systems in the domain of physical medicine and rehabilitation. Information models, classifications and terminologies, such as the “virtual medical record” (vMR), the “international classification of functioning, disability and health” (ICF), the “international classification of diseases” (ICD) and the “systematized nomenclature of medicine—clinical terms” (SNOMED CT), are used for knowledge integration and reasoning. A system is described that supports the measuring of functioning status, diversity, prognosis and similarity between patients in the post-acute stage, thus helping health professionals’ prescription of recommendations.

Apps for mobile devices and web-based platforms are increasingly used in citizen science projects. While extensive research has been done in multiple areas of studies, from Human-Computer Interaction to public engagement in science, we are not aware of a collection of recommendations specific for citizen science that provides support and advice for planning, design and data management of mobile apps and platforms that will assist learning from best practice and successful implementations. In two workshops, citizen science practitioners with experience in mobile application and web-platform development and implementation came together to analyse, discuss and define recommendations for the initiators of technology based citizen science projects. Many of the recommendations produced during the two workshops are applicable to citizen science project that do not use mobile devices to collect data. Therefore, we propose to closely connect the results presented here with ECSA’s Ten Principles of Citizen Science.