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This guideline is an output from the Cos4Cloud project which demonstrates best practice for Citizen Observatories (COs), based on the experiences of the COs involved in the project. It highlights COs best practice, developed from implementation, reviewed in the context of the ECSA Ten Principles of Citizen Science which has been applied as a framework. These examples are produced as guidelines for other COs, initiatives and interested stakeholders which if followed, can produce good outcomes This is one of the best practice guidelines resources highlighting the citizen observatories involved in the development, integration and use of the Cos4Cloud services. These resources demonstrate best practice from these experiences that may be useful for other COs and citizen science initiatives developed and shared by The Open University (OU), in collaboration with project partners, forthe Cos4Cloud Toolbox & Evidence Hub. Best practice guidelines resources designer: Harriett Cornish, OU. Contact: cos4cloud-toolbox@open.ac.uk&nbsp

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.

Citizen science has become an umbrella term that encompasses a growing range of activities, actors, and issues. This chapter examines the potential of citizen science to generate transformative knowledge and argues that civil society organisations (CSOs) are key actors in this regard. However, the roles of CSOs are neglected in the literature on citizen science. We turn to the traditions of community-based research and participatory action research to learn more. With two case studies on health and safety, we show how transformative knowledge enables concerned communities to claim their rights and enriches scientific knowledge generation. Through a socio-historical analysis, we find three main roles grassroots CSOs take on in participatory research: (1) a technical role in the production of data and knowledge; (2) a governance role in the deliberation on research activities and risk assessment; and (3) an advocacy role by campaigning for transformative knowledge. These roles determine the ability of grassroots CSOs to generate legitimacy and rely on CSO members belonging to different spheres of society, scientific skills, and access to marginalised communities. Finally, we discuss the conceptual and practical challenges of accounting for CSOs’ roles in order to build a more just and transformative future through citizen science.

Interest in citizen science is growing, including from governments and research funders. This interest is often driven by a desire for positive environmental impact, and the expectation that citizen science can deliver it by engaging the public and simultaneously collecting environmental data. Yet, in practice, there is often a gap between expected and realised impact. To close this gap, we need to better understand pathways to impact and what it takes to realise them. We articulate six key pathways through which citizen science can create positive environmental change: (1) environmental management; (2) evidence for policy; (3) behaviour change; (4) social network championing; (5) political advocacy; and (6) community action. We explore the project attributes likely to create impact through each of these pathways and show that there is an interplay between these project attributes and the needs and motivations of target participant groups. Exploring this interplay, we create a framework that articulates four citizen science approaches that create environmental impact in different ways: place-based community action; interest group investigation; captive learning research; and mass participation census.

This document has been created by the community of Citizen Observatories and Citizen Science practitioners, as well as key collaborators that attended the Citizen Science working session in the EuroGEO Workshop taking place on July 3-5th, 2019 at the Pavilhão do Conhecimento in Lisbon (Portugal). The event was co-organised by the Portuguese Foundation for Science and Technology (FCT) and the European Commission (EC). This document summarises the current status of Citizen Science in GEO/GEOSS, reflects the vision, and objectives; identifies issues that are blocking its progress and proposes a way forward shared by the Citizen Observatories and Citizen Science projects under the EuroGEO community. Then, it proposes a shared vision, a clear set of objectives and some actions that will pave the way to progress towards that shared vision, based on aggregating citizen science into a federation of data services supported by the Citizen Science community activity in the GEO work program that represents the citizen science community. Finally, it defines a list of recommendations to the European Commission to improve the integration of the European Citizen Science and Citizen Observatories into GEOSS. It thereby emphasises regional strength and competences without losing sight of global collaborations.

In this thesis we propose a framework for designing, developing, a clinical decision support systems (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), and then demonstrate how these services can be integrated into the work-flow of healthcare providers. Furthermore, we focus on supporting spirometry, one of the main diagnostic tools in respiratory disease assessment. We present two methods to offer decision support in assuring the quality of a spirometry test that can be easily embedded into the CDSS framework. The first method is a novel algorithm that relies on a set of rules operating on 23 new parameters to define a high quality test. The second is a machine-learning approach, where we optimise the distinction between a good quality spirometry test and a poor one using a set of supervised-learning classifiers and hyper-parameters, En esta tesis proponemos un marco para el diseño y desarrollo de un Sistema de Soporte de Decisión Clínica (SSDC) que ofrezca un conjunto de herramientas para el diagnóstico y la evaluación de las enfermedades pulmonares. Al mismo tiempo demostramos como estos servicios se pueden integrar en el flujo de trabajo del personal sanitario. Además, nos centramos en la ayuda en espirometría, una de las herramientas de diagnóstico principales en la evaluación de enfermedades pulmonares. Presentamos dos métodos de SSDC que tienen como objetivo asegurar la calidad de las pruebas de espirometría, y que se pueden integrar en el marco del SSDC. El primero es un nuevo algoritmo basado en un conjunto de reglas que definen lo que es considerado como una prueba de alta calidad. El segundo es un enfoque de aprendizaje supervisado donde se optimiza la distinción entre una prueba correcta de espirometría y una de mala calidad

Memoria de tesis doctoral presentada por Sergi Pons Freixes para obtener el título de Doctor por la Universitat Politècnica de Catalunya (UPC), realizada bajo la dirección del Dr. Jaume Piera Fernández del Institut de Ciències del Mar (ICM-CSIC) y del Dr. Luigi Ceccaroni.-- 72 pages, [EN] Water is the most important resource for living on planet Earth, covering more than 70% of its surface. The oceans represent more than 97% of the planet total water and they are where more than the 99.5% of the living beings are concentrated. A great number of ecosystems depend on the health of these oceans; their study and protection are necessary. Large datasets over long periods of time and over wide geographical areas can be required to assess the health of aquatic ecosystems. The funding needed for data collection is considerable and limited, so it is important to look at new cost-effective ways of obtaining and processing marine environmental data. The feasible solution at present is to develop observational infrastructures that may increase significantly the conventional sampling capabilities. In this study we promote to achieve this solution with the implementation of Citizen Observatories, based on volunteer participation. Citizen observatories are platforms that integrate the latest information technologies to digitally connect citizens, improving observation skills for developing a new type of research known as Citizen Science. Citizen science has the potential to increase the knowledge of the environment, and aquatic ecosystems in particular, through the use of people with no specific scientific training to collect and analyze large data sets. We believe that citizen science based tools —open source software coupled with low-cost do-it-yourself hardware— can help to close the gap between science and citizens in the oceanographic field. As the public is actively engaged in the analysis of data, the research also provides a strong avenue for public education. This is the objective of this thesis, to demonstrate how open source software and low-cost do-it-yourself hardware are effectively applied to oceanographic research and how can it develop into citizen science. We analyze four different scenarios where this idea is demonstrated: an example of using open source software for video analysis where lobsters were monitored; a demonstration of using similar video processing techniques on in-situ low-cost do-it-yourself hardware for submarine fauna monitoring; a study using open source machine learning software as a method to improve biological observations; and last but not least, some preliminar results, as proof of concept, of how manual water sampling could be replaced by low-cost do-it-yourself hardware with optical sensors, [CAT] L’aigua és el recurs més important per la vida al planeta Terra, cobrint m és del 70% de la seva superfície. Els oceans representen m és del 70% de tota l’aigua del planeta, i és on estan concentrats més del 99.5% dels éssers vius. Un gran nombre d’ecosistemes depenen de la salut d’aquests oceans; el seu estudi i protecció són necessaris. Grans conjunts de dades durant llargs períodes de temps i al llarg d’amples àrees geogràfiques poden ser necessaris per avaluar la salut dels ecosistemes aquàtics. El finançament necessari per aquesta recol·lecció de dades és considerable però limitat, i per tant és important trobar noves formes m és rendibles d’obtenir i processar dades mediambientals marines. La solució factible actualment és la de desenvolupar infraestructures observacionals que puguin incrementar significativament les capacitats de mostreig convencionals. En aquest estudi promovem que es pot assolir aquesta solució amb la implementació d’Observatoris Ciutadans, basats en la participació de voluntaris. Els observatoris ciutadans són plataformes que integren les últimes tecnologies de la informació amb ciutadans digitalment connectats, millorant les capacitats d’observació, per desenvolupar un nou tipus de recerca coneguda com a Ciència Ciutadana. La ciència ciutadana té el potencial d’incrementar el coneixement del medi ambient, i dels ecosistemes aquàtics en particular, mitjançant l’ús de persones sense coneixement científic específic per recollir i analitzar grans conjunts de dades. Creiem que les eines basades en ciència ciutadana —programari lliure juntament amb maquinari de baix cost i del tipus “fes-ho tu mateix” (do-it-yourself en anglès)— poden ajudar a apropar la ciència del camp oceanogràfic als ciutadans. A mesura que el gran públic participa activament en l’anàlisi de dades, la recerca esdevé també una nova via d’educació pública. Aquest és l’objectiu d’aquesta tesis, demostrar com el programari lliure i el maquinari de baix cost “fes-ho tu mateix” s’apliquen de forma efectiva a la recerca oceanogràfica i com pot desenvolupar-se cap a ciència ciutadana. Analitzem quatre escenaris diferents on es demostra aquesta idea: un exemple d’ús de programari lliure per anàlisi de vídeos de monitoratge de llagostes; una demostració utilitzant tècniques similars de processat de vídeo en un dispositiu in-situ de baix cost “fes-ho tu mateix” per monitoratge de fauna submarina; un estudi utilitzant programari lliure d’aprenentatge automàtic (machine learning en anglès) com a mètode per millorar observacions biològiques; i finalment uns resultats preliminars, com a prova de la seva viabilitat, de com un mostreig manual de mostres d’aigua podria ser reemplaçat per maquinari de baix cost “fes-ho tu mateix” amb sensors òptics, Esta tesis ha sido parcialmente financiada por el antiguo Ministerio de Educación y Ciencia, mediante los programas Junta de Ampliación de Estudios (JAE Predoc, I3P-BPD2005) y Estancias breves I3P del Consejo Superior de Investigaciones Científicas (CSIC)

En aquesta tesi ens centrem en les discapacitats d’origen neurològic. Segons l'OMS, les malalties d'origen neurològic són una de les principals causes de càrrega de malaltia al món. En aquesta tesi es fan servir dues poblacions de pacients: les persones que pateixen lesions medul·lars (LM) i les persones que pateixen dany cerebral adquirit (DCA). Ambdós conjunts de dades han estat proporcionats amablement per l'Institut Guttmann. Més específicament, treballem en quatre dominis de predicció per als pacients amb discapacitats neurològiques: funcions emocionals i urinàries de persones amb LM, i funcions emocionals i executives de persones amb DCA. En la rehabilitació de les persones que pateixen de malalties neurològiques, hi ha dues etapes bàsiques: intrahospitalària i extrahospitalària. En la fase intrahospitalària (aguda), els pacients que han patit una lesió traumàtica o no traumàtica resten a l'hospital en procés de rehabilitació. Generalment, després de romandre uns pocs mesos a l'hospital, tornen a casa i comença la fase extrahospitalària. A partir d’aquest moment, els pacients van un cop l'any a l'hospital de rehabilitació per fer una avaluació integral periòdica, i és llavors quan han de complimentar diversos qüestionaris (depenent de la malaltia que pateixen) amb l'objectiu de mesurar la independència funcional i les variables psicològiques i socials. Aquest estudi es centra en la fase extrahospitalària o fase crònica. Aquesta tesi contribueix a la millora de l'estat de l’art dels sistemes de salut en: la falta d'interoperabilitat, la capacitat limitada per a l'anàlisi temporal o poblacional, la capacitat limitada per al pronòstic, i la capacitat limitada per integrar la informació depenent del temps. En particular, en relació amb la interoperabilitat, aquesta tesi proposa un nou sistema de traducció automàtica a les normes internacionals promogudes per l'OMS i una nova ontologia. En quant a la capacitat per a l'anàlisi temporal o de la població, aquesta tesi proporciona un sistema de seguiment amb tècniques de visualització noves, que permet una millor comprensió de l'evolució d'atributs clínics en individus i poblacions. Més específicament, ens dóna una representació gràfica dels estats i la seva evolució, que permet comparar fàcilment les característiques d'un individu amb les de tota la població. Pel que fa al pronòstic, en aquesta tesi s'ha utilitzat el raonament basat en casos (RBC), una tècnica d'intel·ligència artificial que es fonamenta en la solució de situacions noves, aprenent de situacions similars anteriors ja resoltes. A diferència del nostre sistema de seguiment, en els sistemes de pronòstic basats en RBC el pacient objectiu està relacionat amb un petit grup de pacients similars (en lloc de tota la població dels pacients). Aquesta tesi dissenya i desenvolupa un pronòstic basat en casos utilitzant abstraccions temporals (CAPTA), un sistema de pronòstic amb un nou mètode de raonament basat en casos amb resultats prometedors. Finalment, pel que fa al temps, CAPTA integra la naturalesa temporal d'atributs clínics a través d'abstraccions temporals. CAPTA s’avalua i s'aplica experimentalment en quatre dominis de predicció per als pacients amb discapacitats neurològiques., In this thesis we focus on disabilities of neurological origin. According to the WHO, the diseases of neurological origin are one of the leading causes of burden of disease in the world. Two populations of patients are used in this thesis: people who suffer from spinal cord injury (SCI) and people who suffer from acquired brain injury (ABI). Both datasets have been kindly provided by Institut Guttmann. More specifically, we work in four prediction domains for patients with neurological disabilities: emotional and urination functions of people with SCI, and emotional and executive functions of people with ABI. In the rehabilitation of people who suffer from neurological diseases, there are two basic stages: intra-hospital and extra-hospital. In the intra-hospital (acute) phase, patients who suffered from a traumatic or nontraumatic injury stay in hospital undergoing rehabilitation. After typically a few months in hospital, they return home and the extra-hospital phase starts. Thereafter they go once a year to the rehabilitation hospital for a periodic comprehensive evaluation (PCE), when they are administered several questionnaires (depending on the disease they are suffering from) with the aim of measuring functioning independence, psychological and social variables. This study is focused on the extra-hospital or chronic phase. This thesis contributes to the improvement of the state of the art of healthcare systems by dealing with: lack of interoperability, limited capability for temporal or populational analysis, limited capability for prognosis, and limited capability for integrating time-dependent information. In particular, regarding interoperability, this thesis proposes a new automatic translation system to international standards promoted by the WHO and a new ontology. Regarding the ability for temporal or population analysis, this thesis provides a monitoring system with novel visualization techniques that allows a better understanding of the evolution of clinical attributes in individuals and populations. More specifically, it provides a graphical representation of states and their evolution that permits easily comparing the characteristics of an individual with those of the whole population. With respect to prognosis, in this thesis we have used case-based reasoning (CBR), an artificial intelligence technique based on solving new situations by learning from past similar situations already solved. Unlike our monitoring system, in CBR-based prognosis systems the target patient is related to a small set of similar patients (instead of the whole population of patients). This thesis designs and develops a CAse-based Prognosis using Temporal Abstractions (CAPTA), a prognosis system with a new case-based reasoning method with promising results. Finally, regarding time, CAPTA integrates the temporal nature of clinical attributes through temporal abstractions. CAPTA is applied and experimentally evaluated in four prediction domains for patients with neurological disabilities.

During the last decade, the use of recommender systems has been increasingly growing to the point that, nowadays, the success of many well-known services depends on these technologies. Recommenders Systems help people to tackle the choice overload problem by effectively presenting new content adapted to the user¿s preferences. However, current recommendation algorithms commonly suffer from data sparsity, which refers to the incapability of producing acceptable recommendations until a minimum amount of users¿ ratings are available for training the prediction models. This thesis investigates how the distributional semantics of concepts describing the entities of the recommendation space can be exploited to mitigate the data-sparsity problem and improve the prediction accuracy with respect to state-of-the-art recommendation techniques. The fundamental idea behind distributional semantics is that concepts repeatedly co-occurring in the same context or usage tend to be related. In this thesis, we propose and evaluate two novel semantically-enhanced prediction models that address the sparsity-related limitations: (1) a content-based approach, which exploits the distributional semantics of item¿s attributes during item and user-profile matching, and (2) a context-aware recommendation approach that exploits the distributional semantics of contextual conditions during context modeling. We demonstrate in an exhaustive experimental evaluation that the proposed algorithms outperform state-of-the-art ones, especially when data are sparse. Finally, this thesis presents a recommendation framework, which extends the widespread machine learning library Apache Mahout, including all the proposed and evaluated recommendation algorithms as well as a tool for offline evaluation and meta-parameter optimization. The framework has been developed to allow other researchers to reproduce the described evaluation experiments and make new progress on the Recommender Systems field easier, Durant l'última dècada, l'ús dels sistemes de recomanació s'ha vist incrementat fins al punt que, actualment, l'èxit de molts dels serveis web més coneguts depèn en aquesta tecnologia. Els Sistemes de Recomanació ajuden als usuaris a trobar els productes o serveis que més s¿adeqüen als seus interessos i preferències. Una gran limitació dels algoritmes de recomanació actuals és el problema de "data-sparsity", que es refereix a la incapacitat d'aquests sistemes de generar recomanacions precises fins que un cert nombre de votacions d'usuari és disponible per entrenar els models de predicció. Per mitigar aquest problema i millorar així la precisió de predicció de les tècniques de recomanació que conformen l'estat de l'art, en aquesta tesi hem investigat diferents maneres d'aprofitar la semàntica distribucional dels conceptes que descriuen les entitats que conformen l'espai del problema de la recomanació, principalment, els objectes a recomanar i la informació contextual. En la semàntica distribucional s'assumeix la següent hipotesi: conceptes que coincideixen repetidament en el mateix context o ús tendeixen a estar semànticament relacionats. Concretament, en aquesta tesi hem proposat i avaluat dos algoritmes de recomanació que fan ús de la semàntica distribucional per mitigar el problem de "data-sparsity": (1) un model basat en contingut que explota les similituds distribucionals dels atributs que representen els objectes a recomanar durant el càlcul de la correspondència entre els perfils d'usuari i dels objectes; (2) un model de recomanació contextual que fa ús de les similituds distribucionals entre condicions contextuals durant la representació del context. Mitjançant una avaluació experimental exhaustiva dels models de recomanació proposats hem demostrat la seva efectivitat en situacions de falta de dades, confirmant que poden millorar la precisió d'algoritmes que conformen l'estat de l'art. Finalment, aquesta tesi presenta una llibreria pel desenvolupament i avaluació d'algoritmes de recomanació com una extensió de la llibreria de "Machine Learning" Apache Mahout, àmpliament utilitzada en el camp del Machine Learning. La nostra extensió inclou tots els algoritmes de recomanació avaluats en aquesta tesi, així com una eina per facilitar l'avaluació experimental dels algoritmes. Hem desenvolupat aquesta llibreria per facilitar a altres investigadors la reproducció dels experiments realitzats i, per tant, el progrés en el camp dels Sistemes de Recomanació.

Web services have attracted attention as a possible solution to share knowledge and application logic among different heterogeneous agents. A classic approach to this subject is using SOAP, a W3C protocol aimed to exchange structured information. The Web Services Interoperability organization (WS-I), defines a set of extensions, commonly called WS-*, which further enhance this knowledge exchange defining mechanisms and functionalities such as security, addressability or service composition. This thesis explores a relatively new alternative approach to the SOAP/WS-I stack: REST-based Web services. The acronym REST stands for Representational state transfer; this basically means that each unique URL is a representation of some object. You can get the contents of that object using an HTTP GET; you then might use a POST, PUT or DELETE to modify the object (in practice most of the services use a POST for this). All of Yahoo’s Web services use REST, including Flickr; del.icio.us API uses it; pubsub [http://www.pubsub.com/], Bloglines [http://www.bloglines.com/], Technorati [http://technorati.com/] and both, eBay and Amazon, have Web services for both REST and SOAP. Google seems to be consistent in implementing their Web services to use SOAP, with the exception of Blogger, which uses XML-RPC. The companies and organization that are using REST APIs have not been around for very long, and their APIs came out in the last seven years mostly. So REST is a new way to create and integrate Web services, whose main advantages are: being lightweight (not a lot of extra xml mark-up), human readable results, easy to build services (no toolkits required). Although REST is still generating discussion about possible implementations, and different proposals have been put forward, it provides enough mechanisms to allow knowledge-representations sharing among heterogeneous intelligent services. In this thesis, a novel way to integrate intelligent Web-services is designed and developed, and the resulting system is deployed in the domain of recommendation. Through a mashup, how different services are integrated and how a simple recommendation system consumes data coming from them to provide relevant information to users is presented. Part of this work has been carried out within the context of the Laboranova European project [http://www.laboranova.com/], and has been deployed to integrate a set of applications to create a virtual space to support innovation processes.

Personalization and recommendation systems are a solution to the problem of content overload, especially in large information systems. In this thesis, a personalized recommendation system enhanced with semantic knowledge has been developed in order to overcome the most common limitations of traditional approaches: the cold-start and the sparsity problems. The recommender consists of the following two main components. A user-profile learning algorithm combines user’s feedback from different channels and employs domain inferences to construct accurate user profiles. A recommendation algorithm, using content-based filtering, exploits the semantic structure of the domain to obtain accurate predictions and generate the corresponding recommendations. The system’s design proposed is flexible enough to be potentially applied to applications of any domain that can be properly described using ontologies. In addition to the development of the recommendation system, an existing Web-application in the tourism domain has been extended and adapted in order to be able to integrate the recommender into it. The overall recommendation system has been evaluated and the results obtained indicate that it satisfies the requirements established.

Con este proyecto se quiere mejorar uno de los productos principales de la empresa, que consiste en un sistema de televisión interactiva que permite a los usuarios poder disfrutar de varios servicios tales como una aplicación ofimática, una guía local y la adquisición de contenidos de distintos tipos: películas, series, clips, música, información cultural y videojuegos entre otros. Actualmente existen varios sistemas de recomendación de contenido de vídeo que funcionan relativamente bien, pero únicamente en entornos con un gran número de usuarios que continuamente interactúan con el sistema. Esto implica, que los algoritmos de recomendación de contenido de vídeo actuales sólo tengan cabida en el entorno Web, dónde el número de usuarios puede llegar a ser considerable y se dispone de una alta densidad de información. Se pretende diseñar un sistema de recomendación en el que la precisión de las recomendaciones no dependa tanto de la densidad de información disponible, sino más bien en la calidad de la información, dado que estas son las características del entorno donde se implantará el sistema.