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El interés y la práctica de la ciencia ciudadana (CC) ha aumentado en los últimos años. Esto ha derivado en el uso de páginas web como herramienta de comunicación, recolección o análisis datos o repositorio materiales y recursos. Desde una perspectiva educativa, se espera que al integrar información sobre proyectos de CC en un entorno educativo formal, se inspire a los maestros a crear actividades de aprendizaje. Este, es un caso interesante para usar bots que automaticen el proceso de extracción de datos de webs de CC que ayuden a comprender mejor su uso en contextos educativos. Aunque esta información está disponible públicamente, se deben seguir las reglas de la ley de protección de datos o GDPR. Este artículo tiene como objetivo explicar: 1) cómo la CC se comunica y promueve en los sitios web; 2) cómo se diseñan, desarrollan y aplican los métodos de web scraping y las técnicas de anonimización para recopilar información en línea; y 3) cómo se podrían usar estos datos con fines educativos. Tras el análisis de 72 webs algunos de los resultados son que solo el 24,8% incluye información detallada sobre el proyecto, y el 48,61% incluye información sobre propósitos o materiales educativos., There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to create learning activities. This is an interesting case for using bots to automate the process of data extraction from online CS platforms to better understand its use in educational contexts. Although this information is publicly available, it has to follow GDPR rules. This paper aims to explain (1) how CS communicates and is promoted on websites, (2) how web scraping methods and anonymization techniques have been designed, developed and applied to collect information from online sources and (3) how these data could be used for educational purposes. After the analysis of 72 websites, some of the results obtained show that only 24.8% includes detailed information about the CS project and 48.61% includes information about educational purposes or materials.

This deliverable wraps up the work on “web-based analytics” in WP3 in addition to the previous deliverables on “methods and tools” (D3.1) and on design and implementation of the Analytics Workbench and tools (D3.2). A first add-on is the usage and evaluation experience with the AWB, which is based on several interactive presentations, workshops and webinars. These, in turn, served as a source of data to evaluate this tool suite from a user interface, application and algorithmic perspective. Several case studies included here represent the previously introduced micro, meso and macro perspectives (see D3.1 and D1.2). In spite of the difference in scope, the macro perspective (Twitter analyses) and the micro perspective on participation and engagement share the use of network models and network analysis techniques, whereas the meso level analyses are content-centric. Regarding the interfacing and exportation of analytics results, we address several channels including the information export through the eMagazine as well as through project workshops / webinars and “normal” scientific publications. For the technical outcomes, we report on our open source and open data strategy that relies on open archives and code repositories.