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Citizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.

The authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.

The results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.

In the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.

The purpose of this paper is to present a new approach toward automatically visualizing Linked Open Data (LOD) through metadata analysis.

By focussing on the data within a LOD dataset, the authors can infer its structure in a much better way than current approaches, generating more intuitive models to progress toward visual representations.

With no technical knowledge required, focussing on metadata properties from a semantically annotated dataset could lead to automatically generated charts that allow to understand the dataset in an exploratory manner. Through interactive visualizations, users can navigate LOD sources using a natural approach, in order to save time and resources when dealing with an unknown resource for the first time.

This approach is suitable for available SPARQL endpoints and could be extended for resource description framework dumps loaded locally.

Most works dealing with LOD visualization are customized for a specific domain or dataset. This paper proposes a generic approach based on traditional data visualization and exploratory data analysis literature.