We present a platform and a dataset to help research on Music Emotion Recognition (MER). We developed the Music Enthusiasts platform aiming to improve the gathering and analysis of the so-called “ground truth” needed as input to MER systems. Firstly, our platform involves engaging participants using citizen science strategies and generate music emotion annotations – the platform presents didactic information and musical recommendations as incentivization, and collects data regarding demographics, mood, and language from each participant. Participants annotated each music excerpt with single free-text emotion words (in native language), distinct forced-choice emotion categories, preference, and familiarity. Additionally, participants stated the reasons for each annotation – including those distinctive of emotion perception and emotion induction. Secondly, our dataset was created for personalized MER and contains information from 181 participants, 4721 annotations, and 1161 music excerpts. To showcase the use of the dataset, we present a methodology for personalization of MER models based on active learning. The experiments show evidence that using the judgment of the crowd as prior knowledge for active learning allows for more effective personalization of MER systems for this particular dataset. Our dataset is publicly available and we invite researchers to use it for testing MER systems.

We present a platform and a dataset to help research on Music Emotion Recognition (MER). We developed the Music Enthusiasts platform aiming to improve the gathering and analysis of the so-called “ground truth” needed as input to MER systems. Firstly, our platform involves engaging participants using citizen science strategies and generate music emotion annotations -- the platform presents didactic information and musical recommendations as incentivization, and collects data regarding demographics, mood, and language from each participant. Participants annotated each music excerpt with single free-text emotion words (in native language), distinct forced-choice emotion categories, preference, and familiarity. Additionally, participants stated the reasons for each annotation -- including those distinctive of emotion perception and emotion induction. Secondly, our dataset was created for personalized MER and contains information from 181 participants, 4721 annotations, and 1161 music excerpts. To showcase the use of the dataset, we present a methodology for personalization of MER models based on active learning. The experiments show evidence that using the judgment of the crowd as prior knowledge for active learning allows for more effective personalization of MER systems for this particular dataset. Our dataset is publicly available and we invite researchers to use it for testing MER systems.

Online communities (OC) offer teachers a context for mutual inspiration, collaboration, and professional development. Yet, despite there being several studies analyzing teachers' motivations to participate in these communities, it is still unclear how these motivations relate with the supporting collaborative platforms and how they can serve as an input for defining and prioritizing design requirements. A survey study was conducted with the participants of an open online and a face-toface training course in the different phases of a 'Maker' educational activity, which were introduced to a supporting platform for sharing, exploring, and co-creating learning designs. Information about 170 teachers' self-reported motivations to participate in a collaborative environment and their perceptions about the usefulness of the implemented features was gathered. Findings show that participants' main motivations are not only to gain knowledge, but also include to have fun or to collaborate with the community development. Regarding their perception about the supporting platform, more than the 30% of the participants acknowledged the usefulness of the features implemented and identified the lack of resources and training as the main limitations to participate. Results provide evidence of the importance of the participants motivations to determine design requirements for a platform to enhance collaboration within an OC of teachers.