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Cities with strong local music scenes enjoy many social and economic benefits. To this end, we are interested in developing a locally-focused artist and event recommendation system called Localify.org that supports and promotes local music scenes. In this demo paper, we describe both the overall system architecture as well as our core recommendation algorithm. This algorithm uses artist-artist similarity information, as opposed to user-artist preference information, to bootstrap recommendation while we grow the number of users. The overall design of Localify was chosen based on the fact that local artists tend to be relatively obscure and reside in the long tail of the artist popularity distribution. We discuss the role of popularity bias and how we attempt to ameliorate it in the context of local music recommendation. 

There are many benefits for a community when there is a vibrant local music scene (e.g., increased mental & physical well-being, increased economic activity) and there are many factors that contribute to an environment in which a live music scene can thrive (e.g., available performance spaces, helpful government policies). In this paper, we explore using an estimate of the live music event rate (LMER) as a rough indicator to measure the strength of a local music scene. We define LMER as the number of music shows per 100,000 people per year and then explore how this indicator is (or is not) correlated with 28 other socioeconomic indicators. To do this, we analyze a set of 308,051 music events from 2019 across 1,139 cities in the United States. Our findings reveal that factors related to transportation (e.g., high walkability), population (high density), economics (high employment rate), age (high proportion of individuals age 20-29), and education (bachelor's degree or higher) are strongly correlated with having a high number of live music events. Conversely, we did not find statistically significant evidence that other indica- tors (e.g., racial diversity) are correlated. 

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, RCSB.org), the US Worldwide Protein Data Bank (wwPDB, wwPDB.org) data center for the global PDB archive, provides access to the PDB data via its RCSB.org research-focused web portal. We report substantial additions to the tools and visualization features available at RCSB.org, which now delivers more than 227000 experimentally determined atomic-level three-dimensional (3D) biostructures stored in the global PDB archive alongside more than 1 million Computed Structure Models (CSMs) of proteins (including models for human, model organisms, select human pathogens, crop plants and organisms important for addressing climate change). In addition to providing support for 3D structure motif searches with user-provided coordinates, new features highlighted herein include query results organized by redundancy-reduced Groups and summary pages that facilitate exploration of groups of similar proteins. Newly released programmatic tools are also described, as are enhanced training opportunities. 

Most commercial music services rely on collaborative filtering to recommend artists and songs. While this method is effective for popular artists with large fanbases, it can present difficulties for recommending novel, lesser known artists due to a relative lack of user preference data. In this paper, we therefore seek to understand how content-based approaches can be used to more effectively recommend songs from these lesser known artists. Specifically, we conduct a user study to answer three questions. Firstly, do most users agree which songs are most acoustically similar? Secondly, is acoustic similarity a good proxy for how an individual might construct a playlist or recommend music to a friend? Thirdly, if so, can we find acoustic features that are related to human judgments of acoustic similarity? To answer these questions, our study asked 117 test subjects to compare two unknown candidate songs relative to a third known reference song. Our findings show that 1) judgments about acoustic similarity are fairly consistent, 2) acoustic similarity is highly correlated with playlist selection and recommendation, but not necessarily personal preference, and 3) we identify a subset of acoustic features from the Spotify Web API that is particularly predictive of human similarity judgments.