Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items. In this paper, we reproduce the analyses of Abdollahpouri et al. in the context of music recommendation. Specifically, we investigate three user groups from the LastFM music platform that are categorized based on how much their listening preferences deviate from the most popular music among all LastFM users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users. In line with Abdollahpouri et al., we find that state-of-the-art recommendation algorithms favor popular items also in the music domain. However, their proposed Group Average Popularity metric yields different results for LastFM than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the LastFM dataset we use. Finally, we compare the accuracy results of the recommendation algorithms for the three user groups and find that the low-mainstreaminess group significantly receives the worst recommendations.

Most discussions of the reproducibility crisis focus on its epistemic aspect: the fact that the scientific community fails to follow some norms of scientific investigation, which leads to high rates of irreproducibility via a high rate of false positive findings. The purpose of this paper is to argue that there is a heretofore underappreciated and understudied dimension to the reproducibility crisis in experimental psychology and neuroscience that may prove to be at least as important as the epistemic dimension. This is the communication dimension. The link between communication and reproducibility is immediate: independent investigators would not be able to recreate an experiment whose design or implementation were inadequately described. I exploit evidence of a replicability and reproducibility crisis in computational science, as well as research into quality of reporting to support the claim that a widespread failure to adhere to reporting standards, especially the norm of descriptive completeness, is an important contributing factor in the current reproducibility crisis in experimental psychology and neuroscience.

Addressing issues with the reproducibility of results is critical for scientific progress, but conflicting ideas about the sources of and solutions to irreproducibility are a barrier to change. Prior work has attempted to address this problem by creating analytical definitions of reproducibility. We take a novel empirical, mixed methods approach to understanding variation in reproducibility conversations, which yields a map of the discursive dimensions of these conversations. This analysis demonstrates that concerns about the incentive structure of science, the transparency of methods and data, and the need to reform academic publishing form the core of reproducibility discussions. We also identify three clusters of discussion that are distinct from the main group: one focused on reagents, another on statistical methods, and a final cluster focused the heterogeneity of the natural world. Although there are discursive differences between scientific and popular articles, there are no strong differences in how scientists and journalists write about the reproducibility crisis. Our findings show that conversations about reproducibility have a clear underlying structure, despite the broad scope and scale of the crisis. Our map demonstrates the value of using qualitative methods to identify the bounds and features of reproducibility discourse, and identifies distinct vocabularies and constituencies that reformers should engage with to promote change.

For any scientific report, repeating the original analyses upon the original data should yield the original outcomes. We evaluated analytic reproducibility in 25 Psychological Science articles awarded open data badges between 2014-2015. Initially, 16 (64%, 95% confidence interval [43,81]) articles contained at least one “major numerical discrepancy” (>10% difference) prompting us to request input from original authors. Ultimately, target values were reproducible without author involvement for 9 (36% [20,59]) articles; reproducible with author involvement for 6 (24% [8,47]) articles; not fully reproducible with no substantive author response for 3 (12% [0,35]) articles; and not fully reproducible despite author involvement for 7 (28% [12,51]) articles. Overall, 37 major numerical discrepancies remained out of 789 checked values (5% [3,6]), but original conclusions did not appear affected. Non-reproducibility was primarily caused by unclear reporting of analytic procedures. These results highlight that open data alone is not sufficient to ensure analytic reproducibility.

Context-aware citation recommendation is used to overcome the process of manually searching for relevant citations by automatically recommending suitable papers as citations for a specified input text. In this paper, we examine the reproducibility of a state-of-the-art approach to context-aware citation recommendation, namely the neural citationnetwork (NCN) by Ebesu and Fang. We re-implement the network and run evaluations on both RefSeer, the originally used data set, and arXiv CS, as an additional data set. We provide insights on how the different hyperparameters of the neural network affect the model performance of the NCN and thus can be used to improve the model’s performance. In this way, we contribute to making citation recommendation approaches and their evaluations more transparent and creating more effective neural network-based models in the future.

The Reproducible Software Environment (Resen) is an open-source software tool enabling computationally reproducible scientific results in the geospace science community. Resen was developed as part of a larger project called the Integrated Geoscience Observatory (InGeO), which aims to help geospace researchers bring together diverse datasets from disparate instruments and data repositories, with software tools contributed by instrument providers and community members. The main goals of InGeO are to remove barriers in accessing, processing, and visualizing geospatially resolved data from multiple sources using methodologies and tools that are reproducible. The architecture of Resen combines two mainstream open source software tools, Docker and JupyterHub, to produce a software environment that not only facilitates computationally reproducible research results, but also facilitates effective collaboration among researchers. In this technical paper, we discuss some challenges for performing reproducible science and a potential solution via Resen, which is demonstrated using a case study of a geospace event. Finally we discuss how the usage of mainstream, open-source technologies seems to provide a sustainable path towards enabling reproducible science compared to proprietary and closed-source software.