Posts about reproducible paper (old posts, page 8)

Verifiable Badging System for Scientific Data Reproducibility

Reproducibility can be defined as the ability of a researcher to use materials, procedures or knowledge from a scientific study to obtain results same as that of the original investigator. It can be considered as one of the basic requirements to ensure that a given research finding is accurate and acceptable.This paper presents a new layered approach that allows scientific researchers to provide a) data to fellow researchers to validate research and b) proofs of research quality to funding agencies, without revealing sensitive details associated with the same. We conclude that by integrating smart contracts, blockchain technology, and self-sovereign identity into an automated system, it is possible to assert the quality of scientific materials and validate the peer review process without the need of a central authority.

Science Capsule - Capturing the Data Life Cycle

Science Capsule is free open source software that allows researchers to automatically capture their end-to-end workflows including the scripts, data, and execution environment. Science Capsule monitors the workflow environment to capture the provenance at runtime. It provides a timeline view and a web interface to represent the workflow and data life cycle, and the associated provenance information. Science Capsule also leverages container technologies toprovide a lightweight, executable package of the scripts and required dependencies, ensuring portability and reproducibility.

CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility

The traditional scientific paper falls short of effectively communicating computational research. To help improve this situation, we propose a system by which the computational workflows underlying research articles are checked. The CODECHECK system uses open infrastructure and tools and can be integrated into review and publication processes in multiple ways. We describe these integrations along multiple dimensions (importance, who, openness, when). In collaboration with academic publishers and conferences, we demonstrate CODECHECK with 25 reproductions of diverse scientific publications. These CODECHECKs show that asking for reproducible workflows during a collaborative review can effectively improve executability. While CODECHECK has clear limitations, it may represent a building block in Open Science and publishing ecosystems for improving the reproducibility, appreciation, and, potentially, the quality of non-textual research artefacts. The CODECHECK website can be accessed here:

The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study

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.

Double Trouble? The Communication Dimension of the Reproducibility Crisis in Experimental Psychology and Neuroscience

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.

Mapping the discursive dimensions of the reproducibility crisis: A mixed methods analysis

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.