1,721,114 research outputs found
Replication Data for: Repository approaches to improving quality of shared data and code
No full textThis is supplementary data to the article "Repository approaches to improving quality of shared data and code," and in particular, its first section on completeness of research code.
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Word cloud of Reproducibility and Replicability in Science
No full textThis dataset contains data and code that created a figure for the article Toward Reproducible and Extensible Research: from Values to Action.
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Replication Data for: A large-scale study on research code quality and execution
No full textThis is the accompanying dataset for the article "A large-scale study on research code quality and execution" by Ana Trisovic, Matthew K. Lau, Thomas Pasquier, and Mercè Crosas.
Abstract: The article presents a study on the quality and execution of research code from publicly-available replication datasets at the Harvard Dataverse repository. Research code is typically created by a group of scientists and published together with academic papers to facilitate research transparency and reproducibility. For this study, we define ten questions to address aspects impacting research reproducibility and reuse. First, we retrieve and analyze more than 2000 replication datasets with over 9000 unique R files published from 2010 to 2020. Second, we execute the code in a clean runtime environment to assess its ease of reuse. Common coding errors were identified, and some of them were solved with automatic code cleaning to aid code execution. We find that 74% of R files failed to complete without error in the initial execution, while 56% failed when code cleaning was applied, showing that many errors can be prevented with good coding practices. We also analyze the replication datasets from journals' collections and discuss the impact of the journal policy strictness on the code re-execution rate. Finally, based on our results, we propose a set of recommendations for code dissemination aimed at researchers, journals, and repositories
Co-exposure patterns of heat, wildfire, and wildfire smoke in Western US
No full textWe provide data on three heat-related natural hazards: extreme heat, wildfire burn zones, and wildfire smoke from 2006-2020 in eleven Western US states
SpaCE: The Spatial Confounding Environment
No full textSpaCE: The Spatial Confounding Environment is a benchmarking dataset for causal inference incorporating spatial structure. In particular, SpaCE datasets contain real confounder and exposure/treatment data inspired by environmental health studies. The synthetic outcome and counterfactual are generated according to recommended practices for causal evaluation by mimicking the real outcome data distribution learned with machine learning and neural network methods. Spatial confounding is achieved by masking influential confounders in the learned model
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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