Posts about reproducible paper (old posts, page 4)

Replication and Reproducibility in Cross-Cultural Psychology

Replication is the scientific gold standard that enables the confirmation of research findings. Concerns related to publication bias, flexibility in data analysis, and high-profile cases of academic misconduct have led to recent calls for more replication and systematic accumulation of scientific knowledge in psychological science. This renewed emphasis on replication may pose specific challenges to cross-cultural research due to inherent practical difficulties in emulating an original study in other cultural groups. The purpose of the present article is to discuss how the core concepts of this replication debate apply to cross-cultural psychology. Distinct to replications in cross-cultural research are examinations of bias and equivalence in manipulations and procedures, and that targeted research populations may differ in meaningful ways. We identify issues in current psychological research (analytic flexibility, low power) and possible solutions (preregistration, power analysis), and discuss ways to implement best practices in cross-cultural replication attempts.

Transparency on scientific instruments

Scientific instruments are at the heart of the scientific process, from 17th‐century telescopes and microscopes, to modern particle colliders and DNA sequencing machines. Nowadays, most scientific instruments in biomedical research come from commercial suppliers [1], [2], and yet, compared to the biopharmaceutical and medical devices industries, little is known about the interactions between scientific instrument makers and academic researchers. Our research suggests that this knowledge gap is a cause for concern.

Use Cases of Computational Reproducibility for Scientific Workflows at Exascale

We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use cases: scientific reproducibility of results in the ACME climate simulations and performance reproducibility in molecular dynamics workflows on HPC computing platforms.

Estimating the Reproducibility of Experimental Philosophy

For scientific theories grounded in empirical data, replicability is a core principle, for at least two reasons. First, unless we accept to have scientific theories rest on the authority of a small number of researchers, empirical studies should be replicable, in the sense that its methods and procedure should be detailed enough for someone else to conduct the same study. Second, for empirical results to provide a solid foundation for scientific theorizing, they should also be replicable, in the sense that most attempts at replicating the original study that produced them would yield similar results. The XPhi Replicability Project is primarily concerned with replicability in the second sense, that is: the replicability of results. In the past year, several projects have shed doubt on the replicability of key findings in psychology, and most notably social psychology. Because the methods of experimental philosophy have often been close to the ones used in social psychology, it is only natural to wonder to which extent the results experimental philosophers ground their theory are replicable. The aim of the XPhi Replicability Project is precisely to reach a reliable estimate of the replicability of empirical results in experimental philosophy. To this end, several research teams across the world will replicate around 40 studies in experimental philosophy, some among the most cited, others drawn at random. The results of the project will be published in a special issue of the Review of Philosophy and Psychology dedicated to the topic of replicability in cognitive science.

The state of reproducibility in the computational geosciences

Figures are essential outputs of computational geoscientific research, e.g. maps and time series showing the results of spatiotemporal analyses. They also play a key role in open reproducible research, where public access is provided to paper, data, and source code to enable reproduction of the reported results. This scientific ideal is rarely practiced as studies, e.g. in biology have shown. In this article, we report on a series of studies to evaluate open reproducible research in the geosciences from the perspectives of authors and readers. First, we asked geoscientists what they understand by open reproducible research and what hinders its realisation. We found there is disagreement amongst authors, and a lack of openness impedes the adoption by authors and readers alike. However, reproducible research also includes the ability to achieve the same results requiring not only accessible but executable source code. Hence, to further examine the reader’s perspective, we searched for open access papers from the geosciences that have code/data attached (in R) and executed the analysis. We encountered several technical issues while executing the code and found differences between the original and reproduced figures. Based on these findings, we propose guidelines for authors to address these.

Developer Interaction Traces backed by IDE Screen Recordings from Think aloud Sessions

There are two well-known difficulties to test and interpret methodologies for mining developer interaction traces: first, the lack of enough large datasets needed by mining or machine learning approaches to provide reliable results; and second, the lack of "ground truth" or empirical evidence that can be used to triangulate the results, or to verify their accuracy and correctness. Moreover, relying solely on interaction traces limits our ability to take into account contextual factors that can affect the applicability of mining techniques in other contexts, as well hinders our ability to fully understand the mechanics behind observed phenomena. The data presented in this paper attempts to alleviate these challenges by providing 600+ hours of developer interaction traces, from which 26+ hours are backed with video recordings of the IDE screen and developer’s comments. This data set is relevant to researchers interested in investigating program comprehension, and those who are developing techniques for interaction traces analysis and mining.