Authors : Daniel Nüst, Carlos Granell, Barbara Hofer, Markus Konkol, Frank O Ostermann, Rusne Sileryte, Valentina Cerutti
The demand for reproducibility of research is on the rise in disciplines concerned with data analysis and computational methods. In this work existing recommendations for reproducible research are reviewed and translated into criteria for assessing reproducibility of articles in the field of geographic information science (GIScience).
Using a sample of GIScience research from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, we assess the current state of reproducibility of publications in this field. Feedback on the assessment was collected by surveying the authors of the sample papers.
The results show the reproducibility levels are low. Although authors support the ideals, the incentives are too small. Therefore we propose concrete actions for individual researchers and the AGILE conference series to improve transparency and reproducibility, such as imparting data and software skills, an award, paper badges, author guidelines for computational research, and Open Access publications.
This paper describes a preliminary study of research transparency, which draws on the findings from four focus group sessions with faculty in chemistry, law, urban and social studies, and civil and environmental engineering.
The multi-faceted nature of transparency is highlighted by the broad ways in which the faculty conceptualised the concept (data sharing, ethics, replicability) and the vocabulary they used with common core terms identified (data, methods, full disclosure).
The associated concepts of reproducibility and trust are noted. The research lifecycle stages are used as a foundation to identify the action verbs and software tools associated with transparency.
A range of transparency drivers and motivations are listed. The role of libraries and data scientists is discussed in the context of the provision of transparency services for researchers.
Psychology faces a replication crisis. The Reproducibility Project: Psychology sought to replicate the effects of 100 psychology studies. Though 97% of the original studies produced statistically significant results, only 36% of the replication studies did so (Open Science Collaboration, 2015).
This inability to replicate previously published results, however, is not limited to psychology (Ioannidis, 2005). Replication projects in medicine (Prinz et al., 2011) and behavioral economics (Camerer et al., 2016) resulted in replication rates of 25 and 61%, respectively, and analyses in genetics (Munafò, 2009) and neuroscience (Button et al., 2013) question the validity of studies in those fields. Science, in general, is reckoning with challenges in one of its basic tenets: replication.
Comparative psychology also faces the grand challenge of producing replicable research. Though social psychology has born the brunt of most of the critique regarding failed replications, comparative psychology suffers from some of the same problems faced by social psychology (e.g., small sample sizes).
Yet, comparative psychology follows the methods of cognitive psychology by often using within-subjects designs, which may buffer it from replicability problems (Open Science Collaboration, 2015). In this Grand Challenge article, I explore the shared and unique challenges of and potential solutions for replication and reproducibility in comparative psychology.