Reproducibility: Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Authors are strongly encouraged to make their code and data publicly available whenever possible. Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. The reproducibility factor will play an important role in the assessment of each submission.
The food frequency questionnaire (FFQ) is the most efficient and cost-effective method to investigate the relationship between usual diet and disease in epidemiologic studies. Although FFQs have been validated in many adult populations worldwide, the number of valid FFQ in preschool children is very scarce. The aim of this study was to evaluate the reproducibility and validity of a semi-quantitative FFQ designed for children aged 4 to 5 years.
Replicability is a core principle of the scientific method. However, several scientific disciplines have suffered crises in confidence caused, in large part, by attitudes toward replication. This work reports on the value the computing education research community associates with studies that aim to replicate, reproduce or repeat earlier research. The results were obtained from a survey of 73 computing education researchers. An analysis of the responses confirms that researchers in our field hold many of the same biases as those in other fields experiencing a crisis in replication. In particular, researchers agree that original works - novel works that report new phenomena - have more impact and are more prestigious. They also agree that originality is an important criteria for accepting a paper, making such work more likely to be published. Furthermore, while the respondents agree that published work should be verifiable, they doubt this standard is widely met in the computing education field and are not eager to perform the work of verifying others' work themselves.
When people talk about their data infrastructure, they tend to focus on the technologies: Hadoop, Scalding, Impala, and the like. However, we’ve found that just as important as the technologies themselves are the principles that guide their use. We’d like to share our experience with one such principle that we’ve found particularly useful: reproducibility. We’ll talk about our motivation for focusing on reproducibility, how we’re using Jupyter Notebooks as our core tool, and the workflow we’ve developed around Jupyter to operationalize our approach.
Reproducible research is a concept that has emerged in data and computationally intensive sciences in which the code used to conduct all analyses, including generation of publication quality figures, is directly available, and preferably in open source manner. This perspective outlines the processes and attributes, and illustrates the execution of reproducible research via a simple exposure assessment of air pollutants in metropolitan Philadelphia.
This Request for Information (RFI) seeks public comments on data management and sharing strategies and priorities in order to consider: (1) how digital scientific data generated from NIH-funded research should be managed, and to the fullest extent possible, made publicly available; and, (2) how to set standards for citing shared data and software. Response to this RFI is voluntary. Responders are free to address any or all of the items in Sections I and II, delineated below, or any other relevant topics respondents recognize as important for NIH to consider. Respondents should not feel compelled to address all items. Instructions on how to respond to this RFI are provided in "Concluding Comments."