ReproZip (Rampin et al. 2014) is a tool aimed at simplifying the process of creating reproducible experiments. After finishing an experiment, writing a website, constructing a database, or creating an interactive environment, users can run ReproZip to create reproducible packages, archival snapshots, and an easy way for reviewers to validate their work.
Reproducibility in animal research is alarmingly low, and a lack of scientific rigor has been proposed as a major cause. Systematic reviews found low reporting rates of measures against risks of bias (e.g., randomization, blinding), and a correlation between low reporting rates and overstated treatment effects. Reporting rates of measures against bias are thus used as a proxy measure for scientific rigor, and reporting guidelines (e.g., ARRIVE) have become a major weapon in the fight against risks of bias in animal research. Surprisingly, animal scientists have never been asked about their use of measures against risks of bias and how they report these in publications. Whether poor reporting reflects poor use of such measures, and whether reporting guidelines may effectively reduce risks of bias has therefore remained elusive. To address these questions, we asked in vivo researchers about their use and reporting of measures against risks of bias and examined how self-reports relate to reporting rates obtained through systematic reviews. An online survey was sent out to all registered in vivo researchers in Switzerland (N = 1891) and was complemented by personal interviews with five representative in vivo researchers to facilitate interpretation of the survey results. Return rate was 28% (N = 530), of which 302 participants (16%) returned fully completed questionnaires that were used for further analysis.
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.
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.
We know now that much health and medical research which is published in peer-reviewed journals is wrong, and consequently much is unable to be replicated.[2-4] This is due in part to poor research practice, biases in publication, and simply a pressure to publish in order to ‘survive’. Cognitive biases that unreasonably wed to our hypotheses and results are to blame. Strongly embedded in our culture of health and medical research is the natural selection of poor science practice driven by the dependence for survival on high rates of publication in academic life. It is a classic form of cultural evolution along Darwinian lines.[6, 7] Do not think that even publications in the most illustrious medical journal are immune from these problems: the COMPare project reveals that more than 85% of large randomised controlled trials deviate seriously from their plan when the trial was registered prior to its start. An average of more than five new outcome measures was secretly added to the publication and a similar number of nominated outcomes were silently omitted. It is hardly far-fetched to propose that this drive to publish is contributing to the growth in the number of papers retracted from the literature for dubious conduct along with the increasing number of cases of research misconduct.