Most scientists 'can't replicate studies by their peers.

To a large extent, this reproducibility crisis in basic and preclinical research may be as a result of failure to adhere to good scientific practice and the desperation to publish or perish. This is a multifaceted, multistakeholder problem.

The first IEEE workshop on the future of research curation and research reproducibility was held on 5-6 November in Washington, DC, USA. The workshop brought together stakeholders including researchers, funders, and notably, leading science, technology, engineering, and mathematics (STEM) publishers.

Information Systems, a data science journal published by Elsevier, has devised a solution to the question of reproducibility by establishing a new article type: the Invited Reproducibility Paper. Authors of selected published articles are invited to co-author, with the journal’s reproducibility reviewers, a report by which the experiment described in their published article is reproduced and.

Scientific research has never been a perfect process of discovery. We have never expected to get the results right the first time, and so we have developed multiple methodologies to test different hypotheses to sequentially build a more comprehensive understanding of the world around us.

Yet a cornerstone of science remains the ability to verify and validate research findings, so it is important to find ways to overcome these challenges. The Michigan Institute for Data Science (MIDAS) is pleased to announce the 2020 Reproducibility Challenge.

Papers published in HardwareX complement the original research papers published in the research journals by showing the infrastructure used to conduct the experiments. There is a desperate need to have a high quality repository of state-of-the-art scientific tools which have been validated and tested to produce precise and accurate results.

TY - JOUR. T1 - Reproducibility of Science: Fraud, Impact Factors and Carelessness. AU - Eisner, David. PY - 2017. Y1 - 2017. N2 - There is great concern that results published in a large fraction of biomedical papers may not be reproducible.

Reproducibility in Research. Science depends on curiosity, inspiration, observations, formulations, calculations, resources, communication of ideas and on reproducibility. The reproducibility of scientific experiments and calculations embodies a fundamental aspect of science.

Reproducibility is a best practice in data science as well as in scientific research, and in a lot of ways, comes down to having a software engineering mentality. It is about setting up all your processes in a way that is repeatable (preferably by a computer) and well documented.

The targets are usually papers in what are considered “top journals” or the papers in journals like Science and Nature that seek to maximize visibility. Or, more recently, entire fields of science that are widely publicized - like psychology or cancer biology are targeted for reproducibility and replicability studies.

VALIDATION: The key to experimental repeatability and a sound scientific publication. Whenever I write a paper, review a paper or edit a paper, a key item that I look for is whether the methodology and equipment has been validated, demonstrating the effectiveness (accuracy and reliability) of the research.

Internal validity dictates how an experimental design is structured and encompasses all of the steps of the scientific research method. Even if your results are great, sloppy and inconsistent design will compromise your integrity in the eyes of the scientific community. Internal validity and reliability are at the core of any experimental design.