A presentation that gives an overview of data reproducibility, data reproducibility components and challenges, data reproducibility initiatives, data journals and repositories, university library resources, all within the scope of the health sciences, social sciences, and the arts and humanities disciplines.
Reproducibility of research in Computer Science (CS) and in the field of networking in particularis a well-recognized problem. For several reasons, including the sensitive and/or proprietarynature of some Internet measurements, the networking research community pays limited attentionto the of reproducibility of results, instead tending to accept papers that appear plausible.This article summarises a 2.5 day long Dagstuhl seminar on Encouraging Reproducibility inScientific Research of the Internet held in October 2018. The seminar discussed challenges toimproving reproducibility of scientific Internet research, and developed a set of recommendationsthat we as a community can undertake to initiate a cultural change toward reproducibility ofour work. It brought together people both from academia and industry to set expectations andformulate concrete recommendations for reproducible research. This iteration of the seminar wasscoped to computer networking research, although the outcomes are likely relevant for a broaderaudience from multiple interdisciplinary fields.
Research into text mining based tool support for citation screening in systematic reviews is growing. The field has not experienced much independent validation. It is anticipated that more transparency in studies will increase reproducibility and in-depth understanding leading to the maturation of the field. The citation screen tool presented aims to support research transparency, reproducibility and timely evolution of sustainable tools.
This issue of Cortex plays host to a lively debate about the reliability of cognitive neuroscience research. Across seven Discussion Forum pieces, scientists representing a range of backgrounds and career levels reflect on whether the "reproducibility crisis" – or "credibility revolution" (Vazire, 2018; Munafò et al., 2017) – that has achieved such prominence in psychology has extended into cognitive neuroscience. If so, they ask, what is the underlying cause and how can we solve it?
Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results in a published paper using the original author's raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this paper, we describe our approach to enabling computational reproducibility for the 12 papers in this special issue of Socius about the Fragile Families Challenge. Our approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools enabled us to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on our successes and struggles, we conclude with recommendations to authors and journals.
Reproducibility should be a cornerstone of scientific research and is a growing concern among the scientific community and the public. Understanding how to design services and tools that support documentation, preservation and sharing is required to maximize the positive impact of scientific research. We conducted a study of user attitudes towards systems that support data preservation in High Energy Physics, one of science's most data-intensive branches. We report on our interview study with 12 experimental physicists, studying requirements and opportunities in designing for research preservation and reproducibility. Our findings suggest that we need to design for motivation and benefits in order to stimulate contributions and to address the observed scalability challenge. Therefore, researchers' attitudes towards communication, uncertainty, collaboration and automation need to be reflected in design. Based on our findings, we present a systematic view of user needs and constraints that define the design space of systems supporting reproducible practices.