There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the ‘last mile’ implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.
Multiple mazes are routinely used to test the performance of animals because each has disadvantages inherent to its shape. However, the maze shape cannot be flexibly and rapidly reproduced in a repeatable and scalable way in a single environment. Here, to overcome the lack of flexibility, scalability, reproducibility and repeatability, we develop a reconfigurable maze system that consists of interlocking runways and an array of accompanying parts. It allows experimenters to rapidly and flexibly configure a variety of maze structures along the grid pattern in a repeatable and scalable manner. Spatial navigational behavior and hippocampal place coding were not impaired by the interlocking mechanism. As a proof-of-principle demonstration, we demonstrate that the maze morphing induces location remapping of the spatial receptive field. The reconfigurable maze thus provides flexibility, scalability, repeatability, and reproducibility, therefore facilitating consistent investigation into the neuronal substrates for learning and memory and allowing screening for behavioral phenotypes.
Recently, many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies also involve challenges such as choices about age groupings, modelling across the lifespan, the analyses of longitudinal changes, and neuroimaging data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental cognitive neuroscience, and how preregistration of studies can improve methodological rigor in the field. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.
Increasingly complex statistical models are being used for the analysis of biological data. Recent commentary has focused on the ability to compute the same outcome for a given dataset (reproducibility). We argue that a reproducible statistical analysis is not necessarily valid because of unique patterns of nonindependence in every biological dataset. We advocate that analyses should be evaluated with known-truth simulations that capture biological reality, a process we call “analysis validation.” We review the process of validation and suggest criteria that a validation project should meet. We find that different fields of science have historically failed to meet all criteria, and we suggest ways to implement meaningful validation in training and practice.
WINGS enables researchers to submit complete semantic workflows as challenge submissions. By submitting entries as workflows, it then becomes possible to compare not just the results and performance of a challenger, but also the methodology employed. This is particularly important when dozens of challenge entries may use nearly identical tools, but with only subtle changes in parameters (and radical differences in results). WINGS uses a component driven workflow design and offers intelligent parameter and data selectionby reasoning aboutdata characteristics.
Mechanobiology, the study of how mechanical forces affect cellular behavior, is an emerging field of study that has garnered broad and significant interest. Researchers are currently seeking to better understand how mechanical signals are transmitted, detected, and integrated at a subcellular level. One tool for addressing these questions is a Förster resonance energy transfer (FRET)‐based tension sensor, which enables the measurement of molecular‐scale forces across proteins based on changes in emitted light. However, the reliability and reproducibility of measurements made with these sensors has not been thoroughly examined. To address these concerns, we developed numerical methods that improve the accuracy of measurements made using sensitized emission‐based imaging. To establish that FRET‐based tension sensors are versatile tools that provide consistent measurements, we used these methods, and demonstrated that a vinculin tension sensor is unperturbed by cell fixation, permeabilization, and immunolabeling. This suggests FRET‐based tension sensors could be coupled with a variety of immuno‐fluorescent labeling techniques. Additionally, as tension sensors are frequently employed in complex biological samples where large experimental repeats may be challenging, we examined how sample size affects the uncertainty of FRET measurements. In total, this work establishes guidelines to improve FRET‐based tension sensor measurements, validate novel implementations of these sensors, and ensure that results are precise and reproducible.