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Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts., Data sharing is an important component of reproducible research, but meaningful data sharing can be difficult. Here authors describe a new open source tool, AFQ-Browser, that builds an interactive website allowing visualization and exploratory data analysis of published diffusion MRI data.

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is an important component of reproducible research, it is not always clear how to usefully share data in a manner that allows other labs to understand and reproduce published findings. Here we describe a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a published diffusion MRI study. Because AFQ-browser is portable -- it runs in any modern web-browser -- it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of a diffusion MRI dataset (anatomy, quantitative diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new scientific discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, sharing and aggregating data across labs, and publishing data alongside manuscripts.