Research Associate Professor of Psychological and Brain Sciences and Adjunct Associate Professor of Computer Science at Dartmouth, official Debian Developer, contributor to many open source projects -- Yaroslav (AKA Yarik) is a lead of PyMVPA, NeuroDebian, DataLad and other projects.

The director of the Dartmouth Brain Imaging Center (DBIC), Center for Cognitive Neuroscience (CCN), and the Evans Family Distinguished Professor at Dartmouth. Dr.Haxby has an outstanding history of developing and disseminating in the open novel research methodologies, as well as influential datasets.

Works on the DANDI client and related projects. Skilled in Python testing, packaging, & type-hinting and in continuous integration. Likes abstract mathematics, *nix-based operating systems, and large properly-formatted data files.

Works on DANDI and related projects. Skilled in Python, Gentoo Linux, magnetic resonance imaging, neuroscience, and psychopharmacology.

Loves Python and open-source software. Works on Datalad registry. Currently resides in sunny Southern California with his wife and son.

Austin Macdonald (asmacdo) is obstinately passionate about open source values and is dedicated to developing open science tools as inclusively and transparently as possible. He currently lives in Raleigh with his giant, spastic, and clumsy dog Chonks.

Ph.D. student in Michael Hanke's Psychoinformatics Lab and the lead of the amazing DataLad Handbook. Looks like a Clinical Psychologist on paper, but strives for an open-science conducting, open-software developing secret identity thanks to inspirational influences from Michael, Yarik, Benjamin, and many more. project.

Works on the DataLad project and the magician behind OHBM 2020 Booth Design.

Joey's own introduction "I'm Joey Hess and I write programs" conceals his paramount role in establishing the core of the Debian distribution (debhelper, debian-installer, debconf, pristine-tar, etc.) and his work on variety of other software projects, such as git-annex which we rely upon in the DataLad project.

Franco is the director and founder of the partner project and cloud computing platform brainlife.io.

Formerly a visiting post-doctoral researcher in Dr.Haxby's lab, now a J.-Prof., one of the first Psychoinformaticians, official Debian developer, member of INCF neuroimaging task force -- he is an old-time collaborator and a lead of PyMVPA, NeuroDebian, DataLad, and other projects.

An old-time partner in our "scientific plumbing" business and a lead of DANDI, NiPype and other projects.

A software engineer at brainlife.io. A husband and a father of 2 who likes coding, cooking, bicycling, hiking, gardening, and just about anything else.

Research Software Engineer in the Stanford Research Computing Center. Likes programming, containers, avocados, and open-source development. Developer of Singularity Hub and other related tools for Singularity containers. See a list of work or recent ideas on her personal site.

Jeremy is an Assistant Professor of Psychological and Brain Sciences at Dartmouth and directs the Contextual Dynamics Lab. He enjoys thinking about brains, non-brain brain-related things (e.g. zombies), computers, annoying-to-solve puzzles, and cats.

Luke is an Assistant Professor of Psychological and Brain Sciences at Dartmouth and directs the Computational Social and Affective Neuroscience Lab. His lab develops tools to study the computational processes underlying emotions and social interactions and their neurobiological substrates.

Matt is an Assistant Professor of Psychological and Brain Sciences at Dartmouth. His lab studies how ensembles of neurons across multiple brain areas organize experience in the service of adaptive behavior.

Pythonista and heavy user of open-source offerings. Works on the DataLad project.

A CS undergraduate from Columbia, worked on the DataLad project as a developer intern at Dartmouth in the summer of 2016. "This internship not only made me a contributor to a cool open source project, but through it I gained valuable experience in software development teamwork and tools (Git/GitHub, Travis, etc) which later made academic presentation of the same materials actually make sense and gave me a leg up in my courses. It was an incredible opportunity to work with and learn from such accomplished developers."

Masters student in Psychology / Cognitive Neuroscience at the University of Magdeburg. Oliver started his enthusiasm for open science as an intern at Dartmouth College where he enjoyed a summer full of fMRI data analysis. His current goal is to provide his University with a fully reproducible Masters thesis. Naturally, the perfect way to do it is by using - and contributing to - open software frameworks.

Formerly a Ph.D. student in Cognitive Neuroscience at Dartmouth, holds a M.Sc. in Cognitive Science from the University of Trento, Italy and a degree in Mathematics from the University of Torino, Italy. Co-developer of the DueCredit project.

Formerly a Ph.D. student in Cognitive Neuroscience at Dartmouth. Avid PyMVPA user and NeuroDebian exhibitor at recent SfN and OHBM conferences.

Ph.D. in Psychology, master's degrees in Computer Science, Cognitive Science, and Philosophy of Science, Technology and Society. Developer of PyMVPA, promoter of NeuroDebian, and contributor to other projects.

Neurologist, neuroimaging methods guru, early proponent of reproducible practice and in-depth understanding of the used analysis methodologies, lead of Neuroimaging in Python community and some of its popular software (NiPy and NiBabel) projects and infrastructural solutions, such as Nibotmi which we are contributing to.

Works on the ReproMan and DataLad projects. Kyle is our guide through the Git intricacies and best practices.

Dartmouth undergrad with a passion for learning more about neuroimaging, open science, and reproducible neuroscience. One of NiBabel's active contributors. Editor of select ReproNim training materials. Worked to model relationship between fMRI QA metrics and human scan data under Yarik's wise mentorship.

• Cheng CP and Halchenko YO. A new virtue of phantom MRI data: explaining variance in human participant data. F1000Research 2020, 9:1131 DOI:10.12688/f1000research.24544.1