Our Projects
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Data Bank
Date Completed: In Progress
Main Contributors: Weijie Tan, Joshua Unrau, and Gabriel Devenyi
Description
Data Bank is an open-source web application for managing, versioning, and sharing tabular datasets. Is is developed as a generic tool applicable across a range of research environments.
Key Technologies
TypeScript 
NodeJS 
React 
TailwindCSS 
NestJS 
MongoDB 
Docker -
Douglas-Bell Canada Brain Bank VM hosting
Date Completed: In Progress
Main Contributors: Gabriel Devenyi and Thomas Beaudry
Description
The DNP hosts the Douglas-Bell Canada BrainBank's LORIS instance on a dedicated virtual machine, ensuring it remains accessible and performs reliably. Regular backups are in place to protect their data, and we carry out ongoing maintenance to keep the system stable. We also provide on-call support as needed to address any technical issues.
Key Technologies
libvirt 
Ubuntu 
ZFS -
Open Data Capture
Date Completed: In Progress
Main Contributors: Joshua Unrau, Gabriel Devenyi, and David Roper
Description
Open Data Capture is a web-based application designed for clinical research data collection and performance improvement. It allows clinical researchers to administer data collection instruments, such as forms and interactive tasks, to research subjects.
Key Technologies
TypeScript NodeJS React TailwindCSS NestJS MongoDB Docker -
File server data migration
Date Completed: August 1, 2024
Main Contributors: Thomas Beaudry and Gabriel Devenyi
Description
We provided system administration consulting to securely transfer a large dataset from the DNP file server to a Synology NAS. This process involved meticulous data verification, comprehensive NAS configuration, and ongoing communication with the project stakeholders to ensure all actions aligned with their requirements and addressed their security concerns.
Key Technologies
Synology Ubuntu ZFS -
PEPP Data Cleaning
Date Completed: May 17, 2024
Main Contributors: Weijie Tan and Ryan Haniff
Description
The Prevention and Early Intervention for Psychosis (PEPP) clinic gathered longitudinal data over 15 years, involving more than a thousand participants and thousands of variables. This data was spread across 88 separate SPSS files, with variable names that were inconsistent, often containing typos or ambiguous abbreviations (e.g., "ab_1"). In some cases, identical variable names referred to different items. Furthermore, the coding scheme for values changed over time. For instance, a Likert scale ranging from 0 to 4 at one timepoint might range from 1 to 5 at another. Missing data were encoded using arbitrary numbers in SPSS, which were inconsistent across variables and timepoints. Occasionally, these codes could be misinterpreted as valid responses (e.g., a column with integer responses up to 50 used "77" to indicate missing data). There was no accompanying documentation. Our team developed a reproducible pipeline to consolidate and transform this data into a single, coherent CSV file.
Key Technologies
Python 
Pandas 
NumPy -
PSG Sleep Server
Date Completed: December 1, 2023
Main Contributors: Thomas Beaudry and Gabriel Devenyi
Description
The DNP was contracted to facilitate the acquisition and deployment of a Windows Server. Our team conducted a full installation, setup, and configuration to enable the server to function as a PSG sleep server, being capable of running Natus software for data acquisition from sleep studies. Additionally, we configured the server to perform nightly backups to a DNP-managed file server. This file server is equipped with robust backup capabilities, ensuring secure and disaster-proof data recovery. We then aided them in cloning a file system, and trained them on utilizing the backup software, and restoring data efficiently when needed.
Key Technologies
Windows 
Duplicati Ubuntu ZFS