The ONDRI Neuroinformatics & Biostatistics teams pipelines, partnerships, and products.
Channel: csvconf
Date: 2021-06-11

Presenter:  Derek Beaton 

The Ontario Neurodegenerative Disease Research Initiative (ONDRI) is a large-scale longitudinal project across five disorders spanning 520 participants, each with 3 or more annual assessments, and from 14 sites across the province. ONDRI’s data span lab measures, genetics, ocular imaging, eye-tracking tasks, many cognitive assessments, various brain imaging modalities, and an extensive array of clinical assessments. These data come in many types, shapes, and sizes. Different domain and data experts have different expectations of data standards, how data are curated, and how data are analyzed. However, ONDRI is more than data: it is also 100s of researchers and clinicians coming together to better understand neurodegenerative and cerebrovascular disorders. So, ONDRI’s Neuroinformatics & Biostatistics (NIBS) team built both an infrastructure (data) and a culture (people) around the standardization and integrity of such diverse data. We did this with (1) pipelines: supporting curation from preparation through to analyses and reporting, (2) partnerships: working closely with others on training & collaboration, and (3) products: the tools we build and use; many of whichare publicly available: https://github.com/ondri-nibs. ONDRI’s data standards and outlier analyses are central to the pipelines. Our standards revolve around “data packages” that include data and dictionaries with specific nomenclature and features (e.g., predefined and unambiguous missing data codes). We built the standards around tabular (.csv format) data, then extended standards to “non-tabular” or file-based data, such as neuroimaging. We also perform a battery of (mostly) multivariate outlier analyses. We share standardized reports (written in RMarkdown) for standards and outliers with research and clinical experts to review. These steps identify anomalies and allow us to correct errors. We were able to build a data-focused culture based on shared clinical & research goals, recognizing the difficulty with & importance of data curation, and with an eye toward public release of the data.