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The University of Manitoba Dataverse is a research data repository for our faculty, students, and staff. Files are held in a secure environment on Canadian servers as part of the Borealis network. This repository is Core Trust Seal certified (https://coretrustseal.org)

NORMAN SusDat is a compilation of information provided by NORMAN network members and has merged many chemical lists provided to the NORMAN Suspect List Exchange (NORMAN-SLE) into a common format and includes selected identifiers and predicted values as a service for NORMAN members. NORMAN-SLE, established in 2015 as a central access point for NORMAN members (and others) to find suspect lists relevant for their environmental monitoring question, is a part of this resource.

Online materials database (a.k.a. PAULING FILE project) with more than 2 million entries: physical properties, crystal structures, phase diagrams, available via API, ready for the modern data-intensive applications.

The Open Data Commons for Spinal Cord Injury is a cloud-based community-driven repository to store, share, and publish spinal cord injury research data. There are several challenges for scientific reproducibility and bench-to-bedside translation. For example, only research and data that are published actually get disseminated, a phenomenon known as publication bias. Published research reflects to only a small fraction of all data collected, and data that do not lead to publication are largely ignored, hidden away in filing cabinets and hard drives. This results in an abundance of inaccessible scientific data known as “dark data”. Even when research is disseminated, it is usually in the form of summary reports of aggregated data (e.g. averages across individual subjects) such as scientific articles. The fact that the individual subject-level data are inaccessible further contributes to dark data. The spinal cord injury (SCI) community created the ODC-SCI to mitigate dark data in SCI research. The ODC-SCI also aims to increase transparency with individual-level data, enhance collaboration, facilitate advanced analytics, and conform to increasing mandates by funders and publishers to make data accessible. Members of the ODC-SCI have access to a private digital lab space managed by the PI or multi-PIs for dataset storage and sharing. The PIs can share their labs’ datasets with the registered members of the ODC-SCI community and make their datasets public and citable. The ODC-SCI implements stewardship principles that scientific data be made FAIR (Findable, Accessible, Interoperable and Reusable) and has been widely adopted by the international SCI research community.

The Swissregulon Database contains genome-wide annotations of regulatory sites. The predictions are based on Bayesian probabilistic analysis of a combination of input information including i) Experimentally determined binding sites reported in the literature, ii) Known sequence-specificities of transcription factors, iii) ChIP-chip and ChIP-seq data, iiii) Alignments of orthologous non-coding regions.