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As physicians, scientists and researchers worldwide struggle to understand the novel coronavirus (COVID-19) pandemic, the American Heart Association (AHA) is developing a novel registry to aggregate data and aid research on the disease, treatment protocols and risk factors tied to adverse cardiovascular outcomes.

The Open Energy Data Initiative (OEDI) is a centralized repository of high-value energy research datasets aggregated from the U.S. Department of Energy’s Program Offices, National Laboratories and other collaborators. It aggregates smaller, domain-specific repositories, allows direct data submissions, and supports big data in cloud-based data lakes. Built to enable data discoverability, OEDI facilitates access to a broad network of findings, including the data available in technology-specific catalogs like the Geothermal Data Repository and Marine and Hydrokinetic Data Repository.

Ensembl Plants holds the genomes of plants of significant interest. These range from those of agricultural importance, those which support primary research and of environmental interest. Ensembl Plants datasets are constructed in a direct collaboration with the Gramene resource. The resource holds the genomes of wheat, rice, corn and mouse ear cress amongst others.

This site provides access to the research outputs of the Sarah Lawrence College. Users may set up RSS feeds to be alerted to new content. The interface is available in English.

ModBase (https://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building, and model assessment (https://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (https://salilab.org/modweb).

The Mitochondrial Disease Sequence Data Resource (MSeqDR) is a centralized genome and phenome bioinformatics resource built by the mitochondrial disease community to facilitate clinical diagnosis and research investigations of individual patient phenotypes, genomes, genes, and variants. It integrates community knowledge from expert‐curated databases with genomic and phenotype data shared by clinicians and researchers.

MDF streamlines and automates data sharing, discovery, access and analysis by: 1) enabling data publication, regardless of data size, type, and location; 2) automating metadata extraction from submitted data into MDF metadata records (i.e., JSON formatted documents following the MDF schema) using open-source materials-aware extraction pipelines and ingest pipelines; and 3) unifying search across many materials data sources, including both MDF and other repositories with potentially different vocabularies and schemas. Currently, MDF stores 60 TB of data from simulation and experiment, and also indexes hundreds of datasets contained in external repositories, with millions of individual MDF metadata records created from these datasets to aid fine-grained discovery.