The ScHARe platform is built on a set of established components (Google Cloud Platform , Terra and GitHub ) used in flagship scientific projects at NIH.
ScHARe’s cloud-based platform contains:
Registration is required to access the ScHARe platform, (learn more and register).
STATUS: The ScHARe Datasets collection is accessible to all ScHARe-registered researchers. New datasets are being actively added.
On ScHARe, researchers can access, link, analyze, and export a wealth of datasets relevant to research in health disparities and health care outcomes, including:
Datasets are grouped by these categories:
Register for ScHARe and access the ScHARe Datasets collection in our Terra Workspace.
STATUS: The ScHARe/PhenX Core Common Data Elements are available to all researchers through the National Library of Medicine.
The National Institute on Minority Health and Health Disparities (NIMHD) and the National Institute of Nursing Research (NINR) have developed core common data elements for research. Endorsed by the National Institutes of Health, the ScHARe/PhenX Core Common Data Elements (CCDEs) are standardized questions and responses that can be used across different studies to ensure consistent data collection and facilitate interoperability.
The ScHARe/PhenX CCDEs currently include individual variables, such as demographics and SDOH, as well as project-related variables, such as the NIMHD minority health and health disparity research framework. These CCDEs enable researchers to efficiently design data collection, management, and analysis plans; link and compare data from different sources; and increase sample sizes for studies in smaller populations.
STATUS: The ScHARe Generalized Data Ecosystem Repository is available to all ScHARe-registered researchers.
The ScHARe Generalized Data Ecosystem (GDE) Repository, developed by the National Institute on Minority Health and Health Disparities (NIMHD) and the National Institute of Nursing Research (NINR), enables researchers to meet the requirements of the NIH Grants Management and Sharing policy. The ScHARe GDE Repository allows for the required hosting, management, and sharing of data generated by NIMHD- and NINR-funded research programs, including data on health disparities and health care outcomes. NIH-based researchers, external researchers, and health care organizations can access data within the repository.
The ScHARe GDE Repository is centered on ScHARe/PhenX CCDEs. Researchers using the GDE Repository can map their data to ScHARe/PhenX CCDEs, which facilitates data sharing and accelerates research on health disparities, health care delivery, and health outcomes.
By enabling the uploading, collection, and sharing of data, and the linking of information across different sources, the ScHARe GDE Repository will facilitate research into the mechanisms that foster health disparities and influence health care outcomes.
STATUS: The ScHARe Collaborative Workspaces are available to all ScHARe-registered researchers.
ScHARe is powered by Terra , an open-source data analysis platform based on Google Cloud Platform. Terra was developed by the Broad Institute of MIT and Harvard in collaboration with Microsoft and Verily.
Using ScHARe’s Terra resources, researchers and their collaborators can access and cross-link the same publicly available or controlled-access data. They can also create secure online spaces for collaboratively running large-scale analyses and sharing reproducible results and resources.
ScHARe supports interactive analysis tools such as Jupyter notebooks. Jupyter notebooks are human-readable executable documents that can be run to perform advanced data analyses, including artificial intelligence and machine learning tasks, using coding languages such as Python and R. The platform also supports Dockstore as a repository for Docker-based analysis workflows that allow users to automate basic steps in their analyses.
STATUS: The ScHARe Bias Mitigation Tools are currently under development.
In computer science, algorithms are finite sequences of instructions used to solve specific problems or to perform automated reasoning and decision-making. Algorithms are widely used in healthcare- and policy-related decisions. However, many algorithms operate as “black boxes” offering little opportunity for adequate testing to identify potential biases that can create unfair outcomes, such as privileging one category over another.
Biases can result from missing data and quality problems. Pre-existing social or cultural expectations and design limitations can also introduce biases into algorithms. If not identified, biased algorithms can result in healthcare decisions that lead to discrimination, unequitable healthcare, or unintentional harm to marginalized populations.
ScHARe will provide bioinformatics tools, best-practice workflows, and resources for collaboratively evaluating and mitigating biases associated with datasets and algorithms used to inform healthcare and policy decisions. Collaboratives will strategize on developing better tools to mitigate biases at the design, data, algorithm, model training, and implementation phases.
Page updated Oct. 3, 2024 | created Jan. 18, 2023