data information PLATFORM
Hub for access.
Many challenges face those addressing mental health and substance use problems. There still exists a profound level of misunderstanding and shame when it comes time to ask for help and guidance.
The Recovery Resource Hub is a national initiative funded by Transforming Youth Recovery—a non-profit organization dedicated to the advancement of research-based recovery support services.
Deployed in 2017, data and recovery science researchers rely on the Capacitype platform to help them collect, classify, and analyze the diverse types of resources that can enhance access to pathways to recovery.
Data science is just getting started.
We find ourselves at the very early stages of an emerging field of study and work. The research methods, processes, algorithms, and systems being designed to extract knowledge and insights from structured and unstructured data are evolving on a daily basis.
Our work is firmly focused on making data more helpful and useful in the key moments when people need to determine what is needed to keep themselves, and those they care for, well . To do this, we must study, learn, and invest in new techniques for a rapidly changing landscape.
Current research intended for common good impact.
Data bridges to front lines.
People go online to seek guidance every day—from where to find nearby help to how best to heal. We are working to better understand and support those dedicated professionals and counselors who are assembling resource directories across the nation. Our focus is on ways to make non-formal support and care—those resources outside traditional healthcare systems—more accessible through data bridges that promote confident referrals.
Applied web analytics.
We believe there is another level of web analytics utility that pushes beyond the base measurement, collection, and reporting of data to optimize web usage. Our team is focusing on applied analytics that can enrich the linguistics of search query algorithms and discover socio-referral patterns that can used to better collect and qualify resource data for national and local health and well-being directories and database inventories.
The world is full of unstructured data. And there is a lot of it! In fields of interest, our team is combining disciplines of data science with academic research to create novel classification systems aimed at enhancing our data model taxonomies so as to improve the data mining and collection processes used to transform data into useful and helpful database structures for applied services.
ETL process designs.
If we are to advance in new fields of study, researchers and social scientists need better access to qualified datasets that are specifically curated for their analysis and outcome measurements. Our ETL work is focused in two areas—enhancing our data mining and extraction capabilities and ensuring researchers have access to datasets that can help advance their studies.