Dataset Information

BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science.

ABSTRACT: Data science is a field that has developed to enable efficient integration and analysis of increasingly large data sets in many domains. In particular, big data in genetics, neuroimaging, mobile health, and other subfields of biomedical science, promises new insights, but also poses challenges. To address these challenges, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, including a Training Coordinating Center (TCC) tasked with developing a resource for personalized data science training for biomedical researchers. The BD2K TCC web portal is powered by ERuDIte, the Educational Resource Discovery Index, which collects training resources for data science, including online courses, videos of tutorials and research talks, textbooks, and other web-based materials. While the availability of so many potential learning resources is exciting, they are highly heterogeneous in quality, difficulty, format, and topic, making the field intimidating to enter and difficult to navigate. Moreover, data science is rapidly evolving, so there is a constant influx of new materials and concepts. We leverage data science techniques to build ERuDIte itself, using data extraction, data integration, machine learning, information retrieval, and natural language processing to automatically collect, integrate, describe, and organize existing online resources for learning data science.

SUBMITTER: Ambite JL

PROVIDER: S-EPMC9089329 | biostudies-literature | 2021 Jan-Mar

REPOSITORIES: biostudies-literature

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BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science.

Ambite José Luis JL Fierro Lily L Gordon Jonathan J Burns Gully A GA Geigl Florian F Lerman Kristina K Van Horn John D JD

IEEE transactions on emerging topics in computing 20190306 1

Data science is a field that has developed to enable efficient integration and analysis of increasingly large data sets in many domains. In particular, big data in genetics, neuroimaging, mobile health, and other subfields of biomedical science, promises new insights, but also poses challenges. To address these challenges, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, including a Training Coordinating Center (TCC) tasked with developing a resource for pe ...[more]

PMID: 35548703

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Project description:BackgroundDiabetes is a risk factor for the development of cognitive impairment and possibly for accelerated progression to Alzheimer disease (AD) and other dementias, though the trajectory of cognitive decline in general and in specfic cognitive domains by diabetes is unclear.MethodsUsing the National Alzheimer's Coordinating Center's Uniform Data Det (NACC-UDS) to identify cohorts of elders with normal cognition (N = 7,663) and mild cognitive impairment (MCI, N = 4,114), we compared overall cognitive composite and domain specific sub-scores and their progression over time between diabetic and non-diabetic subjects.ResultsDiabetes was more common among those with MCI (14.7%) than among subjects who were cognitively normal (11.7%). In subjects who were cognitively normal, baseline cognitive composite scores, attention, and executive function sub-scores were lower in diabetics than non-diabetics (by 0.098, 0.066, and 0.015 points, respectively). Over time, cognitive composite score showed subtle worsening in non-diabetics (0.025 points every 6 months), with an additional worsening of 0.01 points every 6 months in diabetics compared to non-diabetics. In the MCI groups, baseline cognitive composite as well as attention and executive domain sub-scores were lower in diabetics than non-diabetics (by 0.078, 0.092, and 0.032 points, respectively). Over time, cognitive composite (by 0.103 points every 6 months) and all domain specific sub-scores showed subtle worsening in non-diabetics, but diabetics had significantly slower worsening than non-diabetics on both cognitive composite (by 0.028 points) and domain specific sub-scores.DiscussionAmong elders, diabetes may be associated with lower cognitive performance, primarily in non-memory domains. However it is not associated with continued worsening, suggesting a static deficit with minimal memory involvement. This data suggest that diabetes may contribute more to a vascular profile of cognitive impairment than a profile more typical of AD.

Dataset Information

BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science.

Publications

BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science.

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