The MIDUS-Colectica Portal

Transcription

1 The MIDUS-Colectica Portal Barry T. Radler, PhD University of Wisconsin-Madison Institute on Aging License: CC BY 4.0 December 7, 2016 European DDI User Conference

2 Overview of Presentation Background on MIDUS study design Longitudinal, cross-discipline characteristics Demonstration of DDI portal Variable-level metadata Search and explore Harmonization Data merge and download Future Directions Documenting more metadata with DDI

3 Background on Baseline Survey: Harvard University MacArthur Foundation funding N=7,108 Ages Satellite studies of stress, cognition

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5 MIDUS Design: Summary of Details Five longitudinal samples 2 National 2 Racial 1 Japanese Multiple waves with 9-10 year interval Multidisciplinary design Research products: N<13,000 25,000+ variables 20 datasets, more coming

6 MIDUS Design: Required Details Five longitudinal samples Multiple waves with 9-10 year interval Multidisciplinary design Research products Wide secondary use of MIDUS Open Data philosophy #1 data download at NACDA (ICPSR) 73k data downloads; 38k users 780+ publications

7 MIDUS-Colectica DDI (3.2) Portal Uses richly structured DDI variable-level metadata for 1. Search Discover variables from across different datasets searching text, labels, concepts. 2. Harmonization (internal, post-hoc) Clarify the related nature of equivalent versions of longitudinal and cross-sample survey variables 3. Custom Data Extract Allow researchers to focus on variables of interest; ease data management burden Facilitate accurate merges across numerous datasets

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20 Future Directions - Ongoing Portal improvements More information about derived or generated variables Provide the code used in their construction Better document non-survey data captures Special protocols, devices, data processing Bio-medical, Experimental, Devices

21 MIDUS Non-survey Data Capture Clinical Exam Sleep Watch Bone Densitometer

22 MIDUS Non-survey Data Capture Psycho-Physiology Experiment Blood, Saliva, Urine

23 MIDUS Non-survey Data Capture EMG EEG

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25 MIDUS Non-survey Data Capture: EEG Example EEG recording ( Question ) EEG activity was recorded using a 128-channel geodesic net of Ag/AgCl electrodes encased in saline-dampened sponges (Electrical Geodesics, Inc [EGI], Eugene, OR). Electrode impedances were reduced to less than 100 KΩ, and analog EEG signals were amplified and sampled at a rate of 500 Hz (band-pass filtered from Hz) with 16-bit precision using an online vertex (Cz) reference. EEG data processing After 60 Hz notch filtering and 0.5 Hz high-pass filtering to remove slow frequency drift, bad channels were identified and removed. Bad sections of data were also removed. Using EEGLAB6, the EEG data was then submitted to a PCA/ICA forcing the identification of 20 components. Components containing obvious eye blinks, eye movements, and other artifacts were then removed from the data. Bad channels were then replaced using a spherical spline interpolation. Epochs of 2 second length were then created. The EEGLAB automated artifact identification routine was then run on these epoched data files, identifying epochs containing deviations of ±100 microvolts, which were then subsequently removed.

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27 Non-survey Data Capture Solutions? Use custom user attributes Integrate with other domain-specific standards (FHIR, CDISC) Crosswalks with other controlled vocabularies or ontologies Bake more documentation into DDI codebooks using extant elements Dagstuhl paper Documenting A Wider Variety Of Data with DDI Use DDI 3.x elements and employ URIs to stand-alone documentation CollectionEvent ProcessingEvent, CommandCode Aggregation Create DDI Profiles of non-survey captures

28 Non-survey Data Capture Future DDI 4/DDI Views Using an information model Expressible in non-xml technologies (RDF, relational DBs, etc) Simplify description and structure Improve documentation Acknowledge different data capture types (terminology) Question Measure Approach different data types (structures) Datum-based, event history Heightened regard for contextual metadata

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30 Thank you! This research is supported by a grant from the National Institute on Aging (R03-AG046312). December 7, 2016 European DDI User Conference