 All Things Data

Standardized data flow for research studies that utilize mobile technologies at the University of Michigan. It depicts how data typically moves from a smart watch or wearable device, into University resources behind a firewall, and finally lands on long-term storage for preservation and analytics.

A listing of U-M and MM offices and research cores that provide data-related consultation services, including those with expertise with mobile data and mental health data.

Data Management and Sharing Plans (DMSPs) are increasingly becoming a requirement when submitting research proposals. Even when not required, proposals with DMSPs are often scored higher than proposals without one. Developing a DMSP helps researchers plan technology use, create a more accurate budget, and assist in the Information Assurance (IA) review process, if needed, for their study. DMSPs are recommended for all studies utilizing wearable and mobile technologies.

This article explores the importance of sharing code, addresses common reservations among researchers, and provides practical advice on how to share effectively. By increasing transparency and releasing code as open source, researchers not only meet the requirements of funding agencies and publications but also stimulate institutional, national, and global research progress.

This article provides general guidance for sharing mobile data, based on the lessons learned by members of MeTRIC (https://metric.umich.edu), and evolving best practices from government and academic institutions worldwide.

Temporary staff may not have Core Image laptops or VPN access, preventing them from using Tableau Desktop to create and publish dashboards. This article discusses an easy solution.

This article discusses an approach to solve the multi-valued field join problem in SharePoint JDBC using Custom SQL with UNION clauses.

This document presents three different sync approaches between MCommunity and SharePoint Lists that work within the security restrictions of Office 365 and Power Automate, including Microsoft's default Data Loss Prevention (DLP) policy which restricts calling 3rd-party APIs directly.

This article shows how researchers can make use of an open source data dictionary tool called SchemaSpy to help create professional, easy-to-understand documentation for their datasets. Specific instructions are provided for Oracle, Microsoft SQL Server, CSV, SQlite, R, and Python + Pandas.

This article introduces MiNap, an application developed to serve as a prototype for sleep medicine research data collection. The article shares an overview of the problem in hand, the designed solution and its future potential in the medical research domain.

This article is a listing of data dictionaries and data models for datasets that utilize mobile data (wearables, mobile apps, surveys, smartwatch apps, phone sensors, and more).

Sleep estimation is a near ubiquitous feature of smart watches and fitness trackers; therefore, sleep parameters have become desirable to include in research as both predictor variables or outcomes. However, not all sleep parameters provided by wearable sleep trackers are considered reliable for the purpose of research and appropriate selection and calculation of aggregate sleep values are required for hypothesis generation and testing.

A compilation of software tools used by MDEN mobile tech researchers for working with mobile data, including: wearable programming, mobile app development, data extraction, data analysis, data presentation and visualizations, and data pipelines.