DOI: 10.5061/dryad.5qfttdzg9
title: Implementing virtual workspaces for clinical research at an academic health center: A case report

description: The challenges of sharing clinical research data are addressed through the
 implementation of cloud-based virtual workspaces, which can enhance
 collaboration among researchers while maintaining data security. In this
 report, the authors provide a case study of the deployment of virtual
 workspaces at UMass Chan Medical School and detailing the formation of a
 Research Informatics Steering Executive workgroup to guide the
 implementation process. The workgroup identified key requirements,
 implemented Amazon WorkSpaces, and established configurable data
 management for research support. The case study also highlights the
 importance of ensuring patient data privacy and security and the value of
 continuous feedback collection. Key lessons from the case study include
 the significance of collaboration, balancing user-friendliness and
 functionality, flexibility in data management, maximizing workspace
 efficiency within budget constraints, and continuous user feedback.
 Successful implementation of virtual workspaces can support secure,
 collaborative research, ultimately advancing medical knowledge and
 improving healthcare outcomes.
description: This dataset was collected from our AWS system, capturing the
 usage of a virtual desktop environment by researchers. The data reflects
 the total hours spent and the associated costs for each session. Each
 entry corresponds to a unique deidentified user ID, ensuring that all
 personal identifiers were removed to maintain confidentiality. The data
 was systematically processed to anonymize user identities while preserving
 the accuracy of usage metrics. The total hours represent the time each
 user spent on the virtual desktop, and the total cost indicates the
 expenses incurred during that period. This information was used to derive
 Figure 2.
description: # Implementing virtual workspaces for clinical research at an academic
 health center: A case report
 [https://doi.org/10.5061/dryad.5qfttdzg9](https://doi.org/10.5061/dryad.5qfttdzg9) ## Description of the data and file structure The data was collected as part of an effort to analyze the usage patterns of a virtual desktop environment by researchers within our organization. The goal of the experimental efforts was to understand how researchers utilize the virtual desktop in terms of time spent and associated costs. This analysis aimed to optimize resource allocation, improve cost efficiency, and identify potential areas for enhancing the virtual desktop experience. The resulting data was used to derive key insights, as illustrated in Figure 2, which highlights significant usage trends and cost implications. ### Files and variables #### File: Fig\_2\_Virtual\_Desktop\_Cost\_Dataset\_-\_Sheet1.csv **Description:**  ##### Variables * Month: The specific month and year when the data was recorded, formatted as YYYY-MM (e.g., 2022-08). * DeidentifiedID: A unique, anonymized identifier assigned to each user to ensure privacy while tracking individual usage patterns. * Total Hours: The total number of hours each user spent using the virtual desktop environment during the given month. * Total Cost: The associated cost incurred by each user for their total hours of virtual desktop usage, calculated in monetary terms (i.e., USD). ## Code/software The dataset is saved in CSV format and can be viewed using free software like Python (3.8+) with Pandas, NumPy, and Matplotlib, or R (4.0+) with the tidyverse and readr packages. It can also be opened in MS Excel or LibreOffice Calc (7.0+) for basic viewing. Provided scripts in Python and R automate the processes of data loading, cleaning, analysis, and visualization, with documentation included for ease of use. ## Access information Other publicly accessible locations of the data: * The data is not currently available in any publicly accessible repositories. Data was derived from the following sources: * The data was derived from usage logs within our AWS system, capturing virtual desktop environment usage by researchers. The logs provided detailed information on user activity, including the time spent and costs associated with each session, which were then anonymized and processed for analysis.