Table 3.
Challenges and insights from final team projects (3–5 students per team)
| Title | Challenges | Insights |
|---|---|---|
| TCGABiolinks: An R‐based, Open Source Tool for Genomic Analysis of Published TCGA data 26 |
Updates to TCGABiolinks were not backward compatible. Initial release of software was in 2015. Modifications were required that prevented exact replication. |
Exciting to see how much data set has grown since 2019 publication. Exciting to be able to replicate findings. Relatively stress‐free experience because of excellent documentation. |
| Meta‐analysis of antidepressant efficacy 27 |
Study transparency was overall quite good. Data set was available on‐line and well‐documented. |
Challenged by calculation of metrics (e.g., credible interval) |
| Association of electronic cigarette use with subsequent initiation of tobacco cigarettes in US youths 28 |
Figuring out what data were used. Inability to replicate the sample because variables to define inclusion/exclusion criteria were not available in the public data set. Figuring out what weights were used. Lack of detail prevented ability to replicate the recoding. |
Publicly available data sets may lack PHI needed to replicate samples. Independent studies using data from national studies may not publish their own data extract. Replication was impossible. |
| Re‐examination of data: EGFR as receptor of interest on monocytes, causal determination of HCMV on EGFR 29 |
No raw images were included in the omics di repository. Authors made data available, but files were too large to process in R Studio; work arounds identified, but package no longer available with latest version of R. Details provided about wet laboratory procedures certain biological descriptions were ambiguous, but nothing about the data cleaning, missing data, statistical techniques used, and testing of assumptions. No response to emails sent to the authors for more information. |
Data access issues and technical challenges were surprising (backward compatibility). Evidence of image compression artifacts, value inversions, narrow cropping; such issues may be a pervasive issue in biological sciences. Need to include data for all components of a study with user‐friendly documentation. The importance of sharing scripts for data cleaning and statistical practices. |
Abbreviations: HCMV, human cytomegalovirus; PHI, protected health information; TCGA, The Cancer Genome Atlas.