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No datasets from study 2 were discarded.
The datasets from Study 1 and Study 2 were combined for a quasi-experimental joint analysis.
We sought primary datasets from study authors, with the following core variables: randomised group, baseline depression measures, follow-up depression measures, age, and sex.
The analyses were performed on the data of study A, except for the test-retest reliability where the datasets from study A and B were combined.
Best practices identified in the checklist include: Isolating the data to be released in a single directory to make it easy to distinguish these datasets from study data that should be maintained at the collaborating site.
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In addition, two datasets from studies of the transformation in aquatic sediment systems (water sediment studies) and one dataset from an aerobic soil metabolism study were provided by UBA.
Here we make use of recently available metagenomic datasets from studies of over two-dozen terrestrial hydrothermal systems in Yellowstone National Park (YNP) and Great Boiling Springs, Nevada, USA (GBS) (Fig. 1) to understand how patterns of gene and genome flow have shaped community architecture.
We used datasets from studies published by Ravel et al. [ 4] and Srinivasan et al. [ 13].
Estimated effect sizes, measurement variability, and correlations between baseline and follow-up measures were estimated using unpublished datasets from studies of physiologic measures in adult and pediatric populations.
As a test of the applicability of the method we use several datasets from studies with non-overlapping samples of DLBCL.
These include datasets from studies of the yeast cell cycle, diauxic shift and gene knockouts [ 9- 15, 16] 16], muscle-expressed genes in C. elegans [ 7], Drosophila [ 8] and E. coli [ 17, 18].
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