Coordinating Interdependencies in an Open Source Software Project: A Replication of Lindberg, et al.

The current study is a full replication (conceptual and empirical) of “Coordinating Interdependencies in Online Communities: A Study of an Open Source Software Project” Lindberg et al (2016), which addresses the question of how OSS communities address unresolved interdependencies. Following the original study, we analyze project development data, archived in the GitHub repository, for the OSS project Rubinius. The analysis explores relationships among development and developer interdependencies as well as activity and order variation. Further, we extend the original study by examining the core relationships in the original study and investigating the external generalizability of the results by replicating the analysis on three analogous OSS projects: JRuby, mruby, and RubyMotion. These offer an opportunity to evaluate the generalizability of the original study to projects of different sizes and amount of activity, yet similar otherwise to the project in the original study. Another extension is the use of an additional control variable, length of activity sequence, which proves to have substantial implications of the study’s focal relationships. We find that three out of the four projects we analyze support the findings of the original study as it pertains to four relationships in the original study: order variation and developer interdependencies, activity variation and developer interdependencies, order variation and development interdependencies, and development and developer interdependencies. We also discuss the implications of our findings, especially in cases where the replication results differ from those in the original study and offer suggestions for future research that can help advance this stream of research

Broad distribution of stick-slip events in Slowly Sheared Granular Media: Table-top production of a Gutenberg-Richter-like distribution

We monitor the stick-slip displacements of a very slowly driven moveable perforated top plate which interacts via shearing with a packing of identical glass beads confined in a tray. When driven at a constant stress rate, the distributions of large event displacements and energies triggered by the stick-slip instabilities exhibit power law responses reminiscent of the Gutenberg-Richter law for earthquakes. Small events are quasi-size independent, signaling crossover from single-bead transport to collective behavior.Comment: 10 pages, 3 figure

Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework

<p>Abstract</p> <p>Background</p> <p>Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across genes and between experiments. As a consequence, these analyses result in biased estimators and posterior probability intervals for gene expression levels in the transcriptome.</p> <p>Results</p> <p>Using the yeast <it>Saccharomyces cerevisiae </it>as an example, we introduce a new Bayesian method of data analysis which is based on a model of SAGE tag formation. Our approach incorporates the variation in the probability of tag formation into the interpretation of SAGE data and allows us to derive exact joint and approximate marginal posterior distributions for the mRNA frequency of genes detectable using SAGE. Our analysis of these distributions indicates that the frequency of a gene in the tag pool is influenced by its mRNA frequency, the cleavage efficiency of the anchoring enzyme (AE), and the number of informative and uninformative AE cleavage sites within its mRNA.</p> <p>Conclusion</p> <p>With a mechanistic, model based approach for SAGE data analysis, we find that inter-genic variation in SAGE tag formation is large. However, this variation can be estimated and, importantly, accounted for using the methods we develop here. As a result, SAGE based estimates of mRNA frequencies can be adjusted to remove the bias introduced by the SAGE tag formation process.</p

Bias correction and Bayesian analysis of aggregate counts in SAGE libraries

<p>Abstract</p> <p>Background</p> <p>Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3' most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power.</p> <p>Results</p> <p>Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used to contrast the accuracy of the various methods. The consequences of tag formation bias are discussed in the context of testing differential expression. A description is given as to how these algorithms can be applied in that context.</p> <p>Conclusions</p> <p>Several Bayesian inference algorithms that account for tag formation effects are compared with the DPB algorithm providing clear evidence of superior performance. The accuracy of inferences when using a particular non-informative prior is found to depend on the expression level of a given gene. The multivariate nature of the approach easily allows both univariate and joint tests of differential expression. Calculations demonstrate the potential for false positive and negative findings due to variation in tag formation probabilities across samples when testing for differential expression.</p