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The tilted-disk model predicts a central hole that should be observed as a mid-eclipse brightening8. [...] photometric and spectroscopic measurements should be made as frequently as possible to create a longitudinal profile of the disk.

the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

The Spack package manager has been widely adopted in the supercomputing community as a means of providing consistently-built on-demand software for the platform of interest. Members of the high-energy and nuclear physics (HENP) community, in turn, have recognized Spack’s strengths, used it for their own projects, and even become active Spack developers to better support HENP needs. Code development in a Spack context, however, can be challenging as the provision of external software via Spack must integrate with the developed packages’ build systems. Spack’s own development features can be used for this task, but they tend to be inefficient and cumbersome. We present a solution pursued at Fermilab called MPD (multi-package development). MPD aims to facilitate the development of multiple Spack-based packages in concert without the overhead of Spack’s own development facilities. In addition, MPD allows physicists to create multiple development projects with an interface that insulates users from the many commands required to use Spack well.

A Linux distribution consists of thousands of packages that are either developed by in-house developers (in-house packages) or by external projects (upstream packages). Leveraging upstream packages speeds up development and improves productivity, yet bugs might slip through into the packaged code and end up propagating into downstream Linux distributions. Maintainers, who integrate upstream projects into their distribution, typically lack the expertise of the upstream projects. Hence, they could try either to propagate the bug report upstream and wait for a fix, or fix the bug locally and maintain the fix until it is incorporated upstream. Both of these outcomes come at a cost, yet, to the best of our knowledge, no prior work has conducted an in-depth analysis of upstream bug management in the Linux ecosystem. Hence, this paper empirically studies how high-severity bugs are fixed in upstream packages for two Linux distributions, i.e., Debian and Fedora. Our results show that 13.9% of the upstream package bugs are explicitly reported being fixed by upstream, and 13.3% being fixed by the distribution, while the vast majority of bugs do not have explicit information about this in Debian. When focusing on the 27.2% with explicit information, our results also indicate that upstream fixed bugs make users wait for a longer time to get fixes and require more additional information compared to fixing upstream bugs locally by the distribution. Finally, we observe that the number of bug comment links to reference information (e.g., design docs, bug reports) of the distribution itself and the similarity score between upstream and distribution bug reports are important factors for the likelihood of a bug being fixed upstream. Our findings strengthen the need for traceability tools on bug fixes of upstream packages between upstream and distributions in order to find upstream fixes easier and lower the cost of upstream bug management locally.

Although meta‐analysis has become an essential tool in ecology and evolution, reporting of meta‐analytic results can still be much improved. To aid this, we have introduced the orchard plot, which presents not only overall estimates and their confidence intervals, but also shows corresponding heterogeneity (as prediction intervals) and individual effect sizes. Here, we have added significant enhancements by integrating many new functionalities into orchaRd 2.0. This updated version allows the visualisation of heteroscedasticity (different variances across levels of a categorical moderator), marginal estimates (e.g. marginalising out effects other than the one visualised), conditional estimates (i.e. estimates of different groups conditioned upon specific values of a continuous variable) and visualisations of all types of interactions between two categorical/continuous moderators. orchaRd 2.0 has additional functions which calculate key statistics from multilevel meta‐analytic models such as I2 and R2. Importantly, orchaRd 2.0 contributes to better reporting by complying with PRISMA‐EcoEvo (preferred reporting items for systematic reviews and meta‐analyses in ecology and evolution). Taken together, orchaRd 2.0 can improve the presentation of meta‐analytic results and facilitate the exploration of previously neglected patterns. In addition, as a part of a literature survey, we found that graphical packages are rarely cited (~3%). We plea that researchers credit developers and maintainers of graphical packages, for example, by citations in a figure legend, acknowledging the use of relevant packages. Streszczenie Chociaż metaanaliza stała się podstawowym narzędziem w ekologii i ewolucji, raportowanie wyników metaanalizy trudne. Aby je ułatwić, wprowadziliśmy wykres orchard, który przedstawia nie tylko ogólne (średnie) oszacowanie efektu i jego przedziały ufności, ale także pokazuje odpowiadającą mu heterogeniczność (jako przedziały predykcji) i wielkości efektów z poszczególnych prób. W drugiej wersji pakietu, orchaRd 2.0 dodaliśmy znaczące ulepszenia, wbudowując w niego wiele nowych funkcjonalności. Zaktualizowana wersja pakietu pozwala na wizualizację heteroskedastyczności (uwzględniającej różne wariancje na poziomach moderatora kategorycznego), średnich brzegowych (np. przy marginalizacji efektów innych niż wizualizowany), średnich warunkowych (np. oszacowań różnych grup dla konkretnych wartości zmiennej ciągłej) oraz wizualizacje wszystkich interakcji pomiędzy dwoma moderatorami, zarówno kategorycznymi jak i ciągłymi. orchaRd 2.0 posiada dodatkowe funkcje obliczające kluczowe statystyki z wielopoziomowych modeli metaanalitycznych, takie jak I2 i R2. Co ważne, orchaRd 2.0 przyczynia się do lepszego raportowania wyników poprzez zgodność z PRISMA‐EcoEvo (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses in Ecology and Evolution). W efekcie, orchaRd 2.0 może poprawić prezentację wyników metaanalitycznych i ułatwić eksplorację wcześniej pomijanych wzorców. Dodatkowo, w ramach systematycznego przeglądu literatury, stwierdziliśmy, że pakiety graficzne są rzadko cytowane (~3%). Zwracamy się z prośbą do badaczy, aby docenili twórców i osoby rozwijające pakiety graficzne, np. poprzez cytowanie wykorzystanych pakietów w opisie wykresów.

IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

The process of standardizing taxon names, taxonomic name harmonization, is necessary to properly merge data indexed by taxon names. The large variety of taxonomic databases and related tools are often not well described. It is often unclear which databases are actively maintained or what is the original source of taxonomic information. In addition, software to access these databases is developed following non‐compatible standards, which creates additional challenges for users. As a result, taxonomic harmonization has become a major obstacle in ecological studies that seek to combine multiple datasets. Here, we review and categorize a set of major taxonomic databases publicly available as well as a large collection of R packages to access them and to harmonize lists of taxon names. We categorized available taxonomic databases according to their taxonomic breadth (e.g. taxon specific vs. multi‐taxa) and spatial scope (e.g. regional vs. global), highlighting strengths and caveats of each type of database. We divided R packages according to their function, (e.g. syntax standardization tools, access to online databases, etc.) and highlighted overlaps among them. We present our findings (e.g. network of linkages, data and tool characteristics) in a ready‐to‐use Shiny web application (available at: https://mgrenie.shinyapps.io/taxharmonizexplorer/). We also provide general guidelines and best practice principles for taxonomic name harmonization. As an illustrative example, we harmonized taxon names of one of the largest databases of community time series currently available. We showed how different workflows can be used for different goals, highlighting their strengths and weaknesses and providing practical solutions to avoid common pitfalls. To our knowledge, our opinionated review represents the most exhaustive evaluation of links among and of taxonomic databases and related R tools. Finally, based on our new insights in the field, we make recommendations for users, database managers and package developers alike.

The increasing interest in open source software has led to the emergence of large language-specific package distributions of reusable software libraries, such as npm and RubyGems. These software packages can be subject to vulnerabilities that may expose dependent packages through explicitly declared dependencies. Using Snyk’s vulnerability database, this article empirically studies vulnerabilities affecting npm and RubyGems packages. We analyse how and when these vulnerabilities are disclosed and fixed, and how their prevalence changes over time. We also analyse how vulnerable packages expose their direct and indirect dependents to vulnerabilities. We distinguish between two types of dependents: packages distributed via the package manager, and external GitHub projects depending on npm packages. We observe that the number of vulnerabilities in npm is increasing and being disclosed faster than vulnerabilities in RubyGems. For both package distributions, the time required to disclose vulnerabilities is increasing over time. Vulnerabilities in npm packages affect a median of 30 package releases, while this is 59 releases in RubyGems packages. A large proportion of external GitHub projects is exposed to vulnerabilities coming from direct or indirect dependencies. 33% and 40% of dependency vulnerabilities to which projects and packages are exposed, respectively, have their fixes in more recent releases within the same major release range of the used dependency. Our findings reveal that more effort is needed to better secure open source package distributions.

The occurrence of collinearity in fMRI-based GLMs (general linear models) may reduce power or produce unreliable parameter estimates. It is commonly believed that orthogonalizing collinear regressors in the model will solve this problem, and some software packages apply automatic orthogonalization. However, the effects of orthogonalization on the interpretation of the resulting parameter estimates is widely unappreciated or misunderstood. Here we discuss the nature and causes of collinearity in fMRI models, with a focus on the appropriate uses of orthogonalization. Special attention is given to how the two popular fMRI data analysis software packages, SPM and FSL, handle orthogonalization, and pitfalls that may be encountered in their usage. Strategies are discussed for reducing collinearity in fMRI designs and addressing their effects when they occur.

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.