One of the guiding principles of open source software development is to use crowds of developers to keep a watchful eye on source code. Eric Raymond declared Linus' Law as "many eyes make all bugs shallow", with the socio-technical argument that high quality open source software emerges when developers combine together their collective experience and expertise to review code collaboratively. Vulnerabilities are a particularly nasty set of bugs that can be rare, difficult to reproduce, and require specialized skills to recognize. Does Linus' Law apply to vulnerabilities empirically? In this study, we analyzed 159,254 code reviews, 185,948 Git commits, and 667 post-release vulnerabilities in the Chromium browser project. We formulated, collected, and analyzed various metrics related to Linus' Law to explore the connection between collaborative reviews and vulnerabilities that were missed by the review process. Our statistical association results showed that source code files reviewed by more developers are, counter-intuitively, more likely to be vulnerable (even after accounting for file size). However, files are less likely to be vulnerable if they were reviewed by developers who had experience participating on prior vulnerability-fixing reviews. The results indicate that lack of security experience and lack of collaborator familiarity are key risk factors in considering Linus' Law with vulnerabilities.

w reviewer homophily. These analyses leverage data from 31 studies, which cumulatively examined 312,740 manuscripts submitted to >640 journals-including Nature Portfolio journals, Science and Proceedings of the National Academy of Sciences. Our dataset represents 4,529,971 author position/demographic/review stage interactions, which upon publication will be the largest publicly available dataset of this kind for future work to build upon. Finally, we describe the current landscape of peer review in the subfields of ecology and evolution by collecting peer review policy data from the websites of 541 journals. Altogether, we find that author demographics predict review outcomes; clear, evidence-based solutions to alleviate review bias are lacking; and relatively few journals are pro-actively combatting bias. Results and discussion Disparate peer review outcomes by author demographicsWe found evidence for disparate peer review outcomes for all demographics that we examined at one or more stages in the review process (Supplementary Tables 3-40). We found the most data on outcomes by author assumed gender (Supplementary Data 1). Assumed female authors had worse or similar outcomes compared with assumed male authors, depending on the author position and review stage (Fig. 2).We found notably lower success throughout the review process for authors with institutional affiliations in Asia, in countries where English is not a primary language and in countries with lower Human Development Indices (HDI; Figs. 2 and 3). Compared with authors with affiliations located in Europe, North America and Oceania, authors with affiliations located in Asia had the most consistent disparities, but authors in Latin America and Africa also often had worse review outcomes. When considering a country's continent, language and HDI

Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double‐observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.

No abstract

No abstract