The rworkflows suite: automated continuous integration for quality checking, documentation website creation, and containerised deployment of R packages

Reproducibility is essential to the progress of research, yet achieving it remains elusive even in computational fields. Continuous Integration (CI) platforms offer a powerful way to launch automated workflows to check and document code, but often require considerable time, effort, and technical expertise to setup. We therefore developed the rworkflows suite to make robust CI workflows easy and freely accessible to all R package developers (https://github.com/neurogenomics/rworkflows). rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 2) an R package to quickly implement a standardised workflow, and 3) a centrally maintained GitHub Action. Each time it is triggered by a push to a GitHub repository, it automatically creates virtual machines across multiple OS, installs all dependencies, runs code checks, builds/deploys a documentation website, and builds/deploys version-controlled containers with a built-in RStudio interface. Additional analyses demonstrate that >50% of all R packages are only available via GitHub, highlighting the need for accessible solutions. Thus, rworkflows greatly reduces the barriers to implementing robust and reproducible best practices

EOSC Synergy WP6: Initial review of systems, initiatives and development of selection criteria of the online learning/training platforms and initiatives

This report describes a review of possible learning platforms and tools, and relevant previous and current projects and initiatives in the area of Open Science and EOSC training and education. It also includes reflections on the criteria we will use to select the platform and tools for the EOSC-Synergy project.European Commission. The report is a deliverable of EOSC-synergy project (INFRAEOSC-05(b)), Grant agreement ID: 857647.Peer reviewe

Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3)

This report records and discusses the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3). The report includes a description of the keynote presentation of the workshop, which served as an overview of sustainable scientific software. It also summarizes a set of lightning talks in which speakers highlighted to-the-point lessons and challenges pertaining to sustaining scientific software. The final and main contribution of the report is a summary of the discussions, future steps, and future organization for a set of self-organized working groups on topics including developing pathways to funding scientific software; constructing useful common metrics for crediting software stakeholders; identifying principles for sustainable software engineering design; reaching out to research software organizations around the world; and building communities for software sustainability. For each group, we include a point of contact and a landing page that can be used by those who want to join that group's future activities. The main challenge left by the workshop is to see if the groups will execute these activities that they have scheduled, and how the WSSSPE community can encourage this to happen

RANG: Reconstructing reproducible R computational environments

A complete declarative description of the computational environment is often missing when researchers share their materials. Without such description, software obsolescence and missing system components can jeopardize computational reproducibility in the future, even when data and computer code are available. The R package rang is a complete solution for generating the declarative description for other researchers to automatically reconstruct the computational environment at a specific time point. The reconstruction process, based on Docker, has been tested for R code as old as 2001. The declarative description generated by rang satisfies the definition of a reproducible research compendium and can be shared as such. In this contribution, we show how rang can be used to make otherwise unexecutable code, spanning from fields such as computational social science and bioinformatics, executable again. We also provide instructions on how to use rang to construct reproducible and shareable research compendia of current research. The package is currently available from CRAN (https://cran.r-project.org/web/packages/rang/index.html) and GitHub (https://github.com/chainsawriot/rang)

BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems

Software Bills of Materials (SBOMs) have emerged as tools to facilitate the management of software dependencies, vulnerabilities, licenses, and the supply chain. While significant effort has been devoted to increasing SBOM awareness and developing SBOM formats and tools, recent studies have shown that SBOMs are still an early technology not yet adequately adopted in practice. Expanding on previous research, this paper reports a comprehensive study that investigates the current challenges stakeholders encounter when creating and using SBOMs. The study surveyed 138 practitioners belonging to five stakeholder groups (practitioners familiar with SBOMs, members of critical open source projects, AI/ML, cyber-physical systems, and legal practitioners) using differentiated questionnaires, and interviewed 8 survey respondents to gather further insights about their experience. We identified 12 major challenges facing the creation and use of SBOMs, including those related to the SBOM content, deficiencies in SBOM tools, SBOM maintenance and verification, and domain-specific challenges. We propose and discuss 4 actionable solutions to the identified challenges and present the major avenues for future research and development.Comment: 11 pages, ICSE 202

Extroverts Tweet Differently from Introverts in Weibo

Being dominant factors driving the human actions, personalities can be excellent indicators in predicting the offline and online behavior of different individuals. However, because of the great expense and inevitable subjectivity in questionnaires and surveys, it is challenging for conventional studies to explore the connection between personality and behavior and gain insights in the context of large amount individuals. Considering the more and more important role of the online social media in daily communications, we argue that the footprint of massive individuals, like tweets in Weibo, can be the inspiring proxy to infer the personality and further understand its functions in shaping the online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users in Weibo is established to train a competent machine learning model, which then successfully identifies over 7,000 users as extroverts or introverts. Systematical comparisons from perspectives of tempo-spatial patterns, online activities, emotion expressions and attitudes to virtual honor surprisingly disclose that the extrovert indeed behaves differently from the introvert in Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study personalities of massive individuals and shed lights on applications of probing personalities and corresponding behaviors solely through online profiles.Comment: Datasets of this study can be freely downloaded through: https://doi.org/10.6084/m9.figshare.4765150.v