An Open-Publishing Response to the COVID-19 Infodemic

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript\u27s figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis

New Technology and Tools to Enhance Collaborative Video Analysis in Live ‘Data Sessions’

The live ‘data session’ is arguably a significant collaborative practice amongst a group of co-present colleagues that has sustained the fermentation of emerging analyses of interactional phenomena in ethnomethodological conversation analysis for several decades. There has not, however, been much in the way of technological innovation since its inception. In this article, I outline how the data session can be enhanced (a) by using simple technologies to support the ‘silent data session’, (b) by developing software tools to present, navigate and collaborate on new types of video data in novel ways using immersive virtual reality technologies, and (c) by supporting distributed version control to nurture the freedom and safety to collaborate synchronously and asynchronously on the revision of a common transcript used in a live data session. Examples of real cases, technical solutions and best practices are given based on experience. The advantages and limitations of these significant enhancements are discussed in methodological terms with an eye to future developments

sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots

Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ?sPlot?, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained: Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain: Global, 0.01?40,000 m². Time period and grain: 1888-2015, recording dates. Major taxa and level of measurement: 42,677 vascular plant taxa, plot-level records.Fil: Sabatini, Francesco Maria. Martin-universität Halle-wittenberg; Alemania. German Centre For Integrative Biodiversity Research (idiv) Halle-jena-leipzig; AlemaniaFil: Lenoir, Jonathan. Université de Picardie Jules Verne; FranciaFil: Hattab, Tarek. Université de Montpellier; FranciaFil: Arnst, Elise Aimee. Manaaki Whenua - Landcare Research; Nueva ZelandaFil: Chytrý, Milan. Masaryk University; República ChecaFil: Giorgis, Melisa Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Vanselow, Kim André. University of Erlangen-Nuremberg; AlemaniaFil: Vásquez Martínez, Rodolfo. Jardín Botánico de Missouri Oxapampa; PerúFil: Vassilev, Kiril. Bulgarian Academy of Sciences; BulgariaFil: Vélez-Martin, Eduardo. ILEX Consultoria Científica; BrasilFil: Venanzoni, Roberto. University of Perugia; ItaliaFil: Vibrans, Alexander Christian. Universidade Regional de Blumenau; BrasilFil: Violle, Cyrille. Paul Valéry Montpellier University; FranciaFil: Virtanen, Risto. German Centre for Integrative Biodiversity Research; AlemaniaFil: von Wehrden, Henrik. Leuphana University of Lüneburg; AlemaniaFil: Wagner, Viktoria. University of Alberta; CanadáFil: Walker, Donald A.. University of Alaska; Estados UnidosFil: Waller, Donald M.. University of Wisconsin-Madison; Estados UnidosFil: Wang, Hua-Feng. Hainan University; ChinaFil: Wesche, Karsten. Senckenberg Museum of Natural History Görlitz; Alemania. Technische Universität Dresden; AlemaniaFil: Whitfeld, Timothy J. S.. University of Minnesota; Estados UnidosFil: Willner, Wolfgang. University of Vienna; AustriaFil: Wiser, Susan K.. Manaaki Whenua. Landcare Research; Nueva ZelandaFil: Wohlgemuth, Thomas. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Yamalov, Sergey. Russian Academy of Sciences; RusiaFil: Zobel, Martin. University of Tartu; EstoniaFil: Bruelheide, Helge. German Centre for Integrative Biodiversity Research; Alemani

sPlotOpen : an environmentally balanced, open-access, global dataset of vegetation plots

Motivation: Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained: Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain: Global, 0.01–40,000 m². Time period and grain: 1888–2015, recording dates. Major taxa and level of measurement: 42,677 vascular plant taxa, plot-level records. Software format: Three main matrices (.csv), relationally linked

Mapping Scholarly Communication Infrastructure: A Bibliographic Scan of Digital Scholarly Communication Infrastructure

This bibliography scan covers a lot of ground. In it, I have attempted to capture relevant recent literature across the whole of the digital scholarly communications infrastructure. I have used that literature to identify significant projects and then document them with descriptions and basic information. Structurally, this review has three parts. In the first, I begin with a diagram showing the way the projects reviewed fit into the research workflow; then I cover a number of topics and functional areas related to digital scholarly communication. I make no attempt to be comprehensive, especially regarding the technical literature; rather, I have tried to identify major articles and reports, particularly those addressing the library community. The second part of this review is a list of projects or programs arranged by broad functional categories. The third part lists individual projects and the organizations—both commercial and nonprofit—that support them. I have identified 206 projects. Of these, 139 are nonprofit and 67 are commercial. There are 17 organizations that support multiple projects, and six of these—Artefactual Systems, Atypon/Wiley, Clarivate Analytics, Digital Science, Elsevier, and MDPI—are commercial. The remaining 11—Center for Open Science, Collaborative Knowledge Foundation (Coko), LYRASIS/DuraSpace, Educopia Institute, Internet Archive, JISC, OCLC, OpenAIRE, Open Access Button, Our Research (formerly Impactstory), and the Public Knowledge Project—are nonprofit.Andrew W. Mellon Foundatio

Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics

Open collaborative writing with Manubot.

Open, collaborative research is a powerful paradigm that can immensely strengthen the scientific process by integrating broad and diverse expertise. However, traditional research and multi-author writing processes break down at scale. We present new software named Manubot, available at https://manubot.org, to address the challenges of open scholarly writing. Manubot adopts the contribution workflow used by many large-scale open source software projects to enable collaborative authoring of scholarly manuscripts. With Manubot, manuscripts are written in Markdown and stored in a Git repository to precisely track changes over time. By hosting manuscript repositories publicly, such as on GitHub, multiple authors can simultaneously propose and review changes. A cloud service automatically evaluates proposed changes to catch errors. Publication with Manubot is continuous: When a manuscript's source changes, the rendered outputs are rebuilt and republished to a web page. Manubot automates bibliographic tasks by implementing citation by identifier, where users cite persistent identifiers (e.g. DOIs, PubMed IDs, ISBNs, URLs), whose metadata is then retrieved and converted to a user-specified style. Manubot modernizes publishing to align with the ideals of open science by making it transparent, reproducible, immediate, versioned, collaborative, and free of charge