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Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations., Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

We describe a novel super-infrastructure for biomedical text mining which incorporates an end-to-end pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature, combining NLP and corpus linguistics methods. The infrastructure permits extreme-scale research on the open access PubMed Central archive. It combines an updatable Gene Ontology Semantic Tagger (GOST) for entity identification and semantic markup in the literature, with a NLP pipeline scheduler (Buster) to collect and process the corpus, and a bespoke columnar corpus database (LexiDB) for indexing. The corpus database is distributed to permit fast indexing, and provides a simple web front-end with corpus linguistics methods for sub-corpus comparison and retrieval. GOST is also connected as a service in the Language Application (LAPPS) Grid, in which context it is interoperable with other NLP tools and data in the Grid and can be combined with them in more complex workflows. In a literature based discovery setting, we have created an annotated corpus of 9,776 papers with 5,481,543 words.

*Introduction* Manually Annotated Sub-Corpus (MASC) Third Release was developed as part of The American National Corpus project and consists of approximately 500,000 words of contemporary American English written and spoken data annotated for a wide variety of linguistic phenomena. The MASC project was established to address, to the extent possible, many of the obstacles to the creation of large-scale, robust, multiply-annotated corpora of English covering a wide range of genres of written and spoken language data. The project provides appropriate data and annotations to serve as the base for a community-wide annotation effort, together with an infrastructure that enables the incorporation of contributed annotations into a single, usable format that can then be analyzed as it is or transduced to any of a variety of other formats. The aim is to offset some of the high costs of producing high quality linguistic annotations via a distribution of effort and to solve some of the usability problems for annotations produced at different sites by harmonizing their representation formats. It also provides data from a much wider variety of genres than are often present in existing multiply-annotated corpora of English, and all of the data in the corpus are drawn from current American English so as to be most useful for natural language processing applications used in the web-based environment. Further information about the pojrect is available at the MASC website. The source texts were drawn from the open portion of the American National Corpus Second Release, which includes written texts and spoken transcripts of American English from a broad range of genres produced since 1990 and from the Language Understanding Annotation Corpus, a collection of various genres inlcuding broadcast, newswire, email, and telephone speech annotated for committed belief, event and entity coreference, dialog acts and temporal relations. MASC Third Release includes the the contents of MASC First

*Introduction * The Manually Annotated Sub-Corpus First Release (MASC I), Linguistic Data Consortium (LDC) catalog number LDC2010T22 and isbn 1-58563-569-3, is the first of three releases of 500,000 words of MASC data developed as part of the American National Corpus (ANC) project. MASC I consists of approximately 80,000 words of contemporary spoken and written American English annotated for a variety of linguistic phenomena. The MASC project is sponsored by the National Science Foundation and was established to address, to the extent possible, many of the obstacles to the creation of large-scale, robust, multiply-annotated corpora of English covering a wide range of genres of written and spoken language data. Researchers from Vassar College, Columbia University and the International Computer Science Institute, University of California at Berkeley are the principal participants the WordNet project provides consulting. The source texts in MASC I are drawn from the open portion of the American National Corpus (ANC) Second Release LDC2005T35, which includes written texts and spoken transcripts of American English from a broad range of genres produced since 1990 and from the Language Understanding Annotation Corpus LDC2009T09, (LU Corpus), a collection of various genres including broadcast, newswire, email and telephone speech annotated for committed belief, event and entity coreference, dialog acts and temporal relations. All of the words of data in MASC I have validated annotations for token, part of speech, sentence boundary, noun chunks, verb chunks, named entities and Penn Treebank syntax. Full-text FrameNet annotations are available for seventeen texts and WordNet word sense annotations are available for 1000 occurrences of each of fifty-three words. Annotations of all or portions of the sub-corpus for a wide variety of other linguistic phenomena have been contributed by other projects. Software and services available from the ANC project website enable transducti