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The IFB Catalogue is a centralized database developed as part of the IFB Distributed national environment of services in Bioinformatics. Its aim is to ensure the visibility and accessibility of the Bioinformatics resources provided and maintained by the french community, whether these are research labs or service platforms, in a structured and open database that guarantees their FAIRness. Such resources can be software tools, databases, computing resources, individual expertises, platforms, trainings and training materials. This catalogue stores the metadata describing their properties (e.g. the licence of a software tool) and linking them (e.g. the publication of a database by a given team). The primary role of the Catalogue is to help users of Bioinformatics services. Through the integration of the data in the IFB website (https://www.france-bioinformatique.fr), it provides an overview of the various resources provided by the community, and supports the needs of end-users (who can perform a given kind of analysis? where is a tool available?). It is also synchronized with related international catalogues (e.g. ELIXIR bio.tools[1] , TeSS[2]), and promotes the reusability of the data through the publication of Bioschemas[3] markup and REST APIs as well as the use of the EDAM ontology[4]. The backend server is available at https://catalogue.france-bioinformatique.fr/. The code of the Catalogue database backend is openly available on https://github.com/IFB-ElixirFr/ifbcat

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus.This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years.A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future. publishedVersion

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future. Stian Soiland-Reyes was supported by BioExcel-2 Centre of Excellence, funded by European Commission Horizon 2020 programme under European Commission contract H2020-INFRAEDI-02-2018 823830. Carole Goble was supported by EOSC-Life, funded by European Commission Horizon 2020 programme under grant agreement H2020-INFRAEOSC-2018-2 824087. We gratefully acknowledge the financial support from the Lorentz Center, ELIXIR, and the Leiden University Medical Center (LUMC) that made the workshop possible. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Peer Reviewed "Article signat per 33 autors/es: Anna-Lena Lamprecht , Magnus Palmblad, Jon Ison, Veit Schwämmle , Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin , Paul Groth , Hans Ienasescu, Pratik Jagtap, Matúš Kalaš , Vedran Kasalica, Alireza Khanteymoori , Tobias Kuhn12, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert9, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft "

The bio.tools registry is a main catalogue of computational tools in the life sciences. More than 17 »000 tools have been registered by the international bioinformatics community. The bio.tools metadata schema includes semantic annotations of tool functions, that is, formal descriptions of tools' data types, formats, and operations with terms from the EDAM bioinformatics ontology. Such annotations enable the automated composition of tools into multistep pipelines or workflows. In this Technical Note, we revisit a previous case study on the automated composition of proteomics workflows. We use the same four workflow scenarios but instead of using a small set of tools with carefully handcrafted annotations, we explore workflows directly on bio.tools. We use the Automated Pipeline Explorer (APE), a reimplementation and extension of the workflow composition method previously used. Moving "into the wild"opens up an unprecedented wealth of tools and a huge number of alternative workflows. Automated composition tools can be used to explore this space of possibilities systematically. Inevitably, the mixed quality of semantic annotations in bio.tools leads to unintended or erroneous tool combinations. However, our results also show that additional control mechanisms (tool filters, configuration options, and workflow constraints) can effectively guide the exploration toward smaller sets of more meaningful workflows.

The FAIR Guiding Principles, published in 2016, aim to improve the findability, accessibility, interoperability and reusability of digital research objects for both humans and machines. The FAIR principles are also directly relevant to research software. In this position paper “Towards FAIR principles for research software”, we summarised and developed a basis for community discussion. At the start, we discussed what makes software different from data concerning the application of the FAIR principles, and which desired characteristics of research software go beyond FAIR. Then, we presented an analysis of where the existing principles can directly apply to software, and where they need to be adapted or reinterpreted. Our next step after the position paper is to prompt for community-agreed identifiers for FAIR research software.Acknowledgments To all the authors of Towards FAIR principles for research software https://doi.org/10.3233/DS-190026, and the numerous people who contributed to the discussions around FAIR research software at different occasions preceding the work on this paper. References Lamprecht, Anna-Lena, et al. (2019) Towards FAIR principles for research software. Data Science. https://doi.org/10.3233/DS-190026 ABOUT THE AUTHOR(S) Dr Paula Andrea Martinez is leading the National Training Program for the Characterisation Community in Australia since 2019. She works for the National Image Facility (NIF). Last year she worked at ELIXIR Europe coordinating the Bioinformatics and Data Science training program in Belgium and collaborated with multiple ELIXIR nodes in the development of Software best practices. Her career, spanning Sweden, Australia and Belgium nurtured her experience in Bioinformatics and Research Software development for complex and dataintensive science. She started a career in Computer Science, later on, interested in research methods development and now outreach and advocacy in data and software best practices

International audience; EDAM is a well-established ontology of operations, topics, types of data, and data formats that are used in bioinformatics and its neighbouring fields [1,2,3] . EDAM-bioimaging is an extension of EDAM dedicated to bioimage analysis, bioimage informatics, and bioimaging. It is developed in collaboration between the NEUBIAS and ELIXIR Europe|ELIXIR-EXCELERATE projects, in contact with Euro-BioImaging and Global BioImaging.EDAM-bioimaging contains an inter-related hierarchy of concepts including bioimage analysis and related operations, bioimaging topics and technologies, and bioimage data and their formats. The modelled concepts enable interoperable descriptions of software, publications, data, and workflows, fostering reliable and transparent, "reproducible" bioimage analysis.EDAM-bioimaging is under active development, with a few alpha releases publicly available. It is used in BISE|biii.eu, the bioimaging tools and resources information portal, and emerging also in descriptions of Debian Med packages available in Debian and Bio-Linux. Development of EDAM-bioimaging has been carried out in a successful open community manner, in a fruitful collaboration between numerous bioimaging experts and ontology developers. The last stable release at the time of poster submission is version alpha05 [4], and the live development version can be viewed and commented on WebProtégé (free registration required). New contributors are warmly welcome![1] Ison, J., Kalaš, M., Jonassen, I., Bolser, D., Uludag, M., McWilliam, H., Malone, J., Lopez, R., Pettifer, S. and Rice, P. (2013). EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics, 29(10): 1325-1332. DOI: 10.1093/bioinformatics/btt113 Open Access[2] Kalaš, M., Ménager, H., Schwämmle, V., Ison, J. and EDAM Contributors (2017). EDAM – the ontology of bioinformatics operations, types of data, topics, and data formats (2017 update) [version 1; not peer reviewed]. F1000Research, 6(ISCB Comm J):1181 (Poster) DOI: 10.7490/f1000research.1114459.1 Open Access[3] Kalaš, M., Ménager, H., Ison, J. and Willighagen, E. (2018). edamontology/edamontology: EDAM 1.21 (Version 1.21). Zenodo. DOI: 10.5281/zenodo.1325952 Open Access[4] Matúš Kalaš, Nataša Sladoje, Laure Plantard, Martin Jones, Leandro Aluisio Scholz, Joakim Lindblad, and contributors (2019). edamontology/edam-bioimaging: alpha05 (Version alpha05). Zenodo. DOI: 10.5281/zenodo.2557012 Open Access

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

Motivation Numerous software utilities operating on mass spectrometry (MS) data are described in the literature and provide specific operations as building blocks for the assembly of on-purpose workflows. Working out which tools and combinations are applicable or optimal in practice is often hard. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design. Results We provide a toolkit to support researchers in identifying, comparing and benchmarking multiple workflows from individual bioinformatics tools. Automated workflow composition is enabled by the tools’ semantic annotation in terms of the EDAM ontology. To demonstrate the practical use of our framework, we created and evaluated a number of logically and semantically equivalent workflows for four use cases representing frequent tasks in MS-based proteomics. Indeed we found that the results computed by the workflows could vary considerably, emphasizing the benefits of a framework that facilitates their systematic exploration. Availability and implementation The project files and workflows are available from https://github.com/bio-tools/biotoolsCompose/tree/master/Automatic-Workflow-Composition. Supplementary information Supplementary data are available at Bioinformatics online.

International audience; The ontology of bioimage informatics operations, topics, data, and formats What? EDAM-bioimaging is an extension of the EDAM ontology, dedicated to bioimage analysis, bioimage informatics, and bioimaging. Why? EDAM-bioimaging enables interoperable descriptions of software, publications, data, and workflows, fostering reliable and transparent science. How? EDAM-bioimaging is developed in a community spirit, in a welcoming collaboration between numerous bioimaging experts and ontology developers. How can I contribute? We need your expertise! You can help by reviewing parts of EDAM-bioimaging, posting comments with suggestions, requirements, or needs for clarification, or participating in a Taggathon or another hackathon. Please see https://github.com/edamontology/edam-bioimaging#contributing. EDAM-bioimaging is developed in an interdisciplinary open collaboration supported by the hosting institutions, participating individuals, and NEUBIAS COST Action (CA15124) and ELIXIR-EXCELERATE (676559) funded by the Horizon 2020 Framework Programme of the European Union. https://github.com/edamontology/edam-bioimaging @edamontology /edamontology/edam-bioimaging

Review history. (DOCX 20 kb)

The objective of EXCELERATE Deliverable 1.8, as stated in the original proposal, was toanalyse the data format-usage landscape of tools registered the ELIXIR Tools and DataServices Registry (bio.tools) to provide a basis for targeted software developments toimprove interoperability of registered tools. We originally foresaw those developments tobe facilitated via a “Matchmaking Service mechanism” possibly including conversion oftools to use common formats, and development of format converter software whereneeded. Whilst such specific developments are laudable, it is now obvious that significantprogress depends on the bioinformatics community at large, and requires vastly greatercapacity than WP1 has at its disposal. Instead, we took a much more targeted approach, focussing - as an exemplar - upon thesystematic curation of tools for proteomics data analysis, and subsequent exploitation ofthe annotations for automated workflow composition. The work addresses the challengeof tool interoperability and is a major stride in delivering a practical “Matchmaking Servicemechanism”. The work is described in two articles: ● Tsiamis, V., Ienasescu, H., Palmblad, M. Schwämmle, V. and Ison J. CommunitycurationofsoftwaretoolsasillustratedformassspectrometrybasedproteomicsInpreparation¨see https://tinyurl.com/proteomics-tools Palmblad, M., Lamprecht, A., Ison, J. and Schwämmle, V. (2018). AutomatedworkflowcompositioninmassspectrometrybasedproteomicsAccepted forpublication in Bioinformatics The first article (Tsiamis et†al†.) describes a systematic approach towards thecomprehensive coverage in bio.tools of the prevalent tools for proteomics data analysis,including expert curation of many tools to a high standard including consistent annotationof data formats and operation using the EDAM ontology. The second (Palmblad etal)explores automated workflow composition from the tool semantic annotation, andprovides a toolkit to support researchers in identifying, comparing and benchmarkingmultiple workflows from individual bioinformatics tools. For this deliverable report, we link to and summarise the publications in press. Theproject files and workflows are freely available: ● https://github.com/bio-tools/biotoolsCompose/tree/master/Automatic-Workflow-Composition

The objective of EXCELERATE Deliverable 1.7 is to provide comprehensive support forusers of the ELIXIR Tools and Data Services Registry ( https://bio.tools ). The core aim isto deliver impact for end-users broadly, making it easier for them to contribute to and usebio.tools. Users are of three types: tool†providers†such as major institutional providersand community-driven collections, tool†developers†including developers of individual tools,workflows and workbench environments, and tool†users†including scientists andbioinformaticians. We originally anticipated to provide direct support to toolusers,primarily by leveraging the existing and highly popular user bioinformatics forums(BioStars, BioPlanet etc) This remains a good objective (highlighted in theEXCELERATE midterm review) but is not yet timely, as it cannot be done in an impactfulway given the resources available to EXCELERATE and sheer number of tool users.

The objective of EXCELERATE Deliverable 1.5 is to review software metrics and assesstheir usefulness in the context of the bio.tools (https://bio.tools) registry, with view toexpose in bio.tools, metrics from scientific performance benchmarking, technicalmonitoring, and of software or service quality in general. The core aim is to provide theend-user with robust, scientifically relevant measures of quality and performance, to guidethem in their comparison and selection of tools through: 1) enabling end-users to sort andfilter bio.tools search results by various criteria, and 2) to render labels on bio.tools ToolCards, perhaps even (eventually) a formal ELIXIR quality stamp.This deliverable report describes research into the problem and implementation ofprototype solutions towards the stated objectives and core aim. This report describeswork done with ELIXIR-EXCELERATE resources.

The objective of EXCELERATE Deliverable 1.6 is to integrate the ELIXIR Tools andData Services Registry (bio.tools) with workbench environments such as Galaxy,respecting the general trend towards the use of workflows as a preferred environmentfor the convenient use of tools and data access, especially when resources must beused in combination with one another. The ELIXIR EXCELERATE proposal envisionedintegration in two ways: A Workbench Integration Enabler service to develop the vision “register your softwareonce - enable its support everywhere”. The idea, technically, is to translate thedescription of any tool or service that is registered in bio.tools, into the metadata formatrequired by the existing major workbench environments. In so, doing, the cost ofmaintaining quality tool description for us in workbench environments is lowered, givinga practical boost to tool interoperability. A utility for en masse registration of services from Galaxy instances. In the context ofa generic registry for bioinformatics software, such as bio.tools, Galaxy instancesconstitute a major source of valuable content (online services and underlying tools).The idea is to provide a sustainable means to harvest this content. We have made major strides in delivering this functionality, having published twopapers with a third in preparation: Ménager, H., Kalaš, M., Rapacki, K. and Ison, J. (2016). Using registries to integratebioinformatics tools and services into workbench environments. Int J Softw ToolsTechnol Transfer, doi:10.1007/s10009-015-0392-zHillion KH, Ison J and Ménager H. (2017). ToolDog – generating tool descriptors fromthe ELIXIR tool registry. [version 1; not peer reviewed]. F1000Research 2017, 6:767 (poster). doi:10.7490/f1000research.1114125.1 Doppelt-Azeroual, O., Mareuil, F., Deveaud, Kalaš, M., Soranzo, N., van den Beek, M., Grüning, B., Ison, J. and Ménager, H. (2017). ReGaTE: Registration of Galaxy Tools inElixir. GigaScience,doi:10.1093/gigascience/gix022 The first article (Ménager et al.) outlines the general strategy behind the WorkbenchIntegration Enabler service, scopes the problem, and provides the technicalgroundwork. The second (Hillion KH et al.) is a poster summarising progress thus farin implementing this service and the basis for a publication in preparation. The third(Doppelt-Azeroual et al.) describes a utility for en masse registration of services fromGalaxy instances, which has been applied to the registration of services from theGalaxy instance hosted by Institut Pasteur, as described in D1.2 report. For this deliverable report, we attach and summarise the publications in press. Thesoftware is available under open source license: ToolDog (MIT license)https://github.com/bio-tools/ToolDog ReGaTE (GPL license)https://github.com/C3BI-pasteur-fr/ReGaTE

The objective of EXCELERATE Deliverable 1.1 is the development of a discoveryportal (bio.tools) built upon a federated curation of a registry of key software resourcesfor bioinformatics worldwide. The core aim is to provide a practical portal that will helpscientists with resource discovery and interoperability. Four reports (D1.1 - D1.4) describe the portal: M12 : prototyping of portal software and registry data model, addition of seed content M24 : consolidation of software features and content to a stable model M36 : expansion of registry content towards comprehensive coverage, with newregistry features M48 : integration with other software systems, registry applications and portal impactevaluation This report (D1.2 at M24) describes a balance of consolidation and expansion of theprototype described in D1.1, which presented 2,550 entries as seed content. In light ofthese data we have validated and improved the model, ontology and software, whilsttaking major strides towards comprehensive coverage of prevalent tools envisioned forD1.3. This deliverable report describes work done with ELIXIR-EXCELERATE resources. Design and development aspects include:production of a stable data model (biotoolsSchema 2.0.0) with improved controlledvocabularies development of an emerging information standard for tools, based upon the model, providing a path for content quality improvement three new EDAM ontology releases revision of the client and server-side software including conformance to the model andnew, community-requested features Operational aspects include: development of the community build-up process contributing to the portal (currently 537 contributors of content) registry growth to 5,952 entries refactored to the model and with preliminary clean-upguided by the information standard 97,262 aggregated annotations on tools including 7477 publication IDs (95% of entries) In addition, we describe specific developments as envisioned for milestones below (alldue in M24), including new prototype user interface components, features, utilities, andcontent developments: M1.7.1 “Implementation of novel highly usable interfaces from analysis of userexperience and usability requirements” M1.3 “Implementation of registry-literature integration” M1.1.2 “EDAM release with coverage of different resource categories and RIs.Implementation of tooling for sustainable community development” M1.4 “Implementation of support for “close to source” resource annotation in keydocumentation generators, and software development frameworks” Our priority to date has been tool and service providers with a focus on content growth(i.e. operating the portal and providing more content). In light of general communityfeedback, and specific recommendations from the EXCELERATE midterm review,focus now shifts to content quality and (somewhat longer term) an increasing priority tofoster the community of tool developers and end-users. Importantly, we now have aclearer picture of the desired “endgame”: to provide a persistent reference tohigh-quality (curated and verified) “canonical” descriptions of unique tools, withinformation about their provision via online services and various downloadableartefacts, and including entries for different versions of a tool, where these have majorfunctional differences. Detailed information on new technical components is included. All aspects of theproject are interdependent and work is ongoing in all areas, to ensure sustainable andhigh quality content growth, develop useful features for end-users and to supportemerging integration scenarios and applications. The report describes progress madethus far and includes a summary of anticipated developments.

The objective of EXCELERATE Deliverable 1.3 is the development of a discovery portal(bio.tools ) built upon a federated curation of a registry of key software resources for 1bioinformatics worldwide. The core aim is to provide a practical portal that will helpscientists with resource discovery and interoperability. Four reports (D1.1 - D1.4) describe the portal: ● M12: prototyping†of portal software and registry data model, addition of seedcontent ● M24: consolidation†of software features and content to a stable model ● M36: expansion†of registry content towards comprehensive coverage, with newregistry features ● M48: integration†with other software systems, registry applications and portalimpact evaluation This report (D1.3 at M36) describes further consolidation†and expansion†of the registryand portal described in D1.2, which presented 5,952 bio.tools entries. We have furtherimproved the model, ontology and software, whilst cleaning and more than doubling theregistry content volume, achieving “near enough” comprehensive coverage of prevalenttools as envisioned in D1.2. This deliverable report describes work done with ELIXIR-EXCELERATE resources. Design and development aspects include: ● production of a major new release of the stable data model (biotoolsSchema 3.0.0)which simplifies the previous stable version 2.0 (described in D1.2) whilstextending the functionality ● the application of the “respectable beta” Tool Information Standard (see D1.5),consolidated from the prototype (see D1.2), to drive content quality improvementin bio.tools ● 3 new EDAM ontology releases ● revision of the client and server-side software including conformance tobiotoolsSchema 3.0.0 Operational aspects include: ● registry growth to 11,479 entries (was 5,952 in D1.2) refactored to the model andincluding major clean-up and improvements guided by the Tool InformationStandard ● 214,771 aggregated annotations on tools (was 97,262 in D1.2) ● content contributor growth to 800 (was 537 in D1.2), following the communitybuild-up process (described in D1.7) ● the portal software has been made available under open license (GPL-3.0). In addition, we describe specific developments as envisioned for milestones below (alldue in M36), including new user interface features, utilities, content developments anddocumentation: ● M1.1.3 “EDAM release with coverage of different resource categories and RIs.Implementation of tooling for sustainable community development” ● M1.7.2 “Implementation of novel highly usable interfaces from analysis of userexperience and usability requirements” Including: ● edamBrowserweb application for browsing resources (in bio.tools, TeSS etcÆ)based on their scientific (EDAM) annotation and community-development ofEDAM. An article describing the work has been accepted for publication in The JournalofOpenSourceSoftware. 2 ● biotoolsSum†utility for search and browsing of local tool collections, embeddableon external Web sites ● edamMap†utility to assist curators in the creation of bio.tools entries; this textmining utility for EDAM has been improved in light of its application to bio.toolscontent import enmasse, and is provided as a Web application and Web APIWe described in D1.2 how focus was shifting to content†quality†and (somewhat longerterm) an increasing priority to foster the community of tool developers and tool end-users. We also summarised an envisioned “endgame”: to provide a persistent reference tohigh-quality (curated and verified) “canonical” descriptions of unique†tools, withinformation about their provision via†online services and various downloadable artefacts,and including entries for different versions of a tool, where these have major functionaldifferences. In this report we summarise our progress towards this endgame. Detailed information on new, or improved technical components is available online. Allaspects of the project are interdependent and work is ongoing in all areas, to drive contentquality improvement, ensure sustainable growth, develop useful features for end-usersand to support emerging integration scenarios and applications.

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

Background:Bioinformaticians routinely use multiple software tools and data sources in their day-to-day work, and have been guided in their choices by a number of cataloguing initiatives. The ELIXIR Tools and Data Services Registry ( bio.tools ) aims to provide a central information point, independent of any specific scientific scope within bioinformatics or technological implementation. Meanwhile, efforts to integrate bioinformatics software in workbench and workflow environments have accelerated, to enable the design, automation, and reproducibility of bioinformatics experiments. One such popular environment is the Galaxy framework, with currently more than 80 publicly available Galaxy servers around the world. In the context of a generic registry for bioinformatics software, such as bio.tools , Galaxy instances constitute a major source of valuable content. Yet there has been, to date, no convenient mechanism to register such services en masse . Findings:We present ReGaTE (Registration of Galaxy Tools in Elixir), a software utility that automates the process of registering the services available in a Galaxy instance. This utility uses the BioBlend API to extract service metadata from a Galaxy server, enhance the metadata with the scientific information required by bio.tools, and push it to the registry. Conclusion:ReGaTE provides a fast and convenient way to publish Galaxy services in bio.tools. By doing so, service providers may increase the visibility of their services, whilst enriching the software discovery function that bio.tools provides for its users. The source code of ReGaTE is freely available on Github, at https://github.com/C3BI-pasteur-fr/ReGaTE .