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Background: Monitoring the ecological status of coastal ecosystems is essential to track the consequences of anthropogenic pressures and assess conservation actions. Monitoring requires periodic measurements collected in situ, replicated over large areas and able to capture their spatial distribution over time. This means developing tools and protocols that are cost-effective and provide consistent and high-quality data, which is a major challenge. A new tool and protocol with these capabilities for non-extractively assessing the status of fishes and benthic habitats is presented here: the KOSMOS 3.0 underwater video system. Methods: The KOSMOS 3.0 was conceived based on the pre-existing and successful STAVIRO lander, and developed within a digital fabrication laboratory where collective intelligence was contributed mostly voluntarily within a managed project. Our suite of mechanical, electrical, and software engineering skills were combined with ecological knowledge and field work experience. Results: Pool and aquarium tests of the KOSMOS 3.0 satisfied all the required technical specifications and operational testing. The prototype demonstrated high optical performance and high consistency with image data from the STAVIRO. The project’s outcomes are shared under a Creative Commons Attribution CC-BY-SA license. The low cost of a KOSMOS unit (~1400 €) makes multiple units affordable to modest research or monitoring budgets.

Linux container technologies, as represented by Docker, provide an alternative to complex and time-consuming installation processes needed for scientific software. The ease of deployment and the process isolation they enable, as well as the reproducibility they permit across environments and versions, are among the qualities that make them interesting candidates for the construction of bioinformatic infrastructures, at any scale from single workstations to high throughput computing architectures. The Docker Hub is a public registry which can be used to distribute bioinformatic software as Docker images. However, its lack of curation and its genericity make it difficult for a bioinformatics user to find the most appropriate images needed. BioShaDock is a bioinformatics-focused Docker registry, which provides a local and fully controlled environment to build and publish bioinformatic software as portable Docker images. It provides a number of improvements over the base Docker registry on authentication and permissions management, that enable its integration in existing bioinformatic infrastructures such as computing platforms. The metadata associated with the registered images are domain-centric, including for instance concepts defined in the EDAM ontology, a shared and structured vocabulary of commonly used terms in bioinformatics. The registry also includes user defined tags to facilitate its discovery, as well as a link to the tool description in the ELIXIR registry if it already exists. If it does not, the BioShaDock registry will synchronize with the registry to create a new description in the Elixir registry, based on the BioShaDock entry metadata. This link will help users get more information on the tool such as its EDAM operations, input and output types. This allows integration with the ELIXIR Tools and Data Services Registry, thus providing the appropriate visibility of such images to the bioinformatics community.

Dendritic cells are sentinels of the immune system distributed throughout the body, that following danger signals will migrate to secondary lymphoid organs to induce effector T cell responses. We have identified, in a rodent model of graft rejection, a new molecule expressed by dendritic cells that we have named LIMLE (RGD1310371). To characterize this new molecule, we analyzed its regulation of expression and its function. We observed that LIMLE mRNAs were rapidly and strongly up regulated in dendritic cells following inflammatory stimulation. We demonstrated that LIMLE inhibition does not alter dendritic cell maturation or cytokine production following Toll-like-receptor stimulation. However, it reduces their ability to stimulate effector T cells in a mixed leukocyte reaction or T cell receptor transgenic system. Interestingly, we observed that LIMLE protein localized with actin at some areas under the plasma membrane. Moreover, LIMLE is highly expressed in testis, trachea, lung and ciliated cells and it has been shown that cilia formation bears similarities to formation of the immunological synapse which is required for the T cell activation by dendritic cells. Taken together, these data suggest a role for LIMLE in specialized structures of the cytoskeleton that are important for dynamic cellular events such as immune synapse formation. In the future, LIMLE may represent a new target to reduce the capacity of dendritic cells to stimulate T cells and to regulate an immune response.

Abstract Background Many bioinformatics tools use reference data, such as genome assemblies or sequence databanks. Galaxy offers multiple ways to give access to this data through its web interface. However, the process of adding new reference data was customarily manual and time consuming, even more so when this data needed to be indexed in a variety of formats (e.g. Blast, Bowtie, BWA, or 2bit). BioMAJ is a widely used and stable software that is designed to automate the download and transformation of data from various sources. This data can be used directly from the command line, in more complex systems, such as Mobyle, or by using a REST API. Findings To ease the process of giving access to reference data in Galaxy, we have developed the BioMAJ2Galaxy module, which enables the gap between BioMAJ and Galaxy to be bridged. With this module, it is now possible to configure BioMAJ to automatically download some reference data, to then convert and/or index it in various formats, and then make this data available in a Galaxy server using data libraries or data managers. Conclusions The developments presented in this paper allow us to integrate the reference data in Galaxy in an automatic, reliable, and diskspace-saving way. The code is freely available on the GenOuest GitHub account (https://github.com/genouest/biomaj2galaxy).

\"FAIR (Findable, Accessible, Interoperable, Reusable) principles\" (Wilkinson et al. 2016) and \"open science\" are two complementary movements in biodiversity science. Although we need to transition to making scientific data and associated material more FAIR, this does not necessarily imply open data or open source algorithms. Here, based on the experience of the French Biodiversity Data Hub (\"Pôle national de données de Biodiversité\" - PNDB), which is an e-infrastructure for and by researchers, we want to showcase how focusing on openness can be a strategy to efficiently reach greater FAIRness. Following DataOne guidance, we can build a complete data/metadata ecosystem allowing us to structure heterogeneous environmental information systems. Using the Galaxy analysis platform and its related initiatives (Galaxy training network, European Erasmus+ Gallantries project, bioconda, bioContainer), we can thus illustrate how we can create transparent, peer-reviewed and accessible tools and workflows and collaborative training material. The Galaxy platform also facilitates use of high performance computing infrastructure through notably the European Open Science Cloud marketplace. Finally, through our experiences contributing to open source projects like EML (Ecological Metadata Language (Michener et al. 1997)) Assembly Line, EDI (Environmental Data Initiative, or PAMPA (Indicators of Marine Protected Areas performance for managing coastal ecosystems, resources and their uses), a French platform to help protected areas managers to standardize and analyse their data, we also show how building open source \"doors\" through the R Shiny programming language to these environments can be beneficial for all.

The global aims of the biodiversity field are to understand the underlying mechanisms of nature, document and capture the state and dynamics of ecosystems, and build predictive models for the future. This understanding is based on access to and use of data, models and analysis tools, produced in ever-greater quantities, and used by diverse communities tackling different aspects of biodiversity from observations, collections, sampling and experimental data. The analysis of biodiversity data is essential for ecosystem services, risk analysis, and human well-being. The impact goes well beyond provisioning for material welfare and livelihoods, to include food security, resiliency, social relations, health, and environmental indicators. Species loss has dramatically accelerated around the world and now poses an existential threat to some ecosystems and susceptible human societies. There is an urgent need to; 1) collect, preserve and share FAIR data on species and ecosystems before they are lost to the scientific record, and, 2) provide automatic workflows producing biodiversity indicators so researchers, planners or policy-makers have evidence-based models to understand the complex dynamics of biodiversity. To accelerate progress, both in the completeness and coverage of data, and in the richness of available information, all relevant sources of data must be aggregated; including sample-based data sets, ecogenomics, molecular research, remote-sensing, literature records, local and regional checklists, and expert knowledge. These resources, records and diverse data types should be used not only as a source of occurrence information, but also as an effective discovery tool on species abundance, community compositions, and interrelated genetic data. Towards these long term aims, the partners of the BiodiFAIRse IN plan to build a virtual research environment and tools, collectively bringing their expertise to FAIR compliance by adapting data exchange standards, promoting the use and mapping of controlled vocabularies and collaborating in the development of registries gathering FAIR research objects and processes, analysis tools, and scalable workflows.

Environmental DNA (eDNA) data enables biodiversity to be monitored at unprecedented resolution and scale. There is great potential in combining knowledge from traditional and innovative methods such as eDNA for biodiversity assessment. eDNA use cases are increasing in aquatic and marine environments, and studies on soils have been developed in recent years. PatriNat*1 (under the guardianship of the French Biodiversity Office (OFB), National Museum of Natural History (MNHN), National Center for Scientific Research (CNRS), and Research Institute for Development (IRD)) is a French data and expertise center working with environmental and research stakeholders to develop data exchanges at all levels. We discuss what eDNA data is (Fig. 1), the different types of data, and the importance of their storage and accessibility. As the amount of eDNA data increases, public agencies need to propose FAIR (Findable, Accessible, Interoperable, Reusable) tools and methods to facilitate their use and foster the development of relevant scientific expertise. We give an overview of the French eDNA data landscape and links with existing standards SINP*2 (National Heritage Inventory Information System), Darwin Core*3(Wieczorek et al. 2012) and workflows (Fig. 2). A priority of eDNA data is to have reliable reference bases and FAIR metadata. PatriNat's new tool will provide access to expertly validated genetic sequence data on species present in France, and is urgently needed for research but also knowledge, monitoring, public policies, and potential law enforcement purposes. We therefore present this technical database built in conjunction with, among other initiatives, DiSSCo*4, iBOL*5 and TaxRef*6 (Gargominy et al. 2022). It will manage 3 data types: Intrinsic sequence data (marker, sequencing methods, etc.). Sequence management (organization, identifiers, URLs, etc.). Voucher data Intrinsic sequence data (marker, sequencing methods, etc.). Sequence management (organization, identifiers, URLs, etc.). Voucher data It will use the nomenclature of Chakrabarty et al. (2013) as well as: The Global Genome Biodiversity Network (GGBN) standard*7 (Droege et al. 2016) The Minimum information about any sequence (MIxS) standard (Yilmaz et al. 2011) The Biological Collection Data (ABCD) standard*8 (Holetschek et al. 2012) The Collections Descriptions terms*9 (Woodburn et al. 2021) The Global Genome Biodiversity Network (GGBN) standard*7 (Droege et al. 2016) The Minimum information about any sequence (MIxS) standard (Yilmaz et al. 2011) The Biological Collection Data (ABCD) standard*8 (Holetschek et al. 2012) The Collections Descriptions terms*9 (Woodburn et al. 2021) The DwC DNA extension*10 can be used for sharing parts of its contents to the Global Biodiversity Information Facility, but referencing sequences associated with specimens/vouchers will require other standards.

The French national biodiversity data hub (“Pôle National de Données de Biodiversité” - PNDB) is a national e-infrastructure created in 2018 and led by the National Museum of Natural History, contributing to the Open Science policy of the Ministry of Higher Education, Research and Innovation (MESRI). PNDB contributes to building an integrative framework taking into account biodiversity over the long term (from the origins of life to future models), at all biological scales (from the molecule to the socio-ecosystem), and in all its interactions, by providing tools and services for the description, access, validation, analysis and reuse of biodiversity data. With the diversity and complementary type of research biodiversity data (information systems, institutional data repositories, research infrastructures as observatories, experimental devices, natural history collections, etc.), but also from public policy data, the missions of the PNDB are deeply based on the FAIR approach (making data Findable, Accessible, Interoperable, Reusable). Thanks to its nomination in 2022 as a thematic reference center of the MESRI, PNDB will contribute to promoting the FAIR approach, will increase the skills (e.g., by training, good practices) of the scientific communities around open science, and stimulate interactions between producers and users of biodiversity data. PNDB has led the French participation to GEO BON (Group on Earth Observations Biodiversity Observation Network) since 2018 and recently shared the lead with public policies information system coordination. Thanks to this co-lead, this national BON proposes an innovative coordination of all biodiversity monitoring programs, from expertise to research around an innovative Essential Biodiversity Variable (EBV) operationalization pilot. This pilot is made of open practical solutions providing a particular high degree of FAIRNess of biodiversity research objects, from data to source codes. PNDB is also a major European point of contact for the DataOne network, who, in combination with the strong link between PNDB and French Global Biodiversity Information Facility (GBIF) node colleagues, allows the dissemination of all types of data through the world in the best manner!

Research processes in biodiversity are evolving at a rapid pace, particularly regarding data-related steps from collection to analysis. This evolution, mainly due to technological advances, offers equipment that is more powerful and generalizes the digitalization of research data and associated products. It is now urgent to accelerate good practices in scientific data management and analysis in order to offer products and services corresponding to the new context, presenting more and more openness, requiring more and more FAIRness (Wilkinson, M.D. et al. 2016). Using Information and Communication Technology (ICT) as international standards and software (Ecological Metadata Language and associated solutions for metadata management, Galaxy web platform for data analysis), we propose, through the national research e-infrastructure called \"Pôle national de données de biodiversité\" (or PNDB, formerly ECOSCOPE), to build a new type of Biodiversity Virtual Research Environment (VRE) for French communities. Although deployment of this kind of environment is challenging, it represents an opportunity to pave the way towards better research processes through enhanced collaboration, data management, analysis practices and resources optimization.

Most biodiversity research aims at understanding the states and dynamics of biodiversity and ecosystems. To do so, biodiversity research increasingly relies on the use of digital products and services such as raw data archiving systems (e.g. structured databases or data repositories), ready-to-use datasets (e.g. cleaned and harmonized files with normalized measurements or computed trends) as well as associated analytical tools (e.g. model scripts in Github). Several world-wide initiatives facilitate the open access to biodiversity data, such as the Global Biodiversity Information Facility (GBIF) or GenBank, Predicts etc. Although these pave the way towards major advances in biodiversity research, they also typically deliver data products that are sometimes poorly informative as they fail to capture the genuine ecological information they intend to grasp. In other words, access to ready-to-use aggregated data products may sacrifice ecological relevance for data harmonization, resulting in over-simplified, ill-advised standard formats. This is singularly true when the main challenge is to match complementary data (large diversity of measured variables, integration of different levels of life organizations etc.) collected with different requirements and scattered in multiple databases. Improving access to raw data, and meaningful detailed metadata and analytical tools associated with standardized workflows is critical to maintain and maximize the generic relevance of ecological data. Consequently, advancing the design of digital products and services is essential for interoperability while also enhancing reproducibility and transparency in biodiversity research. To go further, a minimal common framework organizing biodiversity observation and data organization is needed. In this regard, the Essential Biodiversity Variable (EBV) concept might be a powerful way to boost progress toward this goal as well as to connect research communities worldwide. As a national Biodiversity Observation Network (BON) node, the French BON is currently embodied by a national research e-infrastructure called \"Pôle national de données de biodiversité\" (PNDB, formerly ECOSCOPE), aimed at simultaneously empowering the quality of scientific activities and promoting networking within the scientific community at a national level. Through the PNDB, the French BON is working on developing biodiversity data workflows oriented toward end services and products, both from and for a research perspective. More precisely, the two pillars of the PNDB are a metadata portal and a workflow-oriented web platform dedicated to the access of biodiversity data and associated analytical tools (Galaxy-E). After four years of experience, we are now going deeper into metadata specification, dataset descriptions and data structuring through the extensive use of Ecological Metadata Language (EML) as a pivot format. Moreover, we evaluate the relevance of existing tools such as Metacat/Morpho and DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry) in order to ensure a link with other initiatives like Environmental Data Initiative, DataOne and Long-Term Ecological Research related observation networks. Regarding data analysis, an open-source Galaxy-E platform was launched in 2017 as part of a project targeting the design of a citizen science observation system in France (“65 Millions d'observateurs”). Here, we propose to showcase ongoing French activities towards global challenges related to biodiversity information and knowledge dissemination. We particularly emphasize our focus on embracing the FAIR (findable, accessible, interoperable and reusable) data principles Wilkinson et al. 2016 across the development of the French BON e-infrastructure and the promising links we anticipate for operationalizing EBVs. Using accessible and transparent analytical tools, we present the first online platform allowing the performance of advanced yet user-friendly analyses of biodiversity data in a reproducible and shareable way using data from various data sources, such as GBIF, Atlas of Living Australia (ALA), eBIRD, iNaturalist and environmental data such as climate data.