Increasing understanding of alien species through citizen science (Alien-CSI)

There is no sign of saturation in accumulation of alien species (AS) introductions worldwide, additionally the rate of spread for some species has also been shown to be increasing. However, the challenges of gathering information on AS are recognized. Recent developments in citizen science (CS) provide an opportunity to improve data flow and knowledge on AS while ensuring effective and high quality societal engagement with the issue of IAS (Invasive Alien Species). Advances in technology, particularly on-line recording and smartphone apps, along with the development of social media, have revolutionized CS and increased connectivity while new and innovative analysis techniques are emerging to ensure appropriate management, visualization, interpretation and use and sharing of the data. In early July 2018 we launched a European CO-operation in Science and Technology (COST) Action to address multidisciplinary research questions in relation to developing and implementing CS, advancing scientific understanding of AS dynamics while informing decision-making specifically implementation of technical requirements of relevant legislation such as the EU Regulation 1143/2014 on IAS. It will also support the EU biodiversity goals and embedding science within society. The Action will explore and document approaches to establishing a European-wide CS AS network. It will embrace relevant innovations for data gathering and reporting to support the implementation of monitoring and surveillance measures, while ensuring benefits for society and citizens, through an AS CS European network. The Action will, therefore, increase levels of participation and quality of engagement with current CS initiatives, ensuring and evaluating educational value, and improve the value outcomes for potential users including citizens, scientists, alien species managers, policy-makers, local authorities, industry and other stakeholders

COMPARTIR DATOS DE BIODIVERSIDAD A TRAVÉS DE GBIF COLOMBIA: Una invitación al sector empresarial

La infraestructura Global de Información en Biodiversidad (GBIF) es la red de datos sobre biodiversidad más grande del mundo. Como infraestructura internacional de datos abiertos, permite que cualquier persona pueda acceder, compartir y utilizar información sobre las especies de nuestro planetaBogoáSiB Colombi

Comment opérationnaliser et évaluer la prise en compte du concept ‘FAIR' dans le partage des données: Vers une grille simplifiée d’évaluation du respect des critères FAIR.

National audienceIndexed identifier ? Identification Are each data/dataset identified by an indexed and independant identifier ? Persistent metadata / data link ? Metadata traceability Are the metadata linked to the dataset through a persistent identifier? Metadata & authority linked ? Metadata traceability Are the metadata of each dataset linked to a unique authority (responsible for the datasets at a given time)? Unique, global, persistent ID? Identification Are the data identifiers unique, global and persistent ? Are the data identifiers unique, global and persistent ? Datasets linked to authority ? Metadata traceability Are all datasets linked to an authority (legal entity) through a unique and persistent identifier over time (e.g. institution, association or established body)? In case of a legal reuse restriction (such as personal data, state and public security, national defense secret, confidentiality of external relations, information systems security, secrets in industrial and commercial matters) , is the restriction properly justified?SHARC (SHAring Reward & Credit) est un groupe d’intérêt scientifique interdisciplinaire créé dans le cadre de RDA (Research Data Alliance) dans le but de faciliter le partage des données de recherche (et des ressources) par la valorisation de l’ensemble des activités pré-requises à ce partage, tout au long du cycle de vie des données. Dans ce cadre, un sous-groupe de travail SHARC élabore des grilles d’évaluation des chercheurs afin de mesurer leur niveau de prise en compte des principes FAIR dans la gestion de leurs données.La grille d’évaluation présentée dans ce poster est destinée à être complétée par tout scientifique produisant et / ou utilisant des données. Il s'agit d'un résumé d'une grille d'évaluation plus étendue conçue pour un partage optimal des données (non encore mise en œuvre pour le moment par la plupart des scientifiques).L'évaluation est basée sur les critères de conformité FAIR. Pour remplir cet objectif, la grille affiche le minimum de critères qui doivent absolument être appliqués par les chercheurs pour attester de leur pratique FAIR. Ces critères sont organisés en 5 groupes: «Motivations de partage»; "Trouvable", "Accessible", "Interopérable" et "Réutilisable". Pour chaque critère, 4 degrés d’évaluation sont proposés ("Jamais / Non évaluable"; "Si obligatoire"; "Parfois"; "Toujours"). Au moins un degré mais un seul doit être sélectionné par critère. L'évaluation doit être effectuée pour chaque catégorie F / A / I / R; L'évaluation finale est la somme de chaque degré coché rapportée au nombre total de critères dans chaque catégorie F / A / I / R. Des règles d'interprétation prenant en compte les «motivations du partage» sont proposées

BiodiFAIRse: a Biodiversity dedicated GO FAIR Implementation Network

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

Implementing Nestedness in Darwin Core: An epidemiology case study

Wildlife diseases have an impact on biodiversity, the economy, and public health. Better knowledge of disease patterns would be to the benefit of conservation measures, livestock production and, thus, ultimately human health. Nevertheless, disease surveillance systems operate mostly at a national scale, with incompatible data structures, inhibiting effective data sharing and thus rapid transnational responses to disease outbreak. As the risk of disease to biodiversity, the economy and to public health increases with climate change, land-use change and trade, the necessity for a common data standard to improve data sharing of surveillance efforts is greater than ever to enable transnational proactive and reactive measures to be taken.To address these large issues, a consortium for the European Food and Safety Authority (EFSA; the Enetwild consortium) was formed to collect existing data on wildlife host abundance and distribution in Europe. Alongside data on their associated pathogens, the consortium is attempting to develop data models to aggregate the host data according to the Darwin Core*1 standard.However, the complexity of zoonotic disease and wildlife distribution data is not easily captured in the current version of the Darwin Core standard, often comprising complex data structures with species interactions and partial information. Firstly, zoonotic disease data consist of observations of both the host and the pathogen, whereby each positive case of a disease is associated with the observation of the host species. Secondly, wildlife host distribution data frequently contain detailed information for only some individuals of a group when multiple individuals of the host species are observed simultaneously, such as life stage and sex. In order to capture these types of interactions and subsetting, we need to be able to handle the hierarchical structuring of these data.In an attempt to resolve these issues, a data model in line with the Darwin Core standard was initially developed for wildlife host population data (Enetwild Consortium et al. 2020). Here, we propose a new data model for data on the hosts’ pathogens, and use both data models to demonstrate the model efficacy for complex hierarchical data structures. The epidemiology data model is structured around the existence of a primary occurrence of the host species observation, which may consist of one or many individuals. In order to associate the presence (or absence) of the pathogen to the host, or to provide details on the host group composition, the data model allows child occurrences to relate to the primary, or parent, occurrence at a particular event (Table 1). We propose the implementation of a hierarchical structure of the occurrence extension to allow these two phenomena to be effectively modelled. This hierarchical structuring relies on the adoption of a new term, the ‘parentOccurrenceID’, whereby each 'parent' observation of the host species can be associated with multiple 'child' observations. To this end, we propose the introduction of the parentOccurrenceID term (currently under discussion on GitHub).Using the Darwin Core standard, we propose a data model that would allow effective harmonisation of zoonotic disease data, demonstrating that harmonisation of disease surveillance data in Europe is possible. Finding solutions to capturing complex hierarchical biotic interactions in Darwin Core is currently underway at GBIF*2, 3, although relying on a separate relationship table. However, we advocate for the introduction of the new parentOccurrenceID thanks to its simplicity and very general applicability, being adaptable to any group occurrence where detailed or partial information is available, or to hierarchical interaction relationships such as between hosts and pathogens. We believe the employment of the new term would be complementary to the current GBIF developments, and of benefit to many Darwin Core users where details apply to differing levels of hierarchical occurrences

Three Portals, One Infrastructure: How to manage information with ALA tools

GBIF Togo, hosted at the University of Lomé, has published more than 62,200 occurrence records from 37 datasets and checklists. As a node participant of Global Biodiversity Information Facility (GBIF) since 2011, it has participated actively in several projects including the Biodiversity Information for Development (BID) programme. GBIF facilitates collaboration between nodes at different levels through its Capacity Enhancement Support Programme (CESP). One of the actions included in the CESP guidelines is called ‘Mentoring activities’. Its main goal is the transfer of knowledge between partners, such as information, technologies, experience, and best practices. Sharing architecture and development is the key solution to solving some the technical challenges and impediments (e.g. hosting, staff turnover, etc.) that GBIF nodes occasionally face. The Atlas of Living Australia (ALA) team have developed a feature called ‘data hub’, which allows the creation of a standalone website with a dedicated occurrence search engine that supports data discovery (e.g. specific genus, geographic area) published by particular GBIF nodes. In 2017, a CESP project between the GBIF Benin and the GBIF France led to the creation of a new portal: Atlas of Living Beninises. This portal shared the same back-end database as the Atlas of Living France portal, while at the same time, each portal displayed and managed information relevant only to its region. In 2018, another CESP project between GBIF France and GBIF Togo shared the same goal as the previous one: implement a new Atlas of Living Australia portal for Togo. This goal will be fulfilled using a similar implementation as the previous project: a shared back-end and different front-end. Togo will be the second African GBIF node to implement this kind of infrastructure. This poster will highlight the architecture specific to the Atlas of Living Togo, and present the management procedure that distinguishes data coming from the three different countries

OpenObs: Living Atlases platform for French biodiversity data

The OpenObs project, led by Patrinat, was launched in September 2017, and the first version of this tool was released in October 2020. OpenObs is based on the Atlas of Living Australia platform, supported by the Global Biodiversity Information Facility (GBIF) community, particularly the Living Atlases (LA) collective.OpenObs enables the visualization and downloading of observation data on species available in the National Inventory of Natural Heritage (INPN), the national platform of SINP (Information System for the Inventory of Natural Heritage). It provides open access to non-sensitive public data and includes all available observations, whether they are occurrence or synthesis data.As of July 2023, OpenObs has 134,922,015 observation records, and new data is reguarly added (at least twice a year). Furthermore, the project is constantly evolving with new developments planned, such as a user validation interface and new cartographic tools.We will present the architecture of this LA-based national biodiversity portal (Fig. 1), as well as its planned new functionality and development roadmap

INPN - GBIF Exchanges: Biodiversity data flows at national and international levels

GBIF France, the French node of the Global Biodiversity Information Facility, has been hosted by the National Museum of Natural History (MNHN) since 2006 and is actively involved in various national and international projects related to data mobilization. The INPN (National Inventory of Natural Heritage) information facility, hosted since 2005 by the MNHN, lists the ecological, faunistic, floristic, geological, mineralogical and paleontological data of France and its overseas territories. This system has been recognized since 2012 as the national platform of the French Nature and Landscape Information System. In January 2017, GBIF France and the INPN team were gathered in the Mixed Unit of Natural Heritage Services (UMS PatriNat), which provides expertise and knowledge management missions under the supervision of the French Agency for Biodiversity (AFB), the National Center for Scientific Research (CNRS) and the MNHN. In order to support both systems and enhance biodiversity data quality, a flow of occurrences and taxonomic data has been established between GBIF and INPN. On July 2018, the latest delivery of French data coming from the INPN marked the one billionth occurrence to the GBIF network. Data connected to GBIF from French territories, will be included in INPN in 2019. In this poster, we will explain the detailed process of data exchange between the two platforms, as well as the protocols, the standards and the tools used for data validation, data transformation, data dissemination and data update

Distributions of oxygen and carbon stable isotopes and CFC-12 in the water masses of the Southern Ocean at 30°E from South Africa to Antarctica: results of the CIVA1 cruise

PAR00002959International audienceThis study presents oceanic distributions of stable isotopes (δ18O of water and δ13C of ΣCO2) and CFC-12 from samples collected during the CIVA1 cruise (February/March 1993), across the Southern Ocean, along a meridian section at 30°E, from South Africa (44°S) to Antarctica (70°S). The isotopic measurements show important variations between the subantarctic surface waters with low δ18O–high δ13C values and the antarctic surface waters with very low δ18O–low δ13C values. The surface distributions of δ13C values follow the major frontal oceanic structures; the vertical distribution shows the progressive upwelling from the subantarctic zone to the antarctic divergence of 13C-depleted CO2 derived from remineralization of organic matter. Along the Antarctic continental shelf, between 2500 and 4000 m, a core of water with δ18O values close to −0.1‰ is associated with a relative maximum in CFC-12 concentration, although this core is not detected by its temperature and salinity parameters. This water mass, which corresponds to recently formed deep water, may originate from the eastward extension of the Weddell gyre or from bottom waters coming from the East and formed near Prydz Bay