Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter-or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solv...

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The datasets and code presented in this article are related to the research article entitled “Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets”1. The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes.

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Biodiversity informatics has been characterized as a rapidly growing interdisciplinary field, which aims to bring together the areas of biodiversity and informatics. A study was conducted looking at the current level of activity within the GBIF Participant countries and its associated network in relation to work-based training and/or academic teaching at universities, in the field of biodiversity informatics. It was intended to get an overview of GBIF Node Managers, (hence, member countries), already engaged in developing course curricula, or in providing training, and whether they would be willing to share resources or enter into collaborations, to further elaborate this field of science. This investigation followed a survey approach, conducted globally across the GBIF community to identify the existing capacities and resources within the network. The results indicated that the vast majority of GBIF Nodes survey respondents, are engaged in onsite training activities in biodiversity informatics areas, with a focus on professionals, mostly researchers, policy makers and students. Training includes data digitization, management, publishing, analysis and use, to enable the accessibility of analogue and digital biological data which currently resides as scattered databases/datasets. A list containing the associated URL's for training and dissemination activities in GBIF Nodes has been developed, based on survey results, and will be presented. An initial assessment of the academic teaching activities indicated that many countries across most regions were already engaged in the conceptualisation, development and/or implementation of formal academic programs in biodiversity informatics including Benin, Colombia, Costa Rica, Finland, France, India, Norway, South Africa, Sweden, Taiwan and Togo. This study also identified that digital e-learning platforms were a very important tool to help build capacity in a number of countries. To assess the level of potential in the network to support academic teaching and work-based training, sixty percent indicated that they would be willing to be recruited or commissioned to support teaching activities, demonstrating the value of the Nodes network to support the development of biodiversity informatics globally. The contributions and activities of various nodes across the network will be highlighted and a working high-level curriculum framework will be discussed.

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