GOOS Essential Ocean Variables: the backbone of a sustained and evolving global ocean observing system - Belén Martín Míguez; Emma Heslop; Narissa Bax; Lisandro Benedetti-Cecchi; Gabrielle Canonico; Kim Currie; Karen Evans; Albert S. Fischer; Véronique Garçon; Maria Hood; Johannes Karstensen; Ana Lara-López; et al. (Frontiers in Marine Science, 2026)
The need for ocean information has never been greater. From climate change to food security and extreme events, we need to understand the role of the ocean and better predict change and impact. This is only possible with the sustained collection of a key set of ocean observations. The Global Ocean Observing System (GOOS) coordinates international efforts to collect these Essential Ocean Variables (EOVs), which span physics, biogeochemistry, biology and ecosystem realms. Guided by three expert panels, these EOVs are used to define the needs and design of a sustained, fit for purpose global ocean observing system, aimed at maximizing investments in observing infrastructure. As the GOOS EOVs are increasingly used, it has become important to discuss and refine the understanding of this framework, to ensure that the right balance is struck between their essential nature and the need to expand to new domains and integrate with key global policies. In this paper we provide a description of the EOV framework, discuss some of the challenges in implementing it, and identify a set of recommendations for GOOS and the ocean observing community to take forward. These recommendations include increasing the transparency of the EOV adoption process, and the need to periodically assess the EOVs in consultation with observing communities and with the entities managing other global essential variable frameworks in cross cutting realms such as climate and biodiversity. This will contribute to building a useful and responsive global ocean observing system that delivers the observations required to meet societal needs.

Global ocean indicators: Marking pathways at the science-policy nexus - Karina von Schuckmann; Alex Godoy-Faundez; Véronique Garçon; Frank E. Muller-Karger; Karen Evans; Ward Appeltans; Narissa Bax; Lisandro Benedetti Cecchi; Anthony Bernard; Kim Bernard; Jarret Byrnes; Gabrielle Canonico; et al. (Marine Policy, 2026)

BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series - Maria Dornelas; Laura H. Antão; Amanda E. Bates; Viviana Brambilla; Jonathan M. Chase; Cher F. Y. Chow; Ada Fontrodona‐Eslava; Anne E. Magurran; Inês S. Martins; Faye Moyes; Alban Sagouis; Samuel Adu‐Acheampong; et al. (Global Ecology and Biogeography, 2025)
Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

Validation of pop-up satellite archival tags (PSATs) on Atlantic cod (Gadus morhua) in a Greenland fjord - Julius Nielsen; Daniel Estévez-Barcia; Søren Post; Helle Torp Christensen; Anja Retzel; Lorenz Meire; Frank Rigét; John Frederik Strøm; Audun Rikardsen; Rasmus Hedeholm (Fisheries Research, 2023)

Rise and fall of SARS-CoV-2 variants in Rotterdam: Comparison of wastewater and clinical surveillance - Ray W. Izquierdo-Lara; Leo Heijnen; Bas B. Oude Munnink; Claudia M.E. Schapendonk; Goffe Elsinga; Jeroen Langeveld; Johan Post; Divyae K. Prasad; Christian Carrizosa; Frederic Been; Janko van Beek; Remy Schilperoort; et al. (Science of The Total Environment, 2023)

BioTIME: A database of biodiversity time series for the Anthropocene - Maria Dornelas; Laura H. Antão; Faye Moyes; Amanda E. Bates; Anne E. Magurran; Dušan Adam; Asem A. Akhmetzhanova; Ward Appeltans; José Manuel Arcos; Haley Arnold; Narayanan Ayyappan; Gal Badihi; et al. (Global Ecology and Biogeography, 2018)
Motivation The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km 2 (158 cm 2 ) to 100 km 2 (1,000,000,000,000 cm 2 ). Time period and grain BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format .csv and .SQL.

Visceral fat is associated with brain structure independent of human immunodeficiency virus infection status - Jordan E. Lake; Mikhail Popov; Wendy S. Post; Frank J. Palella; Ned Sacktor; Eric N. Miller; Todd T. Brown; James T. Becker (Journal of NeuroVirology, 2017)

A global database of nitrogen and phosphorus excretion rates of aquatic animals - Michael J. Vanni; Peter B. McIntyre; Dennis Allen; Diane L. Arnott; Jonathan P. Benstead; David J. Berg; Åge Brabrand; Sébastien Brosse; Paul A. Bukaveckas; Adriano Caliman; Krista A. Capps; Luciana S. Carneiro; et al. (Ecology, 2017)
Animals can be important in modulating ecosystem‐level nutrient cycling, although their importance varies greatly among species and ecosystems. Nutrient cycling rates of individual animals represent valuable data for testing the predictions of important frameworks such as the Metabolic Theory of Ecology ( MTE ) and ecological stoichiometry ( ES ). They also represent an important set of functional traits that may reflect both environmental and phylogenetic influences. Over the past two decades, studies of animal‐mediated nutrient cycling have increased dramatically, especially in aquatic ecosystems. Here we present a global compilation of aquatic animal nutrient excretion rates. The dataset includes 10,534 observations from freshwater and marine animals of N and/or P excretion rates. These observations represent 491 species, including most aquatic phyla. Coverage varies greatly among phyla and other taxonomic levels. The dataset includes information on animal body size, ambient temperature, taxonomic affiliations, and animal body N:P. This data set was used to test predictions of MTE and ES , as described in Vanni and McIntyre (2016; Ecology DOI : 10.1002/ecy.1582 ).

Discordant CSF/plasma HIV-1 RNA in patients with unexplained low-level viraemia - Sam Nightingale; Anna Maria Geretti; Apostolos Beloukas; Martin Fisher; Alan Winston; Laura Else; Mark Nelson; Stephen Taylor; Andrew Ustianowski; Jonathan Ainsworth; Richard Gilson; Lewis Haddow; et al. (Journal of NeuroVirology, 2016)

Interaction of brain 5-HT synthesis deficiency, chronic stress and sex differentially impact emotional behavior in Tph2 knockout mice - Lise Gutknecht; Sandy Popp; Jonas Waider; Frank M. J. Sommerlandt; Corinna Göppner; Antonia Post; Andreas Reif; Daniel van den Hove; Tatyana Strekalova; Angelika Schmitt; Maria B. N. Colaςo; Claudia Sommer; et al. (Psychopharmacology, 2015)

Pharmacological interventions in the newborn piglet in the first 24 h after hypoxia-ischemia - Cacha Peeters-Scholte; Evelyn van den Tweel; Tomoaki Ioroi; Ilka Post; Kees Braun; Wouter Veldhuis; Klaas Nicolay; Floris Groenendaal; Frank van Bel (Experimental Brain Research, 2002)

Changes in cerebral haemodynamics, regional oxygen saturation and amplitude-integrated continuous EEG during hypoxia-ischaemia and reperfusion in newborn piglets - Tomoaki Ioroi; Cacha Peeters-Scholte; Ilka Post; Cheraar Leusink; Floris Groenendaal; Frank van Bel (Experimental Brain Research, 2002)