dataesr/scientific-paragraphs-categorization huggingface dataset:
At this time the Steering Committee of the European chapter of the Extracorporeal Life Support Organization (Euro-ELSO) initiated prospective data collection among European and Israeli centres with the intention of providing near real-time information on ECMO use in COVID-19. The study was approved by the Maastricht University Ethical Committee (coordinating center) and is registered under ClinicalTrials. gov identifier: NCT04366921. Data are collected weekly and reported anonymously through the Euro-ELSO website https ://www. euroe lso. net/covid -19/covid -19-surve y/. This voluntary study includes basic data on patients´ age and gender, the details of their ECMO treatment and real-time status (i. e. , ongoing, successfully weaned, or died). Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients.
Acknowledgements We thank below  Euro-ELSO Steering Committee Members  for the approval and a critical review of the study:  M. Velia Antonini  ( Intensive Care Unit, University Hospital of Parma, Parma, Italy );  Nicholas A. Barrett ,  M. D. , ( Guy's and St Thomas' NHS Foundation Trust, London, UK) ;  Mirko Belliato M. D. , PhD. , ( Department of Intensive Medicine ,  IRCCS Policlinico San Matteo Foundation ,.
Climate change is altering the water cycle globally, increasing the frequency and magnitude of floods and droughts. An outstanding question is whether biodiversity responses to hydrological disturbance depend on background climatic context -and if so, which contexts increase vulnerability to disturbance. Answering this question requires comparison of organismal responses across environmental gradients. However, opportunities to track disturbed communities against an undisturbed baseline remain rare. Here we gathered a global dataset capturing responses of aquatic invertebrate communities to river drying, which includes 112 sites spanning a gradient of climatic aridity. We measured the effects of river drying on taxonomic richness and temporal β-diversity (turnover and nestedness components).
Global dataset of invertebrate communities We assembled 13 aquatic invertebrate community datasets from seven countries worldwide (Table  1 , Fig. 1 , Supporting information). Our dataset contained 224 samples collected from 112 stream sites, which were classified as either intermittent (n = 67) or perennial (n = 45; Table  1 , Fig. 1 ). Stream sites covered a wide environmental gradient from firstorder, headwater streams to larger gravel-bed, braided rivers. Samples were collected from sites that ranged from pristine conditions to mildly affected by anthropogenic pressures (primarily agriculture); however, in all cases flow intermittence was the main source of disturbance or stress affecting invertebrate communities. Datasets included samples collected on two occasions from at least one intermittent (INT) and one perennial site (PER): before and after a drying event occurred at INT sites, with visits at PER sites acting as control baselines (i.
e. an experimental design akin to BACI; further details described in Supporting information). Visits took place when streams were flowing (i. e. before flow cessation, and after flow resumption). The timing and duration of river drying events was estimated using a combination of temperature data loggers, discharge gauging stations, modeling and expert opinion. Invertebrate samples were collected at 2-6 sampling stations (4 ± 1. 6, mean ± SD) within a defined site using standardized and consistent sampling methods that targeted the entire invertebrate community (Surber, Hess and kick-net samplers; 250-500 µm mesh size). When samples representing multiple habitats were not composited in the field (i. e. within-site replicates; 8 of 13 datasets), we aggregated samples within each site to create comparable composite samples.
Taxonomy across datasets was homogenized to genus level, and we excluded taxa identified to family or higher taxonomic levels (Supporting information). As a consequence, we included 545 066 individuals (mean ± SD = 71 ± 31%, range = 22-97% of individuals from original datasets) comprising 402 taxa (mean ± SD = 73 ± 19%, range = 53-91% of taxa from original datasets; Supporting information). Although this conservative filtering process likely decreased our ability to detect patterns in the data, it reduced the potential effects that uncontrolled variation in taxonomic resolution could have on the detected patterns.
g. thresholds) comparable across several trait databases, following  Datry et al. (2014)   information). Although assigning traits at the family level rather than species level can reduce the accuracy of trait information, family-level assignment of traits has proven sufficient to detect community responses to drying in a variety of intermittent river systems  (Bonada et al. 2006 , Chessman 2009 , Datry et al. 2014 ). Only taxa with complete trait information were used for trait analysis, comprising 83 out of 114 families and 92 ± 10% of individuals. Taxa excluded from trait analysis are provided in Supporting information.
Regional-scale environmental covariates We chose climatic aridity (hereafter, aridity) as a proxy for climatic harshness because this continuous metric represents a ratio between precipitation and potential evapotranspiration, and can thus be mechanistically linked to the severity of drying stress at a particular site. We used mean aridity index (AI) from the 1950 to 2000 period at 30 arc second spatial resolution, available from the Consortium for Spatial Information's Global-Potential Evapotranspiration and Global Aridity Index dataset  (Zomer et al. 2008; <www. cgiar-csi. org>). Aridity was calculated based on the WorldClim global climate data  (Hijmans et al. 2005) , following: AI = MAP/MAET, where: MAP = mean annual precipitation, and MAET = mean annual potential evapotranspiration. To improve interpretability of this index, we subtracted AI from 1 so that more arid sites had higher AI values.
General relationships between taxonomic and trait richness versus aridity Taxonomic richness was estimated for each sample following  Chao et al. (2014) , which allows for rigorous comparisons of taxa richness when abundances differ between samples. Trait richness was defined as the total number of traits conferring either resistance or resilience to drying within the invertebrate community. For each site, we combined before and after samples. We then tested for relationships between taxonomic and trait richness, and between each richness metric and aridity using linear mixed-effects models (LMM;  Bolker et al. 2009). LMM used flow regime (categorical; INT, PER), time (categorical; before, after) and aridity index (continuous; range 0. 21-1. 46) as fixed-effect terms, and included dataset as a random intercept to account for potential dataset-specific variability in taxonomic richness.
Changes in taxonomic richness and the relative abundance of resistant and resilient taxa At each site, we tested the effects of river drying, aridity and drying duration on changes in taxonomic richness and on the relative abundance of resistant and resilient taxa before and after drying at each site. Taxa were considered either resistant, resilient or both if they had at least one of the traits mentioned above. We fitted LMM using flow regime, aridity and drying duration (21-360 days) as fixed-effect terms, and dataset as a random intercept. We log-transformed taxa richness to meet model assumptions.
We visually assessed compositional changes using Bray-Curtis dissimilarity in a nonmetric multidimensional scaling (NMDS) ordination and tested the effects of flow regime and time of sampling (before versus after drying) on compositional changes using a nonparametric multivariate test (PERMANOVA). All data analyses and visualizations were done in R (ver. 4. 0. 1, <www. r-project. org>), using the packages iNEXT  (Hsieh et al. 2016 ), lme4  (Bates et al. 2014) , betapart  (Baselga 2010) , vegan  (Oksanen et al. 2007 ) and ggplot2  (Wickham 2009).
General relationships between taxonomic richness, trait richness and aridity Local taxonomic richness at sites across the global dataset was 20 ± 5 taxa per sample (mean ± SD), and ranged between 4 and 52 taxa per sample. Mean invertebrate abundance was 2901 ± 1938 individuals per sample, and ranged between 7 and 25 611 individuals per sample. We found a positive, steep relationship between the richness of taxa in a given community and the richness of traits conferring resilience or resistance to drying (prediction 1a). This relationship did not differ between perennial (PER) and intermittent (INT) sites (LMM, Table  2 , Fig. 2a ). Taxa richness was higher at PER sites compared to INT sites, and differences in taxa richness between INT and PER sites increased with aridity (LMM, Table  2 , Fig.
Acknowledgements -We thank  Lawrence Eaton ,  Scott Larned  and  Bruce Chessman  for contributing data for this analysis. Funding -NC was supported by the  French research program Make Our Planet Great Again. AR was supported by funds from the  Univ. of California, Berkeley , and  NSF  # 1802714. RVV was supported by funds from  Univ. of Wisconsin ,  La Crosse. Publication made possible in part by support from the  Berkeley Research Impact Initiative (BRII)  sponsored by the  Berkeley Library.
, 2002; Fedak, 2013; Hussey et al. , 2015). The number of species, studied and the amount of data being collected has proliferated in the last two decades  (Fedak, 2013) , requiring centralized coordination and management of metadata and data collections, and the creation and use of best practices, particularly in relation to data quality control and animal welfare. These activities will be the primary role of AniBOS. Here we present an overview of AniBOS 1.
ANIMAL BORNE OCEAN SENSORS SCOPE AND OBJECTIVES Since 2002, instrumented animals have generated and transmitted via the Global Telecommunication System (GTS) over 650,000 conductivity-temperature-depth (CTD) profiles. The animal-borne ocean observing community started as a series of independent programs to collect animal behavior and oceanographic data. However, the vast amount of generated data necessitated international coordination to provide standardized, quality-controlled data to the oceanographic community  (Roquet et al. , 2014). To facilitate and coordinate this effort, the Marine Mammals Exploring the Oceans Pole to Pole (MEOP 2 ) was born as part of the International Polar Year  (Roquet et al. , 2017; Treasure et al. , 2017). MEOP ensured that these animal-derived physical data were readily and publicly available for operational and scientific applications (Figure  1 ).
In November 2019, a workshop with key researchers and instrument manufacturers prepared, formalized, and outlined the structure, function, and data protocols (collection, quality control and sharing) for a global animal-borne ocean observing system. Following the workshop, an application to the GOOS Observation Coordination Group (OCG) for formal recognition as a contributing network was submitted in April 2020. AniBOS was officially recognized as an emerging GOOS network in June 2020. The overarching goals of AniBOS are to enhance scientific understanding of our changing oceans via in situ data collection by marine animals, with the core mission of making oceanographic measurements and animal movement data from animal-borne instruments including from the most inaccessible and under sampled regions of the global ocean freely available.
, 2015; McLaughlin, 2015; Lennox et al. , 2020) , logistical difficulties and expense of accessing remote regions for deployments, and the high cost of some autonomous vehicles constrains where observations are made. Marine animals carrying oceanographic sensors offer realistic solutions to many of these issues because animals inhabit many of these poorly sampled regions and swim multiple times per day through the water column to depths of several hundred meters (Figures  1, 2 ) and are able to sample temperature and salinity at appropriate resolutions to complement observing networks such as Argo (Table  2 ). OceanOPS (formerly known as JCOMMOPS) will provide key support to AniBOS for mapping coverage relative to other GOOS networks. OceanOPS does this through providing a dedicated focal point, developing specific performance monitoring tools, developing reporting capacity, promoting data and metadata harmonization and enabling operational tools (e.
Species Oceanographic data have been collected using sensors carried by a wide range of marine vertebrates, including marine mammals (phocid seals mainly), seabirds, turtles, sharks and other fish (Figure  3 ). The type of oceanographic data collected and the FIGURE 1 | An overview schematic of the procedures followed to collect oceanographic data using instrumented marine animals (illustrated here with a southern elephant seal). After calibration, instruments are attached to the animal and records hydrographic profiles and behavioral observations during the animal's at-sea foraging trips. The instruments transmit summarized pre-processed data via the Argos constellation of satellites. Hydrographic profiles are then delivered to the Global Telecommunication System (GTS) after near real time quality control.
Geo-Positioning Sensors Animal biotelemetry data are most commonly relayed through Service Argos and its constellation of polar-orbiting satellites FIGURE 3 | Schematic illustrating the Essential Ocean Variables (EOV) that can be measured across all latitudes by animal-borne sensors, using either archival, acoustic or satellite telemetry. These EOV provide key information about the state and dynamics of the ocean and the marine environment. The measurement of vertical temperature and salinity profiles are the most mature and constitute the core activity of AniBOS. (Argos, 2016). Because of limited available satellite bandwidth, continuously recorded measurements on-board the tag must be compressed and summarized prior to being transmitted either pseudo-randomly or following a set of user-defined rules to prioritize near real time CTD data transfer  (Fedak et al.
PDTs are derived in different ways, depending on the instrument type and manufacturer programming. For example, 'Fixed Depth' PDTs report temperature at pre-determined depths corresponding to the World Ocean Database or World Ocean Atlas depth tables. Summary period PDTs, often created by pop-off tags deployed on non-air breathers, by contrast generate profiles based on userdefined bin ranges and the actual depths traversed by the tagged animal during the defined summary period. These PDTs are often created by pop-off tags deployed on non-air breathers. A 'Dive' PDT comes from an air-breathing animal and reports the depthtemperature pairs along the ascent of the dive (i. e. , on the upcast). To maximize the amount of data that is transmitted each time the animal surfaces raw profiles are summarized using a broken stick algorithm that selects and sends only the important inflection points within each profile  (Fedak et al.
Calibration is also performed post-deployment if the tag is recovered. Long-term drift and the impact of biofouling on the conductivity readings have yet to be characterized. It is noteworthy that the electrode-based conductivity cells in the SCOUT-CTD, in contrast to inductive cells used in the SMRU CTD-SRDLs, are not subject to near-field effects that can cause instabilities in the salinity readings  (Hooker and Boyd, 2003) , which are corrected during delayed mode quality control processing of the SMRU CTD-SRDL observations  (Mensah et al. , 2018). The SCOUT-CTD formats the depth, temperature and salinity readings into WOD/WOA (World Ocean Database/World Ocean Atlas) standard fixed depth profiles during the ascent phase of qualifying dives, then takes a Fastloc R GPS snapshot when the animal reaches the surface.
DATA MANAGEMENT Standardized Data and Metadata Standardized data and metadata formats are required for interoperability with other ocean observation sources such as floats and gliders. Although the specific standardized specifications are currently under evaluation and may evolve, an initial standard to be adopted will be consistent with MEOP, using two data formats: time-referenced data and profiling (depthreferenced) data. The Argo data management group develops and maintains such data files 3  , in netCDF format following Climate and Forecast (CF) standards. The Sea-Mammals netCDF data format, currently used to store and distribute data in the MEOP databases 4  has been adapted from the Argo data profile and trajectory format specifications. The NASA/JPL Oceanographic In situ data Interoperability Project (OIIP) has progressed interoperability for marine animal tagging data through their development of the netCDF electronic tag data template specification (nc-eTAG;  Tsontos et al.
Level 1 products and metadata will be sent, within 24 h, to a Global Data Assembly Center (GDAC), where additional scrutiny can be applied prior to public access by end users, national research facilities and aggregated databases. The GDAC is operated using a federated strategy and united by a high level of data format standardization that will make complete metadata (e. g. , compliant with the ISO 19115 standard for geospatial metadata) publicly discoverable, downloadable and queryable via interoperable web services and technologies using standard protocols. Delayed-mode data products (Level 2) are produced (yearly release is targeted), benefiting from the input of regional data experts, researchers, manufacturers and technical experts collaborating on the AniBOS network. The AniBOS Data Committee will supervise this work in association with the DAC's.
These products are subjected to stringent processing that include the quality control of location and ocean profile data. The delayed-mode quality control (DMQC) processes will be standardized globally, using the Level 1 product built by the GDAC as the reference dataset onto which editing, recalibration and data adjustment will be applied. The DMQC builds upon the set of similar procedures that were developed by MEOP, or by different regional teams  (Roquet et al. , 2011 (Roquet et al. , , 2014;; Siegelman et al. , 2019; Jonsen et al. , 2020). New methods to accommodate new tags and/or sensors become available will be developed as necessary. The GDAC provides all Level 2 data publicly available and also archives the data at appropriate national/regional facilities. Currently, the United States Integrated Ocean Observing System (IOOS) Animal Telemetry Network (ATN) and Australia's Integrated Marine Observing System (IMOS) Australian Ocean Data Network serve as regional DACs.
Data Accuracy and Precision All sensor accuracy/precision and calibration attributes will be reported in the metadata products to ensure appropriate handling during QC and creation of derived products. The minimum precision associated with the data provided by a tag is critical and will vary depending on manufacturer and tag type. Similarly, measurement accuracy will need to be specified for all datasets (e. g. , location, temperature, salinity, and chlorophyll-a). Rather than imposing strict minimum thresholds on sensor accuracy and precision, this approach will allow greater assimilation of observation data sets across diverse tag types. To ensure consistent data quality across platforms AniBOS will adopt the Argo flagging system (Argo data management, doi 6  ).
THE ETHICS OF ANIMAL OCEANOGRAPHERS Animals collecting oceanographic data have enhanced greatly our understanding of animal behavior and how the environmental affects this behavior. Central to the ongoing success of AniBOS is ensuring that contributors use the best animal handling practice that prioritizes animal welfare (Table  3 ). The Ethical Advisory Board is a central component of AniBOS (see above). The board's primary focus is to provide ethical oversight of the network activities involving the handling of animals and the attachment of sensors. The AniBOS community has been proactive and already much has been done to ensure animal welfare is at the core of our activities. This has been done through publishing standard operating procedures that describe in detail the capture, handling, instrument attachment and care of animals (Table  3 ).
ANIMAL BORNE OCEAN SENSORS AND THE FUTURE OF OCEAN SCIENCE AND OBSERVATIONS Globally there is a need to increase ocean observing to all parts of the ocean and how this ocean monitoring is translated from observations to applications. AniBOS is committed to helping retain a healthy and resilient ocean, with marine ecosystems that are better understood, protected, restored and managed to promote conservation and food security. AniBOS provides open and equitable access to technology, data and information to narrow knowledge and observational gaps while helping society understand and respond to changing conditions. Ongoing long-term funding is an essential part of maintaining AniBOS and indeed GOOS. Currently AniBOS relies on a mixture of investment derived from several national research projects and in Australia longer term investment from the Integrated Marine Observing System and this represents a vulnerability to the network.
However, to build greater funding security AniBOS has secured endorsement of the network through the UN Decade of the Ocean initiative. International endorsements like this capture the attention of national programs that may facilitate government investment. Indeed, two of the countries currently participating in AniBOS have already made formal budget requests for additional resources to enhance and sustain their national program contributions to AniBOS illustrating national recognition of the network's value and importance. While data sharing within the marine ecology community has previously occurred intermittently a number of studies illustrate and demonstrate the increased productivity by data sharers within the animal tracking community  (Nguyen et al. , 2017; Lennox et al. , 2020) ; several independent initiatives where multiple datasets have already been shared for large synthetic studies, e.
g. , the Retrospective Analysis of Antarctic Tracking Data  (Hindell et al. , 2020; Ropert-Coudert et al. , 2020)  the Global Shark Movement project  (Queiroz et al. , 2019) ; MegaMove  (Sequeira et al. , 2018)  as well as mandated data sharing by data providers  (Hill et al. , 2010). In addition, some data centers are now including the minting of DOI's as part of their data archiving service (e. g. , the U. S. ATN Data Assembly Center). Animal Borne Ocean Sensors is a global inclusive network that has welcomed contributions of data collected from the global ocean from the onset and emphasized the need for community engagement and consideration of currently under-sampled ocean regions as a means to narrow technological, scientific and capacity development gaps  (Fedak, 2004; Hussey et al. , 2015; Yonehara et al.
FUNDING Funding and support for the November 2019 Network Development Workshop was provided by the  Integrated Marine Observing System (IMOS)  and the  Australia Research Council's Special Research Initiative for Antarctic Gateway Partnership  ( SR140300001 ) through the  University of Tasmania's Institute of Marine and Antarctic Studies. IMOS is a national collaborative research infrastructure, supported by the  Australian Government  and operated by a consortium of institutions as an unincorporated joint venture, with the  University of Tasmania as Lead Agent. This research contributes to the  Australian Research Council Discovery  Project  DP180101667  and  DP210103091. SBe was supported under the  Australian Research Council   DECRA DE180100828. IJ was supported by  Macquarie University 's co-Funded Fellowship Program with external partners:  Office of Naval Research  ( N00014-18-1-2405 ); the  Integrated Marine Observing System -Animal Tracking Facility ; the  Ocean Tracking Network ;  Taronga Conservation Society; Birds Canada ; and  Innovasea/Vemco.