dataesr/scientific-paragraphs-categorization huggingface dataset:
Step 4: Taxonomy of Adaptive Solutions for Each Type of Vulnerability A code for an automated text analysis was developed in R by the authors and was used as part of a three-step method analysis. The code is available as supplementary material. First, the code analyzed the frequency of words describing the projects associated with a certain type of risk that were gathered in our database, the classification of these occurrences, and links from the most important to the weakest, which allowed the creation of project categories. Then, the analyzed projects were manually distributed in the created categories. One project can correspond to several categories; in this case, they were each attributed to one of these classes. The text analysis process was restarted in a more detailed way on the projects not assigned at this stage, and this step was repeated until all the projects were classified (or only a few remained to be assigned manually).
The Taxonomic Approach The database of adaptation projects was built from two heterogeneous sources; the number of projects differs and the HPPs are much more detailed. This led us to look for more information in the NAPs when the NDCs were too succinct, and may lead, overall, to an overweighting of the least-developed countries. However, without ruling out any risk of bias, countries that have adopted NAPs have been able to benefit from a financial fund for least-developed countries set up by the Conference of the Parties (COP). It can therefore be assumed that differences in resources did not significantly affect the choices of countries that submitted their NAPs compared to those that submitted only their NDCs. Regarding the universality of the database, a review of the consultation and bidding process undertaken in 2014 and 2015 by multilateral development banks for the benefit of the Parties to the UNFCCC led us to formulate the hypothesis that the adaptation measures recorded in the NDCs were very often designed by independent experts, recommending specific actions for each country, and were designed in a rational manner with regard to climate issues and available knowledge (climate models, knowledge of ecosystems, etc.
), even if they were sometimes merely sketching well-documented climate planning. Thus, for the sake of the argument, we considered this 2015 database from the UNFCCC repertory representative of an independent position of each country. When this was not the case (application of routines or mimicry between several countries, interventions of the same consultants, crude cross-cutting recipes, etc. ), national choices should become more specific and independent each time as the successive revisions of national adaptation documents are submitted (with two major stages in 2020 and 2025). We acknowledge the biases that may arise from potential common decision-makers across countries and the lack of a "bottom-up" type of process in the development of most of these NDCs without this contradicting our hypothesis of independence as a first approximation.
Article 4 of this Agreement calls for these documents to be updated every five years, starting with a new submission of updated targets and means in 2020-2021. The new NDC database "version II" could be screened using our statistical method. It would allow us, on the one hand, to validate the taxonomies: As the transparency increases and the choices of solutions to address a specific risk converge, the taxonomy should be more precise and robust to changes. On the other hand, it would help validate the values of the fitness coefficients. A similar exercise could be carried out in 2025, with new NDCs displaying raised targets in the context of increased transparency. As the objectives and transparency of adaptation actions are progressively updated and strengthened, the NDCs in their successive versions would constitute an example of mankind's reference system for adaptation choices, often in relation to cross-cutting issues.
The comparison of one version with another of the NDCs and NAPs, as well as between stakes, intensities of vulnerabilities, and fitness values for each level of vulnerability, will be rich in lessons. We are aware that a characterization of vulnerabilities at a finer level (regional, for example) would allow a more precise analysis of the correspondence between "keys" (solutions) and "locks" (risks). A complementary analysis using hazard projections from regional climate models could be undertaken on this same 2015 database and, thus, constitute a more accurate version of a reference frame of climate solutions and challenges. It would also be relevant to renew the statistical analysis according to risks projected for 2030 or 2050 in order to complete this analysis of national risks in 2010.
Surber samples were collected after the colonization samples, sieved with a 500 µm sieve and the retained individuals formed the benthic macrofauna. After sorting, macrofaunal samples were preserved in 80% ethanol. The trait dataset used in this study is based on  Magliozzi et al. (2019)  and consists of behavioural, biological, morphological and physiological features reflecting organismal performance and adaptations to environmental pressures. At genus or family level, the macrofauna and meiofauna functional traits were fuzzy-coded using the trait information from  Tachet et al. (2010). Taxon affinities for the trait modalities were expressed as relative abundance distributions so that the sum of trait modality scores for an individual trait and taxon equals one. Some taxa were described as mean trait profiles of their potential families in the corresponding biogeographic area (i.
In every liter of seawater there are between 10 and 100 billion life forms, mostly invisible, called plankton, which form the largest and most dynamic ecosystem on our planet, at the heart of global ecological and economic processes. While physical and chemical parameters of planktonic ecosystems are fairly well measured and modelled at the planetary scale, but biological data are still scarce due to the extreme cost and relative inflexibility of the classical vessels and instruments used to explore marine biodiversity. Here we introduce 'Plankton Planet', an initiative whose goal is to merge the creativity of researchers, makers, and mariners to (i) develop frugal scientific instrumentation and protocols to assess the genetic and morphological diversity of plankton life, and (ii) organize their systematic deployment through fleets of volunteer sailors, fishermen, or cargoships to generate comparable and open-access plankton data across global and long-term spatiotemporal scales.
As proof-of-concept, we show how 20 crews of sailors ("planktonauts") were abl to sample plankton biomass from the world surface ocean in a single year, generating the first citizenbased, planetary dataset of plankton biodiversity based on DNA barcodes. The quality of this dataset is comparable to that generated by Tara Oceans and is not biased by the multiplication of samplers. This dataset has unveiled significant genetic novelty and can be used to explore the taxonomic and ecological diversity of plankton at both regional and global scales. This pilot project paves the way for construction of a miniaturized, modular, evolvable, affordable and open-source citizen fieldplatform that will allow systematic assessment of the eco/morpho/genetic variation of aquatic ecosystems across the dimensions of the Earth system.
Despite the availability of a century's worth of recorded data on oceanic plankton ((24, 25), and see for instance https://www. st. nmfs. noaa. gov/copepod/)), some areas of the ocean (including much of the south and tropical Pacific Ocean) are almost devoid of any biological observations and we do not yet have an informed vision of the global distribution and variation of plankton communities. The largest and longest homogenous survey of plankton life has been conducted using the Continuous Plankton Recorder (CPR), a visionary plankton-scroll instrument created by Sir Alister Hardy in 1931 (  26 ), and towed since then behind ferries and cargo ships, particularly in the North Atlantic, North Pacific, and Southern Ocean south of Australia (  27 ). The CPR database is currently the only basin-wide standardized historical record of ocean plankton life, and it has given rise to keystone studies describing and modelling basin-scale dynamics of plankton community over time and global climate change (e.
The combination of standardized DNA metabarcoding  (34, 35) , metagenomic  (36, 37)  and metatranscriptomic  (38, 39)  datasets has unveiled the basic structure of plankton taxonomic and metabolic diversity  (33, 40) , generating hypotheses about its interactions  (1) , dynamics in the seascape  (3) , and role in emerging ecosystem functions such as the carbon pump  (2, 41). Notably, the Tara Oceans team discovered that the great majority of plankton biodiversity is found in organismal size fractions <100µm, and above all in eukaryotes rather than viruses or prokaryotes  (34, 38). Given the enormous local (e. g. plastics, pollutants  (42, 43) ) and global (e. g. deoxygenation, warming, acidification, freshening, ocean circulation changes  (44, 45) ) anthropogenic pressures on our ocean, we urgently need comprehensive global plankton surveys that merge the spatio-temporal sampling power of the CPR with the systems-biology approach of Tara Oceans.
Only application of a comprehensive and homogenous measure of plankton life from micro-to meso-to global ocean scales  (7, 23, 46)  will provide the data necessary not only to unveil fundamental principles of ecology and evolution of marine life at the ecosystem level of organization, but also to feed mathematical models of the ocean system that integrate physical, chemical, and biological processes. Plankton Planet: toward a participative Oceanography 3. 0 However, this endeavor is hindered by the extreme cost and limited logistical flexibility of classical oceanographic research vessels and instruments, together with the current impossibility to use autonomous samplers (e. g. floats) to sample the biocomplexity of plankton and generate highquality data from it. In this context, the thousands of citizen sailing boats  (47) , professional sailing yachts, the >50,000 cargo ships (https://www.
ics-shipping. org/shipping-facts/shipping-and-worldtrade) and about the same number of global fishing vessels (https://globalfishingwatch. org/datasetsand-code/vessel-identity/) which are navigating the world ocean every day represent an outstanding opportunity. With the miniaturization of sequencing (e. g. 48) and imaging (e. g. 49) devices, the power of cloud computing and artificial intelligence, and the possibilities offered by a participative science engaging actors and volunteers across disciplines and societal/economic activities  (22, 50) , five years ago wea team of researchers, engineers, makers and sailors from France, the US, and New Zealand -started to develop a frugal approach and effective protocols to sample the world ocean plankton for the production of high quality eco/morpho/genetic data in collaboration with recreational and professional mariners.
The Oceanography 3. 0 is independent and universal, based on interconnected individuals and instruments. collect comparable eco-morpho-genetic data on plankton diversity and abundance at a planetary scale, a concept coined Oceanography 3. 0 (Box 1). In this paper, we present the first steps of this cost-effective, eco-friendly, and society-engaging Oceanography 3. 0. We show how the recruitment of the first 20 crews of citizen sailors -the planktonauts-, equipped with a simple kit to sample plankton for DNA-metabarcoding, were able to help generate a scientifically sound, planetary dataset of plankton biodiversity in less than a year. We finally discuss recent developments (see also  (51) ), as well as the 'Plankton Planet' perspectives toward the large-scale deployment of integrated, affordable and portable 'field-eco-scopes' for longterm assessment of ocean life and ecosystems at an unprecedented level of sensitivity.
Integration of P2 data into Tara Oceans data. Tara Oceans (TO) data are today a gold standard for ocean plankton ecology, and we first merged the P2 data into the global TO metabarcoding dataset (Sup. Text 1) for quality checks and comparison of content. As P2 samples were collected by different sailors, sometimes in harsh conditions, we first checked for the presence of obvious biases, such as the sequencing of bacterial contaminants. After taxonomic assignment of the rDNA reads (Sup. Text 1), we found that the percentage of prokaryotic reads per boat matched the expected value observed in TO (given the very large taxonomic spectrum of the eukaryotic PCR primers used that also amplify prokaryotic genes), with just a few outlier samples typically explained by on-board major processing errors (Sup.
P2 samples yielded a total of 57,994 OTUs, as compared to 158,716 for TO surface ocean samples. Phylogenetic breakdown of the rDNA data (Sup. Fig. 7 ) shows the overall taxonomic similarity, in both abundance and richness, between data from P2 and the TO micro-and mesoplanktonic size fractions. While 8,220 OTUs (mostly metazoans and alveolates) were shared exclusively between P2 and the TO larger (>20 µm) organismal size fractions, only 1,981 were common with the smaller (<20 µm) TO plankton size fractions (Fig. 2D ). This emphasizes the consistency of the organisms harvested by P2 and TO in comparable plankton size-fractions, and provides organismal data support for our principal component analysis (Fig. 2A ). Remarkably, however, more than half of all P2 OTUs (37,163) were actually not seen in any plankton size fractions of the TO circumglobal dataset (Fig.
The relatively large, microplanktonic taxa from the genus Protoperidinium were particularly well represented in the P2 dataset, with four OTUs unseen in the TO dataset. Note also the presence of many Blastodinium and Brandtodinium OTUs, which are wellknown parasites  (58)  and photosymbionts  (59)  of respectively copepods and radiolarians, and are thus part of the meso-and macro-plankton in their symbiotic stage. Overall, these analyses confirm the quality of the P2 data to assess plankton diversity from the OTU to the community level. Insights into multiscale plankton ecology using P2 data. We finally used P2 metabarcoding data to explore the consistency of macroecological patterns at both global and local scales, across the data collected by the different boats and planktonauts who sampled plankton independently.
Contextual environmental data. Given the importance of the abiotic environment in structuring planktonic ecosystems  (3, 4) , we also included the measure of basic environmental parameters at each P2 sampling site. Besides UTC date/time for each sampling event, the planktonauts were asked to record surface water temperature using the water temperature sensor of their boat and/or mercury thermometer (Fig. 3A ), and broad-band spectral water reflectance using the Hydrocolor App  (65). In situ temperature data were compared with those from the NASA's MODIS-Aqua satellite, and a good match -on average better than 1°C -was observed. We also developed automated procedures to extract remote sensing ocean color data at each sampling site (https://github. com/OceanOptics/getOC), providing bulk information on chlorophyll-a concentration, as well as suspended dissolved and particulate materials in the target water.
Operational PlanktoKit integrating eco-morpho-genetic data In the pilot stage of 'Plankton Planet', we have witnessed the enormous desire of sailors to sample ocean life during their voyages around the globe. We have also generated what is, to our knowledge, the first citizen-based homogenous planetary dataset to explore plankton biodiversity and ecology. Our objective is now to optimize the P2 sampling protocol and tools for large-scale deployment on navigation loops and transects crossing critical ecological and geographical dimensions of the Earth system. Along the way, we have identified critical challenges to overcome.
Toward a perennial, self-sustainable standardized citizen monitoring of global ocean life The cost of the upcoming P2 PlanktoKit 1. 0 (plankton collecting system, PlanktonScope, DNA-kit, software App to visualize, integrate, and store eco/morpho/genetic information) is estimated at 2,000-3,000 US$ per boat, allowing its deployment on hundreds of seatizen boats. The deployment of the first 100 kits will start in 2021 in the Atlantic and Pacific oceans, and is designed to address topical questions in ocean ecology and to test current models and theories that predict patterns of ocean biodiversity and functionality in relation to the physics of marine water and climate. Based on feedback between these unique data and modelling efforts, we will then fine tune the optimal density of sailboats (or fish boats or cargo ships) and sampling frequency for longer-term deployment of the P2 protocol on key navigation loops and routes that cover the world oceans.
g. on-board sailing boats or cargo-ships), and (iii) makers and freelance engineers who will design and construct the frugal modules to measure given parameters. The Plankton Planet ecosystem will manage communication and provide the overall coordination to ensure that all modules can be fully integrated into the miniaturized field-laboratory and generate good-quality standard data that are immediately shared in public databases. We believe that it is a matter of urgency to quantify and understand the structure, dynamics, and evolution of eco-systems. Over the last century, societies have spent phenomenal amounts of money to discover the properties of the infinitely small (atoms and cells) and the infinitely large (weather, planets and stars, and the universe). But it is in between these extremes that we find arguably the most complex object in the known universe: eco-systems, i.
The Plankton Planet Pilot Project was made possible by seed-funding from the Richard Lounsbery Foundation. Additional funding came from the French Government "Investissements d'Avenir" program OCEANOMICS (ANR-11-BTBR-0008), the 'Plankton Arts' grant from the Fondation d'Entreprise Total, the New Zealand Royal Society Dumont-D'Urville (DDU-CAW1501) and Strategic Seeding (16-CAW-008-CSG) Funds, the Cawthron Institute, as well as the Okeanos, Tara Ocean, and Schmidt Futures Foundations. We are grateful to Pierre Mollo, Francoise Gaill, Eric Karsenti, Paul Falkowski, Étienne Bourgois, Catherine Chabaud, Roland Jourdain, Isabelle Autissier, Raphaela le Gouvello, Daniel Richter, Chris Cornelisen and Gilles Boeuf, for their continuous advice and support. This article is contribution number 1 of Plankton Planet.
P2 protocols: plankton DNA sampling, storing, shipping, extracting, and sequencing After 15-20 min of net towing at a speed of ~2 knots (Sup. Fig. 2 ), 500mL of concentrated seawater were split and manually filtered onto two replicate 10µm polycarbonate filter-membranes (diameter 47mm), dried at 70°C for 5 min in a pan (on the boat gas-cooker), and then stored at room temperature in a hermetic Ziploc bag with granular desiccants (Sup. Fig. 2, 3 ). Upon arrival in a port, samples were shipped to the Roscoff Marine Laboratory (Brittany, France) in a simple envelope via regular mail. In the laboratory, the samples were stored at -80°C and information related to samples was archived in a database together with contextual data. DNA extraction was performed using a protocol modified from the NucleoSpin Plant Midi kit (Macherey-Nagel).
To address general questions of eukaryotic biodiversity over extensive taxonomic and ecological scales, the hyper-variable loop V9 of the Small Sub-Unit (SSU) ribosomal (r) RNA gene was targeted for the generation of amplicons by Polymerase Chain Reaction (PCR). This barcode presents a combination of advantages: (i) it is universally conserved in length and simple in secondary structure, thus allowing relatively unbiased PCR amplification across eukaryotic lineages followed by Illuminasequencing, (ii) it includes both stable and highly-variable nucleotide positions over evolutionary time frames, allowing discrimination of taxa over a significant phylogenetic depth, (iii) it is extensively represented in public reference databases across the eukaryotic tree of life, allowing taxonomic assignment amongst all known lineages.
Sampling coverage and putative cost In order to compare ocean sampling cost between P2 sailing boats and classical oceanographic vessel, we measured the shortest possible route between P2 sampling sites (traveling salesman problem) by a putative oceanographic vessel. For convenience, 144 P2 sampling sites (56% of total sites) from the Pacific (83), Indian  (1)  and Atlantic (60) Oceans with a depth >0m according to the ETOPO1 NOAA database (70) were selected. We then considered two ports as starting points for two navigation loops: Brest for the Atlantic Ocean samples plus 2 Pacific Ocean samples located at the extreme South of South America (62 samples in total); and San Diego for the other Pacific and Indian oceans samples (82 samples in total). For each group of stations, the shortest possible route that visits each station and return to the starting port was defined.
Bioinformatic data processing In order to compare P2 DNA metabarcoding data to the primary Tara Oceans (TO) eukaryotic metabarcoding dataset  (34) , we first merged raw reads from both datasets and applied the following bioinformatics steps. Paired Illumina TM MiSeq reads from the 214 P2 samples and the 883 samples from Tara Oceans (2009-2012) were assembled with vsearch v2. 7. 1 (73) using the command fastq_mergepairs and the option fastq_allowmergestagger. Demultiplexing and primer clipping were performed with cutadapt v1. 9 (74) enforcing a full-length match for sample tags and allowing a 2/3length partial match for forward and reverse primers. Only reads containing both primers were retained. For each trimmed read, the expected error was estimated with vsearch's command fastq_filter and the option eeout.
Each sample was then dereplicated, i. e. strictly identical reads were merged, using vsearch's command derep_fulllength, and converted to FASTA format. To prepare for clustering, samples were pooled and submitted to another round of dereplication with vsearch. Files containing expected error estimations were also dereplicated to retain only the lowest expected error for each unique sequence. Clustering was performed with Swarm v2. 2. 2 (75), using a local threshold of one difference and the fastidious option. The representative sequences of each molecular operational taxonomic unit (OTU) were then searched for chimeras with the vsearch's command uchime_denovo  (Edgar et al. , 2011). In parallel, the OUT representative sequences were taxonomically assigned with the stampa pipeline (https://github.
com/frederic-mahe/stampa/) and a custom version of the Protist Ribosomal Reference database PR2  (76). This custom reference database is an update of the V9_PR2 reference database used for the taxonomic assignation of the Tara Oceans metabarcodes  (34). Clustering results, expected error values, taxonomic assignments and chimera detection results were used to build a raw OTU table. Up to that point, reads that could not be merged, reads without tags or primers, reads shorter than 32 nucleotides and reads with uncalled bases ("N") were eliminated. To create the "cleaned" OTU table, additional filters were applied to retain only: non-chimeric OTUs, OTUs with an expected error per nucleotide below 0. 0002, and OTUs containing more than 3 reads or seen in 2 samples. The final OTU table contained integrated the 214 P2 samples together with the 386 Tara Oceans samples collected in surface waters for 4 organismal size fractions (0.