dataesr/scientific-paragraphs-categorization huggingface dataset:
The Digitisation and Data Staging Module will use cloud-based services to securely host the data generated and needed by the "scientific" community and other users in a 'Data as a Service' (DaaS) model (see below). All partners, including those who do not have capacity to host and mobilise large data collections locally, will have the capacity to analyse data as it streams in from global expeditions and integration activities. Importantly, this module does not seek to be a long-term archive itself, but a working space to promote standardised data handling to speed discovery and transparency. For data types with matching global long-term archives/databases (e. g. INSDC or OBIS), this module provides a local, shared storage solution to prepare Ocean Census data products for submission. For data types without a long-term, multinational archive, this module will provide mid-term storage and preservation of data in the Ocean Census Digital Incubator until viable long-term options are available (see below).
The Data Processing Module will host the software and services to analyse data from Ocean Census, its partners and/or external sources before these data are distributed to partners, decisionmakers and the public. The Data Processing Module will contain collections of software and tools for Ocean Census personnel and collaborators to transform, integrate, analyse, visualise, and communicate the results of the programme's digitisation efforts and it will be combined with data imported through the Cyber-Biodiversity System. It will rely on standardised data in the Digitisation and Data Staging Module and the standard operating procedures developed in the early stages of the programme. This module will offer 'software as a service' (SaaS) via the cloud and will ensure that consistent, traceable, and reproducible methods are used on data and information in the Ocean Census Cyber-Biodiversity System.
For partners without local computing capacity, the Data Processing Module will provide a shared processing facility, while also providing tools that can be used in others' systems to achieve the same Ocean Census-grade standardisation, quality assurance, and interoperability with external systems. We view this as essential in the democratisation of species discovery for the global marine taxonomy community. Building on the two prior modules, the Digital Exchange Module will facilitate access to and the delivery of Ocean Census data between all partners to online repositories and platforms. The Digital Exchange Module will be designed to facilitate delivery of 'gold standard' marine biodiversity data, as well as to ensure that the programme's digital reach and impact are tracked. The Ocean Census Digital Incubator will support communities with developing and adopting new technologies to transform how they digitally capture and communicate data on ocean life.
g. large-scale storage, automation and application of machine learning to imagery data; see below) and country-or region-specific capacity development to adopt or integrate data management and science into workflows, supported by the Ocean Census network of institutions and researchers. We recognise that other cyber-taxonomy platforms exist (e. g. the European Distributed Institute of Taxonomy [EDIT] Platform for Cybertaxonomy;  Berendsohn et al. , 2011)  and, following the philosophy to make use of what has already been developed rather than repeat work, we will integrate the Ocean Census Cyber- Diagram illustrating the digitisation of data related to the collection of a marine organism and the generation of Digital Life Forms, the flow of data into the Ocean Census Cyber-Biodiversity System and flows of data to global databases and data incubators (and return of data to the Cyber-Biodiversity System).
Tools will be generated to translate data to actionable knowledge for different user groups (e. g. policy makers, scientists, industry, civil society). OBIS, Ocean Biodiversity Information System; WORMS, World Register of Marine Species; WOD, World Ocean Database; NODCs, National Oceanographic Data Centres; INSDC, International Nucleotide Sequence Database Collaboration. Urchin icon is available under a Creative Commons License 3. 0 from Iconfinder (https://www. iconfinder. com/). Biodiversity System with these or at the very least ensure it is interoperable with them. We also note that the Ocean Census Cyber-Biodiversity System will be developed through extensive consultation on all aspects of the platform to ensure the system meets the needs of scientists, ocean managers, industry and policymakers.
Once the system is established, applications can be further developed to meet the needs of these user communities. Options may include enhancing use of Ocean Census data in leading decision-support tools and platforms, tailored to public or private sector decision-making needs, or harnessing citizen science initiatives to generate data on ocean life in collaboration with indigenous and local communities. These pathways could be established by strengthening the ocean knowledge base of existing policy-and decision-support platforms, such as the Exploring Natural Capital Opportunities, Risks and Exposure (ENCORE) or the UN Biodiversity Lab, among many others. This will require deeper curation and processing of decisiongrade derivative products from Ocean Census data (e. g. , species occurrence records, abundance, biomass, etc.
, 2022a). Machine learning has been applied to specific identification projects or tasks (e. g. identification of fish or plankton from image data or whales from acoustic monitoring data;  Goodwin et al. , 2022; Katija et al. , 2022). There are developing initiatives to generate global databases for some organisms, including plankton and benthic organisms  (Goodwin et al. , 2022; Irisson et al. , 2022). However, the development of ever more powerful computing capacity, new approaches to pattern recognition and larger collections of images/videos could enable AI to play a greater role in species identification (e. g. MacLeod et al. , 2010)  if taxonomistcurated images were available in the large quantities necessary for producing robust training data. To address this bottleneck problem of labelled high quality training data, the machine learning community has proposed new learning algorithms such as selfsupervised learning that can make use of non-annotated data for improving performance (for instance in plankton image analysis;  Midtvedt et al.
Through building capacity across the biodiversity value chain, we will also provide individual countries with the capability and opportunity to lead surveys in their own waters (providing tailored support as needed) including, for example, for biodiversity discovery and environmental assessment and enhance their capability to engage in management and policy activities. Skills development will be tailored to meet the needs and aspirations of the individuals. Such skills development will likely consist of a mix of hands-on training held during expeditions, general training at national and regional centres, and more specific skills training at the Ocean Census Biodiversity Centres. Technical skills training (e. g. sample processing, database management, communication) will be extended to participants at national and regional centres, while specific specialist training at the Biodiversity Centres will develop specific skills and knowledge (potentially as part of a higher degree).
These institutions have established collections of ocean species and significant infrastructure for taxonomic studies including imaging, sequencing and other advanced technologies discussed above. Their primary activities will include: Ocean Census where they may not be generally available for FIGURE 6 The open network structure of the Ocean Census programme. Core to developing a lasting legacy in acceleration of discovery and description of marine species is the development of a network of Biodiversity Centres and Virtual Taxonomy Networks enabling taxonomists to cooperate and coordinate their activities globally. Expeditions generate samples for species discovery and also stories for ocean literacy. Data generated by expeditions and taxonomists are communicated to the Ocean Census Cyber-Biodiversity System.
We envisage ten or more such networks that could grow from informal taxonomy guilds that already exist for organism groups such as isopods, bivalves, sea urchins, worms, red algae etc. This has proven the most effective way of harnessing taxonomic expertise for projects such as the WoRMS database. An experienced taxonomist within each network will be resourced to organise discussion groups, identification/training workshops (based on specimens from Ocean Census Expeditions), mentorship of emerging experts such as early career researchers, establishment of minimum standards of species description and publication, validation of new species discoveries and descriptions generated by Ocean Census, validation of sequence and distribution records in global databases, preparation of biogeographic datasets, and to organise the sequencing or imaging of pivotal specimens (e.
, 2022a). However, this approach is limited by the lack of sequences that are attributable to known species  (Rogers et al. , 2022a). Because of the integrated approach used by Ocean Census in terms of collection of sequence data alongside species, the programme will greatly assist in populating the reference sequence libraries within t h e B a r c o d e o f L i f e D a t a S y s t e m ( B O L D ; h t t p s : / / www. boldsystems. org/) and global DNA databases (INSDC). This will enhance the use of eDNA approaches for assessment of species diversity and how it changes over time as a result of global or local anthropogenic stressors or as a result of management interventions. eDNA is increasingly being used as a biomonitoring tool, enabling the identification and quantification of species within different marine ecosystems (e.
Summary Ocean Census will become the single largest global programme in history to discover and document ocean life and fulfil a major role in the great scientific challenge to describe and sequence all life on Earth, playing a key role in the protection of the ocean, and thus the Earth and human society that relies on it. Ocean Census will transform our understanding of the diversity and distribution of life in the ocean by accelerating species discovery through four main strategies: • Digitisation of all data associated with the collection of marine species to promote the use of cybertaxonomy including the development of an intermediate step between specimen collection and description of species termed here the Digital Life Form. The Ocean Census Cyber-Biodiversity System will be an integral part of this digitisation strategy collecting, storing and disseminating species data to appropriate existing global databases, archiving data for which such databases do not exist and converting data to knowledge useful to specific user communities (e.
g. scientists, ocean managers, policymakers, industry and civil society). Ocean Census will be organised as an open network of scientists and other specialists focused on the discovery and description of species in the ocean. This will be anchored by Biodiversity Centres and will be accompanied by Virtual Taxonomic Networks which focus on specific taxonomic groups to fulfil the Ocean Census mission. This open network approach will be central to democratisation of marine taxonomy/biodiversity research globally and is supported by the digitisation and data policies and capacity-building activities through Expeditions and Biodiversity Centres. Together these initiatives are essential to maximise human effort on species discovery. Ocean Census also comprises a major public outreach and education element with storytelling on the ocean focused on scientists, policymakers and citizens living in coastal countries hosting Expeditions.
Acknowledgments AR and OS would like to thank  The Nippon Foundation  for funding the  Ocean Census Feasibility Study  and for providing constant and stimulating intellectual input and support to the programme development from  2021-2023. Special thanks is also given to the  Working Groups on Science, Digitisation and Capacity Development  who graciously devoted their time and effort to generation of ideas and concepts for Ocean Census. These are included as authors on the present paper but also include  Ward Appeltans  from the  Ocean Biodiversity Information System  and  Peter Ng  from the  Department of Biological Sciences, National University of Singapore. AR would like to give special thanks to  Sir Charles Godfray ,  Dr Patricia Miloslavich ,  Dr Paul Snelgrove  and  Dr Jyotika Virmani  for providing expert advice through the development of Interim Reports 1 and 2 of Ocean Census.
, 2017; Zittra et al. , 2016). To address the lack of full-length rRNA sequences in public databases, we sought to determine the 28S and 18S rRNA sequences of a diverse set of Old and New World sylvatic mosquito species from four countries representing three continents: Cambodia, the Central African Republic, Madagascar, and French Guiana. These countries, due to their proximity to the equator, contain high mosquito biodiversity  (Foley et al. , 2007)  and have had long histories of mosquito-borne virus circulation  (Desdouits et al. , 2015; Halstead, 2019; Héraud et al. , 2022; Jacobi & Serie, 1972; Ratsitorahina et al. , 2008; Saluzzo et al. , 2018; Zeller et al. , 2016). Increased and continued surveillance of local mosquito species could lead to valuable insights on mosquito virus biogeography.
Using a unique score-based read filtration strategy to remove interfering non-mosquito rRNA reads for accurate de novo assembly, we produced a dataset of 234 novel full-length 28S and 18S rRNA sequences from 33 mosquito species, 30 of which have never been recorded before. We also explored the functionality of 28S and 18S rRNA sequences as molecular markers by comparing their performance to that of the mitochondrial cytochrome c oxidase subunit I (COI) gene for molecular taxonomic and phylogenetic investigations. The COI gene is the most widely used DNA marker for molecular species identification and forms the basis of the Barcode of Life Data System (BOLD)  (Hebert et al. , 2003; Ratnasingham & Hebert, 2007). Presently, full-length rRNA sequences are much less represented compared to other molecular markers.
However, given the availability of relevant reference sequences, 28S and concatenated 28S+18S rRNA sequences can be the better approach for molecular taxonomy and phylogenetic studies. We hope that our sequence dataset, with its species diversity and eco-geographical breadth, and the assembly strategy we describe would further facilitate the use of rRNA as markers. In addition, this dataset enables the design of species-specific oligos for cost-effective rRNA depletion for a broader range of mosquito species and streamlined molecular species identification during RNA-seq.
, 2021 ; M. J. Our score-based filtration strategy, described in detail in the Methods section, allowed us to bioinformatically remove interfering rRNA reads and achieve successful de novo assembly of 28S and 18S rRNA sequences for all our specimens. Briefly, for each Illumina read, we computed a ratio of BLAST scores against an Insecta library over scores against a Non-Insecta library (Figure  2A ). Based on their ratio of scores, reads could be segregated into four categories (Figure  2B ): (i) reads mapping only to the Insecta library, (ii) reads mapping better to the Insecta relative to Non-Insecta library, (iii) reads mapping better to the Non-Insecta relative to the Insecta library, and (iv) reads mapping only to the Non-Insecta library. By applying a conservative threshold at 0. 8 to account for the nonexhaustiveness of the SILVA database, we removed reads that likely do not originate from mosquito rRNA.
Altogether, we were able to assemble 122 28S and 114 18S full-length rRNA sequences for 33 mosquito species representing 10 genera sampled from four countries across three continents. This dataset contains, to our knowledge, the first records for 30 mosquito species and for seven genera: Coquillettidia, Mansonia, Limatus, Mimomyia, Uranotaenia, Psorophora, and Eretmapodites. Individual GenBank accession numbers for these sequences and specimen information are listed in Appendix 1-table  1. sequences and 558 185 sequences, respectively. Following BLASTn analysis against these two libraries, each RNA-seq read is assigned a ratio of BLASTn scores to describe their relative nucleotide similarity to insect rRNA sequences. Based on these ratios of scores, RNA-seq reads can then be filtered to remove non-mosquito reads prior to assembly with SPAdes to give full-length 18S and 28S rRNA sequences.
Comparative phylogeny of novel rRNA sequences relative to existing records To verify the assembly accuracy of our rRNA sequences, we constructed a comprehensive phylogenetic tree from the full-length 28S rRNA sequences generated from our study and included relevant rRNA sequences publicly available from GenBank (Figure  3 ). We applied a search criterion for GenBank sequences with at least 95% coverage of our sequence lengths (~4000 bp), aiming to represent as many species or genera as possible. Although we rarely found records for the same species included in our study, the resulting tree showed that our 28S  (3900 bp) from this study and from GenBank was inferred using the maximum-likelihood method and constructed to scale in MEGA X (S. Kumar et al. , 2018)   Indeed, when concatenated 28S+18S rRNA sequences were generated from the same specimens (Figure  4 ), the phylogenetic tree resulting from these sequences more closely resembles the 28S tree (Figure  3 ) with regard to the basal position of the Mimomyia clade (in teal) within the Culicinae subfamily with good bootstrap support in either tree (84% in 28S rRNA tree, 100% in concatenated 28S+18S rRNA tree).
simpsoni. As GenBank held no records of Ae. opok COI at the time of this study, we instead aligned the putative Ae. simpsoni COI sequences against two sister species of Ae. opok: Ae. luteocephalus and Ae. africanus. We found they shared only 90% and 89% similarity, respectively. Given this significant divergence, we concluded these specimens to be Ae. simpsoni. Ambiguous results were especially frequent among Culex specimens belonging to the Cx. pipiens or Cx. vishnui subgroups, where the query sequence differed with either of the top two hits by a single nucleotide. For example, between Cx. quinquefasciatus and Cx. pipiens of the Cx. pipiens subgroup, and between Cx. vishnui and Cx. tritaeniorhynchus of the Cx. vishnui subgroup. Among our three specimens of Ma. titillans, two appeared to belong to a single species that is different from but closely related to Ma.
Genus colour codes Horreolanus (Ho) Arrenurus (Ar) inferred using the maximum-likelihood method and constructed to scale using MEGA X (S. Kumar et al. , 2018)   1 ). In the 28S rRNA tree with GenBank sequences (Figure  3 ), two Cx. pipiens GenBank sequences formed a clade sister to another containing three Cx. quinquefasciatus GenBank sequences and the "Cx quinquefasciatus MG S74" sequence with 78% bootstrap support. This is in accordance with other studies examining mitochondrial sequences  (Sun et al. , 2019)  and morphological attributes  (Harbach et al. , 2017). This shows that the 28S rRNA sequence can distinguish the two species and confirms that "Cx quinquefasciatus MG S74" is indeed a Cx. quinquefasciatus specimen. However, "Cx quinquefasciatus MG S75" is shown to be basal from other sequences within this Cx.
Relative to 28S sequences, we observed more instances where multiple specimens have near-identical 18S rRNA sequences. This can occur for specimens belonging to the same species, but also for conspecifics sampled from different geographic locations, such as An. coustani, An. gambiae, or Ae. albopictus. More rarely, specimens from the same species subgroup, such as Cx. pseudovishnui and Cx. tritaeniorhynchus, also shared 18S rRNA sequences. This was surprising given that the 18S rRNA sequences in our dataset is 1900 bp long. Concatenation of 28S and 18S rRNA sequences resolved this issue, enabling species delineation even among sister species of Culex subgroups, where morphological identification meets its limits. In Cambodia and other parts of Asia, the Cx. vishnui subgroup includes Cx. tritaeniorhynchus, Cx.
Conclusions Total RNA-seq is a valuable tool for surveillance and virus discovery in sylvatic mosquitoes but it is impeded by the lack of full-length rRNA reference sequences. Here we presented a rRNA sequence assembly strategy and a dataset of 234 newly generated mosquito 28S and 18S rRNA sequences. Our work has expanded the current mosquito rRNA reference library by providing, to our knowledge, the first full-length rRNA records for 30 species in public databases and paves the way for the assembly of many more. These novel rRNA sequences can improve mosquito metagenomics based on RNA-seq by enabling physical and computational removal of rRNA from specimens and streamlined species identification using rRNA markers. Given that a reference sequence is available, rRNA markers could serve as a better approach for mosquito taxonomy and phylogeny than COI markers.
COI Advantages Disadvantages With a larger reference database, the COI is a versatile marker for molecular taxonomy. Being a shorter DNA marker, the COI gene is cost-and time-effective to amplify, sequence, and characterise. Universal primer sets to amplify the COI marker have been developed and tested for many diverse species. All species taxa clustered into distinct clades but with weaker bootstrap support at internal nodes relative to those of the 28S+18S rRNA tree. For An. coustani, and members of Culex species subgroups such as Cx. quinquefasciatus and Cx. tritaeniorhynchus, COI is unable to unequivocally confirm species identity as species can differ by just one nucleotide. Other molecular markers are often used in tandem.
Total RNA sequencing To obtain rRNA sequences, RNA samples were quantified on a Qubit Fluorometer (Invitrogen) using the Qubit RNA BR Assay kit (Invitrogen) for concentration adjustment. Non-depleted total RNA was used for library preparation for next generation sequencing using the NEBNext Ultra II RNA Library Preparation Kit for Illumina (New England Biolabs, Ipswich, Massachusetts, USA) and the NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1) (New England Biolabs). Sequencing was performed on a NextSeq500 sequencing system (Illumina, San Diego, California, USA). Quality control of fastq data and trimming of adapters were performed with FastQC and cutadapt, respectively.
28S and 18S rRNA assembly To obtain 28S and 18S rRNA contigs, we had to first clean our fastq library by separating the reads representing mosquito rRNA from all other reads. To achieve this, we used the SILVA RNA sequence database to create 2 libraries: one containing all rRNA sequences recorded under the "Insecta" node of the taxonomic tree, the other containing the rRNA sequences of many others nodes distributed throughout the taxonomic tree, hence named "Non-Insecta"  (Quast et al. , 2013). Each read was aligned using the nucleotide Basic Local Alignment Search Tool (BLASTn, https://blast. ncbi. nlm. nih. gov/) of the National Center for Biotechnology Information (NCBI) against each of the two libraries and the scores of the best high-scoring segment pairs from the two BLASTns are subsequently used to calculate a ratio of Insecta over Non-Insecta scores  (Altschul et al.