dataesr/scientific-paragraphs-categorization huggingface dataset:
0 with default parameters  14. Because of the relative long length of the PCR product sequenced, ranging from 350 to 700 bp, only 32. 22% of the sequences reached the reverse primer. Therefore, the sequences were trimmed off at 300 bp after the forward primer and clustered using SWARM v1. 2. 8  40  using a local threshold of two differences (d = 2) for accommodating the extreme rate of rDNA substitution known in planktonic foraminifera  41. The obtained MOTUs were assigned to their closest hit in the Protist Ribosomal Reference database (PR2, based on GenBank v. 201)  5  , also truncated at 300 bp after the forward primer, using ggsearch as implemented in FASTA v36. 3. 5c  42. The result of the attribution is given in Supplementary Material 1. The raw sequence data can be downloaded from the European Nucleotide Archive under BioProject PRJEB23355 (https://www.
ebi. ac. uk/ena/data/view/PRJEB23355). Phylogenetics. We used a conservative and strict sequence selection process to analyze the phylogenetic structure of our dataset. In a conservative approach, only MOTUs attributed to foraminifera (Level 3 of PR² database ranked taxonomy), occurring in at least three samples and having a total abundance of at least ten reads were first considered for downstream analysis (Supplementary Fig. 1 ). The most abundant sequence of each MOTU was used as a prefix to search in the metabarcoding dataset the longest sequences of each MOTU that could have potentially reached the reverse primer. We retrieved the most abundant of the longest sequence of each of the retained MOTU to use them as a MOTU "representative sequence". The representative sequences were manually checked, and those having an insufficient length or being potential chimera possibly omitted by UCHIME were excluded from the analysis.
The retained representative sequences were automatically aligned using MAFFT v. 7  43  with 81 sequences representative of foraminifera diversity. We selected 57 planktonic foraminiferal reference sequences representing the morphospecies with an existing barcode with their sub-division into genetic type (or cryptic species) derived from the Planktonic Foraminifera Ribosomal Reference database (PFR²)  35  , and 24 representative sequences of the major groups of benthic foraminifera with multi-locus wall chambered tests (Globothalamea)  44. The best substitution model was selected using jModeltest v. 2  45  and a phylogenetic inference was carried out using PhyML  46  with 1,000 bootstrap pseudo replicates for estimation of the branch support. The resulting tree was visualized with iTOL  47  (Fig.
1 ). The alignment and resulting tree inference are provided in Supplementary Material 2. A molecular nomenclature framework for planktonic foraminifers. No unique rDNA dissimilarity threshold exists to discriminate genuine biological species in foraminifera  33, [48] [49] [50]  , or other group of protists  [51] [52] [53]. The availability of the PFR² database permits to produce a flexible framework that do not rely on fixed threshold but on the extensive taxonomic knowledge produced through single-cell genetic studies and to extend its properties onto the environmental sequences. To parse the different level of diversity of the environmental dataset into meaningful units, we produced a 4-rank molecular nomenclature taxonomic framework (Morphogroup|Genus|Morphospecies|Genetic Type) harmonized between the sequences derived from the reference database and the MOTUs representative sequences.
Assignation of MOTUs to meta-reference database for ecological analyses. The resulting 4-level ranked molecular nomenclature produced by the hierarchal ABGD clustering allowed re-assigning all the rare MOTUs (occurring in less than 3 samples and with less than 10 reads) and potential variants of abundant MOTUs, which were initially not considered in our analysis. To incorporate the maximum of the dataset in the final analysis, we produced a meta-reference database that included the PFR² database, the benthic foraminifera reference sequences available in the PR² database and the representative sequences of the environmental MOTUs. We aligned the framework of the PR² and PFR² on the 4-level ranked nomenclature (Supplementary Material 3) and reassigned all MOTUs with the meta reference database (Supplementary Fig.
3 , Supplementary Material 1). All MOTUs having an identity greater than 95% with sequences in the meta-reference and without ambiguous attribution were retained (Supplementary Fig. 3 , Supplementary Material 1) and merged to produce the final ecological dataset (Fig. 2 , Supplementary Material 4). MOTUs rarefaction curves at each Tara Oceans station, depth of collection, within each plankton size fraction, and for the total dataset were inferred using PAST v 2. 17  56  (Fig. 3 ).
Results Amplicons filtering, phylogenetic diversity, and taxonomic delineation. In total 159,995 raw rRNA sequences reads were obtained from the 32 sequenced samples. 101,362 reads were retained after quality filtering and clustered into 8,729 MOTUs. 907 MOTUs were assigned to eukaryotes other than foraminifera after comparison with the PR² database and were discarded (Supplementary Fig. 1 , Supplementary Material 3). From the 7,822 MOTUs attributed to foraminifera, 176 occurred in at least three samples and had an abundance of at least ten amplicons. Seven MOTUs did not yield sufficiently long sequences to be interpreted in a phylogenetic framework and 14 MOTUs were identified as potential chimeras. Phylogenetic inference including representative sequences of the retained 155 environmental MOTUs and 81 reference sequences of foraminifera resulted in 6 distinct but unsupported clades (Fig.
Five clustered among the Basal clade (Basal A to Basal E), two clustered among the Microperforate clade (Micro A and Micro B) and two clustered among Non-Spinose clade (Non-Spinose A and Non-Spinose B). Finally, we identified three putative morphospecies composed of six sub-lineages with unknown phylogenetic affinities as they clustered into the artificial "Long-Branch II" clade. Meta-reference attribution. The Meta-Reference database included representative sequences of the 155 environmental MOTUs together with 1,342 reference sequences issued from the PR² database and 1,272 issued from the PFR² database and possessed 361 unique taxonomic paths (Supplementary Material 3). After re-assignment of the dataset by the meta-reference, 6,010 out of the 7,822 MOTUs representing 88,734 reads (94. 5% of the foraminiferal amplicons) presented a similarity with reference sequences of 95% or more (Supplementary Fig.
3 ). Among those, we identified 230 rare MOTUs with a high similarity with 15 taxa present only in PR² or PFR² databases (Turborotalita quinqueloba types I and II, Globigerinoides ruber type I, Globigerinoides elongatus, Pulleniatina obliquiloculata type II, Neogloboquadrina sp. , Neogloboquadrina incompta type I, Globorotalia tumida, Globorotalia truncatulinoides type III, Globorotalia scitula, Hastigerina pelagica type II and Globigerinita glutinata type II, Cibicides refulgens, Globocassidulina sp. and Nonion sp. ) that were not considered in the initial analysis. As a result, the final retained dataset for ecological inferences was composed of 69 robustly defined genetic types belonging to 41 morphospecies (Fig. 2 ).
Ecological inferences. Rarefaction curves performed on the ecological dataset showed that saturation was reached in six of the 8 stations and for the total dataset (Fig. 3 ). Overall, saturation was reached at a sequencing depth of 5,000 reads for individual stations and 25,000 reads for size fractions. A higher diversity level was encountered in the micro-plankton [20-180 µm] compared to the meso-plankton [180-2000 µm]. Interestingly, the saturation curves for the total dataset and all the samples recovered in surface waters were similar and offset from the saturation curve for the subsurface community, showing that nearly all encountered species were occurring at the surface. Finally, we assessed the relative abundance of reads from each of the 5 clades in the 3 size fractions. Variation in SSU rDNA copy number within single-cell can potentially bias the estimation of relative abundances in metagenomics surveys  57.
Amongst the 69 lineages detected in the environmental metabarcode, 50 could be attributed to 28 morphospecies for which reference sequences are available in public databases (Fig. 2 ). Only seven of those genotypes have not been previously detected by single cell surveys (Fig. 2 ). Seven genotypes unrelated to any known morphospecies could be attributed to known morphogroups and clustered into four putative morphospecies (Micro A, Micro B, Non-Spinose A, Non-Spinose B; Figs  1  and 2 ). These could correspond to classical morphospecies absent from reference databases such as the Tenuitelids for the Microperforates or the Globorotalids for the Non-spinose. The 12 remaining lineages could not be attributed to any known morphogroup and were assigned to two artificial categories. These lineages occurred in the [20-180 µm] size fraction, which is usually not considered in micropaleontology  59.
Indeed, micropaleontologists classically work with specimen >150 µm because they are easier to handle, can be studied under a stereomicroscope and have more calcite weight for geochemical analyses  60. The barcoding effort that led to the construction of the PFR² database  35  was targeted on iconic morphospecies of planktonic foraminifera used in paleoceanography. The largest specimens more likely to yield enough DNA to be successfully PCR amplified were selected  61. Therefore, it is logical that these 12 unknown genotypes are absent from the current databases because no barcoding effort has been directed so far on the size fraction where they occur. It is likely that some of those 12 lineages, structured into 8 putative morphospecies, could belong to described, but unsequenced morphospecies, such as Orcadia riedeli, Dentigloborotalia anfracta or Berggrenia pumilio which are usually small  (Hemleben 31  ).
Indeed, the non-spinose morphospecies Globoquadrina conglomerata characterized by a slow rate of evolution clustered within the "Basal" clade and the three fast evolving morphospecies Globorotalia truncatulinoides, Globorotalia menardii and Turborotalita quinqueloba clustered into artificial groups (Fig. 1 ). Therefore, it is possible that these environmental genotypes match the documented morphological diversity, although they cluster outside of their "home" clades. At the same time, we note that several abundant morphospecies in the sampled environment such as Trilobatus sacculifer and Globigerinoides ruber were rare in the molecular dataset, which indicates the existence of a PCR bias due to preferential amplification. This illustrates that metabarcoding, even if powerful, can still be partly blind  62  and that we cannot entirely exclude that unknown and ecologically relevant groups could have also been missed during the amplification.
In addition, we followed a stringent path to build our ecological dataset. We chose to retain only the most abundant MOTUs as a first step to build a taxonomic framework aligned with existing databases (our so called meta-reference) and to re-assign the whole dataset as a second step. By doing that we retained ~78% of the MOTUs diversity and ~95% of the dataset volume. Importantly, only after this step, MOTUs belonging to 14 morphospecies present in the reference database but rejected by our initial iteration were retained. Therefore, the same may apply to yet undocumented morphospecies represented by low abundance MOTUs in the dataset. Despite these factors that could under-estimate genuine diversity, rarefaction curves indicated that a near-saturation level of diversity was reached for the total dataset and at all stations (Fig.
Conclusion The Tara metabarcoding survey of eukaryotic plankton diversity estimated the global diversity in the sunlit ocean at ~150,000 MOTUs, which is one order of magnitude higher than the 11,200 cataloged morphospecies described by traditional taxonomy  10. Our results suggest that the implied unknown diversity might not be equally distributed among all planktonic lineages. Whilst some plankton groups are hyperdiverse such as the Diplonemids  63  , our results indicate that at least among the planktonic foraminifera this diversity is limited and not too far from estimates based on classical taxonomy and single-cell barcoding efforts. The case of planktonic foraminifera underlines the crucial role of coverage in reference databases used for the interpretation of metabarcoding data. Our survey shows that the diversity of planktonic foraminifera is finite but also that a considerable part of its biomass, resulting in a flux of at least 25-100 Tg carbon/year to the sea floor  64  , is made of small size taxa that received limited taxonomic attention.
Acknowledgements This work was supported by the  French Government 'Investissement d' Avenir' program OCEANOMICS  ( ANR-11-BTBR-0008 ), the  ANR  project  POSEIDON  ( ANR-09-BLAN-0348 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ) and from the  DFG-Research Center/Cluster of Excellence  'The Ocean in the Earth System'. This article is contribution # 61  of  Tara Oceans.
The European Society of Thoracic Surgeons (ESTS) thymic database is a satellite database of the ESTS registry collecting patients with thymic tumors (thymomas, thymic carcinomas and neuroendocrine thymic tumors) among the ESTS Institutions. The database is free of charge for any ESTS member, easily accessible to any ESTS institution, web-based, prospective, periodically maintained and updated. It may serve as an institutional database for the storage of data about patients with thymic tumors at no cost for each participating institution. It may be used by any ESTS member to propose studies using one of the largest thymic databases worldwide. The present review focuses on the history and the development of the ESTS thymic database (retrospective and prospective) and its structure with a particular reference to the concept of the minimum dataset and the Clinical Care Analysis (CCA) box.
Introduction The European Society of Thoracic Surgeons (ESTS) Database was established in 2001 as an attempt to collect the experience about thoracic surgical procedures for lung cancer among ESTS members. The initial number of participating centres was limited but the project encountered an unprecedented enthusiasm and the number of procedures has steadily increased over the years. The last report in 2017 included more than 110,000 patients from 240 units, 145 of which contributing with more than 100 procedures. Due to the positive feedback from the participating institutions, the core dataset (Lung Cancer Registry) was progressively implemented with some satellite databases dealing with other thoracic diseases and procedures. The Thymic Registry was introduced in 2013 with the intent to provide an up-to-date, online prospective platform for the collection of the claims and EMR (electronic medical record) data of patients with thymic tumors among the ESTS members.
History of the ESTS thymic working group and the ESTS thymic database The ESTS Thymic Working Group was approved and instituted by the ESTS Council in 2010 with the intent of creating an interest group of people in the ESTS dealing with thymic tumors. After the publication of a survey among the ESTS thymic community about the current practice in the management of thymic tumors (1), the first major project of the group was to set up a thymic retrospective database open to all interested ESTS institutions. The feedback from the ESTS community was enthusiastic and we were able to collate more than 2,000 patients treated from 1990 to 2010 from 35 institutions, mostly from Europe but also from Asia and North America. The ESTS thymic retrospective database constituted the largest thymic database ever collected at that time.
The analysis of the results on the overall population of patients with thymic tumors (2) and the subgroup analysis on thymic carcinomas (3) were published and presented at international conferences. Since then, a number of studies using the retrospective ESTS thymic database alone or in cooperation with other international thymic databases were published and presented at ESTS and other international meetings, representing important contributions to the advancement of the knowledge of these rare tumors (4-7). The limitations of the retrospective collection of data with the unavoidable selection and treatment biases led us to consider the creation of a prospective database. The ESTS Council approved the budget for the setup of an online prospective thymic database using the official ESTS platform and in 2013 the thymic database was the first satellite database to be included in the ESTS Registry.
The participation to the ESTS prospective thymic database is strongly encouraged among all ESTS Institutions. Following agreements with other major International thymic organisations, no competition exists with other international thymic datasets and ESTS members are therefore encouraged to upload their patients with thymic tumors into the ESTS thymic registry using the credentials provided by KData Clinical (the official ESTS platform). Instructions how to get access to the ESTS thymic database is provided in the present review (supplementary). It is noteworthy to emphasize that each participating institution can use the ESTS prospective thymic database as its internal database, at no costs. The ESTS thymic registry is periodically updated (the present format was introduced in 2015) and subject to a yearly maintenance (usually at the end of the year).
The results of the thymic database are published as a separate section every year in the ESTS Registry Report (Silver Book). The thymic report is also available in the ESTS website for all ESTS members. One of the most interesting aspects of the thymic database is the possibility for each contributing Institution to propose studies using the data from the thymic registry. We strongly encourage each contributor to propose new studies using the ESTS thymic prospective dataset. This would be an excellent opportunity for young researchers to make important contributions to thymic research using one of the largest thymic databases in the world. A study draft should be sent to the ESTS Thymic Working Group Steering Committee and to the ESTS Database Committee. The draft will be discussed by the Committees and in case of acceptance, the contributor will receive the data for the study.
Our co-authorship policy includes that one person from each center which substantially contributed to the ESTS thymic registry will be included in any manuscript submitted using the ESTS thymic database under a list linked to PubMed. Finally, the ESTS thymic database played a fundamental role in the development of the 8 th edition of the TNM classification of thymic tumors (8) under the coordination of the IASLC Staging and Prognostic Factors Committee (SPFC). Details about the cooperation between ESTS and other major international organisations for the study of thymic tumors will be provided in the last section of the present review.
The structure of the ESTS thymic prospective database The current platform of the ESTS thymic prospective database is effective since 2015, replacing the initial format. The structure includes an introductive section with demographic data which gives access to the Electronic Medical Record (EMR) data sections. There are four EMR data sections, preoperative, intraoperative, postoperative and follow-up. The preoperative section includes two subsections: risk factors (comorbidities), and diagnosis & staging. In the diagnosis & staging subsection, detailed information about the presence of associated paraneoplastic syndromes and characteristics of imaging reports (CT scan, RM and PET scan) are included, based on the ITMIG terminology for radiology reports  (9). The staging subsection contains information about the clinical and pathologic staging according to Masaoka-Koga  (10)  and the IASLC/UICC/ AJCC 8 th edition of the TNM classification  (8).
The intraoperative section includes one subsection where information about the type of the procedure, the final pathologic diagnosis and the resection status are indicated. The postoperative section includes two subsections: perioperative treatments and outcome. In the perioperative treatment subsection information about the use of chemotherapy and radiotherapy is requested, including the intent (induction, adjuvant, palliative) and the date initiated. In the outcome section, the date of discharge and the outcome at discharge are indicated. Finally, in the follow-up section there are information about the date of last follow-up, the vital status and the recurrence status. The number and types of data fields in the thymic registry make it likely that not all information can be uploaded at the same time.
It may happen, for example, that the patient demographics and some basic operative information are initially uploaded, while the histologic and perioperative information need to be added later on, when histology becomes available. One of the most crucial aspects of any prospective database is the completeness rate of the data fields, which may vary from as low as 20% to as high as 90%. The process of completing the missing data fields is sometimes tedious if all data fields are treated as equally important. There are however some data fields which are of particular importance for the analysis. For this reason, we introduced the concept of the "minimum dataset", which includes a limited number of data fields (N=16) which are considered essential in order to analyse the patient record. The list of the essential data fields is indicated in Table  1.
The minimum dataset includes essential information about demographics (gender, age), preoperative EMR (associated paraneoplastic syndromes), intraoperative (histology, final diagnosis, resection status, staging by Masaoka and TNM), postoperative (perioperative treatments, vital status, date of last follow-up). Another interesting resource of the ESTS thymic database is the dashboard or Clinical Care Analysis (CCA) where each contributor can have a visual evidence of the data in form of bar graphs for some essential data fields. Also, the completeness rate for each data field of the minimum dataset is shown in the CCA.
The international collaborative projects involving the ESTS thymic database After a period of relatively tepid interest for the thymic neoplasms at the end of the last century, the last decade has witnessed a tremendous surge of enthusiasm in these rare tumors. The milestone in this process was the creation in 2010 of the International Thymic Malignancies Interest Group (ITMIG) with the aim of gathering individuals from different disciplines (surgery, radiotherapy, chemotherapy, pathology, radiology) and providing them the necessary infrastructures and platforms to advance the research in the diagnosis and treatment of thymic tumors  (11). ITMIG produced an impressive amount of contributions about the definitions of standard terminology in outcome  (12) , pathology report  (13) , minimally-invasive techniques  (14) , imaging reports  (9).
Along with ITMIG, many regional interest thymic groups were created, mostly associated with national or international societies. Among these, the most important are the Japanese Association for Research on the Thymus (JART), the Chinese Alliance on Research in Thymomas (ChART), the Korean Association for research on the Thymus (KART), the Réseau Tumeurs THYMiques et Cancer (RYTHMIC). All these organisations have large thymic databases, although the only online, web-based, prospective thymic databases so far are the ITMIG, ESTS and RYTHMIC databases. ESTS and ITMIG have long been working together for the harmonization of their thymic databases. The ESTS and ITMIG retrospective databases were fairly compatible in terms of data fields and definitions. This resulted in a considerable number of common projects and studies and in the publication of several papers using both databases  (4, 5).