dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall description at diagnosis of FVSG Registry patients with GPA The Registry database enrolled 795 patients with newonset GPA. 18 For the 727 with complete baseline clinical details, including ANCA-immunoassay results, median follow-up was 3. 7 (1. 8-6. 6) years, 62 (8. 5%) were ANCAnegative, 119 (16. 4%) MPO-ANCA-positive and 546 (75. 1%) PR3-ANCA-positive (table  1 ). Their RFS and OS probabilities (table 2) are shown in figure  1A ,B according to ANCA specificity. Comparison of 62 ANCA-negative versus 665 PR3-ANCApositive or MPO-ANCA-positive patients Histological findings supported GPA diagnosis for 29 (47%) ANCA-negative patients. Among the 33 patients with no histological support of a GPA diagnosis, 21 had chronic sinusitis, otitis media or mastoiditis for >3 months, 12 had X-ray evidence of fixed pulmonary infiltrates, nodules or cavitations present for >1 month, seven had 2+ haematuria and 2+ proteinuria on urinalysis, and four had subglottic stenosis, bloody nasal discharge and crusting for >1 month, or nasal ulceration.
Also, it was recently reported that the risk of limited-GPA relapse was similar to that of systemic GPA. 26  Our high relapse and mortality rates for patients with ANCA-negative GPA highlight that it is not always a benign disease and should be managed accordingly, with regular follow-up. FVSG Registry patients with MPO-ANCA-positive GPA were more often older women, as usually reported. 8 9 13  15 16 Our results also agree with the classical 10-year age discrepancy between patients with MPO-ANCA versus PR3-ANCA. 4 8 27-29 Herein, this subset had significantly less frequent arthralgias, purpura or eye involvement, consistent with the data from the only other study that also included >100 MPO-ANCA-positive GPA patients. 9 No such significant disease-manifestation differences were observed in studies with smaller enrolments of MPO-ANCA-positive GPA patients.
10 30 In contrast, better OS of PR3-ANCA-positive patients could be explained by the fact that MPO-ANCA-positive patients were older and had poorer renal outcomes, as in most cohorts. 4 12 28-30 ur results add important clarifying information to the ongoing discussion of the relative contributions of ANCA specificity, as opposed to AAV clinical diagnosis, to GPA phenotype and outcomes. They reconcile the seemingly contradictory literature data and open the way for more tailored treatment, based not only on the AAV type but also on ANCA specificity. Current trials already take this dichotomy into account by stratifying enrolment according to ANCA specificity  35 36  or even by including exclusively patients with one ANCA specificity ( Clinical-Trials. gov Identifier: NCT00405860 or NCT03920722 for MPO-ANCA-positive and NCT03967925 for PR3-ANCApositive patients).
Our data suggest, even among patients receiving a clinically based GPA diagnosis, that those PR3-ANCA-positive are more prone to relapse than those of the two other subsets combined. Patients with critical organ damage that could become life-limiting if further injury occurs (eg, lungs and kidneys) or those at high risk of relapse, such as those who have previously relapsed or whose PR3-ANCA test is positive, should receive longer rituximab maintenance therapy, as already proposed,  33 37  and new therapeutic strategies should be evaluated ( Clin-icalTrials. gov Identifier: NCT03967925). Our study has major strengths. Our Registry collected the clinical characteristics and long-term outcomes of patients, originating from several medical specialties, enabling analysis according to immunoassay results of a truly representative distribution of different GPA presentations and evolutions.
The analysis was conducted retrospectively, engendering all the biases inherent to such a design, including that all relapses occurring during follow-up may not have been captured. However, all patient information was entered prospectively into the FVSG Registry database. Although ANCA-PR3-positivity was associated with a higher relapse risk in the multivariable analysis of our entire GPA population, that finding was mitigated in the subgroup analysis restricted to ANCA-positive patients, perhaps because clinicians may have already prescribed those patients longer maintenance therapies and the detection of a potential small difference may require an even larger sample size. In conclusion, our results finally reconcile the seemingly contradictory data reported in the GPA literature and support the concept that ANCA specificity enables identification of distinct GPA subsets with clinical and prognostic relevance.
Large datasets are highly valuable resources to investigate multi-scale patterns of organisms, and lay foundations for citizen science-based conservation strategies. Here, we used 1,043,262 records from 1708 to 2021 to explore the geography, taxonomy, ecology and distribution patterns of 11,556 fungal taxa in metropolitan France. Our analysis reveals a four-phase pattern of temporal recording, with a main contribution of post-1977 observations in relation with the structuration of associative mycology. The dataset shows an uneven geography of fungal recording. Four clusters of high-intensity sampling scattered across France contrast with poorly documented areas, including the Mediterranean. Basidiomycota and Agaricales highly dominate the dataset, accounting for 88. 8 and 50. 4% of records, respectively.
The dataset is composed of many rare taxa, with 61. 2% of them showing fewer than 100 records, and 20. 5% recorded only once. The analysis of metadata brings to light a preponderance of the mycorrhizal guild (44. 6%), followed by litter saprotrophs (31. 6%) and wood saprotrophs (18. 1%). Highly documented forests (76. 3% of records) contrast with poorly investigated artificial (6. 43%) and open habitats (10. 1%). This work provides the first comprehensive overview of fungal diversity in France and identifies the Mediterranean area and open habitats as priorities to integrate into a global strategy for fungal conservation in France.
Introduction Since the early 21st, the emergence of large citizen-science projects and the development of big-data computing and analytical methods generated an unparalleled use of large fungal datasets as powerful tools to explore the spatio-temporal occurrence of fungal species at national  [1] [2] [3] [4] [5] [6]  and supra-national scales  [7, 8]  (see also  [9, 10]  for molecular database initiatives). Mainly concentrated in the northern parts of the Palearctic and the Nearctic regions, these initiatives poorly concerned continental areas located in the Mediterranean (see  [11, 12]  for few exceptions in the Iberic peninsula), despite the high interest of this area for the conservation of a unique fungal diversity  [13] [14] [15]. During the last decade, this resurgence of interest for old fungal records was essentially driven by an increasing urgency to understand the response of continental biota, including fungi, to the ongoing climate change  [7, 16].
Based on the analysis of a large corpus of data from Northern Europe and Northern America, large datasets proved particularly useful to enlighten the response of fungal diversity to global warming, including shifts in the (1) fruiting phenology (  [1, 17] , see  [18]  for a review), (2) composition of communities  [16] , and (3) fruit body production of edible species  [12]. From an ecological perspective, these large dataset-centered approaches provided new insight into our understanding of the determinants of fungal host shifts (  [3] ; see also  [19] ). Last, large datasets were recently combined at the supra national scale to provide an adapted support for the investigation of macro-ecological questions  [7]. The astonishing diversity of fungi constantly comes in highly patchy communities of organisms at the local scale, with a large proportion of rarely observed taxa  [20] [21] [22] [23].
Long suspected to artefactually result from limited sampling efforts  [20]  which veils the realized communities  [24] , the high proportion of rare species in fungal communities was confirmed by analyses of mycelia, using the metabarcoding of either soil fractions  [25, 26]  or plant fragments  [27, 28]. At large scale, fungal diversity patterns remain poorly investigated (see  [29, 30]  for a molecular-based global assessments of soil diversity), despite the increasing availability of large databases  [7]. In a recent attempt to investigate the large-scale patterns of species distribution in England and Switzerland, [31] observed a lognormal distribution of fruiting species at large scale, strikingly contrasting with the Pareto distribution of local records assembled in this database. These authors suggest that the citizen-science nature of data may explain this pattern.
Further studies using large datasets from other countries are needed to test this hypothesis. Untangling the distribution patterns of fungal species from the location of mycologists able to grasp this information and the geography of their recording activity across the landscape is particularly challenging  [32] [33] [34]. At the national scale, it was evidenced that the uneven distribution of the population, but also the heterogeneity of landcover and the topology of human infrastructure combine their effects to induce highly biased spatial patterns of observations  [6]. However, the geography of fungal data gathered in large databases remains poorly described, and its relationship with the location and the temporal dynamics of the network of mycological associations and fungi field observers has been poorly addressed so far.
In the Palearctic region, initiatives to assemble and analyze large fungal datasets at the national scale took shape in countries from the north-western (Germany, the U. K. , Denmark, Norway, Estonia and the Netherlands) and central parts (Austria, Switzerland and Slovenia) of Europe  [7]. Contrastingly, the southern part of the continent still fails to provide equivalent overviews of the accumulated corpus of fungal records at national scales. Located at the interface between Mediterranean frontage and the continental domain on one hand, exposed to the Atlantic influence in the western part of its territory on the other hand, France presents a remarkable diversity of climatic influences, resulting in a highly diverse mosaic of natural habitats  [35, 36]. In France, a long succession of talented mycologists and a long-term dynamic of local mycological associations contributed to gather a two-century long history of fungal records and collections  [16, 37].
Despite the long-term vitality of French mycology, the first national mycological database was only initiated in 2008  [38, 39]. This collaborative project was developed through the involvement of the Société Mycologique de France (SMF), the Société Mycologique du Nord de la France (SMNF), the Muséum National d'Histoire Naturelle (MNHN) and the University of Lille2, with the support of the ministry of the ecological transition and the Nord-Pasde-Calais region  [39]. Hosted by the association ADONIF since 2015, the French national mycological database encompasses three complementary resources, i. e. , a database of located fungal records (FongiBase), an associated taxonomic reference tool (FongiRef) and the corresponding bibliography (FongiDoc;  [39] ). Online since 2017, this database still waits to be handled in a global analysis of the corresponding patterns of fungal diversity in France.
Here, we analyzed the French national mycological database, which represents the current state of the fungal records accumulated and archived in metropolitan France since 1708  [40]. More specifically, we successively questioned (1) the geographical patterns of 104,3262 located fungal records, (2) the temporal sequence of fungal data acquisition by fungi observers, with particular attention to the contribution of mycological associations, (3) the distribution patterns of fungal alpha diversity across the territory, (4) the taxonomic dimension of observations, (5) the structure of the dataset when considering the abundance patterns of taxa, (6) the contributions of fungal ecological guild to the database, and finally  (7)  the characteristics of fungal records in relation to the FongiBase contributors.
National Fungal Database Fungal records deposited in the French public database are organized into three complementary datasets  [40]  presented hereafter. A first dataset is composed of 1,100,420 fungal records (including lichenized fungi) registered in FongiBase on 25 March 2022. FongiBase records are systematically transferred to GBIF through the French SINP database (https://inpn. mnhn. fr/informations/sinp/ presentation, accessed on 25 July 2022). Each of those inputs corresponds to an individual observation of one or several fungal fruit bodies. The dataset was compiled with data originated from French mycological societies and gathers contributions by independent taxonomists and academic institutes (see Table  S1  for a detailed list of contributors and their respective input). This dataset includes records which were validated by Pierre-Arthur Moreau, and taxonomically assigned using current nomenclature.
0%). The second dataset, FongiRef, provides the current taxonomic framework used for naming records at the validation stage, and the corresponding synonymies used since the observation. As described in  Boury et al. (2018) , Fongibase is coupled with the nomenclatural database FongiRef (fongiref. fongifrance. fr, accessed on 25 March 2022) updated from TaxRef, the French national taxonomic database managed with the French Museum of National History. All taxa cited and analyzed are "retained names" but data include all taxonomic and nomenclatural synonyms recognized by FongiRef/TaxRef. The last dataset, FongiDoc, is a compilation of publications on fungal taxonomy from which bibliographic data referring to observations with location and date were associated to FongiBase.
Assembling of Analysis Matrices From FongiBase, we excluded 57,158 records related to lichenized fungi to analyze the distribution patterns, taxonomy and ecology of 1,043,262 fungal observations. We assembled a matrix associating for all records, seven attributes. The matrix contains, for all collections, (1) the validated taxonomic assignment of the records, (2) the observation date, (3) the GPS coordinates of collecting site (or the center of the corresponding commune as a proxy), (4) the corresponding ecoregion, (5) the identity of the observer, (6) when available, the surrounding habitat and (7) for each species, the trophic guild of belonging. The ecological guild of each species was inferred using the FUNGuild database  [41] , with the FUNGuildR tool  [42]. The species which were absent from FUNGuildR database were assigned to the guild of the corresponding genus when unambiguous or discarded from the analysis when uncertainty remained.
Statistical Analyses All analyses were performed using R software version 4. 1. 2 and RStudio software 2022. 02. 0 + 443 version  [45, 46]. Maps were obtained using IGN map layers (https:// geoservices. ign. fr/adminexpress, accessed on 12 April 2022) and R packages ggplot2, rgdal, and ggspatial  [47] [48] [49]. The density of fungal records was calculated at the department level, as a difference with the national density of records, and expressed in percent. In each department, values were obtained by subtracting the number of observations per square kilometer from the number of observations per square kilometer observed at national scale. The surface of the departments and the number of communes per department were obtained using the French Institute on Economics and Statistics  [50]. The specific richness of ecoregions was rarified with the R package vegan by the rarefy function  [51].
To determine whether the observations and specific richness varied depending on ecoregion, we performed a generalized linear model (GLM) with negative binomial distribution to process overdispersion. Ecoregion was an only categorical fixed factor, and the specific richness and number of records were the explained variables in the model. If the ecoregion effect was significant in the model described above, Tukey multiple comparison tests among the means were performed with package "multcomp"  [53]. The species abundance distributions (SADs) were fitted with R package "sads"  [54] , which uses maximum likelihood methods, and compared with nine different models as in [31].
The French National Dataset From a territory of 543,950 km 2 , the analyzed database FongiBase revealed the presence of 11,556 taxa out of 1,043,262 archived fungal records (Figure  1  and Table  1 ). In Europe, this dataset refers to the largest initiative in terms of the inventoried area, and the fourth database in the number of observations, after the Netherlands (2,094,920 records), Germany (1,530,140 records) and the United Kingdom (1,937,526 records; Figure  1 ).
Spatial Patterns of Fungal Records The current geographic distribution of records shows an uneven density of data across administrative units (Figure  2 ). The highest numbers of records were collected in the departments of Pas-de-Calais (78,199 records; +631. 1% as compared to the national density), Doubs (75,227 records; +603. 3%) and Jura (70,851 records; +562. 4%; Table  S3 ). When considering the density of records, the highest values are registered in the Territoire de Belfort, Doubs and Jura departments, with 18. 8, 14. 4 and 14. 2 records per square kilometer in average, respectively (Table  1  and Table S3 ). At the opposite of the distribution range, the lowest numbers of records have been collected in the departments of Indre (109 records; -99%), Lot-et-Garonne (125 records; -98.
4% of records (Table  S3 ). Last, a set of five departments crossing Montane and Continental domains and comprising Isère, Loire, Rhône, Savoie and Haute-Savoie, represents 15. 2% of located fungal records (Table  S3 ). In three of these clusters (i. e. , northern, western and eastern), taxa accumulation curves tend to saturate, while the Alpine cluster shows an unsaturated sampling (Figure  S2 ). In all, the French dataset FongiBase contains 492,014 records from 46 departments located in the Atlantic ecoregion, 342,124 records from 32 departments located in the Continental ecoregion, 35,202 records from 11 departments located in the Mediterranean ecoregion and 157,558 records from 9 departments located in the Montane ecoregion (Table  1  and Figure  S1 ). On average, the number of fungal records per department is significantly lower in the Mediterranean (i.
Temporal Patterns of Fungal Data Acquisition FongiBase contains records from 1708 to 2022, with 9 records (0. 0009%) related to observations before 1800, 7226 records (0. 7%) related to observations between 1800 and 1900, 28,0438 records (26. 9%) related to observations between 1900 and 2000 and 754,879 records (72. 4%) related to observations since 2000 (Figure  3A ). Similarly, the French database progressively completed its spatial coverage from 7 sampling locations (0. 059%) recorded before 1800, to 637 sampling locations (6%) recorded between 1800 and 1900, 5059 sampling locations (43. 9%) recorded between 1900 and 2000, and 5829 new sampling locations (50. 5%) recorded since 2000 (Figure  3A, B ). The temporal dynamics of accumulation of records and sampling locations show a four-steps pattern.
Taxonomy of Fungal Records Taxa in the Basidiomycota account for 88. 8% (905,400 records) of the recorded observation in the French National fungal database (Figure  4 ). Ascomycota comes second, with 7. 8% (81,622 records) of the total number of observations (Figure  4 ). Two other fungal lineages, i. e. , Zygomycota (0. 009%; 98 records) and Chytridiomycota (0. 001%; 12 records), account for very few in the database (Figure  4 ). Last, Fongibase includes few data of groups traditionally observed by field mycologists, including Myxomycota (0. 3%; 2955 records), Oomycota (0. 004%; 40 records) and Microsporidia (0. 00001%; 1 record). Last, 53,109 data (5. 1%) cannot be satisfactorily assigned to a correct taxonomic position for current limits in the taxonomic base FongiRef. At the lower taxonomic level, Agaricales is the most represented order (50.
Discussion Our report analyses the spatio-temporal, taxonomic and ecological patterns underlying the remarkable species richness of fungi inventoried in the French national mycological database. The analysis revealed four main facets of the distribution of more than one million fungal records gathered across the French metropolitan territory since the 19th century. First, the chronology of fungal data acquisition since the early 19th locates the late 1970s as a breaking point separating (1) ancient data of erratic distribution with low usability for conservation, and (2) modern records associated with high sampling effort at local scale. Second, at the national scale, the geography of fungal records is highly uneven, with four main clusters of intense data acquisition contrasting with the poorly documented southern part of France, including the Mediterranean domain.
Third, the distribution of taxa occurrence shows an overrepresentation of poorly observed/observable organisms, with a majority of taxa recorded less than 10 times. Last, from an ecological perspective, the dataset shows a highly unbalanced distribution of observations, with more than threequarters of records from forest ecosystems, mainly in the ectomycorrhizal guild, while only 4% of data document fungal patterns in grasslands. Considered altogether, these characteristics of the French fungal database highlight the strength of the analyzed dataset, but also alert us about substantial knowledge gaps and methodological issues that should be considered in future strategies aiming at promoting the conservation of fungal diversity in France.
French Metropolitan Territory, a Species Rich Area for Fungi or a Mycologist-Rich Area? As primarily assumed, the French metropolitan territory experiences a particularly high diversity of fungi as compared to most European countries, with more than 7000 expected taxa out of 15,000 European ones  [55]. We here confirm this 15-year-old estimation, by attesting the presence of 11,556 taxa in the French National checklist out of 1,043,262 observations. The analyzed territory a priori appears as remarkably rich, as compared to other European countries of similar sampling effort (i. e. , measured as the number of fungal records), including the United Kingdom (6453 taxa), Germany (5531 taxa) and Switzerland (4974 taxa;  [7] ). This difference in specific richness is unlikely to be explained by differences in sampling efforts among European countries, as reflected by the absence of correlation between the number of taxa and the number of records in France and in the nine countries included in FunClim European database (R = 0.
46 & p = 0. 22; Figure  1 ). Explicitly, the French database harbors a particularly high density of taxa per record, with 1. 11 taxa per 100 records, when 100 fungal observations account for 0. 46 and 0. 42 taxa in the United Kingdom and Germany, respectively (Figure  1 ). More likely, the high fungal specific richness recorded in FongiBase may reflect the large area of the French metropolitan territory as compared to neighboring inventoried countries, and the gamma diversity associated with its remarkable diversity of represented ecoregions (Figure  S1 ) and natural habitats (Figure  8A ). In our analysis, we found a positive and significant correlation between the country area and specific richness of the 10 analyzed territories (R = 0. 79 & p = 0. 006; Figure  1 ). Interestingly, this correlation remains highly significant (R = 0.