dataesr/scientific-paragraphs-categorization huggingface dataset:
Thanks to the standardisation of habitat patches mentioned above, we assumed that similarity in plant composition could substitute the most reliable proxy of dispersal, i. e. genetic similarity, which is restricted to the population level  82. For each pair of habitat patches, we separately calculated the Sørensen similarity index S sør for assemblages of animal-dispersed, wind-dispersed and unassisted species. This index was calculated using the ade4 package  83. The higher the similarity index (S sør ), the higher the similarity between a given pair of habitat patches.
e. expert knowledge) and avoided hierarchisation of habitat types when assigning resistance values. The proportion of woodland, grassland and cropland habitats was calculated from the number of pixels in the circular sliding window with a displacement and interpolation values of one pixel (i. e. 5 m). We tested sliding windows of different diameters of 5 (25 m), 11 (55 m), 21 (105 m) and 41 (205 m) pixels -closely linked to the range of local dispersal distances of plants  41, 89  -to test the sensitivity of the connectivity indices to continuous landscape representation (Fig. 4b ). These resistance maps were produced using Chloe2012 software  90. Landscape connectivity assessment. We calculated landscape connectivity using resistance distance. For each pair of habitat patches, we calculated the resistance distance based on the resistance maps using Circuitscape software  22  (Fig.
4c ). Graphab 3. 0. 2 software 91 was used to generate Euclidean links between each pair of habitat patches for each type of habitat. Pairs of habitat patches located at the boundary of the study area, for which Euclidean links were outside the study area, were excluded from subsequent analyses. We used Pearson correlations to examine relationships among the different resistance distances we calculated for the various diameters (5-205 m) of the sliding windows. Resistance distances obtained from habitats of the same habitat type for the different sliding window sizes were strongly correlated (r ≥ 0. 95, p < 0. 001). We then selected resistance distances obtained from the intermediate-diameter sliding window (11 pixels, i. e. 55 m).
Acknowledgements We are grateful to  Jeremie Guy  and  Juliette Abadie  for assistance in the field,  Maxime Dahirel  for help with the statistical analyses and  Hugues Boussard  for help with Chloe2012 software. This work was funded by the  French Ministry of Agriculture  ( DIVA -Agriconnect  project) and the  Fondation de France  ( BISCO  project) and was supported by landscape databases collected in the LTSER site " Zone Atelier Armorique ". Land-cover maps were established by  Jean Nabucet  ( LETG-Rennes-Costel ) and Kermap.
In amazonia, the knowledge about Fungi remains patchy and biased towards accessible sites. this is particularly the case in French Guiana where the existing collections have been confined to few coastal localities. Here, we aimed at filling the gaps of knowledge in undersampled areas of this region, particularly focusing on the Basidiomycota. From 2011, we comprehensively collected fruiting-bodies with a stratified and reproducible sampling scheme in 126 plots. Sites of sampling reflected the main forest habitats of French Guiana in terms of soil fertility and topography. The dataset of 5219 specimens gathers 245 genera belonging to 75 families, 642 specimens are barcoded. The dataset is not a checklist as only 27% of the specimens are identified at the species level but 96% are identified at the genus level.
tropicalfungi. org) or in Colombia, especially in the terra-firme and white-sand forests  [16] [17] [18]. The last checklist for French Guiana (1996) listed 625 taxa  19  gathered in a very limited number of coastal localities. Evidently, there is an urgent need to systematically collect and document fungi from undersampled areas to fill the knowledge gaps in a region where fungal diversity may be much higher than presently known  [20] [21] [22]. From 2011 onwards, we collected all fruiting-bodies following the same protocol in 126 plots representative of the main forest habitats of French Guiana (Fig. 1 ). We also gathered information on habitats, environment and first taxonomic indications. The resulting dataset provides an unprecedented collection of Basidiomycota for the region, making specimens available for taxonomists, with a molecular barcode for some of them, together with information on ecology and distribution.
We found an extraordinary diversity across forest habitats. The dataset is not a checklist as only 27% of the specimens are determined at the species level and 96% at the genus level. However, the voucher specimens are deposited to herbaria, mainly the Fungarium of the Paris Natural History Museum, for further identification if needed. This tedious, on-going sampling increases the number of families and genera known for the territory as compared with previous collections  4, 19  (Table  1  and Fig. 2 ). The number of species reported from French Guiana increased from 625  23  to 1168. The most abundant families found in French Guiana are also among the most abundant ones found in Amazonian Brazilian forests (http://splink. cria. org. br/, keywords search = Basidiomycota AND Acre, Amapa, Amazonas, Para, Roraima, 16793 records).
Study extent. From 2011, the authors collected sporocarps in French Guiana and inventoried a total of 126 1-ha plots (Fig. 1 ), 6 of which had previously been visited by R. Courtecuisse  19. The Nouragues Ecological Research Station (4°05′N-52°41′W, www. nouragues. cnrs. fr) and the Experimental Station of Paracou (5°18′N-52°53′W, http://paracou. cirad. fr/) are research stations where permanent forest plots are monitored for     19  , Vasco-Palacios  16  , Species Link for Amazonia (splink. cria. org. br, 2019) and this dataset (2019) in gathering specimens. For convenience, only orders that have been more intensively sampled are displayed. We followed the classification proposed by Tedersoo et al. 37. The 126 plots were assigned to one of three topographies according to the classification of Ferry et al.
taxonomic identification. The dataset of 5219 specimens gathers 245 genera belonging to 75 families. Species names of the closest morphospecies were assigned by M. Roy in the field based on existing literature  9, 11, 14, 16, 29. Then, more precise taxonomic identification of all fungi collected was done in collaboration with R. Courtecuisse, C. Decock, T. Henkel, P. -A. Moreau, M. Roy, S. Welti, G. Grühn, J. Fournier, C. Lechat in the field or later by examination of vouchered specimens by A. Verbeken, F. Wartchow and B. Buyck, and using existing literature  29. Homogeneity and consistency of all taxonomic names were controlled afterward. All dry voucher specimens were deposited at one of the following herbaria: LIP herbarium (Lille, Université de Lille, Département de Botanique); PC herbarium (Mycological herbarium of the Paris Natural History Museum, Paris); MUCL, Catholic University of Louvain; HSC, Humboldt State University.
Barcoding. Among the collection, 771 specimens were barcoded as followed. DNA was extracted using the CTAB method  30  , the internal transcribed spacer (ITS1f-ITS1r primers from Taberlet et al. 31  ) was amplified by PCR and sequenced using Illumina Miseq technology (2 × 250 bp) by Fasteris (Plan-les-Ouates, Switzerland) or at the Genotoul platform (www. genotoul. fr). We used tagged primers to distinguish sequences from each specimen. Raw data of the Illumina sequencing were analyzed with the OBITools package  32  as well as scripts in R  33. Briefly, we first conducted paired-end read assembly, read assignment to samples and read dereplication. Low-quality sequences, i. e. those shorter than expected (under 80 bp), containing ambiguous nucleotides, corresponding to singletons and displaying low score paired-end alignments were excluded from the analysis.
Scores of pairwise alignments were calculated with Sumatra package (www. metabarcoding. org) which uses the same clustering algorithm as UCLUST and CD-HIT. This algorithm is mainly useful to detect the 'erroneous' sequences created during amplification and sequencing protocols, deriving from 'true' sequences. For each sample, sequences having pairwise alignments with a score below 97% of similarity were removed and considered as erroneous. Last, for each specimen, the most abundant sequence was kept as representative of the specimen. The last UNITE dataset (https://unite. ut. ee/) was used as reference for the taxonomic assignment of specimen target sequence. The molecular assignation was then compared to the morphological one to confirm the identification of the sequence. In case of discrepancy between the morphological and the molecular identification, the sequence was discarded.
Data Records The dataset contains a record for each sample. Each record contains a unique code identifying the specimen in the collection; a code attributed by the herbarium where it is deposited; a name corresponding to the most precise identification by one of the mycologists involved in this work; the name of the specialist who identified it; the complete description of the sampling plot (city, site, plot, geographical coordinates, altitude, habitat, topography, soil type, substratum and host, name of the collector; collection date), the barcode name (ITS1), the obtained sequence for this barcode and the GenBank accession number of the barcode. The dataset is managed locally in a shared database and is accessible publicly in the GBIF repository (www. gbif. org) under the https://doi. org/10.
acknowledgements Authors are grateful to Régis Courtecuisse, Stéphane Welti and Pierre-Arthur Moreau (University of Lille, France), Cony Decock (Catholic University of Louvain, Belgium), Christian Lechat (www. ascofrance. fr), Jacques Fournier, Gerald Grühn (ONF, Mende, France), Terry Henkel (Humbolt State University, California, USA), Bart Buyck (Department of Systematic and Evolution, Museum National d'Histoire Naturelle, Paris), Annemieke Verbeken (Department of Biology, Gent University, Belgium), Felipe Wartchow (Department of Systematics and Ecology, Universidade Federal da Paraiba, Joao Pessoa, Brazil) for identification of specimens. Authors thank all people who have contributed to complete this database by adding specimens found opportunistically: Amaia Pelozuelo, Hadrien Lalagüe, Axel Touchard, Bertrand Goguillon, Christopher Baraloto, Daniel Sabatier, Emeric Auffret, Eddy Poirier, Thomas Denis, Gunther Fleck, Jérôme Barbut, Jocelyn Cazal, Jennifer Devillechabrolle, Jan Hacker, Marie Fleury, Olivier Bruneaux, Olivier Gargomini, Olivier Tostaing, Pascal Petronelli, Sébastien Brosse, Serge Fernandez, Sophie Gonzalez, Thibaud Decaens, Thierry Munnier, Xavier Desmier, Yannick Estevez, Yves Gaert.
A schematic figure of the RASTA configuration during the HyMeX-SOP1 is given by  Bousquet et al. (2016) , their Fig. 1. The radial velocity measurements are collected at a vertical resolution of 60 m and a time resolution of 250 ms (i. e. 1. 5 s between two measurements of the same antenna). The maximum range is 15 km with a Nyquist velocity of 7. 8 m s -1 (the pulse repetition frequency equals 10 kHz). The data processing described by  Bousquet et al. (2016)  is applied to RASTA wind observations. First, the exact speed of the aircraft and the pointing angles are used to rigorously determine the component related to the aircraft's movement. Doppler measurements are then processed by removing the projection of aircraft ground speed along the six antenna beams. Next, Doppler velocities are unfolded using an in situ wind sensor for the first gate and by applying a gateto-gate correction for the other gates.
RASTA data during the HyMeX first Special Observation Period (SOP1) This study takes advantage of the data collected by RASTA during the HyMeX SOP1, which took place from 5 September to 5 November 2012 over the western Mediterranean  (Ducrocq et al. , 2014). The main goal of the SOP1 was to document the heavy rainfall events that regularly affect north-western Mediterranean coastal areas. During the 2month campaign, approximately 20 rainfall events were documented in France, Italy and Spain  (Ducrocq et al. , 2014). Specifically, the RASTA radar aboard the Falcon 20 collected data during 18 flights in and around mesoscale convective systems in diverse conditions. The data collected by RASTA during the SOP1 offer a wide variety of conditions over land, sea and complex terrains. Among all the observed vertical columns over the SOP1, 72.
RASTA wind data pre-processing First, "super-observations" are created to reduce observation and representativeness errors. They are calculated by interpolating RASTA wind data in the model vertical and horizontal resolutions. This interpolation is done by taking the median value of all data available along the aircraft track within a box of 2. 5 km length between the two half model levels surrounding each model level. Applying a median filter instead of averaging allows the influence of outliers to be reduced due to the difficulty of having high-quality measurements for airborne Doppler radar  (Bosart et al. , 2002). Indeed, after the data processing described in Sect. 2. 1, some spurious data were still occasionally present. Using a median filter instead of a mean filter helps to reduce the weight that these spurious observations can have in the calculation of RASTA wind super-observations.
When the aircraft roll and/or pitch angles are too high (i. e. if d = sin(θ )×R ≥ 2. 5 2 in Fig. 2 , with R being the range from the radar), some data might not be in the same box at a given range from the aircraft (for instance in the box number 3 in Fig. 2 ). Therefore, these data are not taken into account. After this pre-processing, to satisfy assumptions about observation error covariances, which are supposed to be 0 m 2 s -2 , a thinning is applied to RASTA wind superobservations. One super-observation out of three is then assimilated, which is equivalent to approximately one observation every 5 to 9 km depending on the aircraft speed.
During this period, the different assimilation experiments are named CTRL SOP1 , RASTA SOP1 3 h , RASTA SOP1 2 h and RASTA SOP1 1 h. The number of assimilated data over the covered period are represented as a function of the pressure level in Fig. 3  for the three RASTA experiments. Table  1  summarizes the different assimilation experiments. The fourth column shows the percentage of analyses in which RASTA wind data were assimilated over the total number of analyses (360) during the 45-day cycled period for the different RASTA experiments. A larger assimilation window results in assimilating data more frequently, but the time lag between the observation time and the analysis time is greater than 1 h. On the other hand, a smaller assimilation window constrains the number of analyses to those for which the observations are valid near the analysis time.
Therefore, the percentage of analyses in which RASTA wind data were assimilated decreases with the length of the assimilation window from 9. 5 % in the RASTA SOP1 3 h experiment to 7. 2 % in the RASTA SOP1 1 h experiment. Finally, the last column of Table  1  represents the percentage of RASTA wind data which were assimilated among the total number of assimilated data (conventional, GNSS, radar, satellite, RASTA, etc. ) over the entire AROME-WMED domain (represented in Fig. 1 ). This percentage is quite small because of the already dense observing network used in AROME-WMED. Finally, the four different experimental designs are also run on a heavy precipitation event which occurred during the Intensive Observation Period 7a (IOP7a) on 26 September 2012 during the morning. The CTRL IOP7 , RASTA IOP7 3 h , RASTA IOP7 2 h and RASTA IOP7 1 h experiments start at 00:00 UTC on 26 September 2012 and end at 12:00 UTC on 26 September 2012.
Case description: IOP7a The IOP7a precipitation event is one of the most significant episodes that occurred within France during the HyMeX SOP1 campaign  (Hally et al. , 2014). This case study was located over south-eastern France in the area delimited by the red box in Fig. 1 , which has been enlarged in Fig. 4. The precipitation event consisted of a convective line over the mountainous region and a band of stratiform rainfall over the Gard and the Ardèche departments. More than 100 mm of rain was observed between 00:00 UTC on 26 September and 00:00 UTC on 27 September. A first peak of rainfall accumulation is observed in the morning at 08:00 UTC and a second one in the late afternoon at 17:00 UTC. This event is further described by  Hally et al. (2014). During the IOP7a, RASTA data were collected during Flight 15 between 06:10 and 09:45 UTC.
Even though the three RASTA analyses are very similar to each other within their respective assimilation windows t , at 06:30 UTC the RASTA IOP7 (panel c) experiment. This discrepancy is explained by the fact that the aircraft does not have a rectilinear trajectory and passes over the same location several times. In particular, Fig. 4  shows that RASTA collected data at the same location at 06:30 and at 07:00 UTC. In such a case, all data are assimilated as if they were equally valid at the assimilation time T (06:00 UTC here). This overpass explains why the RASTA IOP7 3 h and the RASTA IOP7 2 h are slightly different from the RASTA IOP7 1 h experiment at 06:30 UTC, in terms of both direction and speed. Similarly, there is an overpass of the aircraft at 06:15 and at 07:20 UTC. At this location, while the RASTA IOP7 2 h and the RASTA IOP7 1 h experiments only assimilate the data available at 06:15 UTC, the RASTA IOP7 3 h experiment also assimilates the data collected at 07:20 UTC.
Comparison against conventional observations Averaged over the 45-day experiment, the assimilation of RASTA wind data does not substantially impact the specific humidity and the temperature on both the analyses and the forecasts. Therefore, because the most significant differences only appear on the zonal (u) and meridional (v) wind components, results are only shown for the wind. Calculations are not shown for the analyses but only for the 3 h forecasts. Indeed, since the observations used for the comparisons are all assimilated, the fit to observations is better in CTRL SOP1 than in the RASTA experimental runs. Because RASTA wind data are limited in space around the Mediterranean area (see black lines in Fig. 1 ) and depend on the presence of cloud or precipitation along the aircraft flight path, their assimilation impact is also limited in space.
Impact on rainfall forecasts Forecast scores against rainfall measurements are now calculated over the 35 runs (out of 360) in which RASTA data were assimilated with the largest assimilation window. The verification is conducted using the rain gauge network available from the HyMeX database (https://doi. org/10. 6096/MISTRALS-HyMeX. 904), the locations of which are indicated by the blue markers in Fig. 1. For the comparisons, model outputs are interpolated to the rain gauge station locations using a linear interpolation. Model outputs and rain gauge measurements are then averaged in boxes of 0. 25 • × 0. 25 • within each RASTA-limited validation area. Categorical scores have been calculated: Heidke skill score (HSS), probability of detection (POD) and false alarm ratio (FAR). To avoid the spin-up problem, the first hour of rainfall accumulation has been removed from the calculations.
The HSS, POD and FAR of the 8 h accumulated rainfall forecasts for the three RASTA experiments are displayed in red in Fig. 10  as a function of the rainfall accumulation threshold (mm). The scores of the CTRL SOP1 experiment are also shown in black. The bootstrap confidence intervals are displayed for each threshold by the dashed lines. The impact of the assimilation of RASTA wind data is positive if the red lines are above (below) the black ones for the HSS and POD (FAR). Figure  10  shows that the general pattern is similar for the three RASTA experiments, which indicates that the choice of the assimilation window does not significantly impact the subsequent forecasts. Even though the bootstrap confidence intervals increase with the threshold, differences with the CTRL SOP1 experiment are more pronounced at larger thresholds in any of the three RASTA experiments.
Discussions and conclusions This article reports on the first study in which vertical profiles of wind measured by vertically pointing airborne Doppler W-band radar are assimilated in a kilometre-scale NWP model. The study was performed in a quasi-operational framework with a special version of the Météo-France operational kilometre-scale model AROME with its 3DVAR assimilation system. The data were provided by the airborne Doppler W-band radar RASTA during a 45-day period over a region of the Mediterranean area that is very prone to heavy rainfall. RASTA is a multibeam antenna system that can retrieve the three components of the wind fields, which allows the direct assimilation of the retrieved horizontal wind components. A sensitivity study for the choice of the assimilation window was performed.
A cycling data assimilation experiment has also been conducted over a 45-day period from 24 October to 5 November 2012, for the CTRL experiment and for the three RASTA data assimilation experiments. The comparisons ) and FAR (g, h, i) of the 8 h cumulated precipitation forecasts vs. rain gauge measurements for the three RASTA experiments (in red) and for the CTRL SOP1 experiment (in black). Calculations were performed over the 35 runs in which RASTA wind data were assimilated with the largest assimilation window. The error bars (dashed lines) represent the 90 % bias-corrected and accelerated (BCa) bootstrap confidence intervals (see  Efron and Tibshirani, 1993). against other assimilated observations and rain gauges measurements indicate an overall neutral impact, which is probably due to the small percentage of RASTA wind data which were assimilated among the total number of observations.
Acknowledgements. This work is a contribution to the  HyMeX  programme supported by  MISTRALS, ANR IODA-MED  grant  ANR-11-BS56-0005  and  ANR MUSIC  grant  ANR-14-CE01-0014. This work was supported by the  French national programme LEFE/INSU. The authors acknowledge the  DGA (Direction Générale de l'Armement) , a part of the  French Ministry of Defense , for its contribution to  Mary Borderies 's PhD. The authors thank SAFIRE for operating the French Falcon 20 research aircraft during HyMeX-SOP1. The authors are grateful to  Pierre Brousseau  for his technical help. Two anonymous reviewers are also gratefully acknowledged for their comments, which helped to significantly improve the quality of the article. Review statement. This paper was edited by  Eric Martin  and reviewed by two anonymous referees.
Acknowledgements. C. G. thanks  F. Ribot  for the fruitful discussions. G. S. and Z. R. thank undergraduates  MacKenzy Meis  and  Isabella Cesarone  for their help with the material preparation. The authors thank especially  Dr Qiang Ye  from  Institute for Bioengineering Research Laboratories, University of Kansas  for equipment (Raman, FT-IR and viscometer) training and technical support.
Data accessibility. Raw data are available within the Dryad Digital Repository at: https://doi. org/10. 5061/dryad. bt45780 [36]. Authors' contributions. G. S. participated in design of the project as a senior researcher and helped with drafting the manuscript; C. G. contributed to perform characterizations, data analysis and drafted the manuscript; Z. R. carried out the preparation of all samples, data analyses and drafted the manuscript.