dataesr/scientific-paragraphs-categorization huggingface dataset:
(1993) , and it could be observed also in the test data collected with the depolarization lidar (see Table  1 , and Fig. 3 ) during the summer period. At the ground level the large majority of hydrometeors recorded by the MASC were small particles of non-discernible habit and non-definable riming degree (given the MASC resolution), with an occurrence three times higher than similar measurements conducted in Alpine locations. This is probably the signature of the significant contribution of blowing snow to the near-ground snow flux, that is particularly effective in recirculating small and light particles (e. g. Mann et al. , 2000; Gordon et al. , 2009) , but it could, in a minor part, be the result of the fragmentation of aggregates in the low-level of the atmosphere, where strong katabatic winds blow.
This combination of ground-based and in-situ instruments should be proposed again, and maintained for at least a full year to cover all the seasons. The main limitation of the filter of Pluvio 2 data is that it cannot detect cases when blowing snow occurs together with precipitation. In the case of our measurements, the total accumulated precipitation of the Pluvio 2 in the summer period drops from 66 mm to 52 mm, after the censoring is performed. The removed portion is then 21 % of the total raw accumulated precipitation. Regarding blowing snow, the comparison between the occurrence of signal at the lowest MRR gate and the visual weather reports conducted by Météo France, summarized in Table  4 , confirms that the current MRR configuration does not capture the occurrence of blowing snow events.
5 Summary and conclusions In this paper we present unprecedented observations of precipitation collected at a coastal location of East Antarctica since October 2015. Several remote-sensing and in-situ instruments collected measurements during summer 2015/2016, and one (the MRR) has been operating continuously since then. These instruments have provided an insightful example of their usefulness to monitor precipitation on the Antarctic continent. It has been shown that radar data can be used to remove phantom accumulations from in-situ weighing gauge observations. These accumulations, occurring in high-wind conditions, and tracked down to be due mostly to vibrations, accounted for 21% of the total accumulated precipitation of the summer period. Microphysical observations at the ground level, collected by the MASC in summer, showed that the large majority of hydrometeors (54%) were small ice particles of non-defined habit probably resulting from blowing snow, followed by aggregates (13%), and other hydrometeor types.
Acknowledgements. The authors are thankful to  Météo France , and in particular to the team of  Dumont d'Urville  who provided the access to their in-situ measurements and observations. We thank the  French Polar Institute (IPEV) , in particular  Gregory Tran ,  Doris Thuillier , and  Patrice Godon , who allowed the APRES3 measurement campaign to take place. We thank  Paul Dufay , winteroverer at  DDU , who provided crucial assistance for the operation of the MRR during the winter season. The first author,  Jacopo Grazioli , thanks the  Swiss National Science foundation SNF  for the grant  200021_163287 , financing his participation to the project. The authors acknowledge also the support of the  French National Research Agency (ANR)  to the  APRES3  project and also of  CNES/TOSCA ,  programme EECLAT.
Using a similar background selection as mentioned above, 283 miRNAs were previously detected by RT-qPCR  10  , which converted into 248 unique miRNAs that are currently annotated in the most recent version of miRBase  18. From these, 198 miRNAs were also detected using our current small RNA-sequencing approach. Therefore, the RT-qPCR approach still detected 50 miRNAs that were not identified in the small RNA-sequencing dataset. In contrast, small RNA sequencing detected 259 miRNAs, that were not included in a previous RT-qPCR analysis from the same T-ALL patient population  10. In Fig. 1b  and Supplementary Fig. S1 , the mean expression values for the 574 miRNAs, detected by small RNA-sequencing in primary T-ALL patients, are plotted, with the black dots representing the 280 miRNAs that were previously identified by RT-qPCR and the red dots representing the miRNAs detected for the first time in human T-ALL.
Small RNA-sequencing also enables the detection of so-called isomiRs, i. e. miRNA isoforms that deviate from the canonical sequence by one or a few nucleotide(s)  11, 12. In our small RNA-sequencing dataset, we identified 2139 isomiRs covering 481 different miRNAs, losing some very low expressed miRNAs from the analysis above. Although 154 miRNAs were only represented by one isomiR, some others showed expression of more than 10 different isomiR forms (Fig. 1c ). We observed a positive correlation between the number of isomiRs detected for a specific miRNA and its expression level in human T-ALL (r = 0. 89, Fig. 1d ). For example, we detected 39 different isomiR forms for miR-181a-5p, the miRNA that shows the highest average expression in T-ALL. Remarkably, for 106 out of 481 miRNAs, the canonical miRNA was not expressed in our patient series.
The PCR products were separated using a Pippin Prep System to recover the 147 nt and 157 nt fractions. Sequencing of the small RNA libraries was performed on a NextSeq500 (Illumina), with an average of 14. 4 million reads per sample. After read quality control and adapter trimming, reads were mapped to the reference genome (GRCh38) using Bowtie  33. Raw data files are submitted into the GEO database  34  with accession number GSE89978.
Acknowledgements The authors would like to thank the following funding agencies:  Fund for Scientific Research Flanders ('FWO Vlaanderen ' research projects  GA00113N ,  3G065614 ,  G0C4713N ,  G022216N  and  31500615W  to PVV;  3G056413N  and  3G055013N  to FS;  G037514N  and  GOB2913N  to TT; PhD grant to AW),  Ghent University Research Fund (GOA LNCCA) , the  Belgian Program of Interuniversity Poles of Attraction  ( IUAP P7/03 ) and the  Childhood Cancer Fund Ghent (Kinderkankerfonds VZW).
Acknowledgements This project was supported by the  President 's Emergency Plan for AIDS Relief  through Cooperative Agreement number  PS001426  from the  US Centers for Disease Control and Prevention. We would like to acknowledge the contributions of all members of the  Caribbean LQMS-SIP Technical Working Group (Claudette Brown ,  Jamaica National Agency for Accreditation ;  Eileen Burke ,  Caribbean Epidemiology Center; Ellison Floyd-Tobas, Trinidad & Tobago Laboratory Accreditation Service ;  Floris Gordon ,  African Field Epidemiology Network ;  Wendy Kitson-Piggott ,  Caribbean Med Labs Foundation ;  Vernita Maryat ,  Caribbean Community Regional Organisation for Standards and Quality ;  Mary Nagel ,  Laboratoire de Santé Publique en Haiti ;  Marsha Samaroo ,  Ministry of Health, Republic of Trinidad & Tobago; David Turgeon, US Centers for Disease Control and Prevention ; and  Valerie Wilson ,  Caribbean Med Labs Foundation ).
Background: Understanding the patterns of biodiversity distribution and what influences them is a fundamental pre-requisite for effective conservation and sustainable utilisation of biodiversity. Such knowledge is increasingly urgent as biodiversity responds to the ongoing effects of global climate change. Nowhere is this more acute than in species-rich tropical Africa, where so little is known about plant diversity and its distribution. In this paper, we use RAINBIOone of the largest mega-databases of tropical African vascular plant species distributions ever compiledto address questions about plant and growth form diversity across tropical Africa. Results: The filtered RAINBIO dataset contains 609,776 georeferenced records representing 22,577 species. Growth form data are recorded for 97% of all species.
We propose priority target areas for future sampling efforts, mainly focused on Tanzania, Atlantic Central Africa and West Africa. The observed number of tree species for African forests is smaller than those estimated from global tree data, suggesting that a significant number of species are yet to be discovered. Our data provide a solid basis for a more sustainable management and improved conservation of tropical Africa's unique flora, and is important for achieving Objective 1 of the Global Strategy for Plant Conservation 2011-2020. In turn, RAINBIO provides a solid basis for a more sustainable management and improved conservation of tropical Africa's unique flora.
These results call for immediate international policies to be put in place  [5, 7, 8] ; however, they will be hard to achieve without a better understanding of plant diversity and its distribution. Herbaria around the world are the keepers of such essential data for plants  [7, 9, 10]. Together, they curate a vast databank of about 350,000,000 physical dried plant specimens  [11]. Any conserved specimen provides unique proof of where and when a particular species was present. With ongoing and intensified efforts of natural history institutions to digitize their scientific heritage, the possibility of providing large amounts of data to a much wider audience has rapidly expanded over the last few years  [12] [13] [14] [15] [16]  (see  [17]  for an overview of the number of scanned herbarium specimens in the world's largest virtual herbaria).
During the last 15 years, efforts to database tropical African plants have been undertaken, but generally with the purpose of analysing large scale phytogeographical patterns across Africa  [30] [31] [32]. Thus, these datasets are characterised by an overrepresentation of well-known and comparatively widespread species. More recently, Stropp et al. [25]  extracted and analysed all flowering plant data openly available via the Global Biodiversity Information Facility (GBIF, http://www. gbif. org) for Africa (including Madagascar). They concluded that the quality and completeness of species-occurrence data of flowering plant species available through Biodiversity Information Systems such as GBIF is low. In addition, over half of the records they retrieved did not relate to tropical Africa but to South Africa and Madagascar.
Thus, to date, large uncertainties remain about plant species diversity and distribution across tropical Africa. The digitization and georeferencing of major Africanspecialised herbarium collections have substantially progressed these past years  [16, 17]. When these independent efforts are compiled and concatenated into a single quality-checked comprehensive mega-database, they will provide new and detailed insights into tropical African plant diversity and distribution as never achieved before  [33]. The goal of this paper is to document the main floristic patterns and information gaps for tropical Africa. For this, we use the largest and highest quality mega-database of vascular plant species distributions ever compiled for tropical Africa (RAINBIO,  [33] ). First, we explore the spatial distribution of records and species diversity statistics at regional and country levels and ask the following questions: how is plant species diversity distributed across tropical Africa? How many plant species are there in tropical African forests? How is this diversity partitioned in terms of growth form? Which regions have the highest floristic turnover rates? Second, we analyse the level of botanical exploration across tropical Africa and ask the following questions: have tropical African countries been adequately explored? What regions are well sampled and which are not? Can we identify areas where future sampling would be the most efficient? Finally, we conclude with a series of recommendations in order to improve our knowledge of the floristic diversity of tropical Africa.
Data and study area All analyses were performed using different subsets (i. e. with or without silica gel information, see below) of the RAINBIO database  [33]. In short, RAINBIO is a compilation of: publicly available datasets mainly from international herbaria; personal ones such as datasets on palms, legumes, orchids, Rubiaceae, Marantaceae, flora of Gabon, flora of the Dzanga-Sangha region (Central African Republic); tree plot inventory data from Gabon; georeferenced silica-dried samples for tree species from Central Africa. The data were checked automatically and manually via experts for quality in several ways, including geographic and taxonomic standardisation, merging of duplicate records (originating from different datasets), and exclusion of cultivated/non-native plants. A full description of RAINBIO, how it was compiled and how the data were checked and verified can be found in Dauby et al.
[33]. The RAINBIO dataset contains important additions and a significant increase in data quality compared to the data available through the GBIF (see,  [25]  for biodiversity analyses of data only available via GBIF), and has a strong focus on tropical Africa  [33]. Our study area is tropical Africa, which Klopper et al. [2]  broadly defined as sub-Saharan Africa and excludes southern Africa and Madagascar (Fig. 1 , roughly between 16°N and 20°S). For delimitating this study area, we relied on the ecoregions defined by Olson et al. [34]  and used the eco-region 'south Saharan steppe and woodlands' as the northern limit and ecoregions 'Namibian savanna woodlands' , 'Kalahari xeric savanna' , 'Kalahari Acacia-Baikiaea woodland' , 'Highveld grasslands' and 'Drakensberg montane grassland' as the southern limits.
Sampling unit size In our study, we used two different sampling unit (SU) sizes. First, we simply used a 'fixed-size' SU of 0. 5°(ca. 55 × 55 km at the equator). For certain analyses, however, we used an 'adaptive resolution' SU method  [35]. This approach adapts the size of the SU as a function of a user-defined threshold of minimum occurrence records (see  [35]  for details). This method allows one to consider smaller SUs where record density is high, while increasing SUs size where records are sparsely distributed. The advantage is that record numbers remain broadly constant across SUs. In order to create an adaptive SU grid we uploaded the filtered RAIN-BIO database to the Infomap Bioregions  [35]  website (http://bioregions. mapequation. org). We used the following parameters: maximum cell capacity = 1000; minimum cell capacity = 500; maximum cell size = 8°; minimum cell size = 0.
Species diversity patterns Species richness was estimated at two different levels: (1) for each fixed-size SU of 0. 5°containing at least 100 records, and (2) at country level. Only herbarium specimens were included for this estimation. Plot data and silica gel-dried DNA samples were excluded because of their stronger focus on particular taxa, which are often over-sampled and would introduce a bias. In each case, an observed and estimated total species number was calculated. For the observed species number per country we used georeferenced and non-georeferenced specimens. As species richness estimation is strongly affected by sampling efforts, we computed two complementary richness estimators. First, we calculated a nonparametric species richness estimator  [36] , known as the Chao1  [37] , Ŝ, defined as: Ŝ ¼ S obs þ a 2 1 2a 2 N-1 N Where a 1 is the number of species represented by one specimen (singleton), a 2 is the number of species represented by two specimens (doubleton), S obs is the observed number of species and N is the total number of specimens.
Sampling completeness Two estimates of sampling completeness were computed. First, the relative exploration was estimated as the percentage of species already discovered: observed species divided by the estimate of total number of species by Chao1. Second, the sampling coverage estimator of  [45]  was calculated as: Ĉ N ¼ 1- a 1 N j N-1 ð Þa 1 N-1 ð Þa 1 þ 2a 2 k A sampling coverage of 1 indicates that all species have been collected twice or more, while for a value of 0 all species are known by a single record only. We identified the 'best-explored' countries, namely countries with a relative exploration value above 0. 85 and sampling coverage higher than 0. 95. However, this does not imply they were botanically well known, but rather, compared to others, they had the best data available to date.
Growth form diversity Most species (91%) recorded in RAINBIO were able to be categorised into nine different growth form types (tree, shrub, herb, liana, vine, aquatic herb, epiphyte, mycoheterotroph and parasitic)  [33]. For detailed information on how this was achieved, please see  [33]. In short, main categories were automatically assigned through an ad hoc custom R script by extracting keywords for each habit from available herbarium specimen label note information. A total of 4751 species names (22% of the total filtered dataset, see below) were manually checked by experts in order to confirm automatic assignments (when two or more habits were suggested, 2823 species) and to fill in missing assignments (1928 species). Besides these specific cases, a large majority of habit assignments were confirmed (but no proportion can be provided).
Spatial distribution of data The original unfiltered RAINBIO database comprises distribution data for 25,356 species of vascular plants, 3158 genera and 273 families from a total of 614,022 records. The filtered dataset for tropical Africa that was used for our analyses had a total of 609,776 specimens representing 22,577 species. This represents the largest and highest quality dataset ever compiled for tropical African plants. The records are well distributed but heterogeneous across the continent (Fig. 1a ). The highest collecting efforts are concentrated in West Africa (Liberia to Benin), south-western Cameroon, Equatorial Guinea, Gabon, the region covering the eastern part (Kivu) of Democratic Republic of the Congo (DRC) plus Rwanda and Burundi, and the Eastern Arc Mountain range in Kenya and Tanzania (Fig.
Growth form information Growth form data were recorded for 21,901 species or 97% of the filtered species list of RAINBIO. Our results suggest that the number of herbaceous species is around the same as the number of woody species (Table  2 ). The geographic distribution of plant growth form types across tropical Africa in terms of the proportion of species of a certain type shows contrasting patterns (Fig. 8 ). The herbaceous growth form has a high proportion in drier regions (Sahel, East Africa) where savannah prevails (Fig. 8a ). In contrast, the tree and liana growth forms have high ratios within the rain forest regions where climate seasonality is lowest (Fig. 8c, d ). Shrubs recorded average proportions in the Sahel region and high proportions in the horn of Africa region, for example, Somalia and northern Kenya (Fig.
Discussion The RAINBIO mega-database The basis of the current analyses relies on data available through the RAINBIO mega-database  [33]. As indicated in the methods section, we suggest that RAINBIO is the largest and most accurate database for tropical African plant distributions, as we undertook numerous data validation routines, automatic as well as manual via numerous African flora taxonomic experts. Recently, another dataset has been published  [51] , comprising 3. 1 million records for 40,401 vascular plant species across the whole of Africa. However, this dataset was assembled to test a new method of identifying areas of important biodiversity conservation (Star ratings) and has not been specifically used to explore tropical African plant biodiversity. We stress that, like all existing biodiversity databases, RAINBIO is not perfect.
First, specific data associated with specimens can be erroneous, for example, misidentifications or errors in geo-referencing. Nevertheless, by having expert taxonomists validate large parts of the identifications and by treating information from duplicated herbarium specimens in a systematic manner (see recommendations section), we significantly limited errors and improved the overall quality of RAINBIO. Second, several important herbaria were not directly included such as the Muséum National d'Histoire Naturelle (P), the Conservatoire et Jardin botaniques, Geneva (G), the East African herbarium (EA) and partly the Royal Botanical Gardens, Kew (K) (acronyms follow,  [11] ). In general, specimens within those herbaria have not yet been databased or were not available at the time of this project.
The impact of missing data has yet to be explored and could affect the results in various regions such as East Africa (EA, K) or West/Central Africa (P). However, it is important to underline that (1) a significant part of the data within these herbaria are indirectly available in RAINBIO (via duplicates distributed to other herbaria or via specimen databased with the framework of regional/country floras (e. g. [52, 53] ) or monographic revisions, information of which is included in RAINBIO), and that (2) our paper explores general tropical African plant biodiversity patterns for which RAINBIO provides a sound representation of all data.
In contrast, low record density is not only due to low collecting efforts (Central African Republic, Republic of Congo or Angola) but also to incomplete digitization and georeferencing of existing records, for example, in Angola  [55]  and DRC  [56]. For tropical Africa, we record occurrences for 22,577 species, 2810 genera and 258 families of vascular plants. These values are lower than previous estimates for the region. Indeed, Klopper et al. [2]  recorded a total of 32,424 angiosperm taxa, while Govaerts  [57]  estimated around 29,887 species for the same region. However, these values are not entirely comparable. First, they are based on databases (checklists) of published names (e. g. African Plant Checklist and Database Project; Kew World Checklist) rather than from herbarium specimens.
Due to new insights in taxonomy, a significant number of those names have been marked as synonyms in our list, although we have also added many newly published species names. Second, Klopper et al. 's  [2]  values are for taxa (thus including infraspecific names) and for angiosperms only, while Govaerts'  [57]  are restricted to species and seed plants (gymnosperms and angiosperms). Our values are for species and all vascular Several species-rich areas across tropical Africa (Fig. 2a ) are highlighted based on the Chao1 estimator (but see below), namely the Cameroon/Gabon forests, including Mont Cameroon, the east Kivu region and the Eastern Arc Mountains in Tanzania, with a less pronounced diversity centred in Katanga (south-eastern DRC). These results are consistent with those of several previous studies  [30] [31] [32] , but our results are derived from a significantly larger dataset.
Country-level diversity patterns We provide, for the first time, basic plant biodiversity values for several countries across tropical Africa (Table  1 ). These values are based on the available data within RAIN-BIO, and thus should not be taken as definite. Nevertheless, they provide important insights into the plant diversity of these countries as well as levels of exploration. For some countries, these values are quite close to previous estimates. For example, Onana  [69]  recorded a total of 7850 vascular plant species for Cameroon, whereas we report 6883 species with an estimated total species richness of 8015. The higher recorded total species number of Onana  [69]  could be related to synonymy not taken into account during that study (Onana personal communication). Another example is for São Tomé and Príncipe  [70] , where a total of 803 native flowering plant species (excluding 301 introduced species) plus a single endemic gymnosperm species have been recorded.