dataesr/scientific-paragraphs-categorization huggingface dataset:
This value closely matches our estimates (Table  1 ), which could reflect the inclusion of the vast majority of São Tomé and Príncipe records from herbaria such as BRLU and LISC in our study  [33]. Our study identifies several botanically 'best-explored' countries with Cameroon, Benin and Gabon in the top three (Table  1 ). For some of those countries (i. e. Benin, Gabon and Liberia) a large percentage of existing specimen data (over 90%) is available in RAINBIO and thus could reflect values close to reality. Even for these countries, the average specimen density is well below the threshold of 100 records/100 km 2 (19 for Benin, 35 for Gabon and 16 for Liberia; Table  1 ). In addition, the estimated discover rate (percentage of species yet to be documented based on our estimate of total species richness) varies between 14% and 19% for the top seven countries (Table  1 ).
Thus, even for the top best-explored countries new species are expected to be described or have already been collected but not yet identified. Data from the International Plant Name Index (IPNI, www. ipni. org) for the period 2000 to 2015 reveals that 8162 and 38 new species were described from Benin, Gabon and Liberia, respectively (0. 3%, 2. 7% and 1. 5% of their estimated species richness, respectively). For Gabon, 4710 species were recorded in 2005  [52] , meaning that no less than 526 (10%) new species or new species records have been added these past 11 years (5236 species recorded in RAINBIO). New species and even new genera are still regularly described for Gabon, even in well sampled areas  [71]  such as the Crystal Mountains National Park  [67, [72] [73] [74] [75] [76] [77]. Interestingly, São Tomé and Príncipe show the highest concentration of records (375/100 km 2 , Table  1 ) yet is not amongst the best-explored countries in our study.
For our dataset, this can partly be explained by the targeted scanning of type specimens within the Global Plant Initiative (  [63] , https://plants. jstor. org), artificially increasing the number of species known from a single collection. Still, overall, our results confirm that tropical Africa remains severely under sampled  [24, 25] , even when using the largest homogenised dataset ever complied for the region to date  [33]. Similar results are reported for other tropical regions such as the Amazon basin  [20]  or countries such as Ecuador  [40]. In a world of limited resources, an important question is how to identify priority sampling units  [24] , e. g. SUs where we could expect to significantly add to our knowledge of the African flora. This is not a trivial question, as potentially any SU across Africa deserves more in depth exploration.
Plant growth form dominance across tropical Africa Data on growth form in the tropics is scarce, but some studies show that the contribution of herbaceous species to tropical forest diversity is between 18% and 44%  [83] [84] [85]. Our data suggests that 43. 8% of species are herbaceous across Tropical Africa. This also underlines that savanna and montane vegetation types are well represented in RAINBIO, despite the initial emphasis of our data towards forested regions. It has been shown that on global or continental scales plant growth form is linked with climatic variables  [19, 86, 87]. Although no formal analyses are undertaken here, our data underline general patterns possibly linked to climate (Fig. 8 ). For example, herb species are dominant in the drier parts of Africa (Senegal, Burkina Faso, Malawi, Tanzania, Zimbabwe) whereas trees and lianas are dominant in countries with significant areas of tropical forests (e.
g. Republic of Congo, Gabon, south of Cameroon). In addition, with most of their surface being at higher elevations, Ethiopia, Rwanda and Burundi harbour vegetation with much higher proportions of herbaceous species. As indicated above, the herbaceous component (Fig. 8a ) gives a clear pattern related to the drier regions (Sahel, East Africa) where savanna prevails. Rwanda, Burundi, south-eastern DRC (Katanga), Zambia and Ethiopia, which are dominated by savanna and/or highland vegetation, also stand out as containing a high proportion of herbaceous species. Interestingly, a large New World dataset for geographical trait patterns did not reveal montane areas (such as the Andes or the Rocky Mountains) as exceptionally dominated by herb species  [19]. The moderately high ratio of herb species locally observed on the coast of Ghana and the western tip of Gabon (Port-Gentil) (Fig.
Improve data exchange between datasets The effort to combine several big datasets as well as more non-public specific ones has been a major undertaking  [33]. It has proven to be possible, not overly complicated, though fairly time consuming. For tropical Africa, downloading available data from GBIF will exclude significant records  [25]  mainly from non-public databases from institutions that are either not participating in GBIF or have not yet shared all their data openly. We did not undertake detailed comparisons between GBIF and RAINBIO data as this was out of the scope of this article. Even though GBIF represents the most important source of open data for species distribution information, it has been shown that data quality and reliability may be low, especially in the tropics  [104, 105].
When compiling specimen data from different sources, one is faced with the 'duplicate problem', since plant specimens have several duplicates distributed to different herbaria some are prone to having different identifications and databasing quality, complicating the concatenation of data. Estimates suggest that just under 10% of all records in GBIF are potential duplicates  [106]. Taking into account duplicates when compiling data from different institutes has yet to meet a good automatic solution and has rarely been done in large plant datasets. In the case of RAINBIO, different datasets were identified as 'expert taxonomic benchmarks', i. e. datasets verified by specialists for a given family, against which all duplicates were compared too and updated. We identified 11% of records with at least one duplicate.
Similar approaches to deal with this problem have been undertaken, but at smaller scales (e. g. [105] ). In order to maximise the usefulness of botanical biodiversity data more efforts should focus on resolving this limitation and finding appropriate automatic ways of taking duplicate information into account, especially from important online repositories such as GBIF that contain potentially high levels of duplicated information. Thus, besides past and ongoing major efforts to independently database herbarium specimens and upload them to GBIF, synthesising these datasets must remain a priority.
Improve data reliability Separate, but related to the above point, the reliability of the data is important as it will directly influence the outcome of analyses  [12, 20, 104, 105]. It is well known and understood that natural history specimen datasets have several potential errors  [20, 54, 107]. Two major sources of errors can influence the analyses of such data. First, wrong species identification of specimens will be an important source of error in any dataset. We estimate that the identification error rate in an average herbarium collection can be as high as 10% or even up to 58% for specific groups  [108] , although in the latter figure specimens with a synonymous but otherwise correct name were also counted as errors. In a dataset of 4000 individual trees of the genus Inga, 7% of the identifications proved to be wrong  [109].
By comparing an expert selection of identified duplicates, the identification accuracy in combined botanical datasets like ours can be improved. However, misidentifications are an inherent part of any biodiversity dataset and can never really be completely eliminated at least because of changing taxonomic concepts and the presence of incomplete specimens. The event of DNA barcoding  [110]  could provide added value to accurately identify specimens (from sterile vouchers to species complexes) although bulk barcoding of large collections remains expensive and timeconsuming. We must also consider the level of our taxonomic knowledge for tropical Africa. Every year, hundreds of new plant species are being described (globally, on average well over 2000/year according to data provided by IPNI, www.
ipni. org), and new taxonomic borders for species, genera, etc. are drawn. Bebber et al. [9]  showed that the majority of newly published species names were based on specimens collected long before. Thus, a fair number of new species are awaiting description within one of the world's herbaria. In the future, our advancing knowledge will further refine and improve the quality analyses such as the ones presented above. In addition, reliability of the data can be improved by a structured collaboration continuing to work with taxonomic experts. In the case of RAINBIO, large numbers of records were indeed checked and validated by the project taxonomic experts from numerous families or geographic regions. This greatly improved the accuracy and quality of RAINBIO. Misidentification is sometimes considered as 'background noise' in 'big data' datasets.
Depending on the objectives of the study this can be misleading, leading to erroneous results (e. g. when estimating total species number per country/region; conservation assessments). We advocate a 'compile and check' rather than a 'compile only' approach. Directly involving expert plant taxonomists in such large projects will greatly improve our databases and the subsequent biodiversity analysis. Second, specimens may be incorrectly georeferenced, which in turn affects biodiversity analyses  [105]. However, recent tools or software packages are now available to automatically improve or correct wrong georeferencing in big datasets  [111, 112]. Checking georeferencing precision using such methods should systematically be used in order to improve the precision of these records.
Conclusion The RAINBIO database provides the underlining data to assist in advancing phytogeographical, systematic and ecological research, and enables a wiser sustainable usage and conservation of Africa's unique tropical plant diversity. Only when provided with sound and high quality information on biodiversity distribution, can policymakers take informed decisions about how to manage this fragile resource effectively  [8]. Strangely enough, its strength is also in that it shows clear weaknesses in the amount of botanical data available for tropical Africa. To most efficiently fill the gaps and speed up the work, we should investigate how we might benefit from a much larger group of data collectors. The development of citizen science  [113] , i. e. public involvement in science, has proven its usefulness in a range of biodiversity-related projects (for two recent examples, see  [114, 115] ).
The high sampling frequency is also an advantage for fire observations during brief cloud-free spells in otherwise mostly cloudy regions. In addition it covers higher latitudes than the MODIS fire products since snow and ice pixels are being processed. Despite these advantages the FRP product from SEVIRI does not provide global coverage since it only covers a geographical disc comprising Europe and Africa. Moreover, the SEVIRI FRP level 2 products undergo a different calibration procedure from that of MODIS, and the merging of the two datasets has proven to be a challenge  (Roberts et al. , 2011). Given the difficulties in the use of multiple platform observations, in this paper we propose a new approach to improve the emission estimations from FRP observations based on the idea that weather plays a major role in the ignition, spread and termination of wild fires wherever there is available combustible vegetation and suitable terrain topography  (Flannigan et al.
Model behavior To understand the impact of the new operator on the emissions we concentrate on the prediction of extreme fire. In July 2013 large fires were recorded around the Hudson bay in the Quebec province, Canada. Hundreds of fires consumed a total area of 616,000 hectares during the first two weeks of July 2013. Two major events occurred, the first on 2-6 of July with peak activity on the 4 June (figure  3 ) and the second between 8-10 of July. The smoke plumes produced by the fires in Canada reached Europe on 12 July 2013. Figure  3  show a comparison of FRP budgets over the Hudson Bay area (40-60N, 70-100W) provided by the GFAS using the persistence hypothesis and FWI respectively. Using FWI reduced the total FRP by up to 80% within the Hudson Bay area. The histogram also highlights the number of grid cells for which a positive FRP was estimated, and the number of grid cells for which observational data was not available, i.
Figure1ademonstrates the more obvious consequence of the persistence assumption; the increase in mean fire duration. It shows the difference in fire duration between MODIS and GFAS. The density plot takes into account all fires detected by MODIS instruments on board the Terra and Aqua platforms during the verification period in summer 2013. Fire duration for MODIS is defined by the number of continuous days for which FRP is available and larger than zero. Fire duration in GFAS extends also during observation black-out periods until a valid observation confirms the end of the event. The observed global average fire duration in the MODIS dataset is 4. 2 days, and by assuming persistence GFAS increases the fire duration by 1. 2 days. In the new formulation O takes on values in the interval 0; 1 , and FRP predicted by the new model is therefore by construction expected to be smaller than, or equal to what is predicted by persistence.
Acknowledgements. This work was funded by the  EU  Project  ANYWHERE (Contract 700099 ) and the Global Fire Contract 389730 between the  Joint Research Centre and ECMWF. Angela Benedetti  is thanked for the fruitful discussions concerning the interpretation of the MOPITT comparison. We would like to thank  NASA  and the  MOPITT team  for the ease of access to their data. Luke Jones  is acknowledged for his support in creating Figure 6. Rebecca Emerton  and  Sue Dunning  kindly read the manuscript before submission. 14 Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2017-790Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 26 September 2017 c Author(s)  2017. CC BY 4. 0 License.
FigureFigure 1. 1 Figure1ademonstrates the more obvious consequence of the persistence assumption; the increase in mean fire duration. It shows the difference in fire duration between MODIS and GFAS. The density plot takes into account all fires detected by MODIS instruments on board the Terra and Aqua platforms during the verification period in summer 2013. Fire duration for MODIS is defined by the number of continuous days for which FRP is available and larger than zero. Fire duration in GFAS extends also during observation black-out periods until a valid observation confirms the end of the event. The observed global average fire duration in the MODIS dataset is 4. 2 days, and by assuming persistence GFAS increases the fire duration by 1. 2 days. In the new formulation O takes on values in the interval 0; 1 , and FRP predicted by the new model is therefore by construction expected to be smaller than, or equal to what is predicted by persistence.
Growth occurred in culture media with NaCl concentration ranging from 0 to 15% (w/v) NaCl (optimum, 7. 5% (w/v) NaCl). Agar-grown colonies are beige, circular and shiny with a mean diameter of 1. 2 mm. Bacterial cells were Gram positive, rod shaped and polymorphic. Strain Marseille-P3515 T was motile and spore forming with a positive catalase and oxidase reaction. Strain Marseille-P3515 T exhibited a 97. 96% sequence identity with Bacillus circulans strain NBRC 13626 (GenBank accession no. NR_112632 (Fig. 1 ). We propose to classify strain Marseille-P3515 T as a new species of the genus Bacillus within the family Bacillaceae in the phylum Firmicutes. Strain Marseille-P3515 T is the type strain of 'Bacillus dakarensis' (da. ka. ren 0 sis, N. L. masc. adj. dakarensis, the name of the capital of Senegal where the stool sample was collected).
ne. sa. lou. men 0 sis, N. L. masc. adj. sinesaloumensis, a vast agropastoral area in Senegal where the village of N'diop, where the stool sample was obtained, is situated). Strain Marseille-P2481 T is a Gram-positive, motile, sporeforming organism isolated from a stool sample (1. 7% NaCl) from a 10-year-old boy from N'diop. Catalase activity is positive and oxidase negative. The first isolation was realized in a halophilic medium with 10% (w/v) NaCl, but the strain could tolerate up to 20% (w/v) NaCl. The growing colonies are orange and circular, with a mean diameter of 2 mm. Individual  NMNI cells exhibit a diameter of 1. 9 × 0. 5 μm and have a slightly curved form with a flagellum under electron microscopy. Strain P2481 T exhibited a 16S rRNA gene sequence identity of 97% with Gracilibacillus alcaliphilus strain SG103 T (GenBank accession NR_126185) (Fig.
Individual cells exhibit a diameter of 0. 3-0. 6 × 2. 0-4. 0 μm. Strain Marseille-P3554 T exhibited a 16S rRNA sequence divergence of >1. 3% with Halobacillus yeomjeoni strain MSS-402 (GenBank accession no. NR_043251) (Fig. 3 )  [6]. On the basis of this finding, we propose to classify 'Halobacillus massiliensis' as a new representative of the Halobacillus genus belonging to the family Bacillaceae in the phylum Firmicutes. Strain Marseille-P3554 T is the type strain of 'Halobacillus massiliensis' (mas. si. li. en 0 sis, L. masc. adj. massiliensis, 'of Massilia,' the old Roman name for Marseille, where the strain was isolated). Strain Marseille-P3089 T is also a Gram-positive, motile, spore-forming, catalase-negative and oxidase-positive bacterium. This strain was isolated from a stool sample (2.
1% NaCl) from a 19-year-old man from N'diop. Strain Marseille-P3089 T required salinity ranging from 0. 5 to 20% (w/v) NaCl (optimum, 10% (w/v) NaCl). The colonies are yellow and circular, and entire, smooth and convex, with a 1. 5 mm diameter. Individual cells exhibit a diameter of 0. 3-0. 6 × 2. 0-4. 0 μm. Strain Marseille-P3089 T exhibited a 16S rRNA sequence similarity of 98. 3% with Lentibacillus garicola (strain SL-MJ1, GenBank accession NR_136842), its closest related species with standing in nomenclature (Fig. 4 )  [7]. On the basis of this difference, we propose the creation of 'Lentibacillus massiliensis,' a member of the genus Lentibacillus, family Bacillaceae and phylum Firmicutes. Strain Marseille-P3089 T is the type strain of 'Lentibacillus massiliensis' (mas. si. li. en 0 sis, L.
masc. adj. massiliensis, 'of Massilia,' the old Roman name for Marseille, where the strain was isolated). Strain Marseille-P3587 T was isolated from a stool sample (2. 1% NaCl) from a 7-year-old boy living in N'diop. Growth was first obtained in 15% (w/v) NaCl but occurs at 5 to 20% (w/ v) NaCl, with an optimum at 7. 5% (w/v) NaCl. Colonies are smooth, circular, yellowish and shiny with a mean diameter of 2. 5 mm. Bacterial cells are Gram positive, rod shaped and polymorphic, and the strain Marseille-3587 T is catalase and oxidase positive. The 16S rRNA gene showed 96. 6% sequence identity with Oceanobacillus polygoni strain SA9 (GenBank.
Bacterial cells are Gram positive, rod shaped and polymorphic. Strain Marseille-3532 T is catalase and oxidase positive and exhibited a 98. 2% sequence identity with Oceanobacillus oncorhynchi strain R-2 (GenBank accession no. NR_041240. 1) (Fig. 5 )  [9] , the phylogenetically closest species with standing in nomenclature. Therefore, we proposed to classify the species 'Oceanobacillus timonensis' as a member of the genus Oceanobacillus within the phylum Firmicutes. Strain Marseille-P3532 T is the type strain of the new species 'Oceanobacillus timonensis' (ti. mo. nen 0 sis, L. fem. adj. timonensis, 'related to Timone,' the name of the main university hospital in Marseille, France, where the strain was isolated). Strain Marseille-P3469 T was isolated from a stool sample (2% NaCl) from a 15-year-old adolescent boy in the village of Dielmo.
The first isolation was obtained in a halophilic medium with 10% (w/v) NaCl. However, the strain was able to grow between 0 and 20% (w/v) NaCl, with optimum growth observed at 10% (w/v) NaCl. Agar-grown colonies are cream coloured, circular and shiny, with a mean diameter of 4 mm. Bacterial cells are Gram positive, rod shaped and polymorphic. Strain Marseille-3469 T is catalase and oxidase positive. Strain Marseille 3469 T exhibited a 96. 6% sequence identity with Virgibacillus byunsanensis strain ISL-24 (GenBank accession no. NR_116615. 1) (Fig. 6 )  [10] , the phylogenetically closest species, which led us to classify it as a member of the genus Virgibacillus within the family Bacillaceae in the phylum Firmicutes. Strain Marseille-P3469 T is the type strain of the proposed new species 'Virgibacillus dakarensis' (da.
ka. ren 0 sis, N. L. fem. adj. dakarensis, 'related to Dakar,' the capital of Senegal, where the stool sample was collected). Strain Marseille-P3610 T was isolated in a stool sample (2. 6% NaCl) from a 7-year-old boy living in N'diop. The strain was first isolated in a halophilic medium with 10% (w/v) NaCl but requires 2 to 20% (w/v) NaCl, with optimum growth at 10% (w/ v) NaCl. Colonies are cream, circular and shiny with a mean diameter of 4 mm. Bacterial cells are Gram positive, rod shaped and polymorphic. Strain Marseille-3610 T is catalase and oxidase positive. Strain 3610 T exhibited a sequence similarity of 98% with Virgibacillus carmonensis strain LMG-20964 (GenBank accession no. NR_025481), the phylogenetically closest species (Fig. 6 )  [4]. Therefore, we propose the classification of 'Virgibacillus marseillensis' as a member of the family Bacillaceae in the phylum Firmicutes.
Background: The literature offers competing estimates of disease costs, with each study having its own data and methods. In 2007, the Dutch Center for Public Health Forecasting of the National Institute for Public Health and the Environment provided guidelines that can be used to set up cost-of-illness (COI) studies, emphasising that most COI analyses have trouble accounting for comorbidity in their cost estimations. When a patient has more than one chronic condition, the conditions may interact such that the patient's healthcare costs are greater than the sum of the costs for the individual diseases. The main objective of this work was to estimate the costs of 10 noncommunicable diseases when their co-occurrence is acknowledged and properly assessed. Methods: The French Echantillon Généraliste de Bénéficiaires (EGB) database was used to assign all healthcare expenses for a representative sample of the population covered by the National Health Insurance.
We therefore aimed to develop a comprehensive 'bottom-up' approach using person-level data to estimate the costs of chronic diseases using a medicoadministrative database. One of the main strengths of our approach is that it takes into account the comorbidity issue, a key factor with the older population. Recent strategies using regression-based frameworks  [9] [10] [11] , have also been developed to account for excess spending caused by the presence of comorbidities. Compared to top-down approaches, these types of person-level costing may produce more valid estimates in patients with multiple chronic diseases, as expenditures for comorbidities and complications are better captured  [9]. For this study, we focused on 10 chronic disease groups, namely heart disease, stroke, diabetes, cancers (with a focus on breast, liver, lung, colorectal, stomach, oesophageal, kidney and pancreatic cancers), alcohol use disorders, cirrhosis, neurological disorders, major depression, respiratory illness (chronic obstructive pulmonary disease, asthma), and chronic kidney disease (CKD).