dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Primary data are collected by both professional (from CBN and other organisations) and volunteer. As data originate from various sources (fi eld inventories, scientifi c literature and herbaria), this task involves diff erent trades such as botanists and archivists. Data are entered into CBN databases and then follow a validation process. To insure dataset homogeneity, data records are extracted from CBN databases and provided to the national system in a simplifi ed format that is compatible with the SINP format (edited by MNHN). Th is format is also based on the Data Specifi cation on Species distribution, produced by the INSPIRE Th ematic Working Group Species distribution (http://inspire. ec. europa. eu/). For each observation, key variables are recorded, such as a unique code for the record, valid scientifi c name of the taxon and its identifi er in the French national taxonomic repository (TAXREF).
Additional details including observation and data transmission dates, the collector name, the source basis of record name and geospatial information, including municipality name and code, and the square code of the national grid, are also provided. Th e grid system used for species inventories in France is defi ned by the Muséum National d'Histoire Naturelle: this is a grid composed of cells of 10km by 10km. Information about municipalities are collected from the Insee national repository. When available, additional information is collected such as bibliographic and herbarium references or primary source of the data record. Th e evolution of the dataset is described in the Database history section. Once standardized, data are checked for consistency before being incorporated into the Postgresl/Postgis national database by using Talend Open Studio for Data Integration.
At the end of the process, data are posted on the FCBN geoportal. Sampling description: Most records originate from fi eld inventories (88. 7%), with other records identifi ed in scientifi c literature (11%) and in herbaria (0. 2%). Th e protocols for collection vary over time and between collection sites, but also in response to other projects launched. However, in this fi rst version, SIFlore does not include information on the data collection procedures. It is expected that a simplifi ed description of the diff erent protocols used by each CBN will be provided in the near future. Nevertheless, according to  Vallet et al. (2012) , it is possible to assess survey completeness of regional fl oristic inventories despite heterogeneous sources and protocols, through the use of the Jackknife 1, a non-parametric estimator.
Th is estimator was calculated for each 10km by 10km cell of the French map, aiming to mitigate sampling bias as eff ectively as possible. Th us, occurrences were all generalized to the rank of species (including all infraspecies into this one rank) and only recent data were taken in account (from 1990). Cells with less than 250 species were excluded from the analyses, as they were considered as undersampled and therefore over represented (Figure  3 ). Th e resulting map (Figure  4 ) should allow users to interpret their results appropriately. It has to be noted that the completeness of La Reunion Island survey could not be assured in this work. Indeed, as mentioned above, in 2012, at the time of working on the 1 st version of SIFlore, there had been a lack of La Reunion taxa in the French national taxonomic referential for fauna, fl ora and fungi, TAXREF V5.
As the analysis only takes into account the species which correspond across the two taxonomic guidelines (ITR and TAXREF v5), all of the La Reunion cells had been undersampled, according to Jackknife calculation requirements. Quality control description: Quality control is implemented at diff erent levels, under diff erent responsibilities, throughout the data collection and validation process. Following digitalization, the dataset is fi rst checked by regional data administrators, in order to ensure compliance with the survey protocols. Th e records are individually reviewed according to specifi ed criteria, including the accuracy of the scientifi c name and the correctness of the geographical position entered, according to known chorology. At the national level, a second step is carried out to ensure conformity of the data to the national standards, before compilation.
Dataset history In 1975, the Botanical Conservatory of Brest was created, with the support of the Ministry of Environment. Th is was the fi rst establishment in the world entirely devoted to fl ora conservation. In 1988, the label "conservatoire botanique national" was legally recognized in France. Th ere are currently eleven national botanical conservatories (CBN) in France. As they are mandated to share their expertise to the national and local authorities, CBN have built knowledge databases in which information is structured to allow data sharing. CBN have operational and managerial autonomy. Consequently, their databases diff er in terms of structural design and the information contained. Nevertheless, there is an urgent need to provide information on fl ora distribution at the national level Th e ratio between the observed and estimated richness of species measures the completeness of inventory in each surveyed cell  (Vallet et al.
2012). and, in particular, to defi ne more clearly the fl ora conservation priorities. Furthermore, FCBN is involved in establishing the IUCN Red List of Th reatened Flora at the national level, and in evaluating the conservation status of natural habitats and wild fl ora, according to the Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and fl ora. For these reasons, it was decided to pool all CBN fl ora records and also to apply a subsidiarity relationship between the FCBN and CBN. Th is means that data must only be handled by national botanical conservatories, and that the federation may only facilitate networking and ensure data aggregation and management, without altering the records in any way. In 2010, a working group was created to implement this project.
It includes botanists and data managers from across the CBN, and project facilitators. Due to the heterogeneity of the CBN databases, data cannot easily and quickly be aggregated unless determining a database exchange standard. As such, a standardized format was proposed and, in 2011, 10 taxa were selected for a fi rst trial run. Based on this pilot, the data standard was refi ned. In January 2013, the fl ora regional dataset aggregation was offi cially launched. Data were transmitted through various channels under the scenarios prepared by the working group. Data were then compiled and integrated into a Postgresql/Postgis database using an extract, transform and load system (ETL). In May 2013, 18 million data records were aggregated. In January 2014, an additional 3 million records were integrated.
Dataset description Th e SIFlore dataset is a custom-made SQL view of the global database hosted by the FCBN. Only tracheophyta data are shown. In the current version of the database, the key information provided for each record includes: unique identifi er of the data record in SIFlore, identifi er that points to the data in the original database fi le, source institution and database identifi er, scientifi c name, valid identifi er in the French national taxonomic referential (TAXREF), taxon rank, location of sighting (grid cell and municipality code), date that the fl ora was sighted and name of the data collector.
Acknowledgments We are very grateful to the  French Ministry of Ecology and Sustainable Development and Energy (MEDDE)  for fi nancial support, and to the eleven national botanical conservatories for their enthusiastic participation, collaboration and ongoing support. We would like to express our deepest gratitude to the  CBN directors Pascal Chondroyannis ,  François Dehondt ,  Dominique Dhervé ,  Frédéric Hendoux ,  Laëtitia Hugot ,  Gérard Largier ,  Bertrand Liénard ,  Sylvia Lochon-Menseau ,  Daniel Lucas ,  Coralie Pradel  and  Jean-Marc Valet  for their continuing eff orts in facilitating project implementation. We also would like to thank  Anna Dean  (native English speaker) and  Guillaume Fournié  for checking this paper.
Conservatoire Botanique National méditerranéen de Porquerolles, 34, avenue Gambetta 83400 Hyères, France (j) Conservatoire Botanique National des Pyrénées et de Midi Pyrénées, Vallon de Salut -BP 315-65203 Bagnères-de-Bigorre Cedex, France (k) Conservatoire Botanique National Sud-Atlantique, Domaine de Certes Graveyron 33980 Audenge, France Project description Title: «A dataset on vascular plant distribution covering fi ve centuries of knowledge in France:Results of a collaborative project coordinated by the Federation of the National Bo- tanical Conservatories» is the outcome of a collaboration between 11 Conservatoires botaniques nationaux (CBN) and their Federation (FCBN): (a) Conservatoire Bota- nique National Alpin , Domaine de Charance 05000 Gap, France (b) Conservatoire Botanique National de Bailleul , Hameau des Haendries 59270 Bailleul, France (c) Conservatoire Botanique National du Bassin Parisien, Muséum national d'Histoire naturelle, 61, rue Buff on 75005 Paris, France (d) Conservatoire National Botanique de Brest , 52, allée du Bot 29200 Brest, France (e) Conservatoire National Bota- nique de Corse, Offi ce de l'environnement de la Corse, 14, avenue Jean Nicoli 20250 Corte, France (f ) Conservatoire Botanique National de Franche-Comté, Maison de l'environnement de Franche-Comté, 7, rue Voirin 25000 Besançon, France (g) Conservatoire Botanique National de Mascarin, 2, rue du Père Georges Les colimaçons 97436 Saint-Leu, Ile-de-La-Réunion, France (h) Conservatoire Botanique National du Massif Central, Le Bourg 43230 Chavaniac-Lafayette, France (i).
GA consisted of 132 palms from the "Deli" population derived from four oil palms planted in 1848 in Indonesia  [27] , while the remaining 11 palms originated from Angola. GB consisted of palms from La Mé (106, Côte d'Ivoire), Yangambi (23, Democratic Republic of the Congo), La Mé x Yangambi (5), La Mé x Sibiti (6, Democratic Republic of the Congo) and Nigeria  (5). Pedigree information was available for individuals in both heterotic groups (Additional file 1: Figure  S1 ). However, for Deli individuals, the pedigree was known for a maximum of four generations. In this study, the Deli pedigree was reconstructed with MOLCOANC 3. 0 software, as described in Cros et al. [28] , to obtain kinship coefficients between all Deli palms.
Phenotypic data Phenotypic evaluations were conducted in 26 trials on 30782 A × B oil palms that had been planted on 350 ha at Aek Loba (Indonesia, SOCFINDO estate) between 1995 and 2000 in a study lasting 11 years. The experimental designs of the trials involved randomized complete block designs (RCBD) with five or six blocks and balanced lattices of ranks four or five. The data used in this study was from oil palms which had reached a mature stage 6 to 10 years after planting. Mature bunch harvests were performed every 10 days, and the bunch numbers and weights were recorded. Based on these data, three production traits were analyzed, i. e. fresh fruit bunch yield (FFB, kg/year), bunch number (BN, bunch/year) and average bunch weight (ABW, kg), which is the ratio between FFB and BN estimated annually.
IBD matrix computation QTL mapping using a variance component approach requires identity-by-descent (IBD) relationship matrices as variance-covariance matrices for testing QTL effects at each genetic position. An IBD analysis using SimWalk2 software  [35]  was conducted to estimate the IBD matrices formed by empirical kinship coefficients for each pair of palm trees present in group A and B pedigrees. Simwalk2 implements a Bayesian framework using a Markov chain Monte Carlo (MCMC) algorithm to select the most likely genetic descent graph depicting inheritance patterns within pedigrees  [36]. Empirical kinship coefficients were calculated at every marker position and over a 3 cM interval grid, leading to 922 evaluation points for QTL presence testing using IBD analysis in Simwalk2.
Statistical modeling The model (1) (Phenotypic data section) was used as a null model for QTL mapping. Two other models were designed to test for the QTL presence in each heterotic group by adding a random QTL effect to model  (1). A genome scan was performed by fitting models for groups A and B at k genetic positions used to compute IBD kinship matrices testing for the QTL presence. The models had the form: Y ¼ Xβ þ Z 1 u þ Z 2 s þ Z A g A þ Z B g B þ Z A v A;k þ e ð3Þ Y ¼ Xβ þ Z 1 u þ Z 2 s þ Z A g A þ Z B g B þ Z B v B;k þ e ð4Þ where v A,k and v B,k are q A × 1 and q B × 1 vectors of QTL random effects at the k th genetic position in heterotic group A and B respectively (q A = 146 and q B = 156), with v A,k ~N(0, A A,k σ 2 a A,k ) and v B,k ~N(0, A B,k σ 2 a B,k ). A A,k and A B,k are the IBD kinship matrices at the k th genetic position, calculated in groups A and B, respectively.
The color scale indicates the strength of the correlation, with green and red being perfect positive and negative correlations, respectively. b Relationships between QTL effects for bunch number (BN) and average bunch weight (ABW) in heterotic group B parents are presented using the convex hull encompassing the scatter plot of parental BLUP values (n = 146). Convex hulls are drawn for BLUPs at three QTL positions on linkage group 1 (B_1@159), 15 (B_15@117) and 16 (B_16@57) ABW and pedigree information were combined in multipoint analyses using Simwalk2 Bayesian software to estimate these matrices  [35] , which allowed us to handle such large and complex pedigrees. The pedigree information quality is a key factor in this step. Unlike the group B pedigree, which is well known for the different genetic origins that compose it, the group A pedigree is not fully known in higher generations.
MOLCOANC software  [28]  was used to reconstruct the pedigree, and QTL mapping using the reconstructed pedigree identified two additional QTLs compared to the raw pedigree (Additional file 1: Figure  S4 ). One of them on linkage group 12 is highly supported by a QTL found in the same location for the same traits in a multiparental population involving related palm trees as parents  [6].
g. top of linkage group 14) caused by difficulties in genetic map construction  [33]. For the QTL validation or marker-assisted selection process, target QTL genotypes of pedigree members must be known in order to be able to design efficient crosses that test and combine segregating QTLs. The variance component approach modeled QTL effects as a random term, and estimates of the additive value of each individual at a given QTL were obtained with BLUPs. Despite the normal distribution of BLUPs for QTL effects, the segregation in full-sib families and alleles carried by pedigree members could be inferred qualitatively, e. g. by looking at the pedigree with projected BLUP values (Additional file 1: Figure  S5 ). The Bayesian approach implemented in FlexQTL software offers an interesting way to predict the QTL genotype  [23].
Acknowledgments We acknowledge  SOCFIN Indonesia (Indonesia) and le Centre de Recherches Agricoles sur les Plantes Pérennes (CRAPP, Bénin)  for planning and carrying out the field trials with  Centre de coopération Internationale en Recherche Agronomique pour le Développement (CIRAD, France)  and authorizing use of the phenotypic data for this study. This research was partly funded by a grant from  PalmElit SAS. We thank  P. Sampers ,  C. Carrasco-Lacombe  and  A. Manez  for help in genotyping.
Acknowledgements. We gratefully acknowledge  Axel Timmermann ,  Bette Otto-Bliesner ,  Peter Lawrence , and  Rosie Fisher  for valuable discussions as well as four anonymous reviewers for very helpful comments. We are grateful to the  NCAR in Boulder, USA , for providing the code of the CESM, to the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and to the climate modeling groups for producing and making available their model output. This study is supported by the  Swiss National Science Foundation  (grant no. 200020 147174 ), and the  European Commission   through Seventh Framework Program  ( FP7)  projects  CARBOCHANGE  (grant no. 264879 ) and  Past4Future  (grant no. 243908 ). J. Mignot  has benefited from the support of the  French Agence Nationale de la Recherche  ( HAMOC :  ANR 13-BLAN-06-0003 ).
Introduction Rift Valley fever (RVF) is a zoonotic and vector-borne disease found in many countries of Africa. It has caused "abortion storms" in livestock in numerous countries including Kenya, Tanzania, South Africa and Mauritania, as well as a series of epidemics (200 000 human cases in Egypt in 1977, 155 deaths in Kenya in 2006 and 19 deaths in Madagascar in 2008-2009)  [1] [2] [3]. Due to global changes (intensification of global trade and human mobility, climate change, etc. ), RVF, like other vector borne diseases, has become a genuine threat in other continents  [4]. For instance, RVF was reported in the Arabic peninsula in 2000  [5, 6]. Persistence of RVF was unexpected in Mayotte, a 370 km² tropical island with 200 000 inhabitants in the Mozambique Channel. RVF antibodies in cattle were identified for the first time in 2004 serum databank  [7].
Sensitivity analysis First, we explored the model predictions assuming no vertical transmission in vectors for each of the four seasonal scenarios. Second, to evaluate the effect of variations in the different parameters (including vertical transmission) and their first order interactions on the aggregated outputs presented above, we carried out a sensitivity analysis using a Fractional Factorial Design sampling method  [45]  (using the FACTEX procedure in SAS software. Copyright, SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. , Cary, NC, USA. ) for each of the four scenarios with a variation of 10% below and above the nominal value of each parameter. Following this method, 2187 sets of parameters were computed for each scenario made up of 1500 repetitions.
Observed seroprevalence in Mayotte. Seroprevalence data was collected between 2004 and 2011 in the bibliography and three times in the field between 2012 and 2013 in order to compare the model outcome with observations in Mayotte. Search engines were used to find publications on PubMed (http://www. ncbi. nlm. nih. gov/) and ScienceDirect (http://www. sciencedirect. com/) using key words "Rift Valley fever" AND "Mayotte" in English and French. Thirty-two publications were found. Local veterinary services and research institutions were interviewed to find seroprevalence data in unpublished documents, reports or databases. Original datasets were obtained upon request to the authors in order to specify temporal frames and to calculate confidence intervals when necessary. From May 2012 to April 2013, ruminants were selected to obtain a representative sample of the ruminant population of Mayotte.
We divided Mayotte into five zones to cover each agro-ecosystem (Fig  3 ). The 30 farms included in the study were randomly selected from the most comprehensive database available in Mayotte from the Chambre de l'Agriculture, de la Pêche et de l'Aquaculture de Mayotte (CAPAM, a local public agricultural institution). All ruminants older than six months of age for cattle and three months of age for small ruminants (beyond colostral immunity) belonging to the same owner were eligible for inclusion in the study. Three rounds of blood sampling were conducted. The first round was collected between May and July 2012, the second between September and December 2012, and the third between February and April 2013. Blood samples were taken from all animals each time, regardless of the results of previous samples.
It was not considered in Mayotte, since there are no wild ruminants and since other wild mammals (rats and lemurs) have been tested negative for RVF, so far. Livestock movements may play an important role in the spread of RVF. In Mayotte, there is neither market to gather animals nor transhumance as observed in Eastern Africa. This hypothesis should be further investigated by evaluating more closely movement practices, particularly when the French national identification database (BDNI) will be functional in Mayotte. Nevertheless, mechanisms such as asynchrony of climate between different zones connected by animal movements, as described in Favier et al. [32] , were not necessary to allow for persistence here. The maintenance through illegal animal introduction only, exclusively from Anjouan, 70km distant-island of the Union of the Comoros is unlikely.
Acknowledgments We specially thank the  ARS  for sharing their data ( Mikidache Said ,  Hairya Inoussa ,  Omar Ousseni ,  Lassadi Hassani Ali ,  Ismael Nahouda ,  Mohamadi Madi Saindou ,  Naimoudine Berou  and Mu'uminat Cheick Ahmed who collected the entomological data,  Marina Béral  and  Cyril Czeher  who helped complete the database);  Catherine Cêtre-Sossah  for the share of her serological data and her invaluable comments;  Thomas Balenghien  and  Séverine Licciardi  for their entomological expertise; the  CoopADEM-GDS of Mayotte  and all the farmers for their help in the serological surveys. We also would like to thank  Sandie Arnoux  who helped us in performing part of the sensitivity analysis. Finally we sincerely thank  David Wilkinson  and the two reviewers for their critical and very helpful comments.
Acknowledgements The " Our Planet Reviewed "  PAPUA NUIGINI Expedition  was organized by  MNHN  (marine Principal Investigators:  Philippe Bouchet ,  Claude Payri  and  Sarah Samadi ),  Pro Natura International , and  Institut de Recherche pour le Développement , in partnership with  University of Papua New Guinea ; with funding and support from the  Prince Albert II of Monaco Foundation ,  Total Foundation, Foundation EDF ,  Stavros Niarchos Foundation ,  Entrepose Contracting , and in-kind support from the  Divine Word University ; the expedition operated under a permit from the  Papua New Guinea Department of Environment and Conservation. The authors thank  Matthew Lowe  ( CUMZ ) for permission to examine the type of Actaea spinosissima, and  Paul Clark  (The  Natural History Museum, London ) for facilitating the loan.
These sequences resulted in a final OTU table consisting of 256 OTUs (excluding singletons), with the number of OTUs per sample ranging from 67 to 210. Representative sequences for each OTU were aligned with PyNAST, the taxonomic identity of each phylotype determined using the Green-Genes database  (DeSantis et al. , 2006) , and a tree built using FastTree  (Price et al. , 2009). Rarefaction, to a subsampling depth determined by the minimum number of sequences in the samples (i. e. 3780 sequences), was performed in QIIME on all samples to standardize the sequencing effort. Alpha-diversity was expressed as the Shannon index. Hierarchical cluster analysis was performed and visualized in R 3. 1. 2 (R Core Team, 2015) with Bray-Curtis distances calculated from relative abundances of OTUs (normalized sequence reads).
Acknowledgements We thank all the people in project BASICS involved in this experiment, in particular,  V Balagué ,  R Simó  and  M Vila-Costa  for helping set up the experiment,  J Masmitjà  for the ciliate counts,  J Felipe  for help with cytometry and our missed friend  J Jezbera , who participated in the grazing measurements. We also thank  Markus V Lindh  for insightful discussions. The mesocosm experiment was supported by  EU  project  BASICS  ( EVK3- CT-2002-00078 ). This research was supported by projects from the  European Science Foundation  ( EuroEEFG  project  MOCA ) and the  Swedish Research Council  to JP. KŠ and KH were supported by a  CSIC-CAS  ( Spanish-Czech ) bilateral mobility project awarded to JMG and KŠ. JMG is supported by project  DOREMI  ( CTM2012-34294 ) and the  GRC   2014SGR/1179  from  Generalitat de Catalunya , and FB by a  University of Otago   Research Grant.