dataesr/scientific-paragraphs-categorization huggingface dataset:
Failure to take environmental context into account (e. g. , by analyzing large datasets within which the implications of specialization vary strongly among clades and environmental conditions) risks obscuring important associations. Despite this risk, some taxonomically diverse analyses (e. g. 58 ) have found overall effects on diversification for some traits, notably for zygomorphy, but also for biotic pollination and annuality. Tantalizingly, these are all traits with consistent effects on either speciation or persistence (Table  1 ), although it is also possible that, with further study, traits which appear to have consistent effects on diversification may turn out to have inconsistent effects.
Statistical Analysis All data were analyzed as a completely randomized design with 4 treatments, using one-way ANOVA in SPSS version 18. 0 (SPSS Inc. 2010), where the pen served as the experimental unit for each parameter. Significant differences between means were separated by Tukey's post hoc test. Orthogonal polynomials were performed to study the linear, quadratic and cubic responses of the different  Lactobacillus spp. F-5'-CATCCAGTGCAAACCTAAGAG-3' R-5'-GATCCGCTTGCCTTCGCA-3' Bifidobacterium spp. F-5'-GGGTGGTAATGCCGGATG-3' R-5'-TAAGCCATGGACTTTCACACC-3' Enterobacter spp. F-5'-CATTGACGTTACCCGCAGAAGAAGC-3' R-5'-CTCTACGAGACTCAAGCTTGC-3' E. coli F-5'-GTGTGATATCTACCCGCTTCGC-3' R-5'-AGAACGCTTTGTGGTTAATCAGGA-3' variables for the M-DFCP inclusion in the diets. A threshold level of P < 0. 05 was used to determine significance.
Observation-related products used Three different rainfall datasets are used. To estimate model biases in the representation of rainfall and some of its intraseasonal characteristics, we use GloH2O's Multi-Source Weighted-Ensemble Precipitation version 2. 8 (hereinafter MSWEP). It is a globally gridded rainfall data set at a horizontal resolution of 0. 1º and temporal resolution of up to 3 hours spanning from 1979 to present. It is derived by merging satellite, reanalysis and rain gauge data  (Beck et al. , 2019). MSWEP has been shown to provide the most accurate estimates at daily resolution over West Africa when compared to other 22 daily rainfall products  (Satgé et al. , 2020). To estimate the biases, we average the MSWEP dataset over its whole "historical" period (1979-2020) of gauge-corrected data so as to avoid merging with slightly differently processed near-real-time values.
4. 06 dataset (hereinafter CRU) are used to estimate the AMV impact in observations. They are based on station observations and provided globally over land with a resolution of 0. 5º and span the period from 1901 to present  (Harris et al. , 2020). The period used in this work is 1901 to 2013, which is the maximum possible coincident with the AMV index used to provide the boundary conditions for the sensitivity experiments  (Boer et al. , 2016). In addition, SSTs from the Extended Reconstructed SST dataset version 4 (hereinafter referred to ERSSTv4) are used to estimate the observed AMV pattern. It is a global reconstruction of monthly SSTs provided at a resolution of 2º from 1857 to present  (Huang et al. , 2015). For consistency with the CRU dataset used to estimate the observed AMV impacts on West African rainfall, in this work we use data from 1901 to 2013.
Metrics A rainy or wet day is defined as one in which the total amount of rainfall is above 1 mm  (Hartmann et al. , 2013). This definition has been applied widely over West Africa (e. g. Sanogo et al. , 2015; Diaconescu et al. , 2015; De Longueville et al. , 2016; Diakhate et al. , 2019; Diatta et al. , 2020; Badji et al. , 2022)  and is consistent with the World Meteorological Organization recommendations  (WMO, 2009). This definition is thus used throughout the study for the simulations and MSWEP and CHIRPS datasets. Note, however, that it differs from the one used in the CRU dataset to obtain the number of wet days, for which the threshold is 0. 1 mm  (Harris et al. , 2020). Here we count the number of wet days (n) in the July to September (JAS) summer season. This season represents the mature phase of the West African Monsoon, when rainfall is well developed in the Sahel  (Thorncroft et al.
(2020) , at monthly timescales CHIRPS dataset presents smaller bias with respect to gauge observed rainfall than MSWEP. Fig. 2h  shows that CHIRPS provides wetter conditions when compared to MSWEP (as do other datasets, not shown), so the above values could be underestimating rainfall dry biases over the Sahel by about 0. 5 mm/day (Fig. 3ab ). The north-to-south rainfall biases are consistent with the warm biases of SST simulated by all models in the southeastern tropical Atlantic, which reach values well over 2ºC (not shown). These warm biases are a prominent feature of the current generation of general circulation models  (Richter and Tokinaga, 2020; Farneti et al. , 2022)  were also present in previous model generations like CMIP5  (Mohino et al. , 2019; Farneti et al. , 2022) , and are related to a southward shift of the ITCZ over West Africa with reduced precipitation over the Sahel and enhanced precipitation over the Gulf of Guinea  (Losada et al.
, 2010; Richter and Tokinaga, 2020). The comparison of atmosphere-only and coupled atmosphere-ocean simulations performed in  Roberts et al. (2018)  with the ECWMF-IFS-LR and ECMWF-IFS-HR models provides further evidence to suggest that the main factor explaining the north-to-south rainfall biases over West Africa come from biases in SSTs. According to MSWEP, the number of rainy days in the JAS season (92 days in total) range between above 90 days over the western coast of Guinea to less than 10 in the northern fringe of the Sahel (see contours in Fig. 4 ). On average over the Sahel, it provides 37 rainy days per season. Satgé et al. (2020)  show that MSWEP is the dataset most consistent with gauge rainfall daily records over the Sahel, precisely concerning the detection of rainy days. The CHIRPS dataset shows consistent results over the northern Sahel (Fig.
4h ). However, the number of rainy days is smaller south of 15ºN, especially over the Gulf of Guinea region (Fig. 4h ), despite the enhanced total seasonal amount of rainfall in CHIRPS with respect to MSWEP (Fig. 2h ). The pattern of differences between CHIRPS and MSWEP is similar to the one obtained with two other daily rainfall datasets, the African Rainfall Climatology Version 2 -ARC-2  (Novella and Thiaw, 2013)  and the Global Precipitation Climatology Project (GPCP) Daily Precipitation Analysis Climate Data Record version 1. 3  (Adler et al. , 2017)  (not shown). On average, CHIRPS shows a reduction of 6 days in the number of rainy days over the Sahel with respect to MSWEP (Fig. 3b ), which amounts to an underestimation of nearly 20%. The pattern of model biases in the number of rainy days is consistent with the one in total seasonal rainfall, with an underestimation in the northern Sahel and an overestimation towards the south in both magnitudes (Fig.
2  and 4 panels a-g), and a superposed east-to-west pattern the case of the CNRM-CM6-1 model. Taking CHIRPS differences with MSWEP as a measure of observational uncertainty, the models clearly overestimate the number of rainy days in the coastal region of Ivory Coast (Fig. 4 ). On average, over the Sahel, models present an underestimation in the number of rainy days per season ranging between 1 and 20 days for the IPSL-CM6-LR and ECMWF-IFS-LR models, respectively (Fig. 3b ). However, only in the case of EC-Earth3, ECMWF-IFS-HR and ECMWF-IFS-LR models this underestimation is well beyond observational uncertainty. This suggests that, over the Sahel, the underestimation in seasonal rainfall amounts cannot be only related to the models' tendency to underestimate the number of rainy days. According to the MSWEP database, the mean intensity of rainfall falling on rainy days ranges from well above 12 mm/day in the western coast, close to the Fouta Djallon and Guinea highlands, to below 4 mm/day north of 18ºN (see contours in Fig.
5 ). Rainfall mean intensity is much higher for CHIRPS over the whole West Africa, especially in the Guinea Coast with differences of over 12 mm/day, more than double the estimates obtained from MSWEP locally. Though both datasets show weaker differences in the mean intensity over the Sahel (nearly 4 mm/day on average, Fig. 3a ), it still represents more than a 60% increase in CHIRPS with respect to MSWEP estimates. According to  Satgé et al. (2020)  and the previous results, this difference between datasets could come from both an underestimation of rainy days in CHIRPS and an underestimation of rainfall amounts in MSWEP. In addition, the comparison of extreme rainfall from different datasets for Burkina Faso performed by  Sanogo et al. (2022)  suggests that MSWEP could be underestimating very extreme rainfall values, affecting its estimates of mean rainfall intensity.
This could be connected to a higher disparity in the number of rainy days simulated by the models (a spread of roughly 70% taking into Despite the differences shown by MSWEP and CHIRPS datasets in the JAS total seasonal rainfall, number of rainy days and, especially, the mean rainfall intensity, they both agree on the average monsoon season timing over the Sahel (Fig. 6a ). The average onset date is June 12th (13th), the average cessation date is September 20th (23rd), with a total monsoon season length of 100 (  102 ) days for the MSWEP (CHIRPS) dataset. Most models show average onset dates consistent with the observational estimates over the Sahel, except for the CNRM-CM6-1 in both protocols and the EC-Earth3P-HR in PRIMAVERA which delay the onset by about 21 and 17 days on average, respectively (Fig.
5 days. Though models tend to underestimate the observations (ranging from 31% to 85% of the CRU observational estimate), the underestimation is smaller than for the mean rainfall. Note that a direct comparison between the models and the observation is hindered by the different definitions of rainy days used (the threshold for the former is 1 mm/day, while for observations is 0. 1 mm/day). The patterns obtained are, however, very similar to the ones presented in Fig. 8  when using the same threshold in the simulations as in the CRU dataset to define a rainy day (0. 1 mm/day) (not shown), with a range of averaged impact over the Sahel between 0. 5 days in the ECMWF-IFS-HR model to 1. 5 days for the CNRM-CM6-1 model in the PRIMAVERA protocol. The models' responses to a positive AMV also include an increase of the mean intensity of rainfall over West Africa (Fig.
Acknowledgements We thank  GEO BON  and the  CBD Secretariat  for advice and logistical support. This article is based upon work from  COST Action G-BiKE , CA 18134 supported by  COST (European Cooperation in Science and Technology) , www. cost. eu. This work was conducted as a part of the "Standardizing, aggregating, analyzing and disseminating global wildlife genetic and genomic data for improved management and advancement of community best practices" Working Group supported by the  John Wesley Powell Center for Analysis and Synthesis , funded by the  U. S. Geological Survey. LL was supported Acknowledgements for additional information on funding and support.
indica plants are in general larger than those on A. evenia (Fig. 2d ). This high resemblance is due to their belonging to a same polyploid species complex where A. evenia 2x, A. indica 4x and A. indica 6x forms are present  (Arrighi et al. , 2014; Chaintreuil et al. , 2018). To confirm the visual species identification, we sequenced the nuclear ITS and chloroplastic matK gene and determined the genome size of the specimens JFA29 and JFA109. The obtained data were typical for A. indica 4x plants, confirming the visual identification (Fig. S1 , Table  S2 )  (Arrighi et al. , 2014). In the four collection sites, A. indica plants were stem-nodulated, the nodules varying in shape and size but always with an enlarged base and with a green color (Fig. 2d ). Similar to other Aeschynomene species, pink root nodules could be observed in unflooded conditions, (Fig.
Description of the collecting areas To select collection sites of Aeschynomene and Sesbania species in Madagascar, we made use of general information about their distribution as described in the compendium "The Leguminosae of Madagascar"  (Dupuy, 2002)  and utilized precise location data of previous isolated specimens present in collections through the Global Biodiversity Information Facility (GBIF -https://www. gbif. org/) and the Tropicos database (https://www. tropicos. org/). This resulted in the definition of four collecting areas: 1) the region of Majunga including the vicinity of the Kinkony lake where many temporary to permanent marshes locally named « matsabory » are present. This area also comprises the plain of Marovoay where numerous ricefields form the main rice granary of Madagascar, 2) the Itasy region in the Center of Madagascar where the Itasy Lake and ricefields are present, 3) the Alaotra region in the East side of Madagascar that corresponds to a large basin containing the Alaotra Lake.
Plant and data collection At each sampling location, the presence or receding of water in the aquatic ecosystem was recorded. The stem nodulation status of individuals for each species present in the population was examined and correlated to their positions relative to the flooding area. Whenever possible, three individuals of each species were chosen at random to determine their root nodulation status. Both stems and roots of these individuals were photographed in situ. Plant material was frequently collected for germplasm conservation and production of voucher specimens. The latters were deposited at the Herbarium of the CNARP Institute in Antananarivo (Madagascar).
Gene sequencing and sequence analysis Genomic DNA was isolated from fresh leaves using the CTAB extraction method. The nuclear ribosomal internal transcribed spacer region (ITS: ITS1-5. 8S rDNA gene-ITS2) and the chloroplast matK gene were amplified and sequenced as published in Chaintreuil et al. (2016). Additionnal ITS and matK sequences were retrieved from Chaintreuil et al. (2016, 2018)   obtained by aligning nucleotide sequences with the MUSCLE program that is incorporated in the MEGA X (v10. 1. 8) software. Aligned sequences were further processed in MEGA X using the maximum likelihood approach and the Kimura 2-parameter model with a 1000x bootstrap (BS).
Acknowledgements We thank  Robin Duponnois  ( LSTM Laboratory, IRD ) for providing assistance in developping this research work,  Bernard Dreyfus  ( LSTM Laboratory, IRD ) for his helpful comments on Malagasy legumes, and the  IRD Institute  for funding  Jean-François Arrighi 's mission to Madagascar. We are also thankful to  Dr Eric Giraud  ( PHIM Laboratory, IRD ) for his critical reading of this manuscript. The present work has benefited from the facilities and expertise of the cytometry facilities of Imagerie-Gif (https://www. i2bc. paris-saclay. fr/bioimaging/). This work was supported by the  French National Research Agency  ( ANR-SymWay-20-CE20-0017-04 ).
Remaining settings were identical for all analytical methods with cone temperature set to 350 °C and cone gas to 20 psi. Nebulizing gas was set to 60 psi and collision gas to 1. 6 mTorr. Nitrogen was used as probe and nebulizing gas, and argon as collision gas. Active exhaust was constantly on. Multiple reaction monitoring was used to monitor analyte parent ion to product ion transitions for all analytes. Multiple reaction monitoring transitions and collision energies were optimized by direct infusion experiments. Detailed values for mass transitions can be found in Supplemental Table  3. Both Q1 and Q3 quadrupoles were maintained at unit resolution. Bruker MS Workstation software (Version 8. 2. 1) was used for data acquisition and processing. Linearity in ionization e ciencies were veri ed by analyzing dilution series of standard mixtures.
25 µm lm thickness; Agilent J&W GC Columns) with injection temperature of 270°C, interface set to 250°C, and the ion source to 200°C. Helium was used as the carrier gas at a constant ow rate of 1. 2 mL min -1. The temperature program was 0. 5 min isothermal at 70°C, followed by a 30°C min -1 oven temperature ramp to 210°C and a 5°C min -1 ramp to 330°C, then kept constant during 21 min. Mass spectra were recorded with an m/z 40 to 850 scanning range. Chromatograms and mass spectra were evaluated using the MSD ChemStation software (Agilent). Integrated peaks of mass fragments were normalized for sample dry weight and the respective C32 alkane internal standard signal. For identi cation, the corresponding mass spectra and retention time indices were compared with the NIST20 library as well as in-house spectral libraries.
Mathematical model. Root templates were segmented from an experimental image using the CellSeT image analysis tool  70  (Sup. Fig. 13 ). We used CellSeT to manually assign a cell type to each cell and then read the geometrical and cell-type data into a tissue database (based on the OpenAlea tissue structure  71  ), extending the data structure to incorporate vacuolar compartments within each cell. The geometrical, topological and transporter-distribution data were used to form a system of ordinary differential equations (ODEs) to describe the GA transport, synthesis and degradation within the multicellular root cross-section. Parameters associated with the passive and transporter-mediated transport components were estimated using the oocyte data (Fig. 1A , Fig. 3B ) the remaining parameter values were obtained from the literature (Sup.
Acknowledgments We thank  Daria Binenbaum  for the illustrations and  Peter Hedden  ( Rothamsted Research ) for sharing pGA3ox1-4:GUS seeds. Funding: This work was supported by grants from the  Israel Science Foundation  ( 2378/19  and  3419/20  to E. S. ), the  Human Frontier Science Program  ( HFSP-RGY0075/2015  and  HFSP-LIY000540/2020  to E. S. , H. H. N. -E. and L. R. B. ),  Danmarks Grundforskningsfond  ( DNRF99  to H. H. N. -E. ), the  European Research Council  ( 757683-RobustHormoneTrans  to E. S. ), the  Constantiner Travel Fellowship  (to J. B. ), the  Centre National de la Recherche Scienti que  (to L. S-A. J-M. D. and P. A. ), the  French Ministry of Research and Higher Education studentship  (to L. C. ).
Materials A total of 28 yam genotypes representing white Guinea yam (Dioscorea rotundata) and water or greater yam (Dioscorea alata) were used for this study. Seventeen breeding lines (eight D. alata genotypes and nine D. rotundata genotypes) were obtained from yam breeding programs of the International Institute of Tropical Agriculture (IITA) and National Root Crop Research Institute (NRCRI), Nigeria, and 11 landrace cultivars. Four of these [three D. rotundata (Lasinrin, Awana, Gbongi-Kamilu) and one D. alata (Ewura)] were collected from a contact farmer's field from Iwo, Osun state, Nigeria. The remaining seven [six D. rotundata varieties (Laboko, Kratchi, Kodjèwé, Wété, Dodo, Irindou) and one D. alata variety (Aga)] were obtained from an experimental plot at the AfricaYam project, Benin station, Republic of Benin.
Out of the total 28 yam genotypes, 15 cultivars were used for consumer studies, the remaining 13 genotypes were used for sensory quantitative descriptive analysis (QDA) and other biophysical analyses reported in the study -D. rotundata: TDr1401593, TDr1000048, TDr1400359, TDr0900067, TDr1100180, TDr1401419, TDr IGN 21 (Igangan) and D. alata: TDa1400301, TDa1100224. TDa1215201, TDa1100432, TDa1100201, TDa1100316. In southwest Nigeria, pounded yam samples that were used for consumer testing were prepared from the four landraces -D. rotundata: Lasinrin, Awana, Gbongi-Kamilu and D. alata: Ewura. In southeast Nigeria -D. rotundata: TDr11/0010 TDr1100497 and D. alata: TDa1100477, TDa1100203 were used and in the Republic of Benin six D. rotundata varieties (Laboko, Kratchi, Kodjèwé, Wété, Dodo, Irindou) and one D.
ACKNOWLEDGEMENTS The authors are grateful to  Léa Ollier  and  Pham  and  Thi Ha Giang  ( CIRAD ) and the grant opportunity  INV-008567  (formerly  OPP1178942 ):  Breeding RTB Products for End User Preferences  (RTBfoods), to the  French Agricultural Research Center for International Development (CIRAD), Montpellier, France  by the  Bill & Melinda Gates Foundation 363  (BMGF): https://rtbfoods. cirad. The authors acknowledge the review and editorial comments of  Hernán Ceballos  and proofreading of the manuscripts by  Clair Hershey  to improve the quality.
Setting and sample The Drivers of Food Choice (DFC) program funded 15 projects across West and South East Africa and South and South East Asia between 2016 and 2022. The projects generated evidence on the processes linking individuals' decision making about food purchasing and consumption. The emergent data fomented the formation of the Food Safety Working Group, comprising the lead author (SI) and 6 coauthors (SC, EF, SB, SS, EK, CB). We identified the DFC projects that captured how food safety is conceptualized across different perspectives; some DFC projects did not have information on food safety because the projects that were funded in the DFC portfolio had different aims, not all of which covered food safety. Data from projects in the following 6 countries were included: Ghana  [25] , Guinea  [26] , India  [27] , Kenya  [10] , Tanzania  [28] , and Vietnam  [29]  (Table  1 ).
Pradeilles et al. [25]  used a community-based participatory method (Photovoice) to explore drivers influencing dietary behaviors. Participants were selected from neighborhoods in 2 cities in Ghana: Accra and Ho. Neighborhoods were chosen at random from a compiled list of low-income urban areas. Participants were purposefully recruited through communities, schools, and health services using quota sampling within the 2 selected neighborhoods: James Town in Accra (n ¼ 62) and Ho Dome in Ho (n ¼ 32). Male and female participants aged at least 13 y were selected based on demographic characteristics to ensure variations in participants' views were captured (i. e. , sex, age, body mass index, socioeconomic level, education level, and occupation status). Data were collected between May 2017 and June 2018 by members of the research team who were native speakers but not members of the selected neighborhoods.
The data used in this study came from the Ghanaian subsample of participants selected for the Photovoice study  [25]. In Guinea, Nordhagen et al. [26]  used stratified sampling to purposively select study sites based on their proximal distance to villages and towns. Data were collected from May 2018 to December 2019 through nonparticipant observations at mining sites (n ¼ 25) and markets (n ¼ 8), household surveys (n ¼ 613) and market surveys (n ¼ 4), and in-depth interviews with mothers (n ¼ 45) and food vendors (n ¼ 40). Data collectors were familiar with the local context, fluent in the local languages, and trained rigorously in data collection methods. Households were randomly selected to participate in a household survey if a member was actively engaged in mining and had a child younger than 5 y living in the household.
In-depth interviews were conducted with mothers of children younger than 5 y in mining households and food vendors from markets near the study sites. Interviews with mothers measured 4 dimensions of the food environment: accessibility, affordability, convenience, and desirability. Interviews with vendors measured 2 dimensions: convenience and desirability. This study was conducted according to the guidelines in the Declaration of Helsinki, and all procedures involving research study participants were approved by Guinea's national research ethics council (Comit e National d'Ethique et de Recherche en Sant e, number 080/CNERS/18). Written informed consent was obtained from all subjects. The current study used data from in-depth interviews with mothers and vendors  [26]. Surendran et al. [27]  conducted 2 phases of data collection in Hyderabad, India.