dataesr/scientific-paragraphs-categorization huggingface dataset:
A total of 24 sites in Senegal (20 grassland and 4 savanna woodlands) that were monitored between 1999 and 2017 were provided by the Centre de Suivi Ecologique (CSE;  Diouf et al. , 2015). Data from four sites with contributions of trees as well as grasses (Eguerit, Hombori Hondo, Tara, and Timbadior) and one millet site (Bilantao) were obtained from the Gourma region in Mali, representing a precipitation gradient from 300 to 450 mm yr -1. These field measurements cover the period between 2005 and 2016 and were gathered within the African Monsoon Multidisciplinary Analysis  (Mougin et al. , 2019 (Mougin et al. , , 2009)). Three more fallow and grassland sites are located in the Fakara region in western Niger (Banizoumbou, Tigo Tégui, and Kodey). Measurements here began in 1994, taken by the International Livestock Research Institute (ILRI;  Hiernaux and Ayantunde, 2004) , and continued until 1999 under the responsibility of the AMMA-CATCH observatory  (Cappelaere et al.
, 2009). Furthermore, we gathered biomass production and yield data covering all major crops of the core sites from three sources. The first dataset, containing data for maize, millet, and sorghum for  2004, 2005, 2010, 2015 , and 2016 (with different fertilization treatments and different crop-sowing windows), consists of one site in Benin (Dassari), two sites in Ghana (Vea and Wa), and sites in Mali (Samanko) and Niger (Sadore)  (Faye et al. , 2018). The second dataset, obtained in Upper Ouémé catchment in the Republic of  Benin (for 2001 Benin (for , 2002 Benin (for , and 2003)) , provides information on total aboveground biomass for three sites (Dogue, Beterou, and Wewe) for peanut, maize, and sorghum production with different managements (e. g. , different fertilization types/amounts)  (Dagbenonbakin, 2005).
The third dataset, used for validating LandscapeDNDC, was from cassava fields from the Ikenne and Oke-Oyi sites in Nigeria  (Sobamowo, 2016). Leaf area index (LAI; the one-sided leaf area per unit ground area) data for comparison with measurements were taken at a 4 d temporal and a 500 m spatial resolution from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data (MCD15A3H;  Myneni et al. , 2015). Only pixels where the core sites are located were used. Previous investigations of MODIS data have found them to be the most suitable for representing the seasonality of vegetation dynamics  (Gessner et al. , 2013; Mougin et al. , 2019). We undertook an additional investigation using any data that field site managers could provide, and our findings generally supported the abovementioned view (Fig.
(2018)  grass/woodlands, the PSIM physiological simulation module  (Grote et al. , 2011a; Werner et al. , 2012)  was used, which has been widely applied for forests, including sites where ground vegetation needed to be considered  (Lindauer et al. , 2014; Dirnböck et al. , 2020) , as well as for savanna grasslands  (Grote et al. , 2009b). However, the model does not consider the production of fruits or similar. Therefore, we applied the PlaMo x module  (Kraus et al. , 2016; Liebermann et al. , 2020) , which has been developed particularly to described crop growth based on the same physiological processes as PSIM, for the agricultural plants. While parameters for agricultural crops were mostly defined in a species-specific manner in the model already, information about physiological properties of savanna grass and tree species were newly collected from literature.
If relative soil-water availability is lower than a threshold value, either the stomatal conductance (PSIM) or the photosynthesis itself (PlaMo x ) was linearly reduced  (Leuning, 1995; Knauer et al. , 2015). The threshold is defined as the species-specific parameter H2OREF_GS or H2OREF_A, respectively (Tables  2, 3 ). Interception was calculated from LAI, which is the product of specific leaf area and foliage biomass, and specific interception capacity following  Gash et al. (1995). For soil-water availability, we assumed a rooting soil depth of 1 m for all sites, with no difference between trees and grasses (according to the ISRIC soil database available at https://www. isric. org/ explore/wise-databases, last access: 16 June 2021). Fine-root abundance decreased exponentially with depth, with roots of grasses slightly more concentrated in the upper soil layers than tree roots  (February and Higgins, 2010).
at the Radar4KIT database (DOI: https://doi. org/10. 35097/438; Butterbach-Bahl et al. , 2021)  along with all of the setup and model initialization information (DOI: https://doi. org/10. 35097/437, Rahimi et al. , 2021). It can be additionally downloaded upon request from the following KIT institutional website: https://ldndc. imk-ifu. kit. edu/download/download. php (last access: 16 June 2021). The version used in this study is "win64 ldndc-1. 30. 4". Financial support. This research has been supported by the UP-SCALERS project (grant no. AURG II-1-074-2016), which is part of the African Union Research Grant program financed through the agreement between the European Commission and the African Union Commission (grant no. DCI-PANAF/2015/307-078). We also acknowledge special funding supplied by the Swedish National Space Agency (SNSA).
Acknowledgements. The authors appreciate the fruitful cooperation and data exchange with the EU-funded  CASSECS  project and the staff of the  AMMA-CATCH observatory. Furthermore , some of the climate data were obtained from the  NASA Langley Research Center (LaRC)   POWER  Project, funded through the  NASA   Earth Science/Applied Science Program. We acknowledge support by the  KIT-Publication Fund of the Karlsruhe Institute of Technology.
Data availability. All data regarding soil properties, climate, and management that were used to initialize and run the model, as indicated in the text, are provided in the Radar4KIT database (DOI: https://doi. org/10. 35097/437; Rahimi et al. , 2021)  and are freely downloadable. In addition, evaluation data were obtained from the FLUXNET (Pastorello et al. , 2020; https://daac. ornl. gov/, last access: 16 June 2021), WASCAL Data Discovery Portal (Bliefernicht et al. , 2018; https://wascal-dataportal. org/wascal_searchportal2/, last access: 16 June 2021), CARBOAFRICA project (Bombelli et al. , 2009; https://www. carboafrica. eu/data_en. asp/, last access: 16 June 2021), and AMMA-CATCH database (Gallé and Feller, 2007; http://bd. amma-catch. org/, last access: 16 June 2021) websites with respect to gas exchange; and from the Land Processes Distributed Active Archive Center (LP DAAC) gateway (Myneni et al.
4 w H2OREF_A RWC below which photosyn- 0. 45 x 0. 5 x thesis is decreased (-) MWFM See Table 2 0. 05 y WUEC Water use efficiency 7. 2 z 5. 0 a1 6. 0 a2 3. 0 a3 9. 0 a4 (mg CO 2 g H 2 O -1 ) a Massad et al. (2007), b Groenendijk et al. (2011), c Yu et al. (2001), d Farquhar et al. (1980), e Collatz et al. (1992), f Sonawane et al. (2017), g Von Caemmerer (2000), h Bernacchi et al. (2002), i Boyd et al. (2015), j Martin et al. (2000), k Kattge and Knorr (2007), l Ainsworth and Rogers (2007), m Caldararu et al. (2017), n Vico and Porporato (2008), o Kothavala et al. (2005), p Pallas and Samish (1974), q de Souza Nóia Júnior et al. (2020), r Gleason et al. (2017), s Körner et al. (1979), t Da Matta et al. (2001), u Vu (2005), v Sellers et al. (1996), w Baldocchi and Xu (2005), x from FAO database (http://www.
(2019)  is used to evaluate phenology. This method proved good skill in capturing the principal global phenology cycles  (Peano et al. , 2019)  and integrates a broader spectrum of growing season modes compared to previous techniques (e. g. Murray-Tortarolo et al. , 2013). The set of growing season modes investigated in 4GST are (1) evergreen phenology, (2) single growing season with summer LAI peak, (3) single growing season with summer dormancy, and (4) two growing seasons. 4GST uses LAI data to evaluate phenology. Most ecosystem and climate models introduce leaf area as a fundamental state parameter describing the interactions between the biosphere and the atmosphere. The most common measure of the area of leaves is the LAI, which is generally defined as the one-sided leaf surface area divided by the ground area in m 2 m -2  (Chen and Black, 1992).
Satellite observation To perform a comprehensive global phenology evaluation, a satellite-based observational dataset is required. LAI satellite observations present uncertainties and limitations related to the assumptions and algorithms applied in the LAI calculation (Sect. 4. 2, e. g. Fang et al. , 2013; Forkel et al. , 2015; Jiang et al. , 2017). For this reason, three different satellite observational products are considered in this work. Maisongrande et al. , 2004; Drusch et al. , 2012)  LAI dataset is obtained through a neural network applied on top-of-atmosphere input reflectances in red and near-infrared bands derived from SPOT and PROBA-V. The instantaneous LAI estimates obtained in this way go through a temporal smoothing and small gap filling, which discriminate between evergreen broadleaf forest and no-evergreen broadleaf forest pixels (see  Verger et al.
, 2019; Verger et al. , 2011). It covers the 1999-2019 period with a 10 d temporal frequency and a 1 km spatial resolution. Note that CGLS has a reduced latitudinal coverage compared to MODIS and LAI3g since it covers up to 75 • N versus the 90 • N of the other two products. The 2000-2011 period is common to the three satellite datasets, and it is used in the present analysis. The satellite observations are aggregated into monthly values and regridded, by means of a first-order conservative remapping scheme  (Jones, 1999) , to a regular 0. 5 • × 0. 5 • grid for consistency with the LSMs' output. The regridding operation is directly applied to the gap-filled satellite data. Note that regridding does not employ any specific treatment for differences in land cover. To perform biome-level analysis, the observed ESA CCI land cover map (https://www.
Community Land Model version 4. 5 The Community Land Model (CLM) is the terrestrial component of the Community Earth System Model version 1. 2 (CESM1. 2, http://www. cesm. ucar. edu/models/cesm1. 2/, last access: 1 June 2019). In its version 4. 5 (CLM4. 5, Oleson et al. , 2013)  and biogeochemical configuration (i. e. BGC compset,  Koven et al. , 2013) , it is the land component of the CMCC coupled model version 2 (CMCC-CM2,  Cherchi et al. , 2019). CLM4. 5-BGC features 15 PFTs, in which crop is represented as a generic C 3 crop. The PFTs time evolution follows the area changes described in the Land Use Harmonization version 2 dataset (LUH2,  Hurtt et al. , 2020). CLM4. 5-BGC resolves explicitly plant phenology  (Oleson et al. , 2013; Koven et al. , 2013) , which is described by means of three specific parameterizations: (1) evergreen plant phenology, (2) seasonal-deciduous plant phehttps://doi.
ORCHIDEE The ORCHIDEE model used for the CRESCENDO simulations is the land component of the IPSL (Institut Pierre Simon Laplace) ESM used for the CMIP6 simulations  (Boucher et al. , 2020). The surface heterogeneity is described with 15 different PFTs that can be mixed in each grid cell. The annual evolution of the PFT distribution is derived from the LUH2 database as described in more detail in  Lurton et al. (2019). In each grid cell, the PFTs are grouped into three soil tiles according to their physiological behaviour: high vegetation (forests) with eight PFTs, low vegetation (grasses and crops) with six PFTs, and bare soil with one PFT. An independent hydrological budget is calculated for each soil tile to prevent forests from exhausting all soil moisture. In contrast, only one energy budget (and snow budget) is calculated for the whole grid cell.
Experimental setup In this study, the S3 CRESCENDO simulations were used, characterized by transient CO 2 , climate, and land-use forcing. Each model spin-up is obtained by recycling climate mean and variability from the period 1901-1920 with the pre-industrial (1860) atmospheric CO 2 concentration until carbon pools and fluxes reach a steady state. The 1861-1900 period is simulated using the same climate forcing as the spin-up, but with time-varying atmospheric CO 2 and landuse forcing. Finally, the LSMs are forced over the 1901-2014 period with changing CO 2 , climate, and land-use forcing. All LSMs are commonly driven by the atmospheric forcing reanalysis CRUNCEP version 7  (Viovy, 2018) , and the land-use data are taken from the Land Use Harmonization version 2 dataset  (Hurtt et al. , 2020).
Satellite data Satellite-based LAI datasets have been used in this work as a benchmark for the evaluation of the LSMs' phenology performance globally. However, satellite observations present some caveats and uncertainties (e. g. Myneni et al. , 2002; Fang et al. , 2013; Jiang et al. , 2017). For this reason, three separate satellite LAI products obtained from different acquisition sensors (namely AVHRR for LAI3g, MODIS for MODIS LAI, and SPOT/PROBA VEGETATION for CGLS) have been used in this study. The comparison between these datasets shows major issues associated with LAI products derived from satellite reflectance observations. For example, large differences between LAI3g, MODIS, and CGLS occur at high latitudes and in tropical regions (Fig. 6 ), where thick clouds and snow cover can affect the data reconstruction (e.
Besides, LSMs' crop phenology parameterizations require further development to improve the description of each specific crop. Another limitation of the present evaluation is the monthly temporal frequency. Data at a higher frequency, indeed, might lead to a more detailed bias assessment. The use of a different temporal frequency may also influence phenology type detection. For example,  Peano et al. (2019) , who use 15 d LAI data, detect a slightly different distribution of CLM4. 5 SGS-D and TGS types in Australia, the Horn of Africa, and Brazil. Similarly,  Zhang et al. (2019) , who analyse CLM4. 5 in northeast China with 8 d LAI data, obtain TGS type in areas recognized as SGS-S in the present analysis. LSMs evaluate phenology at the PFT level, but the final LAI values are returned at the grid-cell level.
Code and data availability. The LAI3g satellite observation data are available from Ranga Myneni (http://sites. bu. edu/cliveg/ datacodes/,  Myneni, 2013) ; the MODIS satellite observation data are available from Taejin Park; the CGLS satellite observation data are available from COPERNICUS (https://land. copernicus. eu/ global/products/lai,  Verger et al. , 2019) ; the atmospheric forcing data, CRUNCEP v7, are available from Nicholas Viovy (https://rda. ucar. edu/datasets/ds314. 3/,  Viovy, 2018) ; the land surface models simulations are part of CRESCENDO project and they are stored at the CEDA JASMIN service (http://www. ceda. ac. uk/, Pritchard and CEDA staff, 2018) ; the 4GST python script is available online (https://github. com/daniele-peano/4GST, last access: 31 August 2020, https://doi.
org/10. 5281/zenodo. 4680992, Peano, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-2405-2021-supplement. Author contributions. DP wrote the paper, performed the analysis, and provided CLM 4. 5 data; DH and SM were strongly involved in the discussion of result and in drafting the manuscript; TP provided MODIS data; DW provided LPJ-GUESS data; YF and HL provided CLM 5. 0 data; AW provided JULES-ES data; EJ and CD provided ISBA-CTRIP data; PP provided ORCHIDEE data; JEMSN provided JSBACH data; SZ fostered the analysis; all co-authors discussed the results and contributed to writing the manuscript. Authors after SM are listed in alphabetical order. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. We acknowledge the editor and reviewers for their comments that helped improve this work. This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  641816  (CRESCENDO). DH and AW also received support from the  Met Office Hadley Centre Climate Programme (HCCP)  funded by  BEIS and Defra. David Wårlind  acknowledges financial support from the  Strategic Research Area MERGE  (Modeling the Regional and Global Earth System -https://www. merge. lu. se, last access:  26 August 2020 ) and from the Swedish national strategic escience research programme eSSENCE (http://essenceofescience. se/, last access:  26 August 2020 ) LPJ-GUESS simulations were performed on the Tetralith supercomputer of the  Swedish National Infrastructure for Computing (SNIC) at Linköping University , under project no.
SNIC 2018/2-11  (S-CMIP). Taejin Park  acknowledges support from  NASA 's  Carbon Monitoring System programme  ( 80NSSC18K0173-CMS ). Financial support. This research has been supported by the  European Commission, H2020 Research Infrastructures (CRESCENDO  (grant no. 641816 )), and  NASA's Carbon Monitoring System programme  (grant no. 80NSSC18K0173-CMS ). Review statement. This paper was edited by  Ben Bond-Lamberty  and reviewed by  Matthias Forkel  and one anonymous referee.
MiRNome Analysis RNA from CD11B+ cells from the brains of the LysM Cre/+ : Dicer fl/ + and LysM Cre/+ : Dicer +/+ mice treated with PBS or IL-1β at P5 were extracted using miRNeasy Mini Kit (Qiagen). Quality control, quantification, and annotation was performed using pipeline ncPROseq  27  by Genomeast Platform (Strasbourg, France). It includes quality control, mapping of reads to the mm10 genome assembly (with bowtie version 1. 0. 0, Langmead et al, 2009), annotation with miRBase release 22. 1 for the microRNAs, Repbase and Rfam 11 database for other small non-coding RNAs. The differential expression analysis was performed using DESeq2 (version 1. 26. 0, R package  28  ). Differentially expressed (DE) miRs in microglia were performed as described in the Results section and heatmap was generated using Morpheus https://software.
Microarrays and Data Pre-Processing RNA from CD11B+ cells and O4+ cells from the brains of mice exposed to IL-1β or PBS at P5 were extracted (Trizol) and quality assurance (Agilent100 bioanalyzer) determined by Miltenyi Biotech. RNA sample was hybridized to Whole Mouse Genome Oligo Microarray and Mouse microRNA microarray (Agilent Technologies). Fluorescence signals were detected using Agilent's Microarray Scanner System. The Agilent Feature Extraction Software (FES) was used to read out and process the microarray image files. Differential gene and miR expressions were calculated respectively using Rosetta Resolver gene expression data analysis system (Rosetta Biosoftware) and Gen-eSpringGX software (Agilent Technologies). DE miRs in microglia were performed as described in the Results section.
EV pellets were re-suspended in a volume of 100 μL. NP200 nanopore (100-400 nm diameter range; Izon) was used for sample analysis and the same nanopore was used throughout the experiment. The values for applied voltage, pressure and pore stretch were kept constant for all EV samples and relative calibration particle recordings. CPC200 calibration particles (carboxylated polystyrene particles diluted following the manufacturer's instruction; Izon) were used as standards. Data acquisition and analysis were performed using Izon Control Suite software (version V3. 2).
P2 and P8 Behavioral Tests Ultrasonic vocalizations (USV) are recorded at P2 with an ultrasound microphone (Noldus), sensitive to frequencies of 30 to 90 kHz for 1 minute. The pup isolated to the litter, is placed into a container (H 4,5 cm Â L 10 cm Â l 10 cm), inside of a hermetic polystyrene box (H 23 cm Â L 37 cm Â l 24 cm) to attenuate outside sounds. The box temperature is 23. 2 degrees. The microphone is placed at 10 cm above pup head. USV are recorded and analyzed with UltraVox XT3. 1 software. The nest odor preference test evaluates maternal odor recognition by pups at P8. The measuring platform (L 20 cm Â l 13 cm) was composed of 3 zones (each L 7 cm Â l 13 cm): nest (home nest shavings), neutral (empty zone), and clean zone (clean shaving). The isolated P8 pup was placed in the neutral area and the camera (at 30 cm above) recorded its displacement for 1 minute 3 times in a row.
Statistical Analysis Data were expressed as mean value with standard error of the mean (SEM). Using GraphPad Prism Software, data were tested for normality using the Kolmogorov-Smirnov normality test. Multiple comparisons in the same data set were analyzed by 1-way analysis of variance (ANOVA) or 2-way ANOVA with the Bonferroni post hoc test. Single comparisons were made using two-tailed Student's t test or the Mann-Whitney test.
05. Of these 34 miRs, 17 miRs were significantly up-regulated by IL-1β and 17 miRs were significantly downregulated by IL-1β in microglia (see Table  S1 ). Functional enrichment analysis of these 34 miRs using DAVID 6. 8  43  defined a   S2 ). We then generated a Venn diagram to depict the overlap between the predicted targets of dysregulated microglial miRs established using MirTarget in the online database miRDB (Table  S3 ) and the significant differentially expressed (DE) mRNA by IL-1β exposure in microglia. DE genes of P5 microglia from microarray analysis were identified under two criteria: adjusted p value <0. 05 and FC value: À1. 5 < FC >1. 5  S3 ). Venn diagram revealed that more than 40% (1,371/3,303, p = 2. 3 Â 10 À09 ) of microglial DE genes by IL-1β could be driven by altered levels of miRs (see Fig  3C ).
Acknowledgments This study was supported by grants from  Inserm ,  Université de Paris ,  Horizon 2020 Framework Program  of the  European Union  (grant agreement no. 874721/ PREMSTEM ),  ERANET-NEURON (TRAINS) ,  Fondation de France ,  Fondation pour le Recherche Médicale ,  Fondation Grace de Monaco ,  Fondation des Gueules Cassées ,  Fondation Roger de Spoelberch , and an additional grant from " Investissement d'Avenir -ANR-11-INBS-0011 -"NeurATRIS. The authors acknowledge financial support from the  Department of Health  via the  National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's & St. Thomas' NHS Foundation Trust in partnership with  King's College London and King's College Hospital NHS Foundation Trust. The authors acknowledge financial support from  PhD  fellowship from  Université de Paris (Ecole Doctorale BioSPC  to C.
B. ) the EUR G. E. N. E. (reference # ANR-17-EURE-0013 ) and is part of the  Université de Paris IdEx  # ANR-18-IDEX-0001 funded by the  French Government  through its " Investments for the Future" program. The supporting bodies played no role in any aspect of study design, analysis, interpretation or decision to publish this data. We also want to thank for their precious help  Pierrette Young-Ten ,  Jennifer Hua , and  Irvin Sautet.
, 2018; Sarkizova et al. , 2020) , such as NetMHC  (Andreatta and Nielsen, 2016; Jurtz et al. , 2017; Reynisson et al. , 2020) , are based on artificial neural networks trained in a supervised way to predict peptide presentation from the known peptide-HLA association. They are trained on large datasets at every method release and they provide peptide-scoring schemes that perform best on frequent alleles and well-charac-terized alleles at the time of release. Their accuracy is degraded for rare or little studied HLA-I alleles, which are poorly represented in databases. In that case, another approach is to train unsupervised models of presentation from custom elution experiments with little or no information about the peptide-HLA association. For instance, MixMHCp (Bassani-Sternberg and  Gfeller, 2016; Bassani-Sternberg et al.