dataesr/scientific-paragraphs-categorization huggingface dataset:
3 ), fire emissions before 1997 were based on the median of the six FireMIP scaled time series. We used the average from 1997 to 2003 when combin-ing the various data streams to minimize the impact of interannual variability in the GFED time series, which could result in a mismatch when stitching the FireMIP emissions to the GFED data. Below, we will describe the models we used here, followed by a description of other datasets used and how the various pieces of information were merged to regional time series of emissions for the 1750-2015 period.
JSBACH, LPJ-GUESS, and ORCHIDEE In three of the DGVMs, the SPread and InTensity of FIRE (SPITFIRE) model serves as the fire module  (Thonicke et al. , 2010). SPITFIRE is a process-based global fire model and a further development of the Reg-FIRM approach  (Venevsky et al. , 2002)  but uses a more complete set of physical representations of spread and fire intensity. Precipitation, daily temperature, wind speed, soil moisture, carbon content of the vegetation and litter pools, and the vegetation distribution are used as input for SPITFIRE to calculate rate of spread, fire duration, and intensity. Based on the calculated burned fraction and post-fire mortality of trees, carbon emissions are computed and redistributed over carbon pools. SPITFIRE includes a dynamic scheme for combustion completeness and depends on fire characteristics and the moisture content of different fuel classes  (Lenihan et al.
, 2009). As a further consequence of the more detailed vegetation structure, the size-dependent mortality functions in SPITFIRE have a more realistic impact, whereby small trees have a relatively higher probability of being killed by fires than large trees. For the FireMIP simulations used here, further improvements were made; the calculation of human ignitions was recalibrated and post-fire mortality parameters were updated. For the global vegetation model ORCHIDEE  (Krinner et al. , 2005) , SPITFIRE was adjusted and incorporated by  Yue et al. (2014 Yue et al. ( , 2015)). Most equations from the original SPIT-FIRE model were implemented and run parallel to the STO-MATE submodule, which simulated vegetation carbon cycle processes in ORCHIDEE. Minor modifications were made by  Yue et al. (2014 Yue et al.
Visibility-based fire emission estimates Fire-emitted aerosols lower visibility, and in the frequently burning regions of EQAS  (Field et al. , 2009)  and ARCD  (van Marle et al. , 2017)  visibility observations can be used as a proxy for fire emissions given the reasonable agreement between fire emission estimates from GFED and visibility observations for the overlapping 1997-2015 period. The visibility observations are taken from weather station records from the NOAA National Centers for Environmental Information (NCEI) Integrated Surface Database (ISD). For EQAS, data are available from 1950 onwards, and for ARCD, data are available starting in 1973. Fire emissions in these regions have increased over time, related to migration of humans accompanied with deforestation  (Field et al. , 2009; van Marle et al.
Global Charcoal Database The GCDv3 was, at the time of writing, the most recent version of the GCD  (Marlon et al. , 2016)  and included 736 charcoal records. The records are distributed over five continents with the majority of sites having one record. The sites are not distributed evenly over the globe: many sites (326) are located in the northern part of North America and Europe. Records may lack data from the most recent 250 or so years (the near-surface sediment), which further restricts the charcoal analysis (see Fig. B1 , with locations of charcoal sites and regions in Appendix B). While all of our regions have charcoal records, data density is highest in temperate and boreal regions (in total for five regions; Fig. 3 ). The charcoal records were converted to unitless time series, with a range between 0 and 1, and a decadal time step using methods detailed in  Power et al.
Global fire emissions According to our approach, global biomass burning emissions were relatively stable from 1750 to 2015 (Fig. 5 ). Carbon emissions increased only slightly over the full time period and peaked during the 1990s after which they decreased gradually. Although Africa exhibits a decrease already from 1950 onwards, this decline in emissions was compensated for, especially in the 1990s, by increasing emission in deforestation zones (Fig. 5 ). From 1960 onwards, the interannual variability increased in our dataset as a result of more detailed information from the visibility-based  and  satellite-based (1997-2015)  biomass burning emission datasets. The cyclic variability in the first centuries is related to the use of repeating climate variability in FireMIP. While the increase in IAV is thus partly due to changes on underlying data sources, it has also increased in reality because of the increase in deforestation-based emissions that vary more from year to year than other fire emission sources.
Discussion We found that carbon emissions increased slightly over the full time period and peaked during the 1990s after which they decreased gradually. Africa accounts for a large part (on average, 58 % over our study period) of global fire carbon emissions and the general trend therefore largely mimics that of Africa. The exception is the latter part of our record; from about 1950, African fire emissions decreased while emissions in deforestation zones increased (Fig. 5 ). From 1960 onwards, the interannual variability increased as a result of more detailed information from the visibility record for EQAS and the ARCD and satellite-based biomass burning emission datasets covering the whole globe. This is thus partly an artefact of data availability but also partly real because the interannual variability from deforestation zones is relatively high and its contribution increased over time.
2) dataset  (Hurtt et al. , 2011) , in combination with MODIS MCD12C1 cropland for the year 2012. The reconstructed fire emissions based only on precipitation and changes in cropland as in- put variables showed similar results as the biomass burning emissions based on the median of models for both Southern Hemisphere and Northern Hemisphere Africa from 1950 to 2013 (Fig. 12 ). Although the trends for the two approaches over 1700-1950 agree for NHAF, in SHAF they show opposing trends with an increase from 1750 to 1950 based on models and a slight decrease based on the reconstruction. Future research into the drivers of African fires and how these have changed over time could help to improve these estimates. Emissions from tropical forests are responsible for the global increase we found from 1950 onwards.
13 ). Although the global trends are relatively similar, on a regional scale, differences between our estimates and the data used in CMIP5 are more substantial (See Fig. D1 , with regional comparisons between CMIP5 and CMIP6 estimates in Appendix D). The largest differences were in TENA-E, TENA-W, SHAF, and SARC. In Africa, the continent from which half of all carbon emissions stems, we found that emissions were relatively flat while CMIP5 estimates increased over the past decades, at odds with recent findings that agricultural expansion lowers fire activity  (Andela and van der Werf, 2014). The estimates and trends in EQAS, CEAS, BONA-W, and BONA-E are very similar, and in ARCD as well, although in our estimates the increase there started a few decades later. While our estimates are for several regions driven by consistent data sources, these substantial discrepancies highlight once more that uncertainties are large.
Uncertainties Uncertainties in reconstructing fire emissions are large and stem from uncertainties in the data we used and from our approach of combining the different datasets. For the reconstruction, the fire models and visibility-based fire emissions were used with GFED4s as an anchor point. We have, to some degree, relied on fire models in almost every region, except ARCD and EQAS. The fire models exhibit differences in regional trends, resulting in a range in regional biomass burning emissions. On a global scale, the impact of excluding single models led to relatively small differences up to 3 % (Table  2 ). However, on a regional scale, differences were more profound, with percentages up to 44 % in TENA-E. In regions where models were used in combination with charcoal records, the models had a large influence when the char- coal signal and the models exhibited opposing trends (for example, in EURO and BONA-W), and this also explains why in these regions excluding any of the models would result in a decrease in fire emissions (Table  2 ).
Future model comparisons pinpointing the reasons why models behave differently would help constrain this uncertainty. Furthermore, an in-depth comparison between forest fire statistics from the US and Canada, for example, the Canadian Fire Database (CNFDB;  Stocks et al. , 2002)  and the charcoal time series, may help in better constraining trends in boreal and temperate North America. Given the good agreement between visibility and GFED estimates for the overlapping period in ARCD and EQAS, we feel these regions are relatively well represented. However, this proxy relies on man-made observations leading to inconsistencies. In addition, the locations of the WMO stations were not evenly distributed over the region. Also, little is known about fire history in these regions before visibility observations became available.
There is still much to be gained by collecting more data and using different species. Levoglucosan, for example, is a biomarker for fires and  Kehrwald et al. (2012b)  showed that levoglucosan in a Greenland ice core represents the fire signal from Asian and North American source regions. Other proxy records that could improve regional estimates are char and soot measurements taken from loess. These can be used to validate the estimates in CEAS  (Han et al. , 2010). As the Global Charcoal Database continues to evolve with new data contributions  (Hawthorne et al. , 2017) , regions that are currently undersampled could inform GCD-based biomass burning histories. Finally, the FireMIP exercise may lead to a better representation of the processes driving global fire patterns, which itself will help in developing a more complete understanding of fire since the year 1750.
For a rough indication of uncertainty in these regions, Fig. 14  shows comparisons between our results, char- coal z scores (1750-2000) from GCDv3, and burned area reconstruction by  Mouillot and Field (1900-2000)  for sub-Saharan Africa, Patagonia, boreal Asia, and southeast Asia (Appendix B). The three datasets quantify fire histories using different units, so all datasets were scaled and transposed to the year 2000 value to qualitatively compare the trends. In sub-Saharan Africa, CMIP6 and GCv3 are similar from 1950 to the present, but CMIP6 decreases more rapidly prior to 1950 (Fig. 14 ). The trend in boreal Asia also agrees for a large part, where charcoal estimates exhibit a larger range in variation over time. In Patagonia and southeast Asia, the general trend is increasing, although the peak years differ.
To improve and constrain our dataset, we encourage paleofire researchers to sample their sites in detail for the last 250 years, even though proxy records are currently mostly used for longer (century to millennial) timescales. Pinpointing the reasons behind outliers and opposing trends between the various models will lead to lower uncertainties for studies focusing on past centuries. Climate models should not use the CO 2 emissions (or nitrogen emissions if the nitrogen cycle is included in the model), as forcing because in general these emissions are not net emissions to the atmosphere, but a return pathway of previously sequestered carbon just as respiration is. The exceptions are CO 2 emissions from deforestation and peat fires. However, the models that do not simulate land use change are recommended to use land use change emissions prepared for AR6 (http://www.
Guidance for using this dataset in climate models We have merged satellite-based fire emissions for recent times, charcoal datasets in temperate and boreal regions, visibility records from weather stations over tropical forest regions, and emission estimates from the FireMIP project. Our aim was to make the best use of the strengths of the various datasets using a regional approach. According to our estimates, global biomass burning carbon emissions increased slightly over the full time period and peaked during the 1990s after which they decreased gradually. The global pattern varies somewhat depending on trace gas or aerosol species. Africa accounts for a large part (58 %) of global fire carbon emissions and the general trend therefore mimics that of Africa especially in the early part of our record.
(A2) This scalar P has been normalized to yield a value of 1 in the absence of human activities and therefore describes the degree of human fire suppression. P describes increasing burned area with population density for low GDP, and vice versa for high GDP. GDP data are used for every 5 years from 1890 to 1995. Before and after that date, we keep per capita GDP per region constant in time. HYDE 3. 1 population density values from 1700 to 2000 at a decadal scale were used. Furthermore, historical HYDE 3. 1 cropland fraction from 1700 to 2003 was used. The data of Klein  Goldewijk et al. (2011)  was used to correct SIMFIRE estimates, setting wildfire emissions for croplands to 0. To obtain fire emissions spanning the period from 1700 to 2000, LPJ-GUESS-SIMFIRE was run using daily observed climate data  (Weedon et al.
Acknowledgements. We would like to thank  Kees Klein Goldewijk ,  Benjamin Aouizerats ,  Niels Andela ,  Pierre Friedlingstein , and  Thijs van Leeuwen  for their help and useful discussions. This work was initiated by  Claire Granier ,  Steven Smith , the  CMIP community , and the  Interdisciplinary Biomass Burning Initiative (IBBI). Furthermore, we acknowledge the  PAGES Global Paleofire Working Group  for making the Global Charcoal Database publicly available and supporting fire workshops. Margreet J. E. van Marle  and  Guido R. van der Werf  were supported by the  ERC  (grant no. 280061 ). NSF  award  BCS-1437074  and  BCS-1436496  provided workshop support and funding for  Jennifer R. Marlon  and  Brian I. Magi. PICS CNRS 06484  provided workshop support for ALD. Robert D. Field  was supported by the  NASA   Atmospheric Chemistry Modeling and Analysis Program.
Stijn Hantson  and  Almut Arneth  acknowledge support by the  EU   FP7 projects BACCHUS  (grant no. 603445 ) and  LUC4C  (grant no. 603542 ). Johannes W. Kaiser was supported by the  EU   H2020  project  MACC-III  (grant no. 633080 ). Wolfgang Knorr  acknowledges the support of  PEGASOS  ( EU  contract  265148 ) and  BECC (Biodiversity and Ecosystem  services in a Changing Climate, funded by the  Swedish Government). We thank three anonymous reviewers for their constructive comments and helpful suggestions on an earlier version of the manuscript. Edited by: Samuel Remy Reviewed by: three anonymous referees.
Code and data availability. The Python code that was used to assimilate the raw data and construct the gridded emission estimates is available upon request. This dataset is made available for the Coupled Model Intercomparison Project Phase 6 (CMIP6) analyses at the ESGF repository: at the Earth System Grid Federation (ESGF) repository: https: //esgf-node. llnl. gov/search/input4mips/ (van  Marle et al. , 2016). GFED4s data are publicly available at http://www. globalfiredata. org/data. html (van der Werf et al. , 2017). Charcoal records are available through the Global Charcoal Database version 3: www. paleodata. org (Marlon et al. , 2016). Regional visibility-based fire emissions and regional emissions based on the different fire models can be requested from the corresponding author.
Sequence analyses and curation The ca. 10 9 sequencing reads produced were curated using the OBITools package  63  and R scripts (www. r-project. org). We first assembled paired-end reads and assigned them to their respective samples and taxonomic groups on the basis of the tags and primer sequences, by allowing 2 and 0 mismatches on primers and tags, respectively. Reads were dereplicated, and low-quality sequences excluded (i. e. shorter than 7, 13, 50 and 100 nt for the plant, eukaryota, archaea and bacterial markers, respectively; containing "Ns"; and singletons). We then clustered the remaining unique sequences into operational taxonomic units (OTUs), as follows. We computed pairwise dissimilarities between sequences (i. e. the number of mismatches, allowed to be 0-3) using the Sumatra algorithm  64  , then we formed OTUs using the Infomap community detection algorithm  65.
We excluded OTUs represented by a single sequence because PCR/sequencing almost always produce at least one error on amplified fragments. The true sequence of an OTU was assumed to be the most abundant one in the cluster. OTUs were assigned a taxonomic clade with the ecotag program of the OBITools package. Since this algorithm requires full-length barcodes as reference, we constructed a set of reference databases for each marker by running in silico PCRs with the primer pairs used here (Supplementary Table  7 ). This was done with the ecoPCR program 66 by using Genbank (release 197; ftp://ftp. ncbi. nlm. nih. gov/genbank) and the MOTHUR-formatted SILVA database (release 119; http://www. mothur. org/wiki/Silva_reference_files) as sequence template. We here only considered references with unambiguous taxonomic annotation at the order level.
We also used a reference database of tropical plants occurring at the study site, built with the plant primers, so as to improve plant OTU identification (available in  56  and the Dryad Digital Repository, doi:10. 5061/dryad. 1qt12). At the end of this analysis, we had two taxonomic assignments for each OTU. We gave priority to assignments from SILVA or our local plant database when the similarity between the query and its best match was > 98%. We did so because the SILVA and our local plant databases yield taxonomic annotations that are more reliable than those from Genbank. Otherwise, the taxonomic assignment yielding the highest similarity score was kept. Paired-end reads were assembled, assigned to their respective samples/marker and dereplicated. Low-quality sequences were excluded; the remaining ones were clustered into operational taxonomic units (OTUs) and assigned a taxonomic clade.
We paid particular attention to minimize PCR/sequencing errors, contaminant and false positive sequences as well as potential non-functional PCRs by using several conservative quality criteria. First, we assumed that OTUs peaking in abundance in the negative controls were a contaminant. Any OTU with a best-match similarity in any reference database below < ca. 75% was considered as a chimera or highly degraded sequence. Any OTU that did not fall into the clade targeted by the primer pair was also excluded. Finally, we also curated the dataset from false positives caused by tag-switching events. This phenomenon is suspected to occur during the preparation of the sequencing library and translates into low abundance "contaminant OTUs" in samples coming from other samples that were part of the amplicons multiplex.
To remove them, we considered each OTU separately and set to 0 any abundance representing < 0. 03% of the total OTU abundance in the entire dataset, similarly to  62. This abundance threshold was found to be the one for which most OTUs with unused tag combinations could be removed from the dataset. Verifying the success of our 18,000 PCRs was not possible. Therefore, we used the PCR replicates to filter out potential non-functional PCR reactions, i. e. containing low amounts of reads or high amounts of contaminants or false positives that could not be filtered out with the process described above. First, for each sample we defined an average community (hereafter centroid) by averaging OTUs counts of the four PCR replicates. Second, we compared the distribution of dissimilarities (as defined by Euclidean distances) of PCR replicates with their respective centroid (hereafter d w ) against the distribution of pairwise dissimilarities between all centroids (hereafter d b ).
As we expect d w < d b , we defined the intersection of d w and d b distribution curves as the dissimilarity level above which a PCR replicate is too distant from its centroid to be reliable. Any PCR above this threshold was excluded from the analysis. This process was repeated iteratively until no more PCR were removed from the dataset. If a sample was represented by only one PCR product, then it was excluded during the iterative process. At the end of this procedure, the remaining PCR replicates were summed for each sample. See Supplementary Table  8  for raw and curated datasets statistics. Finally, the sequencing depth of each sample was standardized for each marker by randomly resampling a number of reads equal to the first quartile of read number across samples. Although such procedure has been recently questioned  67  , the data loss caused by rarefaction is minimal when the subsampling size well covers the sample diversity, as it is the case here.
Environmental parameters Soil chemistry and airborne lidar. Total content in soil chemical elements was assessed on one soil sample in two (i. e. every 20 meters). Soils were ground and attacked by hydrochloric and nitric acid. Major element concentration was measured by inductively coupled plasma optical-emission spectroscopy (ICP-OES). Carbon and nitrogen concentration was measured by a CHN elemental analyzer (NA 2100 Protein, CE Instruments). All other chemical elements were quantified by ICP-MS. In total, our dataset included 55 elemental concentrations, which were krigged using an exponential variogram model so as to obtain values for all the points of the initial sampling design. This analysis was conducted with the sp (http://rspatial. r-forge. r-project. org/) and gstat (http://gstat.
Acknowledgements We are indebted to the staff of the  Nouragues Research Field Station (CNRS, French Guiana ) and the  Nouragues Natural Reserve , for commodities and technical help during the field session. We thank  Johan Pansu ,  Guilhem Sommeria-Klein ,  Mélanie Roy  and  Renato Lima  for helpful discussions on the manuscript, as well as the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) and  Pierre Solbes  ( EDB-Calc Cluster ) for providing computing and storage resources. The work was funded by the  METABAR  project ( ANR-11-BSV7-0020 ) and has benefitted from " Investissement d'Avenir " grants managed by  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ;  TULIP :  ANR-10-LABX-0041 ;  OSUG@2020 :  ANR-10-LABX-56 ;  ANAEE-France :  ANR-11-INBS-0001 ).
Background: Whether human papillomavirus (HPV) vaccination could induce or trigger autoimmune diseases (AID) has been questioned, and potentially contributes to low immunization coverage in France. This study evaluated the association between HPV vaccination and the risk of AID using routinely collected data sources. Methods: All girls aged 13-16 years between 2008 and 2012, covered by the general health insurance scheme and without history of HPV vaccination or AID, were included and followed using French nationwide databases. Fourteen neurological, rheumatological, haematological, gastrointestinal or endocrine AID, were identified from ICD-10 codes allocated to hospital stays and long-term illnesses or by marker drugs. Their incidence was compared between girls exposed and non-exposed to HPV vaccination, using a Cox model adjusted for inclusion year, geographic area, socio-economic indicators, healthcare use level and other immunizations.