dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability EUropean Flood Fatalities (EUFF) database  1980-2018 (updated)  is available in the 4TU Centre for Research Data  (Petrucci et al. , 2020 , https://doi. org/10. 4121/uuid:489d8a13-1075-4d2f-accb-db7790e4542f, 2020). The EUFF 2020 database collects 2483 FFs that occurred during a 39-year period in 9 study areas. It includes three files: a) a comma-separated values (csv) file, which 535 contains the data; b) the keyhole mark-up language (kml) file, which provides the location of fatalities on the Google Earth; and c) the readme (txt) file, containing the description of database structure.
Conclusions In the current work, the EUFF 2020 database has been introduced recording data on flood fatalities for 9 areas of 8 European countries for the 1980-2018 period. The potential of the database to analyse various aspects of the structure of the victims due to 540 flood events is also shown. Highlighted conclusions can be shortly summarised as follows: (i) The studied European countries follow various practices for the data collection of flood fatalities on national or regional levels. Similarly, various sources of documentary evidence they do exist. For example, Media information (newspapers, TV, internet reports) have been among the main sources of such records in the past decades. (ii) Despite the limited documentary sources, mainly concerning underreporting issues, bias towards the most severe events and 545 inhomogeneity from one country to another, EUFF 2020 shed light on the fatal flood events occurred in the eight studied countries of the Euro-Mediterranean region.
(iii) The EUFF 2020 database provides both regional and super-regional analyses of floods fatalities concerning their gender, age, conditions, activity of fatalities and dynamics of the accidents, thus contributing to a better understanding of the human exposure to floods associated with the most serious potential consequence, which is its own death. 550 (iv) The EUFF 2020 database, with its great potential to be extended spatially and temporally, represents a unique European database of high scientific and practical potential. One aspect of the database is its vitality, as we continuously strive to improve it by extending the study period and enlarging the domain. The EUFF 2020 database and its high potentials will hopefully motivate and encourage more researchers to enrich it with data on FF available in their own countries.
Acknowledgements RB and JŘ were funded by the  Ministry of Education, Youth , and Sports of the  Czech Republic  for the SustES -Adaptation 565 strategies for sustainable ecosystem services and food security under adverse environmental conditions project, ref. CZ. 02. 1. 01/0. 0/0. 0/16_019/0000797. MCLL and MLL were funded by the  Research State Agency of the Ministry of Science and Innovation and the Ministry of Universities  under the  National  Project  M-CostAdapt  ( CTM2017-83655-C2-2-R ). 570 Thanks to  Dr. Theodoros Baimpos  for proofreading the article.
Chromatin immunoprecipitation and data analysis ChIP was performed as previously described22 with parasites synchronised at 36 hpi. Sonicated chromatin (500 ng DNA content) was immunoprecipitated overnight with 0. 5 μg of anti-HP1 (Genscript) polyclonal rabbit antibody22 and incubated after with 25 μL of Dynabeads Protein G (Invitrogen) for two hours. Subsequent washing, reverse cross-linking and DNA extraction were carried out as described before22. Sequencing libraries were prepared with the immunoprecipitated DNA using the MicroPlex Library Preparation Kit v2 (Diagenode) with the KAPA HiFi polymerase (Kapa Biosystems) for the PCR amplification. For each ChIP sample, a control DNA corresponding to the ChIP input was processed in parallel. Multiplexed libraries were subjected to 150 bp paired-end sequencing on a NextSeq 500 (Illumina)34.
Fastq files were obtained by demultiplexing the data using bcl2fastq (Illumina) prior to downstream analysis. A minimum of two biological replicates were analysed for each clone. Sequencing reads were mapped to the P. falciparum genome35 (PlasmoDB, v29) using Burrows-Wheeler Alignment tool (BWA-MEM) with default settings36. PCR duplicates were removed. ChIP-seq data was normalised over input and peak calling was performed using the MACS237 software with default parameters and a 0. 05 false discovery rate (FDR) cut-off.
For identification the data were searched against the UniProt P. falciparum (isolate 3D7: UP000001450) and the Homo sapiens (UP000005640) databases using SequestHF through proteome discoverer (version 2. 2). Enzyme specificity was set to trypsin and a maximum of two missed cleavage sites were allowed. Oxidized methionine, Nterminal acetylation, and carbamidomethyl cysteine were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 02 Da for MS/MS peaks. The resulting files were further processed using myProMS v3. 940. FDR calculation used Percolator and was set to 1% at the peptide level for the whole study. The label free quantification was performed by peptide Extracted Ion Chromatograms (XICs) computed with MassChroQ version 241.
For protein quantification, XICs from proteotypic peptides shared between compared conditions (TopN matching) were used, with missed cleavages allowed. Median and scale normalization was applied on the total signal of P. falciparum proteins to correct the XICs for each biological replicate (n=5). To estimate the significance of the change in protein abundance, a linear model (adjusted on peptides and biological replicates) based on two-tailed T-tests was performed and p-values were adjusted using Benjamini-Hochberg FDR procedure. P. falciparum proteins with at least three total peptides in all replicates, a 2-fold enrichment and an adjusted p-value < 0. 05 were considered significantly enriched in sample comparisons. ACKNOWLEDGEMENTS 42. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data.
For some fields, we were able to describe crop history only from 2014 onwards, in which case the first sequence was not used (Table  3 ). Maize was the most represented annual crop in the dataset. In order to validate our main results regarding the impact of crop shifts irrespective of the identity of the current crop, all analyses were conducted twice: i/ including all available data; and ii/ including only fields with maize as the current annual crop (i. e. maize monoculture or young tree plantations with maize intercrop). For these analyses, we compared maize fields with no crop shift (n = 17) or two crop shifts (n = 7) because there were not enough maize fields with only one shift (n = 3).
Beforehand we scaled the easting and northing coordinates of each field and added a small jitter (normal noise, mean of 0 and standard deviation of 0. 001) because the function cannot handle duplicate coordinates. Other forms of spatial autocorrelation (gaussian, linear, spherical) were tested but model AIC were similar. All significance values and estimates are extracted from these models. However, to provide estimates of partial R 2 for each single variable (function r2beta, package r2glmm, Kenward Roger method, Jaeger (2017)) we had to use model fitted with the lmer function (i. e. without spatial autocorrelation) due to package compatibility issues. As the autocovariates were not significant, this was unlikely to strongly affect the R 2. Besides, the treatment of interest (number of crop shifts) was not independent of the field itself or the crop type.
Indicator species analysis Our last objective was to determine whether certain species were specifically associated with either high or low frequency of crop shifts. In this regard, we identified groups of indicator species related to each number of crop shifts. As proposed by  Cáceres and Legendre (2009) , indicator species are species that can be used as ecological indicators of environmental and ecological conditions. Their association to a given environment (here, over periods of three years, the number of crops or number of shifts) is based on the specificity and the fidelity of the species as an indicator of the environmental group (i. e. here, fields with a given frequency of crop shifts). We used the indicspecies package (function multipatt, IndVal. g method, Cáceres and Legendre (  2009 )).
custom codes for species absent from the database. Species labelled as "unidentified" correspond to species clearly different from the other species, but which we were unable to identify. Species labelled as "unsure" correspond to plants which might belong to one or the other of the identified species, but could not be identified with certainty (« unsure » species were not included in the analysis). Species number (tree and shrub species) 1 7. 5 (4. 5 -10. 5) a 4. 9 (1. 1 -8. 7) a 2 4. 9 (3. 1 -6. 7) a 6. 6 (4. 7 -8. 5) a 3 3. 8 (0. 5 -7) a 6. 2 (2. 7 -9. 7) a Dominance (all species) 1 0. 8 (0. 6 -0. 9) ab 0. 9 (0. 7 -1. 1) a 2 0. 5 (0. 4 -0. 6) a 0. 7 (0. 6 -0. 8) a 3 0. 5 (0. 3 -0. 7) b 0. 7 (0. 5 -0. 9) a Dominance (herbaceous species) 1 0. 8 (0. 7 -1) a 0. 9 (0. 7 -1. 1) a 2 0. 6 (0. 5 -0.
Acknowledgements This study was realised during a research project of  Sorbonne University and Institut de Recherche pour le Développement ; and was supported by the  ANR HeveAdapt project  (grant  ANR-14-CE030012-04 ) of the  French Agence Nationale de la Recherche. Fieldwork was realised with the cooperation of the  Huai Lang Royal Project Center  and the  Land Development Department of Thailand. We thank two anonymous reviewers for their thoughtful comments.
Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of stateof-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well characterized.
CO2 anthropogenic emissions from NGHGI UNFCCC NGHGI (2019) emissions are country estimates covering the period 1990-2017. The Annex-I parties to the UNFCCC are required to report emissions inventories annually using the Common Reporting Format 185 (CRF). This annual published dataset includes all CO2 emissions sources for those countries, and for most countries for the period 1990 to t-2. Some eastern European countries' submissions begin in the 1980s. Revisions are made on an irregular basis outside of the standard annual schedule.
190 CO2 fossil emissions occur when fossil carbon compounds are broken down via combustion or other forms of oxidation or via non-metal processes such as for cement production. Most of these fossil compounds are in the form of fossil fuels, such as coal, oil, and natural gas. Another category are fossil carbonates, such as calcium carbonate and magnesium carbonate, which are used as feed stocks in industrial processes, and whose decomposition also leads to emissions of CO2. Because CO2 fossil emissions are largely connected with energy, which is a closely tracked 195 commodity group, there is a wealth of underlying data that can be used for estimating emissions. However, differences in collection, treatment, interpretation and inclusion of various factors such as carbon contents and fractions of oxidized carbon, lead to methodological differences (Appendix A, Table  A1 ) resulting in differences of emissions between datasets  (Andrew 2020).
Bottom-up estimates by source category 295 While Figure  1  was made to assist explanation of differences between datasets disaggregated by sector (e. g. , energy industry, transport etc. ), in Figure  2  we present CO2 fossil emissions results from EU27+UK split by major source categories (solid, liquid, gas). As in  Andrew (2020) , we observe good agreement between all data sources and UNFCCC NGHGI (  2019 ) data at this level of regional aggregation. The figure presents estimates for the year 2014, as that was the most recent year when all sources reported estimates. BP 9 (2019), CEDS (v_2019_12_23), and 300 EDGAR 10  v5. 0 (2020) do not publish emissions split by fuel type at the country level and the latter two are shown as dark grey while the former is shown separating gas from liquid/solid.
While the datasets agree well, there are some differences. The EIA (2020) estimate is higher than others, largely because it includes international bunker fuels in liquid-fuel emissions. The IEA (2019) excludes a number of sources from non-energy use of fuels as well as all carbonates. GCP's total matches the NGHGIs exactly by design 305 but remaps some of the fossil fuels used in non-energy processes from 'Others' to the fuel types used. BP, CEDS, and EDGAR v5. 0 all report total emissions very similar to the UNFCCC NGHGI (2019).
375 We aim with this period to bring together all information over a five year period for which values are known in 2018. In fact this can be seen as a reference for what we can achieve in 2023, the year of the first global stocktake, where for most UN Parties the reported inventories will be compiled only up to the year 2021. Given that the GST is only repeated every 5 years, a five-year average is clearly of interest. The CO2 fluxes in Figure  4  include direct and indirect LULUCF on managed land. The total UNFCCC 380 estimates include the total LULUCF emissions and sinks (by the UNFCCC definition) belonging to all six IPCC land classes and HWP (see section 2. 3, Appendix B1, Figure  B1b ). We plot these and compare them with fluxes simulated with statistical global datasets, bookkeeping and biosphere models, sector-specific models and inversion model ensembles.
435 LULUCF and other (environmental changes) processes from class specific models as well as from models that simulate some or all classes. The definitions of the classes might differ from the definition of the LULUCF (FL, CL, GL etc. ) (Figures 6, 7 The Forest Land estimates, which remain in this class (FL-FL) in Figure  6 , were simulated with ecosystem models (CBM, ORCHIDEE, EFISCEN) (described in Appendix A2 and Appendix Table  B1 ), global datasets.
445 (FAOSTAT) and countries' official inventory statistics reported to UNFCCC. The results show that the differences between models are systematic, with CBM having slightly weaker sinks than EFISCEN and FAOSTAT. Starting with year 2000 and towards 2017, the FAOSTAT reports sinks that strengthen over time. Differences between estimates might be due to the use of different input data e. g. CBM and EFISCEN use national forest inventory (NFI) data as the main source of input to describe the current structure and composition of European forest, while FAOSTAT uses input 450 data directly from country submission done under FAO Global Forest Resource Assessments (FRA 15  ) (e. g. carbon stock change calculated by FAO directly from carbon stocks and area data submitted by countries directly). Furthermore, FAOSTAT numbers include afforestation, i.
495  FAOSTAT Forest Land area does not include only forest remaining forest but only the time series of FL, forest land. That means it includes afforestation but not deforestation. This area is based on the same land use/land cover maps, hence relying on net land use changes. From Figure  7  we see that modelled CL-CL inter-annual variabilities simulated by ECOSSE and EPIC-IIASA estimates are consistent, while ORCHIDEE shows a much larger year-to-year variation. The NGHGIs are mostly insensitive to inter-annual variability as the estimations are mainly based on statistical data for 515 surfaces/activities and EFs that do not vary with changing environmental conditions. The three process-based models report sinks in most years (means of -12, -49 and -23 Tg C respectively), contrary to the NGHGIs, which report a small but constant source over the whole period (mean of 5.
745 inversions. The CO2 fossil emissions dominate the anthropogenic CO2 flux in the EU27+UK. Fossil CO2 emissions are more straightforward to estimate than ecosystem fluxes. Different BU methods have only minor differences with respect to the NGHGI. These differences can often be attributed to different definitions or assumptions about activity data or emission factors or by the allocation of fuel types to different sectors (see Fig. 2 , section 3. 2). Currently, TD 750 methods, albeit only a single inversion using CO/NOx proxies to determine CO₂ fossil emissions, show broad agreement with the BU estimates. The TD inversion is not yet capable of verifying the minor differences between the BU estimates. However, a substantial decrease in the level of uncertainty is expected in the near-term with the largescale deployment of observation networks dedicated to detecting fossil fuel emissions (e.
755 assimilation of existing CO2 satellite data are also expected to lead to significant decreases in the uncertainty. The CO2 land fluxes belong to the LULUCF sector, which is one of the most uncertain sectors in UNFCCC reporting due in part to the fact that these fluxes can be either sinks or sources. The IPCC guidelines prescribe methodologies that are used to estimate the CO2 fluxes in the NGHGI, but differences between countries continue to exist due to the use of specific national circumstances (as permitted under the 2006 IPCC guidelines). When we 760 analyzed the estimates from multiple BU sources (inventories and models) we observe similar sources of uncertainties: (a) differences due to input data and structural/parametric uncertainty of models  (Houghton et al. , 2012)  and (b) differences in definitions  (Pongratz et al.
775 All observation-based BU estimates for LULUCF presented in this study show similar magnitudes and trends compared to the NGHGIs but generally differ in the specific values. We notice stronger similarities between NGHGIs and models using national forest inventory data (e,g. CBM, EFISCEN). For cropland and grassland sector-specific models (ECOSSE, EPIC-IIASA) the differences between their results and the NGHGIs are due to differences in input.
825 (ecosystem models, GHG emission models). Two complementary approaches relying on observational data-streams will be combined in VERIFY to quantify GHG fluxes: 1) atmospheric GHG concentrations from satellites and ground-based networks (top-down atmospheric inversion models) and 830 2) bottom-up activity data (e. g. fuel use and emission factors) and ecosystem measurements (bottom-up models). For CO2, a specific effort will be made to separate fossil fuel emissions from ecosystems fluxes. For CH4 and N2O, we will separate agricultural from fossil fuel and industrial emissions. Finally, trends in the budget of the three GHGs will be analysed in the context of NDC targets. The objectives of VERIFY are:.
865 Reporting Format (CRF). This annual published dataset includes all CO2 emissions sources for those countries, and for most countries for the period 1990 to t-2. Some eastern European countries' submissions begin in the 1980s. Revisions are made on an irregular basis outside of the standard annual schedule. For complete description see  Andrew, 2020. Uncertainties: Annex I parties quantitatively estimate their uncertainties of data used for all source and sink categories 870 using the methodologies provided in the 2006 IPCC Guidelines. The 2006 IPCC Guidelines stipulate that the determination of uncertainties is a key element of any complete inventory. Uncertainties are quantified for emission factors, activity data and, in some cases, for emissions. In general, two methods for determining uncertainties are differentiated.
CEDS 920 The Community Emissions Data System has included estimates of fossil CO2 emissions since 2018, with an irregular update cycle. Energy data are directly from IEA, but emissions are scaled to higher-priority sources, including national inventories. Almost all emissions sources are included and estimates are published for the period 1750-2014. Estimates are provided by sector. For complete description see Andrew R. M. , ESSD, 2020.
The Raymond et al. study only provides mean flux densities at the much coarser spatial resolution of the so-called COSCAT regions. All estimates were then downscaled to 0. 1° using the spatial distribution of European inland water bodies. Note that in contrast to  Hastie et al. (2019) , the areal distribution of lakes was extracted from the HYDROLAKES database  (Messager et al. , 2016) , to be consistent 1040 with the estimates of inland water N2O and CH4 presented by  Petrescu et al. , 2020  in review at ESSD. Uncertainty: Monte Carlo simulations were performed to constrain uncertainties resulting from both the pCO2 prediction equation and the choice of the k formulation.
EPIC-IIASA (croplands) The Environmental Policy Integrated Climate (EPIC) model is a field-scale process-based model 1090  (Izaurralde et al. , 2006; Williams, 1990)  which calculates, with a daily time step, crop growth and yield, hydrological, https://doi. org/10. 5194/essd-2020-376 Open Access Earth System Science Data Discussions Preprint.
ECOSSE (grasslands) ECOSSE is a biogeochemical model that is based on the carbon model ROTH-C  (Jenkinson and Rayner, 1977; Jenkinson et al. 1987; Coleman and Jenkinson, 1996)  and the nitrogen-model SUNDIAL  (Bradbury et al. 1993; Smith et al. 1996). All processes of the carbon and nitrogen dynamics are considered  (Smith et al. , 2010a,b). Additionally, in ECOSSE processes of minor relevance for mineral arable soils are implemented as well (e. g. methane 1120 emissions) to have a better representation of processes that are relevant for other soils (e. g. organic soils). ECOSSE can run in different modes and for different time steps. The two main modes are site specific and limited data. In the later version, basis assumptions/estimates for parameters can be provided by the model. This increases the uncertainty but makes ECOSSE a universal tool that can be applied for largescale simulations even if the data availability is limited.
For details see  Friedlingstein et al. , 2019. 1230 Top-down estimates at regional scales (up to 0. 25°x0. 25° resolution) for the period 2006 -2015 are taken from the six models used within EUROCOM  (Monteil et al. , 2019). These inversions make use of more than 30 atmospheric observing stations within Europe, including flask data and continuous observations. The CarboScope-Regional (CSR) inversion system (also included within the EUROCOM ensemble) was also run for the extended period 2006-2018 using four different settings: three network configurations using 15, 40, or 46 sites, and one using 1235 all 46 sites but a factor two larger prior error correlation length scale (200 instead of 100 km).
2019) and BU inventories from EDGAR v5. 0, CEDS, and PRIMAP. EDGAR v5. 0 reports the same sources as the UNFCCC, but CEDS reports emissions from Energy (1A+1B), IPPU and Waste up to 2014, and PRIMAP only for Energy and IPPU. All BU datasets show a good match for overlapping sectors, Energy and IPPU (Fig. 1, sum of sub-280 sectors 1A, 1B).