dataesr/scientific-paragraphs-categorization huggingface dataset:
Aggregation of fodder areas and harvests to categories. The six Eurostat fodder crops (Table As an estimate of cropland area in use, Lassaletta et al. 17  used the sum of harvested crop areas, except when this sum exceeded the total cropland area reported in the FAOSTAT database, in which case the FAOSTAT cropland area was used instead. The sum of harvested crop areas can exceed the FAOSTAT cropland area due to multicropping or intercropping. We followed the same approach as Lassaletta et al. , with the only difference that we used the sum of adjusted crop categories (henceforth called the crop area sum) rather than the sum of individual FAOSTAT crops. Some examples of the crop area sum compared to the FAOSTAT cropland are shown in Fig. 2. Permanent grassland areas, following Lassaletta et al.
17  , were taken from the FAOSTAT database ("Land under perm. meadows and pastures" in the land use dataset). These areas were used in estimates of the allocation of synthetic and manure N inputs to cropland (details below). The FAOSTAT land use data at the time of our study was only available until 2018. We considered two options for filling the data gap in 2019. Eurostat data on permanent grassland and cropland could be used, or the FAOSTAT data for 2018 could be extrapolated constant to 2019. In general, the two methods would produce very similar results. However, since the FAOSTAT and Eurostat data series on permanent grasslands in a few countries differ substantially, we chose to extrapolate the FAOSTAT land use data constant from 2018 to 2019 as it probably gives the most internally consistent results.
Temporary grassland areas were taken equal to the gap-filled crop category Temporary grassland (see Table  3 ). Assessing the accuracy of the estimates of cropland in use. Our approach may lead to an overestimate or an underestimate of actual cropland area in use. The crop area sum may overestimate the cropland in use if some areas are counted more than once due to multicropping or intercropping. This is the reason to set the FAOSTAT cropland area as an upper limit to cropland in use. The reported cropland area may also overestimate the cropland in use since it may include considerable areas of fallow land. In principle, a more direct estimate of cropland in use would be the reported cropland area minus fallow land, but this is not an option since available data on fallow land areas in the FAOSTAT and Eurostat databases are much too incomplete to cover the whole 1961-2019 period.
Instead, to test the accuracy of our estimates, we compared the crop area sum to cropland minus fallow land using data from the Eurostat database which has the most complete records of fallow land. The most common result of this test is that the areas match within a few percent (less than ± 5% difference in 75% of 971 country-years with data on fallow area). See figures in the data record  12  for details. We also compared the crop area sum to the FAOSTAT cropland area. The crop area sum exceeds the FAOSTAT cropland by at least 1% in 163 country-years, about 12% of all 1308 country-years. The only major exceedances, e. g. , more than 10% exceedance for at least 3 years, are in the 1960s and 1970s in Italy, Portugal, and Romania. See figures in the data record  12  for details. At least in Romania most of the difference is explained by inter-and multicropping of cereals with beans and squash or pumpkins which was a common but gradually decreasing practice during the 1960s and 1970s  80, 81.
Synthetic N fertilizer consumption. Time series of agricultural synthetic N fertilizer consumption in European countries are available in several international databases: • FAOSTAT has a dataset representing agricultural use of fertilizers, based primarily on data reported by countries. FAOSTAT fills data gaps using various imputation methods, e. g. , based on trade and production data  83. • Eurostat has a dataset representing agricultural use of fertilizers, based on data reported by countries  84. • Eurostat additionally publishes a dataset representing total national fertilizer sales, using data from Fertilizers Europe  85. • The International Fertilizer Association (IFA) publishes a dataset representing national consumption. It is based on sales data from the fertilizer industry, and gap-filled using production and trade data and N budgeting  86.
In principle, national sales of fertilizers is not equal to agricultural use because (1) stock changes between years can shift agricultural use compared to sales, and (2) sales also cover non-agricultural uses such as parks and lawns. However, due to lack of data, the international datasets of agricultural use do not systematically account for stock changes and non-agricultural use  83, 84. There is no immediately apparent way to judge which of these four datasets best represents the actual history of agricultural use. We therefore inspected and compared the four datasets to assess (1) to what extent they cover the studied time period, (2) whether the data seem consistent and plausible, and (3) how well the datasets agree with each other. The four datasets sometimes disagree considerably. By far, the longest and most complete datasets are provided by FAOSTAT and IFA.
While these two datasets are mostly smooth and consistent, in a few exceptional cases they exhibit implausible jumps in consumption (e. g. , by 50% or more) from one year to another. The FAOSTAT database has somewhat more such episodes, and moreover disagrees with the other three datasets more often than the others. While agreement between several datasets is no guarantee for their correctness, it seems likely that the majority vote of these partly independent estimates is the best estimate. The IFA dataset generally agrees well with the Fertilizers Europe dataset. The Fertilizers Europe dataset arguably exhibits the fewest implausible jumps but covers a shorter time period. Based on these we chose to use the Fertilizers Europe dataset  85  where possible, and in second place the IFA dataset  86.
These datasets together (1) have almost complete coverage of the country-years in this study, (2)  mostly appear consistent and plausible, and (3) usually agree well with a majority vote of the four datasets. We used FAOSTAT data for Finland 1961-1984 and Slovenia 1992-2006 because the IFA data appeared implausible. In addition, a small number of data gaps were filled using FAOSTAT data (Croatia 1992-1993, Czechia and Slovakia 1993) and Eurostat country consumption data (Belgium and Luxembourg 2000-2019). Figures showing the comparison of the four datasets and the selected data are available in the data record  12. Share of synthetic N fertilizer to cropland. Following Lassaletta et al. 17  we calculated the synthetic N fertilizer input to cropland by multiplying the total consumption by country-specific time series of the shares applied to cropland.
However, there are no datasets that enable comprehensive and reliable estimates of synthetic N fertilizer inputs on the level of individual crops or crop categories for the time period and countries covered in this study. As will be detailed below, pan-European crop-and-country-specific data on fertilizer inputs are available only for a few years between the 1990s and today. Further research on this topic would be valuable, and could use, for example, a combination of mass balances, statistical surveys, expert estimates, and crop-specific fertilizer recommendations from throughout the decades in different countries to establish plausible estimates of how synthetic N fertilizers have been allocated between crops. Such an exercise, however, could prove rather labor-intensive and moreover would necessarily imply a level of uncertainty which we have decided is not acceptable in the scope of this study.
Figures illustrating the final results are available in the data record  12. Data collection and interpretation. Three main categories of data were used. First, a few countries (France, Ireland, the Netherlands, and the United Kingdom) have carried out repeated national statistical surveys on grassland and cropland fertilization. Although each of these datasets have their idiosyncracies and required additional data processing (see below), we used them where possible since they are the most consistent and complete datasets available. Second, crop-specific fertilizer datasets from the fertilizer industry have been published in collaboration with FAO during the 1990s and early 2000s  [87] [88] [89]. In addition, a similar unpublished dataset from EFMA  90  (now Fertilizers Europe) gives crop-specific fertilizer rates for the crop year 2005/2006.
These datasets are extremely valuable since they cover all the countries in this study during at least one year. The crop-specific datasets were later aggregated and further processed as explained in the following paragraphs. Third and last, we collected a range of expert estimates and other data from various literature sources. This last category is the least dependable type of data, and we used it only after exhausting other possibilities. However, given the general and long-running scarcity of data on grassland fertilization  91  , there are in many cases no alternatives to expert estimates prior to the 1990s. For all these data sources, as far as possible, we followed the references to the original data source and collected the data from there. In each case the citations in this paper point to the publication from which we collected the data.
A significant complication in using the FAO/EFMA datasets  [87] [88] [89] [90]  is that the crop categorization for some countries includes temporary grassland under the "grassland" item and for other countries under another item, usually "fodder (other)". Since the final goal was to separate permanent grassland from cropland, it was necessary to work out what each "grassland" item refers to. This issue could usually be resolved since these datasets also report crop areas corresponding to the different fertilizer rates. To assign the appropriate land category to such items, we cross-checked the fertilized areas according to the FAO/EFMA datasets against the areas according to FAOSTAT/Eurostat, and thus could in most cases unambigously determine whether the "grassland" fertilizer rates referred to permanent (PG) or total grassland (PG + TG).
We used the FAO/EFMA fertilizer data only when the summed fertilizer quantities and areas matched the previously estimated country-level fertilizer quantities and land category areas. • Aggregation. Several data sources, including national statistical surveys and the FAO/EFMA datasets, report data for individual crops. In these cases, we assigned land category labels (C, PG, C-TG, etc. ) to the individual crops that together constitute such a land category. We then calculated, e. g. , the average application rate on cropland R C as an area-weighted average of the individual crop rates. Where possible, we assigned labels C and PG since this allows the direct calculation of the final result Q C /(Q C + Q PG ). Several datasets, however, only distinguish total grassland (PG + TG) from non-grass cropland (Cin these cases we later estimated permanent grassland rates from total grassland rates (see details below).
• Gap-filling. Several data sources only report rates, not areas or quantities. In such cases, when the land category was known, we used the previously collected area data as necessary to calculate quantities, e. g. , Q C = R C A C. Furthermore, when rates and areas were reported for fertilized grassland (e. g. , R PGf and A PGf ), we calculated the average rate on grassland using grassland areas from FAOSTAT/Eurostat (e. g. , R PG = R PGf A PGf /A PG ). Estimating the share of synthetic N fertilizer applied to cropland. In principle, the share of synthetic N applied to cropland can be calculated as Q C /Q tot using a Q C value from one of the above data sources and Q tot from the IFA/ FAOSTAT databases. Alternatively, if only Q PG is known, the quantity to cropland can be estimated as Q C = Q tot -Q PG.
Based on the FAO/EFMA data and other publications we concluded that the following countries have zero or negligible fertilizer rates to permanent grassland: Bulgaria, Croatia, Estonia, Finland, Greece, Hungary, Latvia, Lithuania, Portugal, Romania, Spain, and Sweden. These countries can conceptually be divided into two groups. One group is the countries in north-east Europe (Sweden, Finland, Estonia, Latvia, Lithuania) which all have substantial areas of temporary grassland. In Sweden and Finland, intensive grassland cultivation is a central part of agriculture but occurs almost exclusively on arable land  92, 93. In Estonia, Latvia, and Lithuania, the grassland cultivation practices have varied substantially through the decades; since 1992 much grassland has been abandoned or very extensively managed  57, 77, 94, 95  , and the FAO/ EFMA datasets  87, 90  suggest that if any synthetic N inputs have been applied to grassland, it has been to temporary grassland.
10 for Czechia and Slovakia in 1993 and extrapolated it to Czechoslovakia 1961-1992. Denmark. Denmark's grasslands are about equal parts temporary and permanent and both categories have been fertilized to varying degrees. Interpretation of the few available data is somewhat involved since some data refer to permanent grassland and others to total grassland. Specifically, three of the FAO/EFMA datasets  [88] [89] [90]  show that the rate ratio R PG /R C fell from about 0. 70 to 0. 17 between 1996 and 2006. An expert estimate from 1980  91  is that permanent grasslands were then fertilized with 150 kg N ha -1 y -1 which translates using Eq. (  5 ) to R PG /R C ≈ 1. 15. The remaining two data points, one for 1966  112  and one for 1993  87  , concern total grassland. To estimate the rate to permanent grassland in these cases we used Eq.
(  4 ) with k = 1. 5 since several data sources  [88] [89] [90] 112  suggest that temporary grasslands have on average received perhaps 50% higher synthetic N rates than permanent. France. France has large areas of both permanent and temporary grasslands which both have received synthetic fertilizer to varying degrees since the 1950s. This development has been quantified in several national surveys. We used rate ratios R PG /R C based on these national surveys as compiled by Le Noë et al. 5. Germany. According to the FAO/EFMA data for Germany  [87] [88] [89] [90]  , the rate ratio R PG /R C decreased from about 0. 8 in 1993 to 0. 6 in 2006. The two earliest FAO/EFMA datasets  87, 88  seemingly refer to total grassland and we therefore estimated R PG /R C using Eq. (  4 ) with k = 1; the estimation is insensitive to the exact value of k since only about 4% of the grassland was temporary at the time.
(  5 ). All the data concern grass vs. non-grass and we estimated the rate ratio R PG /R C using Eq. (  4 ) assuming k = 2 to reflect the higher average production intensity on temporary grassland  116, 118, 126. Italy. In Italy, the FAO/EFMA datasets  [87] [88] [89] [90]  show that the rate ratio R PG /R C has been fairly stable in the period 1993-2006. We extrapolated the average rate ratio R PG /R C ≈ 0. 13 to the whole period 1961-2019. Netherlands. In the Netherlands, synthetic N inputs to grassland increased from some 20 kg N ha -1 y -1 in the 1940s to a peak above 250 kg N ha -1 y -1 in the 1980s before decreasing to about 150 kg N ha -1 y -1 during the 2000s. This development can be fairly well quantified by combining several data sources. For the period 1980-2008, we used data compiled for the Dutch National Emission Model for Agriculture  127  to calculate the rate ratio R PG+TG /R C-TG.
In 1970, a combination of expert estimates  128  and a national survey on grassland fertilization  129  show that R PG+TG /R C-TG ≈ 1. 5. Before 1970, based on grassland fertilizer rates R PG+TG from several sources  [129] [130] [131] [132] [133] [134]  and using Eq. (  5 ) we estimated a linear increase from R PG+TG /R C-TG = 1 in 1961. We then estimated R PG /R C using Eq. (  4 ) with k = 1 since the data do not show any clear differences in fertilizer rates between temporary and permanent grassland  [87] [88] [89] 127. Poland. In the period 1993-2006, the FAO/EFMA datasets  87, 89, 90  show a roughly constant rate ratio R PG /R C ≈ 0. 6. Before the 1990s we have not found quantitative data, although an increasing trend of synthetic N inputs on permanent grassland was noted already in 1965  62  and permanent grassland productivity then increased along with overall synthetic N inputs into the 1970s and 1980s  135.
We therefore extrapolated the average 1993-2006 rate ratio to the whole period 1961-2019. Slovenia. Slovenian grasslands are predominantly permanent. In the period 2006-2012, national statistics 136 show a stable rate ratio R PG /R C ≈ 0. 37 which we extrapolated to the whole period 1992-2019. United Kingdom. The United Kingdom has Europe's longest and most complete dataset of synthetic fertilizer inputs to grassland and crops. Partial surveys of England and Wales started in the early 1940s  137  and have continued in various forms ever since  [138] [139] [140] [141]. Annual time series of fertilizer use on permanent and temporary grassland as well as other crops are available for England and Wales since 1969 and for Great Britain since 1982. Northern Ireland is not included in the annual surveys.
However, for the historical perspective taken here, data are lacking for all but the most basic of the determining parameters. Therefore, in the global study by Lassaletta et al. 17  , a generic 30% loss rate was assumed between excretion and field application. In this European study, we instead use constant country-specific estimates of N loss shares in housing and storage in year 2000 established by Oenema et al. 172. This dataset covers the former EU27 countries, i. e. , all the countries except Croatia, for which we assumed the same loss rate as for Slovenia. For Czechoslovakia and Belgium-Luxembourg, we calculated excretion-weighted averages from their present-day constituents (back-calculated from the per-hectare rates of the paper using the Eurostat Utilised Agricultural Area). The N loss shares estimated for year 2000 by Oenema et al.
However, in all but three countries, the "Other" category has a minor share and we ignored the mismatch, calculating a partition between liquid and solid in proportion to their reported shares (i. e, so that they add up to 100%). In three countries, the "Other" category accounted for more than 20% of in-house N excretion: Hungary (23%), Ireland (99%), and Spain (33%). For these countries we instead compiled data on liquid and solid shares from official country reports to the Convention on Long-Range Transboundary Air Pollution (CLRTAP)  [176] [177] [178]. Having established the liquid/solid division for cattle, we calculated the average 1997-2001 shares of stored manure applied on grassland and non-grassland. For the countries not included in the land allocation dataset, we extrapolated grass/non-grass shares from other countries: from Austria to Slovenia; from Lithuania to Estonia and Latvia; and from Slovakia to Bulgaria, Croatia, and Romania.
technical Validation The dataset presented in this paper builds on careful inspection and cross-comparison of multiple data sources. To the extent possible, all calculation steps have been automated using Python scripts, but the basis of the work has consistently been visual inspection and selection of the most appropriate data from available alternatives. The method section above describes in detail the steps we have taken to guarantee the consistency, completeness, and accuracy of the results. On a more general level, the technical validation in this study can be described as standing on three pillars: 1. Internal consistency has been a guiding principle throughout the process. We have carefully checked or defined variables to achieve internal consistency, e. g. , agreement of total fertilizer consumption and application to cropland and permanent grassland; agreement of crop category harvest = area × yield; etc.
2. Cross-checking of data against multiple independent sources where possible, e. g. , only using crop-specific fertilizer application data if totals agree with national totals; cross-checking cropland and grassland areas between FAOSTAT and Eurostat databases; comparing and hand-selecting fertilizer quantities and crop area data from multiple sources, etc. 3. Detailed scrutiny of visualizations of all included data and of intermediate and final results of calculations. This approach has been invaluable to identify and address data gaps and errors. The text of the method section as well as the figures in the data record  12  give concrete details on key aspects of this work.
Acknowledgements We are extremely grateful for the continuous efforts of many organizations and individuals collaborating to collect and organize high-quality information on agricultural systems: FAOSTAT,  Eurostat ,  IFA ,  Fertilizers Europe ,  UNFCCC , CLRTAP, national statistical authorities, government agencies, and research institutions. This work was conceived and initiated during a pre-doctoral stay of  Rasmus Einarsson  at  CEIGRAM  ( Universidad Politécnica de Madrid ). Rasmus Einarsson acknowledges generous funding from  Göteborgs Handelskompanis deposition , which guaranteed the freedom needed to carry out this work. Alberto Sanz-Cobena  gratefully acknowledges the  Autonomous Community of Madrid  and  UPM  for their economic support through the research project  APOYOJOVENES-NFW8ZQ-42-XE8B5K.
TG Used as a subscript to denote cropland excluding temporary grassland, e. g. , A C-TG. CLRTAP Convention on Long-Range Transboundary Air Pollution EFMA The European Fertilizer Manufacturers Association. (Today known as Fertilizers Europe. ) EU27 Used here to refer to the former European Union 2007-2013, having 27 member states (before Croatia joined to form EU28). EU28 The European Union 2013-2020, with 28 member states. EuropeAgriDB The name of the dataset described in this study. Eurostat The statistics office of the EU. FAO Food and Agriculture Organization of the United Nations. FAOSTAT FAO's statistics organization. FSS Farm Structure Survey, Eurostat's triennial survey of agriculture in the EU. G0000 Eurostat crop code "Plants harvested green from arable land" G1000 Eurostat crop code "Temporary grasses and grazings" G2000 Eurostat crop code "Leguminous plants harvested green" G2100 Eurostat crop code "Lucerne" (alfalfa) G2900 Eurostat crop code "Other leguminous plants harvested green n.
e. c. " TG Used as a subscript to denote temporary grassland, e. g. , A TG. TGf Used as a subscript to denote fertilized temporary grassland, e. g. , A TGf. UNFCCC United Nations Framework Convention on Climate Change Y Variable name referring to crop N yields expressed, e. g. , in kg N/ha/yr. AVariable name referring to crop or land areas, e. g. , A C for cropland area. ACS Annual Crop Statistics (Eurostat dataset). C Used as a subscript to denote cropland, e. g. , A C. C -.
Study design We performed a retrospective, observational, multicentre cohort study analysis using the data from the French National OHCA Registry (ReAC) collected from July 2011 to July 2018. This cohort includes all OHCA patients managed by a physician-staffed mobile intensive care unit (MICU) in France. MICUs consist of an ambulance driver, a nurse and a trained emergency physician experienced in airway management and tracheal intubation as a minimum team. A detailed description of the French emergency medical system (EMS) has been previously published. 19 Briefly, it is a two-tiered system with a fire department ambulance or private ambulance available for prompt intervention and basic life support (BLS), and MICU for advanced life support (ALS) on scene. 20 Importantly, BLS providers are not able to provide advanced airway management (supraglottic airway [SGA] or ETI).