dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Sampling Data on marathon results were obtained through the publicly available database, accessible through the website (http://www. marathonguide. com/results/). This database represents the largest marathon database in the world. Data on UER events were obtained from a publicly available database, accessible through the website of the Deutsche Ultramarathon Vereinigung (DUV) at: https://statistik. d-u-v. org/geteventlist. php. The DUV contains the largest dataset on UER worldwide and has been frequently evaluated within the scientific literature  [10, 15, 16]. Data on race events, race location, race distance, finishing numbers, race time, sex, and nationality were, when available and accessible, analyzed for the time period since the declaration of the COVID-19 pandemic by the WHO in March 2020 [2] until the end of data sampling in October 2020 COVID-19 pandemic period (March 2020-October 2020) and compared to the same time period in the prior year, called the pre-pandemic period (March 2019-October 2019).
Data Analysis Kolmogorov-Smirnov test was applied to test for normality. Descriptive analysis was performed and presented as mean and relative (%) frequency and change. Mean marathon finishing times and age were also presented with standard deviations (SD). An independent t-test was used to test the differences between groups and Mann-Whitney test for not normally distributed data (pre-pandemic vs. pandemic). Statistical significance was set at 5% (p < 0. 05). All analyses were carried out using the Python programming language (Python Software Foundation, https://www. python. org/), Google Colab notebook, and the Statistical Software for the Social Sciences (IBM SPSS v26. Chicago, IL, USA).
Results Data and results for endurance (marathon) running are available in Table  1. The number of marathon finishes according to sexes with monthly breakdowns and percentage change during the COVID-19 pandemic was compared to the pre-pandemic period and is shown in Table  1. Monthly breakdowns were used to demonstrate the evolution of the pandemic, as at different time points, different lockdown restrictions applied throughout the world. A 10. 8-fold drop could be observed in total finishes pre-to pandemic times, with almost no finishes during April/May 2020. Table  1. Data on number of marathon finishes according to sexes with monthly breakdowns and percentage change during the time period of the start of the COVID pandemic (March 2020) until the end of the observation period (October 2020) and comparison to the same time period pre-COVID in 2019 (March-October 2019).
The number of endurance events dropped significantly pre-pandemic to the COVID-19 pandemic period from 213 to 61 races in the database (p < 0. 001), an approximately 3. 5-fold drop. Most events were held in United States (pre-pandemic vs. pandemic 61. 6% vs. 72. 7%), United Kingdom (10. 7% vs. 11. 4%), and Canada (10. 0% vs. 4. 5%), and the majority of finishes originated from the United States (pre-pandemic vs. pandemic 72. 0% vs. 93. 5%). The ratio between finishes per event dropped from 2. 155 pre-pandemic to 700 finishers/event during the COVID-19 pandemic (p < 0. 001), suggesting that events were smaller during the pandemic, as less finishes were observed.
This is something that could be expected, as with national and local lockdowns, restrictions, and bans on travel, very little movement occurred during these time periods  [1, 3]. Overall finishes in endurance running decreased almost 11-fold, while UER finishes decreased 5-fold during the observation period of the pandemic. One explanation may be that endurance running is generally more popular than UER and that the UER community tends to be smaller. Perhaps participation continued in smaller, more local races, as demonstrated by our data that showed that approximately 50 finishes/event in UER were observed compared to 700 finishes/endurance event, although the percentage drop in events for endurance and UER were very similar. Additionally, the number of events in the endurance running database was comparatively smaller than for UER events, which further may add to this.
Limitations Of the two publicly available databases used for this analysis, the marathon results database (http://www. marathonguide. com/results/) is the largest database of marathon results in the world, fully searchable by name, place, or time. However, the majority of data pertain to races held in the United States, Canada, Australia, and New Zealand, and we recognize this as a limiting factor for applicability on races worldwide. The DUV database (https://statistik. d-u-v. org/geteventlist. php) is the largest database worldwide of UER events and has been widely used in the past in the scientific literature  [10, 15, 21]. However, as with any large database, not all results may be complete, and we recognize that this as a limiting factor. Nevertheless, the aim of the study was to examine the impact of COVID-19 on endurance and UER, and both databases provide a sufficiently sizable data sample for analyses.
Methods Identification of putative S-RNase like sequences in Rosa genomes. The annotations (CDS) of two R. chinensis genomes (available at NCBI RefSeq database (www. ncbi. nlm. nih. gov) and GDR database (https ://www. rosac eae. org) Supplementary Table  S1 ) were downloaded, and the sequences showing similarity with reference S-RNases assigned as Rchinensis1 and Rchinensis3, respectively. Moreover, the corresponding genome sequences were downloaded as a FASTA file, and the S-RNase sequences here annotated labeled as Rchinensis2 Figure  6. Positively selected amino acid sites on the predicted 3D structure of the R. chinensis F-box located in the 5` region of the S-RNase. In yellow are highlighted those sites that appear as positively selected in the two datasets analyzed (14 and 13 R. chinensis F-box genes in the vicinity of the S-RNase that are expressed in stamen), in green those that are located in a region not analyzed when 14 F-box genes are considered, and in blue those that change due to alignment gaps in the two datasets analyzed.
and Rchinensis4, respectively. R. multiflora genome (NCBI assembly database, Supplementary Table  S1 ) was also downloaded as a FASTA file, since annotations are not available for this species. To find and extract protein encoding segments larger than 100 bp, we have used getorf, using the emboss Docker image available at pegi3s Bioinformatics Docker images project (htttps:pegi3s. github. io/dockerfiles). Then, we selected the protein encoding segments that show similarity with reference S-RNase sequences, using tblastx (Expect value (e) < 0. 05), as implemented in SEDA  58, 59  , using as query Prunus S3-RNase (AJ298312), Malus Sh-RNase (AB032247), and Fragaria putative S-RNase (gi561957436, gi561674690 and gi561985884)  24. Based on this information, we manually annotated the corresponding genome region to identify the exons of each gene.
For each putative gene we obtained the corresponding amino acid sequence to calculate IP, using ExPASy software  60. We used the same protocol for the genomes here assembled using the short reads of nine Rosa genomes downloaded from NCBI SRA database (Supplementary Table  S1 ). In this case, we used FastQC to evaluate read quality, and Cutadapt to trim reads  61  , and ABySS 2. 0  62  for the de novo assembly, using the Docker images available at pegi3s Bioinformatics Docker images project (htttps://pegi3s. github. io/dockerfiles). Identification of the F-box genes located in R. chinensis chromosome 3 and R. multiflora scaffolds where the S-RNase is located. The protocol presented for the S-RNase like sequences was also used to obtain F-box genes located in R. chinensis chromosome 3, and for the two R.
Such analyses where also performed for the 14 R. chinensis F-box sequences that are in the vicinity of the S-RNase, that cluster with Prunus SLFL, and that are expressed in stamen. Since the inclusion of Rchinensis_Fbox-3 gene sequence excludes from the analyses a large fraction of the 3´region, we performed these analyses after removing this sequence. codeML analyses were also performed for five R. multiflora sc0006888 F-box sequences in the vicinity of the S-RNase. The details of the analyses can be seen at the B + database (bpositive. i3s. up. pt  68  ; Rosa S-locus genes BP2018000004). Model comparisons were M2a-M1a and M8-M7. We consider as.
Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS -"Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC Version 1. 0 at 0. 1°resolution for Europe (Hoffmann et al. , 2021c, b). The plant functional type distribution for the year 2015 (i.
e. LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method based on a cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (submitted). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0. 25 • resolution as input for CMIP6 experiments, to derive realistic LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950.
dataset for RCMs. Reinhart et al. (submitted)  developed a workflow to convert the ESA-CCI LC land cover classes into plant functional types (PFTs) as well as non-vegetated classes such as urban and bare ground for the European domain. The resulting LANDMATE PFT dataset (Version 1. 0)  (Reinhart et al. , 2021b)  shows a good agreement with ground truth observations and thus provides the initial land cover map for the LUT approach. In this study, we introduce the new high-resolution LUCAS LUC historical/future land use and land cover change dataset (Version 1. 0)  (Hoffmann et al. , 2021c, b) , which we prepared to meet the requirements for the next generation RCM simulations for downscaling CMIP6 by the EURO-CORDEX community and in the framework of FPS LUCAS. 2 Generating the LUCAS LUC Dataset.
Workflow The workflow to generate the LUCAS LUC dataset is shown in Fig. 1. It starts with the generation of a PFT based map based on the ESA-CCI LC dataset, the so-called LANDMATE PFT dataset (Sect. 2. 2. 1). The methods and datasets used to create this dataset are described in the companion paper by  Reinhart et al. (2021a). Therefore, only a short description of the basemap development is given in this paper. First, the ESA-CCI LC map for the year 2015, which has a native resolution of ∼300 m globally, is aggregated (global 0. 1 • ). Thereafter, the aggregated ESA-CCI LC map is converted into a set of PFTs. For the conversion of the ESA-CCI LC land cover classes into PFTs, a cross-walking procedure is commonly applied  (Wilhelm et al. , 2014; Li et al. , 2018; Georgievski and Hagemann, 2019; Lurton et al.
, 2020; Reinhart et al. , submitted). Therefore, for each ESA-CCI LC land cover class we set-up a so-called cross-walking table  (CWT) , which defines the composition of this class in terms of PFT fractions. The CWTs are further refined based on climate zones, defined through the Holdridge Life Zones (HLZ;  Wilhelm et al. , 2014). The HLZ concept proposes a global classification of climatic zones in relation to potential vegetation cover dependent on mean annual precipitation data and mean monthly temperature  (Holdridge, 1967). The HLZs are computed from atmospheric observations of temperature and precipitation taken from the E-OBS dataset (within Europe) and the CRU dataset (outside of Europe). Using the higher resolved E-OBS dataset (0. 1 • resolution) instead of the rather coarse CRU dataset (0.
5 • resolution), as it was used by  Wilhelm et al. (2014) , allows for a more detailed representation of HLZs especially in regions with complex terrain. The distribution of C3 and C4 grasses within grassland areas, distinguished by some LSMs, is taken from a separate potential C4 map provided by the North American Carbon Program (NACP) for the  Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The information needed to compute changes in the PFT distribution is taken from the LUH2 dataset (Sect. 2. 2. 3), which provides land-use states and -transitions and land management information at a global 0. 25 • grid from 850 to  2015 (reconstructed)  and from 2016 until 2100 (projected). The land-use classes and subsequently the land-use transitions mainly represent land used by humans through agriculture, livestock farming, forest management etc.
and only distinguish between forested and nonforested vegetation. Thus, they cannot directly be imposed onto the LANDMATE PFTs, which represent mainly the physical land cover. Instead, the LUH2 land-use transitions are translated into annual changes in PFT fractions for the historical period starting 2015 and going back until 1950 using the newly developed land use translator (LUT; Sect. 2. 3). While LUH2 provides transitions of forest vegetation, historical changes in the forest type distributions are taken from a European forest area and species composition dataset provided by  McGrath et al. (2015)  (Sect. 2. 2. 2). By employing the LUT forward in time, the future annual PFT changes are computed from the eight different land-use change scenarios provided by LUH2. In special cases, where a certain vegetation type is not present within a grid cell but should be increased according to the LUH2 and the rules provided by the LUT, a background map of potential vegetation is needed.
This map is constructed from the ESA-CCI LC dataset as well as the CWT used for the LANDMATE PFTs (Sect. 2. 2. 1). The final LUCAS LUC dataset consists of one file containing the annual PFT maps for the historical period from 1950 until 2015  (Hoffmann et al. , 2021c)  and eight different files for the land-use change scenarios for the future period from 2016 to 2100  (Hoffmann et al. , 2021b). For the comparison of the historic land cover changes the ESA-CCI LC based PFT time series and the MODIS PFT dataset are employed (Sect. 3. 1).
LANDMATE PFT dataset Version 1. 0 The LANDMATE PFT dataset Version 1. 0 for the year 2015 is used as a basemap from which the land cover changes are computed with the LUT (Sect. 2. 3). Background maps, required for the LUT, are generated depending on the HLZ for the grass, shrub, and forest PFTs, respectively, based on the CWTs described in  Reinhart et al. (submitted). For example, the background map for the forest PFT group consists of forest PFT fractions that are most likely to grow given the HLZ for each land point respectively. If applied to other regions, this map would need to be adjusted for the dominant vegetation cover of the region, e. g. , for Australia, where temperate broadleaf evergreen forest is one of the dominant forest types. The distribution of the PFTs in the LANDMATE PFT dataset (major PFT class per 0.
European forest area and species composition The dataset from  McGrath et al. (2015)  provides tree species composition on a 0. 25 • grid for Europe from 1600 to 2010, taking into account the conversion of tree species due to forest management. The forest area was reconstructed based on the tree species maps of  Brus et al. (2012) , the land cover map of  Poulter et al. (2015) , and the historical land use maps of  Kaplan et al. (2012 Kaplan et al. ( , 2009)). A detailed description of the tree species dataset is given by  Naudts et al. (2016). While in  Naudts et al. (2016)  Larix sp. (the only deciduous coniferous species present in the dataset) is listed as a tree species, the fractions for this species are zero in the dataset. Consequently, no additional information for the time evolution is available for the deciduous coniferous PFT.
Land-use harmonized dataset version 2 (LUH2) The Land-use harmonized dataset version 2 (LUH2;  Hurtt et al. , 2020)   Future land-use changes are based on the output of different integrated assessment models (IAM) for selected marker SSP-RCP scenarios of which the most imporatnt characteristics are summarized in Table  A1. The Global Land Use Model (GLM2;  Hurtt et al. , 2006 Hurtt et al. , , 2011) )  is employed to translate the LUC information into fractional changes of the land use classes for each 0. 25 • x 0. 25 • grid cell using additional datasets and assumptions as constrains. For three scenarios (i. e. SSP1/RCP1. 9, SSP1/RCP2. 6, and SSP2/RCP45) an additional dataset is provided, which takes into account the future forestation that is present in these scenarios but was not captured in the initial LUH2 dataset (LUH2;  Hurtt et al.
, 2020). The dataset contains the variable "added tree cover", which is the fraction of the 0. 25 • x 0. 25 • grid cell that should be converted from non-forested vegetation to forested vegetation to obtain the correct proportion of future forest cover. et al. , 2015; Naudts et al. , 2016)  to the tree PFTs. Please note that  Naudts et al. (2016)  distinguished the needleleaf species in temperate and boreal, even if they are the same species.
Translating land use changes to PFT changes In order to convert the land use change information given by LUH2 into PFT changes, an algorithm with a fixed set of transition 165 rules is developed (Tables  4  and 5 ). In a first step LUH2 classes are grouped into main land use classes, denoted here as LUT classes (Table  3 ): Crops (CRO), forest (FOR), non-forest vegetation (NFV), rangeland (RAN), pasture (PAS), and urban (URB). For these LUT classes the transitions provided by the LUH2 dataset are aggregated. The aggregated transitions are bilinearly interpolated for the 0. 25 • grid to the 0. 1 • grid also used for the PFT maps derived from ESA-CCI LC (i. e. LANDMATE PFT dataset; Sect. 2. 2. 1), which represent the land cover distribution for the year 2015. The transition rules are defined to ensure that the changes in cropland are as close to the LUH2 changes as possible.
For example, during the transition of forest to cropland (FOR2CRO in table  4 ) the LUT checks if enough forest is available. If this is not the case shrub PFTs are reduced and in a subsequent step also the grass PFTs, given that the sum of forest and shrub PFTs is still smaller than the transitions. The reduction of a PFT group is done until its fraction is zero. For each transition in tables 4 and 5, the relative PFT-fractions remain constant within each PFT group (Table  1 ). For example, an increase in non-forest vegetation would lead to an increase in all shrub and grass PFTs that are present within a grid cell. If a PFT-fraction is not present in a certain grid cell but is supposed to increase, the PFT-fractions are taken from the corresponding background map (Sect. 2. 2. 1). Bare ground and swamps remain unchanged because there is no information about bare ground or wetland changes in the LUH2 dataset and there is no additional information available that could justify a conversion of bare ground or wetlands to vegetation or crops, or any other land cover type, or vice versa.
Accounting for historical forest type distribution For the backward translation, additional information on the relative distribution of broad-and needleleaf forest taken from the McGrath dataset is employed (Sect. 2. 2. 2). To avoid the altering of the basemap derived from ESA-CCI LC the relative fractions of three tree types (Temperate broadleaf evergreen, Temperate deciduous trees, Evergreen coniferous trees) are not directly imposed onto the PFT maps. Instead, only the trend in the relative fraction is used. For every timestep the difference in the relative fractions of the three PFTs are computed. These relative fraction changes are then converted into fraction changes of the individual PFTs by multiplying the relative fraction changes with the sum of the three PFTs.
Adding tree cover for future scenarios For the three scenarios SSP1/RCP1. 9, SSP1/RCP2. 6, and SSP5/RCP3. 4OS, respectively, an additional transition is computed because the afforestation signal is not correctly captured in the LUH2 land-use transitions. After the computation of transitions provided by LUH2 projections, the forest PFTs are increased by employing the added tree cover data (Sect. 2. 2. 3). Here, the same rules as for the transitions from forested vegetation to non-forested vegetation in the backward translation are applied (FOR2NFV in table 5) increasing forest PFTs and reducing non-forested PFTs, starting with shrub PFTs and if their fraction is reduced to zero, grass PFTs.
Comparison with existing PFT-based LULCC datasets The historical trends in the LUCAS LUC PFTs are compared to the trends in the ESA-CCI based PFT time series (i. e. ESA POULTER, Sect. 3. 1. 1) and the MODIS PFT time series (Sect. 3. 1. 2). The PFTs are aggregated to the land cover groups cropland, grassland, forest, and urban according to table 6. In addition, the LUH2 land use states (Sect. 2. 2. 3) are used for the comparison. Note that for this purpose the LUH2 land use class pasture is assigned to grassland because LUH2 does not provide     (2015). In addition to adjustments of the LULC class translation, an urban-PFT is added (Table  A3 ). The resolution of the aggregated PFT maps can be chosen flexibly as required by the user. For the comparison with the LUCAS LUC PFT maps, the PFT maps, denoted as ESA POULTER in the following, are aggregated to a horizontal resolution of 0.
Historical PFT time series based on MODIS The Collection 6 Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) land cover datasets (C6 MCD12Q1) provide ready to use PFT maps as one of their 13 science data sets. For C6 MCD12Q1, several processing steps were refined to eliminate known issues from the MODIS Collection 5 data sets, such as the excessively high interannual variability  (Abercrombie and Friedl, 2015). Additional information on the MODIS data processing can be found at https://lpdaac. usgs. gov/products/mcd12q1v006/. The annual maps are available globally from 2001-2018 in ∼500 m horizontal resolution. The 12 MODIS PFTs follow the PFT definition that was developed for the National Center for Atmospheric Research land surface model (NCAR LSM)  (Bonan et al.
, 2002). Table  A2  shows the 12 PFTs including their description. The 13 science datasets are provided having six different LULC classifications, including the PFT classification. All LULC classification are generated through employment of a supervised classification algorithm (Sulla-Menashe and Friedl, 2018). For the comparison with the LUCAS LUC dataset, the MODIS PFT maps are aggregated to 0. 1 • horizontal resolution.