dataesr/scientific-paragraphs-categorization huggingface dataset:
Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha -1 h -1 yr -1 , with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone. Given the growing concerns about climate change, climatic data is particularly important for the scientific community and society in general, as decisions of individuals, business and governments are dependent on available meteorological data 1. At present, a large number of the large-scale precipitation datasets are publicly available, although formats and completeness of the records vary widely 2.
Since soil erosion is difficult to measure at large scales, models are required for estimating soil loss by water erosion at regional, national and continental scales. Large scale and global model predictions are of utmost importance, since soil erosion is, in addition to soil sealing, the major threat to soil sustainability and consequently to water-and food security. As a consequence, recent developments in the soil modelling and climate change communities aim at addressing major scientific gaps in describing key soil processes such as water erosion  9  , based on updated global datasets. Nonetheless, policy initiatives such as the United Nations Convention to Combat Desertification (UNCCD) for zero net land degradation  10  , the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the Land Degradation and Restoration Assessment  11  , and the Food Agricultural Organisation (FAO) World Soil Resources  12  still remarked the lack of an updated scientific dataset on global soil erosion.
A vital component of such a global soil erosion map is a spatial assessment of rainfall erosivity. However, methods that estimate erosivity based on annual rainfall data are problematic and highly biased, since rainfall intensity is typically not considered  13. In order to include rainfall intensity on the calculation of rainfall erosivity, it is necessary to have high temporal resolution rainfall data for long time periods. In this study, we aim to tackle the challenging task of compiling and processing the first global erosivity dataset from long-term high-resolution rainfall data using sub-hourly and hourly pluviographic records. We have used this global erosivity station dataset combined with a set of exhaustive secondary environment variables to generate a global rainfall erosivity map in order to improve our understanding of the global patterns of high intensity rainfall events.
Results Global Rainfall Erosivity Database -GloREDa. At global scale, this is the first time ever that an erosivity database of such dimension is compiled. The Global Rainfall Erosivity Database, named hereafter as GloREDa, contains erosivity values estimated as R-factors (refer to the method section) from 3,625 stations distributed in 63 countries worldwide. This is the result of an extensive data collection of high temporal resolution rainfall data from the maximum possible number of countries in order to have a representative sample across different climatic and geographic gradients. GloREDa has three components, which are described in the methods section: a) the Rainfall Erosivity database at European Scale (REDES) 14 b) 1,865 stations from 23 countries outside Europe and c) 85 stations collected from a literature review.
The number of GloREDa stations varied greatly among continents (Fig. 1 ). Europe had the largest contribution to the dataset, with 1,725 stations (48% of total), while South America had the lowest number of stations (141 stations or ~4% of total). Africa has very low density of GloREDa stations (5% of the total). In North America and the Caribbean, we collected erosivity values from 146 stations located in 6 countries (Unites States, Canada, Mexico, Cuba, Jamaica and Costa Rica). Finally, Asia and the Middle East were well represented in GloREDa, with 1,220 stations (34% of the total) distributed in 10 countries including the Siberian part of the Russian Federation (Fig. 1b ). The geographic distribution within each continent also differed substantially. For instance, stations in Europe, Oceania and North America covered most of the territory, while those in Africa and South America were largely clustered.
Global erosivity map. The Gaussian Process Regression (GPR) model used to interpolate the erosivity (R-factor) point values to a map showed a good performance for the cross-validation dataset [R 2 = 0. 722, RMSE (Root Mean Square Error) = 1,629 MJ mm ha -1 h -1 yr -1 ]. The annual global erosivity map (Fig. 2 ) is presented at 30 arc-seconds (~1 km) spatial resolution and subdivided in 10 erosivity classes corresponding to the quantiles. The mean of the global R-factor map is 2,190 MJ mm ha -1 h -1 yr -1 with high variability as expressed by the standard deviation of 2,974 MJ mm ha -1 h -1 yr -1. The median (50 th percentile) of the global erosivity map is 1,150 MJ mm ha -1 h -1 yr -1 while 20% of the erosivity values (20 th percentile) are lower than 200 MJ mm ha -1 h -1 yr -1 and the highest 20% (80 th percentile) are higher than 5,200 MJ mm ha -1 h -1 yr -1 (Fig.
2 ). According to the global erosivity map, the highest values are located in south-eastern Asia (Cambodia, Indonesia, Malaysia, the Philippines and Bangladesh), Central Africa (Congo and Cameroon), South America (Brazil, Colombia and Peru), Central America and the Caribbean. The lowest erosivity is mainly located in Siberia, the Middle East, Northern Africa, Canada and Northern Europe. The Polar Regions have been masked out in the global erosivity map. We found that the spatial patterns of the highest erosivity values (Fig. 2 ) are coincident with the corresponding patterns of extreme rainfall events reported by Zipser et al. 14. Zipser et al. 14  defined intense storms based on the convective vertical velocity of rain. The authors compiled a 7-year period (1998-2004) database of intense storms and they generated global maps of extreme rainstorm events based on lightening flash, brightness, temperature and noise.
In South America, Chile has the lowest R-factor with a mean annual value 1,320 MJ mm ha -1 h -1 yr -1 , followed by Argentina (2,232 MJ mm ha -1 h -1 yr -1 ). The rest of the South American countries have high mean erosivity values (>3,700 MJ mm ha -1 h -1 yr -1 ), with the highest ones in Brazil, Colombia and Ecuador (>7,000 MJ mm ha -1 h -1 yr -1 ). The erosivity gradient created by the Andes is clearly visible in the erosivity map. There were few national assessments on rainfall erosivity in south America  [21] [22] [23] [24]. Most of the data used for these studies have been used as input for GloREDa and their spatial patterns are in broad agreement to ours. In North America and the Caribbean, the mean R-factor is 1,409 MJ mm ha -1 h -1 yr -1 with very low values in Canada and the Northern part of the United States, and extremely high values (>8,000 MJ mm ha -1 h -1 yr -1 ) along the Gulf of Mexico and the Caribbean countries.
Discussion The increasing availability of rainfall data with high temporal resolution, the growing computing power, and the development of sophisticated geostatistical models, enabled the development of a global rainfall erosivity dataset at 30 arc-seconds (~1 km) spatial resolution. We acknowledge that this achievement was only feasible through the scientific cooperation between scholars from all over the globe. The global erosivity map was possible thanks to the contribution of data providers (see the long list of meteorological services, organisations, and institutions in the Acknowledgements section), tested methodologies and geostatistical models suitable for such a scale. Comparison with past studies. Compared to previous works on global R-factor estimation  [30] [31] [32]  , our study presents a data-driven approach including measured hourly and sub-hourly data on rainfall intensity for erosivity assessment.
As 12% of GloREDa includes stations with less than 10-years measurements, we recognize that this may cause a bias due to the temporal variation of R-factor. This bias, even if we are convinced that it is limited, should be considered. Other uncertainties are related to the methodological approach such as the a) discrepancies in case of using a different algorithm for rainfall energy estimation (>97. 7% of the calculated R-factor stations are based on the original RUSLE equation); b) the application of temporal resolution calibration factors found in European Union to the global dataset; c) the under-representativeness of measured R-factor data in highlands (11. 6% of the total dataset is located in areas > 1000 m a. s. l. while the respective area amounts up to 20%), tropical and arid areas and d) the Gaussian Process Regression interpolation model.
Implications of global erosivity map and data availability. The new global erosivity map is proposed for global and continental assessments of soil erosion by water, flood risk and natural hazard prevention. Therefore, the aim of the global erosivity dataset is not to challenge other local (or national) erosivity maps, developed from local data with better quality that may not have been available for the present study. Nevertheless, our global R-factor map can potentially cover gaps, where erosivity has not been estimated (due to lack of data), or where it has been calculated solely from rainfall amounts. Current global estimates on soil erosion by water are very uncertain ranging over one order of magnitude from ca. 20 to over 200 Pg yr -1. More accurate global predictions of the rill and interrill soil erosion rates can only be achieved when the RUSLE rainfall erosivity factor is thoroughly computed.
In this study, we present a robust global rainfall-runoff erosivity factor to measure the erosive potential of rainfall, which is a key input in soil erosion prediction models. The global erosivity map represents the first assessment that is solely based on the original RUSLE approach using sub-hourly measured rainfall data for 3,625 stations, providing a solid and harmonised basis for a robust spatial interpolation results. Our results show new insights into the global geography of rainfall erosivity and the global erosivity map which will be publicly available can be employed by other research groups to perform national, continental and global soil erosion modelling. The GloREDa can be considered as an important step to bring together a large group of scientists to advance your understanding of large scale patterns related to land degradation, facing the current policy challenges and demands by the Food and Agriculture Organization (FAO), the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), and the United Nations Convention to Combat Desertification (UNCCD).
Collection of data. At global scale, this is the first time a data collection of observed (measured) high temporal resolution rainfall data takes place. The collection of high temporal resolution rainfall data from the maximum possible number of countries was considered necessary in order to have a representative sample across climatic and geographic gradients. The Global Rainfall Erosivity Database (GloREDa) was compiled based on the following three components: • Rainfall Erosivity database at European Scale (REDES) which includes 1,675 rainfall stations in the European Union and Switzerland (28 countries in total). REDES has been the data source for the erosivity map of Europe  28  and was updated in 2015 to estimate monthly erosivity in Europe  13, 33. • A global data collection of both high resolution rainfall data and calculated erosivity values based on high temporal resolution rainfall data (Table  2 ).
The data collection yielded 1,865 additional rainfall stations (around 52% of the total stations in GloREDa) from 23 countries outside of Europe. For the majority of the stations (located in China, Japan, India, Kuwait, Israel, Turkey, the United States of America, Mexico, Costa Rica, Jamaica, South Africa and Suriname) the calculation of the rainfall erosivity, based on high temporal resolution data, was performed for the first time (the calculation of rainfall erosivity, named as R-factor, is presented in the homonymous sub-section). For other regions, we used published erosivity data that were based on high temporal resolution rainfall datasets. This included countries such as Australia  37  , New Zealand  27  , South Korea 38 , Iran 39 , Malaysia 40 , Colombia 21 , Brazil 22 , Chile  23  , Mauritius 41 and Algeria 42.
• A literature review was used to fill some important data gaps, mainly in Africa, where high temporal resolution rainfall data are scarce. As a result of this exercise, rainfall erosivity values for 85 stations (2. 4% of the whole database) from 13 countries were inserted in GloREDa. These countries included Canada  43  , Argentina  24  , Cuba  44  , Cape Verde 45 , Cameroon  46  , Eritrea 47 , Ethiopia  48  , Kenya  49, 50  , Niger 51 , Nigeria  52, 53  , Rwanda  54  , Tenerife  55  and Zambia  56. Summarizing, we collected high temporal resolution rainfall data for 3,540 stations (97. 7% of the total GloREDa). The total number of observational years equals 59,380; this results in an average of 16. 8 years of high temporal resolution rainfall data per station. As such, GloREDa is the most comprehensive global database including the largest possible number of stations with high temporal resolution rainfall data.
7 mm during a period of 30 min) and, (iii)a rainfall accumulation of less than 1. 27 mm during a period of six hours splits a longer storm period into two storms. The erosivity factor equations and the above mentioned criteria have been developed according to more than 10,000 plot-years of experiments. The R-factor for each station in GloREDa was calculated using the Rainfall Intensity Summarisation Tool (RIST) software developed by the United States Department of Agriculture (USDA).
Interpolation of GloREDa. Given the correlation between the R-factor and monthly climatic data  28  , a regression approach was used to infer the spatial global distribution of rainfall erosivity from a series of independent climatic covariates derived from the WorldClim database  62. The covariates used included average monthly precipitation, average minimum and maximum monthly precipitation, average monthly temperature, precipitation of the wettest month, precipitation of the driest month, and precipitation seasonality, as defined in the WorldClim database (www. worldclim. org). These variables represent long term conditions based on the interpolation of observed data for the period 1960-1990  62. As a result, our erosivity map represents long-term erosivity patterns. Elevation was not included in the model, as it was already used to estimate some of the WorldClim variables.
We subsequently assembled a dataset where each location (i. e. rainfall station) had a mean R-factor (independent variable) as well as values for the climatic variables (independent variables), and used it as input for the interpolation. We used Gaussian Process Regression (GPR)  63, 64  due to the large number of support covariates, their potential collinearity, and the presence of non-linear relationships between the target variable (R-factor) and the covariates. GPR is a non-linear regression approach that can model non-linear relations by projecting the inputs into a higher dimensional space using basis functions, and by creating a regression model in said space. Considering the regression form σ = + f y (x) ( 0, ) n 2  with = f w (x) x T (where y are the observed responses, x the covariates values vector, f a set of functions and w a vector of weights), the GPR uses a projection from the original input space into a feature space using kernel expansion so that f(x) can be rewritten as φ = f x x w ( ) ( ) T where φ is the kernel function.
In this study the Radial Basis Function (RBF) Gaussian kernel was used, which can be written as ′ =     -      σ -′ K x x ( , ) exp x x 2 2 2 where σ is free tunable parameter. The Gaussian kernel is highly adaptable and commonly applied in machine learning  65. The main advantages of GPR are that it can model very complex non-linear relations between covariates and the target variable, and directly model both average and variance estimation, thus providing information about prediction uncertainty. Moreover, GPR is resistant to the issues derived from collinearity among independent variables that can arise in other statistical models. Finally, the GPR has the advantage of adapting in local conditions (climatic) due to its non-parametric nature. In this study the GPR model performance was tested for both, a fitting, and a cross-validation dataset.
Acknowledgements The authors would also like to acknowledge the following services for proving their data:  Bureau of Meteorology in Australia, New Zealand Institute of Water and Atmospheric Research (NIWA), Japan Meteorological Agency (JMA) ,  Korea Meteorological Administration (KMA) ,  National Meteorological Information Center of China, India Meteorological Department, Ministry of Earth Sciences, Iranian Meteorological Organization ,  Department of Meteorology, Directorate General of Civil Aviation -State of Kuwait, Lomonosov Moscow State University, Israel Meteorological Service, Turkish Ministry of Forestry and Water Affairs, U. S. Climate Reference Network (USCRN, NOAA ),  Comision Nacional Del Agua, Servicio Meteorologico Nacional, Mexico, Meteorological service Jamaica, University of Costa Rica (UCR) ,  Centro Nacional de Investigaciones de Café -Cenicafé (Colombia), General Directorate of Water Resources (Chile), Meteorological organization of Suriname, Mauritius Meteorological Services (MMS) ,  Algerian National Agency of Hydraulic Resources.
Workbench and workflow systems such as Galaxy, Taverna, Chipster, or Common Workflow Language (CWL)-based frameworks, facilitate the access to bioinformatics tools in a user-friendly, scalable and reproducible way. Still, the integration of tools in such environments remains a cumbersome, time consuming and error-prone process. A major consequence is the incomplete or outdated description of tools that are often missing important information, including parameters and metadata such as publication or links to documentation. ToolDog (Tool DescriptiOn Generator) facilitates the integration of tools -which have been registered in the ELIXIR tools registry (https://bio. tools) -into workbench environments by generating tool description templates. ToolDog includes two modules. The first module analyses the source code of the bioinformatics software with language-specific plugins, and generates a skeleton for a Galaxy XML or CWL tool description.
Introduction Over the last few years, bioinformatics has played a major role in the field of biology, raising the issue of best practices in software development for the members of the bioinformatics community  [1] [2] [3]. These practices include facilitating the discovery, deployment, and usage of tools, and several helpful solutions are available. Tool discovery is facilitated by various online catalogs and registries  [4] [5] [6]. The ELIXIR Tools and Data Services Registry, bio. tools  7  , describes bioinformatics software using extensive metadata descriptions, supported by the EDAM ontology  8. For software deployment, distribution systems are available  [9] [10] [11] [12] [13]  that let users locally install the tools that they need in convenient, portable and reproducible ways. Workbench and workflow systems such as Galaxy  14, 15  , Taverna  16  or Chipster  17  allow the execution and composition of bioinformatics tools in integrated environments which aim at improved usability, interoperability and reproducibility.
Finally, the Common Workflow Language  18  (CWL) is a recent project that defines a standardized and portable tool and workflow description format, usable across different platforms. All of the above systems rely on components that provide the necessary information to describe, install, or run a specific piece of software. Gathering this information and formatting it into tractable tool descriptions is often a complex and time consuming task for developers. Indeed, it requires a deep knowledge of both the tool itself and the description format. A significant part of the metadata stored in the descriptions is, however, common to registries and workbench environments systems  19  , and strategies relying on a mapping between these different description formats can help avoid redundancy and mislabeling of tools Figure  1 ).
The users of such graphical workbench platforms do not typically handle tool discovery and deployment tasks. Thus, detailed tool descriptions are fundamental, because they are the main source of information for the scientists who use them. Different approaches exist to help improve the quality of the corpus of tool descriptions. (1) Tooling facilitates the creation and validation of the tool descriptions, using Planemo 23 in the case of Galaxy. (  2 ) Community approaches such as the Intergalactic Utilities Commission design and promote best practices for the  ToolDog complements all of these approaches. It leverages the information available in bio. tools to simplify the integration of bioinformatics software into workbench environments.
Methods ToolDog is a command-line utility written in Python. It consists of two modules, which handle (1) the generation of a skeleton for the tool description, based on the analysis of the source code of the tool, and (2) the enrichment of the tool description, using the bio. tools metadata. The tool description generation pipeline (Figure  3 ) leverages bio. tools and includes both a module to generate a tool description using only the registry, as well as a module to enrich an existing tool description with information from the registry.
Source code analysis For a number of bioinformatics tools, a significant part of their description can be extracted from an analysis of the source code. The source code analysis module of ToolDog does this, currently only with python-based tools that use the argparse library for parsing command-line arguments. This module uses the argparse2tool package to retrieve the list of parameters and generate Galaxy or CWL tool description skeletons. To generate such skeletons, ToolDog runs a Docker software container that will download, install, analyze the source code, generate the tool description and then retrieve it. This strategy avoids the pollution of the local user's environment and provides a completely preconfigured, ready-to-use installation of ToolDog.
Tool description enrichment Galaxy and CWL tool descriptions, whether they were manually authored or automatically generated by source code analyses, can be improved by the description enrichment module. This retrieves additional metadata from the corresponding bio. tools entries, and fills in the missing information in the workbench tool description when available. Internally, the input tool description is parsed into an object model of the tool. The metadata from bio. tools are then mapped onto this object model, which is later exported to Galaxy or CWL formats. Parsing and export capabilities of ToolDog leverage the galaxyxml or cwlgen libraries to import and export the updated descriptions.