dataesr/scientific-paragraphs-categorization huggingface dataset:
MS/MS spectra were searched against the A. thaliana proteome database (TAIR10, containing 35 386 entries; http://www. arabidopsis. org/) using the MaxQuant software (version 1. 5. 4. 1). Settings for MaxQuant searches were set as previously described  (Vu et al. , 2016). For the quantitative proteome and phosphoproteome analyses, the 'ProteinGroups' and 'Phospho(STY)sites' output files, respectively, generated by the MaxQuant search were loaded into Perseus software. For phosphoproteome data, only high-confidence hits with phosphorylation localization probability >0. 75 were included in the analysis.
In silico analyses, data visualization, and statistics Centred log2-transformed intensity values of the statistically significant proteins or phosphopeptides (or in some cases distinct phosphosites from the same phosphopeptide) were Z-scored, clustered into groups by a hierarchical clustering analysis based on Pearson correlation, and visualized as heat maps. To generate networks for known and predicted protein-protein interactions, the data sets were loaded to the STRING database (https://string-db. org; version 10. 5) using a high confidence interaction score (>0. 7). As active interaction sources, text mining, experiments, databases, co-expression, neighbourhood, gene fusion, and co-occurrence were selected. (Phospho)proteins that did not have any interaction were removed from the network.
Biological Process terms were retrieved from the TAIR portal (www. arabidopsis. org, Bulk Data Retrieval). Further data visualization was performed in Cytoscape (version 3. 5. 1) on the extracted interaction network and Gene Ontology (GO) annotations. GO enrichment analysis was performed in the PLAZA 4. 0 workbench using entire species as background model and 0. 05 as a P-value cut-off. The HMMER web server was used for the prediction of kinase domains. For Motif-X analyses  (Chou and Schwartz, 2011) , the sequences (limited to 13 amino acids) of up-and down-regulated phosphopeptides were pre-aligned with the identified phosphosite centred. The IPI Arabidopsis Proteome was used as the background database. The occurrence threshold was set at the minimum of five peptides, and the P-value threshold was set at <10 -6.
A normalized early mannitol-triggered differential leaf phosphoproteome A common question related to quantitative phosphoproteomics is whether the measured phosphorylation changes result from changes in kinase or phosphatase activity or from changes in phosphoprotein abundance  (Vu et al. , 2016). For the phosphopeptides for which the proteins were detected in the whole proteomic analysis, we normalized phosphopeptide intensity to protein abundance. A total of 158 phosphoproteins (representing the 172 differentially regulated phosphosites) were mapped on the proteome data set and 32 proteins were found to be overlapping. Sixteen phosphopeptides could not be normalized as they belonged to a group of unique phosphopeptides and did not have a fold change value. Next, the log2 fold change of the protein was subtracted from the log2 fold change of the phosphopeptide.
(Phospho) proteome profiling identifies AHA2 and CRRSP38 as potential leaf growth regulators To assess the involvement of proteins with a differential abundance or phosphorylation status in leaf growth, and thus the quality of our data set, we selected two candidates for phenotypic analysis. From the mannitol-regulated proteome data set (Supplementary Table  S10 ), we selected the poorly characterized CYSTEINE-RICH REPEAT SECRETORY PROTEIN 38 (CRRSP38, AT3G22060). Together with 15 other proteins, CRRSP38 was identified as down-regulated after 30 min of mannitol treatment and was not detected after 4 h of mannitol-induced stress. Transcriptionally, CRRSP38 did not show any mannitol-induced changes at 20 min and 40 min, and an increasing expression from 2 h onwards (Fig. 7A ). We obtained a knock-out allele of CRRSP38 (SALK_151902), referred to as crrsp38-1 (Supplementary Fig.
This is possibly the result of a low coverage of the proteome and/or the limitation of our MS set-up. However, two other phosphoproteins with regulatory links to the GA-DELLA pathway were identified. First, the transcription factor bZIP16 showed a decrease in phosphorylation upon mild osmotic stress. As bZIP16 can repress RGL2 expression  (Hsieh et al. , 2012) , encoding a DELLA protein, we hypothesize that the dephosphorylation of bZIP16 could reduce its repressing activities on RGL2. Interestingly, when exploring bZIP16 in the functional protein association network, bZIP16 interacts with TPR2 (TOPLESS RELATED 2), which in turn is also differentially phosphorylated in our data set and is most probably responsible for the repressing activities of bZIP16. Secondly, TAGK2/CRK2, a kinase that regulates the GA RECEPTOR RING E3 UBIQUITIN LIGASE (GARU) at Tyr321 which results in the disruption of the interaction between GARU and the GA RECEPTOR GA INSENSTIVE DWARF1 (GID1)  (Nemoto et al.
25 per day as a proxy for extreme working poverty. The definition of the working poor rate in the data set is based on the work of  Kapsos and Bourmpoula (2013). The KILM data set provides information on working poverty rates, which can be compared across countries, a critical requirement for the present cross-country study. Another advantage is that the KILM is derived from a micro-based approach to computing the working poverty rates for those countries where direct information from household and labor market surveys is not available. This approach gives robust estimations of the prevalence of working poverty with a minimized prediction error. The data set also provides the share of workers living on less than US$1. 90 per day, as well as the proportion of workers considered to be middle class and higher (living on more than US$13 a day).
Growth volatility is measured as the rolling standard deviation of GDP growth over 10 years and is expected to increase working poverty. As shown by  Blundell and colleagues (2016) , poor households respond to income shocks by increasing their labor supply in order to smooth their consumption. This induces a downward pressure on wages and increases working poverty  (Combes et al. , 2014). Data related to GDP per capita, FDI and trade openness are drawn from the World Development Indicators of the World Bank. Lastly, we expect a positive effect of income inequality through the increase in labor supply. Income inequality is measured by the Gini coefficient of market income obtained from the Standardized World Income Inequality Database (SWIID) calculated by  Solt (2014). The descriptive statistics related to the control variables are presented in Table  1.
Particularly, the introduction of information sharing offices (ISOs), namely, private credit bureaus (CB) and public credit registries, has been identified as a driver of financial development. According to  Djankov, McLiesh and Shleifer (2007) , a private credit bureau is defined as a private commercial firm or non-profit organization which maintains a database on the standing of borrowers in the financial system and has as its primary purpose facilitating the exchange of information among banks and financial institutions. Likewise, a public credit registry (CR) is defined as a database owned by public authorities (central bank or banking supervisory authority) that collects information on the standing of borrowers and shares it with financial institutions. We argue that by reducing information asymmetry, CBs and CRs can enhance financial access.
Second, in order to ensure that our measure of bank penetration does not capture other dimensions of financial development, we introduce two measures of financial development -private credit ratio to GDP and bank concentration 9 -in columns 3 and 4, in line with the work of  Beck, Lin and Ma (2014). Given their potential endogeneity, we introduce their lags of two periods. The related data are available from the Global Financial Development Database of the World Bank. We find that bank concentration tends to increase working poverty, as it may reflect lower access to financial services. 9 The assets of the three largest commercial banks as a share of total commercial banking assets. Consistent with  Jeanneney and Kpodar (2011) , as well as Donou-Adonsou and Sylwester (  2016 ), the ratio of private credit negatively and significantly affects working poverty.
In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models.
, 1988). Infiltration measurements have been largely used to estimate soil saturated hydraulic conductivity. This soil property is a key factor to correctly describe all the components of the soil and land surface hydrological balance and is essential in the appropriate design of irrigation systems. Within the literature it is clear that extensive efforts have been made to estimate this property from basic soil properties using pedotransfer functions (PTFs). PTFs are knowledge-based rules or equations that relate simple soil properties to those properties of soil that are more difficult to obtain  (Van Looy et al. , 2017). Most of these efforts have been based on measurements made on samples of disturbed or undisturbed soil material. With this infiltration database, data are now made available that may contribute to better predicting the saturated soil hydraulic conductivity and demonstrate the effect of, for example, vegetation and land management on the parameters of interest.
Finally, we will provide some first results highlighting the suitability of the database for further research. The main article is also accompanied by a supplement providing more detailed information about the different methodologies to measure soil infiltration. This is added because many of readers are likely not well versed in soil infiltration and its limitations in measurement and modeling. For more detailed information on this, readers could refer to  Smith et al. (2002) ,  Corradini et al. (2017), and Hopmans et al. (2006).
Data collection We collected infiltration measurements from different countries or regions by contacting the data owners or by extracting infiltration data from published literature (Fig. 1 ). To do this, a data request was sent to potential data owners through different forums and email exchanges. The flyer asked data owners to cooperate in the development of the Soil Water Infiltration Global (SWIG) database by providing infiltration data as well as metadata about experimental conditions (e. g. , initial soil moisture content at the start of the experiment and method used), soil properties, land use, topography, geographical coordinates of the sites, and any other relevant information to interpret the data and to increase the value of the database. Infiltration data reported in the literature were digitized and included in the database together with additional information provided in these papers.
Database structure The SWIG database is prepared in *. xlsx with a backup file in *. csv formats containing several datasets. Supplementary data are available at https://doi. org/10. 1594/PANGAEA. 885492  (Rahmati et al. , 2018). The first dataset, named "I_cm", contains cumulative infiltration data in centimeter units and is referred to as "Ixxxx", whereby "xxxx" is the identifier of the individual infiltration test. The corresponding time intervals in hours for the infiltration data are labeled "T_Hour" and named "Txxxx". The constant or varying pressure or tension heads (if any) during infiltration measurements are also reported in another dataset named "Ten-sion_cm". The database also contains additional variables and information relevant to the infiltration data provided by data owners or digitized from articles, as listed in Table  3 , and which is labeled "Metadata".
Additional soil properties were determined by different standards; therefore, data harmonization might be needed for some of those, especially in the case of water content at field capacity, pH, or wet-aggregate stability. Further information on measurement methods is available from references of the data. Since the geometric mean diameter (d g ) and standard deviation (S g ) of soil particle sizes are rarely measured, both parameters were computed using the following equations  (Shirazi and Boersma, 1984) : d g = exp (a), a = 0. 01 n i=1 f i ln D i , (4) S g = exp (b), b 2 = 0. 01 n i=1 f i ln 2 D i -a 2 , (5) where f i is the percent of total soil mass having diameters equal to or less than the arithmetic mean of interval limits (D i ) that define three main fractions (i) of clay, silt, and sand with mean values of 0.
Spatial and temporal data coverage The SWIG database  (Rahmati et al. , 2018)  consists of 5023 soil water infiltration measurements spread over nearly all continents (Fig. 2 ). Data were derived from 54 countries (Table  4 ). The largest number of data sources were provided by scientists in Iran (n = 38), China (n = 23), and the USA (n = 15), whereby one data source might contain several water infiltration measurements. The SWIG database covers measurements from 1976 to 2017. A sparse coverage was obtained for the higher latitudes of the Northern Hemisphere (above 60 • ) including Norway, Finland, Sweden, Iceland, Greenland, and Russia. The lack of reports with infiltration data from most countries of the former Soviet Union as well as the Sahelian and Saharan countries is also notable, as well as the small number of infiltration data from Australia.
Analysis of the database using soil properties Textural information (clay, silt, and sand content) is available for 3842 out of 5023 collected infiltration curves (∼ 76 %). The infiltration measurements cover nearly all soil textural classes according to the USDA classification, except for the sandy clay and silt textural class (Fig. 6 ), which makes the SWIG database a valuable data source for comprehensive studies. To complete the large dataset, the open-access SWIG database might be amended with information regarding those soils poorly or altogether unrepresented by the existing database, including those not usually considered by infiltration studies, such as soils with extremely high stone    (Rubel et al. , 2017; Kottek et al. , 2006). content  (Poesen, 2018). Loam, sandy loam, silty loam, and clay loam contributed with 19, 18, 14, and 13 % (Table  5 ) to the infiltration measurements, respectively.
Infiltration measurements in the SWIG database Different instruments were used to measure soil water infiltration (Table  8 ). About 32 % (1595 out of 5023) of the measurements were carried out using different types of ring infiltrometers. The most frequently used methods are the disc infiltrometer methods (disc, mini-disc, and micro-disc, hood, and tension infiltrometers), which have been used in about 51 % of the experiments. About 5 % of the data were submitted to the database without specifying the measurement method (251 infiltration tests) and around 12 % of the measurements were carried out with other methods not listed above (Table  7 ).
Estimating infiltration parameters from infiltration measurements In order to predict infiltration parameters from infiltration measurements, we classified the SWIG database infiltration curves in two groups: (i) infiltration curves that were obtained under the assumption of 1-D infiltration and (ii) infiltration curves that were obtained under 3-D flow conditions. We fitted the three-parameter infiltration equation of Philip  (Kutílek and Krejča, 1987) , Eq. (  6 ), to the 1-D experimental data and the simplified form of  Haverkamp et al. (1994) , Eq. (  7 ), to the 3-D experimental data: I 1-D = S t 1 2 + A 1 t + A 2 t 3 2 , (6) I 3-D = S √ t + 2 -β 3 K sat + γ S 2 R D (θ s -θ i ) t. (7) We reduced the number of parameters in Eq. (  6 ) by defining A 1 = 0. 33×K sat  (Philip, 1957)  and A 2 = A where A was assumed to be a constant.
By excluding these methods, 596 infiltration curves were excluded from the fitting to Eqs. (  8 ) and (  9 ). In addition, 251 infiltration curves were also excluded from the fitting to Eqs. (  8 ) and (  9 ) as no indication was available on the measurement method used. In total, 4178 infiltration curves were included in our analysis, of which 828 infiltration curves reflected 1-D and 3350 were considered as the results of 3-D infiltration. As no sufficient information was available on the properties of the sand contact layer, we did not correct 3-D infiltration measurements. Finally, the selected infiltration curves were fitted to Eq. (  8 ) or (9) using the lsqnonlin command in Matlab ™. The fitting results of Eq. (  8 ) to the single infiltrometer data are shown in Table  9. R 2 values were higher than 0.
9 in 97 % of the cases and higher than 0. 99 in 77 % of the cases. Fitting Eq. (  9 ) to the 3-D infiltration curves data, R 2 values higher than 0. 9 and 0. 99 were obtained in 94 and 68 % of the cases, respectively. The statistics for the fitting process as well as the fitted parameters of two mentioned models are reported in the SWIG database in an additional dataset labeled "Statistics". For infiltration curves excluded from the analysis, an empty cell is reported. The average values of estimated K sat and sorptivity (S), using Eq. (  8 ) or (  9 ) as well as measured K sat for different soil texture classes extracted from the current database, are reported in Table  10. The measured values of K sat were obtained by other means by the contributors and tabulated in the SWIG database. More detailed information of how K sat was calculated in individual cases can be found in the references linked to those data points.
Comparison between estimated (K sat-es ) and measured (K sat-m ) values of K sat (Table  10 ) reveals that there is reasonably good agreement between measurements and estimation, except for loamy sand (with mean K sat-es = 62 cm h -1 vs. K sat-m = 25 cm h -1 ), sandy loam (with mean K sat-es = 32 cm h -1 vs. K sat-m = 41 cm h -1 ), silt loam (with mean K sat-es = 27 cm h -1 vs. K sat-m = 3 cm h -1 ), and silty clay (with mean K sat-es = 26 cm h -1 vs. K sat-m = 45 cm h -1 ) textural classes. However, the only significant difference between measured and estimated K sat values was found for the silt loam textural class (Table  10 ) applying an independent t test. We also compared our estimated K sat values from the infiltration measurements from the SWIG database with K sat values from databases that have been published in the literature (Table  11 ).
The validity of our estimated K sat values is confirmed by comparing the order of magnitude of the difference between these values, and those tabulated in previous studies, for the various different soil classes. Some of these databases like that of  Clapp and Hornberger (1978)  and  Cosby et al. (1984)  have been used to parameterize land surface models. Most of the K sat values in the listed databases have been obtained from laboratory-scale measurements often performed on disturbed soil samples. In most of the reported databases K sat is controlled by texture, with the highest mean values obtained for the coarse-textured soils and the lowest mean values for the fine-textured soils. This is not the case for the K sat values obtained from the SWIG database. Clayey soils have a mean value that is similar to the coarser textured soils.
This may be partly explained by the fact that the measurements collected in the SWIG database are obtained from field measurements on undisturbed soils. It was observed that the standard deviation of K sat in the SWIG database is typically larger than the standard deviations obtained from the databases in the literature. This indicates that texture is apparently not the most important control on K sat values. However, one would also pose that much of the lack of correlation between soil texture and predicted K sat from the SWIG database is related to the lack of soil structural information, such as macro porosity quantification or other possible soil attributes. Indeed, many of the datasets presented in our paper on saturated and near-saturated flow can be used to infer the state of the soil's structure, namely its macroporosity, by using the slope of the near-saturated conductivity curve, via Philip's "flow-weighted mean poresize" analysis.
White and Sully (1987)  have discussed this in a great detail. Zhang et al. (2015)  is another example of where tension infiltrometers can be used to describe the temporal dynamics of the macroporosity which characterizes soil structure. This could inspire researchers to collect such information when conducting additional soil infiltration measurements and include this in the database in the future. This finding indicates that present parameterization in current land surface models, which are mainly based on texture, may severely underestimate the variability of K sat. In addition, it shows that also mean values are not dominantly controlled by textural properties. Other land surface properties such as land use and crusting may, in fact, be much more important.
Exploring the SWIG database using principal component analysis In order to demonstrate the potential of the SWIG database for analyzing infiltration data and for developing pedotransfer functions, principal component analysis (PCA) was performed and biplots were generated to show both the observations and the original variables in the principal component space  (Gabriel, 1971). In a biplot, positively correlated variables are closely aligned with each other and the larger the arrows the stronger the correlation. Arrows that are aligned in opposite directions are negatively correlated with each other and the magnitude of the arrows is again a measure of the strength of the correlation. Arrows that are aligned 90 • to each other show typically no correlation. Figures  7  and 8  show the results of two PCAs.
Texture class Measured Independent t test Estimated by Eq. (  8 ) or (  9 ) between measured and estimated K sat tion to properly estimate K sat and S. However, the SWIG database provides additional information such as land use, initial water content, and slope that might prove to be good predictors. A further analysis in this respect is however beyond the scope of this paper. More importantly, the present analysis in combination with the results provided in Table  11  shows that a texture-dominated derivation of K sat values, as implemented in most land surface models, does not provide adequate means to estimate K sat. n a S (cm h -0. 5 ) K sat (cm h -1 ) n a K sat (cm h -1 ) df t.
Potential error and uncertainty in the SWIG database Similar to any other databases, the data presented in the SWIG database may be subject to different error sources and uncertainties. These include (1) transcription errors that occurred when implementing the measurement data into the EXCEL spreadsheets, (2) inaccuracy and uncertainties in determining related soil properties such as textural properties, (3) violation of the underlying assumption when performing the experiments, and (4) uncertainty (variability) in estimated soil hydraulic properties due to the different measurement methods. Unfortunately, none of these errors or uncertainty sources are under the control of the SWIG database authors, and quantification of these sources is often difficult, since the required information is often lacking.