dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability All data used in this study are freely available. Spatial patterns of mean annual rainfall erosivity over the European region (Fig. 1c ) are freely available from the ESDAC (European Soil Data Centre) dataset at https ://esdac. jrc. ec. europ a. eu/conte nt/globa l-rainf all-erosi vity. Anomalies in the spatial pattern of annual mean rain rates over the Mediterranean region (Fig. 4d ) are obtained from NCEP (US National Center for Environmental Prediction)/NCAR (US National Center for Atmospheric Research) reanalysis data at https ://psl. noaa. gov/cgi-bin/ data/getpa ge. pl. The proxy-based multi-decadal winter NAO reconstruction (Fig. 4a ) is by Climate Explorer Climate Change Atlas of the Dutch Royal Netherlands Meteorological Institute (KNMI) at http://clime xp. knmi.
Karst aquifers provide drinking water for 10% of the world's population, support agriculture, groundwater-dependent activities, and ecosystems. These aquifers are characterised by complex groundwater-flow systems, hence, they are extremely vulnerable and protecting them requires an in-depth understanding of the systems. Poor data accessibility has limited advances in karst research and realistic representation of karst processes in large-scale hydrological studies. In this study, we present World Karst Spring hydrograph (WoKaS) database, a community-wide effort to improve data accessibility. WoKaS is the first global karst springs discharge database with over 400 spring observations collected from articles, hydrological databases and researchers. The dataset's coverage compares to the global distribution of carbonate rocks with some bias towards the latitudes of more developed countries.
Large-scale modelling and comparative water resource research have shown the great value of large datasets in hydrology 11. Numerous studies have applied these large datasets for several purposes such as model evaluation, global parameter estimations, impact studies, statistical and comparative analyses. For instance, large-scale hydrological models such as WaterGAP 12 used discharge data from the Global Runoff Data Centre (https://www. bafg. de/GRDC) for parameter estimation. Likewise, streamflow data from the Model Parameter Estimation Experiment (MOPEX)  13  and the Global Runoff Data Centre (GDRC) were combined to derive global base flow indexes and recession constants 14. Streamflow observations of near-natural catchments obtained from UNESCO's European Water Archive (EWA) were used to investigate the streamflow trends across Europe and differentiated the impacts from climatic variability and anthropogenic drivers 15.
karst areas as karst spring discharges are under-represented. Access to data has been identified as a major impediment in quantifying karstification, modelling flow dynamics and transport processes of karst groundwater  2, 17. Studies involving large-scale parameter estimation or comparative studies in karst hydrology are still fairly rare and unrealistic representations of hydrological processes in karst regions can still be found in many large-scale hydrological models  17, 18. The need to advance research in karst hydrology especially on larger spatial scales, combined with the importance and peculiarities of karst aquifers, therefore requires a consolidated, global database for karst systems. Recent advances in providing large-scale information on karst aquifers include the development of World Karst Aquifer Map (WOKAM)  4, 19  , which is the first to accurately map karst regions worldwide, or the SNO KARST database  20  that provides long-term observations of hydro meteorological and geochemical variables of several karst observatories across France, including karst spring discharge.
The SNO KARST also offers a new tools for characterizing and modelling flow in karst aquifers  21  or assess their vulnerability  22. With the WoKaS database, we provide the first known effort to create a global database of karst spring discharge observations. It is the result of an intense and global effort to make a large number of karst spring hydrographs accessible for karst researchers and the wider hydrological communities. Data from individual researchers and research networks like the Karst Commission of the International Association of Hydrogeologists (IAH) was combined with karst spring hydrographs from national databases and digitized spring discharge data from scientific publications. Access to WoKaS database will motivate large scale and comparative karst hydrology studies, help to improve representation of karstic processes in large-scale models, improve management of karst groundwater, and will promote international and interdisciplinary collaborations.
Methods The development of the WoKaS database followed three steps: (i) identification of karst spring locations across the globe; (ii) sourcing for discharge observations of the identified springs; and (iii) evaluation of collected datasets, which included technical validation and quality assessment. The workflow of these steps is illustrated in Fig. 1. Identifying karst spring locations. Firstly, we assembled the list of karst springs in countries with carbonate outcrops identified from the World Karst Aquifer Map  4, 19. For each country with carbonate outcrops, we performed an extensive literature search with a set of keywords consisting of: (1) country's name; (2) karst; (3) spring; and (4) hydrology. From all the identified materials (articles, conference proceedings, reports, theses, news bulletins, books), we extracted karst spring names, location coordinates, elevation as well as land cover, catchment area (km 2 ), defined as the topographic boundary within which the spring is located, recharge area (km 2 ), defined as the area contributing to the recharge of the aquifer drained by the spring, and factors influencing discharge if such information were available.
Several spring locations were also collected from WOKAM, which provides a list of relevant karst springs for each country, and from reviewing national databases. Data acquisition. Time series of karst spring discharge observations were collected from three sources: (1)   published data including scientific articles, reports and theses; (2) project partners and collaborators; and (3) public databases of national hydrological services. For each source, the method for data extraction, collection and gathering were different.
To extract the spring discharge observations from the published articles, theses and reports, we used WebPlotDigitizer (https://github. com/ankitrohatgi/WebPlotDigitizer). WebPlotDigitizer is an open source, web-based, semi-automatic digitization tool developed with HTML5 that works on most common web browsers. The hydrographs were cut out from the original publications, saved as image or pdf files and imported to WebPlotDigitizer. The raw discharge values for the total duration of the observation period were then extracted. Python codes for daily time step interpolation were used to post-process the extracted raw values. Spring discharge observation time step are not usually stated in publications. Therefore, when the temporal resolution of the observation was unknown, the interpolation time step used was irregular and dependent on the resolution of the extracted figure: plot quality, number of plotted variables, and length or duration of the hydrograph.
For instance, hydrographs that covered longer time periods only show seasonal and annual events, hence, a discharge variability could only be captured on monthly time steps. Whereas, if the observation period was shorter, individual events could be identified and discharge values could be extracted on a daily temporal resolution. research partners and collaborators. Additional data were acquired through the karst research community. Calls for data contribution were made at conferences, through social media platforms (Twitter and Facebook) and emails soliciting data support for the database to various research commissions, institutes, working groups and researchers with relevant datasets.
Hydrological agencies. A large number of karst spring discharge observations were obtained from national hydrological services that provide online access to their datasets. In total, we collected discharge datasets from ten national databases mainly in Europe and the United States of America (Table  1 ). Most of this data is in the public domain or published under the creative commons (CC-BY) license and could be directly combined with the data obtained from other sources (see above). Data from databases (Banque Hydro, eHYD, LUBW and NRFA) that do not provide their discharge data under the open data or CC-BY license are made available only as annual averages in the data repository  23. To access this data at daily resolution, we provide an automatic download routine written with R, which extracts the karst spring discharge time series from the databases' webpages directly.
Data records The WoKaS database includes over 400 karst spring discharge observations from more than 30 countries across the globe covering a wide range of hydrologic and climatic diversity. The datasets which are freely available for download  23  are accessible in comma-separated values (CSV) file format. Time series datasets cover time spans ranging from a couple of months to a maximum of 120 years (Fig. 2a ). Over 60% of the dataset is made by discharge time series observations of up to 20 years and within this subset, over 90% of the time series have discharge observations greater than a year, more than 65% cover an observation periods greater than 5 years and above 35% have more than 10 years of discharge observations. More than 30% have time series measurements of > 20 years (Fig. 2a ).
Considering all collected datasets with those from databases without CC-BY license, which are available as annual averages  23  (see hydrological agencies subsection), ca. 40% of the datasets have temporal resolution of ≤1 day and above 20% have a year resolution (Fig. 2b ). If the datasets from databases without CC-BY license are substituted by higher resolution time series data which are accessible through the download routine (see hydrological agencies subsection), the percentage of datasets with ≤1 day temporal resolution increased to almost 60%. Subsequently, datasets with a year temporal resolution are reduced by 20% (Fig. 2b ). Dataset completeness describes the percentage of total discharge observations of a dataset without missing values. More than 90% of the datasets in the WoKaS database are gap-free for the obtained hydrograph duration (Fig.
2c ). accuracy and quality of datasets. The accuracy and quality of the datasets were defined based on four criteria (Table  2 ): (1) if the discharge observation measurement is known, (2) recognition of individual events on the spring's hydrograph, (3) recognition of seasonal events on the spring's hydrograph and (4) identification of recession events on the hydrograph. These criteria (mostly based on the data source -see subsection data acquisition) were used to assign five quality classes, from A (very high) to C3 (very low). Generally, datasets from hydrological databases, research partners and collaborators fall within the quality class A or B. Since digitized datasets were extracted from hydrograph plots, inaccuracies were inherited from quality and observation length of the hydrograph plot that was to be digitized.
For example, when discharge observation covered several years, only seasonal variability was visually identifiable and individual discharge events could not be recognised. Meanwhile, both seasonal variability and individual events are visually recognisable for discharge observations extending over fewer years (<5years). Consequently, the digitized datasets are sub-divided to class C1 (individual events identifiable, recession periods recognizable), C2 (individual events identifiable, recession periods not clearly recognizable) and C3 (individual events not identifiable, recession periods not clearly recognizable). Based on the defined quality classes, a high percentage of WoKaS datasets are of good quality (Fig. 3 ), approximately 62% and 20% of the datasets are of class A and B respectively (Fig.
Measurement type known Individual event Seasonality Individual recession A Very High ✓ ✓ ✓ ✓ B High ✗ ✓ ✓ ✓ C1 Medium (digitized < = 5 years) ✗ ✓ ✓ Recognisable C2 Low (digitized > 5 years) ✗ ✗ ✓ Recognisable C3 Very Low (unclear, poor plot) ✗ ✗ ✓ ✗ Table  2. Datasets quality description. The symbol "✓" indicates that the corresponding requirement is fulfilled and "✗" indicates that the requirement is not fulfilled. 80% of the WoKaS datasets (see subsection spatial representativeness of datasets). Similarly, class B datasets are distributed within these latitudes in Europe and Asia, and also in Australia. The class C datasets are found in the Middle East, Asia and Southern Africa, these are the regions where spring discharge datasets have been digitised from publications due to the scarcity or unavailability of direct spring discharge observations.
technical Validation The collected datasets were evaluated by: (1) determining the accuracy of the identified spring locations with respect to carbonate rock outcrop of WOKAM in order to exclude non-karstic springs and (2) determining the spatial representativeness of the database for karst areas by comparing the distribution of the identified locations over all latitudes with the distribution of carbonate rock outcrops over the world's land surface. accuracy of identified locations. A fundamental prerequisite for a spring to be considered karstic is that, it must be fed by a karst aquifer. The recharge area of karst aquifer can lie exclusively (autogenic recharge) or partially (allogenic recharge) within carbonate rock areas  9. In some cases, recharge of karst aquifers is partly due to groundwater flow coming from adjoining aquifers, such as the alluvial ones.
Spatial representativeness of datasets. Likewise karst landscape areas, karst springs are not evenly distributed globally. Consequently, it is important to ensure that the WoKaS database is representative of karst's landscape distribution. Therefore, we compared the frequency of karst areas over all latitudes with the frequency of spring locations over the same latitudes. Using 30° grid steps, we found that the distribution of karst areas resembles the distribution of WoKaS spring locations (Fig. 4 ) but with a considerable bias towards the wealthier and data-rich regions of Europe and North America. At those latitudes (30°N-60°N), we found approximately 50% of the total global karst area and 80% of the WoKaS datasets. More (financial) resources and attention have been directed towards hydrological studies and monitoring in these regions, which is a common and well-known problem of the global representativeness of experimental hydrology  25.
Usage Notes Data repository organisation. WoKaS is a compilation of volumetric karst spring discharge observation datasets from different sources (see subsection data acquisition). The data repository  23  holds a single packaged zip archive named "WoKaS_Data_Record", which contains a "read_me" text file that provides guide information for users and four subfolders: WoKaS_Hydrograph_Metafile, WoKaS_Hydrograph_Datasets, Auto_Download_ Routine and WoKaS_Summary_Plot. The contents of the files and subfolders contained in the zip archive are described below: (A) WoKaS_Hydrograph_Metafile contains a xlsx file "WoKaS_Metafile" which provides a summary of all WoKaS datasets attributes. The file includes information on the dataset's country's name, assigned WoKaS identifier, spring's name, local gauge station identifier which is the assigned identifier in the database of origin of dataset, spring's coordinates, spring's discharge observation length (years), quality flags (as described in subsection accuracy and quality of datasets), dataset's source name and the source type which indicates online or offline accessibility of the datasets (see subsection data acquisition and Table  1 ).
Discharge observation measured at gauge stations located farther downstream of the spring's outlet are usually influenced by superficial flow. When information about the influence of superficial flow is available, it is provided in the "Additional information" section of the metafile. A more comprehensive explanation of the used terms and content of the metafile is provided in the "read_me" document file included in the zip file archive. (B) WoKaS_Hydrograph_Datasets includes over 400 CSV files of karst spring discharge measurements in cubic metres per second (m 3 /s). Headers providing meta-information such as the source of the dataset, spring's name, local gauge station identifier, location coordinates in WGS 84 as well as measurement time format are appended to the csv files. The discharge observations provided in the data repository  23  can be static or dynamic.
Datasets obtained from research partners, collaborators and publications are static because they are not updated periodically. Conversely, datasets from hydrological databases (see subsection hydrological agencies) are dynamic and periodically updated through the individual agencies. For users who want the updated datasets, they can be obtained directly from the source online database via an automatic download routine (see below). As described in the Methods sections, for some sources (Banque Hydro, eHYD, LUBW and NRFA), complete datasets at higher temporal resolution are only obtainable through the automatic download routine (see information in "Additional information" column of the metafile). (C) Auto_Download_Routine includes the R script files for downloading the dynamic datasets from the hydrological agencies online databases.
The R codes allow the user to access and download the most recent version of the discharge datasets in their original temporal resolution from the online databases of the hydrological agencies. The downloaded datasets from the different online databases are standardised, having same format as described in "B" above. The newly downloaded version of the dynamic datasets are saved in WoKaS_Dynamic_Datasets folder, which is automatically created while the download routine code is run. In case of changes within the hydrological databases online access link system, the R codes might stop working. However, the R code will be frequently maintained and an updated version will be available on GitHub (https://github. com/KarstHub/WoKaS). (D) WoKaS_Summary_Plot subfolder contains a pdf file also named "WoKaS_Summary_Plot which includes the hydrograph plots of all the spring discharge datasets.
Datasets naming convention. The naming convention used for the datasets is a combination of the International Organisation for Standardisation Alpha-2 (ISO 2) country's code, and a four-digit serial number followed by the spring's name. The ISO 2 code and four-digit serial number are separated by a hyphen "-" and an "@" sign between the serial number and the spring's name. For example, a dataset with the name "FR-0050@ Cent-Fonts" means: 'FR' = ISO-2 country's code for France '0050' = WoKaS database assigned serial number 'Cent-Fonts' = Name of the spring. recommended usage for datasets. Based on the assigned quality classes for the datasets, we provide recommendation on the usage and application of the datasets. The "very high" and "high" quality datasets (Class A and B) are appropriate for all hydrological analyses including statistical and comparative analyses, model evaluation and calibration, impact studies and process understanding.
The C1 datasets are suitable for discharge's trend analysis, event-based process understanding and water balance estimation. It should be noted that human impacts such as groundwater pumping for drinking and irrigation could affect spring discharge and trends can't be solely attributed to climatic and landscape changes. In the comment section of the metafile, information about human impacts are only included when available. C2 and C3 quality datasets are most suitable for analysis that does not require much accuracy, such as computing annual averages or monthly spring discharge variations. The focus of the WoKaS database is to provide easy access to spring discharge dataset, the present structure of the database does not distinguish among different aquifer recharge processes that fed the karst springs (see subsection accuracy of identified locations).
code availability The R code to download datasets directly from the hydrological databases and to combine them with the spring discharge time series obtained from the other sources (see above) is available at https://github. com/ KarstHub/WoKaS. The code is provided in R programming language version 3. 5. 0, and commented following a recommended programming comment guidelines  24. Comprehensive instructions on how to run the code and system requirements are provided by a "README" file included in the GitHub repository.
Country Database/agency name Database access Automatic download France Ministère de l'Écologie, du Développement Durable et de l'Énergie (BANQUE HYDRO) http://www. hydro. eaufrance. fr/indexd. php Yes France SNO KARST, OSU OREME https://data. oreme. org/observation/snokarst No Austria Bundesministerium für Nachhaltigkeit und Tourismus (eHYD) https://ehyd. gv. at/ Yes Germany Bayerisches Landesamt für Umwelt https://www. gkd. bayern. de/de/grundwasser/quellschuettung//tabellen Yes Germany Landesanstalt für Umwelt Baden-Württemberg (LUBW) http://udo. lubw. baden-wuerttemberg. de/public/pages/selector/index. xhtml Yes Ireland Environmental Protection Agency (EPA HydroNet) http://www. epa. ie/hydronet/#Groundwater Yes Slovenia Agencija Republike Slovenije za okolje (ARSO) http://vode. arso. gov.
An initial homology model suggested strong feasibility of a similar interaction mode and highlighted seven amino acids on the face of the BTN2A1 IgV domain incorporating the C, C', F (legend continued on next page) Immunity  52, 487 488 489 490 491 492 493 494 495 496 497 498 March 17, 2020 491  and G b strands (CFG face), equivalent to the region of BTNL3 IgV domain involved in binding Vg4, as candidates for alanine mutation (Figure  3C ). Individual BTN2A1 alanine mutants were generated for these seven residues. Of these, four completely abrogated BTN2A1 binding to Vg9Vd2 TCR (R65A, R124A, Y126A, E135A), a fifth marginally decreased affinity (K79A), Y133A did not affect binding, and S72A increased affinity (K d 10-15 mM) (Fig-  ures 3D  and S3B ). These results allowed generation of an improved, mutationally informed model of BTN2A1/Vg9 interaction using the high-ambiguity driven protein-protein DOCKing (HADDOCK) software, analysis of which outlined a molecular rationale for the effect of each mutation (Figure  S3C ; STAR Methods).
RNAseq analysis of Radiation Hybrids Knowing the differences in antigen presentation of the various radiation hybrid cell lines, we performed RNA seq to identify those human Chr 6-encoded genes that are expressed in each hybrid line. Cells were stored in TRIzol Reagent (Invitrogen) and total RNA was extracted. Sequencing libraries were produced with an Illumina Truseq RNA preparation kit as described by the supplier's protocol and were sequenced with an Illumina HiSeq4000. Sequence reads were mapped to the human genome (hg38) with STAR (version STAR_2. 50a) and read counts of gene transcripts were determined using gtf file Homo_sapiens. GRCH38. 84. gtf and featur-eCount (v1. 5. 0-p1). Cell lines were then compared for presence, i. e. , expression, of human Chr 6 genes. To filter out reads descending from mouse or hamster cells, all fastq-files were initially mapped against the mouse genome (Mus musculus, version GRCm38) using STAR and the corresponding Gene Transfer Format (gtf) file (version 87).