dataesr/scientific-paragraphs-categorization huggingface dataset:
Data visualization Single-cell Multilayer Viewer, was developed as a serverless software for the visualization of up to 5 layers of information. It is available on GitHub repository: https://github. com/ MarionPerrier/scMLV. Single-cell Multilayer Viewer allows us to merge and visualize up to 5 layers of quantitative and qualitative variables, alone and in combination. Code and software availability Single-cell Multilayer Viewer (scMLV) developed for this study is available on GitHub repository: https://github. com/MarionPerrier/scMLV Single-Cell Signature Scorer and Single-Cell Virtual Cytometer are available at: https://sites. google. com/site/fredsoftwares/products. Seurat v3. 2. 2 is available from the Comprehensive R Archive Network (CRAN) and further details in the installation could be found on https://satijalab.
Implementation For large integer arithmetic computations, we used the libraries GMP  [6]  and NTT  [9]. The NTT library supports multithreading without an additional memory overhead and performs integer multiplication faster than GMP routines when the operands are sufficiently large. This setup is similar to the solution given in  [5]. To generate a list of primes, we used the implementation of the sieve of Eratosthenes provided by the FLINT library  [8]. The source code of our implementation is available at https://github. com/milostatarevic/left-factorial. To compute all remainders r p for primes p in an interval (m, n], we implemented the following four phases. 2. 1. Phase 1: computation of the P i,j. This phase consists of two parts. First, we generated a list of primes in (m, n], then we computed and stored all P i,j using a product tree.
g. protein kinases, phosphatases, transcription factors, etc. ), and 2) functional molecules that are directly involved in tolerance to stress, such as various antioxidants, chaperones, osmotic protectants, etc. (Trudić et al. , 2013). Large collections of oak expressed sequence tags (ESTs) have been generated from various tissues and developmental stages, including 130,000 Sanger sequences and approximately 2. 5 million reads available from public databases  (Ueno et al. , 2010). This catalog constitutes a useful resource for detecting candidate genes controlling traits of interest for the development of new genetic markers for genetic approaches (linkage mapping and QTL detection, association mapping) or for dissection of genetic architecture of adaptive traits  (Alberto et al. , 2010; Casasoli et al.
Morphometric analyses For all individual trees, diameter at breast height (DBH [cm]), absolute height of trees  (H [m] ) and height to the crown (HC [m]) were performed. DBH was measured with classic caliper (Haglöf, Sweden) whereas H and HC were measured using "Vertex III" (Haglöf Sweden AB) apparatus. All statistical analyses for morphometric parameters were performed in Statistica version 13 (TIBCO Software Inc. 2017). One-way analysis of variance (ANOVA) was conducted to determine the statistical significance of the four oak ecological groups to (1) DBH, (2) H and (3) HC. All statistical analyses were performed at p≤0. 05 level of significance.
Process of reverse transcription and construction of cDNA was done by iScript cDNA Synthesis Kit ® from Bio-Rad Company according to the manufacturer's instructions. Two housekeeping genes (18s with 103 % efficiency and efβ with 99 % efficiency) were used to normalize the data and the main criterion for their selection was their stability across biological samples. Prior to gene primers design in Primer 3® software  (Rozen and Skaletsky, 2000)  a search of gene sequences for Arabidopsis thaliana was done in TAIR database (The Arabidopsis Information Resource, 2015). After that, Blast analysis was performed against oak EST database in order to identify and select orthologues based on their e-value (Table  1  and Table 2 ). We checked the specificity of the amplification product using conventional agarose gel electrophoresis and afterwards we performed the quantification.
Acknowledgement This research was conducted under the short-term scientific mission "Drought resistance candidate genes and their expression in the Quercus robur L. species from Srem provenance using qPCR" within  COST Action FP 0905: "Biosafety of  forest transgenic trees: improving the scientific basis for safe tree development and implementation of  EU policy directives. " We would like to thank  Mr Ed Bauer  (retired) from the  USDA Forest Service, Northern Research Station, Institute for Applied Ecosystem Studies, Rhinelander, WI USA  for English and style editing.
Data collection and harmonisation Data collection is done either manually (mostly by email) or is automated, depending on the degree of automation at the data provider side. Although standards for in situ soil moisture data collection are available  (Colliander et al. , 2017; Montzka et al. , 2020) , there is no general agreement within the community, and it is not prescribed for participation in the ISMN. Thus, the data being contributed to the ISMN are heterogeneous with regard to units (e. g. volumetric soil moisture, water depth, mass, soil saturation, and plant-available water), installation depth, integration length, and positioning of the sensors (vertical and horizontal), the metrical system, the sampling interval, and the time zones used for the measurement time stamps. The first harmonisation step for all data and metadata involves the conversion of units to internationally agreed scientific units (e.
, 2011b). All soil moisture measurements provided to the ISMN are converted to volumetric soil moisture in cubic metres by cubic metre (hereafter m 3 m -3 ). Since the majority of networks already shares their measurements in volumetric soil moisture units, often no unit conversion is needed. For the other (mostly historical) networks, measurements are converted to volumetric units using metadata on soil properties (in case measurements are provided as saturation level or plant-available water) and/or the thickness of the soil layer represented by the measurement (in case measurements are provided as water height or mass;  Dorigo et al. , 2011b). Note that, even if all measurements are harmonised in terms of units, differences in sampling volumes related to the sensor design and installation are not accounted for.
For each network, the download contains (i) the measurements and their quality flags, (ii) information about the file data organisation, (iii) a description of the ISMN quality flags, (iv) a metadata file compliant with ISO 19115 and INSPIRE (Infrastructure for Spatial Information in the European Community) metadata standards, and (v) information about static site characteristics (e. g. land cover, climate class, and soil characteristics). The extracted data set files are formatted according to either the CEOP (Coordinated Energy and Water Cycle Observations Project; Williams, 2010) standard or a slightly modified version of the CEOP format which improves data usability  (Dorigo et al. , 2011b). These standards use the ascii format, but a NetCDF format is foreseen for the near future.
Data provider and user involvement The ISMN is entirely built on the voluntary, free-of-charge contributions from scientific and operational providers. This prevents the ISMN from being too prescriptive towards the data providers in terms of delivery intervals, automation, and formatting. Hence, a careful balance is needed between in-W. Dorigo et al. :  A decade of ISMN clusiveness, on the one hand, and data quality standards, on the other. The ISMN facilitates between users and data providers by reporting data quality issues and user feedback to the providers every 6 months. This is done by means of a report on data usage statistics for each individual network, e. g. on the number of downloads, the usage of their data in scientific publications, and flagging statistics. Together with obtaining visibility and citations, obtaining feedback on data usage and data quality is one of the primary motivations for data providers to join the ISMN.
More than 3000 active users have registered since 2009 (status in July 2021). Data download is free of charge but user registration (compliant with the latest European Union General Data Protection Regulation, EU GDPR, privacy standards) is required to prevent illegal redistribution of the data or theft of ground equipment and to track (undisclosed) statistics on data usage that are fed back to the data providers, e. g. by regular reports. News feeds on the ISMN web page and a biannual newsletter inform the users about new networks, new data sets, data quality issues, important publications, workshops, and more. In addition, a dedicated forum and classical email exchange allow users to raise and receive response to issues. Moreover, open-source software packages are available for reading and plotting the data (see the data availability section).
On the other hand, the ISMN flagging reduces the unbiased root mean squared difference (ubRMSD) for both comparisons. The benefit of excluding spurious values is also obvious in Fig. F1a , where points are located below the 1 : 1 line. Again, the benefit is less clear for ESA CCI (Fig. F1c ). Although validations of a satellite and a model-based are not directly comparable, one reason for the different impact is that the ESA CCI retrievals were already flagged in the production process for values outside valid geophysical range, inconsistencies, dense vegetation, freezing, and snow cover, while this is not the case for the ERA5 model data. Consequently, a positive effect of the ISMN flags is more effective for data sets that were not a priori masked than for data sets that were already filtered for spurious observations.
Other quality indicators The automated quality control algorithms offer insight into the quality of the respective measurement time series but not necessarily of the usability of the data sets for specific applications. Gruber et al. (2013)  adopted a triple collocation approach (TCA) to characterise the representativeness errors of ISMN data for coarse-scale (∼ 25 km) use. TCA is a statistical analysis using a combination of three data sets with independent error structures to estimate the random error variance in each of these data sets. Here, we apply the TCA to estimate the representativeness errors of the ISMN data of all networks with sufficient sampling in the period 2001-2019 for application at the coarse scale. ESA CCI SM passive soil moisture (v5. 2; Gruber et al. , 2019b; Dorigo et al.
, 2017)  and top-layer ERA5 volumetric soil water content  (Hersbach et al. , 2020)  are used to complement the triplets. Spatial collocation is carried out using a nearestneighbour method, with a maximum distance of 30 km, while the temporal collocation uses a maximum time difference of 1:20 h between the triplets. An exception was solely made for the PBO_H2O network because its observations are provided daily at 12:00 UTC, while ESA CCI SM is given at 00:00 UTC daily. All measurements that cover, at least partly, the 0. 00-0. 07 m depth interval are used. Systematic differences between the data sets, i. e. multiplicative and additive biases, are removed by scaling ERA5 and ERA CCI SM soil moisture data sets to the in situ data prior to the TCA. The results for different networks are quite diverse (Fig.
7 ). For example, the spread of errors is relatively large for ORACLE but small for others (e. g. African Monsoon Multidisciplinary Analysis -Coupling the Tropical Atmosphere and the Hydrological Cycle (AMMA-CATCH) or Sungkyunkwan University (SKKU)). The median errors per network vary between 0. 03 and 0. 05 m 3 m -3 , with some outliers in both directions. Note that the triple collocation analysis estimates the combined representativeness and sensor errors, although the latter are assumed to be small compared to the natural spatiotemporal variability  (Gruber et al. , 2013). There is a clear trend of decreasing mean errors with increasing sensor depths (Fig. 8a ), which is likely due to a reduction in high-frequency signals and sensor perturbations with depth. Note that this decreasing trend is observed despite the increasing discrepancy in depth support between the ISMN data and the two surface soil moisture data sets (ERA5 and ESA CCI SM).
However, this is not confirmed by our triple collocation results. Possibly, the advantage of the larger spatial support of these systems is counteracted by their lower signal-to-noise ratio. Error information at the site, sensor, or data set level is currently not routinely available for the stations in the ISMN but would be required for a proper weighting of individual stations in large-scale validations  (Gruber et al. , 2018 (Gruber et al. , , 2019b)). 4 Impact of the ISMN on Earth system sciences.
User uptake As mentioned earlier, over 3000 users have registered to the ISMN, while 20-30 new users register each month. Most users are based in the USA, China, India, and Europe (Fig. 9 ). When asked for the intended use of the data, the four main GEO benefit areas are water, disaster, agriculture, and climate sectors, all with a similar share between 16 % and 30 %. Most users come from research organisations (41 %), higher or secondary education (32 %), and non-profit organisations (19 %). Only few users come from public bodies (6 %) or private companies (2 %). The large uptake of the ISMN for soil moisture studies is particularly due to the simplicity of accessing and using multiple data sets from a wide variety of networks. Initially, the ISMN was established to facilitate the calibration and validation of SMOS-based soil moisture products  (Dorigo et al.
Scientific studies Soil moisture measurements from the ISMN have been widely used as reference data sets for the development and evaluation of satellite soil moisture products, which are mostly global coarse-scale surface soil moisture products (Table  E1 ; n = 212). Although initially the goal was to support algorithm development and validation of the SMOS satellite, which was indeed the case for 85 studies, many other satellite missions profited from the ISMN data, most notably SMAP (n = 52), Advanced Scatterometer (ASCAT; n = 50), AMSR-E (n = 46), and ESA CCI (n = 34). Fortuitously, the data have also been discovered for the evaluation of soil moisture products from less used sensors, including the Chinese Feng-Yun 3B, HY-2, and Gaofen-1 satellites  (Parinussa et al. , 2014a (Parinussa et al.
Sheng et al. , 2019; Helgert and Khodayar, 2020)  or directly derive soil moisture from high-resolution synthetic aperture satellites like Sentinel-1 (e. g. Rodionova, 2019b; Foucras et al. , 2020). It should be noted that the ISMN and its contributing networks are mostly designed for analysing time series, thus lacking reference data to assess spatial patterns in the data, particularly in high-resolution products (de Jeu and  Dorigo, 2016). Soil moisture measurements from the ISMN have been used as a training set for various data-driven approaches. In situ observations were ingested into machine learning frameworks together with several ancillary predictor variables, either to simulate soil moisture at a very high spatial resolution  (Zappa et al. , 2019)  or to create long-term records  (O and Orth, 2021).
Greifeneder et al. (2021)  developed a machine learning approach to estimate surface soil moisture from Landsat optical and thermal and Sentinel-1 SAR imagery in the Google Earth Engine, using ISMN soil moisture as target variable. Large-scale monitoring networks are necessary to build reliable models for spatially wide analysis, while dense networks are ideal for accurate localised models  (Senanayake et al. , 2019; Abbaszadeh et al. , 2019). Usage-oriented evaluation studies have focussed on the intercomparison of multiple coarse-scale satellite products, using the ISMN data as a reference, either to select the best performing sensor for a specific application or geographic region (e. g. Beck et al. , 2021; Karthikeyan et al. , 2017)  or to combine them in an optimal way to build a superior product (e.
g. Liu et al. , 2011; Hagan et al. , 2020). Since direct satellite observations of soil moisture are only possible for the surface layer, most studies concentrate on the evaluation of surface soil moisture. Yet, various studies also focus on products derived from surface soil moisture that represent moisture in deeper (root zone) layers, either through exponential filtering (e. g. Paulik et al. , 2014; Tobin et al. , 2017) , empirical models  (Pradhan, 2019) , or by assimilating them in land surface models (e. g. Pablos et al. , 2018; Blyverket et al. , 2019a). Occasionally, the ISMN is used for the evaluation of other satellite data sets, e. g. soil temperature data to validate the freeze/thaw state  (Rautiainen et al. , 2016; Hu et al. , 2019).
ISMN as part of operational services Because of its operational nature and advanced quality control procedures, the ISMN has been identified as the primary reference data source for future operational validation systems for global satellite-based soil moisture products  (Bayat et al. , 2021). But, already today, the ISMN is an integrative part of several operational satellite soil moisture production chains. - Recently, the Quality Assurance for Soil Moisture (QA4SM) service (https://qa4sm. eu, last access: 28 October 2021) was initiated to bring together validation methodologies, community protocols, reference data, and satellite observations to evaluate and intercompare soil moisture data products in a coherent, standardised, and transparent way. In situ data sourced from the ISMN are an integral part of this validation system  (Scanlon et al.
Model development and validation In situ measurements are the most important reference source when assessing the performance of land surface models, re-analyses products, and hydrological models. Although also satellite observations are a valuable validation source (e. g. Szczypta et al. , 2014) , these measure only the upper ∼ 5 cm of the soil and, hence, do not allow for the validation of deeper layers. Besides, state-of-the-art reanalysis products like ERA5 Hersbach et al. , 2020 assimilate satellite soil moisture observations so that, for these products, in situ data remain the only truly independent validation reference. Since the sampling rate (hourly) of the ISMN observations is generally higher, and their spatial support lower than that of most models  (Reichle et al. , 2004) , model evaluation is, in principle, not limited by the spatial and temporal resolution of the in situ data, although representativeness issues often remain.
Among others, ISMN data have been used to validate new highresolution reanalysis products over the USA  (McDonough et al. , 2018)  and Europe  (Naz et al. , 2020). Apart from soil moisture, soil temperature data have also been drawn from the ISMN for model validation purposes (e. g. Wang et al. , 2016; Albergel et al. , 2015). Besides the evaluation of hydrological or land surface model improvements, the ISMN has also frequently served model development in a more fundamental way. For example,  Hartmann et al. (2015)  developed a large-scale karstic groundwater recharge model over Europe and the Mediterranean and calibrated and evaluated this model with observations of actual evapotranspiration from FLUXNET  (Baldocchi et al. , 2001)  and soil water content data from the ISMN. Martínez-Fernández et al.
(2021)  used data from the ISMN to assess the fraction of precipitation that is stored in the soil profile. Calvet et al. (2016)  used soil moisture and temperature data from the SMOSMANIA (soil moisture observing system -meteorological automatic network integrated application) network contained in the ISMN to derive pedotransfer functions for the soil quartz fraction in southern France. Pal et al. (2016)  developed a statistical model to estimate the vertical soil moisture profile using SCAN data of the ISMN as source, while, on a similar note,  Shin et al. (2018)  developed a non-parametric evolutionary algorithm to predict soil moisture dynamics using ISMN data over Oklahoma and Illinois. Similarly,  Jalilvand et al. (2018)  estimated the drainage rate from surface soil moisture drydowns from ISMN data.
METHODOLOGY Procedure and measurement Subjects were recruited among men seeking treatment for CSBD at clinics in Warsaw, Poland. Healthy controls (HC) were recruited online. For both group participation in the study were connected to the monetary gratification. All subjects completed questionnaires designed to measure symptoms of CSBD: the Sexual Addiction Screening Test  (Gola, Wordecha, et al. , 2016; Gola, Skorko, et al. , 2016) , the Brief Pornography Screening Test  (Kraus et al. , 2020) , and the Hypersexual Behavior Inventory  (Reid, Garos, & Carpenter, 2011; Reid, Garos, Carpenter, et al. , 2011). We also collected data from questionnaires measuring affective processing, compulsivity, and impulsivity: the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ,  Torrubia, Avila, Molto, & Caseras, 2001; Wytykowska, Białaszek, & Ostaszewski, 2014) , the State-Trait Anxiety Scale (STAI,  Spielberger, 1989; Wrze sniewski, Sosnowski, & Matusik, 2002) , the Hospital Anxiety and Depression Scale (HADS,  Zigmond & Snaith, 1983) , the Obsessive-Compulsive Inventory -Revised (OCI-R,  Foa et al.
Analysis of the dataset Data analysis was performed using MATLAB R2017a (The Math Works Inc. Natick, USA), the SPSS statistics package (link: https://www. ibm. com/products/spss-statistics), and JASP software  (JASP Team, 2019). First, subject characteristics and questionnaire scores were compared using FDT -1A; the task consists of emotional and control blocks, in which subjects see three stimuli on the screen, one at the top and two below it, and are asked to choose which of the two lower stimuli corresponds to the upper one; FDT is designed to measure the processing of emotions, RAT -1B; the participant is asked to bet in a lottery of various winning probability and ambiguity level, as well as various payoff amounts; the task is used to study the decision-making process under two conditions of uncertainty, involving either risk or ambiguity, EST -1C; the emotional version of the Stroop Task, in which emotional words are displayed in various colors and the subject is asked to indicate the right color; the reaction time is significantly different if the words are emotionally charged (as opposed to neutral words), so that EST can be used to study emotions processing, IDT -1D; IDT consists of two phases: the cue phase (presentation of icons representing different types of rewards, with payoff value and probability of win) and the reward-processing phase (designed to measure the subjective hedonistic value of the reward); the two phases are separated by a discriminatory task (the subject must press a button as soon as possible in response to figures), which, if the correct key was pressed sufficiently fast, is followed by a reward displayed on the screen (either a picture of a naked woman in erotic trials, or a picture representing a sum of money in monetary trials), SDT -1E; the participant estimates the risk of contracting a sexually transmitted disease for each hypothetical sexual partner, and also his readiness to have sex with the partner, either immediately but without a condom, or with a condom, but not immediately (with a delay varying from 1 h to 3 months); the task measures the discounting of sexual activity, LT -1F; a blue or red door appears on the screen and the participant is asked to decide whether to open the door or not; after the decision a reward may be displayed (erotic or monetary), with a probability indicated by the color of the door; LT makes it possible to study the process of probability learning in the context of sexual/monetary rewards, ANT -1G; a row of five arrows, pointing in the same or in different directions (congruent vs.