dataesr/scientific-paragraphs-categorization huggingface dataset:
Integrated development environments (IDEs): facilitating the use of R IDEs are software applications that are used to facilitate coding by providing the code editor, compiler, or interpreter and debugger within a single graphical user interface (GUI). A range of IDEs exist for R, such as Eclipse -StatET, (an Eclipse-based IDE; e. g. des  Riviêres and Wiegand, 2004; Wahlbrink, 2016) , Emacs Speaks Statistics (ESS; an add-on package for GNU Emacs;  Rossini et al. , 2004 ), Microsoft's R Tools for Visual Studio (RTVS; a plug-in for the Microsoft Visual Studio IDE; e. g. Beard, 2016) , RKWard (which integrates with the KDE desktop environment;  Rödiger et al. , 2012) , Tinn-R (a replacement for the basic code editor designed to replace Rgui;  Faria et al. , 2008) , or Vim or Neovim (a terminal-based working environment designed to facilitate working in a pure command-line interface; e.
The RStudio environment makes it straightforward for hydrologists to conduct a range of tasks (e. g. visualize data; create dynamic graphs or web applications with shiny; use version control; or develop and view R Markdown presentations, for example in LaTeX, HTML, or presentation slides), all within the IDE. Other advantages of RStudio include the easy access to the help window, the ease of package management and updating, code debugging, or even the easy development of packages in one location. All of these benefits contribute to facilitating open hydrological science. RStudio version 1. 2 includes many new features to help interact with other programming platforms, such as the reticulate  (Allaire et al. , 2018b)  and r2d3  (Luraschi and Allaire, 2018)  packages, which make it easy to call Python and D3 from within R sessions and R Notebooks.
Database connections can be established using a range of packages such as RPostgreSQL  (Conway et al. , 2017)  or RSQLite  (Müller et al. , 2018). Distributed data frames can be implemented using packages such as sparklyr  (Luraschi et al. , 2018). As the volume of available data increases and hydrologists use a greater number of models and ensembles, parallel computing -where many calculations are carried out simultaneously instead of sequentially -has become an essential tool in computational hydrology and has sped up analyses. For instance, instead of using traditional for-loops (an approach where one action is carried out over a dataset iteratively), the data may be broken down into groups (e. g. by year or by season), and functions can be applied to each group in parallel. The performance boosts that can be achieved by parallelizing the code (i.
Other packages that are widely used for parallel computing in hydrology and other areas include the snowfall  (Knaus, 2015)  package, which facilitates the development of parallel R programs, e. g. by including extended error checks. Additionally, some packages have parallel functionalities that are integrated directly within the package, such as the h2o package  (LeDell et al. , 2018)  that provides an interface to H2O, a scalable platform offering parallelized implementations of supervised and unsupervised machine learning algorithms.
Sharing R code with the community Open research practices bring significant benefits to researchers  (McKiernan et al. , 2016) , such as increases in citations, media attention, potential collaborators, job opportunities, and funding opportunities. Most importantly, sharing code increases the likelihood that an approach will be used by other scientists in their research and saves new users the trouble of "reinventing the wheel" and writing codes that have already been developed by others. There are many different platforms for sharing and publishing R code such as GitHub, Figshare, and RPubs or Plotly (also for dashboards and interactive plots). To obtain a DOI, users may wish to use repositories such as Zenodo, a general purpose open-access repository which allows researchers in the sciences and humanities to deposit datasets, research software, and create reports.
This feedback between users and developers is one key route to scientific progress. Users can identify issues or suggest improvements by commenting on online collaboration platforms (e. g. GitHub or GitLab) or by emailing the developer or maintainer. Most packages are hosted on these repository hosting platforms before becoming avail-able via the CRAN archive, ensuring that a certain standard and best practice are met. Although CRAN itself does not have any mechanism to check the quality or cleanness of the code, there is a suite of packages that are used to (i) ensure clean code and documentation that follow a widely accepted style guide, such as the lintr  (Hester, 2018), goodpractice (Csardi and Frick, 2018) , and hunspell  (Ooms, 2018)  packages and to (ii) rigorously test the functions against predefined outcomes, using for example the testthat package  (Wickham, 2011).
In comparison with GitHub, CRAN is an organized repository which provides executables for several platforms, whereas finding an hydrological R package on GitHub can be difficult. The comparison between the two is somewhat like visiting a curated art auction versus an antique store. Additionally, CRAN has more thorough checks and gives visibility and/or searchability to a package, also signalling that the author intends to maintain the package over the long term. Developing an R package requires a structured approach, just like writing a scientific paper. There are many generalist resources for writing R packages, such as the R package book (see http://r-pkgs. had. co. nz; Wickham, 2015)  or a suite of video tutorials on YouTube. The main requirements for publishing a package are listed on the CRAN website.
Harmonizing hydrological workflows One of the main challenges today for hydrological workflow harmonization is to standardize calls for data which are structured in different ways by data providers around the world. While the number of packages for hydrological data retrieval has grown considerably (see Table  1 ), the packages are structured differently because they were set up independently and because the underlying hydrological and hydrometric datasets differ from country to country. As more nations develop similar hydrological data acquisition packages, we believe it would be worth implementing a common syntax and data output form. For example, it would be ideal to use consistent APIs and output objects across packages. Suggesting the "ideal" format is beyond the scope of this paper, but a potential task force might be set up to help draft a way forward for the community.
APIs: hydrological data acquisition and provision APIs play a key role in hydrological data acquisition and provision and are likely to become increasingly important in the future. An API is a set of code which usually includes subroutine definitions, communication protocols, and tools for building software and interacting with different datasets. APIs are specific to the use case by definition, but the interface is often provided in HTTP (i. e. web protocol) so that requests and responses can be made and received by a wide range of languages or systems. For R users, hydrological interaction with HTTP APIs usually comes in the form of data acquisition packages such as the aforementioned packages rnrfa  (Vitolo et al. , 2016a) , tidyhydat  (Albers, 2017) , or daymetr  (Hufkens et al. , 2018).
Teaching hydrology in R Due to its relative ease of use and open-source nature, R is increasingly being used as an interactive tool for teaching in the hydrological sciences. Many examples of typical hydrological analyses can be found online as tutorials (see for example,  Hurley, 2018). The USGS also publish relevant examples as a hydrological blog at https://owi. usgs. gov/blog, which may be of interest to the community. R packages such as airGRteaching or TUWmodel have also recently been developed to facilitate teaching of hydrological modelling. The TUWmodel package  (Viglione and Parajka, 2019)  provides a lumped conceptual rainfall-runoff model based on the well-known HBV model  (Bergström and Forsman, 1973)  and is designed for educational purposes. The airGRteaching package  (Delaigue et al.
There is now an increasing number of online applications that allow beginners to learn R in a sandbox, i. e. a virtual space for testing coding online. Sandboxes are particularly useful for introducing basic methods in computational hydrology without having to master the technicalities of R. Examples of R sandboxes include the RStudio Cloud (https://rstudio. cloud), which can be used to learn, teach, or test R code. Another sandbox, initially called R-Fiddle, is now called datacamp-light (https://github. com/datacamp/ datacamp-light). Many other R sandboxes can be found online. Thus, rather than installing R on multiple PCs, sandboxes can be used by educators or lecturers by asking students to connect directly to the web.
Developing the community: short courses, help desks, and meet-up events This paper reflects the strong collective desire to develop the community of computational hydrologists in R. As mentioned in Sect. 2. 5, the R-Hydro community has been meeting regularly in recent years at the EGU General Assembly during the short course Using R in Hydrology. This course is run annually by the YHS and is typically attended by a wide range of hydrologists, ranging from beginners to more experienced users (Fig. 2 ). The resources and teaching presentations from the short course are made available to the community online on the YHS GitHub pages (https://github. com/hydrosoc/) and archived on the Zenodo repository (see  Boessenkool et al. , 2017; Slater et al. , 2018; Hurley et al. , 2019). These presentations include a range of topics, such as obtaining, cleaning, and visualizing hydrological data with R; parallel and high-performance computing for hydrologists; automating tasks; using R Shiny with hydrological data; modelling the hydrological cycle in snow-dominated catchments; community-led initiatives; using R as a GIS; discharge time-series visualization; extreme value statistics; hydrological modelling; trend analysis; accessing hydrological data using web APIs; extracting gridded netCDF data; report generation; and other hydrological tasks in R.
com/rusersoxford). There is also an ongoing push for greater inclusivity of underrepresented groups in programming, as exemplified by the rapid growth of the global R-Ladies movement (Fig. 7 ). In addition, the computational community has been trying to provide support to other programmers by running coding help desks such as at the American Geophysical Union (AGU) 2018 fall meeting in the career centre. Geoscientist volunteers ran the desk in 2018 to provide perspective and advice to other coders, with a range of short, 10 min tutorials on topics such as keeping track of code versions with GitHub, making high-quality plots, or using a new package or library. We anticipate that such help desks, short courses, and meet-up sessions will continue to help grow the computational hydrology community in future years.
Acknowledgements. We thank two anonymous reviewers,  Michael Stoelzle , and  Paul Astagneau  for comments that improved the paper. We also thank all those who have contributed to the  R-Hydro community  and the R community more broadly, whether by developing and sharing their code or helping and teaching others to use these tools. In particular, we thank the guest speakers who participated in the Using R in Hydrology EGU short course. Review statement. This paper was edited by  Erwin Zehe  and reviewed by  Michael Stoelzle  and two anonymous referees.
28, 2019 The McClear service has several hundreds of users, made of academics, researchers, consultants and companies in various domains. They provide valuable validations of the McClear service and feedbacks on its limitations, from which we identified several issues in the model  This paper describes how we tackled several of these issues to yield  We assessed the benefits of the changes by comparing  and by comparing against ground measurements of SSI. Section 2 is an overview of. Section 3 lists the identified drawbacks; it describes the changes brought to overcome these issues and shows the improvements. Section 4 presents the measurements of the SSI performed in the BSRN network that serve as a reference to quantify the uncertainties of. The measurements dataset is exactly the same as used by  Lefèvre et al.
One can see abrupt changes of the slope of the curve in the blue curve at 05:00 UT and 19:00 UT. We looked for a new function with a limited number of parameters that does not exhibit such discontinuities and offers a better accuracy than the MLB. In the process, about 50 either physically or numerically motivated fit functions were created by using different interpolation techniques: piecewise linear interpolation, polynomial fitting, and barycentric interpolant. Interpolations of G, B, and D and several other related quantities such as KT, log(G), etc. were tested. Furthermore, several interpolation variables linked to θ such as θ itself, cos(θ), arccos(θ), log(1 + cos(θ)), or outputs of various airmass functions were evaluated. Performances of each function were assessed by comparing its outputs against a reference dataset of SSI.
1°. We computed the differences between the reference dataset and the results of a given function, and then their mean and standard deviation. Furthermore, we created bins of θ and for each bin, we computed the mean, the minimum, and the maximum of the differences. The Figs. 2, 3 and 4 are plots of these values for several functions. Fig. 2  is the plot for G given by the 5-piecewise MLB interpolation. One may see that the amplitude of errors is large for θ < 60°, the minimum is less than -2 W m -2 and the maximum is greater than 3 W m -2. As θ increases, the error tends to be positive yielding an overestimation of G. We observe bounces at 60°, 75°, 80°, 85°and 89. 9°which induce strong changes in slope at these nodes. Such changes in slope are visible in daily profiles of SSI and clearness indices as already illustrated in Fig.
By the means of Chebyshev polynomials of the first kind, we obtain the nine Chebyshev nodes, i. e. the values of log(1 + cos(θ)) that are optimal for the computation of a n over the range [0, 90]°, with respect to the maximum absolute error within this range. For each of the 1000 cases, B * and D * at each of these nine nodes, and then B and D were computed. Fig. 5  exhibits the mean, maximum and minimum of the differences between the reference datasets and the results of the selected function for G. One may see that the errors are very small, i. e. less than 0. 7 W m -2 , for any θ. There are no discontinuities in the first derivatives. Similar results are attained for B and D. The case of θ > 90°has not been treated. However, the present approach may be extended to such cases provided lib-Radtran is run with an appropriate solver such as MYS-TIC  (Emde et al.
(2013). Here, the aerosol mixture was classified as "urban" during the day at the BSRN station Carpentras (France). From 08:00 to 11:30 UT, the mixture was erroneously classified as "maritime polluted", yielding a 3 % increase in G. The issue was fixed by replacing the classification by a smoother approach proposed by  Ceamanos et al. (2014b). It consists in firstly computing a partial SSI for each aerosol component and then computing the SSI for the actual atmosphere by mixing the partial SSIs. Implementing their approach was not straightforward because the CAMS outputs have to be mapped onto the libRadtran inputs. The database OPAC in libRadtran comprises among others, microphysical and optical properties of 10 aerosol components:   et al. (2009)  mentioned that optical properties of aerosols in CAMS are taken from the OPAC aerosol components.
The BSRN dataset and the protocol for validation After   2 ). These stations experience various climates as shown in Table  3. Table  4  reports the number of measurements in cloud-free conditions and the means of G, B, KT, and KT B. The validation was made by comparing G and B of the reference dataset and the McClear-v3 estimates for coincident instants and location. The discrepancies at each instant were computed by subtracting the observations from the McClear-v3 estimates and they were summarized by the bias, the standard deviation, and the root mean square error. Relative values were expressed with respect to the means given in Table  4. The Pearson correlation coefficients, slopes and offsets of the leastsquares fitting lines were also computed. This comparison was performed for G, B, KT, and KT B at each site, and all instants from all years.
The results hereafter will be presented for G and B, the former combining the over-and underestimation of its direct and diffuse components. The pyranometers and pyrheliometers measure the solar radiation in the range  [285, 2800]  nm approximately while McClear provides the SSI for the range  [240, 4606]  nm. The difference in G and B in cloud-free conditions amounts to a few W m -2. For the sake of this validation, a set of abaci was computed that fits the spectral range of the measurements. Similarly, we computed A limitation of the validation is the lack of large θ in the dataset from  Lefèvre et al. (2013). Because of their criteria for selecting cloud-free instants, all instants are within [sunrise + 90 min, sunset -90 min]. Hence, the improvements in interpolating the SSI as a function of θ angle will not be fully evidenced here.
having shown the improvements brought by McClear-v3 regarding several artifacts identified in McClear-v2, this section is dedicated to the validation of McClear-v3 against measurements of SSI performed at ground stations. We used the same reference dataset 156 B. Gschwind et al. : Improving the McClear model estimating the downwelling solar radiation Meteorol. Z. (Contrib. Atm. Sci. ) List of BSRN stations, their country, their geographical coordinates, and the period of measurements. Amsl: above mean sea level.
Acknowledgments The research leading to these results has partly received funding from the  Copernicus Atmosphere Monitoring Service , a program being operated by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Union. The authors thank all operators of the ground stations of the BSRN network for their valuable measurements and the  Alfred-Wegener Institute  for hosting the BSRN website. The authors thank the team developing libRadtran (http://www. libradtran. org, last access:  2018-04-19 ). They thank  X. Ceamanos ,  C. Emde  and  B. Mayer  for their help about the modeling of the aerosols. They thank  L. Saboret  and  E. Wey  for their valuable help in setting up the McClear-v3 application on the Web. They also thank  W. Wandji Nyamsi ,  Y. Aoun ,  S.
europa. eu/project/rcn/101810_en. html). Haplotypes inferred in phased DNA are combinations of multiple SNPs across the human genome and supposedly better capture the genetic information in diploid individuals at the population level (https://cordis. europa. eu/result/rcn/195753_fr. html). This study was aimed to perform a fine-scale haplotype mapping of genes of BCAA metabolism or its regulation in two European populations (French and Romanians) affected by MetS with various degree of IR in the frame of MEDIGENE program exploring Mediterranean populations. In a subgroup of French subjects, data were also available to tentatively correlate findings with in vivo insulin sensitivity measured by IVGTT and BCAA plasma levels. This paper describes the identification of four genomic regions close to MUT, AACS, SLC6A15 and PRKCA genes associated with IR and dissects the complex association signal delivered by finescale haplotype mapping compared with independent SNPs.
Patient's data were recorded through a secure portal (https://xz. univ-montp1. fr) using the MEDIPAD software and were stored in Medigene, Anthropological & Genetic DataBase (MAGDB)  [14]. For replication, we used an extended sample (n = 832) from Southern Europe (Turkey, Italy, Greece and Spain). IR was assessed using the Homeostasis Model Assessment (HOMA IR ) index. Insulin resistant (IR) subjects were defined based on fasting insulin + 2 SEM in normal weight controls (cutoff values were 14. 6 and 8. 9 μU/ml for French and Romanians, respectively). In the subgroup of French subjects, HOMA IR was compared with the insulin sensitivity index (S I ) from IVGTT and expressed in min -1 /(μU ml -1 ) x 10 -4 , as previously described  [33]. Plasma BCAA were detected by liquid chromatography-mass spectrometry (LC-MS) and expressed in μmol/L.
Genotyping and imputation Genomic DNA was genotyped with the customized MEDISCOPE chip by Affymetrix (Santa Clara, CA) containing 758,000 SNPs as previously described  [34]  using facilities of the Biocomputing (BC/SNP max ) platform (https://bio. tools/BC_SNPmax). Before imputation, all studied genes were at SNP density identical with the regular EUR 500k chip. Genotyping was performed at the IDIBAPS core facilities (Barcelona, Spain) using the GeneTitan instrument. Quality control (QC) was performed following Affymetrix protocols and yielded a mean call rate for SNPs > 97%. SNPs with Minor Allele Frequency (MAF) < 0. 01 or divergent from Hardy-Weinberg (HW) equilibrium (P < 10 -4 ) were excluded. Principal Component Analysis (PCA) was performed on 654,000 SNPs, merged with HapMap data and pruned (r 2 > 0.
8). For imputation, genotyped data were imputed with BEAGLE 4. 1 program using the 1000 Genomes Project (Phase III) (www. 1000genomes. org/data) as SNP reference panel  [35]. Imputed SNPs (conform-gt) were filtered for accuracy metrics (AR 2 > 0. 86) and MAF > 0. 01  [36, 37]. Genomic locations (GRCh37/hg19, by February 2009) were retrieved from the Axiom Analysis Suite 1. 1 from the Mapfile (MEDISCOPE) and then extended to 10 kb upstream and downstream. ).
Haplotype-mapping Haplotype mapping was conducted in two steps. In the first step, to identify the most significant hits for association, we applied a sliding window procedure using SNP & Variation Suite v8. 4. 4 program (Golden Helix. Inc. , Bozeman, MT, www. goldenhelix. com) with a length of 4 SNPs, the expectation/maximization (EM) algorithm for haplotype inference (frequency threshold of 0. 01) and Gabriel's method for linkage disequilibrium (LD) blocks  [38]. In a second step, after location of positive hits among genes, longer haplotypes of multiple SNPs were analyzed in HAPLOVIEW 3. 1  [39]. In this program, LD blocking was checked and refined and the association signal was analyzed in detail. When indicated we also used the "four gamete" rule for LD blocking. As a rule, we extended analysis of the initial hit with one LD block upstream and downstream in the respective gene.
Haplotype pairs were assigned to individuals in the population by PHASE 2. 1 program  [40]. After the location of regions of interest, a number of 104 SNPs corresponding to these regions were used to compare the haplotype approach with that of independent SNPs in association, replication and power calculation. The power calculated (PBAT) in the same Golden Helix program, which indicated that a power at 0. 85 would be expected from SNPs with MAF > 0. 2 and displaying effects with OR � 2. 5.
In ANOVA, statistical significance was determined at 2n chromosomes and for plasma BCAA level, extreme values ± 2SD for the entire population were excluded, while significance among groups was determined by a posteriori Student-Newman-Keuls test. To correct HOMA IR values for the body mass index (BMI), we calculated predicted values of the HOMA IR as a function of BMI in each ethnic sample and separately in men and women and then residuals were calculated as previously shown  [41]. Values of S I were obtained using the minimal model calculation from IVGTT and tested as 1/S I as previously reported  [33]. HaploReg v. 4. 1 program was used to predict transcriptional activity of SNPs in each haplotype (http://archive. broadinstitute. org/mammals/haploreg).