dataesr/scientific-paragraphs-categorization huggingface dataset:
This has not been achieved before using C with a spectroscopic survey and the constraint is as tight as the one obtained from the state-of-the-art Dark Energy Collaboration analysis, using a combination of DES galaxy clustering & weak lensing, Planck, JLA, and BAO (DES Collaboration et al. 2017). 3. For the models and datasets considered, we find very high values for the Bayes factor, R, when combining BOSS C s, Planck, and JLA. We highlight: R BOSS+PLANCK+JLA 4 × 10 4 for ΛCDM and R BOSS-10%+PLANCK+JLA 3 × 10 5 for ΛCDM varying neutrino masses. 4. The Bayes factor can also be used for model selection. Considering the combination of datasets, the Bayes factor between ΛCDM and wCDM is R w,Λ = P( ì D BOSS+Planck+JLA |wCDM) P( ì D BOSS+Planck+JLA |ΛCDM) = 0. 67 where ì D here represents the overall combination of data vectors.
This indicates that this combination prefers slightly ΛCDM to wCDM, although no strong conclusion can be made. 5. We find a small tension between BOSS C s and Planck for S 8 in all models considered, with BOSS preferring smaller values. For example, for ΛCDM: S 8 = 0. 715 +0. 072 -0. 064 (BOSS) S 8 = 0. 850 +0. 023 -0. 021 (Planck) although the combination of these datasets prefers higher values such as Planck (see table  4 ) and the Bayes factor suggest the datasets are compatible. We conclude that such tension can be investigated further with this method as LSS data increases in size and depth. 6. Even though we do not show these results, we performed a cosmological analysis using a ΛCDM model but fixing m ν = 0eV and compared with the ΛCDM results from Section 6. 4, which has a m ν fixed to 0.
Cosmological for the ΛCDM + m ν model, using the 10% ma x cut, varying now 6 cosmological parameters, including the sum of neutrino masses considering only one massive species. This figure contains a combination of sampled and relevant derived parameters from the chains: Ω m , Ω b , S 8 , h, n s , and m ν. Note that the Planck chains also varied τ r ei o. The blue contours where estimated from the BOSS C s data alone using the SH16 likelihood; the green contours are a combination the BOSS likelihood and JLA data; the red contours are the Planck high-TT, TE, EE and low-P likelihood results; finally, the purple contours are a combination of the three probes: BOSS C , JLA and Planck (also high-TT, TE, EE and low-P). For this scale cut, the combination of datasets yields an upper bound on m ν < 0.
Using the method proposed by  McLeod et al. (2017)  the spectroscopic sample has the potential of "fixing" the photometric redshift limitations when probing the photometric clustering redshift distribution together with the cosmological parameters. With increasingly large future photometric and spectroscopic surveys such as LSST (LSST Dark Energy Science Collaboration 2012), DESI  (Levi et al. 2013 ), J-PAS  (Benitez et al. 2014) , and Euclid  (Laureijs et al. 2011) , the future of precision cosmology lies in our ability to combine datasets from across the entire electromagnetic spectrum. The angular power spectrum approach offers a unified framework for coherently combining different datasets in order to obtain maximal information from each  (Joachimi & Bridle 2010; Kirk et al. 2015; McLeod et al.
Figure13. 2D m × S 8 marginalised credible intervals for a ΛCDM cosmology. In this figure we show in detail the cosmological results from Section 6. 4 for BOSS C s only (blue); BOSS C s plus JLA (green); BOSS C s plus JLA and Planck (purple); together with results using the post-reconstruction full shape (incl. f 8 (z)) from Alam et al. (2016)  consensus results (pink, dotdashed), and Planck alone (red). In order to compare our results to a weak-lensing probe, we also show the results from Hildebrandt et al. (2017) (grey, dashed). For details on the external datasets, see Section 6. 3.
Figure18. Cosmological constraints for the wCDM model varying now 6 cosmological parameters. This figure contains a combination of sampled and relevant derived parameters from the chains: Ω m , Ω b , S 8 , h, n s , and w 0. Note that the Planck chains also varied τ r ei o. The blue contours where estimated from the BOSS C s data alone using the SH16 likelihood; green contours are a combination the BOSS likelihood and JLA data; red contours are the Planck high-TT, TE, EE and low-P likelihood results; finally, the purple contours are a combination of the three probes: BOSS C , JLA and Planck (also high-TT, TE, EE and low-P). The apparent cuts in the Planck alone contours are due to the prior in h. Note, again, that none of the results here use Planck Lensing data.
Figure22. Cosmological for the ΛCDM + m ν model, using the 10% ma x cut, varying now 6 cosmological parameters, including the sum of neutrino masses considering only one massive species. This figure contains a combination of sampled and relevant derived parameters from the chains: Ω m , Ω b , S 8 , h, n s , and m ν. Note that the Planck chains also varied τ r ei o. The blue contours where estimated from the BOSS C s data alone using the SH16 likelihood; the green contours are a combination the BOSS likelihood and JLA data; the red contours are the Planck high-TT, TE, EE and low-P likelihood results; finally, the purple contours are a combination of the three probes: BOSS C , JLA and Planck (also high-TT, TE, EE and low-P). For this scale cut, the combination of datasets yields an upper bound on m ν < 0.
ACKNOWLEDGEMENTS We would like to acknowledge the  BOSS Collaboration  for their great work and for making their data public. AL and BM thank the support of the Brazilian people through the  Science without Borders CNPq Brazil  fellowships. BM and FBA acknowledge support from the  European Community  through the  DEDALE grant  (contract no. 665044 ) within the  H2020 Framework Program  of the  European Commission. FBA acknowledges the  Royal Society  for support via a  Royal Society URF. AC acknowledges the  Royal Astronomical Society  for support via a summer bursary. OL acknowledges support from an  European Research Council Advanced  Grant  FP7/291329  and support from the  UK Science and Technology Research Council (STFC)  Grant No. ST/M001334/1. MM acknowledged the support from the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement No  6655919.
Furthermore, the relative motions between the signal source and receiving sensors lead to the Doppler effect, further complicating the mechanisms of the signal distortions  [17] [18] [19]. Therefore, retrieving the desired information directly from the distorted signals is difficult. Passive acoustic detections are used to estimate the directions by analyzing the power of the synthetic beams from different directions. The conventional (or Bartlett) beamformer is a natural extension of the classical Fourier-based spectral analysis  [20]  to sonar array data and has been widely used. For arbitrary geometric arrays, the algorithm maximizes the beamforming output power of a given input signal. Given the direction of arrival θ, the received array data with additive noise can be written as x(t) = a(θ)r(t) = a(θ) L ∑ j=1 A j (t)s i (t -τ j (t)) + a(θ)n(t) (4) where x(t) can be considered as a multichannel random process, a(θ) = [a 1 (θ), • • • , a N (θ)] T is the steering vector, and N is the number of elements in a geometric array.
Temporal Efficiency Acoustic target tracking is a typical real-time application, being constrained by the time interval of the data sampling. In this paper, the processing time of each snap must be shorter than the snap interval if the real-time processing capacity is desired. This subsection estimates the running time of the proposed method. We base the algorithm implementing on Matlab 2017b within 64-bit Windows-10 operation system. The processor is Inter (R) Core (TM) i7-8550U, CPU @1. 80 GHz. Because the extraction process of PCA spectrum possesses high data dependency, resulting in low parallelism, we do not make any parallel optimizations on the final implementation. The running time measurement includes the PCA enforcement and tracking processes of each snap. Figure  20  compares the measurement results of four different implementations.
The new scheme by  Pflug et al. (2019)  showed improvement in simulating SWE compared to the default model  (Liston and Elder, 2006a)  which tended to strongly overestimate the SWE at a Snow Telemetry station (SNOTEL) in the Washington Olympic Mountains, USA. The default liquid water percolation scheme did not allow the release of meltwater runoff during the accumulation season, causing a large over-accumulation of SWE. This process is typical of warm Mediterranean snowpack, such as the Mount Lebanon snowpack. In this study, we make use of a newly available snow dataset  (Fayad et al. , 2017b)  to evaluate this new percolation scheme in SnowModel to compute the SWE distribution and its evolution, for the first time, in three basins in the warm Mediterranean mountains of Lebanon. We evaluate the model outputs using both in situ (continuous snow depth records at AWS and snow course measurements) and remote sensing data (MODIS snow cover area).
The different land classes are needed to account for the snow holding capacities and canopy radiation effects  (Liston and Elder, 2006a) , however bare soil is the largely dominant class in the snow dominated region of the study area. SnowModel was forced using the half-hourly meteorological data collected at the AWSs (LAQ, MZA, CED) for three snow seasons between 2013 and 2016 (01 November to 30 June). The data were taken from  Fayad et al. (2017c)  and only reformatted to match SnowModel input format. We used the following input meteorological variables: precipitation (LAQ, MZA), air temperature (LAQ, MZA, CED), air relative humidity (LAQ, MZA, CED), wind speed and direction (LAQ, MZA, CED). No observations were available for LAQ AWS during snow season 2013-2014  (Fayad et al. , 2017b). We did not use the shortwave radiation measurements because of the large data gaps.
Model evaluation The validation dataset includes (1) half-hourly snow height (HS), snow albedo and incoming shortwave radiation collected at the AWS from W1314 to W1516, (2) bi-weekly manual snow density measurements collected near the AWS during the snow seasons of W1415 and W1516, and (3) daily snow cover area (SCA) observations from MODIS from W1314 to W1516. All these data are fully described in  Fayad et al. (2017b)  and are available as open data in a public repository  (Fayad et al. , 2017c). Nearly continuous snow depth is available for water years W1415 and W1516 at LAQ and for water years W1314, W1415 and W1516 at MZA and CED. Half-hourly snow depth records were averaged to the daily timestep. We computed the daily albedo of the surface below AWS from half-hourly upward and downward-looking pyranometers measurements following  Stroeve et al.
(2013) , i. e. by computing the ratio of the daily incoming and reflected shortwave radiation totals. Summer albedo values ranged between 0. 2 and 0. 4 depending on the site and the year, hence we removed albedo values lower than 0. 5 to keep only snow albedo measurements. The calculated daily snow albedo values and the incoming shortwave radiation were averaged to monthly values. The MODIS dataset is a daily cloud-free time series of snow cover maps providing the snow presence and absence at 500 m resolution (binary SCA). The model outputs at 100 m resolution were converted to binary SCA using a threshold of 10 mm w. e. of SWE.
Introduction During the last decade, the joint developments of measurement devices and software environments advanced biomechanics investigations toward increasingly complex movements performed in ecological conditions. For example, most of the motion analysis systems currently track participants' movement outdoor in natural light or musculoskeletal software's make motion analysis and dynamic simulation accessible to a wide range of researchers. OPENSIM  (Delp et al. 2007 ), an open-source musculoskeletal software, has become the mainstay in numerous biomechanical studies by providing a full access to source code, and then allowed researchers to extend their capabilities to an infinity of situations. However, for outdoor sport activities such as rowing, acquisition of the rower's kinematics on-water is still challenging.
Most of rowing biomechanics studies are performed on ergometer while significant differences were observed with on-water conditions  (Miarka et al. 2018). In other words, rower's movement on-water must be estimated with another method. The rower's lower-limb kinematics can be modelled as a closed-loop kinematic chain by assuming that the feet and the pelvis are always in touch with the foot stretchers and the seat, respectively. The aim of this study is to develop a closed-loop multibody model to assess the rowers' lower-limb kinematics being usable during on-water training sessions with few measurements tools. By this way, we suggest that the kinematics of the hips, knees and ankles can be reasonably estimated via the knowledge of the translation and rotation of the pelvis and feet only. In order to assess the performance of this method, the comparison to a traditional inverse kinematics openloop method based on data measured with a motion capture system was carried out in laboratory.
Methods Eleven female (age: 26 ± 6 years; mass: 71 ± 6 kg; height: 1. 74 ± 0. 04 m) and 17 male rowers (age: 23 ± 9 years; mass: 74 ± 6 kg; height: 1. 81 ± 0. 05 m) participated to an experimentation by performing 45 seconds on a fixed and a free-floating ergometer (RowPerfect, The Netherlands) at 20, 32 and maximal stroke rate. The movements were recorded by 16 optoelectronic cameras (Oqus 7, Qualisys, Sweden) thanks to 40 reflective markers. Participants performed 4 functional movements to calculate lowerlimb joints centres of rotation for the ankles, knees  (Ehrig et al. 2006 ) and hips  (Halvorsen 2003). The global frame was defined by anteroposterior (AP), medio-lateral (ML) and vertical axis. First, a closed-loop lower-limbs chain model (30 relative joint coordinates) was developed with the multibody software ROBOTRAN  (Fisette and Docquier 2019).
When the pelvis tilt furnished by OPENSIM was compared with the raw data, the mean RMSD was still 11 (Figure  2 ). The first axis (ML) used to the frame embedded to the pelvis were not defined was similar for the 2 software. The intermediary axis (%AP) is defined differently: based on 4 markers placed on the pelvis for ROBOTRAN and on a predictive method to estimate the tubercle for OPENSIM. The same comparison was performed with the absolute angles of the thighs, shanks and feet with mean RMSD differences of 5 , 6 and 14 , respectively. A major difference in the definition of the foot between ROBOTRAN (rotation around APaxis) and OPENSIM (sequence or rotations around AP and vertical axis to model the role of the subtalar joint) may explained the largest RMSD for this segment. Further developments on ROBOTRAN must be undertaken to improve the physiological modelling of this joint.
Many tools and methods have been developed to facilitate the processing and analysis of metabolomics data; most seek to solve a specific challenge in the multi-step data processing and analysis workflow. This review provides an overview of the metabolomics-related tools that are made available as packages (and a limited number of non-trivial, non-packaged scripts) for the statistics environment and programming language R  [7]. We have included packages even if they are not anymore part of current CRAN or Bioconductor, i. e. , they are available as archived versions only. We have not included packages described in the literature if they are no longer available for download at all. We did include packages that are currently available, but not yet published in the scientific literature. The package did include packages that are currently available, but not yet published in the scientific literature.
Different approaches exist for this metabolite annotation step, ranging from (usually insufficient) database lookup of exact mass (MS) or chemical shift (NMR) alone, to the use of fragmentation patterns obtained in tandem MS experiments, which can be searched against spectral databases or analysed with in silico algorithms, to spectral searching or de novo structure elucidation using combinations of NMR experiments (often 1D and 2D). Large parts of the metabolomics software landscape in general have been covered in reviews, recent ones include the large list of software packages  [8]  first described by Spicer et al. [9] , and a series of annual reviews covering the list maintained by Misra and others  [10] [11] [12] [13] , a review by Kannan et al. [14]  and the review focussing on approaches for compound identification of LC-MS/MS data by Blaženović et al.
The R Package Landscape The core of the R language was started in 1997 and provided the basic functionality of a programming language, with some functions targeting statistics. The real power driving the popularity of R today is the huge number of contributed packages providing algorithms and data types for a myriad of application realms. Many packages have an Open Source license. This is not a phenomenon exclusive to R, but is rather a positive cultural aspect of bioinformatics software being mostly published under Open Source license terms, regardless of the implementation language. An R interpreter can be embedded in several other languages to execute R code snippets, and R code can also be executed via different workflow systems (e. g. , KNIME or Galaxy, see Section 2. 9), which is beneficial for analysis workflows, interoperability and reuse.
9). In addition to a rich development infrastructure (website, developer infrastructure, version control, build farm, etc. ) there are regular workshops for developers and users. To enable reproducible research, BioC runs bi-annual software releases tied to a particular R release, thus ensuring and guaranteeing interoperability of packages within the same BioC release and allowing to install BioC packages from a certain release to reproduce or repeat old data analyses. On both CRAN and BioC, each package has a landing page pointing to sources, build information, binary packages and documentation. On BioC, packages are sorted (by their respective authors) into "BiocViews", where most packages are targeting genomics and gene expression analysis, and the most relevant ones for metabolomics are Cheminformatics (containing 11 packages), Lipidomics  (11) , SystemsBiology  (66)  and, of course, Metabolomics  (56).
Bioconductor workflows (organised as separate BioC View  [18] ) provide well documented examples of typical analyses. For community support, BioC maintains mailing lists, a web-based support site, slack communication channels and more. Both CRAN and BioC have a well-defined process for accepting new packages, and the respective developer guidelines (see guidelines for CRAN  [19]  and for BioC  [20] ) cover the package life-cycle from submission, updates and maintenance, to deprecation/orphaning of packages. In the case of BioC, new submissions undergo a peer review process, which also provides feedback on technical aspects and integration with the BioC landscape. A smaller number of packages are also hosted on sites like rforge. net (https://rforge. net/), r-forge. wu-wien. ac. at (http://r-forge.
wu-wien. ac. at/)  [21] , or sourceforge. net (https://sourceforge. net/) (SF). The non-profit initiative rOpenSci  [22]  maintains an ecosystem around reproducible research, including staff and community-contributed R packages with additional peer review. Currently, there are no specific metabolomics related packages. The GitHub (and also GitLab, Bitbucket) hosting services are not specific to R development, but have gained a lot of popularity due to their excellent support for participation and contribution to software projects. The maintenance of BioC packages on one of the git-based sites has become easier since the BioC team migrated to git as its version control system. A downside of these generic repository hosting sites is that there is no central point of entry, and finding packages for specific tasks is difficult compared with dedicated platforms and relies on search engines and publications.
Also, while these hosting services make it easier to provide packages that do not meet BioC and CRAN requirements (e. g. , rinchi, due to limitations in the InChI algorithm itself), it also allows users to postpone (or circumvent entirely) the review process that helps ensure the quality of BioC contributions. In addition to generic search engines like Google. com or Bing. com, the rdrr. io is a comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R-Forge. Initially, its main purpose was to find R packages by name, perform full-text search in package documentation, functions and R source code. Recently, it also serves as a hub to actually run R code without local installation, see Section 2. 9.
Dependencies and Connectivity of Metabolomics Packages Code reuse and object inheritance can be a sign of a well-connected and interacting community. At the useR!2015 and JSM2015 conferences, A. de Vries and J. Rickert (both Microsoft, London, UK) showed the analysis of the CRAN and BioC dependency network structure  [23] [24] [25]. Compared to CRAN, BioC packages had a higher connectivity: "It seems that the Bioconductor policy encourages package authors to reuse existing material and write packages that work better together". We repeated such an analysis  [26]  with the packages mentioned in this review and created a network of reverse dependencies (i. e. , the set of packages that depend on these metabolomics related packages in BioC or CRAN). The resulting network is shown in Figure  2.
R-Packages for Metabolomics This section reviews packages, relates some of those with similar functionality, and mentions how some of the packages can be used together. The sections in this review are ordered according to specific analytical approaches and the individual required steps. (2) packages from the review without reverse dependencies; and (3) data packages. Some packages from the review are not in current versions of CRAN or BioC. An interactive version of this figure is also available online (rformassspectrometry. github. io/metaRbolomics-book, Appendix 2) and as Supplemental File S2.
Profile Mode and Centroided Data The mass spectra can be recorded in profile (also called continuum) mode, but are often 'centroided'. Centroiding is, in effect, a process of peak detection for a profile mode mass spectrum (hence in the m/z dimension, not in a chromatographic dimension)-a gaussian region of a continuum spectrum with a sufficiently high signal to noise ratio is integrated to give a centroided mass (a "stick" in the mass spectrum as opposed to a continuous signal) and integrated area under the curve. This results in data of reduced size-what was many m/z-intensity pairs is reduced to a single m/z-intensity pair. Practically, this reduces the file size considerably, and many data processing tools (e. g. , centWave in xcms) require MS data that has been centroided. The centroiding can be done either during acquisition on the fly by the instrument software, or as an initial processing step.
Structuring Data and Metadata The result from the pre-processing is usually a matrix of abundances, rows being features (or features grouped into compounds/molecules) and columns being the samples. Within the statistical community, it is common nowadays to manipulate data matrices with rows as observations and columns as features, this difference stems from the early days, when spreadsheet programs could only handle a limited number of columns smaller than the number of e. g. , genes. Such matrices can be easily encapsulated into an ExpressionSet class from Bioconductor's Biobase package  [34] , the more recent SummarizedExperiment defined in the SummarizedExperiment  [35]  package or the mSet class from the metabolomics focussed Metabase  [36]  package. The main advantage of such objects is their inherent support to align quantitative data along with related metadata (i.