dataesr/scientific-paragraphs-categorization huggingface dataset:
| FUTURE DIRECTIONS The BIEN database represents an unprecedented source of global botanical data that currently focuses on the New World. The BIEN database, workflow, and r package are under continuous development. We plan on expanding the BIEN database to increase coverage of other continents as well as incorporating additional types of trait data. Another priority is to develop an API to allow easier access to the BIEN database by other programs. As development proceeds, we will add functionality to allow users to contribute their own data and range maps. Accessing these data will allow studies at an unprecedented scale with more complete, high quality datasets than smaller scale initiatives might achieve on their own. This is an important step toward improving our understanding of global patterns of biodiversity and developing predictive ecological theory at the largest of scales.
, the generation of the BIEN database consists of a linked workflow that (1) standardizes taxonomy by correcting spelling of species names and updating synonyms to currently accepted names via the Taxonomic Name Resolution Service or TNRS (Boyle et al. , 2013); (2) detects and flags observations with erroneous geographic coordinates; and (3) flags cultivars and non-native species via the Native Species Resolver (http://bien. nceas. ucsb. edu/bien/tools/nsr/). Coordinates were flagged as erroneous if they fell outside the specified political regions, if the latitude was exactly 0 or 90 degrees, the longitude was exactly 0 or 180 degrees, or if the point fell in the ocean. The detection of cultivars and non-native records relies on native species lists, which are not available throughout the New World, so this filtration is imperfect.
Any publications using data obtained from BIEN should cite the original publications and acknowledge data providers when possible. Studies utilizing large amounts of BIEN data should seek solutions that ultimately lead to an entry in the Web of Science so that data providers and original publications are given credit. Normal ethics apply to co-authorship of scientific publications. Users of data stored in BIEN should consider inviting the original data contributor to be a co-author(s) of any resultant publications if that contributor's data are a major portion of the dataset analysed, or if a data contributor makes a significant contribution to the analysis of the data or to the interpretation of results. Similarly, studies utilizing significant fractions of BIEN data and derived data products (species lists, geographic ranges, etc.
ACKNOWLEDGEMENTS We thank  Irena Šímová  for input on the manuscript,  Scott Chamberlain  for valuable comments on R programming, our beta-testers for their input, and our CRAN reviewers for their cheerful comments and assistance. We thank  Scott Chamberlain ,  Robert Freckleton  and an anonymous reviewer for their helpful comments on both the manuscript and the r package. N. M. -H. acknowledges support by a  Villum Foundation  Postdoctoral Fellowship. We gratefully acknowledge the  Canada Foundation for Innovation for funding the infrastructure for Canadensys. This work was conducted as a part of the  Botanical Information and Ecology Network (BIEN) Working Group (PIs BJE ,  Richard Condit , B. B. , S. M. D. , R. K. P. ) supported by the  National Center for Ecological Analysis and Synthesis  (funded by  NSF  Grant # EF-0553768 ), the  University of California, Santa Barbara , and the  State of California.
Serialization The previous command and answer definitions are given as OCaml datatypes, and implemented by a reasonably straightforward OCaml interpreter. However, the browser side, and, in particular, the Web Worker API, requires messages to be JSON objects. What is the best way to relate our OCaml datatypes to their JSON representation? We think that the direct manipulation of JSON objects in OCaml would have consumed most of our available development time. Thus, core to our design is the use of the new PPX system for OCaml meta-programming, which supports the automatic generation of serializers. In particular, we use the ppx_yojson [32] package, which will automatically generate serialization functions for the definitions of our small DSL and -more importantly -for core Coq data types. Indeed, this has proven to be a great choice, resulting in a very low overhead when tracking upstream changes, exporting complex structures, or just experimenting.
A Coq datatype is declared serializable using pragmas, such as in the following declaration: type feedback = [% import : Feedback. feedback ] [ @@deriving yojson ] The above code will generate the following functions: We can use this method to export any data type exposed in the OCaml plugin API of Coq. As the jsCoq system matures, support for querying more Coq objects is gradually introduced. For example, using this mechanism, we can reliably recognize and handle user-specified notations, printing them in an enhanced way if desired; this is hard to do without serialization support due to the highly flexible Coq pretty-printing engine.
Practical Validation So far our jsCoq system has behaved in a stable way, filling a niche for casual Coq users, tutorials, and people willing to try Coq add-ons in an easy way. Indeed, even at this early stage, jsCoq has already been used to support some Coq courses and a tutorial, including: • "Winter School Advanced Software Verification and Computer Proof", Sophia Antipolis, January 18-22, 2016; • "Mathematical Components, an Introduction" and ITP tutorial, Nancy, August 27, 2016; and • the "Mathematical Components" book. These two courses total around one hundred jsCoq users. Feedback was quite positive, as the tool worked well for everybody and allowed them to experience Coq; the main complaints came from the immaturity of the user interface, in particular the fact that our choice of UI panels does not adapt well to different screen sizes.
Regarding the jsCoq instance hosted in our servers, it would be safe to say that more than a thousand unique users have tried to access it. However, note that we do not track or gather any personally identifiable information, and so this estimate has been done on traffic data only and may not be very exact. There is also interest in using jsCoq to teach established Coq classes; work is underway and we believe this could happen soon, as the tool approaches its 1. 0 release. Additionally, the tool has been proved valuable in question and answers sites such as http://stackoverflow. com, where it allowed users to share runnable Coq code snippets. Performance-wise, our experimental findings are consistent with the predictions by the js_of_ocaml developers: jsCoq usually runs on par or faster than the bytecode version of Coq.
Conclusion We have introduced and detailed jsCoq, a new web-based execution platform and user environment for the Coq interactive proof assistant. jsCoq is just a set of static HTML/JavaScript files; thus it can be easily hosted by anyone, and used locally or offline. Even though jsCoq is still a work in progress, we believe that the possibilities are exciting, and we think it is not too presumptuous to state that this experiment has brought some fresh air to the area of Coq user interfaces. The tool already enjoys some practical impact: it has been used to give some courses, with more courses and tutorials planned in the near future.
Air shower and detector simulations Air showers are simulated using the CORSIKA simulation package (version 7. 4387)  [5]. The interactions of hadronic particles in the Earth's atmosphere are treated using QGSJET-II-04 at high energies and FLUKA for energies below 200 GeV. The electromagnetic interactions are treated with EGS4. The observation level for ALTO is set to 5. 1 km above sea level, which is the altitude of the ALMA site in Chile [6], and areas near the LLAMA site in Argentina  [7]. The generated air shower particles are fed into a detector simulation code specifically developed for ALTO, based on the GEANT4 package  [8]. The code includes the design and geometry of each of the detector components along with all material properties such as density and refractive index as well as reflectivity, absorption and scattering coefficients of photons as function of wavelength.
Acknowledgements The  ALTO  project is being supported by the following Swedish private foundations or public institutes: the  Crafoord Foundation , the  Foundation in memory of Lars Hierta , the  Magnus Bergvall's Foundation , the  Crafoord stipendium of the Royal Swedish Academy of Sciences (KVA) , the  Märta and Erik Holmberg Endowment of the Royal Physiographic Society in Lund , the  Längmanska kulturfonden , the  Helge Ax:son Johnson's Foundation and Linnaeus University. We also thank the  Swedish National Infrastructure for Computing (SNIC) at Lunarc (Lund, Sweden). We would like to thank  Bertrand Vallage  from  CEA/Saclay (France)  for providing us with two ANTARES optical modules. We would like to thank  Jean-Pierre Ernenwein  from  CPPM/Marseille (France)  for providing the ANTARES surface array and the scintillator liquid, for his extremely valuable expertise on scintillator materials characteristics, and for his novel ideas on the PMT installation and choice.
Acknowledgements We thank  Dr V. Martinez  of  Univ. Edinburgh for useful technical discussions;  Pr C. Douarche  for discussions and for providing different strains of fluorescent bacteria. N. F. M. thanks support by the  Pierre-Gilles de Gennes Foundation. P. B. thanks the  C'Nano Ile de France  for Post-doctoral financial support. We acknowledge the financial support of the  ANR 2015 "Bacflow". A. L. and N. F. M. acknowledge support from the  ERC   Consolidator Grant PaDyFlow  under grant agreement  682367.
(1998) , to model the complex process of molecular cloud disruption. For the diffuse component the attenuation factor, A diff λ , is calculated from the tabulated radiative transfer results of  Ferrara et al. (1999) , which are computed using the code described in  Bianchi et al. (1996). For this purpose it is assumed that the ratio of vertical to radial scalelengths of the disc is h z /h R = 0. 1, and that the dust and stars have the same scaleheight i. e. h z (dust) = h z (stars). These assumptions are discussed in more detail in  Granato et al. (2000). The central optical depth is calculated according to equation (  8 ) and the Ferrara et al. tables are interpolated to get the total attenuation as a function of wavelength, A diff λ. If a galaxy is undergoing a starburst a similar procedure is followed but using the gas mass and radial scalelength of the forming bulge/spheroid (burst), which are predicted by galform.
We show the comparison to the data in Fig. 8. The agreement between the model predictions and the observations for MS galaxies is extremely good (for z 0. 5), which is remarkable given that the SEDs of galaxies were not considered in calibrating the model. For SB galaxies (bottom row) the agreement is generally good for z 2. To further investigate how our predictions compare to the observations, we compare their dust temperatures, dust masses and infra-red luminosities as a function of redshift in Fig. 9. To do this in a consistent way, we fit a modified black body (MBB), L λ = 4π M dust κ 0 (λ/λ 0 ) -β B λ (T d ), (18) to both the observed and predicted photometry, noting that this form is not equivalent to the one assumed for the dust emission in galform (Equation  14 ) and has only a single dust temperature (whereas the galform dust model assumes that there are two temperatures).
ACKNOWLEDGEMENTS The authors would like to thank  Daniel Scharer  for discussions that led to some of the developments in this paper, and  Peter Mitchell , for providing us with the SED fitting code used in Appendix A. We also acknowledge insightful comments from the referee, which allowed us to clarify many points made in the manuscript. WIC and CGL acknowledge financial support and fruitful discussions from the  Munich Institute for Astro-and Particle Physics (MIAPP) DFG cluster of excellence 'Origin and Structure of the Universe ' workshop entitled 'The Star Formation History of the Universe', held in Munich in August 2015. This work was supported by the  Science and Technology Facilities Council  [ ST/K501979/1 ,  ST/L00075X/1 ]. CMB acknowledges the receipt of a  Leverhulme Trust Research Fellowship.
g. Guinehut et al. , 2012; Le Traon, 2013). This fact demonstrates the very strong and unique complementarities of the two observing systems  (Le Traon, 2013). The Argo network in the Mediterranean Sea presently consists of around 80 operating floats deployed in the frame of the MedArgo program (http://nettuno. ogs. trieste. it/sire/ medargo/active/index. php). The specific semi-enclosed morphology with a large fraction of coastal areas, shallow bathymetry and circulation structures of the basin make profilers programmed with the Argo standard global parking depth of 1000 m inappropriate for this program  (Poulain et al. , 2007). This is why a parking depth of 350 m was chosen for the Mediterranean basin. The objective was to track the intermediate waters throughout the Mediterranean which are mostly composed by LIW.
(2009). These authors evaluated the quality of global real-time altimetric products by comparing them with independent in situ tide gauges and drifter data. Moreover, our procedure does not include the validation of the outcomes of the OSSEs against a reference observing system experiment (OSE) using real data  (Hoffmann and Atlas, 2016). Thus, our approach can be qualified as a simplified OSSE. This study will assess the scales covered by altimetry which are larger than 100 km  (Pujol and Larnicol, 2005). Notice that the scales mentioned in this paper allude to a definition based on the diameter of individual structures, usually referred to as "feature scales". The paper is organised as follows: the datasets are described in Sect. 2. Section 3 details both the processing sequence developed to compare the altimeter data with Argo in situ measurements and the quantification of the differences between Argo and SLA.
ARGO dataset We use delayed mode quality-controlled temperature and salinity (T-S) profiles from 2003 to middle 2015 as obtained from the Coriolis Global Data Assembly Centre (http://www. coriolis. eu. org, ARGO GDAC global distribution database) in the Mediterranean Sea (Fig. 1 ). Dynamic height (DH) was computed at 5 m depth as an integration of the pressure, temperature and salinity vertical profiles through the water column, using a reference level at 400 and 900 dbar (close to 400 and 900 m, respectively). The choice of these reference levels is conditioned by the availability of the climatology used to compute DH anomalies. This issue will be addressed later. An additional quality control criterion relative to both the profile's position and the pressure, temperature and salinity measurements was applied: only profiles with a quality position flag of 1 (good data) were employed.
(2013 Pujol et al. ( , 2016)). The spatial resolution of the dataset is 1/8 • × 1/8 • and the time period used in this work spans from January 1993 to December 2014. The quality of this product can be estimated among others by comparison with in situ Argo data. Notice that the availability of altimetry and Argo data does not match. Therefore, a common period spanning the period from January 2003 (beginning of the Argo dataset) to December 2014 (ending of the altimetric data analysed in this study) has been used in both datasets. Moreover, to perform this comparison, it is critical that altimetry and Argo data have the same interannual temporal reference  (Legeais et al. , 2016). We estimate DHAs from Argo data through a synthetic mean Argo DH referred to the time period between 2003 and 2011.
Error estimates from comparison of Argo dynamic heights and altimetry sea level anomalies This section focuses on the comparison of altimetry data with Argo DH in order to estimate the differences between Argo DHA and altimeter SLA needed to specify observation errors in our OSSEs. In addition, this analysis can contribute to the validation of satellite SLAs with an increased confidence. A sensitivity analysis of the method of comparison of both datasets is provided. This analysis mainly focuses on the impact of the reference depth selected in the computation of the Argo DH on the comparison with specific altimetric SLA gridded merged product for the Mediterranean Sea.
Method for comparing altimetry and in situ Argo data The comparison method of altimetry with Argo data consists of co-locating both types of datasets since spatial and temporal sampling of altimetry and Argo data are different  (Valladeau et al. , 2012). Altimeter grids and synthetic climatologies were spatially and temporally interpolated at the position and time of each in situ Argo profile, which are considered as reference, by using a mapping method based on an optimal interpolation scheme. This considerably reduces errors due to different sampling characteristics of altimeter and in situ data. As mentioned before, the period investigated extends from January 2003 to December 2014. Then, statistical analyses are performed between both datasets. Co-located altimeter and Argo DH differences are analysed in terms of the standard deviation (SD) for the two reference levels used to compute DHAs from the Argo profiles (namely 400 and 900 dbar).
The correlation between both datasets was 0. 80. In order to study the impact of the reference level, we repeated the analysis using the shallower reference level Table  1. Comparison of correlation and standard deviation (cm) of the differences between the new AVISO product for the Mediterranean Sea and Argo data referred to both 400 and 900 dbar (sub-columns on the left). Sub-columns on the right display the results of the robustness experiments in terms of standard deviations (see text for details). DHA referred to 400 dbar has been computed for all valid Argo profiles and those reaching 900 m depth for comparison purposes. The number of Argo platforms and vertical profiles used are also shown.
Consequently, DHA referred to 400 dbar was recomputed but using all the available profiles reaching 400 m depth. Now, the number of T-S Argo profiles used to compute DH increased to 2258, thus corresponding to 41 Argo floats. Notice that this more comprehensive number of Argo profiles is almost 6 times larger than the profiles used to compute DHAs referred to 900 dbar. The SD of the differences of SLA-DHA was 4. 92 cm while the correlation between both datasets decreased to 0. 76. In the framework of our OSSE, this SD value can be considered as an error estimate of the Argo DHA with respect to altimeter SLA in the Mediterranean Sea for the time period investigated. Furthermore, this result represents an improvement of 14 % in terms of signal variance with respect to the one obtained from the differences between SLA and DHA referred to 900 dbar.
Experiments design This section describes the different elements of the OSSEs conducted in the Mediterranean Sea. A flow chart of the methodology developed is provided in Fig. 3. The specific altimetry-gridded merged product for the Mediterranean Sea, described in Sect. 2. 2, has been used as the nature run (NR) component of the OSSEs. Namely, we use daily SLA maps throughout 2014. The region considered covers the entire Mediterranean basin. The original altimetry dataset has a spatial resolution of 1/8 • × 1/8 • and presents 17 283 grid points (see Table  2 ). We obtain synthetic observations from the nature fields by sub-sampling the NR with the different spatial resolutions displayed in Table  2. The aim is to reproduce some possible configurations of the Argo array network in the Mediterranean Sea.
Discussion The Argo network in the Mediterranean Sea presently consists of around 80 operating floats drifting with less than 2 • mean spacing. Even though this array improves the global coverage of the Argo network, it only captures the large-scale circulation features of the basin. In this work, we have investigated which configuration in terms of the spatial sampling of the Argo array in the Mediterranean would be necessary to recover the mesoscale dynamics in the basin as seen by altimetry. The monitoring of the mesoscale features is not an  Argo program target. However, this issue is of concern since it can help the current ocean state estimates. To do that, we have conducted several observing system simulated experiments (OSSEs) in the basin. We have followed a simplified OSSE approach by contrast to the comprehensive approach, including an equivalent observing system experiment.
In addition, the computation of the differences between altimetry and Argo data referred to both 400 and 900 dbar revealed a SD of SLA-DHA differences 1. 67 cm lower (in terms of variance) when computing DHA referred to 400 dbar. This fact, together with both a higher correlation coefficient between both datasets and the larger number of available profiles, suggests that it is preferable to consider the 400 dbar level as the reference level to compute DHA from Argo data in the Mediterranean basin. This leads to a SD of the differences between both datasets of 4. 92 cm (equivalent to 90 % of SLA signal variance). Conversely, one would expect better results when using 900 dbar as a reference level because the physical content (variance) of a larger fraction of the water column is considered when computing Argo DH.
However, the more comprehensive number of available Argo profiles when using 400 dbar as reference level, and thus the larger coverage of the Argo network, seem to play a more critical role in the comparisons with altimeter data in the Mediterranean basin than the deep sampling of the steric signal. On the other hand, the climatology used here to compute DHA could be not as accurate at 900 m due the lower number of historical data available at that depth, then resulting in larger SDs of the differences between both datasets. Nonetheless, the evaluation of this climatology is out of the scope of this paper and it will be addressed in further investigations. Another interpretation of the results obtained here could be done in terms of the dynamics of the water masses residing in the Mediterranean Sea.
Furthermore, the depth of the LIW core in most of the Mediterranean basin is also the reason for choosing 350 m as the parking depth for the Argo floats in the Mediterranean  (Poulain et al. , 2007). Results reported from the regular-gridded experiments have shown that the reconstructed SLA maps from a configuration similar to the current Argo array in the Mediterranean (spatial resolution of 2 • × 2 • ) are not able to capture the mesoscale features of the basin. As a consequence, these maps only retrieve 21 % of SLA signal variance. This is an expected result because the initial target of the Argo program is to monitor the large-scale ocean variability. Increasing the resolution, reconstructed SLA fields from a 0. 75 • × 0. 75 • grid box of SLA observations retrieve 66 % of SLA signal variance.
Acknowledgements. The research leading these results has received funding from the  European FP7  under the  E-AIMS (Euro-Argo Improvements  for the  GMES Marine Service ) project (code:  312642 ) and the  Sea Level Thematic Assembly Center (SL-TAC) of the Copernicus Marine and Environment Monitoring Service (CMEMS). We gratefully acknowledge the constructive comments by two reviewers, which helped to improve the paper. Edited by: A. Sterl Reviewed by: two anonymous referees.
To do so, a battery of advanced management services (e. g. , Demand side management, minimization of transmission losses, etc. ) is designed. The DECF framework belongs to those services and consists of two main modules, Alliance Builder and Seller2Buyer Matcher. The Alliance Builder component is provided to allow the MG to cluster its components according to several criteria as described in the following. The Seller2Buyer Matcher allows to align the schedule the collaboration between components.