dataesr/scientific-paragraphs-categorization huggingface dataset:
Temporal heterogeneity A number of studies rely on S2M for the analysis of meteorological and snow cover trends at climatic scale  (Spandre et al. , 2019a; Lopez-Moreno et al. , 2020). For these applications, the main caveat in using S2M lies in the temporal heterogeneity of the dataset, in particular the strong changes in 2 m temperature observations across the mountain regions considered during the full time period of the reanalysis. The S2M reanalysis for the period 1991-2020 is produced using significantly more observations than the simulation over the period  1961 -1990 (Fig. 4 (Fig. 4 ), and we established in Sect. 4. 3. 3 that this has a significant impact on the simulated 2 m temperature trends. This is superimposed on changes in meteorological guess in 2002 (from ERA-40 to ARPEGE) and changes intrinsic to ERA-40 and ARPEGE: assimilation of satellite observations in ERA-40 is known to be responsible for temporal breaks  (Sturaro, 2003; Sterl, 2004).
Future updates The S2M dataset is intended to be updated every year to extend its time coverage period and take into account evolutions of the various components of the model chain. This may impact, in the future, the simulations presented in this article. The replacement of ERA-40/ARPEGE by ERA-5 is under preparation and is expected to reduce temporal heterogeneities -although changes in observation data are likely to remain the dominant source of heterogeneity. It is also planned to expand the S2M reanalysis to lower-lying mountain ranges Vosges, Jura and Massif Central.
Data availability The S2M dataset is freely available on the AERIS data center on the following https://doi. org/10. 25326/37#v2020. 2. The S2M data are provided by Météo-France -CNRS, CNRM, Centre d'Études de la Neige, through AERIS  (Vernay et al. , 2022). To access the data five fields are required. -Versions. To date the most recent version is 2020. 2. -Areas. The areas are Alpes (Alps), Pyrénées (Pyrenees), Corse (Corsica) massifs or Postes (stations). For the massif areas specify also the geometry type (flat or with 20 and 40 • slopes). -Products. The product "meteo" is for meteorological variables (see Sect. 3. 1. 2) and "snow" is for snow cover and soil variables (see  Sect. 3. 1. 3). -Begin year. Data will be extracted starting at 1 August of the year given as "Begin year". -End year.
Conclusions This study introduces and describes the latest release of the meteorological and snow cover reanalysis S2M covering the 63-year period from 1958 to 2021 for the French mountainous areas (French Alps, Pyrenees and Corsica). It includes the description of the different models and data used to produce this dataset, a comprehensive list of the parameters forming the dataset itself in its specific geometry and the technical access to the database. An evaluation of the simulation quality is provided by comparison to in situ observations of snow depth as well as an overview of known and potential uses of this dataset, as well as a series of caveats associated with the use of this dataset. Yearly updates of this reanalysis will extend the period in the future and could lead to significant updates of the current S2M data by including model or input data modifications.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-14-1707-2022-supplement. Author contributions. MV was in charge of SAFRAN code development and maintenance and ran SAFRAN simulations. ML was in charge of SURFEX/Crocus code development and maintenance and ran SURFEX/Crocus simulations from a code release provided by RN after various technical developments. PH implemented the MEPRA module in SURFEX to allow including mechanical diagnostics in the dataset. SM initiated the idea to publish this dataset, identified the solution to optimally share this dataset and led the discussion with the partners of the AERIS. MV produced most of the final results and wrote the manuscript with inputs from ML and SM and contributions of all authors. DM provided Figs.
Acknowledgements. This work was made possible thanks to the vision and pioneering work of people involved in the early steps of the  SAFRAN-Crocus-MEPRA model chain  ( Eric Brun ,  Yves Durand ,  Eric Martin ,  Gérald Giraud  and  Laurent Mérindol ). The homogenized series of observation used for the evaluation of this dataset were made and provided by  Brigitte Dubuisson  ( Météo-France ,  Direction de la Climatologie et des Services Climatiques, Toulouse, France ). Elements of Fig. 3 were provided by  Bertrand Cluzet  (now at the  WSL Institute for Snow and Avalanche Research SLF ). CNRM/CEN is part of  LabEx OSUG@2020. Review statement. This paper was edited by  Ge Peng  and reviewed by  Kristian Förster  and one anonymous referee.
An appropriate selection of the training and validation sets and size of each dataset is crucial to avoid overfitting or bias in the selection of those datasets and guarantee generalization of the results. This question of the selection of features during training and validation of the ML algorithms has been largely discussed. One-by one backward and forward approaches  (Evin et al. , 2018)  can be tested and compared to the pseudo-wrapped approach in order to avoid the possible effect of feature order. Optimization is then performed parameter type by parameters type (EDA, Cardiac, Eye-tracking data and driving behavior). Other approaches could then be considered for further optimization. This exploratory work aims at (1) assessing the capacity of different ML techniques to classify personality traits under different driving contexts, and (2) assessing the benefit of personality traits clustering based on behavioral and physiological data while evaluating the relative contribution of the different types of bio-signals to improve the accuracy of the algorithm.
The level (1, 2 or 3) and situation (left-turn, overtaking car, pedestrian crossing) coding was tested as a pre-analysis of the ML algorithms. In order to record the physiological signals, during the test each participant was equipped with 5 electrodes from a Biopac MP150 system (BioNomadix system). Such electrodes were located under the last left rib, on the right clavicle and on top of the right hip of the participant for cardiac activity measurements. EDA signals was computed from sensors located on the left-hand index and middle finger (sampling rate of 1 kHz, hardware band-pass filtering 35 Hz). Additionally, each participant wear eye-tracking glasses (Pupil Core glasses from Pupil Labs UG, Berlin, Germany) with post-processing from the pupil Labs software.
Physiological measures Cardiac signal-based features included: the mean of the R to R wave interval length (RR), the standard deviation of the RR intervals (SDNN) and the root mean squared of the successive differences between adjacent RR intervals (RMSSD). Detection of R peaks was automatically performed using HRV Analysis  (Pichot et al. , 2016)  and visually assessed. The EDA signal consisted in the acquisition of the amplitude of skin conductance level. A decomposition of the skin conductance into tonic and phasic components was carried out by the Ledalab software (v3. 4. 9) written in MATLAB and available online (www. ledalab. de). Then an amplitude threshold of 0. 02 μs  (Benedek & Kaernbach, 2010)  was set to discriminate relevant phasic responses. Several features were computed from the EDA phasic responses detected: the number of responses (EDA_nb), the mean amplitude of response (EDA_ampMeanScrs), the standard deviation of amplitude of peaks (EDA_SDAmpScrs), the mean latency which correspond to the mean delay between two peaks (EDA_latMean) and the standard deviation of latency as the standard deviation of the delay between two peaks (EDA_SDLat) were computed.
Eye movement measures The eye-tracking data (collected at 200 Hz with the Pupil Core system from Pupil Labs) consisted in four features associated with fixations. All visual gaze located in a 0. 5 • visual angle during 66 ms was considered as a fixation. Such implementation of fixation detection on Pupil Labs software is a dispersion-based method. The number of fixations, the total duration of the fixations (NB_Fix), the mean duration of the fixations and the standard deviation of the duration of the fixation were computed. Occurrence of the total duration of the fixations and mean duration of the fixation in the final results had then be computed as a preliminary step before final optimization. For biosignal, outliers were withdrawn using a threshold of mean value ±3 standard deviations.
Optimization of feature by personality traits Based on previous results, the optimization of feature selection by the one by one backward and approach has been furtherly used. First, a feature selection for behavioral and eye tracking data was performed and then for eye tracking data. The results presented in Table  4  summarize the best prediction based on ROC AUC for each personality trait with the optimized selection of features. Only first three best algorithms were reported. Best results were found for the Openness personality trait followed by the Agreeableness and Neuroticism. All best ROC AUC results when comparing algorithms were found for RF, followed by J48 and NN. J48 presented ROC AUC reduced by 3. 1% when compared to RF and NN implemented as MP were found to be 14. 5% lower than RF result in average.
Environmental Impact: Global Warming Potential The GWP to 100 years was calculated using the CML-IA method  [47]. The life cycle inventory was modeled in the OpenLCA software, using primary data for direct inputs and the European Reference Life Cycle Database (ELCD) V3. 2 for background processes. The complete inventory data and LCA assumptions are discussed in previous works on restoration mortars  [48, 49]. Results of these detailed LCAs were fitted to Equation (4), by using only the dominant contributions to GWP, as a means of having a tool to quickly estimate the GWP of restoration mortars. The emission factors (EFs) for binders, aggregates, and other essential inputs, required to use Equation (  4 ) are listed in Table  3. These EFs include those of silica sand production  [50] , lime production  [51] [52] [53] , and calcareous sand production  [54, 55].
Anatomical observation (light microscopy) P. lividus development, from fertilization to juvenile stages, was recorded on specimens mounted alive. P. lividus embryos, larvae, and juveniles were imaged using a Zeiss A2 (Axio Imager 2) light microscope (Zeiss, Jena, Germany), under either brightfield, differential interference contrast (DIC), or polarized light. P. lividus larvae were also imaged, under dark-field, using a Zeiss SteREO Discovery V20 microscope (Zeiss, Jena, Germany). Metamorphosis was imaged using a Zeiss Axio Observer Z1 microscope  (Zeiss, Jena, Germany). In all cases, images were captured using the Zen software suite (Zeiss, Jena, Germany). To record embryos, larvae, and juveniles, specimens were mounted in 0. 2 µm filtered seawater, between a slide and a cover slip, using clay to generate spacers and enable orientation of the specimens.
Observation and imaging of the labeled specimens were carried out using a Leica SP8 confocal microscope (Leica, Wetzlar, Germany). For each sample, series of optical sections were taken at a z-step interval of 1-2 μm and multichannel acquisitions were obtained by sequential imaging. Confocal optical sections were compiled into maximum intensity z-projections using ImageJ version 1. 44o  (Schneider et al. , 2012) , and scale bars were added using the same software. In situ hybridization assays were carried out as previously described in  Robert et al. (2014). The riboprobe used for the P. lividus myosin heavy chain (mhc) gene corresponded to a 2066 base pair-long partial cDNA (GenBank accession number: OM307457). The probe was used at a final concentration of 1 ng/μL, and images were acquired using a Zeiss A2 (Axio Imager 2) microscope (Zeiss, Jena, Germany) under differential interference contrast (DIC) light.
Second, a hole was present in the left larval epidermis, in front of the rudiment (Figure  15B ). This hole, identified as the vestibular pore  (Smith et al. , 2008) , arose either from the bridge between the larval epidermis and the vestibular roof or resulted from a de novo fusion of these two tissues (data not shown). In addition, at the competent pluteus stage, the larvae displayed some behavioral changes. First, ciliary beating of the epaulettes (Figure  15C ) was reduced periodically, and the larvae thus sank to the bottom of the culture vessel (data not shown). Second, while at the bottom FIGURE 14 Development of the pedicellariae and the genital plates in Paracentrotus lividus larvae. In (A-N), images are from larvae at the 8-arm pluteus stage (8-arm). In (A), the image was acquired using dark-field light microscopy.
Conclusion Taken together, this study represents a comprehensive description of P. lividus development, providing a complete compendium of its embryonic, larval, and adult periods. This dataset thus represents a useful framework for future research on this species as well as for future comparisons with other animals, echinoids, echinoderms, and beyond. Our results will also be useful for developing reliable rearing conditions for echinoid aquaculture, as they provide easily recognizable characteristics to identify specific developmental stages during different periods of the life cycle. With the advancement of genome editing techniques in marine organisms and the relatively short generation time of P. lividus, this work also creates a solid basis for the creation of stable transgenic lines in this echinoid species, opening the door for genetic approaches in this animal model system.
Funding This work was supported by the  Centre National de la Recherche Scientifique (CNRS)  to all authors, by the  French Ministry of Research and Technology  ( 2173/2015 ) and the  Japan Society for the Promotion of Science  ( PE18758 ) to LF, by the  ANR  grant  MusMod4EDC  ( ANR-21-CE34-0006-02 ) to MS, and by the  André Picard Network  ( ANR-11-IDEX-0004-02 ) and a  CNRS Institute of Biological Sciences  grant ( DBM254552 ) to JC.
Some of these have modeled enzyme demands of metabolic reactions by constraining metabolic networks with kinetic parameters and physiological limitations of cells, such as a crowded intracellular volume  [16] [17] [18]  , a finite membrane surface area for expression of transporter proteins  19  and a bounded total protein mass available for metabolic enzymes  [20] [21] [22] [23] [24] [25]. All of these modeling frameworks have been successful at expanding the range of predictions of classical FBA, providing explanations for overflow metabolism and cellular growth on diverse environments for Escherichia coli  [16] [17] [18] [19] 21, 23, 25  , Saccharomyces cerevisiae  22, 25, 26  , Lactococus lactis  27  , and even human cells  20, 24. However, these modeling approaches were applied to metabolic networks of extensively studied model organisms, which are usually well represented in specialized resources for kinetic parameters such as the BRENDA  28  and SABIO RK  29  databases.
cerevisiae, Yeast7  38. This method extends the classical FBA approach by incorporating a detailed description of the enzyme demands for the metabolic reactions in a network, accounting for all types of enzyme-reaction relations, including isoenzymes, promiscuous enzymes and enzymatic complexes. Moreover, GECKO enables direct integration of proteomics abundance data, if available, as constraints for individual protein demands, represented as enzyme usage pseudo-reactions, whilst all the unmeasured enzymes in the network are constrained by a pool of remaining protein mass. Additionally, this method incorporates a hierarchical and automated procedure for retrieval of kinetic parameters from the BRENDA database, which yielded a high coverage of kinetic constraints for the S. cerevisiae network.
Results Community development of GECKO. To ensure wide application and enable future development by the research community, we established the GECKO toolbox as open-source software, mostly encoded in MATLAB. It integrates modules for enhancement of GEMs with kinetic and proteomics constraints, automated retrieval of kinetic parameters from the BRENDA database (python module), as well as simulation utilities and export of ecModel files compatible with both the COBRA toolbox  44  and the COBRApy package  45. The development of GECKO has been continuously tracked in a public repository (https://github. com/SysBioChalmers/GECKO) since 2017, providing a platform for open and collaborative development. The generation of output model files in. txt and SBML L3V1 FBC2  46  formats enabled the use of the ecYeastGEM 1 structure as a standard test to track the effects of any modifications in the toolbox algorithm through the use of the Git version control system, contributing to reproducibility of results and backwards compatibility of code.
As the BRENDA database  47  is the main source of kinetic parameters for GECKO, all of the available k cat and specific activity entries for non-mutant enzymes were retrieved. In total, 38,280 entries for 4130 unique E. C. numbers were obtained and classified according to biochemical mechanisms, phylogeny of host organisms and metabolic context (Brenda kinetic data analysis section in the Supplementary Information File 1), in order to assess significant differences in distributions of kinetic parameters. This analysis showed that not all organisms have been equally studied. While entries for H. sapiens, E. coli, R. norvegicus, and S. cerevisiae account for 24. 02% of the total, very few kinetic parameters are available for most of the thousands of organisms present in the database, showing a median of 2 entries per organism (Fig.
1a ). The analysis also showed that kinetic activity can differ drastically, spanning several orders of magnitude even for families of enzymes with closely related biochemical mechanisms (Fig. 1b ). Finally, it was also observed that k cat distributions for enzymes in the central carbon and energy metabolism differ significantly from those in other metabolic contexts across phylogenetic groups of host organisms (life kingdoms, according to the KEGG phylogenetic tree  48  ), even without filtering the dataset for entries reported exclusively for natural substrates, as previously done by other studies  49  (Fig. 1c ). In the new version of the GECKO toolbox (GECKO 2. 0), a modified set of hierarchical k cat matching criteria was implemented to address how k cat numbers depend on biochemical mechanisms, metabolic context and phylogeny of host organisms.
The modified parameterization procedure enables the incorporation of kinetic parameters that have been reported as specific activities in BRENDA when no k cat is found for a given query (as the specific activity of an enzyme is defined as its k cat over its molecular weight), adding 8,118 new entries to the catalog of kinetic parameters in the toolbox. A phylogenetic distance-based criterion, based on the phylogenetic tree available in the KEGG database  48  , was introduced for cases in which no organism-specific entries are available for a given query in the kinetic parameters dataset. Specifically, where GECKO 1. 0 chooses k cat available in BRENDA regardless of organism, GECKO 2. 0 chooses the values available in BRENDA for the phylogenetically closest organism by iteratively introducing a wildcard into the E.
0 counterpart yielded an average relative error of 32. 07%. The introduction of manually curated k cat numbers in a metabolic network has been proven to increase the quality of phenotype predictions for S. cerevisiae  22, 25, 38  ; nevertheless, this is an intensive and time-consuming procedure that is hard to ensure for a large number of models subject to continuous modifications. In order to ensure applicability of the GECKO method to any standard GEM, a unified procedure for curation of kinetic parameters was developed based on parameter sensitivity analysis. For automatically generated ecModels that are not able to reach the provided experimental value for maximum batch growth rate, an automatic module performs a series of steps in which the top enzymatic limitation on growth rate is identified through the quantification of enzyme control coefficients.
For such enzymes, the E. C. number is obtained and then its correspondent k cat value is substituted by the highest one available in BRENDA for the given enzyme class. This procedure iterates until the specific growth rate predicted by the model reaches the provided experimental value. Finally, as the first version of the toolbox relied on the structure and nomenclature of the model Yeast7, its applicability to other reconstructions was not possible in a straightforward way. In order to provide compatibility with any other GEM, based on COBRA  44  or RAVEN  50  formats, all of the organism-specific parameters required by the method (experimental growth rate, total protein content, organism name, names and identifiers for some key reactions, etc. ) can be provided in a single MATLAB initialization script, minimizing the modifications needed for the generation of a new ecModel.
ecModels container is an automatically updated repository. Several GEMs that have been published are still subject to continuous development and maintenance  [1] [2] [3] 5, 6  , this renders GEMs to be dynamic structures that can change rapidly. In  rder to integrate such continuous updates into the enzyme-constrained version of a model in an organized way, an automated pipeline named ecModels container was developed. The ecModels container is a continuous integration implementation whose main functionality is to provide a catalog of ecModels for several relevant organisms that are automatically updated every time a modification is detected either in the original GEM source repository or in the GECKO toolbox, i. e. , new releases in their respective repositories. The pipeline generates ecModels in different formats, including the standard SBML and MATLAB files, and stores them in a container repository (https://github.
This pool is then constrained by experimental values of total protein content, and calibrated for batch simulations using experimental measurements of maximum batch growth rates on minimal glucose media, thus providing a functional ecModel structure ready for simulations. For ecHumanGEM just the unbounded ecModel files are provided, as this is a general network of human metabolism, containing all reactions from any kind of human tissue or cell type for which evidence is available, and therefore not suitable for numerical simulation. As H. sapiens is the most represented organism in the BRENDA database, accounting for 11% of the total number of available k cat values (Brenda kinetic data analysis section in the Supplementary Information File 1), kinetic parameters from other organisms were not taken into account for its enhancement with enzyme constraints.
ecHuman1 provides the research community with an extensive knowledge base that represents a complete and direct link between genes, proteins, kinetic parameters, reactions and metabolites for human cells in a single model structure, subject to automated continuous update by the ecModels container pipeline. Visualization of GECKO simulations in the Caffeine platform. We implemented simulations with ecModels in Caffeine, an open-source software platform for cell factory design. Caffeine, publicly available at http://caffeine. dd-decaf. eu, allows user- friendly simulation and visualization of flux predictions made by genome-scale metabolic models. Several standard modeling methods are already included in the platform, such as  13  C fluxomics data integration, and simulation of gene deletion and/ or overexpression, to interactively explore strain engineering strategies.