dataesr/scientific-paragraphs-categorization huggingface dataset:
Following Cox (2008) cited in INSPIRE MIG (2016), an Observation is an action whose result is an estimate of the value of some property of the feature-of-interest, at a specific point in time, obtained using a specified procedure. The information describing the observation data, such as variable name (ObservedProperty concept), feature of interest involved in the observation (FeatureOfInterest concept), acquisition and processing methods (Procedure concept), observation result (Result concept) were based on the O&M OGC standard(OGC, 2013). This kind of information is necessary to understand how the data was acquired and to allow its reuse. The Result concept refers to the data itself. It contains some metadata on data values (quality flag description, missing value) and the data filename. For an Observation that represents time-series measurements (of a property made repeatedly with the same procedure in a feature of interest), the Result 12 https://github.
com/theia-ozcar-is/data-model-documentation/tree/master/standard-mapping 13 https://github. com/theia-ozcar-is/data-model-documentation/tree/master/pivot-data-model 14 ISO 19115: https://www. iso. org/standard/53798. html. csv file, which contains only one time series of a given variable at a given location. The format of the. csv file was standardized with a common syntax: the data file must have a short metadata header and data values must be formatted in the form of tuples: date, location, value, quality flags.
15 https://github. com/theia-ozcar-is/data-model-documentation/blob/master/pivot-datamodel/description_champs_JSON_v1. 1%20EN. pdf16 CSW (Catalog Service for The Web): http://www. opengeospatial. org/standards/cat 17 SOS (Sensor Observation Service): http://www. opengeospatial. org/standards/sos.
of different options to organize datasets and of the interest of implementing standardized web services. A positive outcome of the collective discussions around the design of the Theia/OZCAR IS was the creation of a community amongst IT and scientists dealing with data management within the OZCAR-RI. Members of this community now know each other and is able to share experiences and skills. The pivot data model was designed to document time series. However, the inventory of all the data collected in the observatories highlighted other kinds of data such as 2D maps, 2D or 3D profiles (e. g. geophysical measurements), soil profiles, soil cores, experiments and even model simulations. The proposed pivot model will be extended to accept metadata related to these kinds of data using the generic O&M classes of our data model.
In addition, the GeoJSON format can handle 2D or 3D geometries. However, we have not yet tested it with this kind of data. Furthermore, MongoDB is schemaless, so evolutions are much easier to handle than in standard relational databases. The choice of implementing the pivot data model in JSON has the advantage that JSON objects are directly imported into the MongoDB database without having to be mapped into java objects in the import module code or the backend code. This will contribute to facilitate evolutions in the pivot data model. It was already extended to enrich time series description without particular difficulty.
for a given catchment (Xue et al. , 2019), supporting both faceted search and map 21 https://www. hydroshare. org/ the map interface, HydroShare uses Google Maps and Google Map clustering facilities, while Theia/OZCAR IS uses the open-source JavaScript library Leaflet. Regarding the implementation of the faceted and full text search, HydroShare is based upon the SOLR 22 search engine, which is designed to provide an efficient, advanced, full-text and faceted search capabilities. Theia/OZCAR IS uses the capabilities of the MongoDB database, which provides a less efficient search engine and.
Acknowledgements The authors thank all the participants of the " Tour de France " meetings, and those that contributed to the working groups during the OZCAR Fréjus meeting in April 2018. The authors are grateful to the  Theia technical team  ( N. Baghdadi ,  A. Sellé ,  S. Debard ) and more generally the interpole technical group led by  F. Genova , including Odatis, Form@terre, Aeris, Theia and PNDB. The persons involved in the  Trajectories  project from  Grenoble Alpes University , and in the  Zone Atelier Network  are also thanked for fruitful exchanges. The work is conducted within the  OZCAR-RI , supported by the  French Ministry of Research , French research institutions, universities and the  French Agence Nationale de la Recherche (ANR). The work is also part of the Theia land data pole of the  Data Terra Research Infrastructure.
Digital Objects. http://www. ifs. tuwien. ac. at/dp/ipres2010/papers/massol-6. pdf[Accessed 24 February 2019] Molénat, J. , et al. , 2018. OMERE: A Long-Term Observatory of Soil and Water Resources, in Interaction with Agricultural and Land Management in Xue, Z. K. , Couch, A. and Tarboton, D. 2019. Map based discovery of hydrologic data in the HydroShare collaboration environment. Environmental Modelling &Software, 111, 24-33. Zaslavsky, I. , et al. , 2011. The Initial Design of Data Sharing Infrastructure for the Mediterranean Hilly Catchments. Vadose Zone Journal, 17(1). Critical Zone Observatory. ed. Proceedings of the Environmental Information doi:10. 2136/vzj2018. 04. 0086 Management Conference, iEM'2011, 2011 Santa Barbara, CA, 28-29 September, Nord, G. , et al. , 2017. A high space-time resolution dataset linking meteorological 145-150.
Slot Geometrical Features Definition Compared to the works found in the literature, where simplified models of slot have been taken into account, in this work, both a standard slot (STSL) model and the non-standard (NSTSL) ones are used. The slot profiles are defined with the presence of an insulation sheet and, therefore, its thickness d ins is taken into account. In general, the slot insulation sheet is pre-folded and does not substantially modify the slot cross-section profile available for the positioning of the wires. The geometries and the related contour data of a standard slot, typically used for wires, and a non-standard one are shown in Figure  2a ,b  [16] , respectively. Energies 2020, 13, x FOR PEER REVIEW 5 of 20 slot filling factors obtainable in many cases of study. The algorithm has been implemented in the Matlab environment and it is described in detail below.
V. power supply. The waveforms recorded in each node are averaged over 512 shots with a 1 GHz band-bass oscilloscope. In order to reconstruct the EF maps and sequences over the time, a Python script was developed to process the data. The outcome is the data processing is a 2D map resulting from an overlay of a quiver plot describing the direction of the EF vector and a contour plot with a color-map scaling the magnitude of the EF strength according to (1). Last but not least, the alignment of the probe axis have been performed before the measurements. A clear evidence of a correct alignment is demonstrated in figure  4a ) where the IW is still far away from the probe, confined within the glass tube. In this case, the EF lines configuration refers to a pin-to-plane geometry where -in good assumption-the EF distribution in the vicinity of the grounded plane is homogeneous and uniform  [27].
Acknowledgment The author would like to thank Kapteos collaborators for the helpful discussions concerning the use of the EO probe for vectorial and real time analysis of the electric field. Thanks to  X. Damany  for the fruitful discussions on this topic. S. Dozias  is acknowledged for the electronic engineering and technical support. Data and graphs have been exclusively processed with open-source libraries SciPy, NumPy [38],  Veusz  and  Matplotlib  [39].
Such tools are at the core of this survey. Tools for visualization of macromolecular structures have emerged from the longstanding need for molecular graphics and are accessible to scientists with broadly varying backgrounds as previously reviewed in  (4). In this mini-review we focus on a few essential and generic tools along with some novelties and more singular solutions for specific needs. We will not focus on commercial closed source software, although there are definitely tools available, amongst which we may mention Yasara (5) and Maestro  (6) , both freely accessible. Commercial tools often integrate or interface with modelling features and may preferably be used in industry settings or for occasional lay usage because of their user guidance, support and commercial ecosystem. The scope of applications we target with this mini-review ranges from very generic needs to specific usages.
Swiss army knives for general needs A few molecular visualization packages have been used by the community for many decades and provide robust visualization capacities suitable for a large audience. Among these tools we will briefly discuss the Chimera (latest version called ChimeraX), JMol (and more recently JSmol), PyMol and VMD softwares, which all serve a large user base. Such legacy packages appear as a safe choice for visualization-based projects, with a stable codebase and API to interact with. Their usability has been refined over time, and although not necessarily easy to use for newcomers, they are well adapted to the tasks related to molecular visualization. Each tool may feature many functionalities and provide tutorials, scripts and extensions that enrich the user's experience.
PyMol  (18)  was launched at the end of 1999 and, as its name suggests, is built around a python scripting environment. This tool is very popular with experimentalists as it provides useful features for crystallographic and NMR-derived structures. It generates publication quality figures and provides convenient molecular editing and atom selection functionalities. Since its inception in 1993, the VMD software focused on the graphical analysis of molecular dynamics data, hence its acronym Visual Molecular Dynamics. It manages even quite large systems smoothly and provides many extensions, in particular for advanced visual analysis, through plugins. An important feature common to all these packages is the possibility to automate and script tasks for easy re-use and batch deployment. Despite their long history, all packages still provide regularly novel functionalities and adapt to the progressing hardware technologies, for instance in the graphics card market.
net which has recently been released. UnityMol experiments with WebVR have been reported as well  (25). Another wide-spread and very common need is to generate illustrative and compelling images for publications, which has been reviewed by  Goodsell and Jenkinson (27). David Goodsell has provided for many years the captivating images of the molecule of the month  (28) , which can now be reproduced by everyone with the Illustrate software  (29). Previously, the introduction of ambient occlusion lighting with the Qutemol software  (30)  had already led to a significant improvement in the depth perception from static illustrative images. The possibility to continuously vary the abstraction level added further possibilities  (31). For particularly eye-catching results, tools from the cinema and animation industry can be used as well  (32) (33) (34) (35) (36).
Such publication quality images can be produced with the majority of packages described here, either directly within the package or through a subsequent ray-tracing step. Using interactive raytracing  (37, 38) , very high quality images can be produced almost on the fly and explored interactively. Real-time, often GPU-powered, path-tracing approaches become more widely accessible, for instance through the Blender game engine  (30). Alternative approaches attempt to produce visually similar results  (39). The visual exploration of specific data sets represents an important aspect of protein visualization. Historically this aspect is illustrated by the need to visualize experimental electron density maps in crystallography, spawning many tools, among which O  (40, 41) , Coot (42), CCP4mg  (43)  and uglymol  (44)  just to mention a few.
More generally the advanced exploration of specific experimental data may represent a specific feature of some packages, such as Chimera, with a focus on CryoEM data. Similarly, through its tight integration with the protein data bank, Mol* provides many useful features for crystallographic data. A particular challenge may be the joint analysis of data spawning different data spaces. This task is nicely illustrated by the visual analytics approach implemented in the Aquaria software  (45, 46)  enabling a user to link the evolutionary dimension of sequence-space with structure space. The MolArt structure annotation and visualization tool pursues similar avenues  (47). Molecular dynamics simulations -and more generally computational simulation approaches including normal modes, Poisson-Boltzmann electrostatics, finite element and elastic network models -represent another source of specific data to be visualized.
Historically, the VMD package was the first to focus on molecular simulations, but many others now provide such possibilities, most recently for instance MDsrv  (38)  or HTMoL  (39)  on the web  (40)  and Unitymol in VR. Such virtual reality exploration (48) of protein structures addresses the need to render the complex spatial features of macromolecules very efficiently in immersive 3D thereby helping shape perception and benefiting from advanced interaction devices. Several packages have reported VR capacities such as Molecular Rift  (49) , ChimeraX  (50) , UnityMol  (25, 51) , Narupa  (52) , Nanome  (53)  and CootVR.
Several of these databases already exist, such as  (67) (68) (69) , to mention only a few, however interactive visual analysis (and visual analytics in particular) is currently rather limited. Whereas the overwhelming part of protein visualization so far may have focused on displaying existing models, the future may focus more intensely on methods to visually create complex molecular landscapes from scratch. Novel technology may be instrumental to this task  (70) (71) (72) (73). A final general and more philosophical comment about the progress of molecular graphics will conclude our collection of thoughts on future trends and perspectives. There still exists a huge potential for substantial improvements of visualization methods by better integration of different scientific communities, in particular computer scientists involved in the field of computer graphics and biologists with visualization needs.
Conclusion Protein visualization is nowadays a vibrant and striving field, with an extraordinary longevity since the first drawings by hand that date back before the 1960s. It has reached a broad audience in diverse subfields of modern biology and is ubiquitous on the web, in publications and on a broad variety of display devices used by researchers, from mobile phones to 3D theaters. This success has been driven by the availability of high-quality software tools, often developed and refined collectively by the scientific community. Beyond the classical axiom: "bigger (structure), larger (dataset), faster (visualization)", future new developments related to molecular visualization of protein structures will serve our increasing need to integrate heterogeneous data and to efficiently use new hardware devices (from VR/AR headsets to smartphones).
Acknowledgments. This work was supported by the " Initiative d'Excellence" program  from the  French State  (Grant " DYNAMO ",  ANR-11-LABX-0011  and grant " CACSICE ",  ANR-11-EQPX-0008 ) and received funding from the  ANR-16-CE05-0024   ENZYMOR  project. X. M. and M. B. thank  Sesame Ile-de-France  for co-funding the display wall used to assess several software packages described in this review. M. C. acknowledges support from  CNRS-MITI  grants  PEPS MPI 2018  and "Modélisation du vivant" 2019. X. M. and M. B. thank  UCB Biopharma  for support.
The diversity of T-cell receptor (TCR) repertoires is achieved by a combination of two intrinsically stochastic steps: random receptor generation by VDJ recombination, and selection based on the recognition of random self-peptides presented on the major histocompatibility complex. These processes lead to a large receptor variability within and between individuals. However, the characterization of the variability is hampered by the limited size of the sampled repertoires. We introduce a new software tool SONIA to facilitate inference of individual-specific computational models for the generation and selection of the TCR beta chain (TRB) from sequenced repertoires of 651 individuals, separating and quantifying the variability of the two processes of generation and selection in the population.
A. Data source and modeling strategy P i post (σ) = Q i (σ)P i gen (σ). (B) To learn these models, for each individual we separated sequences into productive and nonproductive sequences. Nonproductive sequences are free of selection effects and were used to learn the generation model, Pgen, using the IGoR software  [15]. Most productive sequences are subject to selection and were used to learn the selection model, Q, by matching the statistics of the data with those of sequences generated synthetically with Pgen (using the OLGA software  [16] ) and weighted by Q. Once the model is learned, the probabilities of amino-acid TRB sequences preand post-selection can be calculated using OLGA and SONIA. We analyzed previously published RepSeq data from a large cohort study  [14]  consisting of TRB nucleotide sequences from blood samples of 651 healthy individuals.
Productive sequences were used to learn individual-specific selection models Q i (σ) as in Ref. [17] , by comparing them with simulated productive sequences generated from the individual specific generation models. A new software package, SONIA, was developed to perform the Q i (σ) inference. For each sequence in each individual the algorithm computes two probabilities: its generation probability, P i gen , and its post-selection probability in the periphery, P i post. We then use them to estimate the intra-and inter-person variability.
For example, some of the features of the TRB designated by (CASSGRQGVATQYF, TRBV06-05, TRBJ02-05) are 'CDR3 length 14', 'S in position 2 from the left', 'Y in position -2 from the right' and 'V gene is TRBV06-05'. To facilitate the definition and learning of such selection models, we introduce the software package SONIA. SONIA allows for a flexible definition of model features and infers the selection factors q f using MLE. The input to SONIA is a list of selected amino acid sequences and, if needed, their V and J gene choice. By default SONIA uses P gen as provided by an IGoR inferred model (using OLGA as a generation engine  [16] ), but it can also take as an input a custom sample of pre-selection sequences. This can be useful for identifying selection pressures during immune challenges using different choices of pre and post-selection repertoires (see Methods for details).
Fig. 5E  shows the distribution of these entropies across the pop-ulation. The width of the distributions of log 10 P is strongly correlated with their means across individuals and also from pre-selection to post-selection (Fig. 5F ), suggesting again a single parameter driving individual variability, possibly the average number of N insertions. We also plot the P post distributions of TRBs from the VDJdb database that are known to be specific to human viruses  [19]  (Fig. 6 ). There does not appear to be a substantial shift in the post-selection probability of these viral-specific sequences as compared to productive TRBs from blood. A similar absence of bias was previ- ously reported for the distribution of generation probabilities  [16] , suggesting that the VDJ recombination process is not explicitly skewed towards generating these viralspecific sequences.
This suggests that the main statistical effects of thymic selection captured by our model are mostly universal, probably driven by positive selection for amino acids that makes a folding functional receptor. The effect of HLA specific positive and negative selection, on the other hand, might not be well captured by this kind of a model, which focuses on finding broad sequence features rather than specific sequences to harness more statistical power, although variations in the V and J selection factors may reflect HLA types. Our approach thus complements the strategy of looking for associations of particular TCRs with HLA type, which was previously applied to the same dataset  [12]. An obvious limitation of this and other studies of that dataset is that it comprises a restricted subset of the human population.
While in this study we used SONIA for the purpose of comparing peripheral to pre-selection repertoires, the software is written to be flexible in several ways. First, if can be used to infer selection factors between any two repertoires (observed or generated), by inferring selection factors that match the statistics of the two samples. Second, SONIA can go beyond selection pressures on single amino acids, allowing features of pairs or motifs of amino acids. Finally, SONIA can be applied to other chains than TRB, notably the alpha chain of the TCR (TRA) as well as immunoglobulin IgH. SONIA's flexibility opens up the possibility of using SONIA to find statistical correlations in various biological or clinical contexts. SONIA could be applied to samples that are known to have responded to some perturbations, for example after vaccination or infection  [24, 25].
A. Data The data used for the inference of both the VDJ generation models and the subsequent selection models are the Adaptive Biotechnologies sequenced TRB repertoires of Emerson et. al. [14]. An initial quality control pass was done over the 664 individuals to ensure at least 10,000 unique out of frame sequences to be used to infer the VDJ generation model. 651 individuals passed this threshold and all were used in the subsequent analyses. All analyses were done on unique nucleotide reads, discarding any cell count information. This is done to ensure that each sequence is reflective of a single recombination event, which is an important restriction when modeling VDJ recombination and thymic selection. For some selection modeling purposes (e. g. modeling antigen exposure), cell counts may be incorporated.
D. SONIA implementation SONIA is a python software built to define and infer feature-defined selection models. SONIA has built in procedures for defining and identifying sequence features of CDR3 sequences. SONIA also ships with the prepackaged selection models of LengthPosition and Left+Right features. With a feature model defined, SONIA takes as an input a list of productive amino acid CDR3s, along with any aligned V/J genes. This list of observed CDR3s can be either reduced to unique sequences (useful when learning thymic selection and the background statistics are based on unique sequences) or sequences taken with their clonality to account for a non-flat clone size distribution. As an optional input, SONIA can read in baseline CDR3 and aligned V/J genes to use as the background that the selection model is learned from.
E. Distributions of probabilities We produced the distributions of P gen , Q, and P post shown in Fig. 5A -C by comparing the productive data sequences of each individual to a synthetic sample of productive sequences generated from P i gen of that individual using OLGA  [16]. The number of generated sequences for each individual were matched to the number of productive data sequences. For each dataset, we calculated P i gen using OLGA, and Q i and P i post using SO-NIA's Left+Right model. The Q-weighted curves are determined by weighting each generated sequence by its selection factor Q i and then renormalizing. For Fig. 5D , we used 300,000 scenarios, nucleotide, and amino acid sequences were generated from each individual's VDJ generation model. Again, we used OLGA to compute the various generation probabilities P i , where P i is P i scenario , P i,nt gen , or P i gen.