dataesr/scientific-paragraphs-categorization huggingface dataset:
Results In this section, we exploit the experimental data by splitting our analyses in three parts. The first subsection exploits the extraction of all viewpoints in order to analyze the dynamics of the community. The second subsection exploit the topic hubs (TH-X) series of extractions to analyze the final structure of the medium. The third subsection exploits the guests' trail (GUEST-A) and other guests' trail (GUEST-B) series of extractions to analyze the mutual influence between the collective and the individual in terms of interactions with the medium and knowledge acquisition. Our analysis is purely descriptive: the main goal of the analysis is to gain a better and deeper insight in the interaction phenomena that occurred in the Garden-party experiment in order to design future experiments including statistical inferences.
Overview of the Extracted Data The TH-X series exploits the trails by all the guests around the topic hubs. The chosen radius is 1. 5. Figure  8  summarizes the neighborhood of each topic hub in terms of respective populations of agents, documents, events, topics, and topic hubs. It must be noted that the topic hubs do not operate a partition of the resources; each resource can be directly or indirectly connected to several topic hubs. It appears on Figure  8  that each extraction captures an average of 592 and a maximum of 667 resources among the 784 potentially reachable resources. It must be noted that the topic hubs do not operate a partition of the resources; each resource can be directly or indirectly connected to several topic hubs. It appears on Figure  8  that each extraction captures an average of 592 and a maximum of 667 resources among the 784 potentially reachable resources.
The Garden-party experiment gathered thirty volunteers called guests who spontaneously, for two and a half months, built together and browsed through a digital corpus of about eight hundred resources including forty external authors, four hundred documents, and three hundred topics. Interlinking these resources, the guests have weaved a web of nine thousand connections reflecting logical links, emotional or rational reactions as well as happy encounters. The experimental data issued from the Garden-party are analyzed through two comparisons bearing on the proximities measured between the guests and the topic hubs (themes of interest). Both comparisons focus on a sample of eleven guests and seven topic hubs (aggregators of topics). The first comparison is between the KDNA proximities (computed along the personal trails) and the SEAc proximities (self-evaluated proximities); in seven cases, we observe a similarity between KDNA profiles and SEAc profiles.
Context. Grain surface chemistry is key to the composition of protoplanetary disks around young stars. Aims. The temperature of grains depends on their size. We evaluate the impact of this temperature dependence on the disk chemistry. Methods. We model a moderately massive disk with 16 different grain sizes. We use the 3D Monte-Carlo POLARIS code to calculate the dust grain temperatures and the local UV flux. We model the chemistry using the 3-phase astrochemical code NAUTILUS. Photo processes are handled using frequency-dependent cross-sections, and a new method to account for self and mutual shielding. The multi-grain model outputs are compared to those of single-grain size models (0. 1 µm), with two different assumptions for their equivalent temperature. Results. We find that the Langmuir-Hinshelwood (LH) mechanism at equilibrium temperature is not efficient to form H 2 at 3-4 scale heights (H), and adopt a parametric fit to a stochastic method to model H 2 formation instead.
Another improvement was achieved with the development of grids of thermochemical models allowing multiwavelength fitting of dust and molecular lines such as DIANA  (Woitke et al. 2019 ). However, these elaborate approaches use a single grain temperature which is either fixed or derived from a dust radiative transfer simulation while the temperature of a grain depends on its size (e. g. Wolf 2003) , larger grains being colder. Chapillon et al. (2008)  found in the disk of CQ Tau, whose temperature is well above the CO snowline temperature, an important CO depletion. They suggested that CO may remain trapped onto larger grains that are cold enough to prevent thermal CO desorption. Heese et al. (2017)  have investigated the dust temperature variations with grain size and position (radius and altitude above the midplane) in a typical TTauri disk using the 3D dust radiative transfer code Mol3D  (Ober et al.
Model description To estimate the impact of the grain temperature onto the chemistry, we couple the radiative 3D Monte-Carlo code POLARIS with the astrochemistry code NAUTILUS. This requires to independently assume a gas and dust disk structure, a gas temperature, a dust distribution and settling. Moreover, the coupling between the two codes requests for NAUTILUS a new method to calculate the dust extinction, the self and mutual shielding using the UV flux calculated by POLARIS in each disk cell. Figure  1  is a scheme summarizing how we proceed.
Chemistry simulations The NAUTILUS gas-grain code  (Ruaud et al. 2016 ) is used to perform chemistry simulations. NAUTILUS is dedicated to computing chemical evolution in different interstellar environments and uses the rate equation approach  (Hasegawa et al. 1992; Hasegawa & Herbst 1993)  to calculate the abundance as a function of time. The NAUTILUS gas-grain code is a 3-phase model  (Ruaud et al. 2016 ) that includes gas-phase chemistry and chemically active grain surfaces and mantles. Thus, aside from the gas-phase chemistry, NAUTILUS covers the physisorption of neutral species on the surface, the diffusion of these species and their reactions, desorption of species from the surface and repopulation of the surface by species from the mantle as the species on the surface evaporate. The latest version of NAUTILUS, the Nautilus Multi Grain Code (NMGC) 1  0D model  (Iqbal & Wakelam 2018) , can perform simulations using a full grain distribution in size.
The atomic H problem The NAUTILUS code initially implemented the H 2 formation through the Langmuir-Hinshelwood (LH) mechanism that considers physisorption on grain surfaces. The LH mechanism is efficient only over a relatively narrow temperature range (5-15 K on flat surfaces  Katz et al. 1999; Vidali et al. 2004 Vidali et al. , 2005)). In the case of multiple grain sizes, dust settling implies that only the small grains remain in the PDR regions of the disk. These small grains illuminated by the UV field from the star can get significantly warmer at the disk surface, and the H 2 formation via the LH mechanism as treated in most astrochemistry codes becomes inefficient, leading to much smaller H 2 formation rate than in the case of a single, larger, equivalent-area, grain size with lower surface temperature.
In order to have a broad view on how our chemical models react to different intensities of UV flux, we adopt two regimes of radiation. One regime uses the spectrum of a relatively high-UV emitting TTauri star and another uses the spectrum of a relatively low UV emitting one, hereafter called HUV and LUV, respectively. For the HUV regime we use the spectrum of TW Hya  (Heays et al. 2017)  while for the LUV we use the one of DM Tau. Both FUV spectra are provided in the CTTS FUV spectra database 3  (France et al. 2014). The blue solid line in Fig. B. 3 exhibits the spectrum of TW Hya as provided by  France et al. (2014)  before it is attenuated by the disk. The chemical abundances in the disk are calculated using the time-dependent equation embedded in NAUTILUS. We use atomic initial abundances described in Table  B.
Summary. We presented here a new chemical model of protoplanetary disks that consistently accounts for the impact of grain sizes on the dust temperatures and UV penetration. Representative results were obtained for a typical disk around T Tauri stars, using parameters derived from the Flying Saucer observations  (Dutrey et al. 2017). We coupled the chemical code NAUTILUS and radiative transfer code POLARIS to use a consistent distribution of grain sizes and temperatures, accounting for size-dependent dust settling. We implemented in NAUTILUS a new method to account for self and mutual shielding of molecules in photo processes, method that accounts for the frequency dependence of photorates. The outputs of multi-grain models are compared to those of single grain size, using two different assumptions for the single-grain temperature: equal to the (assumed) gas temperature, or to the area-weighted mean temperature of the multigrain models.
Acknowledgements. We thank  E. Bron  for fruitful discussions about the H 2 formation in PDRs and  M. Ruaud  for a detailed reading of the article. We also thank an anonymous referee who helped to improve the paper. This work was supported by the  "Programme National de Physique Chimie du Milieu Interstellaire" (PCMI)  from  INSU/CNRS. J. K. acknowledges support from the  DFG  grants  WO 857/13-1 ,  WO 857/15-1 , and  WO 857/17-1.
Results and discussion The numerical procedure described in the previous section is applied to address the pressurized elastoplastic thin and thick hollow cylinders including inertia effects. The material obeys to the Tresca criterion with Young modulus p(t) = { p 0 t t 0 if 0 ≤ t ≤ t 0 p 0 if t 0 ≤ t , ( 46 ) where p 0 and t 0 are the initially given pressure and time, respectively, as shown in Fig. 3. The algorithm is implemented in Matlab. The minimization algorithm fmincon is used to find the local minimum of the constrained minimization problem  (43) ,  (45). Based on our observations, it is preferred to impose a tiny tolerance for the equality optimization constraints. This tolerance is chosen so that the numerical minimum of the cost function is close to zero, which is the theoretical value.
Nowadays quantum chemical calculations are widely used to generate extensive datasets for machine learning applications, however, generally these sets only include information on equilibrium structures and some close conformers. Exploration of potential energy surface provides an important information on ground and transition states, but analysis of such data is complicated due to the number of possible reaction pathways. Here, we present RePathDB, a database system for managing 3D structural data for both ground and transition states resulted from quantum chemical calculations. Our tool allows to store, to assemble and to analyze reaction pathway data. It combines relational database CGR DB for handling compounds and reactions as molecular graphs with a graph database architecture for the pathway analysis by graph algorithms.
Introduction Public and commercial chemical databases collect, curate and index thousands of chemical data records daily. They range from gigantic libraries like ZINC 1 (over 700M 2D structure records, as of 2018) or CAS registry 2 (over 163M small molecules) including purchasable chemical compounds, to smaller, more focused databases such as ChEMBL 3 (over 1. 9M structures with bioactivity records) or DrugBank  4  (about 14 000 registered drugs, with biological targets and clinical information). Experimental reaction data is also quite abundant: CAS REACT  5  includes more than 128 million reactions and synthetic preparations, while Reaxys 6 has over 53M reactions. All these databases greatly assist organic and medicinal chemists, chemoinformaticians in their research. However, conventionally the databases deal with the relational tables of 2D molecular structures, since this format is more convenient to handle and most algorithms such as structural search etc.
, are well implemented only for 2D structures. Needless to say, the sources of 3D structures are also quite scarce -they can be obtained either via crystallographic studies, or by quantum chemical calculations. Datasets of 3D structures calculated by quantum chemistry have started emerging recently with the growing interest of application of machine learning methods to emulate QC. The most prominent QM7 and QM9 sets  7  are based on GDB virtual database which assembles combinatorially enumerated structures containing up to 7 or 9 heavy atoms, respectively. The PubchemQC 8 dataset was created in a similar manner: it contains extracted from Pubchem database structures for which the geometries and energies of the ground and excited states were calculated using TD-DFT method. The largest available set of structures calculated by QC is ANI set 9,10 containing > 5M structures.
Conventional relational databases architecture can hardly be used for the storage and analysis of this information because (i) both 3D and 2D structures must be stored; (ii) equilibrium, transition and intermediate states should be stored in different relational tables; and, therefore, (iii) building reaction pathways would require multiple joinings of these tables. In this case, represent of the ensemble of data as a graph looks more convenient, because it allows to apply various graph theory algorithms (e. g. , shortest path search) for the analysis of pathways. Recently, application of a graph database architecture for chemical data has been reported 18 as a tool for merging data from ChEMBL abd DrugBank. In this work, we report a new reaction database management system combining relational and graph architectures which allow to consider both 2D and 3D objects.
Condensed Graph of Reaction In silico handling of chemical reactions can be significantly simplified by the Condensed Graph of Reaction (CGR) approach  19. In this framework, the structures of reactants and products are merged into a single molecular graph (Figure  1 ). The CGR edges correspond either to standard chemical bonds or to "dynamic" bonds describing transformations. Similarly, dynamic atoms may also be designated as atoms that change a property (e. g. , formal charge) during the transformation. In such a way, one can consider a CGR as a pseudomolecule for which some types of molecular descriptors can easily be computed followed by their application in data analysis and statistical modeling tasks. 20 This approach has been successfully applied to various tasks, such as similarity search in reaction databases,  19, 21  building quantitative structure-reactivity models,  [22] [23] [24] [25]  assessment of tautomer distributions,  26, 27  prediction of activity cliffs,  28  classification of enzymatic transformations,  29  prediction of reaction conditions  30, 31.
Database management All new types of entities in this project (see "Results and Discussion" section) are implemented as extensions of Neo4J database 34 (v. 3. 3) nodes, via neomodel Python library (v. 3. 3. 2). CGRdb and CGRtools (v. 4) Python libraries have been used for storing and management of molecular structures, encoding reactions as Condensed Graphs, and substructural and similarity searches. CGRdb is based on PostgreSQL. As the developed library requires an extended list of external DB tools to be properly installed and set up, it is also available as a Docker container. Feeding the data to the database requires Neo4J, CGR DB to be launched on the server, and the following command to be run with appropriate information (hosts, ports, etc. ): python -m repathdb -pg POSTGRES_CONNECTION_INFO ("//user:port@host:port/table") -nj BOLT_CONNECTION_INFO("bolt://login:pass@host:port") populate -f FOLDER -s FILE_TYPE "Populate" command above will launch he script that will search the specified folder (-f option) for files (-s option) and will parse these files to extract 3D coordinates and the energy of structures.
Web interface Web interface is implemented with Plotly Dash framework  35  as it is the most convenient modular framework that can include a wide variety of widgets on a web page. MarvinJS web plugin (v. 20. 2. 0, 2020, ChemAxon, http://www. chemaxon. com) is used for the search query editor. 3D structure visualization is implemented as a custom JavaScript widget based on the 3DMol. js library  36.
RePathDB architecture The developed database and database management system combines two architectures -a relational database for molecular graphs (for both compounds and reactions) and related properties, implemented using CGR DB library, and a graph database to represent the relationships between the entities, built using Neo4J. As a DBMS developed specifically for dealing with chemical reactions using the CGR concept, the purpose of CGR DB in here is two-fold. First, CGRtools library serves as the base of CGR DB and allows to parse a variety of chemical formats, including XYZ format storing 3D coordinates of all atoms of the system. Second, it is used to perform fast search by chemical structures (substructural or similarity search) for both molecules and reactions, represented as molecular graphs.
On the other hand, the relationships between entities in RePathDB are managed by a graph database, rather than by the conventional "key-attribute" paradigm of a relational table. This is especially convenient when the nature or the number of relations for each entity is not known beforehand. Another advantage is the accessibility of graph algorithms. The set of pathways of a reaction can be imagined as a graph, therefore, such architecture facilitates the exploration and analysis by graph algorithms. For example, the shortest path between a reactant and a product can be easily found via a graph. To implement the graph architecture (see the schematic view on Figure  2 ), we introduce entities (graph nodes) of the following types: 1) Molecule (M) represents an individual chemical compound (e.
Atomic coordinates and energy of every S as well as atom-to-atom mapping of S into C are stored in the database. 5) Two types of Transition state (TS) are considered: true or approximate ones. Each TS relates two S. In 2D perspective, TS corresponds to a CGR of encoding a given Reaction. 3D atomic coordinates and energies of transition states are stored in the entities. 6) Molecular formula (B) represents the full atomic composition of the studied system and is the same for all pathways considered in the given study. These pathways can be viewed as different parts of the same PES. 7) Single system (explored PES for one reaction or one reaction mapping) may include hundreds of single transformation paths and, therefore, will accommodate many connections C -R -C -R -C …, etc. Schematically, the architecture of the database for a single transformation is shown on.
3. An atom failing the full valence check searches connected neighbors for other non-full valences. If found, related bond order is changed to double or triple. 4. If the valence check still fails and there are no neighboring atoms with vacancies, formal atomic charges are assigned instead. Related software tool (XYZ parser) has been implemented in CGRtools library. The log file parser used to extract an information from the GRRM/AFIR output files is specialized for this format only. The parser searches files with specific headers and processes them only if they have, at least, three structures: initial and final equilibrium states, and the maximum energy point (real or approximate transition state). The parser checks the existence of transition state (maximum energy) and ignores the files lacking this information.
Technically, a search can be launched using either command line or web interface. When web interface is used, a query involving given initial and final structures can be prepared with the MarvinJS structure editor. Structural keys (fragments) implemented in CGR DB are used in substructure and Tanimoto similarity searching algorithms. Once both reactant and product molecules are detected, the graph database will find the most optimal path between them. The optimality metric is estimated as the sum of all energy barriers along the path, i. e. the differences of energies between a transition state and the preceding equilibrium state are summed up to form the score. Only the nodes belonging to the reduced graph are considered (see Figure  3 ). In this case, the lowest energies among corresponding 3D configurations are used to assess the energy barriers.
Transition states as 3D CGRs GRRM/AFIR calculations allow to extensively explore the PES to find numerous transition states of a given system. The number of these TS may be great: e. g. , for Wohler's urea synthesis pathways 152 true TS (representing slow processes like chemical bond rearrangements) and over 5000 approximate TS (representing fast processes like conformational changes) were found. On the other hand, they represent a smaller number of actual transformations (R in Figure  2 ). In the database, all reactions are encoded into Condensed Graphs of Reactions (CGR). Ensemble of transition states (TS) associated with a given R are simply different 3D geometries corresponding to the same transformation, and thus can be represented by a single 3D CGR (see example on Figure  4 ). A special extension for 3D CGR has been developed and implemented into CGRtools library (also available in web interface as 3Dmol.
Conclusions In this paper we present a new database architecture for storage and analysis of 3D chemical structures calculated by quantum chemistry. While the management of data related to single molecules is possible with conventional relational databases, they are less suitable for chemical reaction data. Specifically, the pathway of chemical transformations during the exploration of PES is more easily understood as a graph. Therefore, we propose a database system that combines the graph and relational architectures and all their advantages. Since the number of 3D structures obtained via the exploration of PES even for one reaction system may be overwhelming, our tool circumvents this by relating all equilibrium structures to molecular graphs, and all transition states to Condensed Graphs of Reactions.
Code availability Described Python library is freely available at GitHub: https://github. com/icreddcheminfo/RePathDB. We also use following freely available libraries: CGRtools (https://github. com/cimm-kzn/CGRtools), CGR DB (https://github. com/stsouko/CGRdb), Neo4J Graph Database (https://neo4j. com/neo4j-graph-database/), Plotly Dash (https://plotly. com/dash/), D3. js (https://d3js. org).
Models and simulations We used the 13 dynamic vegetation models that are part of the TRENDY v8 model ensemble  (Friedlingstein et al. , 2019) : CABLE-POP, CLASS-CTEM, CLM5. 0, ISAM, ISBA-CTRIP, JSBACH, JULES-ES, LPX-Bern, OCN, OR-CHIDEE, ORCHIDEE-CNP, SDGVM, and VISIT. In these models, vegetation cover is either dynamically simulated (CABLE-POP, JULES-ES, LPX-Bern, and SDGVM) or prescribed (CLASS-CTEM, CLM5. 0, ISAM, ISBA-CTRIP, JS-BACH, OCN, ORCHIDEE, ORCHIDEE-CNP, and VISIT). All models used identical forcing inputs and followed a common simulation protocol over the period 1700-2018 (see Fig. C1  and description in the Appendix A for more detailed information). However, each model used a model-specific interpretation of the land-use change forcing. We remapped all model outputs and satellite datasets (see below) to a common 0.
Gross primary production and phenology We compared the simulated GPP phenology cycle to GPP derived from solar-induced chlorophyll fluorescence (SIF) (GOSIF-GPP;  Li and Xiao, 2019). To ensure the GOSIF-GPP phenology accurately captured phenology, we also compared these data to a satellite-derived leaf area index (LAI) product (modified Copernicus Service information, 2020; see Fig. C2 ). GOSIF-GPP is based on the OCO-2-based SIF product (GOSIF) and derived by assuming linear relationships between SIF and GPP estimated from eddy covariances sites. Uncertainties were accounted for by using eight different SIF-GPP relationships (derived universally and biomespecific, with and without intercept at both site and grid cell levels). The 0. 05 • dataset covers the period 2000 to 2018 at an 8 d temporal resolution that we aggregated to a monthly time step.