dataesr/scientific-paragraphs-categorization huggingface dataset:
2017 , Andr e et al. (2017) , http://propagationtool. cdpp. eu/). The data used by both tools are listed at http://storms-tools. irap. omp. eu/PropagationTool/CDPP_PropagationTool_fichiers/listing. html. Once this option is activated the related images are mapped onto the solar surface and displayed according to their corresponding times (updates in the scene depends on respective time resolution). Further extension of 3DView mapping functionalities should be available in the coming months. First, in the frame of Europlanet 2020 RI, a direct connection to the APIS database (Auroral Planetary Imagery and Spectroscopy,  Lamy et al. , 2015)  will allow to display HST images of auroral emissions at giant planets (this is made possible via EPN-TAP, see Section 6). APIS holds FITS files which retain all information on geometry and enable a correct data projection.
Science case 1: observations and hybrid simulations compared at Mercury This science case aims to demonstrate the ability of VO tools to combine successfully observations and global simulation results in order to easily and quickly determine information on the different plasma regions encountered by a spacecraft. We focus on the hermean environment explored by the MESSENGER mission  [Solomon et al. , 2007]. We attempt to determine the location of the Bow Shock crossings and when MESSENGER spacecraft is magnetically connected to the planet, and eventually to determine if the spacecraft was magnetically connected to reconnected field lines. In this science case we will use two VO-tools, AMDA and 3DView as well as MESSENGER (from NASA/PDS and available in AMDA) data and the Latmos Hybrid Simulation database  (Modolo et al.
Note that similar frames are defined for other moons DIIS for Dione, ENIS for Enceladus, etc. These frames were specifically coded by the tool developers. Once the scene has been built it is possible to get the 3D perspective of the flyby by, for instance, displaying the approach phase with the altitude profile or look at instrument FOV (which are opened in a separate windows as in Fig. 16 ). At the time of the paper preparation there is no Cassini data available on databases directly connected to 3DView for this time period; this does not preclude external data to be uploaded by the user. The last step concerns the ability of 3DView to discover planetology data worldwide from the initial context of the scene (Fig. 21 ). This is done via the VESPA interface available in the Science menu. To date, as explained in Section 6, the VESPA infrastructure enables us to search for data in 33 different planetology and heliophysic services compliant with EPN-TAP (see also  Erard et al.
Once the "Search" button is hit the VESPA registry is consulted and services with corresponding data returns corresponding files. For the short time interval of the scene the search returns no data. By extending the time frame (for instance changing the start year to 2006) will provide answer from the "titan" service of PADC/LESIA; this database displays vertical profiles of temperature and composition in Titan's atmosphere obtained by Cassini/CIRS from remote sensing techniques. Thumbnails of the available profiles are displayed in the interface. To access the data themselves, as they cannot be displayed in 3DView "as is", it is necessary to send them to an external tool, either by downloading them first, or by directly sending them via SAMP to the companion tool (both possibilities are offered by right clicking on the chosen file).
For instance in the present case TOP-CAT  (Taylor, 2005)  can easily plot such profiles. Similarly the EPN-TAP interface shows that (1023) results exist in the APIS database which stores PDF and FITS observations of Titan atmosphere; the FITS files may be sent to Aladin for visualization. This use case has shown how to easily get the 3D perspective of a particular planetary configuration, and moreover how current interoperability infrastructure enables to extend the richness of data without an a priori knowledge of the user.
Next we use the time shift functionality to display JUICE orbit 36. 5 years later. 'Future' orbits are displayed in green, past ones in red. Then we open the 'Remote data (IMPEx)' menu to access Galileo data in the Ganymede environment from the AMDA database (the original public data come from the Planetary Data System). The magnetic field amplitude is plotted along the Galileo trajectory. These data are then compared to one recent hybrid simulation (2016-03-19) with the LatHyS code (for instance  Leclercq et al. , 2016a,b; Modolo et al. , 2016 Modolo et al. , , 2017)). This simulation was especially designed to be compared with the G1 and G2 flybys. The simulation cube is then explored by different means. First the amplitude of the simulated magnetic field (interpolated along Galileo trajectory) is compared to the observed one and show remarkable agreement.
Similarly the simulated magnetic field vector is plotted along JUICE trajectory as arrows in the 3D scene but also as a simple time series plot. More globally a cut in the magnetic field amplitude 3D cube in performed in the GPHIO XZ plane. 3D cubes can be investigated in several ways: downloading the whole cube on the user disk may take time but more slicing options are then available subsequently; a lighter approach may be to only download pre-calculated 2D slices. This depends on how the modeller database is constructed but both approaches are possible within the IMPEx framework and handled in 3DView. The 2D cut shows a shape corresponding to the magnetic bending of Ganymede magnetic field in the Jovian plasma flow. Only the magnetic field was chosen here for conciseness (and not to overload the Figure ) but other data can of course be explored similarly.
Conclusion This paper presents the main features of a 3D space physics data visualization tool, 3DView, designed by IT engineers and scientists under the supervision of the French Plasma Physics Data Center, the CDPP. The initial "simple" orbit functionalities have been extended following 1/the involvement of CDPP in several science and interoperability EU projects, and 2/the willingness of CNES to support software for a larger community. The main focus of development in recent years has been towards the integration of a broad range of data, from in-situ measurements, to models and numerical simulations. In this context integration means either direct upload by the user or the use of interoperability standards which facilitate the connection between tools and databases, for instance SAMP for data transfer or EPN-TAP for data discovery.
Along side the community of scientists, the tool has attracted an audience among students which benefit from the 3D representations of data and get an easier understanding of some space physics concepts. Hands-on and tutorials demonstrating 3DView functionalities are regularly offered at the Masters Degree level and during summer schools. Finally, the next envisioned developments concern two aspects: first, the connection to new databases to increase the possibility of data ingestion; for instance a connection prototype to the ESA/Cluster Science Archive is in place, and the planned increase of services in the VESPA frame will naturally increase 3DView potentialities. Second, new science functionalities are currently being developed and among them a conjunction finder between space born and Earth ground based facilities.
This will greatly help the ionospheric community to identify relevant case studies, access the data and display them in the same tool. "2D" functionalities will also be enhanced, in particular surface mapping, for Earth, but also at giant planets (with auroral emission data from the APIS database). In conclusion 3DView is conceived and developed as a multi-purpose, multi-thematic analysis tool which can have varied usages from precise science cases, to model exploration and mission preparation.
21. T117 flyby of Titan by Cassini (left), and (right) the contextual data discovery with EPN-TAP. The target is automatically set to Titan and, for an extended time range, the VESPA services which hold Titan related data are seen in the 'Services' column. The 'titan' service for atmospheric profiles shows 1386 results, 4 of which are displayed as thumbnail. This profile can then be downloaded or sent to companion tools for analysis. One can see that the APIS database also holds 1023 results corresponding to an HST campaign on Titan in 2009.
Acknowledgments The CDPP wishes to warmly thank all members of its  User Committee  ( CDPP CU ) for the time spent at extensively testing functionalities and for the judicious discussions and comments. Many users, and among them numerous students, are also thanked for their useful contribution at enhancing the tool by reporting bugs, typos, …The 3DView development team is grateful to the  ViSBARD (NASA/SPDF ) team for long-standing inspiration. The CDPP, and in particular the 3DView software, is Fig. 25. A composite view of a 3DView scene (above) and ConnectSolo image (below) in the Solar Orbiter/Solar Probe Plus context (see text for details).
Experimental The TRWAXS measurements were performed on beamline ID09 at the European Synchrotron Radiation Facility (ESRF, Grenoble). The experimental setup is described in the literature,  64  whereas the specic details of the present study are summarized in the ESI. † All scattering patterns were measured with the FReLoN area detector,  65  azimuthally integrated and reduced to difference scattering curves DS(q,t) (with t being the time delay between the laser and X-ray pulses, q ¼ (4p/l)sin(2q/2) with l and 2q being the X-ray wavelength and the scattering angle respectively) according to standard procedures. 66, 67 To follow the structural dynamics of [Fe(btbip) 2 ] 2+ in a 9 mM MeCN solution, the data was collected in two separate runs using different X-ray energies, 25. 2 keV and 18 keV.
By using a multilayer monochromator, the X-ray energy bandwidth of the undulator harmonics were reduced to 1. 55% and 1. 9% for the 25. 2 keV and 18 keV runs respectively, which greatly enhances the structural sensitivity compared to the conventional raw undulator pink beam measurements while maintaining high Xray ux. 67 Note that using high energy X-rays gives access to a wider range of scattering vectors q, allowing for a more precise structural analysis. 68 The higher X-ray ux and detector efficiency at 18 keV made the 18 keV measurements four times faster per time delay than at 25. 2 keV. The 18 keV dataset was therefore acquired with more laser/X-ray time delays to better resolve the excited state kinetics of the molecule. By contrast, the 25. 2 keV run was done with only one time delay, 150 ps, in order to obtain the best possible structural renement of the MC state.
The interpretation of the TRWAXS data was conducted according to the established framework,  69  where the experimental curves are tted with theoretical signals from three contributions: the changes in the solute structure, solvation shell rearrangements (also referred to as the cage term) and the changes in the bulk solvent structure as a result of (adiabatic) heating. We note that the inclusion of the density change component in the solvent response did not improve the ts in the time range 0-400 ps (see ESI †) unlike in recent work on aqueous solutions of similar Fe II complexes. 29, 70 The analysis is based on structural models of the complex and the surrounding solvation shell (cage term) which were calculated by DFT and classical molecular dynamics (MD) simulations, respectively. The difference signal for the bulk solvent temperature increase was measured according to the standard procedure using azobenzene molecules.
While the latter has been traditionally seen as a necessary condition for the structural renement using TRWAXS,  [87] [88] [89] [90] [91] [92] [93] [94] [95]  the present work, together with the recent experiments on [Co(terpy) 2 ] 2+ ,  79  extend the applicability of the TRWAXS technique signicantly. The many time points in the 18 keV dataset were used to analyze the excited state kinetics of [Fe(btbip) 2 ] 2+. Here we used the global t approach,  66, 69  which ties the reaction kinetics to the hydrodynamic state of the liquid via the conservation of mass and energy (see ESI † for details). Fig. 4a  shows a comparison of the tted theoretical and experimental DS(q,t) curves for a subset of time points. For all time points we nd a very good agreement between the experimental data and the model t.
Acknowledgements The DTU-affiliated authors would like to gratefully acknowledge DANSCATT for funding the beam time efforts and the  Danish Council of independent research  grant  DFF -4002-00272. PP acknowledges nancial support from the  Knut and Alice Wallenberg Foundation  as well as the  Swedish Energy Agency (Energimyndigheten) , as well as support from the  Swedish national supercomputer facilities NSC  and  LUNARC. KSK.
INTRODUCTION A plethora of SPARQL endpoints 1 is proliferating on the Internet thus allowing ordinary users to specify their queries either via APIs or manually. This phenomenon is leading to a democratization of query formulation. The queries are collected into log files by their respective owners and represent a valuable resource for understanding users' preferences and needs in terms of query specification, but also for guiding us in research on query language design, query evaluation and benchmarking  [1, 2]. Motivated by previous studies on SPARQL log analysis  [3, 4] , we could harvest a large and varied corpus of SPARQL query logs amounting to a total of 180M queries  [1] , which is several orders of magnitude more than earlier analytical studies on SPARQL query logs. In this demonstration, we showcase DARQL, a tool for deep and fast analysis of large SPARQL query logs.
SYSTEM AND MAIN COMPONENTS A query builder lets the user modify the features of the queries under scrutiny to respectively enlarge or restrict the scope of the analyzed portion of the corpus. Since our tool is deployed on top of a relational DBMS with a web-based front end, each search on the corpus corresponds to an SQL query issued on the database. The user can also manually modify this query and rerun a deeper or coarser analysis at will. Internally, the system consists of the following main components: • a batch processing system (for loading and analyzing query logs, writing to files), • a PostgreSQL 10 database, and • a GUI served from a connecting back-end. We refer to Figure  1  for a slightly more detailed overview. We describe the main components next.
Loading and Analyzing Query Logs We release DARQL as open source project and therefore also discuss some aspects that will not be part of the demo, such as the batch processing system. Before the user can start exploring queries through the database interface, the queries need to be analyzed, deduplicated, and stored into the system. To this end, we provide scripts that can handle three main log formats: CLF-based logs (Common Log Format) used by most web servers, delimiter-based line log formats (e. g. CSV, TSV), or multi-line delimited formats. Every query in the log is parsed and stored into the database. For queries that do not parse, we record this in the database and do not perform further analysis. Every query that parses is run through an extensive set of analytical tests: 1 parse test, 31 keyword tests (query type, operators, solution modifiers, aggregation operators,.
If we discover duplicate queries we do not re-run query analysis, but simply record its occurrence by referring to the first occurrence and store the new origin (log file and line number). As such, our system can display, for each query, how many duplicates were found and where. The batch processing system can write output to either files or databases. It also allows logs to be rewritten in different formats, and deduplicated in a normalized form. The analysis output can be transformed to spreadsheets.
Database The database is a PostgreSQL 10 system that stores the results of each analysis for every query. For duplicate log entries it stores the origin of each entry. We tested the database with the dataset of roughly 180M SPARQL queries from  [1]. This dataset is about two orders of magnitude larger than those found in earlier studies. We used PostgreSQL 10 in order to utilize new parallelization features for queries and joins, which after appropriate tuning resulted in much faster query times compared to PostgreSQL 9. 6. We tested the system through a Web interface on our server 2  and noticed that most queries that our Query Builder generates are done in less than a second (e. g. 200ms). 3 Count queries are generally more expensive in PostgreSQL and take between 2-4 seconds on large sets (50-100 million queries) and are faster if subsets are smaller.
We use the edges s → h and h → o (in blue) and h → p (in orange). Figure  3 (d) has such a visualization for the SPARQL query in Figure  3(b). The visualizer currently uses several graph layout algorithms (cose, cose-bilkent, concentric, breadthfirst, grid, and circle) and can readily switch between them. This gives users a quick idea of the query's structure. Finally, we provide a datasets panel (shown in Figure  4 ), which shows general statistics of the data that is currently in the database, ordered by origin. The panel contains four columns: name of the dataset ("originMajor"), total number of queries ("total"), number of unique queries ("unique"), and finally the number of unique queries that can be parsed ("unique_valid"). Duplicates are tested globally, so we may classify a query from dbpedia_15 as a duplicate if it already occured in dbpedia_12.
) or on the entire corpus. Expert Search in SQL. The predefined user interface only generated SQL queries to the database that test conjunctions of conditions. Using the direct SQL interface, we show that if the user wants, also more advanced conditions can be queried. For example, one can search the union of all queries that have a minimum size and those that have cycles. Furthermore, it is possible to explore queries in the context of time. Although only some logs have timestamps, most log files have names that indicate a date. Figure  3 (a) shows five queries from January 17th, 2016, for example. Using the SQL editor, queries coming from a specific date (or month, or year) can be found as well. With this date, we could perform complex interesting queries with a temporal aspect. For instance, we could inspect how many queries were submitted on the same day, or try to find days or time spans that have the most or least queries.
In particular, we wished to assess the clinical acceptability of the BOADICEA model and the BWA. In a previous article  (Bredart et al. 2018) , we reported on clinicians' perceptions of the BOADICEA model and BWA v3 tool in terms of usability (e. g. , data entry timing, risk communication format). In this study, we explored the relationship between the perceived importance attributed to BC risk factors by professional profile, in relation to respondents' knowledge and usage frequencies of the BWA as well as of six other BC risk assessment tools commonly used in family cancer clinics. The results of our analyses will help to identify perceived deficiencies in the BWA and guide further software development, so that the next version of the tool will enable more comprehensive BC risk assessment in genetics and oncology practice.
2004 (Evans et al. , 2005) )  and data entry timing using these tools; and (4) socio-demographic and professional background (Supplementary material A). The questionnaire was developed in line with BRIDGES program objectives, which specified an assessment of the acceptability of the BOADICEA model and the BWA in clinical practice. As the next BWA update is expected to integrate non-genetic and additional genetic BC risk factors, we assessed the degree of importance attributed to familial, hormonal, reproductive and lifestyle risk factors to BC risk assessment  (Harvie et al. 2015). For each BC risk factor, five response options from least to most important were provided (see Table  4 ). We assessed BWA use with that of six other BC risk assessment tools that could be used in family cancer clinics, implementing the Gail  (Gail et al.
Data analysis As shown in Fig. 1 , 525 and 203 respondents' data were extracted from the BOADICEA website and NGS survey sources respectively. The response rate obtained from the BOADICEA website survey source could not be estimated as the survey was sent to registered individuals who might no longer use the tool, and tracking the tool use is not legally permitted. Among respondents contacted through NGS, the response rate was 43. 7% (203 respondents out of 464 NGS members, excluding those who responded through the BOADICEA website). Based on psychometric analyses  (Husson et al. 2011; Jolliffe 2002) , we developed indicators of genetic clinical activity level (i. e. one scale comprising six survey items), and of importance attributed to BC risk factors (i. e. five single items addressing familial and personal cancer history, breast tumour pathology, BMI and breastfeeding), and three multi-item scales corresponding to hormonal BC risk factors (i.
All of these studies have focused on the analysis of substitution effects on the PR structure complexed with only one FDA-approved drug, and they have not considered the diversity in PIs. Moreover, they have highlighted the effects of amino acid changes on the different interactions established between PR and the drug but they have provided little information regarding the structural changes in PR itself induced by these mutations. Thus, the structural characteristics leading to the differing patterns of PI sensitivity have not been adequately explored to date. Currently, there are approximately 600 PR1 structures and 19 PR2 structures available in the Protein Data Bank (PDB)  20. These structures correspond to different forms (bound or unbound) complexed with different ligands. Performing a systematic comparison of the ligand-binding sites extracted from diverse PR1 and PR2 structures could provide information concerning the effects of variations between PR1 and PR2 sequences, regardless of the complexed ligands that improve the understanding of PR2 resistance to PIs.
1C ). PR ligand set. The 24 bound PR structures were complexed with 12 different ligands: three FDA-approved drugs, four chemical compounds and five peptides (Fig. 2 ). This ligand set is diverse in terms of size, ranging from 54 atoms for the peptide extracted from the 2MIP (PDB code) structure to 70 atoms for APV extracted from the 3S45 (PDB code) structure (Fig. 2 ). Certain of these ligands are flexible. This flexibility resulted from different sources. We observed small shifts in the superimposed PR1 and PR2 ligands, such as for the GRL and 065 ligands (Fig. 2 ). Several ligands had the capacity to bind PR with different orientations in both PR1 and PR2, such as IDV and C20, or in one PR, such as APV and DRV, which bind to PR1 with two orientations (Fig. 2 ). We used all these superimposed ligands to estimate PR pockets that allowed us to consider the ligand diversity and flexibility.
PR pocket set. Ligand-binding pockets were extracted from each PR structure (PR unbound and bound) by determining their atoms situated in proximity to the ensemble of all superimposed ligands of the set (see Material & Methods). This resulted in a set of 28 pockets extracted from the PR set, called the PR pocket set. Each pocket corresponds to a larger region capable of binding all ligands of the dataset. Figure  3  shows pockets extracted from six PRs (3 PR1 and 3 PR2 structures): two were extracted from unbound structures, hereafter referred to as unbound pockets, and four were extracted from bound PR structures, hereafter referred to as bound pockets. Unbound pockets had different geometries compared to bound pockets. Indeed, the pocket lid, composed of flap atoms, was missing in the unbound pocket.