dataesr/scientific-paragraphs-categorization huggingface dataset:
It provides functionalities similar to pathway databases-content annotation, interactive exploration and network-based data visualization  17  -but for user-drawn CellDesigner diagrams. 18 With the support of GoogleMaps API, the platform enables interaction with elements of displayed diagrams. It is worth noting that the 'map staining' approach to visualization of experimental data is a compelling idea for comprehensive representation of high-dimensional datasets. In the light of the increasing interoperability of molecular interaction data, and growing knowledge on mechanisms of biological systems, it is important to continue the development of technologies supporting contextualized systems biology repositories. Of particular importance are areas of curation support, and knowledge sharing and re-use.
Automatic annotation using online bioinformatics resources The platform features configurable automated annotation tools. These annotators use either the name of an element or its MIRIAM identifier, fetch automatically information from a corresponding database, and update annotations of elements in the uploaded network. Currently available annotators use the following: -Element name: BioCompendium (http://biocompendium. embl. de) and HGNC. 19 MIRIAM identifier: ChEBI,  20  Ensembl,  21  Entrez,  22  Uniprot 23 and Gene Ontology. 24 notated resources are hyperlinked to the corresponding online resources and are immediately accessible for interactive exploration. A set of rules for automatic verification of the annotation of uploaded content can be configured using the administrator panel. Warning messages are displayed when annotation type requirements are not fulfilled.
For instance, when a 'PubMed' field is required for the annotation of interactions, warnings will be generated for each interaction, which has no PubMed annotation. Such a rule-based control supports quality and completeness checks of the uploaded models, which is especially important for large diagrams. Visualization of structured content via GoogleMaps API Upon upload, molecular interaction networks in the SBGN-compliant format are translated to the data model and uploaded to the database. The visualization is generated using the Google Maps API. Importantly, the visualization provided by the MINERVA platform is interactive for both elements and interactions. Annotation on the elements and interactions are visible in the left panel of the interface. The contents are searchable, and multiple-element queries are allowed.
Functionalities for content exploration Online queries for drug targets via DrugBank and CHEMBL. MINERVA features a built-in interface to DrugBank  25  and CHEMBL  26  databases. The 'Drug' tab provides an additional query interface, accepting names of drugs as search items, which are used as a query to DrugBank and CHEMBL. The retrieved drug targets are displayed on the visualized network. Users can search for multiple, comma-separated terms, allowing to look for elements subject to potential drug-drug interaction. Figure  1a  illustrates a query for two Parkinson's disease drugs, levodopa and carbidopa, with their targets displayed in the Parkinson's disease map (see the section 'MINERVA use cases'). One can see the semantic zoom areas-gray for pathways, colored for compartments-automatically generated by the platform.
The MINERVA platform allows for an upload of experimental datasets. Registered users have access to an additional menu, allowing them to upload and manage experimental results in a simple format. The uploaded data are used to generate a custom overlay by highlighting elements with an indicated intensity and color and assigning line width and line color to interactions. The uploaded dataset may contain different types of elements, allowing visualization of multiple data types. Figure  1c  shows a visualization of a Parkinson's disease-relevant transcriptome dataset. The bottom part of the left panel shows the 'User-provided overlays' menu, allowing for an online upload of datasets by registered users. Feedback to content moderators. Networks displayed on the MINERVA platform are interactive, and allow direct annotation by the user.
Interfacing knowledge repositories and databases The MINERVA platform reaches out to other knowledge repositories by handling pure SBGN format in parallel with CellDesigner files. Nevertheless, lot of biological knowledge is present in less-stringent format within a vast amount of currently available literature. For instance, to date, PubMed indexes more than 24 million articles. Knowledge bases established by mining available full-text articles or abstracts for molecular relations are a promising target to explore, cross-link and thus contextualize this rich content. Structured knowledge on molecular interactions is available in large-scale databases like String  28  or via integrative curation of MIntAct. 6 Querying these databases may support curators with an unbiased source of standardized information.
Graphical layout is a high-quality information, accompanying molecular interactions in repositories like Reactome,  7  WikiPathways (http://www. wikipathways. org) and others. 30, 31 Novel interfaces need to be designed and developed, allowing to assemble molecular interaction networks from these heterogeneous sources. A common denominator for a graphical layout is needed for this purpose, and SBGN is a good candidate for this purpose. An existing link to drug-related databases allows adding an additional information layer on top of a molecular network displayed in MINERVA. Interfaces to other database resources, including experimental datasets, like GEO or ArrayExpress,  32  would allow for further accretion of information sources, leading to refined hypothesis generation. The mapping between several database-specific or commercial identifiers might be addressed by tools such as BridgeDB.
Conclusions We present the MINERVA platform, a standalone webserver allowing to visualize, annotate and manage molecular interaction networks in SBGN-compliant formats. Our platform is easy to set up, and offers unprecedented integration of SBGN data model with external databases and user interface. Web-based, interactive, automatically annotated content can be enhanced and commented by users, and exported in different formats, including SBGN models, network structure or images. The MINERVA platform allows hosting of different molecular interaction networks in parallel, giving the users and administrators a flexible tool to set up and use a library of different molecular maps. Future development of the platform foresees linking to a broader range of knowledge bases, improved querying and advanced visualization functionalities.
Implementation and availability MINERVA is a web service using the Java Server Faces 2 technology. The server side, including data parsing, integration, annotation and verification, is implemented in Java 8. The platform uses the Postgres SQL database for data storage and the Hibernate framework as a middle layer between the web server and database. The user-web interface is generated using JSF and PrimeFaces. The displayed content is visualized by Google Maps API, dedicated JavaScript and CSS. MINERVA is available free of charge under Affero GPL 3. 0. Commercial use, where source code is not disclosed, requires a separate license. MINERVA can be downloaded as a virtual machine snapshot or as a Docker container from the project website (http://r3lab. uni. lu/web/ minerva-website/). Moreover, a Debian package is provided, allowing setup via the 'apt-get install' command.
Web-based approach MINERVA is a web-based platform hosting molecular interaction networks encoded as process diagrams in SBGN and CellDesigner formats. The platform allows direct exploration of the content, including lookup of multiple terms, hyperlinks to available annotations and visualization of high-throughput datasets. Explanatory graphics, like illustration of organs, organelles or pathways, can be displayed and linked with the molecular content of the map, further facilitating the exploration. Each instance of MINERVA platform is an autonomous, standalone webserver. A web-based administration interface allows to manage the hosted content and registered users, independently from the MINERVA development team. Figure  2  summarizes the architecture of MINERVA.
Content curation in SBGN format The platform accepts content in SBGN-compliant formats, curated using either CellDesigner software (http://www. celldesigner. org) or SBGN-specific editors (http://sbgn-ed. org). Upon upload, the input file is translated into the data model supported by MINERVA platform. In this regard, MINERVA provides flexibility in the choice of curation tool, and a possibility to translate between formats, as the direct export of the content allows choice between CellDesigner and pure SBGN format (see the section 'Functionalities for content exploration'). MINERVA handles, at the moment of publication, 43 different MIRIAM identifiers  40  (see Supplementary Table  1  for a complete list). Elements or interactions manually annotated with one of these identifiers will be visualized with a hyperlink cross-referencing to the MIRIAM-indicated database.
A completely prognostic plant phenology including leaf critical age, maximum LAI (leaf area index), senescence, plant tissue allocation, and leaf photosynthetic efficiency, which varies depending on the leaf age, is also taken into account. The soil water budget and the exchanges of energy and water between the atmosphere and the biosphere are calculated in SECHIBA  (Krinner et al. , 2005). The Choisnel hydrological scheme is used with a 2 m soil column represented by two moisture layers: a superficial layer and a deep layer  (Ducoudré et al. , 1993). The biogenic emission scheme, of which we present a new version, is embedded in this module  (Lathière et al. , 2006). In ORCHIDEE, ecosystems are represented by 13 plant functional types (PFTs, listed in Table  1 ). Each PFT is representative of a specific set of plant species that are grouped according to plant physiognomy (tree or grass), leaf shape (needleleaf or broadleaf), phenology (evergreen, summergreen or raingreen) and photosynthesis type for crops and grasses (C3 or C4).
We chose these compounds because measurements have shown that they are emitted from vegetation in the greatest abundance and because of their importance in atmospheric chemistry, in particular regarding secondary organic aerosol formation. We mentioned that the emission module has also been modified to include a light dependency for almost all compounds emitted. In the previous module version, indeed, isoprene was the only compound dependent on both light and temperature, while the others were only dependent on temperature. As detailed in Sect. 1, most recent field campaigns highlight, for a large number of plants, the dependency of monoterpenes, sesquiterpenes and oxygenated BVOC emissions on radiation as well. Adopting a detailed parameterization is not yet possible because of the lack of data at global scale.
two simulations for the year 2006 performed by both models, where we output two test species, the first one totally dependent on light (LDF = 1) and the second one totally independent of light (LDF = 0) (ORC_LDF and MEG_LDF); the output time frequency is 1 h for this run. All simulations are performed at the global scale with a spatial resolution of 0. 5 • × 0. 5 •. We use the CRU-NCEP v5. 2 meteorological forcing database (http://esgf. extra. cea. fr/thredds/catalog/store/p529viov/ cruncep/V5_1901_2013/catalog. html) providing temperature, pressure, humidity, wind speed and shortwave solar radiation. This forcing is based on the 6-hourly 2. 5 • NCEP/NCAR re-analysis  (Kalnay et al. , 1996)  combined with the CRU TS 2. 1 monthly anomalies  (Mitchell and Jones, 2005). The run sets 3 and 4 are carried out for the year 2006, which is estimated as an averaged year regarding the BVOC emissions calculated by MEGAN and ORCHDEE in the 10 years of simulation.
For the ORCHIDEE model a spin-up of 20 years is first performed to balance the leaf stock. The spin-up is based on a 10year loop using meteorological forcing for the year 1989, followed by a 10-year simulation from 1990 to 1999. In ORCHIDEE, the global vegetation distribution for the 13 PFTs is prescribed using the land-use history (LUHa. rc2) related to the year 2000  (Hurtt et al. , 2006). The database can be found at http://esgf. extra. cea. fr/thredds/catalog/ work/p86ipsl/IGCM/BC/SRF/OL2/PFTmap_1850to2005_ AR5_LUHa. rc2/catalog. html. In MEGAN the distribution for the 16 PFTs is consistent with the Community Land Model v4  (Lawrence and Chase, 2007)  and related to the year 2000. Table  1  gives the global surfaces covered by the different PFTs in ORCHIDEE and MEGAN. For the present work, MEGAN in the standard configuration is forced by the LAI retrieved by MODIS  (Yuan et al.
Differences between ORCHIDEE and MEGAN emission algorithms While starting from a similar approach, the ORCHIDEE and MEGAN emission modules differ significantly in their pa-    et al. (2003) ,  Hayward et al. (2004) ,  Karl et al. (2004 Karl et al. ( , 2007 Karl et al. ( , 2009))   rameterization and variable description and calculation. We list the main differences below. 1. One of the principal differences in the two emission schemes is the approach on LAI. ORCHIDEE calculates the LAI at each model time step for each PFT and grid cell, taking a full plant phenology scheme and the environmental condition (temperature, radiation, precipitations, CO 2 , etc. ) into account, while the MEGAN stand-alone version used in this study does not compute the LAI; rather, it has to be provided as an external forcing averaged over the vegetated part of the grid cell.
Global budgets As already discussed at the end of the Introduction, the validation of BVOC emissions at the global scale is a complex issue because of the poor data coverage in many regions and the general lack of year-round measurements. Satellite observations provide very useful information, especially regarding the order of magnitude and the seasonal and regional variability of emissions, but the most abundant VOC species are not directly measured (such as isoprene and monoterpenes). Satellite measurements are also subject to large uncertainties arising from difficulties in the retrieval of the atmospheric concentration of short-lived compounds from space or in separation of the different sources (for instance, terrestrial biogenic, anthropogenic, oceanic) and the various compounds themselves.
2 ). The northern and southern tropics have an opposite seasonal cycle, with isoprene emissions coming mostly from the northern tropics between March and October and from the southern tropics for the rest of the year (Fig. 3 ). The overall stable global emission budget is generally characteristic of the compounds for which tropical regions are strong emitters all year round, such as sesquiterpenes (Table  3  and Fig. 3 ). On the other hand, the global BVOC emissions for which temperate regions are strong emitters will have a more marked seasonal cycle (Fig. 2 ), such as for methanol and isoprene in OR-CHIDEE. Indeed, the two models exhibit a very different interseasonal variation in terms of isoprene global emissions. Sindelarova et al. (  2014 ) compared the monthly isoprene emissions time series from different data sets, showing, for some of them, an inter-seasonal variation similar to ORCHIDEE, and, for others, no seasonal cycle.
(2011)  pointed out some weaknesses in the ability of ORCHIDEE to correctly model the LAI seasonal cycle, especially in the equatorial forest (Amazonia, central Africa, Indonesia) where a poor correlation of model output with satellite data was demonstrated. In general, quite large and comparable incertitude is found when different LAI databases are compared. Krinner et al. (2005)  found that the difference between OR-CHIDEE and MODIS satellite LAI  (Myneni et al. , 2002)  is as much as the difference between the satellite data that they used and an alternative satellite vegetation cover data set  (Tucker et al. , 2001). Therefore given the many existing limitations, we cannot precisely estimate to which extent OR-CHIDEE LAI is reliable. It is likely that the ORCHIDEE LAI modelization has room for improvement, and a possible component to be upgraded is the allocation of the different carbon stocks, but further investigations are needed.
Conclusions The main objectives of this study were to (i) present the new version of the BVOC emission module embedded in the OR-CHIDEE model, (ii) provide BVOC emission estimates for the 2000-2009 period for a large diversity of compounds, (iii) compare the ORCHIDEE model results to emissions calculated by MEGAN in terms of global, regional and seasonal patterns and (iv) investigate how the uncertainty linked to some key variables or parameters such as the LAI and the LDF could affect the BVOC emission estimate in the two models. The new ORCHIDEE emission module now considers many speciated monoterpenes and bulk sesquiterpenes, which have been shown to be important regarding SOA formation, uses updated EFs and includes developments in the physical processes related to BVOC formation, such as the emission dependence on light for almost all compounds, a multi-layer calculation of diffuse and direct radiation and shaded and sunlit leaves over LAI layers.
The outcome of this analysis provides some guidelines for future developments of BVOC emission models at the global scale. In particular, the following issues should be carefully addressed. -LAI uncertainties are still extremely high, and have a considerable impact on emissions. Improvements in LAI modelling or estimation at the global scale are essential. -EF allocation is a big concern because of its high variability. A proper way to assign statistically robust values at a global scale has not yet been found. Significant improvement can only be achieved by increasing the observation data coverage of many regions and performing long-term measurements. -LDF parameterization is still oversimplified, and has a significant impact on emissions. Future developments should, therefore, improve LDF parameterization accuracy, for example, by including PFT dependency.
As for EFs, more reliable results can only be achieved by increasing observation coverage. -The rather low number of PFTs in global models is a limiting factor in accurate emission estimates. Further analysis will certainly be needed to include other important parameters/variables in the investigation, for example, leaf temperature vs. air temperature usage, leaf age classes, parameters in the Guenther formulation and the soil moisture activity factor. Finally, it is worth mentioning that, besides model intercomparison, there is a strong need to evaluate model results against emission observations. This has already been done in other domains, for example, in atmospheric chemistry modelling  (Mann et al. , 2014; Tsigaridis et al. , 2014). In the case of BVOCs, however, observational data are very challenging to acquire, especially on the long-term scale.
Acknowledgements. We thank  Cathy Nangini  for her very useful comments and corrections,  J. -Y Peterschmitt  for his help in data visualization and  A. Guenther  for meaningful discussions. We gratefully acknowledge support from the project  ÉCLAIRE  (grant agreement no. 282910 ),  PEGASOS  (grant agreement no. 265148 ),  MACC   II  project (grant agreement no. 283576 ) and  MACC-III  (grant agreement no. 633080 ) funded under the  EC Seventh Framework Programme. This work was partly funded by the  DGAC  under the  TC2  project. This work was performed using DSM-CCRT resources under the  GENCI  ( Grand Equipement National de Calcul Intensif ) computer time allocation and the  GENCI  project. Edited by: J. Rinne Reviewed by: two anonymous referees.
Method A web application (www. MEmind. net) was developed to assess patients and collect data regarding gender, age, diagnosis and treatment. We analyzed antipsychotic prescriptions in 4345 patients attended in five Psychiatric Community Mental Health Centers from June 2014 to October 2014. The web-application reported average daily dose prescribed for antipsychotics, prescribed daily dose (PDD), and the PDD to defined daily dose (DDD) ratio.
The increasing availability of smartphone technology permits the gathering of naturalistic data that can be processed immediately and provide instantaneous decision making assistance to clinicians. A pioneer project in the 1980s programmed drug monitoring systems to identify evidencebased medication practices in 11 New York State Institutions for Mental Health and Developmental Disabilities. The results of the study showed that the surveillance techniques improved prescribing practices  [21]. EHRs are now used routinely and are a major source of structured data. In a naturalistic, observational, retrospective, non-interventional study, Gavirina et al. [22]  were able to describe prescription habits in a sample of 1700 patients suffering from schizophrenia. The data were gathered from computerized or e-medical records that were registered in the electronic medical record and implemented in all centers of the network that performed the assessment.
However, these systems present some limits that are related to the methodology of such studies; observational studies based on retrospective analysis of EHR data. For example routine practice data are collected for billing or institutional purposes. The re-use of these data to advance clinical research can be challenging  [23]. The timing, quality, and comprehensiveness of clinical data are often not consistent with research standards  [24]. Accuracy (correctness) of data relies on correct and careful documentation, which is often difficult to perform though most EHR used in routine. Furthermore, due to the architecture of traditional EHR, data cannot be processed instantaneously to deliver a CDSS, which requires a double task of de-identification of the data, and statistical analysis out of the software core.
The growth and popularity of mHealth apps (health-related software applications), their ease of use and their cost of development compared to traditional software make them particularly suitable for the task of prescription monitoring. Our Hypothesis was that a web application developed for this study may be able to describe clinician prescriptions. The objective of this study was to describe, via the Memind web-application, the prescription patterns of antipsychotics in a naturalistic outpatient setting in five Psychiatric Community Mental Health Centers in Madrid, Spain. We focused primarily on off label uses and antipsychotic polypharmacy.
Material The web application was specifically developed for Android and OSX Smartphones, Tablets and the Mac and PC versions of Mozilla firefox and Google Chrome. It is available in three languages (English, French, Spanish). The web application has two distinct interfaces. The "electronic health record" view is designed for use by health care providers during clinical rounds and medical or nursing visits. It has been designed to cover all the data acquired during a standard psychiatric evaluation, including sociodemographic, diagnostic and pharmacological treatment information. Furthermore, it is based on commonly stored information in mental health management. A large customizable choice of relevant scales can be added by the care provider to the basic evaluation. The patient view was also developed and will allow patients to be monitored with ecological momentary assessment (EMA) tools in future studies.