dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). A recently developed stressor-AOP network webserver, integrating information from the ToxCast and AOP-wiki databases, revealed that many chemical stressors can putatively interfere with 1 or several AOPs  (Aguayo-Orozco et al. , 2019). Due to the rapidly expanding available toxicology data (including omics), data-driven and computer-based tools are now believed to be a realistic option for the development of nonanimal-based hazard and risk assessment. Pipelines for generating and enriching AOP descriptions including literature mining and integration of diverse data sources have been proposed recently  (Carvaillo et al. , 2019; Nymark et al. , 2018). Such integrative approach is essentially qualitative, and will be improved in the future to generate more quantitative models, that will take into consideration dose and time effects.
A next step would be to include, for example, the dose-dependent activation of MIEs or detailed pathways of toxicity. Another aspect will be to establish quantitative AOP networks. A recent study examined how AOPs can be used to develop computational pathway-based quantitative models that will be useful for regulatory chemical safety assessment  (Perkins et al. , 2019). All such improvements could be envisaged by considering other data sources (eg, joint pathways analysis from cross-omics studies and databases such as Effectopedia and the human toxome knowledgebase). Compared with BPA, the effects of BPF on human health are poorly characterized. Most studies focused on its physiological and endocrine activities. Few models have been used to characterize such effects in humans; for example, a fetal testis assay developed in 3 different species (mouse, rat, human) showed that nanomolar concentrations of BPA, BPS, and BPF are able to reduce basal testosterone secretion in the human ex vivo model  (Eladak et al.
Figure1. Overview of the systems toxicology strategy for developing adverse outcome pathway (AOP) networks for bisphenol F (BPF). In a first step, BPF-protein associations were extracted from the Comparative Toxicogenomics database and the ToxCast database. Then creation of protein complexes by integration of the first-order protein partners (P, dashed line) to the extracted proteins (P, full line) using a high confidence interactome based on experimental evidences (InWeb data source). The next step consisted of performing a biological enrichment of the protein complexes to statistically rank pathways linked to them. Finally, different data types were integrated from various sources (literature, databases) using AOP-helpFinder and by manual curation, to build comprehensive mechanisms between BPF and toxic effects, to develop an individual AOP and AOP network for which BPF may be a stressor.
Technology and architecture A Media Wiki  (MediaWiki, 2018)  platform is used as a repository of the Scenarios and the LEL, as well as a tool to derive the Tests. Many extensions have been added to Media Wiki. An ad-hoc collaborative catalog and editor was added to manage Scenarios and LEL. This tool is enriched with a semantic Media Wiki extension that allows the semantic support and the creation of forms to make the CRUD operations (create, retrieve, update and deleted) in a more user-friendly way. Finally, the semantic extension also provides a query language for semantic searches in order to implement the derivation of tests. Wikis are tools that allow users with no knowledge in programming to easily edit information on the web. This feature fosters the participation and collaboration of non-technical users.
In traditional wikis there are three different elements to build each page: (i) the code that can be edited by every user and it is stored in the server; (ii) a template that defines the elements and their position; and (iii) the HTML code provided in real time by the server with the page requested by the user, it is a combination of (i) and (ii). There are many software systems to implement wikis. Media Wiki is an open source implementation written in PHP that uses the MySql database engine. Wikipedia and other projects of Wikimedia use Media Wiki. Since it relies on the wikitext format, users with no knowledge of HTML or CSS can easily edit the pages. Another advantage of Media Wiki is the management of the links between pages. Although the destination of a link does not exist, the link can also be written and Media Wiki shows it anyway, when the user clicks the link, Media Wiki allow to create the page.
Another important feature is the configuration management. Media Wiki stores in a database all the different versions of each page, so, all previous versions can be accessed is necessary. Media Wiki has been extended with the Semantic Media Wiki framework that allows retrieving information from the Wiki in a similar way that data is retrieved from a database. In a Semantic Media Wiki, each piece of data is represented by a triplet consisting of a subject, a predicate, and object. Every element of this triplet corresponds to a category of the LEL glossary, and it is also related to the template used in the description of the Scenarios. Some attributes as Title, actors, and resources uses some the categories, but the episodes are described with the triplet. Thus, the Semantic Media Wiki framework provides the infrastructure to organize and store the information and a query language to implement the derivation rules in a straightforward way.
Another technology added to Media Wiki is Page Forms, also called Semantic Forms. This extension allows to add, edit and retrieve data using customized forms. It is based on templates that are used in Pages called Special Pages. Another extension used is Input types, which allows defining the basic elements and their attributes. Nevertheless, the episodes of the Scenarios are complex structures (they are a set of triplets), thus another extension was needed to deal with them: the Semantic Internal Object extension. Figure  1  shows a conceptual model that describes the types of Scenarios and LEL. Every LEL symbol has two attributes: notion and impact (behavioral responses). There are 4 categories of LEL Symbols but only 3 were used in this approach: Subject, Object, and Verbs. The Scenarios is the core of the approach.
Acknowledgements Authors of this publication acknowledge the contribution of the Project  691249 ,  RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems  (www. ruc-aps. eu),funded by the  European Union  under their funding scheme H2020-MSCA-RISE-2015 References (Bjarnason and Borg, 2017) Bjarnason E. and Borg M. ( 2017) " Aligning Requirements and Testing: Working Together toward the Same Goal ," in IEEE Software, vol. 34, no. 1, pp. 20-23. (Boucher and Mussbacher, 2015) Boucher M. , Mussbacher G. ( 2017) " Transforming Workflow Models into Automated End-to-End Acceptance Test Cases ," in Proceedings -2017 IEEE/ACM 9th International Workshop on Modelling in Software Engineering, MiSE 2017, 2017, pp. 68-74.
Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO 2 , nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe.
Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2. 6, RCP4. 5, RCP6. 0, RCP8. 5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a "SQLite" relational database or "ASCII" flat file version (at https://.
The process of formal calibration, comparison and evaluation of complex vegetation models is often hindered by the availability and the harmonization of suitable data. The data necessary to drive a vegetation model are often complex and need to be compiled from different data sources (e. g. Bagnara et al. , 2019). In particular for model comparisons, besides data for the evaluation of individual models, common input and driving data for process-based vegetation models are needed to ensure fair comparisons between the participating models. Although model comparisons have a long tradition in vegetation modelling  (Cramer et al. , 1999 (Cramer et al. , , 2001;; Bugmann et al. , 1996; Morales et al. , 2005) , they have often been limited by overall data availability and comparability. Common databases that are ready to use for thorough model evaluation would allow the community to gain a better appreciation of model differences, explore structural uncertainties and provide a basis for more systematic ensemble projections of climate impacts.
Recently, several initiatives have started compiling model evaluation, input or driving data for a wide range of applications of process-based vegetation models  (Huntzinger et al. , 2013; Kelley et al. , 2013; Warszawski et al. , 2014; Sitch et al. , 2015; Collier et al. , 2018). Although these initiatives have leveraged important scientific progress, many of them have focussed on the global scale, mostly providing evaluation, input and driving data from global products. Such global products generally lack the breadth and depth of process-level detail required to rigorously assess model performance at smaller scales as for example they lack longterm and detailed measurements of forest stand structure. The database for the project "Towards robust projections of European forests under climate change" (hereafter PRO-FOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale.
It has been designed to fulfil two objectives: to allow for a thorough evaluation of complex, processbased vegetation models using multiple data streams covering a range of processes at different temporal scales and to allow for climate impact assessments by providing the latest climate scenario data. The PROFOUND DB only provides data for individual forest stands but contains a number of elements that are de-signed to foster comparison of both global/regional models and local models. The climate data, for example, are provided locally (or bias-corrected using local data) in the same way that stand-scale vegetation models would need them and also extracted from global gridded datasets that global vegetation models would use. The PROFOUND DB is also designed to allow for disentangling of uncertainties that affect quantitative model predictions in ecology (see  Lindner et al.
, 2014, and Dietze, 2017 , for an explanation of different uncertainty types), for example by facilitating standardized evaluations of structural or process uncertainties via model comparisons. Model input and driver uncertainty are addressed through a wide range of climate data from different sources, covering the full range of Representative Concentration Pathways (RCPs). Collalti et al. (2018 Collalti et al. ( , 2019)) , for example, have used the PROFOUND DB to study the effects of thinning on carbon use efficiency across a combination of all four RCPs and five global climate models. Finally, parametric uncertainty can be assessed through the wide range of data that can be used for inverse calibration. In the following we describe the main components of the PROFOUND DB  (Reyer et al. , 2020)  and an R Package  (Silveyra Gonzalez et al.
, 2020)  developed to explore the database and allow rapid and easy access for modellers. 2000-2008 D  1996-2014 D  1996-2014 D  1998-2010 D 1996-2008 D  1901-2010 D 1960-2013 D 1996-2012 D   Reanalysis  climate   1901-2012 D  1901-2012 D  1901-2012 D  1901-2012 D 1901-2012 D  1901-2012 D 1901-2012 D 1901-2012  The forest sites featured in the PROFOUND DB were selected to provide a wide array of data sources across a European gradient. We focussed in particular on providing long time series of tree-and stand-level growth and yield as well as carbon cycle data available from eddy-flux measurements because these variables are most commonly in calibrating and evaluating process-based vegetation models. The selected sites spread along a wide climatic gradient across Europe (Fig. 1 , Table  3 ) and cover some of the most common European forest types, as well as the main central European forest management history of favouring monospecific, evenaged forests or mixtures of two tree species.
We compiled the data from existing data sources and collected the definitions of variables, their units and information about the main measurement methods from the site principal investigators (PIs) and from official descriptions of the data to harmonize the variables as much as possible. The overall guiding principle for the compilation of the data was to provide data that can be easily used by modellers for setting up and evaluating their models. In order to allow for data uncertainty to be reflected in model calibration studies, we also included uncertainty estimates for the measured data, such as those available for carbon flux measurements (see Sect. 3. 2. 9), wherever possible.
Data sources The PROFOUND DB provides information on the site, soil and forest stand as well as data for climate, atmospheric CO 2 concentration, nitrogen deposition, carbon fluxes, atmospheric heat conduction and remote sensing at a range of different temporal resolutions (i. e. from 30 min to decadal measurements). Table  1  provides an overview of the different data types and their temporal resolution available in the PRO-FOUND DB. All variables available are listed in Tables S1-S13 in the Supplement. In the following we describe how the   Pilegaard et al. , 2003). individual sub-datasets of the PROFOUND DB have been brought together and describe the key variables and characteristics of each dataset.
Nitrogen deposition The nitrogen deposition data, reported as total deposition of reduced and oxidized wet and dry nitrogen deposition, respectively, have been extracted for each site of the PRO-FOUND DB from two different datasets which serve different purposes. -EMEP data. For detailed model evaluation studies that require the best possible estimates of local nitrogen deposition, we extracted data from the "Co-operative programme for monitoring and evaluation of long-range transmission of air pollutants in Europe" (EMEP) for the time period 1980-2014 (EMEP/CEIP, 2014a, b). Sea-salt-corrected data are available from 1980 to 1995 in 5-year steps and from 1986 to 2014 at annual time step and are derived by atmospheric transport modelling  (Simpson et al. , 2012). -ISIMIP data. For model simulation studies, we also provide nitrogen deposition estimates based on atmospheric chemistry modelling for a historical time period (1861-2005) and four future scenarios, where nitrogen deposition follows the four RCPs.
The data are further described in  Lamarque et al. (2013a, b) , sea-saltcorrected and consistent with the global nitrogen deposition data provided within ISIMIP  (Frieler et al. , 2017). The data are taken from the global dataset without further corrections and hence are not intended to represent realistic, local forecasts but rather rough estimates of future nitrogen projections. For the 1980-2014 time period, the ISIMIP data are typically lower and less dynamic than the EMEP estimates (Fig. 5 ). However, while they do not seem suitable for historical model evaluations, they cover a much longer time period and are clearly interesting for scenario studies because they feature different nitrogen deposition pathways consistent with RCP climates and CO 2 pathways. It is also important to note that measured throughfall of NO 3 and NH 4 is on average lower than modelled total deposition, due to canopy uptake  (Marchetto et al.
Forest inventory data For each site, the PROFOUND DB provides information about the forest stand at tree and stand level (Table  4 ). The data are available for different time periods and have different measurement intervals, but generally they cover mostly the second half of the 20th century and the first decade of the 21st century (Table  1 ). The data also cover a wide array of height-age and diameter at breast height (DBH)-age relationships (Figs. 6 7 ). For seven out of nine sites individual tree DBH and height measurements are available. The time series length ranges between 15 and 65 years within the time period 1948-2015. For the Sorø site, the DBH and heights have been reconstructed from tree-ring data  (Babst et al. , 2014) , and the full stand reconstruction is available from 1996 to 2010 at annual resolution (see Sect.
Flux data The carbon fluxes, i. e. net ecosystem exchange (NEE), ecosystem respiration (RECO) and gross primary production (GPP) are taken from the Tier One FLUXNET2015 dataset (http://fluxnet. fluxdata. org/, last access: 5 June 2020). We provide estimates of fluxes calculated using different estimates for gap-filled and partitioned fluxes to give a rough estimate of the uncertainty added to the long-term budgets in the process. NEE data are filtered using two different methods to calculate uStar thresholds  (Barr et al. , 2013 , and a modified version of  Papale et al. , 2006 ; see also  FLUXNET2015, 2017. Daytime (i. e. Lasslop et al. , 2012)  and night-time (i. e. Reichstein et al. , 2005)  refer to whether ecosystem respiration parameters were estimated from only night-time fluxes or also using daytime data (zero intercept of GPP light response curve).
In many cases the number of accepted night-time fluxes is low and the temperature range is narrow, which leads to high uncertainty in the estimated respiration. This can be improved by also using daytime fluxes. On the other hand in the daytime method the uncertainties of photosynthetic light, temperature and possible vapour pressure deficit (VPD) responses may be attributed to respiration parameters. Further information about the daytime and night-time methods is available in  Lasslop et al. (2010)  and  Reichstein et al. (2005)  and also FLUXNET2015 (2017). We also extracted different uncertainty estimates for each variable. Additionally, we provide time series of the sensible and latent heat flux, soil (soil water and soil temperature) and meteorological variables at a 30 min time resolution from the FLUXNET2015 database including measurement uncertainty estimates.
Remote sensing data The PROFOUND DB includes remote sensing information at different spatial scales and temporal frequencies, specific for each product. We included five MODIS products (ORNL DAAC, 2008a-e) and several vegetation indices calculated from the surface reflectance data for each of the forest sites. The original MODIS scenes are available at the NASA Land Processes Distributed Archive Center (LP DAAC) (https: //lpdaac. usgs. gov/, last access: 5 June 2020). The specific time series included in the PROFOUND DB were downloaded from the Land Product Subset Web Service of the Oak Ridge National Laboratory Distributed Active Archive  Center (ORNL DAAC) (https://daac. ornl. gov/MODIS/, last access: 5 June 2020). The ORNL DAAC MODIS subsetting web service is implemented to allow users access to massive amounts of remote sensing data  (Santhana-Vannan et al.
, 2011). In addition, a second set of vegetation indexes was calculated from the reflectance values. A summary of this information is shown in Table  6. The full list of variables and how they were aggregated is provided in Table  S10. The main difference among the forest sites is the data quality, which is highly dependent on the presence of clouds. When possible, low-quality observations have been substituted by interpolated values, otherwise the cell was left blank. In any case the alteration of the original data was minimal. It is also important to note that the size of the pixel is large compared to the plot size of the forest stands, which means the pixel data also contain other vegetation types than the ones present at the sites. Three general types of data are included: (1) geophysical variables as measured from the MODIS sensor, i.
, 2005; Khanna et al. , 2007; Palacios-Orueta et al. , 2012). LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. The FPAR is the fraction of photosynthetically active radiation (400-700 nm) that is absorbed by the canopy  (Myneni, 2015). Gross primary productivity and net photosynthesis estimations are based on the light use ef-Table  6. Summary of the remote sensing data included in the PROFOUND DB. VIS, NIR and SWIR are the visible, near-infrared and shortwave infrared regions of the electromagnetic spectrum. NDVI: normalized difference vegetation index; EVI: enhanced vegetation index; FPAR: fraction of photosynthetically absorbed radiation; LAI: leaf area index; GPP: gross primary productivity; NDWI: normalized difference water index; AR: angle at red; ANIR: angle at NIR; AS1: angle at shortwave infrared 1; AS2: angle at shortwave infrared 2; SANI: shortwave angle slope index; SASI: shortwave angle slope index.
Sorø (DK) The ICOS site Sorø (DK-Sor in the FLUXNET and ICOS databases) is located in Denmark at an elevation of 40 m a. s. l. The climate is warm temperate and fully humid with a mean annual temperature of 9 • C and annual precipitation sum of 774 mm during the period 1996-2010. The soil has been classified as Alfisols and Mollisols. Potential natural vegetation is deciduous broadleaved forest dominated by Fagus sylvatica. Other species occurring in the area are Fraxinus excelsior, Larix decidua, Picea abies, Quercus spp. and Acer spp. However, the region is mostly used as cropland. Data on tree DBH are reconstructed from tree-ring measurement  (Babst et al. , 2014)  and historical management information for the time period from 1994 to 2017. Stand data are derived from these data for the time period from 1994 to 2017 (see Sect.
The PROFOUND R package (ProfoundData) The ProfoundData R package provides functions to access the PROFOUND DB (Figs. S2  and S3 ). The ProfoundData package plus a detailed vignette explaining the functionalities are available on CRAN (https://CRAN. R-project. org/ package=ProfoundData, last access: 5 June 2020). The Pro-foundData package serves as an interface for users that want to access the PROFOUND DB as a relational database via the R statistical software  (R Core Team, 2016). The following main functions are included to achieve this goal: -"getData" to download data (data can be downloaded for one forest site and one underlying dataset at a time); -"browseData" to check the available forest sites, datasets, variables for a dataset, datasets for a forest site and the database version, metadata, data policy and original data source; -"plotData" to quickly inspect any variable of the datasets visually; -"summarizeData" to summarize data from the database; -"queryDB" to pass self-defined queries; -"writeSim2netCDF" to write netCDF files and can be used to convert data (and other files such as model simulation output) into netCDF files.