dataesr/scientific-paragraphs-categorization huggingface dataset:
The impact of these withheld/added data are assessed by comparing the OSE with the control simulation in which only the regular data are assimilated. Although the methodology of OSEs is simple, it is widely applied due to its applicability to any ODAP system including highly non-linear or stochastic systems, and ability to directly give the differences caused by assimilation of targeted data (e. g. ,  Oke and Schiller, 2007; Turpin et al. , 2016; Xue et al. , 2017b). In the OSE framework, reduction of errors cannot be estimated accurately because the true state is unknown. Instead, errors are evaluated using reference data, which are preferably independent from the data assimilation process performed in advance in the OSEs in order to perform fair evaluation  (Fujii et al. , 2015b). Adjoint based sensitivity methods are also very applicable for OS-Eval.
In particular,  Langland and Baker (2004)  proposed a technique to compute the variation in forecast error due to the assimilated data using an adjoint model, which is often called Forecast Sensitivity Observation Impact (FSOI) and widely used for monitoring observation impacts in weather centers. Meanwhile, Degrees of Freedom for Signal (DFS) are used to indicate how effectively signals which can potentially improve analysis accuracy are observed (e. g. ,  Cardinali et al. , 2004). In an Ensemble Kalman Filter (EnKF) system, FSOI, and DFS are evaluated based on statistical information of ensemble members. In addition, the analysis uncertainty (i. e. , error variance) in an ODAP system can also be exploited for OS-Eval because the impact of assimilating specific observations can be measured by the uncertainty reduction.
OSE Studies for Evaluation of TAO/TRITON Array The TAO/TRITON array provides subsurface temperature and salinity data in the equatorial Pacific, which was vital for an ocean data assimilation system to provide oceanic initial condition for coupled atmosphere-ocean models in ENSO forecasts before the Argo era  (McPhaden et al. , 1998). However, the rapid increase in the number of Argo floats in 2000s was presumed to reduce the importance of the TAO/TRITON array as the primary provider of tropical Pacific subsurface data, which in view of the costs of maintaining the array make it crucial to reassess the importance of TAO/TRITON data in seasonal predictions. ECMWF 1 evaluated the impacts of TAO/TRITON, as well as Argo and satellite altimetry data, in their operational seasonal forecasting system through OSEs, and find complementarity among those data in late 2000s  (Balmaseda et al.
, 2007; Balmaseda and Anderson, 2009). Fujii et al. (2011)  carried out a similar OSE study to evaluate the impacts of TAO/TRITON data on the ENSO forecasts using the JMA 2 's seasonal forecasting system at the request of JAMSTEC 3 , the agency deploying TRITON buoys as a part of the array west of 165 • E. These results were shared in the GOV OS-Eval TT  (Oke et al. , 2009) , and presented in OceanObs'09  (Balmaseda et al. , 2010). In spite of these efforts, the data received from the TAO buoys deployed by NOAA 4 as a part of the TAO/TRITON array east of 165 • E was severely reduced in the period of 2012-2014  (Tollefson, 2014). In addition, the number of the TRITON buoys has also started to reduce since 2013. The influence of the reduction of subsurface temperature and salinity data in the equatorial Pacific was discussed in the Tropical   (Xue et al.
, 2017b)  were summarized in order to highlight the importance of the array for seasonal forecasting  (Fujii et al. , 2015a). Consequently, the workshop recommended the scientists to make a proposal of reorganizing the TPOS, which includes buoys, Argo, other insitu measurements and satellite data, in a more efficient and sustainable way by launching the TPOS2020 project  (Smith G. C. et al. , 2019). During the TPOS2014 workshop, inconsistency among the impacts of the subsurface data was recognized in OSE results. For example, the results in JMA (Figure  1 ) indicates that removal of only near-equatorial buoys causes larger decrease of Anomaly Correlation Coefficients (ACCs) compared to the removal of all TAO/TRITON buoys in the forecast Sea Surface Temperature (SST) averaged in the NINO3 region for 9-12 months leadtime and in the NINO4 region for 5-12 months lead-time although Argo and TAO/TRITON data have positive impacts in all cases.
, 2012; Reul et al. , 2013; Toyoda et al. , 2015)  show that SSS measured from space can bring new information. In the framework of the SMOS-Nino15 ESA project 8  , OSEs were designed to explore the potential impact of SMOS, Aquarius and SMAP SSS data assimilation using the ODAP systems of UKMO 9  and Mercator Ocean. The project was expected to produce an Observation Impact Statement (OIS; see section Community Collaborations) based on the OSE activities for the GOV OS-Eval TT. The 2015/16 ENSO event has been chosen as a case study for this project. The ODAP systems are based on the same Ocean General Circulation Model (OGCM) (NEMO;  Madec, 2016)  but with different settings and atmospheric forcing fields. The data assimilation techniques are also quite different; Mercator Ocean uses a reduced order, localized Kalman Filter approach with a 1-week assimilation window and UKMO uses a 3-dimensional Variational (3DVAR) approach with a 1day assimilation window.
Assimilated data sets in the control simulation are along-track satellite Sea Surface Height (SSH), SST, and in-situ temperature and salinity profiles. The SSS data assimilation is restricted to the 40 • S-40 • N area, due to increasing uncertainty in the salinity observations at higher latitudes. For both systems, specific bias correction schemes have been adapted to estimate the bias for the different SSS datasets. Performing OSEs with different ODAP systems and data sources allow for more robust conclusions to be made on the sensitivity of ocean estimates to SSS data assimilation. The success of assimilating SSS data was confirmed by the reduction seen of the Root Mean Square (RMS) differences between SSS observations and model forecast (Figure  2 ). In both systems, the differences are around or >0.
SST and in-situ profiles are assimilated in all other experiments. In addition, synthetic SSH data along the tracks of conventional altimeters were assimilated in OSSE1. Only the synthetic data of SWOT were assimilated in OSSE2. Note that they include the nadir altimeter in the middle of the swaths of the SWOT satellite. Synthetic SSH data of both the nadir altimeters and SWOT were assimilated in OSSE3. All experiments are performed with the same initial conditions and forcing for 1 year January to December 2009. A coherency analysis  (Thomson and Emery, 2014)  was done to investigate the reliability of the SSH signal in the OSSEs at the different spatial scales (Figure  7C ). If 0. 6 is chosen as a threshold for reasonable performance, the figure implies that assimilation of nadir and SWOT altimeter data performs well for wavelengths down to 110 km in OSSE3, while OSSE1 performs well-down to 180 km.
Since the absolute values of DFS have little physical meaning on their own, the relative fraction of each observation type to the total DFS are presented. The restart files from a TOPAZ ensemble have been extracted in the end of a 25-years reanalysis  (Xie et al. , 2017)  in May 2017 which assimilated SST, along-track SSH, sea ice concentrations, in-situ temperature and salinity profiles. We compare the relative impact of SKIM surface currents to that from the along-track SSH which also informs surface currents (Figure  10 ). In view of the above simplifying assumptions on the observation errors and the fact that the simulated SKIM data does not cover the TOPAZ area completely, the quantitative share of DFS (22. 6% for SSH against 10. 2% for SKIM) are only indications that the SKIM dataset is potentially valuable even in the presence of a good observing network as of May 2017.
Use of Multi-System Ensemble Spread The spread of a multi-system ensemble can be used for evaluating the uncertainty of ODAP products. The International Real-Time Ocean Reanalysis Intercomparison project  (Xue et al. , 2017a) , provides beneficial information for OS-Eval as the ensemble spread can be monitored as the observing network changes in time. The project was started in 2014 following to the recommendation of the TPOS2014 workshop (see section OSE Studies for Evaluation of TAO/TRITON Array). In the project an ensemble of nine ODAP products has been routinely collected at NCEP, and the temporal and spatial variations of the ensemble spread of total temperature and anomalous temperature have been monitored and used to discern the influences of the TPOS data on the uncertainty of the ODAP products in near real time.
(2015a). A main reason of the effort did not fully succeed is probably the lack of financial support, needed to perform such a resourceconsuming experiment at the same time in different groups. Nevertheless, multi-system OSEs for the evaluation of satellite salinity data have been successfully conducted using the UKMO and Mercator Ocean ODAP systems with financial support from ESA for the SMOS-Nino15 project (section OSEs for Evaluating Satellite SSS Data Impacts During the El Nino 2015 Event). In addition, a multi-system OSSEs are also conducted for evaluation and design of the Atlantic in-situ observation network within the H2020 AtlantOS project (section Evaluation of the Observing System Network in the Atlantic). This activity is feasible due to the close relationship among European countries and financial support from the European Union.
Nature Run and Calibration of OSSEs OSSEs are widely used for the evaluation of future observing systems and network designs as introduced in section OSSEs. A clear advantage of OSSEs is that wide range of observation data, even data that do not exist in reality, can be evaluated by synthesizing the data from a simulated true state. Another advantage of OSSEs is that estimation errors in each experiment are easily calculated using the Nature Run as the true state. However, several limitations are identified through the H2020 AtlantOS project (see section Evaluation of the Observing System Network in the Atlantic). In particular, it should be noted that OSSEs operate under the following two limitations: the dependency on the performance of the Nature Run, and the difficulty in synthesizing observation data with realistic error properties.
Particularly notable is that excluding Argo data results in an increase in surface height errors as well as temperature and salinity. Even though all the altimeter data were assimilated it is necessary to have a good T-S structure in the model in order to obtain the full benefit of altimeter data assimilation. The trial in UKMO, thus, demonstrated the potential of NRT-OSEs to evaluate observing systems in near real time. However, it is important to note that these results may not be robust due to the system-dependency as discussed in section Limitations of OS-Eval and System Dependency. The hope was that NRT-OSEs would become routine and would be run by other operational centers which would have given a better picture of the benefits of the components of the observing system in near real time.
As of yet this has not happened and this remains the only example of NRT-OSEs in the ocean. The main barrier to wider implementation is the expense in terms of computer and human resources of running an additional version of the operational suite. The importance of near real time evaluation was also highlighted in the TPOS2014 preliminary workshop. Although the influence of the reduction of the number of TAO data on seasonal predictions and other operational ocean services had to be evaluated promptly, there were few ways to examine the impacts at that time. Finally, the spread of the equatorial Pacific temperature anomaly fields among ocean reanalyses of operational centers are examined  (Fujii et al. , 2015a). The spread increased after the reduction of TAO data, implying it caused the increased uncertainty.
The communication is necessary to build up the understanding on the potential of ODAP systems in the observational community to help in securing financial and human resources and to set up an adequate infrastructure in operational/research centers for OS-Eval activities. As a tool of the communication, the GOV/OceanPredict OS-Eval TT now plans to publish the Observing Impacts Statement (OIS;  Lea, 2012)  ideally once a year for a specific observing platform based on integration of OS-Eval results within the TT. The SMOS-Nino15 work (section OSEs for Evaluating Satellite SSS Data Impacts During the El Nino 2015 Event) highlights the interest of space agencies in developing OIS to justify follow-on missions for SSS. OISs, thus, support observational agencies to secure resources for sustaining and improving ocean observing systems.
• Implementation of standard reporting in the form of OISs is needed to support decision-making and to provide quantitative demonstrations of data impacts that may strengthen the case for funding and improvements to ocean observing systems. Finally, we recommend the continued development of OS-Eval activities at international level with the support of the international ODAP (e. g. , OceanPredict and CLIVAR-GSOP) and observational communities. This is mandatory to guide the development of the observing system and to monitor its impact. and NU: Limitations of OS-eval and system dependency. YF and ER: Multi-system evaluation. YF: Nature run and calibration of OSSEs. DL and YF: Near-real-time evaluation. GL, YF, and CD: Community collaborations.
YF, P-YL, and PO: Conclusion. The manuscript is edited by YF with the support of YK, SP, CD, VK, MM, ER, PO, and DL. FUNDING SSS OSE studies at CLS, Mercator Ocean, and UKMO were funded through the ESA SMOS-Nino15 project. OSE studies in ECMWF have been partially funded by the EU-H2020 program through the AtlantOS project (633211). OSSE studies on in-situ observations in Mercator Ocean and CNRS/IGE have been funded by the EU-H2020 program through the AtlantOS project (633211). YF and SM are supported by JSPS KAKENHI Grant Number JP15H03727. YF is also supported by JPS KAKENHI grant Number JP17H00728. GH and VK are supported by the NOAA Hurricane Sandy Disaster Relief Act [OAR-M8R2WHSP01 and NA13OAR4830224] and from the NOAA Quantitative Observing System Assessment Program [QOSAP; OARP8R2W02PQF and NA15OAR4320064].
Research objectives This paper details and describes the Biomolecular Network Ontology (BNO) which is freely available at https://github. com/AliAyadi/ BNO-ontology-version-1. 0 and can be viewed using the standard ontology visualization editor Protégé. The BNO aims at giving a formal and semantic representation that models all the necessary biological knowledge to study and reason on complex biomolecular networks. This semantic representation wishes to meet the following goals: (1) Determine the structure of a biomolecular network by identifying the specific functions of all molecules and the different nature of the interactions they provide; (2) Disturb the network with stimuli by changing the concentration of an element and observe its response; (3) Reason and infer new knowledge; Moreover, through the application of the BNO ontology, we will understand how a cell works through the semantic interpretation of knowledge involved in the network's behaviour, and identify the different states of the biomolecular network over time and through the simulation of its behaviour.
Instances define individuals that are the members of a class. Finally, properties are used to define the relationships among the concepts. OWL defines two properties: object properties which define relationships among couples of individuals, and datatype properties which define the relations between individuals and a data type value  [6]. In addition, under OWL language we can query the content of an ontology using the SPARQL query language and reason about these classes and individuals through Semantic Web Rule Language (SWRL). It is important to be clear about why an ontology is being developed and what its intended uses are. Ontology engineering proposes various methodologies for the process of developing an ontology. We briefly cite some methods: the TOVE (TOronto Virtual Enterprise) methodology proposed by and M.
The main properties and data types After the definition of the major BNO concepts and in order to describe the semantic relations among them, we need to define the domain, range, property type, and inverse properties as constraint conditions. The different properties and data types of the BNO ontology are explained below. • hasBehaviour(object1, object2): where ob-ject1 is a BiomolecularN etwork and object2 is a Behaviour. • hasInteraction(object1, object2):where ob-ject1 is a BiomolecularN etwork and object2 is an Interaction. • hasN ode(object1, object2): where object1 is a BiomolecularN etwork and object2 is a N ode. • hasSource(object1, object2): where object1 is an Interaction and object2 is a N ode. • hasEnd(object1, object2): where object1 is an Interaction and object2 is a N ode. • hasState(object1, object2): where object1 is a N ode and object2 is a N odeState.
Within these three sub-ontologies, we are more interested in the molecular functions ontology and the cellular components ontology. We chose to use the Gene Ontology for the following reasons, (1) it is an initiative of several genomic databases such as the Saccharomyces Genome Database (SGD), the Drosophila genome database (FlyBase), etc. to build a generic ontology for describing the role of genes and proteins, (2) it is the most developed and most used in biology (since 2000), and (3) it provides annotation files about a large number of cellular entities. • The Simple Event Model ontology  9  proposed by Van Hage et al. [40]  provides the necessary knowledge for the description of events. The ontological architecture of the Simple Event Model ontology consists of four basic classes, Event that specifies what is happening.
As presented in Figure  11 , Protégé provides the SWRLTab as a development environment for working with SWRL rules and creating SQWRL queries. In it, we can edit and execute SWRL rules for querying the BNO ontology in order to infer new knowledge. That way, a query in the BNO ontology with SWRL rules can be used to detect, for example, the transition states of each molecular components over the simulation time, to identify exactly the molecular components involved in a particular interaction, etc. These results can be used by software applications through APIs, such as Jena Semantic Web Toolkit  [52]  and Apache Jena  [53].
Conciseness: The terms of the BNO ontology were checked with the help of expert biologists, we assume that the ontology does not contain any redundant terms. Consistency: This criterion ensures that the logical axioms are satisfiable and consistent. The satisfaction of the logical axioms is recognized when it is possible to find a situation under which all the axioms are true, and their consistency when it is impossible to find a contradiction within the axioms. As detailed in the previous section, reasoning in the BNO ontology was performed using two reasoning mechanisms. Firstly, via an SWRL rule-based system using the latest version of Her-miT reasoning plugin in the Protégé environment version 1. 3. 8. 3. And secondly, through a reasoning which is written in MATLAB/SIMULINK development environment.
Discussion This section states the main strengths and weaknesses of our approach and how to overcome them. The "case study" presented in this paper represents a "proof of concept" since it demonstrates the logical consistency of the approach and validates the relevance of the BNO ontology. Moreover, we developed a Matlab tool, a qualitative simulator, available on https://github. com/AliAyadi/ QualitativeReasoningInMATLAB to check and compare the results in the paper with the results obtained using a qualitative tool. Results demonstrate how the proposed semantic approach provides a powerful formalism for modelling and representing complex biomolecular networks. Indeed, we have seen that our approach is able to: • enrich and infer new knowledge, • detect more properties and relationships among the molecular components, • provide useful inferring knowledge and rich semantics allowing biologists to model the dynamical behaviour of complex biomolecular networks.
The Analysis of the RPG Session After the sessions, the content (i. e. , the exchanges during the RPG sessions, the debriefings, and the post-session interviews) was integrally transcribed. We conducted the analysis with Nvivo 11 Pro software (version 11. 4, QSR International, Melbourne, Australia)  [36]. We structured our analysis on four hypotheses on the possible effects of the RPG on social learning that arose from literature. These hypotheses were transformed into "parent nodes" in the Nvivo software. For each hypothesis, we identified specific contributions of the RPG session to social learning by coding informative segments from the transcription, and aggregated them into "child nodes" (Table  1 ). Increased mutual understanding  [16, 37, 38]  Enabling: • Expression of a need to be understood • Experiencing the constraints and challenges of others • Expressing one's own difficulties Improved ability to deal with uncertainties  [39, 40]  Identifying different kinds of uncertainties: • Lack of access to information • Knowledge gap • Ambiguity Willingness to explore innovative methods of governance  [15, 41]  Encouraging collective discussion on:.
Acknowledgments: The authors would like to thank  Diane Esquerré  and  Christine Hervé (Dynafor)  for their support in the design and facilitation of the SECOLOZ RPG and the managers of the  Cévennes National Park  for their help and trust:  Stéphane Baty ,  Stéphan Garnier ,  Grégoire Gauthier ,  Jocelyn Fonderflick ,  Julien Buchert , and  Franck Duguépéroux.
On the eve of component procurement, this paper discusses the present physics basis for the first ITER tungsten (W) divertor, beginning with a reminder of the key elements defining the overall design, and outlining relevant aspects of the Research Plan accompanying the new "staged approach" to ITER nuclear operations which fixes the overall divertor lifetime constraint. The principal focus is on the main design driver, steady state power fluxes in the DT phases, obtained from simulations using the 2-D SOLPS-4. 3 and SOLPS-ITER plasma boundary codes, assuming the use of the low Z seeding impurities nitrogen (N) and neon (Ne). A new perspective on the simulation database is adopted, concentrating purely on the divertor physics aspects rather than on the coreedge integration, which has been studied extensively in the course of the divertor design evolution and is published elsewhere.
Emphasis is placed on factors which may increase the peak steady state loads: divertor target shaping for component misalignment protection, the influence of fluid drifts, and the consequences of narrow scrape-off layer heat flux channels. All tend to push the divertor into an operating space at higher subdivertor neutral pressure in order to remain at power flux densities acceptable for the target material. However, a revised criterion for the maximum tolerable loads based on avoidance of W recrystallization, sets an upper limit potentially ∼50% higher than the previously accepted value of ∼10 MW m -2 , a consequence both of the choice of material and the finalized component design. Although the simulation database is currently restricted to the 2-D toroidally symmetric situation, considerable progress is now also being made using the EMC3-Eirene 3-D code suite for the assessment of power loading in the presence of magnetic perturbations for ELM control.