dataesr/scientific-paragraphs-categorization huggingface dataset:
4. 1. 5. Scale Challenges faced. A number of case studies reported challenges related to scale, spatial extent and spatial resolution. Working at large scales (e. g. large regions, national, international) leads to challenges for validation, particularly of social science methodologies, and increases the challenge of convening representative stakeholder groups (Hungary, Essex, Brazil). Both fine and broad scales can pose challenges for obtaining datasets at the right spatial resolution (Barcelona). Some methods are more appropriate for certain scales (e. g. the Cairngorms case found ESTIMAP to be appropriate for the national park scale, but too coarse when zoomed in) and method scale can limit utility for particular decision needs (e. g. see Section 3. 2. 6). Spatial extent may need to vary to cover different ES.
In the Portuguese case, the default case study boundary needed to be extended to cover the spatial extent of marine ecosystems and pollination services relevant to the study. Successes and opportunities. Whilst data availability can be a key constraint, in many cases spatial challenges can be resolved with the application of resources and expertise, particularly GIS skills which facilitate the transfer of datasets between scales (Oslo). There are many cases where methods were successfully combined at the same scale (e. g. Loch Leven, Warwickshire) or adapted to transfer across scales (e. g. Barcelona, Helsinki, Oslo; Section 3. 3. 2). Methods can be combined to make up for deficiencies in spatial resolution. For example, the Cairngorms case study combined local surveys that were impossible to conduct at the national park level with integrated mapping-modelling that was suitable only at a broader scale.
With sufficient time, methods can be nested and cross-scale approaches can be taken  (Patagonia, Sierra Nevada; Martín-López et al. , 2017). Cross-scale approaches can be very important for integrated valuation as values can vary with spatial resolution, because of the level of aggregation and the spatial context. For example, the individual value of a street tree is different to the value of a similar tree in a park or forest (Oslo). Even where spatial challenges persist, e. g. for methods linked by input-output data (Section 3. 3. 1) and direct-overlay comparisons (Section 3. 3. 4), they can contribute to case-based learning (Section 3. 3. 2) by allowing stakeholders a more nuanced understanding of an issue.
Considering the evolution of the semantic wiki engine based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires existing ontologies to be imported. Most of them use the RDF-based (Resource Description Framework) systems in conjunction with the standard SQL (Structured Query Language) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for SMW (Semantic MediaWiki)  is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology based architecture, which aims to implement this idea. The architecture mainly includes three layered functional architectures: Web User Interface Layer, Semantic Layer and Persistence Layer.
It reduces (instantaneously) distances between civilizations. Thus, it is an opportunity to disseminate the local knowledge on a large scale. But, this is not sufficient to make the Internet the ultimate solution to the African culture vulgarization. It does not create anything itself. It is the African responsibility to build the content of the "empty shell" that is the Internet and make the rational use and enjoy the opportunities it offers. To get there, new computational technologies (semantic technologies) are needed to manage these large repositories of sociocultural data and to discover some useful patterns and knowledge from them. Semantic Web vision proposed by Tim Berners-Lee, revolutionized the Web architecture. The Web architecture switched from the documentary graph to the published and interconnected databases with capabilities to "understand" their semantics and reason on them.
Technically, are introduced into the Web architecture stack, the RDF data model and the URI standard for modeling and identifying resources on the Web. As result, the Web is spreading in the World and the World is spreading in the Web with issues such as cultural "digital divide". Indeed, a cultural void in the Web of Data is the lack of that culture at the applicative level (e-tourism, etc. ) of the Web of Data. In this paper, we present the architecture of a sociocultural knowledge co-construction platform, developed to enable some communities to share and co-build their cultural heritage. Thus, the architecture relies essentially on the USCO (Upper-Level Sociocultural Ontology)  (Diallo et al. , 2014)  ontology, which allows communities to share and co-construct their socio-cultural knowledge.
The modeling choice, is to model the deictic time by associating the properties of the effective date associated with the deictic time.  Ontoshare : Ontoshare is a virtual activity ontology in the case of the sociocultural knowledge sharing. It designs how Internet users could share and co-construct the content of a sociocultural Knowledge Management System (KMS). The developed architecture is based on three entities: 1) Semantic MediaWiki (SMW), which is the base of the platform, 2) Virtuoso, which is the triple store (RDF database) and Exhibit, which is the data visualization tool. Figure  1  shows the different layers of the architecture. This architecture consists of three layers: A Human-Machine-Interface (HMI) supported by SMW and Exhibit. This layer allows users to participate in the co-construction of knowledge.
Semantic MediaWiki (SMW) Semantic MediaWiki (SMW) is an extension of MediaWiki, the Wikipedia-based wiki engine (semantic-mediawiki. org, 2017). Unlike traditional wikis that can only contain texts that can be not 'understood' or evaluated by software agents, SMW adds semantic annotations to the Wiki pages using an extension of the MediaWiki script language. Thus, SMW enables MediaWiki to provide functions such as a collaborative knowledge base. MW has a scripting language to describe the content of the Wiki pages. This language was extended by SMW with the following three sets of semantic annotations: Classes, Properties, Axioms and Instances. Thus, the collection of semantic data in SMW is made by allowing users to add annotations on the articles pages through special tags. Each item corresponds exactly to an ontological element, that is to the following RDF types: owl:Thing, rdfs:Class, owl:ObjectProperty, owl:DatatypeProperty and owl:AnnotationProperty.
Semantic Layer The main role of the semantic layer is to link the Knowledge Base: 1) to Exhibit for better visualization, 2) to the external resources to feed the Knowledge Base, and 3) to the defined rules to deduce implicit relations in the Knowledge Base. All these actions are done through the Jena framework. It provides an API for retrieving and writing data in RDF graphs. The graphs are represented as an abstract model. A template can come from data, files, databases, URIs, or a combination of them. A model can also be queried by a SPARQL endpoint. Thus, to allow access to the data stored in Virtuoso, the triple store provides a Jena RDF Data Provider, which is a service for the Jena framework and allows Semantic Web applications to directly query a Virtuoso Knowledge Base. Query Engine (See Figure  1 ) is a SPARQL endpoint interface provided by Virtuoso.
Persistence Layer Typically, SMW stores data in a Relational Database. However, for RDF data, the best option is to use a data format that organizes information in the form of a triples rather than in relational database tables. Indeed, the use of a triple store has several advantages : 1. Better performance for information retrieval: RDF databases are configured to respond to SPARQL queries, which is a W3C recommendation. SPARQL offers better performance in information retrieval than the use of SQL queries in a relational database. Indeed, SPARQL queries make it possible to benefit from the semantics of the RDF data 2. An additional interface: with a SPARQL endpoint (Web query interface), it is possible to make SPARQL queries on the data without resorting to the interface of SMW. This allows other applications to access the data.
A reasoning functionality: semantic Web languages such as RDFS and OWL offer powerful modeling features such as subsumption relations between the concepts or characteristics of certain properties as transitivity. Thus, an RDF database can infer on these characteristics to get answers. The reuse of ontologies and the integration of data: it is possible to add the external data in the RDF database and to use SMW to update them. Thus, the RDF database can be seen as a platform for data integration and ontologies reuse.
Physical separation of resources: Separating the data used by MW from SMW provides a way to distribute tasks across multiple servers. In particular, complex queries, which consume a large amount of computational power, may thus not affect the basic operation of the Wiki. Thus, SMW can operate with five different triple stores. Table  2  compares these triple stores. A graph database represents the data in graph structures composed of nodes and edges. This allows easy processing of the data by a calculation of the specific properties of the graph, such as the number of steps (nodes) needed to go from one node to another. A triple store implements a graph model that interprets the predicate as the label of a link between subject and object. Among the triple stores (see Table  2 ), Virtuoso has been used.
It is a hybrid database and a middleware that supports relational data, graphical and RDF data, web-server applications, and so on. By integrating Virtuoso with SMW, only the semantic data is migrated into Virtuoso. As shown in Figure  1 , all textual data remains in the MySQL database that is used by MediaWiki, and all annotations made in the Wiki are stored in Virtuoso. For the better scaling, the database is divided into three warehouses (graphs): 1) a warehouse where the semantic data are stored (annotations from the Wiki), 2) a warehouse where the USCO ontology is stored, and 3) a warehouse , where the HuTO ontology is stored. Since SMW allows importing vocabularies, the vocabularies of HuTO and USCO are imported into the platform. This allows users to annotate the resources of the Wiki using the vocabularies of HuTO, USCO and Ontoshare.
Acknowledgement In memory of  Papa Fary Diallo , who brutally left us while we were discussing on this paper. The authors would like to thank the  CEA-MITIC (Centre d'Excellence Africain en Mathématiques, Informatique et TIC ) , the  University of Maria (UMa)  and  LANI (Laboratoire d'Analyse Numérique et d'Informatique  ) for the financial support.
Background: We investigated the feasibility of left ventricular (LV) and right ventricular (RV) volume and function estimation using a first-pass gated 15 O-water PET. This prospective study included 19 patients addressed for myocardial perfusion reserve assessment using 15 O-water PET. PET data were acquired at rest and after regadenoson stress, and gated first-pass images were reconstructed over the time range corresponding to tracer first-pass through the cardiac cavities and post-processed using TomPool software; LV and RV were segmented using a semiautomated 4D immersion algorithm. LV volumes were computed using a count-based model and a fixed threshold at 30% of the maximal activity. RV volumes were computed using a geometrical model and an adjustable threshold that was set so as to fit LV and RV stroke volumes.
Such cardiac function assessment using first-pass gated PET has been poorly investigated in humans since only two studies are reported on the feasibility of LV function assessment using first-pass gated 15 O-water PET  [22, 23]. TomPool is a free software developed in Montpellier University Hospital and designed for semi-automated post-processing of equilibrium blood-pool SPECT images. It has previously been validated against CMR for LV and RV function assessment  [8]. It relies on a watershed immersion algorithm for LV and RV segmentation and a deformable reference model for time-activity curve fitting  [24, 25]. The aim of the present work was to investigate the feasibility of LV and RV volume and function measurement using the first-pass 15 O-water PET in a cohort of patients referred for myocardial perfusion assessment at rest and after vasodilator stress.
Patients Nineteen patients aged 47-75 years (median 66 years, 14 men and 5 women) were prospectively recruited from the outpatients of the Nuclear Medicine Department at Caen University Hospital from December 2015 to November 2016 in the frame of the WATER-DAY study (ClinicalTrials. gov unique identifier NCT 02278497) aiming to compare myocardial perfusion and flow reserve using dynamic myocardial SPECT to 15 O-water PET and endovascular fractional flow reserve measurement  [26]. Inclusion criteria were the presence of at least one significant (≥ 50%), non-occlusive, coronary stenosis on percutaneous coronary angiography, and no history of myocardial infarction. All patients underwent both myocardial SPECT and 15 O-water PET within 3-17 days (median 10 days), without any relevant cardiovascular event or change in cardiotropic medication between the two examinations.
Myocardial SPECT acquisition and analysis All SPECT acquisitions were carried out using a dedicated CZT cardiac SPECT camera (D-SPECT; Spectrum-Dynamics, Biosensors, Caesarea, Israel) with the patient in supine position. Rest and stress acquisitions were performed in the same session. Rest images were acquired during 5 min, 6-7 min after injection of 2. 5 MBq/kg of 99m Tc-sestamibi. For stress imaging, 7 MBq/kg of 99m Tc-sestamibi was injected at peak hyperaemia following IV injection of 400 μg of regadenoson, and data were acquired 6-7 min later over 2 min. Gated SPECT images were reconstructed using the manufacturer's dedicated software and post-processed using commercially available software (Corridor4DM, INVIA, Ann Arbor, MI)  [27]  to obtain LV end-diastolic volume, end-systolic volume, stroke volume, and ejection fraction.
O-water PET acquisition and analysis All PET acquisitions were achieved using a GE Discovery VCT RX scanner (GE Healthcare, Buc, France) and started with a low-dose transmission CT scan for attenuation correction. Two intravenous injections of 15 O-water (1. 5 to 3 MBq/kg) were performed, each one being simultaneously performed with the start of a dynamic gated 4′30″ emission scan. The second emission scan was performed at peak vasodilator stress following IV injection of 400 μg regadenoson. After correction for random coincidences, scatter, and dead time, the acquired list-mode data was reconstructed twice. First, a dynamic 24-frame sequence (14 × 5″, 3 × 10″, 3 × 20″, and 4 × 30″) was obtained using Fourier rebinning and 2D filtered back-projection. It allowed to extract a time-activity curve (TAC) in a region of interest (ROI) centred on the cardiac area.
TACs were characterized by a sharp early peak corresponding to tracer first-pass through the right then left cardiac cavities followed by a sustained decreasing plateau corresponding to myocardial uptake then wash out. End of tracer first-pass was detected by visual inspection as the TAC transition point between the peak and the plateau. A second image reconstruction was then performed over the first-pass time range using 3D OSEM (nine subsets, two iterations) with eight-interval gating. Reconstructed images were re-oriented into cardiac canonical axes using CardIQ software on a dedicated workstation (Advantage Windows 4. 4, GE Healthcare, Buc, France). Final image matrices were sampled on a 64 × 64 × 47 grid with 3. 27 mm cubic voxels. First-pass gated blood-pool images were post-processed using in-house software (TomPool, freely available for download at http://scinti.
The interface of the system must be designed to provide non-expert users all the information required to choose the models and appropriately interpret the results. Moreover, procedures like installation, maintenance, documentation, validation and versioning, which are common in software development, must be also implemented for the models and for the prediction platform in which they are implemented. In this article we describe our experience in the eTOX project and the lessons learned after 7 years of close collaboration between industrial and academic partners. We believe that some of the solutions found and the tools developed could be useful for supporting similar initiatives in the future.
, 2013) , TransQST  (Maldonado et al. , 2017) , iPiE  (Bravo et al. , 2016)  and eTOX  (Cases et al. , 2014; Sanz et al. , 2017; Steger-Hartmann and Pognan, 2018)  deserve to be highlighted. In particular, the eTOX project, funded by the Innovative Medicines Initiative, constituted a pioneering exercise of extracting and integrating in vivo data from legacy resources at the pharmaceutical industry, and exploiting such data for readacross and the development of predictive models, since one of the aims of the eTOX project was to set up an integrated system for the prediction of toxicological endpoints, with a focus on organ and in vivo endpoints. The project faced many challenges, some of which have been described in previous publications  (Cases et al. , 2014; Sanz et al. , 2015 Sanz et al.
Platform for Model Development and Production Most of the eTOX models were developed by academic partners and SMEs, located in different European countries. Therefore, the architecture of the system to be developed should support independent and concurrent model development, while model prototypes should be made accessible to the end users (pharmaceutical companies) for early testing. This software platform, designed to increase the model development efficiency, should be compatible with the local deployment of the final system. The final version must be installed physically at the computational resources of the pharmaceutical companies, since the end users considered that only an installation behind the company firewalls guarantees that they could be used on highly confidential compounds corresponding to drug candidates under development.
These requirements made necessary the adoption of technical solutions that facilitate the remote access to the models and the portability of its software implementation, which consisted of two layers of containerization. The outer layer consisted in a self-contained virtual machine (VM), configured to expose a REpresentational State Transfer (REST) web service  (Fielding, 2000)  to predict the properties of query compounds. VMs were installed at the partners facilities, thus making possible that models developed at remote sites were immediately accessible through a centralized web server which shows all available models through a single graphical interface (see Figure  1 ). The physical location of the server running the computations was completely transparent to the end user. Figure  2  shows a schematic representation of the setup that was adopted for the development and production of the eTOX models.
Model Development and Maintenance Apart from connecting the individual models to the eTOXsys prediction system, eTOXlab provides additional support for the model development, maintenance, and documentation. Regarding model development by diverse teams of modelers, it is important to make use of common tools providing consistent solutions for tasks that need to be carried out by the different models. An example of this is the structure normalization,  since the end user expects that the input structure is internally normalized and processed in the same way by all the models to which it is submitted. The use of a common modeling framework allows employing a common workflow for the building of all the models and for carrying out predictions with them, where the same software tools are used at each step, thus guaranteeing that the results are consistent; an example is structure normalization.
The ADAN methodology can also be applied to non-QSAR models  (Capoferri et al. , 2015). Another task that can be facilitated by the use of a modeling framework like eTOXlab is the maintenance of the models. Given that models are not static entities, once they are developed, they should evolve along the time by incorporating new compounds to the training series, updating of the software used at the different steps or refining the modeling workflow. In any case, every improvement produces a new version of the original model (see Figure  5 ). In production environments, where important decisions can be based on model results, it is important to maintain a wellordered inventory of all models and versions developed and use unique identifiers for each of them. As a minimum, the system must allow to reproduce predictions made by any model version.
Structure Confidentiality The eTOX project was a collaborative effort involving several major pharmaceutical companies, which contributed data generated and stored in-house for the training of predictive models. Sharing this information posed a major problem, in particular when it involved the structure of confidential compounds. Predictive models should ideally be built using all available structures and biological annotations available, irrespectively of the partner who contributed this information. Unfortunately, the data protection policies of the different industrial partners imposed obvious limitations, difficult to overcome. At the beginning of the project we hoped to be able to develop and implement new structure-masking algorithms able to hash the structures into representations usable for building models, but resilient to any effort to reverse-engineer the algorithm and guess the original chemical structure.
Our hope was not unfounded, and different similar methods have been published in the past  (Tetko et al. , 2005; Masek et al. , 2008). For this particular purpose, we obtained excellent results using a simple random permutation of the molecular descriptors generated by methods like GRIND or GRIND2  (Pastor et al. , 2000; Durán et al. , 2008). The permuted vector of descriptors does not allow guessing the original structure, since the permutation destroyed any link between the value of the variables and their physicochemical interpretation. Moreover, this approach is resistant to brute-force methods  (Faulon et al. , 2005; Filimonov and Poroikov, 2005) , since these methods require the application of the same algorithm to a comprehensive database of structures, and a key element of the hashing algorithm (the random seed of the permutation) is never shared or revealed.
The robustness of the algorithm was carefully tested and further demonstrated by code-breaking challenges at the project consortium level, where the hashed representation resisted any effort to identify the original structure. In these exercises, we also demonstrated that the hashed representation preserved all the information existing in the original molecular descriptors, and the models derived from them had equivalent quality. Unfortunately, in these exercises we found that, beyond the robustness of our masking algorithm, it was impossible to convince the pharmaceutical companies to implement it in the eTOX project since, given the high corporate sensitiveness on the issue, such implementation would require costly external audits that we could not afford. For this reason, we adopted an alternative approach: if the confidential data cannot be taken out of the companies' internal repositories, we can move the whole model building system to the companies, so the models can be built there.