dataesr/scientific-paragraphs-categorization huggingface dataset:
The students could drop their vizualisations onto the 3D map and animate a presentation by going through the different maps. They have proposed still image of geographic data or animated simulation with multi-agent model. The aim of this project was also to show how tangible models could help in understanding urban projects. In addition to the 3D scene, we also provided the students with a tangible model of the neighbourhood that was built using white Legos. The white color allows to easily project information into the model. Rather than only seeing their maps in the digital 3D scene, students could project them onto the tangible model. Figure  11  shows an example projection on the model. We calibrated a video-projector such that the 3D scene in UD-Viz was matching with the tangible model, and we projected the slideshow module on the model.
Code and data availability. The source code of the ORCHIDEE-C lateral model developed in this study is available online (https://doi. org/10. 14768/f2f5df9f-26da-4618-b69c-911f17d7e2ed, Zhang et al. , 2021)  from 22 July 2019. All forcing and validation data used in this study are publicly available online. The specific sources for these data can be found in Table  1. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esd-13-1119-2022-supplement.
Food microbiology researchers, risk assessment agencies and food business operators rely heavily on the reuse of knowledge that is available as data, models and tools. Unfortunately, such knowledge reuse remains challenging, as food safety data sets, models and tools are usually only available in platform-dependent or software-dependent formats that rarely comply to the Findability, Accessibility, Interoperability, and Reusability data principles. In recent years, the Risk Assessment Modelling and Knowledge Integration Platform (RAKIP) Initiative developed the socalled Food Safety Knowledge Exchange (FSKX) format. This development was accompanied by the creation of open-source software that facilitates the adoption of FSKX. Future work within RAKIP will focus on creating semantic interoperability in FSKX-related solutions and on the extension of the FSKX format towards other food microbiology knowledge.
RAKIP was initiated in 2016 and is continuously funded by three institutions with food safety risk assessment mandates: ANSES, BfR and DTU Food. Over the years, RAKIP followed the strategy outlined by Plaza-Rodriguez et al. in 2015  [13]  and created critical resources that enable efficient exchange of risk assessment models and data  [14] [15] [16] [17] [18]. For example, during the generation of risk assessments, there might be the need to combine a consumer phase model for exposure assessment from one author with the dose-response model created by another. If these models are only available in different formats or tools, their re-use by risk assessors might be hampered. For that purpose, missing resources that could facilitate the efficient exchange of data and models between software solutions were developed.
This strategy takes into account that numerous software solutions exist in the field that may only slowly take up the new RAKIP resources. The RAKIP Initiative and activities in other bioscience disciplines  [19]  also support the Farm to Fork Strategy at the heart of the European Green Deal by enabling efficient information exchange on the basis of harmonized information exchange formats  [6]. This EU strategy aims at transforming food systems to better readjust food security, food safety, biodiversity, environmental costs and ethical objectives. Efficient information exchange and integration of knowledge between sectors and disciplines is an important element to reach the shared goals of the European Green Deal  [5]. A transformation of such impact needs to be facilitated by IT-based resources for information exchange, which are currently not available.
If data would be provided in semantically interoperable ways, this would support researchers and even allow computer-based algorithms to integrate available data from different sources in a meaningful way. Large international initiatives like EOSC Life (https:// www. eosc-life. eu/) create new solutions: for example, the RO-Crate standard for packaging research data with its metadata  [27] , which will be taken into account by the RAKIP Initiative in decisions to further enhance the resources supporting efficient knowledge exchange in the food microbiology sector. This also includes considerations of what tools need to be developed to reduce the workload for knowledge creators.
Available resources facilitating knowledge exchange in the domain of food safety risk assessment For the food safety risk assessment domain, the RAKIP project created the following infrastructural resources to enable efficient exchange of domain-specific data and models: 1. A file format called Food Safety Knowledge Exchange (FSKX) that allows for the exchange of executable models, for example, microbial growth and inactivation models, source attribution models, and risk assessment models, together with linked information like metadata, underlying experimental or observational data, simulation scenarios, and simulation results, in a standardized way (https://foodrisklabs. bfr. bund. de/ fskx-food-safety-knowledge-exchange-format/). The FSKX file format can also comprise 'joined' models, that is, models that are composed of several model modules, where each module is an individual FSKX file.
2. A community-driven harmonized annotation schema for food safety knowledge (e. g. data and models), called Generic Metadata Schema (GMS), with integrated controlled vocabularies that make use of existing code lists and information exchange standards  [14]. GMS also allows the easy creation of knowledge-type specific metadata schemata. This helps to capture only relevant meta-information that is specific to certain model classes or knowledge domains. This platform was also used to establish a number of web services for execution, creation, editing, and joining of FSKX models  [18]. All 'FSK-Lab'-based software solutions currently support the execution of models created in the scripting language R (www. rproject. org) and Python (www. python. org) and the joining of FSKX models. The RAKIP project website https://foodrisklabs.
Development needs to extend the Food Safety Knowledge Exchange (FSKX) format into a standard facilitating data-driven innovations in food microbiology So far, RAKIP has primarily been focused on microbiological food safety and risk assessment knowledge. As the idea to promote knowledge exchange is relevant in other domains as well, the RAKIP Initiative plans to expand its scope, for example, towards the One Health sector, which implies the involvement of a broader range of experts and expertise. In order to adopt the available FSKX-related resources also in various other food microbiology domains, a number of additional infrastructural developments are needed. This work must target four main areas: 1. the FSKX format itself, 2. the software supporting FSKX, 3. FSKX-compliant content in knowledge repositories, 4.
training and support for information providers and users. For area 1, the work will address three aspects: a) make the necessary adjustments so that FSKX can be used for information objects from other food disciplines, such as microbial data analysis pipelines, food tracing data, food authenticity data, whole-genome sequencing (WGS) data, and so on; b) extend FSKX such that the provisioning of semantically interoperable meta-information is supported, that is, via linking the controlled vocabularies in GMS to relevant ontologies; and c) provide options to use FSKX for the exchange of protected data and models, for example, as encrypted information objects. For area 2, investments need to be made into a) the automated generation of open-source software libraries in different popular programming languages, which can be integrated into new and existing software tools and reduce the maintenance effort for software developers (including the support for backward compatibility between different FSKX versions), b) the improvement of the usability and functionalities of the existing FSKXcompliant software solutions (a significant development effort must thereby be directed towards technical solutions, e.
g. Application Programming Interfaces (APIs), that make use of the semantically interoperable metainformation provided within FSKX objects in the future), and c) the provisioning of secure, cloud-based computational resources that allow to combine and execute FSKX information objects from different programming languages. For areas 3 and 4, an ongoing investment in human resources will be required to create a critical quantity of knowledge in FSKX-based knowledge repositories like RAKIP-Web (https://knime. bfr. berlin/landingpage/ RAKIP-Model-Repository) and to provide training and education. Collaborative international support will be essential to assure continued commitment.
This lack of interoperability is in our opinion partly due to a lack of knowledge and adoption of food-related ontologies like FoodOn  [33]  and a lack of customized software tools like SciLite  [34]  or 'ISA tools' (https://isa-tools. org/ [35] ) that support food microbiology researchers in their knowledge-sharing efforts. Also, tools and platforms facilitating the easy exploitation of semantically interoperable metainformation, as it is available in other disciplines (e. g. like DisGeNET-RDF (https://www. disgenet. org/rdf) or Omics DI https://www. omicsdi. org/), are missing in the food microbiology sector. As a consequence, it will be necessary to bring together several stakeholders from the food microbiology community to work synergistically towards the objective of FAIR data.
A roadmap towards open infrastructural resources enabling data-driven innovations in food microbiology In order to accomplish the development goals outlined above, it is important to implement measures that can provide long-term financial and organizational support. The foundation of the RAKIP Initiative by DTU Food, ANSES and BfR is therefore an opportunity for the food microbiology sector, as this initiative allows the linking of long-term governmental support with the developments funded by research grants. Specifically, the RAKIP Initiative is committed to supporting the development and provisioning of infrastructural resources that facilitate efficient information exchange in microbial and chemical risk assessment, risk-benefit analysis and food microbiology. It is already planned to expand the RAKIP Initiative network towards other risk assessment institutions (e.
g. EFSA, SSI, WHO) and universities (e. g. University Cordoba, University of Donja Gorica etc. ). An open-access knowledge base and a well-defined standardized knowledge exchange format will also be beneficial for the private sector and may support collaboration where possible. A combination of efforts of the RAKIP Initiative with existing European infrastructure providers like ELIXIR (https://elixir-europe. org/)  [19]  would further open up new opportunities for funding, synergistic development, visibility, and end-user support. On a technical level, the following development tasks will be addressed by the RAKIP Initiative in the near future: • Creation of a technical framework to maintain and extend the FSKX format. This framework provides a) an intuitive graphical user interface (GUI) for collaborative curation of the GMS and the data/modelclass specific metadata schemata and b) an automated software code generation pipeline that transforms agreed FSKX changes into software code libraries for different programming languages and tools, also maintaining backward compatibility wherever possible.
• Maintain and improve the functions and user-friendly application of existing FSKX-related software tools, like RAKIP-Web, FSK-Lab, the FSK2R R library, the services offered via the D4Science VREs  [36] , as well as 3rd party tools. In parallel, the RAKIP Initiative will continuously extend the amount of FSKX-compliant content in, for example, the RAKIP-Web model repository and provide individual support for researchers who wish to convert their own data or models into the FSKX format even if this will not be deposited in RAKIP-Web. This includes the organization of regular workshops, webinars and elearning courses, and the active communication and dissemination of results at relevant international conferences. In the mid-term perspective, the RAKIP Initiative aims at developing into a service provider for infrastructural resources that supports interoperability of data and services and that is closely connected to similar initiatives from other life science and One Health domains.
An important issue when sharing models remains the model code quality verification. Indeed, the computer programs used in risk assessment modeling are often complex and expensive to develop. However, the model code is rarely formally verified. So far, the main code review approach is to compare the generated model results with the ones from the original publication. Even though other and better solutions can be envisaged, this responsibility will stay with any platform owner or journal reviewer in the foreseeable future. The resources generated by the RAKIP Initiative will support their work, as the original model code can be shared with FSKX, even if it was created in various programming languages.
Unlike the current state of the art  [4] , the approach does not explicitly rely on clearly visible vessels. 2. We experimentally validated our approach on 6 in vivo TTTS video sequences, which are an extended version of the publicly available fetoscopy placenta dataset  1. The extended dataset used in our experimentation has been made available, under the fetoscopy placenta dataset webpage, for reproducibility. 3. We show that FlowNet-2 pretrained on non-medical data reliably initializes fetoscopic mosaics, provided that inconsistent motions due to floating particles and specularities are identified and filtered with RANSAC. Note that FlowNet-2 cannot be fine-tuned in a supervised way on fetoscopic data due to the lack of groundtruth. While self-supervised fine-tuning  [18]  could be an option, our experimental results show that pretrained models are sufficiently reliable for fetoscopic mosaicking.
Point correspondences from optical flow Optical flow aims at generating a 2D displacement vector field between a source and target images (i. e. , a flow field). To perform optical flow between consecutive frames, we use the FlowNet-2  [15]  a deep learning architecture, with its parameters pretrained on the flying chairs synthetic dataset  [1]. While the training data is very far from representative of fetoscopic video appearance and characteristic motions, its very large size and accurate groundtruth enable training a network that focuses on capturing the fundamental geomet-ric relationships between local appearance changes rather than learning application-specific priors. This has shown a great generalizability power in computer vision problems, and we show (in Sec. 3. 4) that it is also a reliable backbone for fetoscopic video.
Dataset description For qualitative and quantitative analysis, we use the same fetoscopic dataset presented in  [3]  but with additional frames as shown in Table  1. The extended dataset will be released with this publication. The dataset consists of 6 in vivo fetoscopic video sequences taken from different TTTS laser therapy surgeries. Each video sequence has varying conditions of occlusion, texture, lighting, and floating particles. 2 Enblend: http://enblend. sourceforge. net. The first column in Table  1  shows the number of frames in the original dataset from  [3] , while the last column shows the number of frames in the extended dataset.
Comparison methods We compare our method against the complete pipeline of the current state-of-the-art vessel-based approach presented in  [4] , and classic Lucas-Kanade (LK) pyramidal registration. We perform an ablation study of different components of our own pipeline. We replace FlowNet-2 with Recurrent All-Pairs Field Transforms (RAFT)  [29]  as the Optical Flow backbone. Note that RAFT has been recently reported as a top performer on widely popular optical flow benchmarks such as Sintel  [8]  and KITTI  [22]. We replace LM+RANSAC with robust nonlinear regression method for utilizing a smooth approximation function of the absolute loss(soft l1 loss)  [31]  as our robust registration (RR) method. Additionally, we investigate the outliers detected by RANSAC to show their correlation with the frequent floating All experiments were done in python using NVIDIA k80 GPUs from Google Colaboratory.
FlowNet-2  [15]  was used with pretrained weights provided by the authors. 3 The datasets used to obtain the pretrained weights were fly-ingChairs  [11] , flyingThings  [21] , and ChairsSDHoM  [15]. RAFT  [29]  was used with pretrained weights provided by the authors. 4 The datasets used obtain the pretrained weights were flyingChairs  [11] , flyingThings  [21] , Sintel  [8] , KITTI  [22]  and HD1K  [16]  datasets. RANSAC threshold used for all sequences was ||P D -P R || 2 ≤ 6 ( 3 ) where P D are the destination points and P R are the reprojected points. This was determined empirically by viewing removed pixels.
It is noteworthy that our proposed method, its RAFT variant, and the RR variant are the only ones where these clear failures do not occur in any of the sequences. We also note that to perform evaluation using RAFT and RR, we use the same pipeline described in Section 2, but we replace FlowNet-2 with RAFT and LK+RANSAC with a RR method. Such failures, even if they happen on a single frame window, can invalidate the addition of all subsequent frames to a mosaic The first column shows final mosaics on various sequences from the extended dataset using our method while the second column show final mosaics on the same sequences using the state-of-the-art approach from  [4]. The third column shows SSIM time plots which plot the SSIM of the registration of consecutive images. The red colored plot is our method while the blue is the baseline (Table  2 ).
Funding This work was supported by the  Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)  [ 203145/Z/16/Z ]; the  Engineering and Physical Sciences Research Council (EPSRC)  [ EP/P027938/1 ,  EP/R004080/1 ,  EP/P012841/1 ,  NS/A000027/1 ]; the  H2020 FET (GA863146 );  Wellcome  [ WT101957 ]; the  Royal Academy of Engineering Chair in Emerging Technologies Scheme  and the  Nigerian Petroleum Trust Development Fund  ( PTDF) Overseas Scholarship program.
B. Principal Node (PRIN) These nodes are used to detect, localize and classify targets. Since the sensors considered here are more heavy (because of power supply to satisfy computing demand), these nodes are mostly in a sleep mode and are woken up by WUNs when targets are detected in the field of view of the sensors coupled to these nodes. 1) Optronic Node: This node uses an RGB camera to detect targets in the image (see Figure  2b ). The camera is calibrated on the node and the position of the node can be estimated (based on the board localization sensors), or registered by an operator. This node is based on an Intel NUC6i5SYH (I5-6260U, 8GB RAM) that runs a Linux based OS. The deep learning object detection, named YOLO  4  , is implemented using OpenCL to run on the embedded GPU, reaching 5Hz as frequency.
The outputs of the algorithm are bounding boxes labelled with classes. The bounding boxes are converted to corresponding angles and sent to the fusion node. Upon request a thumbnail image of one object can be sent to the operator. 2) Radar Node: This node uses a "PSR-500 perimeter surveillance radar" from EPSI-ITNI (see Figure  3 ). This radar has 8W of consumption, a size fixed to 370(l) × 150(w) × 53(mm), a weight equals to 1. 8kg and emits in C band frequency with FMCW waveforms. This node is based on an Intel NUC6i5SYH (I5-6260U, 8GB RAM) that runs a Linux based OS. A processing tool-chain is boarded on NUC to provide measurements of moving targets. Those measurements are transmitted to a fusion node at 3Hz with limited classification information.
Background: Even if English is the leading language for international communication, it is essential to keep in mind that research runs at the local level by local teams generally communicating in their local/national language, especially in Europe among European projects. Objective: Therefore, the European Federation for Medical Informatics -Working Group on Health Informatics for Inter-regional Cooperation" has one objective: To develop a multilingual ontology focusing on Health Informatics and Digital Health as a collaboration tool that improves international and, in particular, European collaborations. Results: We have developed the Medical Informatics and Digital Health Multilingual Ontology (MIMO). Hosted on the Health Terminology/Ontology Portal (HeTOP), MIMO contains around 1,000 concepts, 460 MeSH Descriptors, 220 MeSH Concepts, and more than 300 newly created concepts.
" In other words, there is an "IS_A" relation between "medical informatics" and "information science," where "medical informatics" is the narrower concept. No such tree exists in SNOMED CT  [22]  or in  NCIT [23] , where only a tree of "software" exists in these two reference terminologies, and both are currently integrated into MIMO. The Thus, in the example of the MeSH Descriptor "Diagnostic and statistical manual of mental disorders," MIMO has applied the relations between this MeSH Descriptor and the four MeSH Concepts and were integrated into MIMO, which is not the case in the MeSH thesaurus. The previous concepts included in the dictionary were disseminated in the various hierarchies of MIMO. Specific hierarchies were created with medical concepts, which do not exist in other systems, methods, and terminologies of reference: in particular, "Supervisory Control And Data Acquisition (SCADA)"  [27, 28]  or "diseases linked to digital health"  [29, 30].
The MIMO team's interest focused on the following terminology server: a cross-lingual health terminology and ontology server (HeTOP; URL: https://www. hetop. eu/hetop/rep/en/terminologies/ [31] ), which contains more than 2 million concepts, 100 terminologies, and ontologies, 55 languages, including non-European languages (e. g. , Mandarin, Arabic, Korean or Japanese)  [32]. Moreover, the Generic Model of HeTOP is compatible with the ISO 25,964 standard and allows interoperability with other vocabularies platforms  [33] [34] [35]. The proofof-concept of the MIMO was integrated, in mid-2019, into the HeTOP cross-lingual terminology server and is available to the consortium using an identification  [36]. Furthermore, we have evaluated the interest of MIMO by the "Health Informatics and Digital Health" community both at the individual and collective levels.
The advantages of using HeTOP as the hosting platform of MIMO are: • The navigability capabilities between the different available thesauruses and ontologies allowing discovering if a search term exists in MIMO and provided resources; • The interface is translated into 12 languages (partially or fully processed both by domain experts and online AI-based systems) to facilitate the platform used by a large community not always comfortable with English, similarly to other well-known environments; • The possibility to get dynamically generated links to relevant search query results on PubMed, or at this time, CISMeF, the Catalog and Index of French Language Health Resources on the Internet  [38] ; • The frequent updates of the terminologies and ontologies hosted allowing to be enriched thanks to alignment processes MIMO, when relevant dynamically; • HeTOP comprises capabilities of controlled vocabularies alignment and mapping  [39, 40].