dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We are grateful to  Claude Parsot  for helpful discussions and comments, and to  Nathalie Sauvonnet  for her initial input to the project and support. The plasmids pMK101 [IpaB cloned in pET21a(+)] and pMK001 [IpgC cloned in pET28a(+)] were kindly provided by  Michael Kolbe. The SAXS measurements were performed on the BM29 BioSAXS beamline at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. We are grateful to the  Local Contacts at ESRF  for providing assistance in using the BM29 BioSAXS beamline. The visit to the BioSAXS beamline was financially supported by  ESRF. Molecular graphics were produced using the  UCSF Chimera package from the Resource for Biocomputing, Visualization , and  Informatics  at the  University of California, San Francisco  (supported by  NIH   P41 RR-01081 ).
FUNDING The project was funded by the  European Research Council (ERC)  Grants  232798  and  339579  (PS). MF was supported by the  French Medical Research Foundation  ( SPF20121226366 ). AG was supported by  FP7-PEOPLE PIAPP-GA-2008-217768. SC was funded by  E. E. & GSRT action "Support of postdoctoral researchers"  ( LSI-1808 ). SC and EM were supported by a grant from the  Hellenic Foundation for Research and Innovation (HFRI). We acknowledge UTechS UBI support from the  French Government (Agence Nationale de la Recherche, ANR) :  Programme Investissements d'Avenir France BioImaging (FBI ,  N°ANR-10-INSB-04-01 ) and the  Investissement d'Avenir programme, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABEX-62-IBEID ).
(B) Constraint-based Multiple Alignment, COBALT (Papadopoulos and Agarwala, 2007). Part of the alignment, focusing on the C-terminal part of major translocators from T3S systems of different origin, is presented. Multiple sequence alignment columns with no gaps are colored. Higher conserved columns in red, less conserved ones in blue and non-conserved in grey. Accession numbers for the protein sequences used are provided in Materials and Methods section Modeling of the IpgC/ IpaB Complex.
g. , other people cannot be trusted, there is something wrong with me as a person) to the self and world more broadly is another strong predictor of prognosis, any effect of intervention on generalized posttraumatic appraisals could represent an alternate mechanism through which autobiographical memory-based training programs help to improve PTSD. We therefore sought to evaluate the potential of a memory flexibility training (MemFlex) program in ameliorating autobiographical memory deficits and other cognitive predictors of PTSD. The MemFlex intervention is a workbook-based program that has previously proved efficacious in improving both autobiographical memory deficits and symptoms of depression  (Hitchcock et al. , 2016 (Hitchcock et al. , , 2018 (Hitchcock et al. , , 2021)). Adaptation of MemFlex techniques has also yielded promising findings in individuals experiencing psychosis  (Edwards et al.
Autobiographical memory deficits have been observed across a variety of languages and cultures (e. g. ,  Jobson et al. , 2016 Jobson et al. , , 2018)) , thus the intervention may be suitable for translation into different languages. Because the initial face-to-face session has now been effectively delivered by undergraduate and postgraduate students without any counseling or therapeutic training, the intervention could lend itself well to delivery by nonexperts (e. g. , teachers, other health practitioners, minimally trained volunteers). In the future, it may be worth exploring whether this faceto-face session could be replaced by an additional workbook session or potentially a video to allow flexibility in delivery. Because these applications may help to improve access to cognitive-based intervention for PTSD that is low cost, low intensity, focused on the individual's positive experiences, and able to be facilitated by nonexperts, a scaled-up trial of MemFlex is now needed to evaluate treatment efficacy and the mechanisms driving any treatment effects.
Open Practices All data have been made publicly available via OSF and can be accessed at https://osf. io/u2hqf. The design and analysis plans for the experiments were preregistered at https://clinicaltrials. gov/ct2/show/NCT03634709. This article has received badges for Open Data and Preregistration. More information about the Open Practices badges can be found at https://www. psychologicalscience. org/ publications/badges.
Research infrastructures, including advanced computing and storage infrastructure, in environmental science, are essential requirements for scientists in this domain to understand and analyze the sophisticated earth system 5. Interdisciplinary research communities and research infrastructures collaborate with the neighboring disciplines, namely atmosphere, biosphere, hydrosphere, and geosphere. Internal cooperation across different realms resulted in the formation of distinct research traditions, skills, and cultures. The interconnected essence of the earth system, on the other hand, requires the scientific community to transcend well-established divisions between disciplines and domains and work toward a common understanding of the world as a whole  6. The data from the ICOS 1 Research Infrastructure, for example, aids climate science by informing scientists and the general public on natural and human-caused greenhouse gas emissions and uptake from the ocean, land ecosystems, and atmosphere.
It gives access to high-quality data processed by the Thematic Centers as raw, near real-time, and final quality-controlled data and supplemented with elaborated (model) data and analyses, almost always licensed under a CC4BY 2 license. The IAGOS 3 research infrastructure provides atmospheric composition information, including greenhouse gas observations from commercial aircraft. IAGOS data are used by researchers worldwide for process studies, trend analysis, validation of climate and air quality models, and spaceborne data retrievals validation. Aerosols and their precursors are monitored by the ACTRIS 4 research infrastructure. Aerosols have a significant impact on the Earth's radiation balance, and consequently, the climate. Their levels are inextricably linked to human activity and emissions.
Accordingly, a knowledge management system is required for research communities that (1) discover cross-domain knowledge and capture them automatically, (2) answer any domain question without any limitation to its current search space, (3) deal with noisy sets of retrieved documents, likely consisting of many irrelevant documents and semantically and syntactically ill-formed documents, (4) have an advanced search engine to interpret and reformulate queries by information retrieval algorithms, (5) return a set of recommended solutions (answers) based on the retrieved documents, and (6) visualize its outcomes to facilitate the data analysis for research communities. This paper introduces a novel Knowledge management system, called ENVRI-KMS, to meet the ENVRI research community's requirements and make the research assets Findable, Accessible, Interoperable, and Reusable (FAIR 10 ) for the community.
Additionally, it validates and collects required data from users. The service layer abstracts the functionality that the ENVRI-KMS offers; it can be roughly split into three sub-layers, namely: (1) The Application sub-layer provides customized application logic (e. g. , FAIRness Gap Analysis, Engineering support, or discovery knowledge from ENVRI community) based on the data passed from the underlying discovery sub-layer those results up to the User Interface Component. (2) The Discovery sub-layer provides the functionality for searching the ENVRI-KMS, ranking the results, and recommending relevant content. (3) The Content sub-layer provides functionality for managing the content in the ENVRI-KMS, typically in a pipeline covering: ingesting information, the transformation from information to knowledge, quality control of the knowledge generation, CRUD (Create, Read, Update, Delete) of the ENVRI-KMS content, and the provenance of these activities.
The storage layer at the bottom is responsible for data storing and access. The data storage options needed in this project include RDF Triple Store and Inverted Index. Currently, information collected in the ENVRI-KMS consists of two main parts, as illustrated in Figure  3. The structured data in the ENVRI-KMS is based on RDF and mainly includes: (1) OIL-e (ontology of the ENVRI Reference Model) based ENVRI RI description, (2) description of the service portfolio from the previous project, and the possibly new ones in ENVRI-FAIR, (3) FAIRness principles and the results of assessing the ENVRI research infrastructures, and (4) demonstrators for tackling the known gaps, e. g. , those being identified during the FAIRness assessment. The versions of the structure data currently can be managed via version control systems.
User-friendly and customizable user interface. As already analyzed, although the Knowledge management systems provide a GUI for search and query, their targeted users are knowledge base administrators considering the technology barriers. For general users without much technical knowledge of the SPARQL or triplestores, a straightforward user interface for searching and exploration is expected to increase the user experience. Scaling and increasing performance. Apache Jena Fuseki does not currently support horizontal scale-up, but there are workaround solutions like coordinating the updates from a staging server and publishing (read-only) to external clients. Based on the comparison, it is clear that no one single solution satisfies all the requirements. The optimal solution should be combining existing options, and other software such as Blazegraph could be a candidate.
Design research questions To answer the first two design research questions (RQ1 and RQ2), we have conducted an extensive literature review besides a set of expert interviews with domain experts at the RIs to build the search space (including webpages, datasets, etc. ) of the ENVRI-KMS and capture knowledge systematically. The current search space 11 of the ENVRI-KMS includes all research infrastructures which are mentioned on the ENVRI community knowledge base 12. It is essential to highlight that the search space is not limited to the initial sets and grows automatically. Accordingly, the third design research question (RQ3) can be addressed based on the natural language processing approach and Open Semantic Search that we have employed in the implementation of the ENVRI-KMS 30. To answer the fourth design research question (RQ4), we have evaluated a set of technologies that can be employed to store and retrieve data.
envri. eu/ components, other infrastructure selections, and other aspects needed to satisfy all requirements 40. According to Avgeriou et al. 41  , failing to make architectural design decisions during software development has well-known implications, such as costly system evolution, weak stakeholder communication, limited reusability of architectural assets, and poor traceability between specifications and implementation. In order to make the design decisions to design the architecture of the ENVRI-KMS, we analyzed several alternative tools that could be used to build the fundamental components of the knowledge base. Selecting the right database system(s) (DBMS) was one of those design decisions. The DBMS selection problem is a subclass of the Commercial off-the-shelf (COTS) selection problem, and both problems are a subclass of Multi-Criteria Decision-Making (MCDM) problems 42.
Accordingly, we used a decision support system that has been introduced by Farshidi et al. 43  to evaluate potential alternative solutions that we can employ to store and retrieve data. After performing an extensive evaluation, we decided to use Apache Solr to indexing the search entities and MySQL to manage user profiles and user search histories. Judging the suitability of a set of technologies, such as programming languages, for developing a knowledge base system is a non-trivial task. For instance, a purely functional language like Haskell is the best fit for writing parallel programs that can, in principle, efficiently exploit huge parallel machines working on large data sets 44. However, while developing a dynamic website, a software engineer might consider ASP. net as the best alternative, and others might prefer using PHP or a similar scripting language.
It is interesting to highlight that successful projects have been built with both: StackOverflow is built in ASP. net, whereas Wikipedia is built in PHP. Furthermore, a software engineer might prefer particular criteria, such as scalability in enterprise applications, whereas other criteria, such as technology maturity level, might have lower priorities. We realized that we needed to select the right programming language ecosystems for developing the ENVRI-KMS. We used the decision model in the knowledge base of the decision support system 45 to evaluate potential programming languages that we can use to develop the ENVRI-KMS. Note, as mentioned earlier, we use an open-source tool, called open semantic search, in which its backend was implemented in PHP and Python, as the initial phase of the development process of the ENVRI-KMS.
So that the first two solutions for us were these programming languages. However, the decision support system suggested C# , Java, and Ruby as three more alternative solutions. Finally, we decided to continue using Python, as we had more experience with it and found so many open-source projects on Github, which were implemented in Python, that could boost the development process. Some issues were discovered regarding the cross-referencing of statements between knowledge bases (named graphs). A workaround published in a newsgroup provided a potential fix for static data but would have to be extended for a continuously growing data collection. A possible solution would be to store information that is expected to change/grow, e. g. , the entity descriptions and the user terminology collected from the RDF forms, in a typical named graph and to configure Ontowiki filters for its efficient navigation while storing more static content, such as external ontologies, in separate graphs.
Santoro et al. 55  investigated the relationship among knowledge management systems, open innovation, knowledge management capacity, and innovation capacity. Farshidi et al. introduced a framework and knowledge management system to build decision models for database management systems 56 , cloud service providers  57  , software architecture patterns 17,58 , model-driven development platforms  59  , programming languages 45 and blockchain platforms  60. Based on the literature study phase of the research, we realized that publications could be categorized into knowledge management approaches and knowledge management systems. There is a vast amount of literature about knowledge management approaches, such as 47-49,51,55,  [61] [62] [63] [64] [65] [66]  , that are research papers that mainly introduce methodologies that can be employed to discover, gather, manage, and apply a particular type of tacit or implicit knowledge.
Alternatively, a significant organization. (3) Knowledge Discovery that refers to the process of finding explicit knowledge in data and emphasizes the "high-level" application of particular data mining methods. The main goal is to extract such knowledge from data in the context of large databases. (4) Knowledge Acquisition which is the process used to define the rules and ontologies required for a knowledge-based system and is the process of extracting, structuring, and organizing knowledge from one source, usually human experts. (  5 ) Knowledge Representation that translates information from the real world into a machine-understandable form and then utilizes acquired knowledge to solve complex decision-making problems. (6) Decision-Making Process which is a reasoning process based on assumptions of values, preferences, and beliefs of decision-makers.
Data availability Underlying data Mendeley Data: ENVRI-KMS. https://doi. org/10. 17632/ ntxypfsvds. 1 21 This project contains the following underlying data: • Knowledgebase-discussion. xlsx (Raw survey outcome data) Extended data • Analysis of the Survey: https://doi. org/10. 17632/ ntxypfsvds. 1 21 • Technology review, system design, documentation of the implementation, a demo of the ENVRI-KMS: https:// doi. org/10. 17632/n3khm4pnsd. 1 30 This project contains the following extended data: • ENVRI-KMS (1). pdf (Summary of data analysis) • Knowledgebase-discussion. pdf (Visualized survey outcomes) Data are available under the terms of the Creative Commons Attribution 4. 0 International license (CC-BY 4. 0).
(a)) directly and enter her search query in the search box and see the results immediately (See Figure 5 (b)). The results and their relevance to RIs and be visualized based on the graph visualization of the ENVRI-KMS (See 5 (c)). Note, the searcher can limit the search space of the ENVRI-KMS to a particular category, such as Webpages and RIs, as well. For instance, Figure5 (d)shows the dataset search of the ENVRI-KMS. The ENVRI-KMS can automatically capture, extract, and index knowledge regarding research assets based on the URL of the RIs (See Figure5 (e)). Additionally, knowledge curators can ingest research assets manually to the knowledge base of the ENVRI-KMS (See Figure5 (f)). Note, the ENVRI-KMS checks the indexed documents periodically to keep its knowledge base always up-to-date.
○○○ 2 : 2 It would be interesting to read more about what is different about a knowledge management system for environmental science. Is the rationale for developing the new software tool clearly explained? PartlyIs the description of the software tool technically sound? PartlyAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? NoIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? PartlyCompeting Interests: I am co-chair of the Research Data Alliance Metadata Interest Group with one of the authors, Keith Jeffery. The work of that group is not related to this paper. This conflict of interest did not affect the objectivity of my review.
31. 43% R21 One of the search criteria in the ENVRI-KMS should be the geolocation of datasets. 48. 57% R22 The metadata of the search space entities, such as datasets and APIs, should be available through the ENVRI-KMS. 20. 00% R23 Continuous integration and continuous delivery (CI/CD) should be supported by the ENVRI-KMS. 28. 57% R24 Different user groups, such as researchers, knowledge curators, developers, and high-level managers, should receive suggestions from the ENVRI-KMS. 17. 14% R25 The ENVRI-KMS should categorize and classifies its knowledge base contents. 37. 14%.
Research Infrastructures, such as ACTRIS and ANAEE, are the primary sources of knowledge that contain knowledge assets, including webpages, datasets, APIs, service catalogs, publications, design decisions, best practices, devices, and data provenance. The Sitemap Extractor explores and extracts the site structure (the list of URLs) of the RIs. Then, the Web Crawler browses the extracted URLs and by employing NLP & ETL (Natural Language Processing and Extract/Transform/Load) techniques, such as Named-Entity Recognition (NER) and Relation Extraction (RE), tries to index documents and classify the extracted knowledge. Typically, a web crawler is a bot or software agent. It starts with a list of URLs to visit, called the seeds. As the crawler visits such URLs, it identifies all the hyperlinks on webpages by the aim of a sitemap extractor, and adds them to the list of its URLs to visit, called the crawl frontier.
decision support system for pattern-driven software architecture. In Proceedings of the 14th European Conference on Software Architecture, ECSA 2020. ACM, 2020; 1: 1-12. Reference Source 18. Castellano G, Vessio G: Towards a tool for visual link retrieval and knowledge discovery in painting datasets. In Italian research conference on digital libraries. Springer, 2020; 105-110. Publisher Full Text 19. Farshidi S: SiamakFarshidi/solr-php-ui: ENVRI-KMS (Version 1. 0). Zenodo. 2021. http://www. doi. org/10. 5281/zenodo. 4882766 20. gra. fo. : Envri-fair research infrastructures. 2021. Reference Source 21. Farshidi S, Zhao Z: Envri-kms. 2021. Reference Source 22. Mentimeter: An application for creating interactive presentations & meetings. 2021. Reference Source 23. Martin P, Remy L, Theodoridou M, et al.
Acknowledgements. The chemistry-transport simulations were performed using the high-performance computing resources of TGCC (Très Grand Centre de calcul du CEA; allocation no. GEN10274) provided by  GENCI  (Grand équipement national de calcul intensif). The authors acknowledge the editor and the two anonymous reviewers for their valuable comments and feedback. Financial support. This research has been supported by the  Agence de l'Innovation de Défense  (grant no. NETDESA 2018600074 ). Review statement. This paper was edited by  Aurélien Dommergue  and reviewed by three anonymous referees.
Phirbo algorithm overview Our algorithm is based on the assumption that the degree of similarity between phage and host sequences is proportional to the overlap between ranked similarity matches of each sequence to the same reference data set of prokaryotic sequences. Specifically, to compare a pair of phage (P) and host (H) sequences, we first perform two independent BLAST searches against the reference database of prokaryotic genomes (D)-one BLAST search for phage and the other for the host query (Fig. 1a ). The two lists of BLAST results (Fig. 1b ), P → D and H → D, contain prokaryotic genomes ordered by decreasing sequence similarity (i. e. , bit-score). To avoid a taxonomic bias due to multiple genomes of the same prokaryote species, we rank prokaryotic species according to their first appearance in the BLAST list (Fig.
1c ). In this way, both lists represent phage and host profiles consisting of the ranks of top-score prokaryotic species. The properties of these lists (Fig. 1c ) can be characterized by four features: (i) species listed at the top of each ranking are more important (similar) to the query than those listed at the bottom; (ii) the lists may not be conjoint (some species may appear in one ranking but not in the other); (iii) the ranking lists may vary in length (BLAST may return few prokaryotic matches in response to virus sequences in contrast to thousands of matches in cases of multiplespecies prokaryotic families); (iv) two or more species from the database may achieve the same BLAST score and, therefore, occupy the same position on the ranking list (Fig. 1c ). A recently introduced similarity measure used for comparing the rankings of Web search engine results  [24] , the Rank-Biased Overlap (RBO), satisfies these four conditions.
Phirbo preserves BLAST top-ranked host predictions We further evaluated the host prediction accuracy of Phirbo by selecting a top-scored prokaryotic sequence for each virus without thresholds on score values  [14, 16, 17, 19]. Briefly, host prediction accuracy is calculated as the percentage of viruses whose predicted hosts have the same taxonomic affiliation as their respective known hosts (if multiple top-scoring hosts are present, the prediction is scored as correct if the true host is among the predicted hosts). Phirbo restored all hosts predicted by BLAST in the data sets by Edwards et al. and Galiez et al. , achieving the same prediction accuracy as BLAST across all taxonomic levels (Table  1 ). Of note, BLAST found multiple different host species with equal scores for 14 phage genomes.