Onderwyseresse se verwagtinge van tweede of addisionele taal graad R-leerders se kommunikasievaardighede in 'n bepaalde geografiese area van Bloemfontein (Afrikaans)

Abstract in Afrikaans Onderwyseresse is dikwels onseker oor die wyse waarop hulle tweede of addisionele taal graad R-leerders behoort te onderrig. Gevolglik stel hulle dieselfde kommunikasie-eise en op dieselfde wyse aan tweede taal leerders as aan moedertaalleerders. `n Groot aantal Suid-Afrikaanse graad R-leerders se basiese interaktiewe taalvaardighede in Engels as onderrigtaal, is dikwels nie op peil aan die einde van graad R nie, omdat hulle nie Engels magtig is by toetrede tot graad R nie. Gevolglik kan hierdie leerders se kognitiewe akademiese taalvaardighede nie toereikend ontwikkel in Engels nie en beskik hulle nie aan die einde van graad R oor doeltreffende kommunikasievaardighede vir skool-gereedheid nie. Vir die doel van hierdie studie is daar spesifiek gefokus op graad R-onderwyseresse asook op leerders se kommunikasievaardighede vir skoolgereedheid. `n Transformerend-opeenvolgende ontwerp bestaande uit `n gemengde metodiek van kwalitatiewe en kwantitatiewe benaderings in drie fases is vir die uitvoer van die studie geselekteer. Fase een het bestaan uit `n literatuurstudie, vir fase twee is graad R-onderwyseresse as deelnemers geselekteer en by fase drie is data van fases een en twee met mekaar vergelyk. Data-insameling het deur middel van vraelyste, `n onderhoudskedule vir semi-gestruktureerde onderhoude, en `n skedule vir die fokusgroepbespreking geskied. `n Omvattende ontwikkelingsgebaseerde lys voorvereistes van kommunikasievaardighede (die lys), is in die literatuur vir spraak-taalpatologie geïdentifiseer asook kommunikasievaardighede wat deur die amptelike graad R-kurrikulum geïmpliseer word. Resultate van fase twee het getoon dat graad R-onderwyseresse nie al die kommunikasievaardighede van graad R-leerders in ooreenstemming met die lys vereis het nie. Volgens fase drie se bevindinge het die onderwyseresse meer kommunikasievaardighede van leerders verwag as wat deur die graad R-kurrikulum geïmpliseer word. Die kliniese waarde van die studie is geleë in die feit dat riglyne verskaf word vir interaksie tussen graad R-onderwysers en leerders vir die fasilitering van taalontwikkeling by die leerders. Die navorsingsbevindinge beaam vorige bevindinge dat graad R-onderwysers kan baat by addisionele opleiding oor spraak-taalontwikkeling en kommunikasievaardighede vir skoolgereedheid. Hierdie addisionele opleiding kan gesamentlik volgens die model van samewerkende dienslewering deur die onderwysers en die spraak-taalterapeut beplan word. Die navorsingsresultate kan aangewend word as uitgangspunt vir toekomstige navorsing oor die problematiek wat deur hierdie navorsing geïdentifiseer is. Op grond van die gevolgtrekkings is kliniese implikasies en aanbevelings vir toekomstige navorsing geïdentifiseer. Die data wat tydens die navorsing ingesamel is, word as betekenisvol beskou vir die ontwikkeling van `n aanvullende program vir onderwysers om jong kinders se taalontwikkeling te fasiliteer.Dissertation (Master of Communication Pathology)--University of Pretoria, 2007.Speech-Language Pathology and Audiologyunrestricte

Informative Armstrong RDF datasets for n-Ary relations

The W3C standardized Semantic Web languages enable users to capture data without a schema in a manner which is intuitive to them. The challenge is that, for the data to be useful, it should be possible to query the data and to query it efficiently, which necessitates a schema. Understanding the structure of data is thus important to both users and storage implementers: The structure of the data gives insight to users in how to query the data while storage implementers can use the structure to optimize queries. In this paper we propose that data mining routines be used to infer candidate n-ary relations with related uniqueness- and null-free constraints, which can be used to construct an informative Armstrong RDF dataset. The benefit of an informative Armstrong RDF dataset is that it provides example data based on the original data which is a fraction of the size of the original data, while capturing the constraints of the original data faithfully. A case study on a DBPedia person dataset showed that the associated informative Armstrong RDF dataset contained 0.00003% of the statements of the original DBPedia dataset.https://www.iospress.nl/bookserie/frontiers-in-artificial-intelligence-and-applicationsam2019Informatic

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

Armstrong Relations for Ontology Design and Evaluation

Abstract. A challenge in ontology design is to ensure that the ontology accurately represents the application domain and application constraints. Motivating scenarios provide the motivation for the representation choices made during design, and competency questions are subsequently used to evaluate the design. In this paper we show how the notion of Armstrong relations from relational database theory can be used to generate motivating scenarios and competency questions for uniqueness constraints and not null constraints

A Simple Standard for Sharing Ontological Mappings (SSSOM)

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Are they associated in some other way? Such relationships between the mapped terms are often not documented, leading to incorrect assumptions and making them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Also, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. The Simple Standard for Sharing Ontological Mappings (SSSOM) addresses these problems by: 1. Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. 2. Defining an easy to use table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data standards. 3. Implementing open and community-driven collaborative workflows designed to evolve the standard continuously to address changing requirements and mapping practices. 4. Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases, and survey some existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable, and Reusable (FAIR). The SSSOM specification is at http://w3id.org/sssom/spec.Comment: Corresponding author: Christopher J. Mungall <[email protected]

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

“Be sustainable”: EOSC‐Life recommendations for implementation of FAIR principles in life science data handling

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large‐scale data‐driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross‐disciplinary work done within the EOSC‐Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC‐Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive‐ and industry‐related resources, by means of cross‐disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences

Preprint: "Be Sustainable", Recommendations for FAIR Resources in Life Sciences research: EOSC-Life's Lessons

"Be SURE - Be SUstainable REcommendations" The main goals and challenges for the Life Science (LS) communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable LS resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European LS Research Infrastructures (RIs), it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable FAIR data management, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences. IN PRESS EMBO Journal: https://www.embopress.org/journal/14602075This research is mainly a product of the EOSC-Life European programme funding from the European Union's Horizon Europe research and innovation programme under grant agreement Nº824087. Complementary support was provided through EU funded project AgroServ (grant agreement Nº101058020), EU funded project BY-COVID (grant agreement Nº101046203), EU funded project DANUBIUS-IP (grant agreement Nº101079778), EU funded project EMPHASIS-GO (grant agreement Nº101079772), EU funded project FAIRplus (IMI grant agreement Nº802750), EU funded project FAIRsharing (Wellcome grant agreement Nº212930/Z/18/Z), EU funded project ISIDORe (grant agreement Nº101046133), EU funded project Precision Toxicology (grant agreement Nº965406), UKRI DASH (grant agreement NºMR/V038966/1). Special thanks to T. Biro and her radical collaboration team from Research Data Alliance who gave us great inspiration on how to lead this radical collaboration work

EOSC-Life Report on the work of the Open Call Projects

This Deliverable 3.3 is a report on the Digital Life Sciences Open Call and two Internal Calls organised by EOSC-Life WP3. The organisation of these Calls followed the successful integration and support of 8 Demonstrator projects&nbsp;which provided the first concrete use cases in the initial phase of EOSC-Life. The three Calls overall supported 11 scientific user projects, selected to facilitate integration of concrete use-cases across Life Sciences domains into the European Open Science Cloud (EOSC)&nbsp;framework. Through the Calls, the practical goal was to facilitate co-creation of an open, digital collaborative space for life science research by developing FAIR&nbsp;tools, workflows, resources, infrastructures, and guidelines together with the EOSC-Life RIs experts and communities. We report in this Deliverable the following achievements: Organisation of the EOSC-Life Open and Internal Calls; Integrating and training the EOSC-Life WP3 Open Call&nbsp;and Internal Call&nbsp;project teams in EOSC-Life; Activities for connecting project teams with EOSC-Life and LS-RI communities and dissemination of projects outcomes to broader communities; Work done in the individual projects, their results, and impact of developed resources; Recommendations from the EOSC-Life WP3 project teams and the EOSC-Life community for future Open Calls. </ol