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Abstract: Nedavni razvoj orodij umetne inteligence (UI), kot je ChatGPT, je v visokem šolstvu sprožil različne odzive, od strahu pred vplivi na akademsko integriteto, do pomislekov glede učinkovitosti orodij in priložnosti za inovacije. Usmerjanje v ta razvoj kot del poučevanja in učenja je ključnega pomena, nekateri premisleki pa bi lahko podprli nove načine etične uporabe teh orodij. Obstaja veliko orodij umetne inteligence, ki vplivajo na oblikovanje in ocenjevanje pedagoških tečajev. Ta vir se osredotoča na ChatGPT. Poleg tega se to gradivo osredotoča na ocenjevanje, zlasti na to, kako sodelovati s svojo učno skupino in študenti, da bi razumeli, kaj je ChatGPT, kako se lahko uporablja pri delu študentov in kako se lahko uporablja kot del ocenjevanja.
Keywords: umetna inteligenca, ChatGPT
Published in DKUM: 06.05.2025; Views: 0; Downloads: 4
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Abstract: Sensitive data requires careful consideration and adherence to best practices to ensure its confidentiality, integrity, and availability. Essential steps in handling sensitive data are identification and classification of sensitive data, implementation of data access control, encryption of sensitive data, secure storage and transmission, implementation of data breach response plan, backup and monitoring usage of data, complying with regulation, and disposing of data securely. Misconduct in handling sensitive data can compromise data confidentiality, integrity, and availability. These include data breaches (unauthorised access or disclosure, theft, insider threats, falsification, fabrication, imputation, and amputation of data), failure to comply with data protection regulations, inadequate data security practices, improper retention and disposal of data, and failure to report data breaches and incidents. In this presentation Milan Ojsteršek presents how to manage sensitive data, desensitise it, and which are the most common breaches in handling sensitive data incidents. This presentation was given at the 4th ENAI Academic Integrity Summer School 2024, 16th – 21th September 2024, University of Konstanz, Germany.
Keywords: open science, metadata, research data management, sensitive data, academic integrity, data management ethics, research misconduct, licensing of open data, FAIR, Slovenian open access infrastructure
Published in DKUM: 18.04.2025; Views: 0; Downloads: 2
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Abstract: This document has been developed by the Interoperability Task Force of the EOSC Executive Board FAIR Working Group, with participation from the Architecture WG. Achieving interoperability within EOSC is essential in order for the federation of services that will compose EOSC to provide added value for service users. In the context of the FAIR principles, interoperability is discussed in relation to the fact that “research data usually need to be integrated with other data; in addition, the data need to interoperate with applications or workflows for analysis, storage, and processing”. Our view on interoperability does not only consider data but also the many other research artefacts that may be used in the context of research activity, such as software code, scientific workflows, laboratory protocols, open hardware designs, etc. It also considers the need to make services and e-infrastructures as interoperable as possible. This document identifies the general principles that should drive the creation of the EOSC Interoperability Framework (EOSC IF), and organises them into the four layers that are commonly considered in other interoperability frameworks (e.g., the European Interoperability Framework - EIF): technical, semantic, organisational and legal interoperability. For each of these layers, a catalogue of problems and needs, as well as challenges and high-level recommendations have been proposed, which should be considered in the further development and implementation of the EOSC IF components. Such requirements and recommendations have been developed after an extensive review of related literature as well as by running interviews with stakeholders from ERICs (European Research Infrastructure Consortia), ESFRI (European Strategy Forum on Research Infrastructures) projects, service providers and research communities. Some examples of such requirements are: “every semantic artefact that is being maintained in EOSC must have sufficient associated documentation, with clear examples of usage and conceptual diagrams”, or “Coarse-grained and fine-grained dataset (and other research object) search tools need to be made available”, etc. The document finally contains a proposal for the management of FAIR Digital Objects in the context of EOSC and a reference architecture for the EOSC Interoperability Framework that is inspired by and extends the European Interoperability Reference Architecture (EIRA), identifying the main building blocks required.
Keywords: technical interoperability, semantic interoperability, organizational interoperability, legal interoperability, EOSC, metadata crosswalk, reference architecture
Published in DKUM: 21.09.2021; Views: 1050; Downloads: 69
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Abstract: This resource provides crosswalks among the most commonly used metadata schemes and guidelines to describe digital objects in Open Science, including: - RDA metadata IG recommendation of the metadata element set, - EOSC Pilot - EDMI metadata set, - Dublin CORE Metadata Terms, - Datacite 4.3 metadata schema, - DCAT 2.0 metadata schema and DCAT 2.0 application profile, - EUDAT B2Find metadata recommendation, - OpenAIRE Guidelines for Data Archives, - OpenAire Guidelines for literature repositories 4.0, - OpenAIRE Guidelines for Other Research Products, - OpenAIRE Guidelines for Software Repository Managers, - OpenAIRE Guidelines for CRIS Managers, - Crossref 4.4.2 metadata XML schema, - Harvard Dataverse metadata schema, - DDI Codebook 2.5 metadata XML schema, - Europeana EDM metadata schema, - Schema.org, - Bioschemas, - The PROV Ontology.
Keywords: crosswalk, metadata, EDMI metadata set, Dublin CORE, Datacite 4.3 metadata schema, DCAT 2.0 metadata schema, UDAT B2Find metadata recommendation, OpenAIRE Guidelines for Data Archives, OpenAire Guidelines for literature repositories 4.0, OpenAIRE Guidelines for Other Research Products, OpenAIRE Guidelines for Software Repository Managers, OpenAIRE Guidelines for CRIS Managers, Crossref 4.4.2 metadata XML schema, Harvard Dataverse metadata schema, DDI Codebook 2.5 metadata XML schema, Europeana EDM metadata schema, Schema.org, Bioschemas, The PROV Ontology
Published in DKUM: 21.09.2021; Views: 2030; Downloads: 75
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Abstract: Glavna tema diplomske naloge je raziskovanje in primerjava nekaterih najbolj razširjenih algoritmov za določanje sopojavnosti besed v besedilih. Teoretično so razloženi pojavi kolokacij, njihova osnova in statistično ozadje. Nato so opisani trije najpogostejši algoritmi, ki slonijo na različnih pristopih: T-test, Pearsonov hi-kvadrat in algoritem PMI. Ti opisi so podprti s primeri izračuna vrednosti algoritmov. Praktični del vsebuje implementacijo predobdelave besedila in iskanja statističnih podatkov, sledi pa uporaba algoritmov nad temi podatki. Za konec je podana še primerjava teh algoritmov na osnovi dobljenih rezultatov.
Keywords: algoritem, sopojavnost besed, besedilo, primerjava
Published in DKUM: 02.11.2020; Views: 973; Downloads: 67
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