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1.
Kako odkriti pogodbeno goljufanje?
Phillip Dawson, Wendy Sutherland Smith, Kevin Dullaghan, 2025, other educational material

Abstract: Pogodbeno goljufanje (angl. contract cheating) je oblika neprimernega akademskega ravnanja, ko nekdo za izdelavo dela uporabi tretjo osebo, pa če za to plača ali dolguje uslugo ali ne. V letih 2016 in 2017 je ekipa CRADLE sodelovala z ocenjevalci iz Univerze Deakin iz Avstralije pri eksperimentalni raziskavi za izboljšanje odkrivanja pogodbenega goljufanja. Skupaj so opredelili priporočila o tem, kako izboljšati odkrivanje goljufanja pri sklepanju pogodb. Ta priporočila so v tem viru prevedena v slovenski jezik.
Keywords: pogodbeno goljufanje, odkrivanje pogodbenega goljufanja
Published in DKUM: 12.05.2025; Views: 0; Downloads: 5
.pdf Full text (350,61 KB)

2.
Prvi odziv na ocenjevanje in ChatGPT pri vaših učnih enotah
Lorelei Anselmo, Tyson Kendon, Beatriz Moya, 2025, other educational material

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
.pdf Full text (450,19 KB)

3.
Kako se izogniti plagiatorstvu in katere so najpogostejše napake v znanstvenih delih?
Milan Ojsteršek, 2025, other educational material

Abstract: Predstavitev je namenjena vsem, ki želijo spoznati kakšni so vzroki za plagiatorstvo in katere so najpogostejše napake, ki jih lahko ovrednotimo kot plagiatorstvo, v znanstvenih delih. Najprej spoznamo pojem plagiatorstva in katere vrste plagiatov poznamo. Nato opišemo tri načine, kako se izognemo plagiatorstvu. Kako ugotovimo, da gre za plagiat in kako delujejo programi za ugotavljanje podobnosti v besedilih predstavimo zatem. Na koncu predstavitve podamo nekaj primerov besedil s 40% podobnostjo, v katerih morajo bralci ugotoviti ali so besedila plagiat ali ne. Predstavitev zaključimo z navodili, kako se preventivno izogniti plagiatorstvu oziroma kako ga preprečiti.
Keywords: akademska nepoštenost, detekcija plagiatorstva, preventiva ska nepoštenost, plagiatorstvo, preprečevanje plagiatorstva
Published in DKUM: 06.05.2025; Views: 0; Downloads: 1
.pptx Full text (7,39 MB)
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4.
Data management and academic integrity
Milan Ojsteršek, 2024, other educational material

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
.pptx Full text (16,66 MB)

5.
Webinar: Data Management and Ethics
Milan Ojsteršek, Matjaž Divjak, 2024, scientific film, scientific sound or video publication

Abstract: Video recording and presentation slides of a lecture by Milan Ojsteršek and Matjaž Divjak for the HybridNeuro webinar "Data Management and Ethics", which was held online on April 8th 2024 at Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia. More info including additional materials (slides, example dataset): https://www.hybridneuro.feri.um.si/webinars/data_management_and_ethics.html The HybridNeuro project combines the expertise of leading European partners in the field of Neural Interfaces to set up new pathways of analyzing human motor system and human movements and transfer the academic research into clinical and industrial practice. Link: https://www.hybridneuro.feri.um.si/ This project has received funding from the Horizon Europe Research and Innovation Programme under GA No. 101079392, as well as UK Research and Innovation organisation (GA No. 10052152). This video is available under the Creative Commons Attribution 4.0 International licence (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Keywords: HybridNeuro project, webinar, presentation, data management, data annotation, open science, open access, data repository, FAIR data, metadata
Published in DKUM: 09.05.2024; Views: 201; Downloads: 7
.pdf Full text (5,54 MB)
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6.
EOSC interoperability framework : Report from the EOSC Executive Board Working Groups FAIR and Architecture
Oscar Corcho, Magnus Eriksson, Krzysztof Kurowski, Milan Ojsteršek, Christine Choirat, Mark van de Sanden, Frederik Coppens, 2021, scientific monograph

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
.pdf Full text (1,06 MB)
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This document is also a collection of 2 documents!

7.
EOSC Interoperability Framework Reference Architecture (Version 2.0)
Magnus Eriksson, Mark van de Sanden, Krzysztof Kurowski, Frederik Coppens, Oscar Corcho, Milan Ojsteršek, Christine Choirat

Abstract: The EOSC Interoperability Framework Reference Architecture contains framework definitions and uses abstract Building Blocks as a tool to group functionality that will be needed to meet the requirements for the EOSC Interoperability Framework (EOSC-IF). The base of the EOSC-IF Reference Architecture has been derived from the European Interoperability Reference Architecture (EIRA) developed by ISA2. This is made available as an Archimate file.
Published in DKUM: 21.09.2021; Views: 1013; Downloads: 24
File (1,20 MB)
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8.
Crosswalk of most used metadata schemes and guidelines for metadata interoperability (Version 1.0)
Milan Ojsteršek, 2021, complete scientific database of research data

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
.xlsx Research data (169,58 KB)
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9.
Izdelava pogovornega robota z rekurentno nevronsko mrežo LSTM : diplomsko delo
Tomaž Piko, 2020, undergraduate thesis

Abstract: V diplomskem delu so v prvem delu najprej predstavljeni pogovorni roboti in njihovi tipi, nato rekurentne nevronske mreže ter delovanje različnih celic, ki jih pri njih najpogosteje srečujemo. V drugem delu pa je prikazan primer implementacije in učenja rekurentne nevronske mreže LSTM (Long Short-Term Memory) ter izdelava mobilne aplikacije, v kateri lahko pisno komuniciramo z izdelano mrežo oziroma našim pogovornim robotom v slovenskem ali angleškem jeziku.
Keywords: pogovorni roboti, rekurentne nevronske mreže, celica LSTM, obdelava naravnih jezikov
Published in DKUM: 03.11.2020; Views: 1115; Downloads: 80
.pdf Full text (1,27 MB)

10.
Primerjava algoritmov za določanje sopojavnosti besed v besedilih : diplomsko delo
Klemen Pal, 2020, undergraduate thesis

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
.pdf Full text (686,66 KB)

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