Towards Semantic Data Management Plans for Efficient Review Processing and Automation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00375870" target="_blank" >RIV/68407700:21240/24:00375870 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5220/0012837900003756" target="_blank" >https://doi.org/10.5220/0012837900003756</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0012837900003756" target="_blank" >10.5220/0012837900003756</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Semantic Data Management Plans for Efficient Review Processing and Automation

Popis výsledku v původním jazyce

In recent times, Data Management Planning has become increasingly crucial. Effective practices in data management ensure more precise data collection, secure storage, proper handling, and utilization beyond the primary project. However, existing DMPs often suffer from complex structures that impede accessibility for humans and machines. This project aims to address these challenges by converting DMPs into formats that are both machine-actionable and human-readable. Leveraging established DMP templates and relevant ontologies, our methodology involves analyzing diverse approaches to achieve this dual functionality. We assess machine-actionability through comparative evaluations using AI and NLP tools. Furthermore, we identify gaps in ontologies, laying the groundwork for future enhancements in this critical area of research.

Název v anglickém jazyce

Towards Semantic Data Management Plans for Efficient Review Processing and Automation

Popis výsledku anglicky

In recent times, Data Management Planning has become increasingly crucial. Effective practices in data management ensure more precise data collection, secure storage, proper handling, and utilization beyond the primary project. However, existing DMPs often suffer from complex structures that impede accessibility for humans and machines. This project aims to address these challenges by converting DMPs into formats that are both machine-actionable and human-readable. Leveraging established DMP templates and relevant ontologies, our methodology involves analyzing diverse approaches to achieve this dual functionality. We assess machine-actionability through comparative evaluations using AI and NLP tools. Furthermore, we identify gaps in ontologies, laying the groundwork for future enhancements in this critical area of research.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 13th International Conference on Data Science, Technology and Applications

ISBN

978-989-758-707-8

ISSN

2184-285X

e-ISSN

2184-285X

Počet stran výsledku

8

Strana od-do

543-550

Název nakladatele

SciTePress

Místo vydání

Madeira

Místo konání akce

Dijon

Datum konání akce

9. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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