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1.
Advanced Quantitative Research Methods in Nursing
Lucija Gosak, Leona Cilar Budler, Roger Watson, Gregor Štiglic, 2024

Abstract: The publication "Analysis of quantitative research data in nursing research: A guide to SPSS" provides nursing students and nurses with the knowledge and skills to interpret the different statistical methods in their field, which can improve users' skills in collecting, analysing and interpreting results from clinical practice, thus contributing to improving the quality of health care. It provides detailed instructions on how to use IBM SPSS and perform statistical analyses that nurses need to be familiar with as they use and generate data in their daily work with patients. The main aim of patient care is to provide high quality, evidence-based care, so nurses have a duty to keep up to date with the latest research and evidence and apply it to their work. The knowledge gained in this book can also help nurses to better understand and interpret previously published results, and thus critically assess the validity and reliability of the results they will use in clinical practice.
Keywords: quantitative analysis, statistics, IBM SPSS, reliability, validity, data analysis
Published in DKUM: 18.07.2024; Views: 42; Downloads: 2
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2.
Disaggregated data on age and sex for the first 250 days of the COVID-19 pandemic in Bucharest, Romania
Marian-Gabriel Hâncean, Maria Cristina Ghiţǎ, Matjaž Perc, Jürgen Lerner, Iulian Oană, Bianca-Elena Mihǎilǎ, Adelina Alexandra Stoica, David-Andrei Bunaciu, 2022, original scientific article

Abstract: Experts worldwide have constantly been calling for high-quality open-access epidemiological data, given the fast-evolving nature of the COVID-19 pandemic. Disaggregated high-level granularity records are still scant despite being essential to corroborate the effectiveness of virus containment measures and even vaccination strategies. We provide a complete dataset containing disaggregated epidemiological information about all the COVID-19 patients officially reported during the first 250 days of the COVID-19 pandemic in Bucharest (Romania). We give the sex, age, and the COVID-19 infection confirmation date for 46.440 individual cases, between March 7th and November 11th, 2020. Additionally, we provide context-wise information such as the stringency levels of the measures taken by the Romanian authorities. We procured the data from the local public health authorities and systemized it to respond to the urgent international need of comparing observational data collected from various populations. Our dataset may help understand COVID-19 transmission in highly dense urban communities, perform virus spreading simulations, ascertain the effects of non-pharmaceutical interventions, and craft better vaccination strategies.
Keywords: disaggregated data, age, sex, COVID-19, pandemic, Romania
Published in DKUM: 15.07.2024; Views: 43; Downloads: 2
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3.
Big data in sports : a bibliometric and topic study
Ana Šuštaršič, Mateja Videmšek, Damir Karpljuk, Ivan Miloloža, Maja Meško, 2022, review article

Abstract: Background: The development of the sports industry was impacted by the era of Big Data due to the rapid growth of information technology. Unfortunately, that has become an increasingly challenging Issue. Objectives: The purpose of the research was to analyze the scientific production of Big Data in sports and sports-related activities in two databases, Web of Science and Scopus. Methods/Approach: Bibliometric analysis and topic mining were done on 51 articles selected after four exclusion criteria (written in English, journal articles, the final stage of publication, and a detailed review of all full texts). The software tool used was Statistica Data Miner. Results: We found that the first articles appeared in Scopus in 2013 and WoS in 2014. USA and China are countries which produced the most articles. The most common research areas in WoS and Scopus are Public environmental and occupational health, Medicine, Environmental science ecology, and Engineering. Conclusions: We conducted that further research and literature review will be required as this is a broad and new topic.
Keywords: big data, sport, bibliometric study, topic study, health care management, services, decision making
Published in DKUM: 05.07.2024; Views: 76; Downloads: 3
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4.
Performance-oriented UWB RTLS decision-making approach
Simona Šinko, Enej Marinič, Blaž Poljanec, Matevž Obrecht, Brigita Gajšek, 2022, original scientific article

Abstract: When introducing new technologies, companies are repeatedly faced with choosing between solutions from different providers. Regardless of all the good technical characteristics of the technology, if it is chosen inappropriately, it can prove to be a cost driver instead of something that brings added value to the system. Aware of this, we considered selecting a real-time location system (RTLS) based on Ultra-wideband technology in the indoor work environment. In practice and theory, it has been proven that the introduction of the RTLS can have highly positive effects on performance and business sustainability indicators. When reviewing the literature, it was noticed that authors solely focus on the technical properties of the systems and prices when giving guidelines on selecting the optimal RTLS. This article aims to provide advanced guidelines for UWB RTLS selection, proposing a phased selection process which is the main novelty proposed and investigated in this research. The guidelines are based on fragmented recommendations in the scientific literature that have been identified, gathered, considered, and reasonably allocated to the advanced performance-oriented phased selection process. In practice, this approach enables decision-makers to choose the most efficient and most appropriate UWB RTLS for specific logistics systems.
Keywords: decision-making, location data, RTLS, UWB, technology selection
Published in DKUM: 04.07.2024; Views: 85; Downloads: 10
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5.
An efficient iterative approach to explainable feature learning
Dino Vlahek, Domen Mongus, 2023, original scientific article

Keywords: data classification, explainable artificial intelligence, feature learning, knowledge discovery
Published in DKUM: 13.06.2024; Views: 90; Downloads: 9
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6.
Categorisation of open government data literature
Aljaž Ferencek, Mirjana Kljajić Borštnar, Ajda Pretnar Žagar, 2022, review article

Abstract: Background: Due to the emerging global interest in Open Government Data, research papers on various topics in this area have increased. Objectives: This paper aims to categorise Open government data research. Methods/Approach: A literature review was conducted to provide a complete overview and classification of open government data research. Hierarchical clustering, a cluster analysis method, was used, and a hierarchy of clusters on selected data sets emerged. Results: The results of this study suggest that there are two distinct clusters of research, which either focus on government perspectives and policies on OGD, initiatives, and portals or focus on regional studies, adoption of OGD, platforms, and barriers to implementation. Further findings suggest that research gaps could be segmented into many thematic areas, focusing on success factors, best practices, the impact of open government data, barriers/challenges in implementing open government data, etc. Conclusions: The extension of the paper, which was first presented at the Entrenova conference, provides a comprehensive overview of research to date on the implementation of OGD and points out that this topic has already received research attention, which focuses on specific segments of the phenomenon and signifies in which direction new research should be made.
Keywords: open government data, open government data research, hierarchical clustering, OGD classification, OGD literature overview
Published in DKUM: 12.06.2024; Views: 76; Downloads: 2
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7.
Cyber (in)security of personal data and information in times of digitization
Miha Dvojmoč, Mojca Tancer Verboten, 2022, original scientific article

Abstract: In an employment relationship, work and pay are no longer the only important aspects, as importance is increasingly shifting toward obligations concerning the protection of personal data and privacy arising from the prohibition of causing harm to the employer and the duty of loyalty to the employer. The article deals with the constitutionally protected right to privacy and the protection of personal data from the point of view of ensuring cyber security at the employer. The employer is obligated to protect the right to privacy by legal provisions, whereas from the point of view of ensuring the protection of privacy and information, the employer must protect, first and foremost, the personal data of employees. The main purpose of the legal protection of personal data is the lawful and fair processing of the personal data of individuals. Employers are thus facing an increasing number of risks related to the safety of employees and the security of business processes, and it is therefore important to establish comprehensive corporate security to ensure adequate security across all levels.
Keywords: cyber security, protection of personal data, protection of privacy, cyber threats, protection of business secrets
Published in DKUM: 11.06.2024; Views: 91; Downloads: 4
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8.
Neo4j graph dataset of cycling paths in Slovenia
Alen Rajšp, Iztok Fister, 2023, other scientific articles

Keywords: data mining, geographical data, graph database, OpenStreetMap, route generation, sports training
Published in DKUM: 10.06.2024; Views: 85; Downloads: 7
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9.
A review of federated learning in agriculture
Krista Rizman Žalik, Mitja Žalik, 2023, review article

Keywords: federated learning, agriculture, architecture, data partitioning, federation scal, aggregation algorithms, communication bottleneck
Published in DKUM: 05.06.2024; Views: 91; Downloads: 5
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10.
Simulated and experimental HDEMG signals of biceps brachii muscle for analysis of motor unit merging
Aleš Holobar, Jakob Škarabot, Dario Farina, 2024, complete scientific database of research data

Abstract: This dataset contains a collection of simulated and experimental surface HDEMG recordings of the biceps brachii muscle during the isometric elbow flexion. Simulated data contains 50 recordings: 5 subjects and 5 excitation levels, each with and without added noise. Experimental data contains 16 recordings: 2 subjects with 4 excitation levels and 2 repetitions of each level. Synthetic data was simulated using the cylindrical volume conductor model [1] and the motor unit recruitment and firing modulation model proposed in [2]. Each recording is 20 seconds in length with 90 HDEMG channels sampled at 2048 Hz and is stored as a 2D matrix of raw EMG values in Matlab’s MAT format. Experimental surface EMG data was recorded on two volunteers during isometric contractions at constant force level. Each recording is 25 seconds in length with 64 HDEMG channels sampled at 2048 Hz and is also stored as a 2D matrix of raw EMG values in Matlab’s MAT format. The dataset is approximately 1.5 GB in size.
Keywords: surface high density electromyogram (HDEMG), motor unit, spike train, motor unit merging, simulated data, experimental data, biceps brachii, dataset
Published in DKUM: 30.05.2024; Views: 120; Downloads: 4
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