Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
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Experiences of Dyslexic Software Engineers - A Qualitative Study
(Association for Computing Machinery, Inc, 2026-07-09) Cruz, Marcos Vinicius; Verma, Pragya; Liebel, Grischa; Department of Computer Science
Dyslexia is a common learning disorder that primarily impairs an individual’s reading and writing abilities. In adults, dyslexia can affect both professional and personal lives, often leading to mental challenges and difficulties acquiring and keeping work. In Software Engineering (SE), reading and writing difficulties appear to pose substantial challenges for core tasks such as programming. However, initial studies indicate that these challenges may not significantly affect their performance compared to non-dyslexic colleagues. Conversely, strengths associated with dyslexia could be particularly valuable in areas like programming and design. However, there is currently no work that explores the experiences of dyslexic software engineers, and puts their strengths into relation with their difficulties. To address this, we present a qualitative study of the experiences of dyslexic individuals in SE. We followed the basic stage of the Socio-Technical Grounded Theory method and base our findings on data collected through 10 interviews with dyslexic software engineers, 3 blog posts and 153 posts on the social media platform Reddit. We find that dyslexic software engineers especially struggle at the programming learning stage, but can succeed and indeed excel at many SE tasks once they master this step. Common SE-specific support tools, such as code completion and linters are especially useful to these individuals and mitigate many of the experienced difficulties. Finally, dyslexic software engineers exhibit strengths in areas such as visual thinking and creativity. Our findings have implications to SE practice and motivate several areas of future research in SE, such as investigating what makes code less/more understandable to dyslexic individuals.
Domain Diversity, Motivation, Inclusion, and Feedback in Software Modelling Education
(Association for Computing Machinery, Inc, 2026-07-17) Graßl, Isabella; Lazik, Christopher; Chakraborty, Shalini; Liebel, Grischa; Goulão, Miguel; Tan, Shin Hwei; Khomh, Foutse; Department of Computer Science
Student engagement is critical for effective learning in software modelling, yet fostering motivation and inclusivity remains a challenge. While existing research has focused on modelling tools, notations, and assessment, little attention has been given to how the choice of problem domains and the diversity, relatability, and cultural perspectives they bring shape students' learning experiences. This study explores how problem domains and teaching methods influence motivation, engagement, inclusiveness, and feedback in modelling education. To investigate these dimensions, we conducted parallel surveys with 90 students and 22 educators. Our findings reveal disconnects between educator assumptions and student preferences: Students show greatest motivation for socially relevant domains and prefer choice in selection, while educators overestimate interest in study-related domains. The study identifies how minor design choices can exclude students. Students perceive feedback as meaningful when visibly acted upon. These findings suggest inclusive domain selection is central to student motivation; thus, we recommend student-centred domain selection.
Scale-free to Pareto–Tsallis transitions in the distributions of waiting times : weather, sea-level, currency trading and automotive datasets
(2026-09) Gogîţă, Paul Adrian; Dumitru, Tudor Gabriel; Constantin, Florin Ioan; Diac, Tudor Andrei; Neagoe, Alexandra Florentina; Raportaru, Mihaela Carina; Nicolin-Żaczek, Alexandru; Department of Engineering
We report a series of detailed statistical analyses on the distributions of waiting times pertaining to a diverse set of complex systems, including terrestrial and space weather, sea-level variations, currency trading (for both fiat and cryptocurrencies), and synthetic automotive data. Given a generic time series, we define a waiting time as the shortest time interval needed to find an entry of value of at least (Formula presented) (Formula presented), with (Formula presented) (Formula presented) a given threshold, after a certain entry of value (Formula presented) (Formula presented) was observed. Going through the entire time series we obtain the complete set of waiting times for a specific value of (Formula presented) (Formula presented) and can determine their distribution. This distribution can be seen as a dynamic fingerprint of the process to which the time series pertains and is particularly useful to directly compare the dynamics of otherwise very different systems. To this end, we show that the aforementioned distributions have a prominent scale-free character for small values of (Formula presented) (Formula presented) for all datasets under scrutiny, while for large values of (Formula presented) (Formula presented) the observed distributions of waiting times converge to a Pareto–Tsallis distribution. We identify the threshold values (Formula presented) (Formula presented) at which this transition occurs using the goodness-of-fit indicators, and further substantiate these results by analyzing the behavior of the generalized Kullback–Leibler divergence. Our results are robust across all of the considered datasets.
Oxygen Field Dynamics in Bioconvection and the Spatial Organization of Multispecies Aerobic Bacteria
(2026-07-01) Gallardo-Navarro, Oscar; Arbel-Goren, Rinat; Dassa, Bareket; August, Elias; Stavans, Joel; Department of Engineering
Emergent self-organization is a hallmark of natural bacterial communities, whose spatial structures and dynamic gradients are shaped by the complex interplay among bacterial diversity, oxygen and its consumption, and motility and by hydrodynamic flows. Key aspects of the interplay between oxygen gradients and bacterial community spatial structure remain obscure. Here we aim to elucidate the role that oxygen plays in the self-organization of multispecies bacteria in the water column, focusing on oxytactic bioconvection suspensions of naturally coexisting aerobic bacteria and on self-organization near air-water interfaces. Combining microscopy, mapping of the oxygen field and controlled external oxygen levels, we show that species-specific oxygen affinities and consumption rates induce the formation of distinct bacterial layers near air-water interfaces, resulting in dynamic segregation during multispecies bioconvection, and play a key role in determining the nature of bioconvective patterns in single species suspensions. We further find that oxygen and bacterial fields are tightly coupled and fluctuate with similar spatiotemporal scales, giving rise to oxygen advection and a well-defined oxic-anoxic boundary. Together, our results illuminate the fundamental role that oxygen gradients play in multispecies bacterial active matter and its spatial self-organization, with implications for the formation and stability of ecological niches in aquatic and sedimentary environments.
Objective stratification of knee osteoarthritis stages using a semi-supervised learning approach on multimodal MRI-CT cartilage features
(2026-06-25) Ciliberti, Federica Kiyomi; Maruotto, Ida; Jonsson, Halldor; Gargiulo, Paolo; Department of Engineering
Introduction – Knee osteoarthritis (KOA) is a chronic and progressive joint disease that affects middle-aged and older adults. Early detection is crucial to prevent progression toward joint replacement and improve long-term outcomes, yet current diagnoses are strongly influenced by subjective symptoms, especially pain perception, which varies widely across individuals and does not reliably reflect structural degeneration. This study introduces a semi-supervised learning (SSL) framework for characterizing KOA stages through combined MRI and CT-derived cartilage features. Methods – A cohort of 133 knee scans was analyzed, including 36 expert-labeled cases categorized as healthy, early degeneration, or advanced degeneration. These labels served as seeds for graph-based SSL using Label Propagation and Label Spreading, producing pseudo-labels for the remaining samples. Results – Label stability across ten Monte Carlo runs demonstrated high agreement (0.91 (Formula presented) 0.14) and substantial reliability (Fleiss’ kappa = 0.781). Supervised classifiers trained on the SSL-labeled dataset achieved robust performance, with Support Vector Machines and Logistic Regression yielding the highest weighted F1-scores (0.84 and 0.81, respectively). Statistical analysis confirmed significant differences among the three classes for all extracted features. Discussion – The volume-to-surface ratio and density heterogeneity demonstrated the strongest discriminatory power, reflecting progressive cartilage thinning, surface irregularity, and increasing structural heterogeneity consistent with KOA pathophysiology. These results show that combining expert knowledge with SSL enables reliable KOA stratification even with limited labeled data, offering meaningful insights into cartilage degeneration and laying the foundation for quantitative and more objective imaging-based biomarkers and future continuous scoring systems.
Flokkar í Opnum vísindum
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- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts