Abstract A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.
Alba Ordonez, Theodor Johannes Line Forgaard, David Wade
et al.
We present the NCS-models, a family of seismic foundation models pretrained on a large share of full-stack seismic cubes from the Norwegian Continental Shelf (NCS) available through the public DISKOS database. The model weights are open-sourced for the wider geoscience community. Foundation models trained with large-scale self-supervision are emerging as a promising basis for automatic seismic interpretation. However, most existing seismic models rely on limited or proprietary datasets, and it remains unclear how well natural-image foundation models transfer to seismic data. Our goals are to develop basin-scale seismic foundation models, provide practical recipes for scalable 3D training, and quantify the effects of basin-targeted pretraining and token dimensionality on downstream interpretation performance. Using masked autoencoders with Vision Transformer backbones, we pretrain models on a DISKOS-derived corpus of 3D time- and depth-migrated seismic volumes. The NCS-model variants use 2D, 2.5D multi-view, and 3D tokenization within a matched training setup. Transfer is evaluated on interpretation benchmarks using frozen backbones and a simple k-nearest neighbor classifier. Baselines include an ImageNet-pretrained MAE, a frontier vision foundation model, and a globally pretrained seismic model. Natural-image pretrained models do not reliably transfer, reflecting the large domain gap between natural images and seismic data. Seismic pretraining is necessary for robust transfer, and large-scale basin-targeted pretraining yields further gains over a smaller globally pretrained seismic baseline. The NCS-models achieve the best overall performance without fine-tuning, while 2.5D tokenization offers the strongest accuracy-efficiency tradeoff and the embeddings support similarity search for interactive interpretation.
Øyvind Sandbakk, Espen Tønnessen, Silvana Bucher Sandbakk
et al.
Abstract Background World-class coaches collect training data from their athletes systematically and exhibit an experimental mindset when making individual training adjustments in response to this data and other forms of feedback. However, the methods, expertise, and insights of highly accomplished endurance coaches is so far almost untouched in the scientific literature. The aim of this study was to provide a synthesis of common features and sport-specific variations in best-practice training characteristics within Olympic endurance sports as described by world-class Norwegian coaches. Methods A multiple case-study design was used, and twelve successful male Norwegian coaches served as key informants. Together, they were responsible for athletes winning more than 380 international medals, representing long-distance running, biathlon, rowing, cross-country skiing, speed skating, road cycling, swimming, and triathlon. The study design included: (1) an extensive, email-administered and Word™-based questionnaire related to training characteristics at the macro-, meso-, micro-, and session-level; (2) cross-referencing data with historically reported training logs from successful athletes; (3) in-depth and semi-structured in-person interviews with each coach; (4) a review process among authors and coaches. The data collection was undertaken in 2022. Results All coaches adhere to a traditional periodization model, including a gradual shift towards lower overall training volume and more competition-specific training as the competitive period approaches. The coaches also employ a pragmatic approach to align training organization with the various constraints faced in the training process. Another common emerging feature was an emphasis on high volume of low-intensity training combined with 2–3 weekly “key workout” days consisting of 3–5 intensive training sessions. Finally, coaches across all sports focused on achieving high training quality by optimizing training sessions, systematically controlling the load-recovery balance, and ensuring optimal preparations for major competitions. Substantial sport-specific differences were evident in terms of volume, frequency, intensity distribution, and application of strength and cross training, mainly due to variations in exercise mode constraints (i.e., mechanical, and muscular loading), competition distance, and organizational aspects. Conclusions This study offers novel insights into best-practice training characteristics in Olympic endurance, shedding light on both commonalities and sport-specific variations. These insights can be used to generate new hypotheses to be further elucidated and contribute to the development of evidence-based training practices.
We present HySemRAG, a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG) to automate large-scale literature synthesis and identify methodological research gaps. The system addresses limitations in existing RAG architectures through a multi-layered approach: hybrid retrieval combining semantic search, keyword filtering, and knowledge graph traversal; an agentic self-correction framework with iterative quality assurance; and post-hoc citation verification ensuring complete traceability. Our implementation processes scholarly literature through eight integrated stages: multi-source metadata acquisition, asynchronous PDF retrieval, custom document layout analysis using modified Docling architecture, bibliographic management, LLM-based field extraction, topic modeling, semantic unification, and knowledge graph construction. The system creates dual data products - a Neo4j knowledge graph enabling complex relationship queries and Qdrant vector collections supporting semantic search - serving as foundational infrastructure for verifiable information synthesis. Evaluation across 643 observations from 60 testing sessions demonstrates structured field extraction achieving 35.1% higher semantic similarity scores (0.655 $\pm$ 0.178) compared to PDF chunking approaches (0.485 $\pm$ 0.204, p < 0.000001). The agentic quality assurance mechanism achieves 68.3% single-pass success rates with 99.0% citation accuracy in validated responses. Applied to geospatial epidemiology literature on ozone exposure and cardiovascular disease, the system identifies methodological trends and research gaps, demonstrating broad applicability across scientific domains for accelerating evidence synthesis and discovery.
Tobias Verheugen Hvidsten, Maximilian Roithner, Fred Espen Benth
et al.
Electric vehicle batteries have a proven flexibility potential which could serve as an alternative to conventional electricity storage solutions. EV batteries could support the balancing of supply and demand and the integration of variable renewable energy into the electricity system. The flexibility potential from electric vehicles, in distinction to conventional battery storage, depends on the vehicle user's willingness and opportunity to make their vehicle available for flexibility. This rate of participation is often not considered in studies, despite the impact electric vehicle flexibility could have on the electricity system. This work presents a modelling study of the Norwegian electricity system, demonstrating how a future net-zero electricity system can benefit from electric vehicles in terms of integrating renewables and balancing supply and demand, while considering the rate of participation. Our findings show electric vehicles' potential to eliminate the need for stationary battery storage with just 50% participation in vehicle-to-grid. We find that the flexibility of electric vehicles contributes to relative reductions in the total cost of the electricity system by almost 4% and 15% assuming 100% participation in flexible charging and vehicle-to-grid, respectively.
Muhammad Shahzad Javed, Karin Fossheim, Paola Velasco-Herrejón
et al.
Environmental movements and climate strikes have made it apparent that youth feel excluded from the ongoing energy transformation process, highlighting the crucial need for their engagement to achieve a socially accepted transition. This interdisciplinary study focuses on the Norwegian electricity system and involves conducting educational workshops with high school students aged 15 to 16 to ascertain their perspectives towards a net-zero energy system. The workshops were structured into three segments, starting with the dissemination of common knowledge about energy and climate, followed by interactive activities designed to explore and develop an understanding of various aspects of energy transition. Three rounds of questionnaires, administered at distinct time intervals, assessed changes in students' attitudes and socio-techno-economic preferences. Our findings show that 33\% of pupils favored exclusively offshore wind as a main energy source, while 35\% opted to combine it with solar energy, indicating that over 68\% viewed offshore wind as favorable. Although 32\% supported some form of land-based wind turbines, there was strong disagreement about wind parks in agricultural, forested, and residential areas. Preferences also exhibited considerable regional variation; solar installations were favored in southern and southeastern Norway, while wind farms were suggested for central and northern regions. Pupils emphasized energy independence, showed reluctance towards demand response, prioritized reducing emissions and preserving biodiversity over minimizing electricity costs. Despite cost-minimization being core to most energy system models, youth deemed it the least important factor, highlighting a disconnect between modeling priorities and their perspectives.
The plant kingdom is vital to all other life on earth and is a fundamental part of children’s culture. Yet, we have little knowledge about how plants are currently represented in picturebooks aimed at children. In this article, I conduct a quantitative analysis of plant motifs in picturebooks published in Sweden and Norway in 2020. Inspired by concepts drawn from ecocritical children’s literature research and critical plant studies, the article investigates which plant species and types the books contain and how they are presented through text and pictures. Although the study finds that plants are prevalent motifs in contemporary Swedish and Norwegian picturebooks, it also shows that vegetal entities are often backgrounded and depicted as generic plant types.
There are great expectations for the use of AI in Norway. On the other hand, it is reported that the adoption of AI in Norway is slower than expected in both the private and public sectors. Using responses from NOKIOS Technology Radar 2017-2021, IT in Practice surveys conducted by Ramboll in 2021-2024, as well as another national survey as part of a five-year cycle, this article looks at reported and planned use of AI with a focus on local (municipalities) and national government agencies. IT in practice is distributed to a large number of Norwegian public agencies, with a response rate of over 5o percent. The most recent data (2024) presented in this article is based on responses from 335 public organizations, with 237 municipalities, and 98 public organizations at the national or regional level. The survey confirms that the use of AI is still at an early stage, although expectations are high for future use. -- Det er store forventninger til bruk av KI i Norge. På den annen side rapporteres det at adopsjonen av KI i Norge går tregere enn forventet både i privat og offentlig sektor. Ved hjelp av svar fra NOKIOS teknologiradar 2017-2021, IT i Praksis undersøkelser utført av Rambøll i 2021-2024, samt en annen nasjonal undersøkelse som en del av en femårig syklus, ser vi i denne artikkelen på rapportert og planlagt bruk av KI med fokus på lokale (kommuner) og nasjonale offentlige etater. IT i praksis distribueres til en lang rekke norske offentlige virksomheter, med en svarprosent på over 50 prosent. De nyeste dataene (2024) presentert i denne artikkelen er basert på svar fra 335 offentlige organisasjoner, med 237 kommuner, og 98 offentlige organisasjoner på nasjonalt eller regionalt nivå. Undersøkelsen bekrefter at bruken av KI fortsatt er på et tidlig stadium, selv om forventningene er høye til fremtidig bruk.
Sílvia O. Sequeira, Ekaterina Pasnak, Carla Viegas
et al.
Microbial contamination poses a threat to both the preservation of library and archival collections and the health of staff and users. This study investigated the microbial communities and potential health risks associated with the UNESCO-classified Norwegian Sea Trade Archive (NSTA) collection exhibiting visible microbial colonization and staff health concerns. Dust samples from book surfaces and the storage environment were analysed using culturing methods, qPCR, Next Generation Sequencing, and mycotoxin, cytotoxicity and azole resistance assays. Penicillium sp., Aspergillus sp., and Cladosporium sp. were the most common fungi identified, with some potentially toxic species like Stachybotrys sp., Toxicladosporium sp. and Aspergillus section Fumigati. Fungal resistance to azoles was not detected. Only one mycotoxin, sterigmatocystin, was found in a heavily contaminated book. Dust extracts from books exhibited moderate to high cytotoxicity on human lung cells, suggesting a potential respiratory risk. The collection had higher contamination levels compared to the storage environment, likely due to improved storage conditions. Even though, overall low contamination levels were obtained, which might be underestimated due to the presence of salt (from cod preservation) that could have interfered with the analyses. This study underlines the importance of monitoring microbial communities and implementing proper storage measures to safeguard cultural heritage and staff well-being.
Abstract This article focuses on three motherhood tropes, which became narrative convention in Norwegian migration literature in the period 1986–2010. I categorize these motherhood tropes as: (1) the cultured mother; (2) the creative mother; and (3) the appropriation of Henrik Ibsen’s infamous character Nora Helmer. Illustrating these tropes shows a critique of motherhood found in some of Norway’s migration literature. However, Mala Naveen’s novel, Desiland (Desi Land), published in 2010, incorporates and challenges these three motherhood tropes by breaking with convention. I argue that Naveen performs disidentification, or an identity in difference, and calls for a renegotiation or remapping of the current paradigm of Norwegian migration literature, which limits minority women’s artistic representation.
Gunilla Borglin, Cecilia Olsson, Hanne Aagaard
et al.
Introduction Knowledge about long-term care services ability, regardless of if the service is home-based or facility-based, to provide an optimal and comprehensive fundamental nursing care (understood as focusing on physical, relational and psychosocial needs) consistently over time is sparse. Research into nursing indicates the presence of a discontinuous and fragmented healthcare service, and that fundamental nursing care such as mobilisation, nutrition and hygiene among older people (65 years and above) seems to be, regardless of reasons, systematically rationed by nursing staff. Thus, our scoping review aims to explore the published scientific literature on fundamental nursing care and continuity of care targeting older people’s needs while also describing identified nursing interventions with the same foci in a long-term care context.Methods and analysis The upcoming scoping review will be conducted in accordance with Arksey and O’Malley’s methodological framework for scoping studies. Search strategies will be developed and adjusted to each database, for example, PubMed, CINAHL and PsychINFO. Searches will be limited to the years 2002–2023. Studies focusing our aim, regardless of study design, will be eligible for inclusion. Included studies will be quality assessed and data will be charted using an extraction form. Textual data will be presented through a thematic analysis and numerical data by a descriptive numerical analysis. This protocol adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol checklist.Ethics and dissemination The upcoming scoping review will take into consideration ethical reporting in primary research as part of the quality assessment. The findings will be submitted to an open-access peer-reviewed journal. Under the Norwegian Act on Medical and Health-related Research, this study does not need ethical clearance by a regional ethical review authority as it will not generate any primary data or obtain sensitive data or biological samples.
Martijn Vermeer, Jacob Alexander Hay, David Völgyes
et al.
Background: The mapping of tree species within Norwegian forests is a time-consuming process, involving forest associations relying on manual labeling by experts. The process can involve both aerial imagery, personal familiarity, or on-scene references, and remote sensing data. The state-of-the-art methods usually use high resolution aerial imagery with semantic segmentation methods. Methods: We present a deep learning based tree species classification model utilizing only lidar (Light Detection And Ranging) data. The lidar images are segmented into four classes (Norway Spruce, Scots Pine, Birch, background) with a U-Net based network. The model is trained with focal loss over partial weak labels. A major benefit of the approach is that both the lidar imagery and the base map for the labels have free and open access. Results: Our tree species classification model achieves a macro-averaged F1 score of 0.70 on an independent validation with National Forest Inventory (NFI) in-situ sample plots. That is close to, but below the performance of aerial, or aerial and lidar combined models.
John Bjørnar Bremnes, Thomas N. Nipen, Ivar A. Seierstad
During the last two years, tremendous progress in global data-driven weather models trained on numerical weather prediction (NWP) re-analysis data has been made. The most recent models trained on the ERA5 at 0.25° resolution demonstrate forecast quality on par with ECMWF's high-resolution model with respect to a wide selection of verification metrics. In this study, one of these models, the Pangu-Weather, is compared to several NWP models with and without probabilistic post-processing for 2-meter temperature and 10-meter wind speed forecasting at 183 Norwegian SYNOP stations up to +60 hours ahead. The NWP models included are the ECMWF HRES, ECMWF ENS and the Harmonie-AROME ensemble model MEPS with 2.5 km spatial resolution. Results show that the performances of the global models are on the same level with Pangu-Weather being slightly better than the ECMWF models for temperature and slightly worse for wind speed. The MEPS model clearly provided the best forecasts for both parameters. The post-processing improved the forecast quality considerably for all models, but to a larger extent for the coarse-resolution global models due to stronger systematic deficiencies in these. Apart from this, the main characteristics in the scores were more or less the same with and without post-processing. Our results thus confirm the conclusions from other studies that global data-driven models are promising for operational weather forecasting.
Nils Melsom Kristensen, Paulina Tedesco, Jean Rabault
et al.
Knowledge about statistics for water level variations along the coast due to storm surge is important for the utilization of the coastal zone. An open and freely available storm surge hindcast archive covering the coast of Norway and adjacent sea areas spanning the time period 1979-2022 is presented. The storm surge model is forced by wind stress and mean sea level pressure taken from the non-hydrostatic NORA3 atmospheric hindcast. A dataset consisting of observations of water level from more than 90 water level gauges along the coasts of the North Sea and the Norwegian Sea is compiled and quality controlled, and used to assess the performance of the hindcast. The observational dataset is distributed in both time and space, and when considering all the available quality controlled data, the comparison with modelled water levels yield a mean absolute error (MAE) of 9.7 cm and a root mean square error (RMSE) of 12.4 cm. Values for MAE and RMSE scaled by the standard deviation of the observed storm surge for each station are 0.42 and 0.54 standard deviations, repsectively. When considering the geographical differences in characteristics of storm surge for different countries/regions, the values of MAE and RMSE are in the range 5.7-13.9 cm and 7.6-17.8 cm respectively, and 0.33-0.46 and 0.42-0.59 standard deviations for the scaled values. The minimum and maximum values for water level in the hindcast are -2.60 m and 3.92 m. In addition, 100-year return level estimates are calculated from the hindcast, with minimum and maximum values of, respectively, -2.75 m and 3.98 m. All minimum and maximum values are found in the southern North Sea area.
Alexandra-Ioana Albu, Gabriela Czibula, Andrei Mihai
et al.
With the recent increase in the occurrence of severe weather phenomena, the development of accurate weather nowcasting is of paramount importance. Among the computational methods that are used to predict the evolution of weather, deep learning techniques offer a particularly appealing solution due to their capability for learning patterns from large amounts of data and their fast inference times. In this paper, we propose a convolutional network for weather forecasting that is based on radar product prediction. Our model (<i>NeXtNow</i>) adapts the ResNeXt architecture that has been proposed in the computer vision literature to solve the spatiotemporal prediction problem. <i>NeXtNow</i> consists of an encoder–decoder convolutional architecture, which maps radar measurements from the past onto radar measurements that are recorded in the future. The ResNeXt architecture was chosen as the basis for our network due to its flexibility, which allows for the design of models that can be customized for specific tasks by stacking multiple blocks of the same type. We validated our approach using radar data that were collected from the Romanian National Meteorological Administration (NMA) and the Norwegian Meteorological Institute (MET) and we empirically showed that the inclusion of multiple past radar measurements led to more accurate predictions further in the future. We also showed that <i>NeXtNow</i> could outperform <i>XNow</i>, which is a convolutional architecture that has previously been proposed for short-term radar data prediction and has a performance that is comparable to those of other similar approaches in the nowcasting literature. Compared to <i>XNow</i>, <i>NeXtNow</i> provided improvements to the critical success index that ranged from 1% to 17% and improvements to the root mean square error that ranged from 5% to 6%.
Artikkelen tar form som en refleksjon over et utprøvende undervisningsopplegg med studenter i norskdidaktikk, høsten 2020. Med utgangspunkt i teorifeltet litterære geografier brukte vi geografi som analytisk linse i arbeidet med den svenske barneromanen Mördarens apa av Jakob Wegelius (2014). Studentene kartla romanens geografiske steder på et kart i Padlet, de analyserte de visuelle kartene i innsidepermene og reflekterte over sentrale handlingssteder i romanen. Utprøvingen indikerer at denne analytiske tilnærmingen hjalp studentene med å lese analytisk og stimulerte til kritiske refleksjoner. Likevel må det arbeides videre med å stimulere til nærlesing. Undervisningssekvensen er en del av en serie geografiske utprøvinger med det formålet å utvikle en spatial litteraturdidaktikk. Denne didaktikken skal svare på en utfordring i den nye læreplanen, LK20, nemlig: Hvordan kan vi utvikle kritisk lesing gjennom utforskende og tverrfaglig arbeid med skjønnlitteratur?
Context: The efficient processing of Big Data is a challenging task for SQL and NoSQL Databases, where competent software architecture plays a vital role. The SQL Databases are designed for structuring data and supporting vertical scalability. In contrast, horizontal scalability is backed by NoSQL Databases and can process sizeable unstructured Data efficiently. One can choose the right paradigm according to the organisation's needs; however, making the correct choice can often be challenging. The SQL and NoSQL Databases follow different architectures. Also, the mixed model is followed by each category of NoSQL Databases. Hence, data movement becomes difficult for cloud consumers across multiple cloud service providers (CSPs). In addition, each cloud platform IaaS, PaaS, SaaS, and DBaaS also monitors various paradigms. Objective: This systematic literature review (SLR) aims to study the related articles associated with SQL and NoSQL Database software architectures and tackle data portability and Interoperability among various cloud platforms. State of the art presented many performance comparison studies of SQL and NoSQL Databases by observing scaling, performance, availability, consistency and sharding characteristics. According to the research studies, NoSQL Database designed structures can be the right choice for big data analytics, while SQL Databases are suitable for OLTP Databases. The researcher proposes numerous approaches associated with data movement in the cloud. Platform-based APIs are developed, which makes users' data movement difficult. Therefore, data portability and Interoperability issues are noticed during data movement across multiple CSPs. To minimize developer efforts and Interoperability, Unified APIs are demanded to make data movement relatively more accessible among various cloud platforms.
Rebecca E. Ross, Genoveva Gonzalez-Mirelis, Pablo Lozano
et al.
Sea pens are considered to be of conservation relevance according to multiple international legislations and agreements. Consequently, any information about their ecology and distribution should be of use to management decision makers. This study aims to provide such information about six taxa of sea pen in Norwegian waters [Funiculina quadrangularis (Pallas, 1766), Halipteris spp., Kophobelemnon stelliferum (Müller, 1776), Pennatulidae spp., Umbellula spp., and Virgulariidae spp.]. Data exploration techniques and ensembled species distribution modelling (SDM) are applied to video observations obtained by the MAREANO project between 2006 and 2020. Norway-based ecological profiles and predicted distributions are provided and discussed. External validations and uncertainty metrics highlight model weaknesses (overfitting, limited training/external observations) and consistencies relevant to marine management. Comparison to international literature further identifies globally relevant findings: (a) disparities in the environmental profile of F. quadrangularis suggest differing “realised niches” in different locations, potentially highlighting this taxon as particularly vulnerable to impact, (b) none of the six sea pen taxa were found to consistently co-occur, instead partially overlapping environmental profiles suggests that grouping taxa as “sea pens and burrowing megafauna” should be done with caution post-analyses only, (c) higher taxonomic level groupings, while sometimes necessary due to identification issues, result in poorer quality predictive models and may mask the occurrence of rarer species. Community-based groupings are therefore preferable due to confirmed shared ecological niches while greater value should be placed on accurate species ID to support management efforts.
Science, General. Including nature conservation, geographical distribution