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Replication Data for: Spotting the lect: Asymmetric literacy competencies in multilectal contexts
No full textDescription of dataset: The dataset contains quantitative experimental data from a language classification task collected from 9–10th grade lower secondary school pupils (n = 398) from three Norwegian municipalities. The municipalities were located in Eastern Norway (Telemark county), Northern Norway (Nordland county), and Western Norway (Vestland county). In this task, the participants were asked to classify single sentences as either standard or dialectal Norwegian. The sentences were presented in standard written Norwegian (Bokmål and Nynorsk varieties) and various versions of dialect writing (Eastern, Northern, and Western). Participants' accuracy and reaction times were measured. The file with experimental data also contains by-participant measures of average reading speed in the two standard varieties of written Norwegian (Bokmål and Nynorsk) for a subset of 288 participants, which was derived from a separate study. The file further contains information on some of the participants' (n = 179) writing habits in private digital communication, expressed as the number of words the participants shared with the researchers, as well as the number and proportion of words containing speech-like deviations from the standard. The dataset also includes qualitative background questionnaire data collected from a subset of the same participants (n = 352), which includes information about their age, gender, and various language use patterns, both oral and written. The dataset further includes all stimuli used in the experimental task, the list of questions from the background questionnaire, the analysis code, the OpenSesame experiment file, and the consent form template.Abstract of the related publication: This study investigates how Norwegian adolescents with different literacy profiles perceive and distinguish standard and dialectal varieties of written Norwegian. Using a variety classification task, we analyzed response accuracy and reaction times (RTs) among 398 lower secondary school pupils across three regions: Eastern Norway (primarily Bokmål-literate), Northern Norway (exposed to Bokmål and dialect writing), and Western Norway (familiar with Nynorsk, Bokmål, and dialect writing). Participants with broader literacy profiles (Western Norway) achieved higher accuracy and showed longer RTs, indicating more thorough processing, while those with more limited exposure (Eastern Norway) employed more reductive or guessing-based strategies. However, even for Western participants, distinguishing the lesser-used standard, Nynorsk, from dialect writing was more challenging than distinguishing Bokmål. A supplementary analysis revealed that frequent use of dialect writing in private digital communication did not undermine standard literacy overall, although in the Western group it was associated with slightly lower accuracy, highlighting the vulnerable status of Nynorsk. These findings suggest that exposure to multiple written varieties enhances metalinguistic awareness and processing depth, while revealing regional asymmetries in the attainment of the two written standards. The study underscores the importance of explicitly addressing both standard varieties and dialect writing in education, with broader relevance for contexts where minoritized or non-standard varieties coexist alongside a dominant written language.</p
Supporting Data for: Dual functionality in whole blood microsampling: Combining plasma separation and immunoaffinity clean-up for LC-MS based protein analysis
No full textThis dataset contains supplementary data related to the following journal article: “Dual functionality in whole blood microsampling: Combining plasma separation and immunoaffinity clean-up for LC-MS based protein analysis” by Ago Mrsa, Bjarke Strøm Larsen, Trine Grønhaug Halvorsen and Léon Reubsaet.
The supplementary data consists of mass spectrometric files in the .RAW file format
The Norcoast25 - a regional marine radiocarbon calibration curve for the period Bølling to the Early Holocene for the Norwegian coast (14.4–11.0 ka BP).
No full textThis is data to the Norcoast25 calibration curve used to calibrate marine 14C ages to calendar years along the coast of Norway. All details of how the Norcoast25 calibration curve was constructed are explained in the related publication (see below). The data is stored in two different versions: The file Norcoast25.14c can be downloaded to the calibration program OxCal. The file Norcoast25.txt contains the same data. The files have three columns; Column A is the calibrated date BP (CAL_BP), Column B is the 14C age (14C_age) and column C is the error to the 14C age (14C_Error).
To calibrate a 14C date with the Norcoast25 curve use the program OxCal, download the file Norcoast25.14c to the bin folder of OxCal, ex. C:\Oxcal\bin. Then, to calibrate a 14C age with the Norcoast25 curve you must write it into the OxCal code. For instance:
Plot()
{
Curve("Norcoast25","Norcoast25.14c");
R_Date("TUa-1902",13110,125);
};
It is also possible to use Norcoast25 with the calibration program CALIB, but then you would have to rename the Norcoast25.14c file to marine20.14c in the downloaded version.</p
Supplementary data for: A governance-aware systems thinking architecture for sustainability at scale
No full textThis dataset supports the paper “A Governance-Aware Systems Thinking Architecture for Sustainability at Scale.” The study addresses a key challenge in large-scale sustainability research: how to ensure transparency, consistency, and auditability when integrating heterogeneous evidence and systems thinking approaches.
The paper introduces STAI³RS, a governance framework designed to ensure rigor and reproducibility in sustainability synthesis. STAI³RS stands for Scalable, Transparent, Analytical, Interpretable, Reliable, Reproducible, Robust, and Systematic, and provides cross-cutting principles and procedural cues for applying systems thinking methods at scale.
Building on this governance layer, the paper presents SEEDS (Systems Evidence Extraction and Decision Support), a six-component operational model that structures sustainability synthesis from problem framing to decision support. SEEDS integrates boundary governance, evidence extraction, harmonization, synthesis, and iterative learning into a transparent and auditable workflow.
The dataset includes materials that document the governance diagnostics and operational implementation of these frameworks. In particular, Table S2 presents a case study demonstrating the SEEDS workflow in practice through a large-scale feedstock evidence mapping study spanning over 130,000 peer-reviewed studies across biomass- and waste-to-X conversion technologies. This case study illustrates how governance rules, audit trails, and harmonization procedures enable reproducible synthesis across three large scientific corpora.
SEEDS_components_in_practice
These materials support reuse, verification, and extension of governance-aware systems thinking approaches in sustainability research and decision support
Replication Data for: "Above- and belowground communities in boreal forests are tightly linked along common ecological gradients"
No full textThis is a collection of files used in a soil fungal diversity study in the Solhomfjell natural reserve with focus on the relationship between fungal and plant diversity
The dataset is made up of a main set of predictor variables and a table of ITS2 OTUs curated and standardised for metabarcoding community analysis. In addition, there is supplied supporting and raw files
Replication Data for: Sensor Comparison for Multi-Modal Motion Estimation in Upper Body Stroke Rehabilitation
No full textThis dataset contains data within the field of human motion estimation for stroke rehabilitation. It is used in the article named "Sensor Comparison for Multi-Modal Motion Estimation in Upper Body Stroke Rehabilitation". This dataset comprises motion data collected from 16 healthy participants (5 women and 11 men, aged 23-43) engaged in upper body movements as part of a study on stroke rehabilitation and range of motion assessment. Participants performed 12 distinct movements, being recorded by four different sensors: a marker-based optoelectronic motion capture system, eight inertial measurement units (IMUs), one webcam and one stereocamera. The data collection adhered to ethical standards established by the Declaration of Helsinki, ensuring participant anonymity and informed consent
Supplementary to: Stimulate, ventilate, collaborate – A qualitative study underpinning the development of an educational guide to newborn resuscitation for midwifery students
No full textThis is an educational guide to newborn resuscitation for midwifery students, supplementary to: Stimulate, ventilate, collaborate – A qualitative study underpinning the development of an educational guide to newborn resuscitation for midwifery students. The development was created in workshops including midwives and midwifery students using the ADDIE model
Identification of salmon allergens from Atlantic salmon (Salmo salar) with mass spectrometry
No full textThe following dataset was obtained from mass spectrometric analysis from the Proteomics and Metabolomics Core Facility at UiT The Arctic University of Norway. The dataset is from different tissues from Atlantic salmon: raw muscle, cooked muscle, mucus, and skin. The purpose of the dataset was to identify known and possibly novel allergens from Atlantic salmon
Replication Data for: Plasma Density Estimation from Ionograms and Geophysical Parameters with Deep Learning
No full text
This dataset contains the replication data and code for the article "Plasma Density Estimation from Ionograms and Geophysical Parameters with Deep Learning". The project introduces "Kian-Net", a deep learning model designed to estimate electron density profiles in the ionosphere by fusing ionogram images with geophysical parameters.
The Kian-Net model utilizes a fusion architecture (FuDMLP) combining an IonoCNN (Convolutional Neural Network) for processing ionogram images and a GeoDMLP (Deep Multilayer Perceptron) for processing geophysical parameters. These two branches are fused to predict plasma density profiles. The model is trained using plasma density profiles observed by the EISCAT UHF radar as ground truth.
The dataset is organized into modules:
1. Training_KIAN_Net/: Scripts and source data for training the model.
2. Testing_KIAN_Net/: Scripts for evaluating the model on independent test days, including pre-trained weights.
3. Predicting_KIAN_Net/: Scripts for generating predictions on new data.
4. Plotting/: Scripts and data for generating the figures presented in the publication.
Data ranges from 2012 to 2022 and includes magnetometer data and ionograms from the Tromsø Geophysical Observatory, geophysical data from OMNIWeb, and EISCAT UHF radar data.
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Computational fluid dynamic simulation of a pump-turbine
No full textThe data correspond to the computational fluid dynamic simulations of a reversible pump-turbine. A three-dimensional simulation of the turbine was conducted under different operating conditions, design load, high load and part load. Prior to the simulations, several observation points were created inside the numerical model that allowed to acquire time-dependent data of pressure fluctuations