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Supplementary data and code for: Replacing natural gas in Norwegian methanol production: A national-scale MILP optimization of a waste-to-methanol supply chain
No full textThis dataset accompanies the paper "Replacing natural gas in Norwegian methanol production: A national-scale MILP optimization of a waste-to-methanol supply chain". It contains all input data, processing code, optimization results, and supporting documentation needed to reproduce the study in full.
The study develops a deterministic mixed-integer linear programming (MILP) model to evaluate whether domestic Norwegian waste and biomass resources can support a national upstream supply chain for methanol production, as a long-term substitute for natural gas. The model covers 356 mainland Norwegian municipalities and five industrial hub nodes as potential conversion sites, 110 approved coastal ports as aggregation and export points, and six feedstock-specific thermochemical conversion modules: hydrothermal liquefaction of food waste and sewage sludge, fast pyrolysis of woody waste and forest harvest residues, and thermal cracking of household and industrial plastic waste. For each module, bio-oil yield, capital cost, operating cost, electricity demand, external heat demand, and emission intensity parameters are derived from the peer-reviewed techno-economic literature using triangular fuzzy numbers and defuzzified into single planning values before optimisation.
The model determines which conversion plants should be activated, how much feedstock should be processed at each site, and how the resulting waste- and biogenic-derived oil (WBD-oil) should be routed to coastal ports, under four cost- and emission-constrained scenarios. Each scenario is solved using a lexicographic two-pass procedure: the first pass maximises annual WBD-oil delivered to port, and the second pass minimises total annual system cost while preserving the maximum output achieved in the first pass. Transport distances between supply nodes and ports are computed using the Open Source Routing Machine (OSRM) with real Norwegian road-network data, capturing the routing constraints imposed by fjord and mountain geography.
The deposit is organised into four folders. The input data folder contains all raw feedstock statistics from Statistics Norway (SSB), municipality boundary and port registry geospatial files, and four parameter workbooks documenting the techno-economic, yield, emission, and minimum capacity assumptions with full literature traceability. The pipeline folder contains six sequentially numbered Python scripts covering node construction, port table assembly, distance matrix computation, cost and emission parameter generation, MILP optimisation, and results analysis, each accompanied by a detailed code manual. The results folder contains 31 publication-quality figures and 10 statistics tables generated by the analysis script, consolidated into a single Excel workbook. The data descriptor folder contains a variable dictionary covering every column in every deposit file, provided in both Word narrative and Excel lookup table formats. An interactive Leaflet.js map visualising the optimised supply chain across all four scenarios is included at the deposit root
Replication Data for: Gender, Political Orientation, and Public Reactions to Ministerial Comebacks after Scandals
No full textThis dataset can be used to replicate the findings in the paper ""Gender, Political Orientation, and Public Reactions to Ministerial Comebacks after Scandals" (abstract below). The data we analyse is a survey experiment fielded in the Norwegian Citizen Panel (round 29) with two treatments, which was preregistered in OSF, and which asks people the extent to which they agree or disagree with parties (Conservative/Labour) allowing their (female/male) politicians to become a minister after having committed various transgressions (for example given friends an advantage in awarding public office). The read-me-file has links to the codebooks and methodological report for the Norwegian Citizen Panel round 29. The raw data can be accessed either via a csv-file or a sav file (attached), and contains the relevant variables for the analysis in the Norwegian Citizen Panel for that round. The R script attached outlines both the data wrangling executed to prepare the experiment for analysis (recoding of variables etc), and the code for the analysis itself (index construction, descriptive analysis, and regression analysis). The response-ids have been altered in this version of the dataset.
Abstract:
Political misconduct is a widespread phenomenon that frequently brings careers to an abrupt end. While prior research has examined how either politician gender or party affiliation shape citizens’ willingness to forgive wrongdoing, we explore how these factors interact with the ideological leaning of the citizen. Focusing on violations of ethical guidelines, including harassment and financial misconduct, we theorize that if left-leaning citizens are more concerned with gender balance in politics, and right leaning citizens exhibit stronger out-group hostility, the result is a comparatively more lenient treatment of Conservative women. We test this theory in a multi-party setting using a survey experiment in Norway, a closed-list PR-system where voters can only indirectly influence parties’ decisions regarding scandalized politicians. Respondents were asked whether the party should allow a politician to return to a ministerial post after wrongdoing. We show that citizens are more lenient towards Conservative women, which is due to left-leaning citizens going soft on them. There is also a tendency that Labor women are treated more harshly by right-leaning citizens, while there is no similar difference between male politicians. Our findings thus explicitly show that citizens turn to gender cues when evaluating the future career of scandalized politicians from their out-groups.</p
Francis-99 Workshop 2: Transient operation
No full textThe Francis-99 workshop series aims to provides an open platform to the hydropower researchers and possibility to explore their capabilities and to enhance their computational modelling skills. The objective is to provide high quality experimental data of selected test cases from the Waterpower laboratory (NTNU). The open data may include three-dimensional model, geometry, mesh for simulations and the experimental data. Then, organize a workshop for the researchers to present their research work and publish verified and validated data through Francis-99 workshop proceeding.
The first workshop focused on steady state operating conditions of the turbine. The data on the first workshop are available on DataverseNO (https://doi.org/10.18710/HKQ2RF). This workshop focuses on transient operation of the turbine, including the load variation form the design load to the high load and part load. The data includes both pressure measurements at selected locations in the turbine and velocity measurements in draft tube. Velocity was measured using PIV in the draft tube. The PIV data were collected at two sections of the draft tube, Line 1 and Line 2. A potential user can use these data and validate the numerical model. Detailed information about the data, numerical model, boundary conditions, mesh for the numerical model is available in the attached report
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
SENSE. Analysis of Erasmus+ projects for relation to STEAM
No full textThis dataset is from the Horizon project SENSE. The New European Roadmap to STEAM Education.
This dataset presents a structured analysis of ten European Union-funded projects selected for their relevance to the SENSE. project’s vision of future-making STEAM education. The projects were chosen based on their contributions to themes such as the integration of arts and sciences, spatial design, citizen science, social inclusion, and participatory educational practices.
This dataset was created for contribution to SENSE. deliverable report 3.4, "Report on Knowledge and Practices for future-making STEAM education in Europe", and includes comparative insights across four dimensions:
Spatial configuration: How learning environments and physical spaces were used or reimagined.
Art & Science: The nature and depth of integration between artistic and scientific practices.
Links to SENSE.: Specific synergies with the SENSE.STEAM methodology and roadmap.
Each project entry includes a brief description and a hyperlink to the original project page or documentation. The dataset supports the development of the SENSE. methodology by identifying transferable practices, gaps, and opportunities for innovation in STEAM education across Europe.
This dataset is intended for researchers, educators, and policy-makers interested in STEAM education, transdisciplinary learning, and participatory pedagogies. It contributes to the methodological foundation of the SENSE. Roadmap and informs the design of STEAM Labs and future educational interventions.</p
Appendices to: The Austronesian languages of eastern Indonesia and Timor-Leste: unravelling their prehistory and classification
No full text The files in this data set are the appendices from Grimes and Edwards' 2026 book "The Austronesian languages of eastern Indonesia and Timor-Leste: unravelling their prehistory and classification", published by Language Science Press. Appendices A and B are identical to those in the book (without footnote), while appendices C and D differ slightly, as explained in the readme
Appendix A lists the languages that appear in the book, along with the sources of data for each language. It also lists the ISO 639-3 code, Glottocode (Hammarström et al. 2023), and geographical coordinates for each language. For languages in our target region, this list is comprehensive (based on current knowledge), though it does not systematically list every variety of each language where these are known to be dialects in the linguistic sense. For a small number of languages in our target region, no data is presented in this book – usually because no data is available. These languages are listed, but no source is given.
Appendix B presents data for the word ‘banana’ from 391 languages/varieties. This tally includes 329 Austronesian languages and 62 Papuan languages. 192 of the Austronesian languages are within our target region.
Appendix C presents the full data available to us relating to marsupial terms in Austronesian languages of Sulawesi and Linguistic Wallacea, including parts of Indonesian west Papua. It provides fuller data than the discussion in chapter 14 of the book. Different variations of **kVndoR(a) ‘cuscus’ (§14.3), and **mantəR ‘cuscus’ (§14.4) and formally close words are found in different Wallacean subgroups, and also in Papuan languages. 502 terms for marsupials (and similar mammals) are presented from 288 languages/varieties, of which 231 are Austronesian, and 57 are Papuan.
Appendix D is a 494-item word list designed to help collect data useful for comparative purposes for languages of eastern Indonesian and Timor-Leste. There is a printable PDF version of the wordlist for use in the field, as well as a tab-separated spreadsheet version for electronic entry of the data. Synopsis of book
For 150 years there has been a question over how the Austronesian languages of eastern Indonesia and Timor-Leste fit into the Austronesian world. The area is severely under-documented. There has been no consensus on the classification of these languages, and scholars admit to being perplexed. This is the first systematic attempt at subgrouping the whole region based on historical phonology, supplemented by morphosyntax and the lexicon. Insights from archaeology, DNA studies, and awareness of long-term contact with Papuan languages inform this study.
Nine Wallacean subgroups are identified, along with their internal structures. Light is she2026d on languages whose classification has been unclear. Discontinuities in the historical phonology suggest different groups speaking different Austronesian languages got off different boats at different places, probably at different times. No evidence is found supporting a monolithic Austronesian advance through the region, nor a common Austronesian parent language below PMP that links all Wallacean subgroups.
Speakers of SVO Austronesian languages with prepositions, preverbal negation, numbers before nouns, and post-posed possessors came into contact with speakers of languages of unrelated Papuan families, with postpositions, clause-final negation, numbers following nouns, preposed possessors, and other features of SOV languages. Austronesian languages adopted these features but not uniformly, such that features attributed to contact are uneven across the region. Some are not found in some subgroups or branches within subgroups. Distribution maps of phonological, grammatical, and lexical features show many features are not found in all subgroups, do not align with each other, and some are found outside the region. Austronesian languages in the region are a kind of uneven hybrid that make them typologically different from Austronesian languages to the west and north.
The study evaluates earlier proposals along with new possibilities to link subgroups in different ways, but finds no exclusively shared innovations inherited from a common parent. Scenarios are explored of how Austronesian-speaking peoples came into the region. The uneven distribution of various features is addressed. Implications are many, and warrant a revised picture of the Austronesian world.
Several factors enabled this more in-depth study than has been previously possible. Both authors have extensive experience in the region. Many Dutch-era sources have become accessible online. Recent publications and unpublished data have been shared by others. This enabled the authors to glean data from 517 Austronesian and Papuan languages from within the region as well as to the west and east of it, providing context. Within the region, data have been gleaned from 292 varieties (256 Austronesian, 36 Papuan), some of which are now extinct. The volume is data rich with 334 data tables, 78 figures (including 32 maps), and 195 numbered examples/lists of data.</p
Supplement to: Self-reported disability among Norwegian children: Prevalence and Methodological insights into the Relational model of disability
No full textR script for analysis codes for creating variables and for analysis for the article Self-reported disability among Norwegian children: Prevalence and Methodological insights into the Relational model of disability.
Privacy: Raw data cannot be shared publicly. A mock dataset or variable structure is provided for testing
Note: All personal identifiers have been removed. Municipality names are anonymized or aggregated into Urban/Rural categories.Related article abstract:
Introduction: Leisure participation supports children's health, social inclusion, and well-being, yet children with disabilities (CWD) often face barriers to participate in organised and physically demanding activities. This study examined differences in leisure-time patterns between children with and without disabilities.
Methods: Cross-sectional data from 6,049 Norwegian children aged 10–13 years were analysed. Leisure time was assessed across six domains using twenty-two indicators. Latent Class Analysis identified leisure-time profiles, and multinomial logistic regression examined associations between disability status and profile membership, adjusting for sociodemographic factors.
Results: Five leisure profiles emerged: Home-oriented (21%), Social-oriented (14%), Aesthetic-oriented (20%), Physically-oriented (31%), and Screen-oriented (14%). In the adjusted model, and when comparing to children without disability and having the physically-oriented as the reference group, CWD were more likely to belong to the Screen- (OR = 2.41, 95% CI: 1.80–3.21) and Social-oriented (OR = 2.04, 95% CI: 1.52–2.74) profiles.
Discussion: CWD were less likely to be in profiles characterised by organised and physical leisure activities and more likely to be in profiles dominated by screen-based and informal activities, indicating persistent barriers to inclusive leisure-time participation. These findings underscore the importance of developing strategies that promote accessible, organised, and physical activity-based leisure opportunities, such as sports, while also ensuring sustained access to inclusive informal and digital spaces, such as neighbourhood facilities and e-sports. These efforts would support the social lives and well-being of CWD.<p
SENSE. interview needs and challenges of SENSE. target groups
No full textThis dataset is from the Horizon project SENSE. The New European Roadmap to STEAM Education.
To consolidate and enrich the results of the initial stakeholders mapping conducted during the STEAM DNA Workshop, direct involvement of the target groups was sought. Through interviews, a collection of ideas, insights, and feedback was gathered, with the aim of gaining a deeper understanding of the needs and constraints faced by participants in the SENSE. project.
See a full description of methodological information in the published report "SENSE. The New European Roadmap to STEAM Education Deliverable 3.3 – Report on Stakeholder Challenges and Needs for future-making STEAM Education in Europe", published June 2023 and accessible at the following link:
https://sense-steam.eu/wp-content/uploads/2024/03/D3.3-%E2%80%93-Report-on-Stakeholder_EU-Portal.pdf
In order to conduct the local interviews and to subsequently analyse the responses comprehensively, the following protocol was established.
Firstly, based on the landscape of STEAM beneficiaries identified during the STEAM DNA Workshop, specific target groups were defined:
Young people (aged 19-25)
Parents and/or educators of young people aged 13-18
Educational institutions (such as school headmasters, teachers from secondary education, educators, explainers, educational programs developers, science communicators)
Business (Companies/ Industries/ social enterprises interested in a skilled and creative workforce)
Policy makers (such as municipalities, local administrators, local, regional, and national officers of the ministries of education)
Secondly, in order to ensure a broad representation of all target groups, each partner was asked to identify at least two or more representatives of their beneficiaries, taking into account the categories mentioned above. Given the heterogeneity of the Consortium partners and their varying degrees of involvement with different target groups in their daily work, the involvement of all partners as a whole Consortium in this assessment activity allowed the full spectrum of participants to be concretely involved. This approach also lays the foundation for the participation and organisation of the upcoming local STEAM workshops of WP4.
Each partner contributed to the survey. Oral interviews were preferred and could be conducted in person, by telephone or by online teleconference. If necessary, partners had the flexibility to use other types of data collection.
The proposed questions were the following:
What are the constraints of current science education in your context and from your point of view?
What do you see as the main barriers to participation, especially for girls?
What strategies and approaches have you found useful to promote inclusion and participation in science education?
In what way do you think the integration of science and arts can improve STEM education of pupils from different backgrounds and contexts?
What benefits do you foresee for you or your organization from the completion of the SENSE. project?
Do you have any suggestions for us?
The interviews began with a brief overview of the SENSE. project, its objectives and how the opinions and information gathered would be used. A sample letter of introduction was shared with the partners, which could be adapted to suit individual local contexts and circumstances. The letter is included in SENSE. Deliverable 3.3 Annex 1, mentioned above.
Following the interviews, each partner completed an electronic questionnaire (Google Form) for each interview, documenting the responses collected (see Google Form in the SENSE. Deliverable 3.3 Annex 2, mentioned above). If an answer was too long, the interviewer and the interviewee agreed on a summarising sentence to be included in the form without any changes.
With regard to data management and protection, the information was collected in accordance with the respective national regulations.</p
Replication Data for: Municipalities as buyers of reclaimed building material: Previous experience and future outlook
No full textData was collected from 104 municipalities in Norway via phone interviews administered by Norstat AS. The data collection period was 19th August till 5th September 2024. The study aims to investigate factors that would convince public buyers to incorporate recycled construction materials in their tender process. For this purpose, municipal representatives were provided with sixteen enablers to score on a 4-point Likert scale. Furthermore, the survey questions inquired about their past experience with such materials and their expected future use. Record # represents the identifying number for each survey-taker. Information regarding municipality name and survey-taker details (department, position) have not been disclosed in the data file for privacy reasons. The corresponding README file details each variable and the answer options provided to the survey-takers