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Sentinel-2 imagery 2024
No full textThis dataset provides a pan-European Sentinel-2 composite image mosaic for the year 2024. The high-resolution (10 m) imagery is part of the PathFinder collection and it was used to create the 2024 maps, together with auxiliary geospatial layers, and National Forest Inventory (NFI) data. The composite images were produced by Terramonitor (https://www.terramonitor.com/). For methodology used in the compositing, see the following publication: Miettinen, J., Carlier, S. et al. (2021) Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling. International Journal of Remote Sensing 42: 9492-9514. https://doi.org/10.1080/01431161.2021.1998715</a
Replication Data for: The effect of season, somatic cell count and bulk milk storage time on the sensory and chemical characteristics of an aged hard goat milk cheese
No full textThis dataset contains chemical, physicochemical, and sensory data generated in a study investigating chemical and sensory changes during ripening of a hard-type goat milk cheese. The dataset includes measurements of free amino acids, organic acids, volatile compounds, basic composition (pH, dry matter, fat, protein), and quantitative descriptive sensory analysis (QDA).
Samples were collected from cheeses produced across different production seasons and farms and analysed at multiple ripening times durring 2021-2022. All cheeses were manufactured under standardised cheesemaking conditions.
Each data file contains sample identifiers (SampleID), production season, farm, milk storage time prior to cheesemaking, ripening time, variable name, numerical value, unit, and analytical method
Replication data for: The role of perceptual salience in L2 morphology acquisition: Attention, awareness, and intake
No full text Dataset description : The dataset includes 68 participants reading a total of 254 sentences (8 learn-practice, 140 learn, 6 test-practice, 80 test) in Englishti, an English-based semi-artificial language incorporating high- and low-salient morphemes reflecting the meaning of possessive determiners (Simoens et al., 2017). Eye-tracking data focuses on two specific Interest Areas (IA): IA7, which contains the target morpheme, and IA5-8, which covers a broader area surrounding the morpheme. In addition to the eye-tracking data, the dataset includes basic demographic data (age, gender), participants' awareness interview scores, Working Memory scores as assessed by the Reading Span Task (van den Noort et al., 2008), and implicit learning ability as reflected by the Serial Reaction Time task (Kaufman et al., 2010). Abstract of the publication : Acquiring morphology poses a considerable challenge in second language acquisition (SLA), highlighting the need to explore methods that facilitate this task for L2 learners. One potential facilitator is salience, which is theorized to aid language acquisition by directing learners’ attention to certain linguistic elements (Goldschneider & DeKeyser, 2001). To empirically investigate the impact of one type of salience, perceptual salience, a text-based eye-tracking experiment was conducted with 68 L1 Dutch speakers who read 240 sentences in Englishti, an English-based semi-artificial language featuring perceptually high-salient (-ulp) and low-salient (-o) morphemes according to length (Simoens et al., 2017). Within an implicit learning paradigm, participants were assigned to intentional or incidental learning contexts. The task consisted of two phases: a learning phase involving input flooding of the target morphemes followed by content-related questions, and a testing phase where participants completed a grammaticality judgment task on Englishti sentences, half of which were familiar from the learning phase and half of which were new. The results revealed a significant influence of salience, mediated by learning context and English proficiency, on fixation durations, thus empirically confirming the effect of perceptual salience on attention allocation in L2 morphology acquisition
Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
No full textThe data set consists of simulated time‑series measurements from two gas‑lifted subsea oil wells, used to develop and evaluate data‑driven virtual flow metering (VFM) models for oil and gas flow rate prediction.
Purpose:
To assess a range of machine learning algorithms (10 methods, including LSTM, MLP, XGBoost, SVR, tree‑based and linear methods) for predicting multiphase flow rates in subsea oil production, and identify which give the lowest prediction error.
To study the impact of measurement noise, the effect of noise filtering (median filter), and the quantification of prediction uncertainty (via 95% confidence intervals in XGBoost) in a VFM context.
Scope:
Two wells (Well 1 and Well 2) are considered, each represented by an open‑loop simulation model of a gas‑lifted oil well derived from Janatian et al. (2022).
For each well, 5 762 samples of process data are generated and split into 70% training and 30% test sets using a time‑series split; key input variables include bottom‑hole and wellhead pressures and temperatures plus choke opening, with oil and gas flow rates as targets.
The study covers the full workflow: data collection from the simulator, preprocessing (scaling, time‑series splitting, noise injection and filtering), model training and hyperparameter tuning, performance comparison via MAPE, and uncertainty quantification.
Nature of the data:
Synthetic, model‑generated process data rather than field measurements: data come from a validated dynamic model of gas‑lifted wells, not directly from a physical asset.
Multivariate, time‑series data at sample‑level resolution, comprising sensor‑like inputs (pressures, temperatures, choke openings) and corresponding oil and gas flow rates over time for each well.
Used primarily as a benchmarking set for supervised learning: different regression algorithms are trained and tested on identical data to compare prediction accuracy, robustness to impulse noise, and the effect of noise reduction and uncertainty quantification techniques.</p
SENSE. Survey data from the Louvre Citizen Science and Art-Practices Workshop
No full textThis dataset is from the Horizon project SENSE. The New European Roadmap to STEAM Education.
The dataset originates from a three-phase survey conducted during a workshop designed to assess participants’ evolving self-perception regarding various aspects of citizen science (CS) and artistic practices. The survey was administered at three distinct moments throughout the workshop, allowing for a progressive evaluation of participants’ attitudes, confidence, and understanding of citizen science practices and its possible relationship with artistic practices. The resulting data capture how participants’ perspectives developed over time, providing insights into the workshop’s impact on their engagement with interdisciplinary and participatory approaches. Participants were member or related member of organizations that conformed the SENSE. project.
The implementation of the workshop was aimed to:
Reflect about how CS can be related to a variety of educational contexts to change the way we approach STEM, and the way we understand and run a scientific research project.
Reflect on the implementation of CS and artistic practices in local contexts.
Build spaces of interaction among the workshop participants, allowing the participants to collect and experiment CS strategies that might be valuable to the SENSE. project partners and their communities.
Set the conditions to the consortium and associated partners to further reflect on citizen science and art practices in a proactive manner.
Explore and contribute to the essence of the theoretical and practical foundations of SENSE.STEAM.
The workshop was divided in 4 major blocks. To start the workshop, we stressed the need to work cooperatively in CS research. We thus intentionally built an activity to discover the diversity of workshop participants profiles (skills, competences, and attitudes). After this interactive session, we started the Context block. Workshop participants were introduced to Citizen Science with an initial plenary talk and then learned about 4 different CS and Art experiences in a conversational mode and in small groups. Those projects were already developed by 4 different consortia members. After a discussion, we started the research-in-the-field block. Participants actively went through the different steps of a CS project with different tools and methods to succeed in the creation and manage their own CS projects, according to their realities. The research topics were selected by Louvre and the topics were motivated by art-inspired perspectives. The research was highly contextualised (as most of the CS projects), inside the Louvre rooms. A discussion with all participants wrapped up the workshop. After sharing the results of their research-in-the-field, participants critically reflected on the whole experience and shared their own visions about CS and Art practices. This final discussion under the form of a round table helped to offer further guidance to STEAM Labs interested in CS practices and on how they can converge with Art practices.
The survey being implemented is a simplification and adaptation of the survey Universitat de Barcelona performed to analyse the dynamics of the participation of librarians and users in a local CS project (Cigarini, Bonhoure, Vicens and Perelló, 2021). The approach is based on the Theory of Planned Behavior (Ajzen, 1991) and assumes that the intention to remain engaged in activities is best predicted by positive views (attitudes), favourable opinions held (subjective norms), and individual perceived ability to be engaged in the activities (perceived behavioural control) (Ajzen, 1991). The survey also included a question on whether workshop participants consider that CS and artistic practices are related to each other.
To dynamically monitor the views and opinions of the workshop participants, three almost identical surveys were launched in three different moments of the workshop.
The three moments were:
Survey 1. Launched before starting the workshop. Workshop participants were asked to answer the survey after the Welcoming session (Day 1) and before
introducing CS and Art practices.
Survey 2. Launched during the workshop. Workshop participants were asked to answer the survey after the Session 3: Test and Action Plan).
Survey 3. Launched when the workshop ended (Day 2).
The dataset is a simple table including all responses (csv format) and the questions of the survey are in a separate file (docx format). The workshop participants were asked to fill in an online questionnaire, and before starting the workshop, they signed an informed consent. To ensure anonymity, the participants were assigned an ID number to complete the three surveys.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Cigarini, A., Bonhoure, I., Vicens, J., & Perelló, J. (2021). Public libraries embrace citizen science: Strengths and challenges. Library & Information Science Research, 43(2), 101090. https://doi.org/10.1016/j.lisr.2021.101090</p
Supplementary database for: Flexural Behavior of Mechanically-Connected Timber–Concrete Composite Floors: Database Development and Critical Review of Full-Scale Experiments
No full textThis experimental database encompasses parameters collected from 417 full-scale experimental specimens on mechanically jointed Timber Concrete Composite (TCC) beam and slabs subjected to short-term static bending tests. Parameters include general study details such as reference, test setup, specimen type, specimen count, and identification. Geometric properties are documented through span length, concrete and timber dimensions (thickness and width), and interface thickness. Shear connection information captured includes type of shear connector, shape, dimensions (depth, length, spacing), and arrangement patterns. Material properties recorded involve timber type, mechanical properties, and concrete type along with its compressive strength. Additionally, experimental observations cover reinforcement details, loading protocols, maximum load, maximum deflection, primary and secondary failure modes, and interface slip
Supplementary material for "Dependent-Marked Anticausatives in Old Norse-Icelandic: Modeling Productive and Unproductive Alternations"
No full textThis dataset provides the material on which the analysis in the article: "Dependent-Marked Anticausatives in Old Norse-Icelandic: Modeling Productive and Unproductive Alternations" is based, an article which is coming out later this year in the journal Functions of Language.
The dataset contains 119 pairs of linguistic examples from Old Norse-Icelandic. The first example of each pair represents a causative construction, while the second example of the pair represents a corresponding anticausative construction. Each example consists of three lines: a) the example line, b) the glossing line, and c) the translation line, according to the tradition in linguistics. Thus, each pair of examples consists of six lines.
The dataset was gathered during three different research projects. The first period was funded by the Norwegian Research Council (NonCanCase, grant nr. 205007) during Catrine Sandal’s work (PI Jóhanna Barðdal). The second period of data gathering was funded by the European Research Council (EVALISA, grant nr. 313461) during Sigríður Sæunn Sigurðardóttir’s research assistantship at Ghent University (PI Jóhanna Barðdal). And, finally, the third period of the data gathering was during a project on Language Productivity at Work, funded by Ghent University’s Research Fund's Concerted Research Action Scheme (BOF-GOA grant nr. 01G01319, Co-PI Jóhanna Barðdal).</p
Volumetric three-dimensional experimental measurement of a model wind turbine wake
No full textThis dataset contains time-resolved, volumetric, three-dimensional velocity fields of the near wake of a model wind turbine (WT). The velocity fields were obtained experimentally using particle tracking velocimetry (PTV), specifically the Shake-the-Box (STB) algorithm. The Eulerian velocity fields were calculated from the particle tracks using Vortex-in-Cell # (VIC#).
Four different cases are available, they differ in inflow free stream turbulence level (5% or 10%) and number of WTs present in the flow at the time of measurement (1 WT or 2 WTs).
In addition to the velocity fields, the rotor position for each time step, as well as characteristic scales, are included in each case.
The dataset supports the findings in the related publication
Replication Data for: Bisection of predictive land cover data models
No full textGround truth for sample points used to validate two predictive peatland models along with estimators used to bisect the models to create peatland map
Maker centered learning - cultivating creativity in tomorrows schools
No full textThe primary objective of this project was to analyse creative maker-centered learning and pedagogical practices for systematically educating creativity in primary and secondary schools, and to develop scientific knowledge on accessible educational practices that cultivated learners’ creativity and innovation capabilities to help bridge the ingenuity gap in future education. The secondary objectives were to: (1) enhance knowledge of how creativity was deliberately educated in school makerspaces and how maker-centered learning supported the development of creative capabilities and digital competences; (2) develop knowledge of how maker-centered learning could be applied in pedagogical practices to systematically educate creativity and innovation in basic education; and (3) generate knowledge on how teachers could effectively facilitate and scaffold students’ innovation processes in maker-centered projects.
The project results demonstrated how design and creativity could be systematically integrated into regular educational practices. Maker-centered learning proved applicable across educational levels and generated actionable pedagogical models that contributed to the advancement of STEAM education and the professional development of teachers in STEAM subjects