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Replication Data for: The effect of turbulence on a flexible finite wing: forces, deflections and the wingtip vortex
No full textThis dataset contains the data required to reproduce the results in the journal article: "The effect of turbulence on a flexible finite wing: forces, deflections and the wingtip vortex". It contains experimental results obtained in the wake of a flexible finite wing, specifically capturing the wing tip vortex, at different turbulent conditions set using an active turbulence grid. The wing tip vortex was captured using cross-stream stereoscopic particle image velocimetry (PIV). Digital Image Correlation (DIC) was employed to study the deformation of the flexible wing under the different turbulent conditions. The performance of the wing (lift and drag) was measured using a force balance. Characterisation of the turbulent freestream generated by the active grid was carried out using hot-wire anemometry (HWA)
Supporting data for: Development of cost-effective process for phyco-remediation of dairy wastewater and valorization of algal biomass for production of biofuel and biochemical: A sustainable approach towards bio-refinery
No full textThis dataset supports the research project titled: "Development of cost-effective process for phyco-remediation of dairy wastewater and valorization of algal biomass for production of biofuel and biochemical: A sustainable approach towards bio-refinery."
Purpose: The dataset was generated to explore sustainable strategies for wastewater treatment and biomass valorization using microalgae-based systems. It aims to enable resource recovery from dairy wastewater through integrated remediation and bio-conversion processes.
Nature: The dataset is organized into three sections, each corresponding to a key objective of the project:
Objective 1 – Phyco-remediation of dairy wastewater (DW): Includes experimental data on the design and performance of microalgae consortia for DW treatment. This section supports the published article: Co-Cultivation of High-Value Microalgae Species with Filamentous Microalgae for Dairy Wastewater Treatment.
Objective 2 – Valorization via hydrolysis: Contains data on the synthesis and characterization of a multienzyme magnetic nanocatalyst (ME-MNC) for the hydrolysis of microalgal biomass. Data include enzyme immobilization optimization, material characterization (XRD, FTIR, FESEM, TGA), and recovery of sugars and proteins.
Objective 3 – Biochemical and biofuel production: Presents data from fermentation experiments using hydrolyzed biomass as feedstock. It compares the performance of free and immobilized Actinobacillus succinogenes cells in producing organic acids, including yield and reusability.
Scope: The dataset covers raw measurements, processed data, and supporting documentation for all three project objectives. It includes metadata, analysis outputs, and experimental protocols. Refer to the folder structure for details on specific contents and conditions.</p
Applying an MCDM method on six green methanol production technologies
No full textDATASET MIGRATED FROM FIGSHARE: Calculation sheet for DEMATEL applied on indicators for green methanol technologies</p
Python code for rule-based NLP extraction of keywords from circular economy definitions
No full textDATASET MIGRATED FROM FIGSHARE: This repository provides Python code and a dataset for a rule-based NLP analysis of 500 peer-reviewed circular economy definitions. The Python script enables automated keyword extraction to identify 10R strategies (R0-R9) and beyond, offering structured insights into CE definitions. ChatGPT 4o is used for optimization and debugging. Key Features✅ Python-based rule-based NLP model for keyword extraction.✅ Google Colab-ready script for real-time text analysis.✅ Dataset of 500 analyzed CE definitions with extracted R-strategy and sustainability-related keywords.✅ Manual & user guide to ensure easy reproducibility.</p
List and lexical-semantic analysis of 600 predefined keywords for rule-based NLP to extract themes within and beyond R-strategies in circular economy definitions
No full textDATASET MIGRATED FROM FIGSHARE: Thematic identification was applied in this study through a combination of deductive and abductive reasoning to ensure both structured categorization and flexibility in capturing emerging concepts. The process began with a deductive approach, selecting initial keywords for each circular economy strategy based on the 10R framework. These keywords formed the foundation for strategy classification. An iterative abductive refinement followed, where definitions were manually reviewed to identify missing or alternative terms, which were then systematically integrated. During this process, the initial keyword set was used as input for the previously developed NLP model. The model was continuously tested and refined, enabling data-driven expansion while maintaining alignment with the 10R framework.Moreover, a linguistic categorization analysis (lexical-semantic analysis (Hua et al., 2015)) was conducted, classifying all keywords into three groups: lexical matches (direct variations of the strategy name), semantic equivalents (synonyms), and contextual inferences (concepts requiring interpretation). </p
Supplementary chapter and frequency data on sustainability-oriented economic models
No full textDATASET MIGRATED FROM FIGSHARE: This supplementary file provides an integrated resource consisting of (1) a descriptive chapter that offers an overview of more than 20 sustainability-oriented economic models, grouped into socially inclusive, technological/innovation-driven, and integrated sustainable development clusters, outlining their core principles, historical context, and relevance to sustainable development; and (2) detailed frequency data from Scopus title searches, presenting the academic prominence of each model and its synonyms. These data serve as a proxy for scholarly attention and complement the main text’s analysis, particularly supporting Figure X, by clarifying the relative positioning of the circular economy within the broader sustainability landscape.</p
Supplementary material: Detailed temporal analysis of CE–SDG mapping dynamics
No full textDATASET MIGRATED FROM FIGSHARE: This supplementary material provides detailed temporal data on the mapping of 2,700 circular economy (CE) indicators to the 17 Sustainable Development Goals (SDGs), covering the period 2011–2024. It includes year-by-year distributions for each SDG, identification of emergence years based on a defined threshold method, and supporting data for temporal clustering and pattern analysis. These results offer additional insights into the evolution of CE–SDG alignment and highlight emerging trends, gaps, and shifts in thematic emphasis over time.</p
Python code for a stratified random sampling of circular economy indicators
No full textDATASET MIGRATED FROM FIGSHARE: This Python script performs stratified random sampling of 2,713 Circular Economy (CE) indicators based on their SDG classification for expert reviews. It ensures proportional representation by sampling approximately 5% of indicators from each SDG group while guaranteeing at least one sample per SDG when present. These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.</p
Replication data for: "Family breakup dynamics in a promiscuous solitary mammal"
No full textDATASET MIGRATED FROM FIGSHARE: Datafiles and R code used for all analyses, figures, tables, and Supplementary Materials included in the manuscript: "Family breakup dynamics in a promiscuous solitary mammal".Please see ReadMe file for more information regarding all the files.</p
Search strategies for a review article on intersectorial collaboration and young people not in education, employment or training (NEET)
No full textDATASET MIGRATED FROM FIGSHARE: Search strategies for a review article on cross sector collaboration regarding youth not in education, employment, or training are described.</p