cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tgxw2g4stx.1
title: [Dataset] Eco-friendly nanocomposite scaffolds: physicochemical analysis of starch matrices integrated with Turbinaria ornata-mediated CuO nanoparticles

description: This dataset comprehensively characterizes starch-based nanocomposite scaffolds reinforced with copper oxide nanoparticles (CuO NPs) synthesized using Turbinaria ornata, a brown algae, as a green reducing and stabilizing agent. The study employs UV-visible spectroscopy (UV-Vis) to verify CuO NP formation via absorbance peaks and monitor reaction kinetics. Fourier-transform infrared spectroscopy (FT-IR) identifies functional groups in the algal extract and elucidates interactions between starch polymers and nanoparticles, such as hydrogen bonding or electrostatic forces. X-ray diffraction (XRD) confirms the crystalline phase and purity of biosynthesized CuO NPs, while scanning electron microscopy (SEM) reveals the scaffold’s porous architecture, surface topography, and homogeneous nanoparticle dispersion. Thermal gravimetric analysis (TGA) evaluates the nanocomposite’s thermal stability and decomposition behavior, highlighting the reinforcing effect of CuO NPs on starch matrices. Dynamic light scattering (DLS) measures nanoparticle hydrodynamic size, polydispersity, and colloidal stability in suspension, critical for assessing biocompatibility. The dataset underscores the synergy between eco-friendly synthesis (using Turbinaria ornata extract) and enhanced scaffold functionality, such as improved mechanical strength, thermal resistance, and controlled degradation. These insights advance sustainable biomaterial design, particularly for tissue engineering, wound healing, or drug delivery, where biodegradable starch-algae nanocomposites offer alternatives to synthetic counterparts. Researchers can utilize this integrated analytical approach to optimize algal-mediated nanoparticle synthesis, tailor scaffold properties, and validate eco-conscious strategies for developing high-performance nanohybrid systems. The work bridges green chemistry principles with material science, emphasizing scalability and environmental safety in nanotechnology applications.
DOI: 10.17632/mmyv8mmkbx.1
title: [Dataset] Green nanotechnology in biomaterials: characterization of starch-based scaffolds with Turbinaria conoides-synthesized copper oxide nanocomposites

description: This dataset offers a holistic analysis of starch-based nanocomposite scaffolds functionalized with copper oxide nanoparticles (CuO NPs) synthesized using Turbinaria conoides, a marine brown algae, via an eco-friendly green chemistry approach. UV-visible spectroscopy (UV-Vis) confirms CuO NP formation through characteristic absorption peaks, while Fourier-transform infrared spectroscopy (FT-IR) identifies bioactive phytochemicals in the algal extract responsible for nanoparticle reduction and stabilization, as well as molecular interactions (e.g., hydrogen bonding) between starch polymers and CuO NPs. X-ray diffraction (XRD) validates the crystalline structure and phase purity of the biosynthesized nanoparticles, and scanning electron microscopy (SEM) reveals the nanoscaffold’s interconnected porous network, surface roughness, and uniform nanoparticle distribution. Thermal gravimetric analysis (TGA) assesses thermal degradation patterns, demonstrating enhanced stability of the nanocomposite due to CuO NP reinforcement. Dynamic light scattering (DLS) provides insights into nanoparticle size distribution, zeta potential, and colloidal stability, critical for evaluating biocompatibility. The study emphasizes the role of Turbinaria conoides extract in enabling energy-efficient, nontoxic synthesis of CuO NPs while improving scaffold functionality, such as mechanical resilience and controlled biodegradation in physiological environments. These starch-algae nanocomposites hold promise for sustainable biomaterial applications, including tissue regeneration, antimicrobial wound dressings, or eco-friendly packaging. By integrating green nanotechnology with advanced characterization techniques, this dataset provides a blueprint for designing scalable, environmentally benign nanohybrid systems. Researchers can utilize these findings to refine algal-mediated synthesis protocols, tailor scaffold architectures, and validate the performance of bio-derived nanomaterials in multidisciplinary contexts, bridging ecological sustainability with cutting-edge material science.
DOI: 10.17632/xtwhbc25hj.1
title: [Dataset] Multidisciplinary evaluation of biofabricated starch nanoscaffolds incorporating phyto-synthesized CuO nanoparticles from Dictyota extract

description: This dataset provides a multidisciplinary assessment of starch-based nanoscaffolds integrated with copper oxide nanoparticles (CuO NPs) synthesized using Dictyota sp., a brown macroalga, as a sustainable phytoreduction agent. UV-visible spectroscopy (UV-Vis) confirms CuO NP formation via characteristic plasmonic absorption bands, while Fourier-transform infrared spectroscopy (FT-IR) identifies functional groups in the algal extract (e.g., polyphenols, polysaccharides) involved in nanoparticle synthesis and stabilization, alongside interactions (e.g., hydrogen bonding) between starch polymers and CuO NPs. X-ray diffraction (XRD) validates the crystalline structure and phase purity of the nanoparticles, and scanning electron microscopy (SEM) reveals the nanoscaffold’s porous architecture, surface morphology, and homogeneous nanoparticle dispersion. Thermal gravimetric analysis (TGA) quantifies thermal stability, demonstrating enhanced degradation resistance of the nanocomposite due to CuO NP reinforcement. Dynamic light scattering (DLS) profiles nanoparticle hydrodynamic size, polydispersity, and colloidal stability, essential for biocompatibility evaluation. The dataset underscores the efficacy of Dictyota extract in enabling eco-friendly CuO NP synthesis while improving scaffold properties, such as mechanical robustness, controlled biodegradation, and thermal resilience. These biofabricated nanocomposites hold potential for biomedical applications, including tissue engineering, antimicrobial coatings, or drug delivery systems, where sustainable materials are prioritized. By combining green synthesis with advanced analytical workflows, this resource bridges phytochemistry, material science, and nanotechnology, offering insights into scalable, low-toxicity biomaterial design. Researchers can leverage this multidisciplinary framework to optimize algal-mediated synthesis protocols, correlate structural features with functional performance, and engineer starch-based nanohybrids for targeted ecological and biomedical innovations. The work highlights the role of marine algae in advancing eco-conscious nanotechnology and circular material economies.
DOI: 10.17632/763h4wwkrj.1
title: [Dataset] Sustainable nanohybrid systems: Physiochemical properties of Gracilaria corticota extract-mediated copper oxide nanoparticle-loaded starch scaffolds

description: This dataset delivers a comprehensive evaluation of starch-based nanoscaffolds reinforced with copper oxide nanoparticles (CuO NPs) synthesized using Gracilaria corticota, a red algal extract, as a sustainable reducing and capping agent. UV-visible spectroscopy (UV-Vis) confirms CuO NP formation through distinct plasmon resonance peaks, while Fourier-transform infrared spectroscopy (FT-IR) identifies bioactive compounds (e.g., sulfated polysaccharides, phenolics) in the algal extract responsible for nanoparticle synthesis and stabilization, alongside molecular interactions (e.g., hydrogen bonding) between starch matrices and CuO NPs. X-ray diffraction (XRD) validates the crystalline phase and purity of the nanoparticles, and scanning electron microscopy (SEM) reveals the nanoscaffold’s interconnected porous structure, surface topography, and uniform CuO NP dispersion. Thermal gravimetric analysis (TGA) demonstrates enhanced thermal stability of the nanocomposite, attributing improved degradation resistance to nanoparticle reinforcement. Dynamic light scattering (DLS) profiles nanoparticle size distribution, polydispersity, and colloidal stability, crucial for assessing biocompatibility and application suitability. The dataset underscores the role of Gracilaria corticota in enabling eco-friendly CuO NP synthesis while optimizing scaffold performance, including mechanical strength, thermal resilience, and controlled biodegradation. These nanohybrid systems exhibit promise for sustainable biomedical applications, such as tissue engineering scaffolds, antimicrobial wound dressings, or eco-friendly packaging materials. By integrating green nanotechnology with advanced physicochemical characterization, this work highlights scalable strategies for designing low-toxicity, high-performance biomaterials. Researchers can leverage this multidisciplinary dataset to refine algal-mediated synthesis protocols, correlate structural properties (e.g., porosity, crystallinity) with functional outcomes, and advance circular economy principles in nanomaterial development. The findings bridge marine bioresource utilization with material innovation, emphasizing environmentally conscious alternatives to conventional synthetic nanocomposites.
DOI: 10.17632/99bn3x4yzb.1
title: [Dataset] Advanced biopolymer scaffolds: Physiochemical features of Chondracanthus exasperatus-functionalized copper oxide-starch nanocomposites

description: This dataset provides a detailed physicochemical evaluation of starch-based biopolymer scaffolds reinforced with copper oxide nanoparticles (CuO NPs) synthesized using Chondracanthus exasperatus, a red macroalga, as a green reducing and stabilizing agent. UV-visible spectroscopy (UV-Vis) confirms CuO NP formation through characteristic absorption peaks, while Fourier-transform infrared spectroscopy (FT-IR) identifies bioactive algal compounds (e.g., carrageenans, sulfated polysaccharides) involved in nanoparticle synthesis and stabilization, alongside molecular interactions (e.g., hydrogen bonding) between starch matrices and CuO NPs. X-ray diffraction (XRD) validates the crystalline structure and phase purity of the nanoparticles, and scanning electron microscopy (SEM) reveals the scaffold’s porous architecture, surface morphology, and uniform dispersion of CuO NPs. Thermal gravimetric analysis (TGA) assesses thermal stability, demonstrating enhanced degradation resistance due to nanoparticle reinforcement, while dynamic light scattering (DLS) quantifies hydrodynamic size, polydispersity, and colloidal stability of the NPs, critical for biocompatibility.
The study highlights how Chondracanthus exasperatus extract enables eco-friendly CuO NP synthesis while improving scaffold functionality, including mechanical durability, thermal resilience, and controlled biodegradation. These nanocomposites exhibit potential for advanced biomedical applications, such as tissue engineering matrices, antimicrobial coatings, or eco-conscious packaging, where sustainability and performance are prioritized. By integrating algal-mediated green synthesis with advanced analytical techniques, this dataset bridges marine bioresource utilization with material innovation, offering a template for designing low-toxicity, high-efficiency nanohybrid systems. Researchers can leverage these insights to optimize algal extraction protocols, tailor scaffold porosity and crystallinity, and validate eco-friendly strategies for scalable nanomaterial production. The work underscores the synergy between biopolymer science and green nanotechnology, advancing circular economy principles in the development of next-generation biomaterials.
DOI: 10.17632/y89s8vn8r4.1
title: Sound and cutting force components in longitudinal turning of C45 steel at different cutting regimes

description: This repository is compiled with the aim to explore the potential of using cutting sound for estimating values of cutting force components (tangential, radial, axial). Data was obtained by performing 100 experiment trials in longitudinal turning of unalloyed medium carbon steel C45E. Every trial was carried out with different combination of cutting parameters (cutting speed, depth of cut, feed rate). For each trial, sound and cutting force components were recorded. 

Repository consists of three folders, "raw_data", "notebooks" and "datasets". Folder "raw_data" holds 100 raw sound signals and measurements of cutting force components. In addition, 100 one-second sound segments were extracted from stable cutting period of the raw sound signals. Folder "notebooks" provides Python code for extracting sound and force features from one-second sound segments and raw measurements of cutting force components. Finally, folder "datasets" is consisted of sound and force features extracted with provided Python notebooks. Additional data in this folder lists all combinations of cutting parameters used in experiment trials.
DOI: 10.17632/vf8822rd8j.1
title: [Dataset] Eco-conscious biomaterial design: Analytical insights into starch nanoscaffolds with green-engineered copper oxide nanoparticles using marine Sargassum wightii extract

description: This dataset presents a detailed physicochemical characterization of starch-based nanoscaffolds integrated with copper oxide nanoparticles (CuO NPs) synthesized via an eco-friendly approach using Sargassum wightii, a marine brown algal extract. UV-visible spectroscopy (UV-Vis) confirms CuO NP formation through characteristic plasmon resonance peaks, while Fourier-transform infrared spectroscopy (FT-IR) identifies bioactive compounds (e.g., polyphenols, polysaccharides) in the algal extract responsible for nanoparticle reduction and stabilization, alongside interactions (e.g., hydrogen bonding) between starch polymers and CuO NPs. X-ray diffraction (XRD) validates the crystalline phase and structural purity of the nanoparticles, and scanning electron microscopy (SEM) reveals the nanoscaffold’s porous architecture, surface topography, and homogeneous CuO NP dispersion. Thermal gravimetric analysis (TGA) demonstrates enhanced thermal stability and delayed degradation of the nanocomposite, attributed to nanoparticle reinforcement. Dynamic light scattering (DLS) profiles hydrodynamic size, polydispersity, and colloidal stability of the CuO NPs, critical for assessing biocompatibility.
The study emphasizes the sustainable synergy between Sargassum wightii’s phytochemicals and starch matrices, yielding scaffolds with improved mechanical strength, thermal resistance, and controlled biodegradation. These nanohybrid systems hold promise for eco-conscious biomedical applications, such as tissue engineering, wound healing, or drug delivery, where low environmental impact and high performance are prioritized. By merging green nanotechnology with advanced analytical workflows, this dataset bridges marine bioresource valorization with material innovation, offering a scalable strategy for eco-friendly biomaterial development. Researchers can leverage these insights to optimize algal-mediated synthesis protocols, tailor scaffold porosity and crystallinity, and validate the ecological and functional advantages of marine algae-derived nanocomposites. The work underscores the potential of Sargassum wightii in advancing circular economy principles, reducing reliance on toxic chemicals, and fostering sustainable alternatives in nanotechnology-driven biomaterial design.
DOI: 10.17632/zp8nkgg5kr
title: Correlation of Clinical Scores with Remote Assessments of Accelerometry Output of a Low-Cost Quantitative Continuous Measurement of Movements

description: This dataset accompanies the research poster titled “Correlation of clinical scores with remote assessments of accelerometry output of a low-cost quantitative continuous measurement of movements.” It focuses on the reliability and validity of using wearable accelerometers to remotely evaluate motor function in individuals with Parkinson’s disease (PD) and compares those results to standard clinical scores obtained through in-person assessment.

The study collected accelerometry data from participants performing predefined motor tasks. These data were then processed to extract movement features such as amplitude, frequency, and signal stability. Clinical motor scores were concurrently collected using established neurological scales. The dataset includes both raw and summarized data from individuals with PD and age-matched typically developing (TD) controls.

The primary analysis involved assessing the internal consistency and test-retest reliability of the accelerometer-derived features using Cronbach’s Alpha and Intraclass Correlation Coefficients (ICC). High internal consistency (e.g., Cronbach’s Alpha = 0.918 for PD group) and strong reliability in average measures (ICC = 0.918 for PD group) support the use of remote motor assessments as a valid tool for clinical or research purposes.

This description style is in line with other published Mendeley Data entries such as:

Yoon et al. (2021) – Wearable-based gait and balance features dataset for fall risk prediction in elderly (https://data.mendeley.com/datasets/dyhgxdkf5y/1)

Giggins et al. (2022) – Accelerometer-derived physical activity and balance metrics in Parkinson’s disease (https://data.mendeley.com/datasets/xmzz5mfxv9/2)

The dataset is intended for researchers interested in neurodegenerative disease monitoring, remote assessment technologies, movement disorders, and wearable sensor applications. It can be reused for meta-analyses, validation studies, and the development of digital health tools, particularly in resource-limited or telehealth contexts.
DOI: 10.17632/zp8nkgg5kr.1
title: Correlation of Clinical Scores with Remote Assessments of Accelerometry Output of a Low-Cost Quantitative Continuous Measurement of Movements

description: This dataset accompanies the research poster titled “Correlation of clinical scores with remote assessments of accelerometry output of a low-cost quantitative continuous measurement of movements.” It focuses on the reliability and validity of using wearable accelerometers to remotely evaluate motor function in individuals with Parkinson’s disease (PD) and compares those results to standard clinical scores obtained through in-person assessment.

The study collected accelerometry data from participants performing predefined motor tasks. These data were then processed to extract movement features such as amplitude, frequency, and signal stability. Clinical motor scores were concurrently collected using established neurological scales. The dataset includes both raw and summarized data from individuals with PD and age-matched typically developing (TD) controls.

The primary analysis involved assessing the internal consistency and test-retest reliability of the accelerometer-derived features using Cronbach’s Alpha and Intraclass Correlation Coefficients (ICC). High internal consistency (e.g., Cronbach’s Alpha = 0.918 for PD group) and strong reliability in average measures (ICC = 0.918 for PD group) support the use of remote motor assessments as a valid tool for clinical or research purposes.

This description style is in line with other published Mendeley Data entries such as:

Yoon et al. (2021) – Wearable-based gait and balance features dataset for fall risk prediction in elderly (https://data.mendeley.com/datasets/dyhgxdkf5y/1)

Giggins et al. (2022) – Accelerometer-derived physical activity and balance metrics in Parkinson’s disease (https://data.mendeley.com/datasets/xmzz5mfxv9/2)

The dataset is intended for researchers interested in neurodegenerative disease monitoring, remote assessment technologies, movement disorders, and wearable sensor applications. It can be reused for meta-analyses, validation studies, and the development of digital health tools, particularly in resource-limited or telehealth contexts.
DOI: 10.17632/jrs2y2hp7v.1
title: [Dataset] Bioactive nanostructured platforms: Physicochemical assessment of starch scaffolds hybridized with Sargassum muticum-copper oxide nanoparticles

description: This dataset offers a comprehensive physicochemical evaluation of starch-based scaffolds functionalized with copper oxide nanoparticles (CuO NPs) synthesized using Sargassum muticum, a brown macroalga, via an eco-friendly green synthesis approach. UV-visible spectroscopy (UV-Vis) confirms CuO NP formation through characteristic plasmon resonance peaks, while Fourier-transform infrared spectroscopy (FT-IR) identifies bioactive algal compounds (e.g., polyphenols, polysaccharides) involved in nanoparticle reduction and stabilization, as well as molecular interactions (e.g., hydrogen bonding) between starch polymers and CuO NPs. X-ray diffraction (XRD) validates the crystalline structure and phase purity of the nanoparticles, and scanning electron microscopy (SEM) reveals the scaffold’s porous architecture, surface morphology, and uniform nanoparticle dispersion. Thermal gravimetric analysis (TGA) assesses thermal stability, demonstrating enhanced degradation resistance due to CuO NP reinforcement, while dynamic light scattering (DLS) quantifies hydrodynamic size, polydispersity, and colloidal stability of the NPs, critical for biocompatibility.
The study highlights the role of Sargassum muticum extract in enabling sustainable CuO NP synthesis while improving scaffold functionality, including mechanical robustness, thermal resilience, and bioactive properties. These hybrid nanocomposites exhibit potential for advanced biomedical applications such as tissue engineering, antimicrobial coatings, or targeted drug delivery, where eco-conscious design and performance are paramount. By integrating algal-mediated green synthesis with advanced analytical techniques, this dataset bridges marine bioresource utilization with material innovation, offering insights into scalable, low-toxicity biomaterial development. Researchers can leverage these findings to optimize synthesis protocols, tailor scaffold porosity and crystallinity, and validate the ecological and functional benefits of marine algae-derived nanocomposites. The work underscores the synergy between green nanotechnology and biopolymer science, advancing sustainable alternatives for next-generation bioactive platforms in healthcare and environmental applications.
DOI: 10.17632/pngxtjmtt3.1
title: MSRCMPMT for Project Scheduling

description: This test dataset contains sample project information for the multi-project scheduling problem. There are 12 test cases available, each containing two related files (BW and FW). The corresponding resource file is also included.