cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18375959
title: The current situation and managerial implications for responsible tourism development in Kien Giang Province based on tourist surveys

description: Responsible tourism is a development trend in the tourism industry aimed at maximizing positive aspects and minimizing negative impacts from tourism. Besides achievements, tourism in Kien Giang Province also reveals many limitations that can be addressed through the approach of responsible tourism development. With the purpose of analyzing the current situation of responsible tourism development in Kien Giang Province based on content and territory according to tourists' perceptions, this study was conducted. Research data were collected via a survey with questionnaires administered to 400 tourists. Descriptive statistics and one-way ANOVA analysis methods were used to analyze the data. The research results reflect the highlights and limitations in the development of responsible tourism in Kien Giang regarding aspects such as product development, communication, labor usage, tourism management, community actions, operation of food service establishments, and accommodation facilities. Additionally, the differences in the development level of these aspects by surveyed locations were also clarified.
DOI: 10.5281/zenodo.18375958
title: The current situation and managerial implications for responsible tourism development in Kien Giang Province based on tourist surveys

description: Responsible tourism is a development trend in the tourism industry aimed at maximizing positive aspects and minimizing negative impacts from tourism. Besides achievements, tourism in Kien Giang Province also reveals many limitations that can be addressed through the approach of responsible tourism development. With the purpose of analyzing the current situation of responsible tourism development in Kien Giang Province based on content and territory according to tourists' perceptions, this study was conducted. Research data were collected via a survey with questionnaires administered to 400 tourists. Descriptive statistics and one-way ANOVA analysis methods were used to analyze the data. The research results reflect the highlights and limitations in the development of responsible tourism in Kien Giang regarding aspects such as product development, communication, labor usage, tourism management, community actions, operation of food service establishments, and accommodation facilities. Additionally, the differences in the development level of these aspects by surveyed locations were also clarified.
DOI: 10.34740/kaggle/dsv/14625418
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.18374439
title: UNB Inputs
DOI: 10.5281/zenodo.18374438
title: UNB Inputs
DOI: 10.25921/z0pk-pv81
title: Surface underway measurements of partial pressure of carbon dioxide (pCO2) during the Antarctic Research and Supply Vessel (ARSV) Laurence M. Gould expeditions in the Southern Ocean, South Pacific Ocean, Drake Passage and South Atlantic Ocean in 2020 (NCEI Accession 0225445)

description: This dataset includes surface underway, chemical, meteorological and physical data collected from ARSV Laurence M. Gould in the Southern Ocean, South Pacific Ocean, Drake Passage and South Atlantic Ocean from 2020-02-16 to 2020-12-27. These data include partial pressure (or fugacity) of carbon dioxide - atmosphere, partial pressure (or fugacity) of carbon dioxide - water, sea surface salinity, sea surface temperature and barometric pressure. The instruments used to collect these data include carbon dioxide (CO2) gas analyzer and shower head chamber equilibrator for autonomous carbon dioxide (CO2) measurement.
DOI: 10.58032/agh/rtezdn
title: Low-cost halloysite-fly ash composite for the removal of pharmaceuticals, dyes and surfactants from contaminated water - research data

description: The file contains raw data regarding solid state characterization of the tested powder composite containing halloysite and fly ash and experiemental conditions and data collected during sorption experiments of dyes, pharmaceuticals and surfactant on this composite.
DOI: 10.5285/48e39c93-2405-99f3-e063-7086abc003fa
title: Stainless Steel CTD water column samples (macronutrients, chlorophyll-a, particulate carbon, C-fixation, d18O) and upcast sensor measurements (temperature, salinity, oxygen, optical backscatter, beam transmittance and attenuation, PAR, fluorescence) from cruise DY167 (GApr19), July-August 2023.

description: This dataset contains sampled biogeochemical properties (nitrate + nitrite, silicate, phosphate, nitrite, dissolved oxygen, chlorophyll-a, particulate carbon, carbon fixation rates and oxygen isotopes) from Niskin bottles fired on 31 stainless steel CTD casts and the accompanying upcast sensor measurements (temperature, salinity, density, dissolved oxygen, optical backscatter, beam transmittance and attenuation, upward photosynthetically active radiation (PAR), and chlorophyll-a fluorescence). The dataset also contains the percent contribution of meteoric water, sea ice melt, and Atlantic Water, which were derived from d18O and salinity. The samples were collected during cruise DY167 (GEOTRACES cruise GApr19) in July-August 2023 in the Norwegian and Barents Seas and cover the relatively warm Atlantic Water inflow, fresher water on the Spitzbergen Bank, coastal/glacial outflow around Svalbard, and Arctic water around sea ice. Samples were taken from 20 L Niskin bottles that were fired between 4 and 12 different depths on the upcasts. The samples were filtered through a 0.2 um acetate membrane filter to determine nutrients (using colorimetric method and a Bran and Luebbe QuAAtro 5-Channel Nutrient Auto-analyser); through a 0.7 um (GF/F) filter to determine particulate carbon (using elemental analysis), carbon fixation rates (using isotope tracer incubations and isotope ratio mass spectrometry), and chlorophyll-a (frozen for 24 hours then measured using a Turner Design Field fluorometer); unfiltered samples were analysed for dissolved oxygen (using the standard Winkler technique, titration was done manually), and oxygen isotopes (following a CO2 equilibration method). Raw sensor data were processed and averaged within 5 second windows around the time of each bottle firing, and data were calibrated against independent in-situ samples. These data were collected to better determine how temperature, light, nutrients, and iron control the distribution and activity of nitrogen fixing organisms in the Arctic Ocean. This dataset was generated by the National Oceanography Centre (NOC), the University of Liverpool (UoL), the University of Southampton (UoS), British Geological Survey (BGS) and British Antarctic Survey (BAS) under Natural Environment Research Council (NERC) Discovery grant Nitrogen fixation in the Arctic Ocean, N-ARC (grant numbers NE/T000570/1 and NE/T001240/1) and grant Biogeochemical processes and ecosystem function in changing polar systems have global impacts, BIOPOLE (grant number NE/W004933/1).
DOI: 10.5281/zenodo.18365927
title: Adult Beginner Piano Learning Trajectories under ABRSM and YAMAHA Examination Systems: A 2025 Practice-Based Dataset

description: This dataset documents learning trajectories of adult beginner piano students (aged 25–75) engaged in structured music education under the ABRSM and YAMAHA examination systems during 2025, with longitudinal projections into 2026. The data encompasses two distinct geographic and cultural contexts: Taiwan, which is transitioning into a "hyper-aging society" by 2025 with a focus on Phase Three Senior Citizen Education, and Australia, where instrumental music is increasingly linked to resilience and wellbeing in lifelong learning frameworks.

The dataset is derived from long-term teaching practice in community colleges, private studios, and digital platforms. It captures the transition from foundational technical skills to musical interpretation, specifically tracking responses to the ABRSM 2025-2026 Piano Syllabus (which features expanded popular and jazz repertoire ) and the YAMAHA "Well-being" centric 2025 management plan.

The primary objective is to provide empirical, practice-based evidence on how adult learners progress in piano performance, music literacy (including MIDI-based accuracy), auditory skills (imitation and hearing), and emotional engagement. The dataset supports interdisciplinary research across music education, gerontology, arts-based intervention, and human-centered AI applications.
DOI: 10.5281/zenodo.18365928
title: Adult Beginner Piano Learning Trajectories under ABRSM and YAMAHA Examination Systems: A 2025 Practice-Based Dataset

description: This dataset documents learning trajectories of adult beginner piano students (aged 25–75) engaged in structured music education under the ABRSM and YAMAHA examination systems during 2025, with longitudinal projections into 2026. The data encompasses two distinct geographic and cultural contexts: Taiwan, which is transitioning into a "hyper-aging society" by 2025 with a focus on Phase Three Senior Citizen Education, and Australia, where instrumental music is increasingly linked to resilience and wellbeing in lifelong learning frameworks.

The dataset is derived from long-term teaching practice in community colleges, private studios, and digital platforms. It captures the transition from foundational technical skills to musical interpretation, specifically tracking responses to the ABRSM 2025-2026 Piano Syllabus (which features expanded popular and jazz repertoire ) and the YAMAHA "Well-being" centric 2025 management plan.

The primary objective is to provide empirical, practice-based evidence on how adult learners progress in piano performance, music literacy (including MIDI-based accuracy), auditory skills (imitation and hearing), and emotional engagement. The dataset supports interdisciplinary research across music education, gerontology, arts-based intervention, and human-centered AI applications.
DOI: 10.5285/48c1d94c-1647-8717-e063-7086abc0f520
title: Titanium CTD water column samples (macronutrients, chlorophyll-a, trace elements) and upcast sensor measurements (temperature, salinity, oxygen, optical backscatter, beam transmittance and attenuation, PAR, fluorescence) from the Norwegian and Barents Sea on cruise DY167 (GApr19), July-August 2023

description: This dataset contains sampled biogeochemical properties (nitrate + nitrite, silicate, phosphate, nitrite, chlorophyll-a, and dissolved iron and manganese) from Niskin bottles fired on 22 titanium CTD casts and the accompanying upcast sensor measurements (temperature, salinity, dissolved oxygen, optical backscatter, beam transmittance and attenuation, upward photosynthetically active radiation (PAR), and chlorophyll-a fluorescence). The samples were collected during cruise DY167 (GEOTRACES cruise GApr19) in July-August 2023 in the Norwegian and Barents Seas and cover the relatively warm Atlantic Water inflow, fresher water on the Spitzbergen Bank, coastal/glacial outflow around Svalbard, and Arctic water around sea ice. Samples were taken from 10 L Niskin bottles that were fired on the upcasts. The samples were filtered through a 0.2 um acetate membrane filter to determine nutrients (using colorimetric method and a Bran and Luebbe QuAAtro 5-Channel Nutrient Auto-analyser) and trace elements (using offline pre-concentration and extraction and Inductively Coupled Plasma Mass Spectrometry (ICP-MS)); and through a 0.7 um (GF/F) filter to determine chlorophyll-a (frozen for 24 hours then measured using a Turner Design Field fluorometer). Raw sensor data were processed and averaged within 5 second windows around the time of each bottle firing, and salinity and chlorophyll-a data were calibrated against independent in-situ samples. This dataset was generated by the National Oceanography Centre (NOC), the University of Liverpool (UoL), and the University of Southampton, under Natural Environment Research Council (NERC) Discovery Science grant "Nitrogen fixation in the Arctic Ocean", N-ARC (grant numbers NE/T000570/1 and NE/T001240/1).
DOI: 10.25921/f94g-zp40
title: Surface underway measurements of partial pressure of carbon dioxide (pCO2) during the Antarctic Research and Supply Vessel (ARSV) Laurence M. Gould expeditions in the Southern Ocean, South Atlantic Ocean in 2021 (NCEI Accession 0246983)

description: This dataset includes surface underway, chemical, meteorological and physical data collected from ARSV Laurence M. Gould in the Southern Ocean and South Atlantic Ocean from 2021-05-04 to 2021-12-31. These data include partial pressure (or fugacity) of carbon dioxide - atmosphere, partial pressure (or fugacity) of carbon dioxide - water, sea surface salinity, sea surface temperature and barometric pressure. The instruments used to collect these data include carbon dioxide (CO2) gas analyzer and shower head chamber equilibrator for autonomous carbon dioxide (CO2) measurement.
DOI: 10.3886/e244662
title: admission

description: University of Benin 2025/2026 Admission List (1st & 2nd) is Out☎(08053738196) DR Ben for Admission Help into Third and Final Batch Call Registrar : 08053738196 To check your admission status on the Admission List, Processing Of Admission Into Any Department And Purchasing Of 2025/2026 Last Supplementary Form For New Student Into Any Program, call the School's Admissions Office now on 08053738196
DOI: 10.5285/48d50e6a-e137-e1e3-e063-7086abc09a72
title: Underway biogeochemical samples data (macronutrients, chlorophyll-a, dissolved trace elements, particulate carbon, carbon fixation, d18O) and water mass fractions from a towfish at 3 m in the Norwegian and Barents Sea on cruise DY167 (GApr19), July-August 2023

description: This dataset contains measurements of dissolved inorganic macronutrients (nitrate + nitrite, silicate, phosphate, nitrite), chlorophyll-a, dissolved trace elements (iron and manganese), particulate carbon (PC), carbon fixation rates (C-fix) and oxygen isotopes (d18O) from an underway towfish deployed from the RRS Discovery during cruise DY167 (GEOTRACES cruise GApr19). The dataset also contains the percent contribution of meteoric water, sea ice melt, and Atlantic Water, which were derived from d18O and salinity, as well as navigational (latitude, longitude) and bathymetric data. The samples were collected at 2 hour intervals from water pumped by the towfish at approximately 3 metres depth in the Norwegian and Barents Seas and cover the relatively warm Atlantic Water inflow, fresher water on the Spitzbergen Bank, coastal/glacial outflow around Svalbard, and Arctic water around sea ice. The samples were filtered through a 0.2 um acetate membrane filter to determine nutrients (using colorimetric method and a Bran and Luebbe QuAAtro 5-Channel Nutrient Auto-analyser) and trace elements (using offline pre-concentration and extraction and Inductively Coupled Plasma Mass Spectrometry (ICP-MS)); through a 0.7 um (GF/F) filter to determine particulate carbon (using elemental analysis), carbon fixation rates (using isotope tracer incubations and isotope ratio mass spectrometry (IRMS)), and chlorophyll-a (frozen for 24 hours then measured using a Turner Design Field fluorometer); unfiltered samples were analysed for oxygen isotopes (following a CO2 equilibration method). These data were collected to better determine how temperature, light, nutrients, and iron control the distribution and activity of nitrogen fixing organisms in the Arctic Ocean. This dataset was generated by the National Oceanography Centre (NOC), the University of Liverpool (UoL), the University of Southampton (UoS), British Geological Survey (BGS) and British Antarctic Survey (BAS) under Natural Environment Research Council (NERC) Discovery grant Nitrogen fixation in the Arctic Ocean, N-ARC (grant numbers NE/T000570/1 and NE/T001240/1) and grant Biogeochemical processes and ecosystem function in changing polar systems have global impacts, BIOPOLE (grant number NE/W004933/1).
DOI: 10.5281/zenodo.18323831
title: Benchmarking Conceptual Rainfall–Runoff Models in Peninsular India: Simulated Streamflow and Evaluation Metrics (CAMELS-IND, MARRMoT)

description: This dataset accompanies the manuscript “Benchmarking and Selecting Optimal Hydrological Models for Large-Sample Applications Considering Complexity and Uncertainty” (Mangukiya et al., under review, Water Resources Research). It provides simulated streamflow outputs and evaluation metrics for 47 conceptual rainfall–runoff models applied across 159 watersheds in Peninsular India.

Model simulations were generated using the Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) (Trotter & Knoben, 2023), which implements 47 established hydrological models within a unified and consistent framework. Model forcing data and catchment attributes were derived from the CAMELS-IND dataset (Mangukiya et al., 2025), which provides hydrometeorological time series and physiographic information for catchments in Peninsular India.

The archive contains daily simulated streamflow time series for all models and watersheds, along with a comprehensive set of performance metrics computed during the evaluation period. While the associated manuscript primarily reports Nash–Sutcliffe Efficiency (NSE) and percent bias in high-flow volumes (FHV), this dataset additionally includes correlation coefficient, root mean square error (RMSE), Kling–Gupta Efficiency (KGE), percentage bias (PBIAS), and flow-duration-curve-based errors.

Contents:



simulated_streamflow: Daily simulated streamflow time series for all models and watersheds

evaluation_metrics: Model performance metrics computed for each watershed for the validation period

best_performing_model.csv: Summary of best-performing models per watershed based on selected evaluation criteria


All simulations were produced using a consistent single-parameter-set calibration framework, as described in the associated manuscript. Users are encouraged to interpret the results within the context of the adopted calibration and benchmarking approach.

This dataset is intended to support transparency, reproducibility, and future comparative studies on large-sample hydrological modeling, model benchmarking, and structural uncertainty.

Related datasets and software:



CAMELS-IND : Mangukiya, N. K., et al. (2025). CAMELS-IND: hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India (v2.2). Zenodo. https://doi.org/10.5281/zenodo.11118408

MARRMoT : Trotter, L., & Knoben, W. J. M. (2023). MARRMoT (v2.1.2). Zenodo. https://doi.org/10.5281/zenodo.2482541


Related publications:

Trotter, L., Knoben, W. J. M., Fowler, K. J. A., Saft, M., & Peel, M. C. (2022). Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1. Geoscientific Model Development, 15, 6359–6369. https://doi.org/10.5194/gmd-15-6359-2022

Mangukiya, N. K., et al. (2025). CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India. Earth System Science Data, 17, 461–491. https://doi.org/10.5194/essd-17-461-2025
DOI: 10.5281/zenodo.18323832
title: Benchmarking Conceptual Rainfall–Runoff Models in Peninsular India: Simulated Streamflow and Evaluation Metrics (CAMELS-IND, MARRMoT)

description: This dataset accompanies the manuscript “Benchmarking and Selecting Optimal Hydrological Models for Large-Sample Applications Considering Complexity and Uncertainty” (Mangukiya et al., under review, Water Resources Research). It provides simulated streamflow outputs and evaluation metrics for 47 conceptual rainfall–runoff models applied across 159 watersheds in Peninsular India.

Model simulations were generated using the Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) (Trotter & Knoben, 2023), which implements 47 established hydrological models within a unified and consistent framework. Model forcing data and catchment attributes were derived from the CAMELS-IND dataset (Mangukiya et al., 2025), which provides hydrometeorological time series and physiographic information for catchments in Peninsular India.

The archive contains daily simulated streamflow time series for all models and watersheds, along with a comprehensive set of performance metrics computed during the evaluation period. While the associated manuscript primarily reports Nash–Sutcliffe Efficiency (NSE) and percent bias in high-flow volumes (FHV), this dataset additionally includes correlation coefficient, root mean square error (RMSE), Kling–Gupta Efficiency (KGE), percentage bias (PBIAS), and flow-duration-curve-based errors.

Contents:



simulated_streamflow: Daily simulated streamflow time series for all models and watersheds

evaluation_metrics: Model performance metrics computed for each watershed for the validation period

best_performing_model.csv: Summary of best-performing models per watershed based on selected evaluation criteria


All simulations were produced using a consistent single-parameter-set calibration framework, as described in the associated manuscript. Users are encouraged to interpret the results within the context of the adopted calibration and benchmarking approach.

This dataset is intended to support transparency, reproducibility, and future comparative studies on large-sample hydrological modeling, model benchmarking, and structural uncertainty.

Related datasets and software:



CAMELS-IND : Mangukiya, N. K., et al. (2025). CAMELS-IND: hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India (v2.2). Zenodo. https://doi.org/10.5281/zenodo.11118408

MARRMoT : Trotter, L., & Knoben, W. J. M. (2023). MARRMoT (v2.1.2). Zenodo. https://doi.org/10.5281/zenodo.2482541


Related publications:

Trotter, L., Knoben, W. J. M., Fowler, K. J. A., Saft, M., & Peel, M. C. (2022). Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1. Geoscientific Model Development, 15, 6359–6369. https://doi.org/10.5194/gmd-15-6359-2022

Mangukiya, N. K., et al. (2025). CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India. Earth System Science Data, 17, 461–491. https://doi.org/10.5194/essd-17-461-2025
DOI: 10.5281/zenodo.18366196
title: Keyboard Music Learning, Emotional Regulation, and Cognitive Engagement in Older Adults: A Taiwan–Australia Pilot Dataset (2026)

description: Description

This pilot dataset documents keyboard-based music learning practices among older adults (aged 60–80) in Taiwan and Australia. In 2026, Taiwan transitioned into a "hyper-aging society," prompting the "Phase Three Senior Citizen Education Project (2025-2028)" , while Australian music education increasingly emphasizes instrumental music as a tool for resilience and social flourishing.

The dataset focuses on three core dimensions:

1.Cognitive Engagement: Measured via the Stroop Test (executive function), MMSE (global cognition), and auditory-motor coordination. 

2.Emotional Regulation: Tracked through self-reported mood states (POMS 2nd Edition) and observed resilience during technical challenges. 

3.Practice Trajectories: MIDI-based data capturing tempo stability (BPM variance), pitch accuracy, and repertoire retention over 12 months.

The objective is to provide an empirical basis for music education as a non-pharmacological, dignity-preserving intervention . This dataset is intended for use in gerontology, music pedagogy, and human-centered AI for health innovation.