cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fgv56hzxnt
title: Dataset on the Performance of a Photovoltaic Solar Water Pump in Coffee Plantations Using Response Surface Methodology (RSM)

description: This dataset was generated to investigate how key environmental and operational factors influence the performance of a photovoltaic (PV) solar-powered water pumping system installed in a coffee plantation in Chiang Mai Province, Thailand. The research hypothesis posits that the combined effects of solar irradiance, panel inclination, and panel surface temperature significantly influence pumping efficiency, and that an optimal operating point can be identified using a structured statistical approach.

The data were collected through 15 experimental runs designed using Response Surface Methodology (RSM), which enabled the systematic variation of three independent variables: solar irradiance (300–900 W/m²), panel inclination (15–35°), and panel surface temperature (30–60 °C). For each condition, the pumping efficiency (%) was measured, and both raw and processed data—including residual diagnostics, ANOVA outputs, regression coefficients, and response surface parameters—are provided. These elements allow users to fully reproduce the experimental workflow, model-building process, and optimization procedure.

The dataset shows that solar irradiance is the most influential factor affecting pumping efficiency, followed by panel surface temperature and panel inclination. Efficiency generally increases with higher irradiance and moderate tilt angles, while excessively high panel temperatures lead to performance losses due to reduced photovoltaic electrical output. Statistical analyses confirm a highly reliable quadratic regression model (R² = 0.9972), with strong agreement between adjusted and predicted R², indicating excellent model adequacy and predictive capability.

Visual outputs—including perturbation plots, 3D surface plots, and contour plots—illustrate the main and interaction effects of each variable. These plots reveal a clear nonlinear behavior in system performance, with all three factors exhibiting curvature consistent with the quadratic model. The optimal operating conditions were identified at a solar irradiance of 600 W/m², a panel inclination of 25°, and a panel temperature of 45 °C, corresponding to a predicted pumping efficiency of 76.3–77.0%.

This dataset can be interpreted as a validated reference model for the design and optimization of PV-powered water pumping systems operating in agricultural environments, particularly in highland regions with fluctuating solar and temperature conditions. Researchers may reuse the data to calibrate simulation tools, develop predictive models, compare alternative pump designs, or assess how environmental variability impacts renewable-energy-based irrigation performance. The combined inclusion of raw data, statistical modeling files, and visual analyses ensures transparency and reproducibility, offering a comprehensive resource for renewable energy and agricultural engineering research.
DOI: 10.3535/mcz-wyw-2dw
title: Ochropacha duplaris (Linnaeus, 1761)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/pwp-w6r-k97
title: Opigena polygona [Denis & Schiffermüller], 1775

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.17632/fgv56hzxnt.1
title: Dataset on the Performance of a Photovoltaic Solar Water Pump in Coffee Plantations Using Response Surface Methodology (RSM)

description: This dataset was generated to investigate how key environmental and operational factors influence the performance of a photovoltaic (PV) solar-powered water pumping system installed in a coffee plantation in Chiang Mai Province, Thailand. The research hypothesis posits that the combined effects of solar irradiance, panel inclination, and panel surface temperature significantly influence pumping efficiency, and that an optimal operating point can be identified using a structured statistical approach.

The data were collected through 15 experimental runs designed using Response Surface Methodology (RSM), which enabled the systematic variation of three independent variables: solar irradiance (300–900 W/m²), panel inclination (15–35°), and panel surface temperature (30–60 °C). For each condition, the pumping efficiency (%) was measured, and both raw and processed data—including residual diagnostics, ANOVA outputs, regression coefficients, and response surface parameters—are provided. These elements allow users to fully reproduce the experimental workflow, model-building process, and optimization procedure.

The dataset shows that solar irradiance is the most influential factor affecting pumping efficiency, followed by panel surface temperature and panel inclination. Efficiency generally increases with higher irradiance and moderate tilt angles, while excessively high panel temperatures lead to performance losses due to reduced photovoltaic electrical output. Statistical analyses confirm a highly reliable quadratic regression model (R² = 0.9972), with strong agreement between adjusted and predicted R², indicating excellent model adequacy and predictive capability.

Visual outputs—including perturbation plots, 3D surface plots, and contour plots—illustrate the main and interaction effects of each variable. These plots reveal a clear nonlinear behavior in system performance, with all three factors exhibiting curvature consistent with the quadratic model. The optimal operating conditions were identified at a solar irradiance of 600 W/m², a panel inclination of 25°, and a panel temperature of 45 °C, corresponding to a predicted pumping efficiency of 76.3–77.0%.

This dataset can be interpreted as a validated reference model for the design and optimization of PV-powered water pumping systems operating in agricultural environments, particularly in highland regions with fluctuating solar and temperature conditions. Researchers may reuse the data to calibrate simulation tools, develop predictive models, compare alternative pump designs, or assess how environmental variability impacts renewable-energy-based irrigation performance. The combined inclusion of raw data, statistical modeling files, and visual analyses ensures transparency and reproducibility, offering a comprehensive resource for renewable energy and agricultural engineering research.
DOI: 10.3535/rff-ac6-w2b
title: Exaeretia ciniflonella Zeller, 1846

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/6sp-c0e-b08
title: Ochropacha duplaris (Linnaeus, 1761)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/hqz-5td-v9j
title: Scopariinae

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/k8r-jzy-jql
title: Scopariinae

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/ld6-gcm-lwk
title: Helotropha leucostigma Hübner, 1808

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/00g-pep-748
title: Scopariinae

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/24w-nc9-rbc
title: Scopariinae

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/7jf-9jk-1pc
title: Conistra (Conistra) vaccinii (Linnaeus, 1761)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/agk-350-z5q
title: Agriphila selasella Hübner

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/0qm-7b0-6ax
title: Orgyia (Telochurus) recens (Hübner, 1819)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/7k5-v8s-dg4
title: Phengaris Doherty, 1891

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/er7-9jj-s0b
title: Dichonia (Griposia) Tams, 1939

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/gyh-98t-n4r
title: Conistra (Conistra) vaccinii (Linnaeus, 1761)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.17632/z2syry92fm
title: Data on pump efficiency optimization and social return of a solar-powered water pump in agricultural applications

description: This dataset presents experimental and socio-economic data used to evaluate and optimize the performance of a photovoltaic (PV) solar-powered water pumping system installed in agricultural areas of northern Thailand. The research hypothesis assumed that solar irradiance, PV tilt angle, and panel temperature (or ambient temperature) jointly influence pump efficiency in a nonlinear manner and that the system performance can be optimized using the Response Surface Methodology (RSM). The data include measurements of pump efficiency under 15 experimental conditions based on a Box–Behnken design, covering solar irradiance (300–1000 W/m²), PV tilt angle (10–35°), and temperature (30–60 °C). These measurements were collected under actual field conditions in coffee- and crop-growing areas in Chiang Mai and Lampang Provinces.

The dataset shows that pump efficiencies ranged from 51.4–85.2% depending on operating conditions. The highest efficiency (≈85%) occurred at moderate irradiance (≈800 W/m²), a tilt angle of 20–25°, and temperatures around 40–45 °C. Extremely high temperatures (&gt;45 °C) reduced efficiency due to thermal losses in the PV modules. Statistical analysis confirmed that solar irradiance was the most influential factor, followed by panel temperature and tilt angle. The developed quadratic regression model provided an excellent fit (R² &gt; 0.99), and diagnostic plots confirmed model adequacy, normality of residuals, and strong agreement between predicted and experimental values. Response surface plots and interaction contours illustrate how combinations of the three variables affect performance and identify the optimal region.

Beyond technical performance, the dataset also includes economic and social impact data derived from Social Return on Investment (SROI) analysis. Costs, energy use, and stakeholder feedback were collected through field surveys, interviews, and operational records. The SROI analysis compares the solar PV pumping system with a conventional groundwater pumping system and quantifies social and economic benefits for farmers and local communities over a three-year period. The results indicate consistently increasing social value, lower energy costs, reduced fuel dependency, and improved long-term sustainability.

Together, the dataset provides comprehensive technical, environmental, and socio-economic information supporting the optimization, performance modeling, and feasibility assessment of PV water pumping systems in rural agricultural regions. These data can be used for system design, RSM-based optimization, validation of numerical models, and sustainability assessments in similar climatic or agricultural contexts.
DOI: 10.3535/5ty-zxh-9y7
title: Mythimna (Mythimna) impura Hübner, 1808

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/exl-hg7-58w
title: Lycaena phlaeas (Linnaeus, 1761)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.17632/z2syry92fm.1
title: Data on pump efficiency optimization and social return of a solar-powered water pump in agricultural applications

description: This dataset presents experimental and socio-economic data used to evaluate and optimize the performance of a photovoltaic (PV) solar-powered water pumping system installed in agricultural areas of northern Thailand. The research hypothesis assumed that solar irradiance, PV tilt angle, and panel temperature (or ambient temperature) jointly influence pump efficiency in a nonlinear manner and that the system performance can be optimized using the Response Surface Methodology (RSM). The data include measurements of pump efficiency under 15 experimental conditions based on a Box–Behnken design, covering solar irradiance (300–1000 W/m²), PV tilt angle (10–35°), and temperature (30–60 °C). These measurements were collected under actual field conditions in coffee- and crop-growing areas in Chiang Mai and Lampang Provinces.

The dataset shows that pump efficiencies ranged from 51.4–85.2% depending on operating conditions. The highest efficiency (≈85%) occurred at moderate irradiance (≈800 W/m²), a tilt angle of 20–25°, and temperatures around 40–45 °C. Extremely high temperatures (&gt;45 °C) reduced efficiency due to thermal losses in the PV modules. Statistical analysis confirmed that solar irradiance was the most influential factor, followed by panel temperature and tilt angle. The developed quadratic regression model provided an excellent fit (R² &gt; 0.99), and diagnostic plots confirmed model adequacy, normality of residuals, and strong agreement between predicted and experimental values. Response surface plots and interaction contours illustrate how combinations of the three variables affect performance and identify the optimal region.

Beyond technical performance, the dataset also includes economic and social impact data derived from Social Return on Investment (SROI) analysis. Costs, energy use, and stakeholder feedback were collected through field surveys, interviews, and operational records. The SROI analysis compares the solar PV pumping system with a conventional groundwater pumping system and quantifies social and economic benefits for farmers and local communities over a three-year period. The results indicate consistently increasing social value, lower energy costs, reduced fuel dependency, and improved long-term sustainability.

Together, the dataset provides comprehensive technical, environmental, and socio-economic information supporting the optimization, performance modeling, and feasibility assessment of PV water pumping systems in rural agricultural regions. These data can be used for system design, RSM-based optimization, validation of numerical models, and sustainability assessments in similar climatic or agricultural contexts.
DOI: 10.3535/a8p-t8m-7th
title: Lasionycta Aurivillius, 1892

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/km9-q98-y0p
title: Caradrina (Platyperigea) montana Bremer, 1864

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/38h-f8c-y26
title: Mythimna (Mythimna) impura Hübner, 1808

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/3v3-5v3-dk3
title: Mythimna (Mythimna) impura Hübner, 1808

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/syd-sd6-4br
title: Catoptria falsella [Denis & Schiffermüller], 1775

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/w4q-qq1-jmr
title: Autographa Hübner, 1821

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/4tb-plf-07b
title: Lycaena phlaeas (Linnaeus, 1761)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/v70-txj-pym
title: Catoptria falsella [Denis & Schiffermüller], 1775

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/h41-616-s7w
title: Dypterygia scabriuscula (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/qc8-klj-g2v
title: Acronicta (Subacronicta) megacephala ([Denis & Schiffermüller], 1775)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/3lc-2c4-z37
title: Dypterygia scabriuscula (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/td3-y9v-esh
title: Colobochyla salicalis ([Denis & Schiffermüller], 1775)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/27n-hl7-dnr
title: Cucullia umbratica (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/7dg-fx9-9ej
title: Orthosia (Monima) populeti Fabricius, 1781

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/7he-k2a-wn9
title: Eugnorisma (Metagnorisma) depuncta Linnaeus, 1761

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/mps-01x-7z4
title: Dahlica (Dahlica) charlottae (Meier, 1957)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.17632/275grzntd3.1
title: North Eastern Indian Numismatics

description: This dataset contains a carefully selected collection of 1,750 high-resolution tiff images of ancient and mediaeval coins from the Northeastern regions of India with a total of 72 kings from all four regions. The images of this dataset have been taken from Assam, Tripura, Koch Bihar, and Jaintiapur. This dataset will support research of numismatics, archaeology, cultural heritage digitisation, and image classification with artificial intelligence.
The coins have been collected from verified private collections and five reputed auction houses: Marudhar Arts, Oswal Auction, Classical Numismatic Gallery, David Feldman, and Jain Auction House with their permissions. Each coin was examined visually and authenticated using standard numismatic references to confirm its dynasty, mint, denomination, motif type, and inscription style. The coins' inscriptions are written in Assamese, Bengali, Sanskrit, Arabic, and various local scripts.
To enable machine learning applications, all images went through standard preprocessing steps, including background removal, noise reduction, and resizing to 128 × 256 pixels. The front and back design was kept perfect for each image. The dataset comes with a detailed annotation file that documents metadata, including class labels and numismatic features. It delivers a systematic hierarchical model covering 72 defined coin classes across four regions of the Northeastern Indian dataset.
The main idea behind this dataset is that modern deep learning models can be trained to classify ancient North-Eastern Indian coins. This finding remains reliable despite many challenges, including surface wear, variations in minting techniques, and the inherent complexity of coin designs. To evaluate the dataset, the four different deep learning architectures were used: ResNet-50, ResNet-152, DenseNet-121, and EfficientNetV2-B3, where EfficientNetV2-B3 had the highest accuracy at 93.20% and an F1-score of 92.80%. These results confirm the dataset's reliability and suitability for coin recognition and classification.
This dataset fills a significant gap since coins from the North-Eastern Indian are historically rare, intricately designed, and not well represented in existing digital collections. Its research potential includes automated classification and feature extraction for ancient coins, comparative studies in motif analysis and history, and analysis of minting patterns. By improving machine learning pipelines for ancient coin recognition, the dataset enhances both numismatic research and machine learning methods.
DOI: 10.3535/49g-95v-fyv
title: Amphipoea fucosa Freyer, 1830

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/r2m-p15-r8a
title: Cyaniris semiargus (von Rottemburg, 1775)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/yp7-6mr-f1v
title: Leptidea

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/2nw-b55-e2v
title: Amphipoea fucosa Freyer, 1830

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/fkb-cx2-5vg
title: Agrochola (Anchoscelis) Guenée, 1839

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/2qw-lyr-cte
title: Drepana falcataria (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/az7-0eb-v9k
title: Dasypolia (Dasypolia) templi Thunberg, 1792

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/21z-wk0-pfm
title: Tholera decimalis Poda, 1761

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/wxy-nyh-5f0
title: Orthosia (Monima) populeti Fabricius, 1781

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.17632/92cvzj65ky
title: WeatherDrive: A Real-World Driving Dataset Collected Under Diverse Weather Conditions
DOI: 10.3535/5ly-jn4-cbw
title: Mythimna (Mythimna) impura Hübner, 1808

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/91v-7f6-ks3
title: Orthosia (Monima) populeti Fabricius, 1781

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.17632/92cvzj65ky.1
title: WeatherDrive: A Real-World Driving Dataset Collected Under Diverse Weather Conditions
DOI: 10.3535/dy0-53p-3gq
title: Orthosia (Monima) populeti Fabricius, 1781

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/m9a-9mw-n0s
title: Satyrium Scudder, 1876

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/9pz-h4p-g9x
title: Tholera decimalis Poda, 1761

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/m21-vf8-yln
title: Dasypolia (Dasypolia) templi Thunberg, 1792

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/n7k-2d2-2l0
title: Lithophane (Lithophane) consocia Borkhausen, 1792

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/pq1-erq-ml9
title: Acronicta (Subacronicta) megacephala ([Denis & Schiffermüller], 1775)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/lm5-lkj-aw4
title: Mesapamea Heinicke, 1959

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/vp5-ytq-rc9
title: Acronicta (Subacronicta) megacephala ([Denis & Schiffermüller], 1775)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.