cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6
title: Data underlying the publication: Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations

description: This data includes the files for developing a workflow to simulate fed-batch fermentations using a hybrid modeling approach based on flow-informed compartment models (CFD-CM) and a machine learning (ML) method. The proposed workflow circumvents the need for re-calibration of the compartment model upon changes in the working volume and stirring rate of the system. This is done using an inferring module based on a neural network. The methods to deploy the framework are described in the publication 'Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations'. The dataset includes the case and data files from FLUENT to generate the parameterization of the compartment models (i.e., intercompartmental fluxes - .csv files) used for training and testing of the neural network, which is also included. These files aim to ensure the reproducibility of the results presented in the corresponding publication.
DOI: 10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6.v1
title: Data underlying the publication: Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations

description: This data includes the files for developing a workflow to simulate fed-batch fermentations using a hybrid modeling approach based on flow-informed compartment models (CFD-CM) and a machine learning (ML) method. The proposed workflow circumvents the need for re-calibration of the compartment model upon changes in the working volume and stirring rate of the system. This is done using an inferring module based on a neural network. The methods to deploy the framework are described in the publication 'Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations'. The dataset includes the case and data files from FLUENT to generate the parameterization of the compartment models (i.e., intercompartmental fluxes - .csv files) used for training and testing of the neural network, which is also included. These files aim to ensure the reproducibility of the results presented in the corresponding publication.
DOI: 10.4121/0a08d2ec-8959-403f-afea-2b085dc9f3a6.v2
title: Data underlying the publication: Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations

description: This data includes the files for developing a workflow to simulate fed-batch fermentations using a hybrid modeling approach based on flow-informed compartment models (CFD-CM) and a machine learning (ML) method. The proposed workflow circumvents the need for re-calibration of the compartment model upon changes in the working volume and stirring rate of the system. This is done using an inferring module based on a neural network. The methods to deploy the framework are described in the publication 'Dynamic compartment models: Towards a rapid modeling approach for fed-batch fermentations'. The dataset includes the case and data files from FLUENT to generate the parameterization of the compartment models (i.e., intercompartmental fluxes - .csv files) used for training and testing of the neural network, which is also included. These files aim to ensure the reproducibility of the results presented in the corresponding publication.
DOI: 10.6084/m9.figshare.28456973.v1
title: Translating argumentation structures in the Holy Quran

description: This study examines Quranic argumentation structures and their translation strategies. Quranic verses with argumentative structures were identified based on a literature review and specific criteria. Four Quran translations, representing diverse approaches, were selected for comparative analysis. Using an analytical comparative method, the study assessed the accuracy and quality of translations through a four-point evaluation scale. Each translation was analyzed in relation to the original Quranic text to identify translation strategies.Findings reveal that Quranic discourse employs various argumentation techniques, including mental reasoning, inferential evidence, and rhetorical strategies. Translators used different methods, ranging from direct translation to paraphrasing. The study emphasizes the importance of consulting exegesis books to ensure translations align with scholarly interpretations. These findings offer valuable insights into Quranic argumentation and its translation.
DOI: 10.6084/m9.figshare.28456973
title: Translating argumentation structures in the Holy Quran

description: This study examines Quranic argumentation structures and their translation strategies. Quranic verses with argumentative structures were identified based on a literature review and specific criteria. Four Quran translations, representing diverse approaches, were selected for comparative analysis. Using an analytical comparative method, the study assessed the accuracy and quality of translations through a four-point evaluation scale. Each translation was analyzed in relation to the original Quranic text to identify translation strategies.Findings reveal that Quranic discourse employs various argumentation techniques, including mental reasoning, inferential evidence, and rhetorical strategies. Translators used different methods, ranging from direct translation to paraphrasing. The study emphasizes the importance of consulting exegesis books to ensure translations align with scholarly interpretations. These findings offer valuable insights into Quranic argumentation and its translation.
DOI: 10.6084/m9.figshare.28456946
title: Transcription Data

description: This qualitative study assesses the effectiveness of a hybrid learning model in international laser dentistry courses developed by the Laser Dentistry Research Group at Khon Kaen University, Thailand. The model combines online theoretical education with hands-on experience through synchronized simulation laboratories. It aims to address the limitations of traditional face-to-face learning, especially during the COVID-19 pandemic, by offering accessible and interactive learning methods.Data for this study were collected via in-depth interviews with participants from both online and onsite courses held in 2020 and 2021. These interviews explored the participants’ experiences, challenges faced during the courses, and the perceived effectiveness of the hybrid model in delivering both theoretical knowledge and practical skills. The data were analyzed using a combination of manual coding and AI-assisted analysis to ensure accuracy and reliability.<b>Methodology</b>:<br>The research methodology involved qualitative data collection through structured interviews. Participants were selected from those enrolled in international laser dentistry courses, and the data collection was approved by the relevant ethical review board at Khon Kaen University to ensure compliance with legal and ethical standards. The data analysis process utilized both human coding and AI tools to validate the findings.<b>Key Findings</b>:<b>Hybrid Model Effectiveness</b>: The combination of online lectures and real-time simulation laboratories successfully bridged the gap between theoretical learning and practical application, receiving positive feedback from all groups.<b>Knowledge Retention</b>: Both online and onsite learners showed comparable levels of knowledge retention, indicating the hybrid model’s effectiveness in ensuring sustained learning outcomes.<b>International Collaboration</b>: The model facilitated successful collaboration among learners from diverse linguistic and cultural backgrounds, supported by online resources and peer interactions.This research underscores the potential of hybrid learning models as an adaptable and sustainable solution in specialized fields like laser dentistry, ensuring the transfer of both theoretical knowledge and practical skills in an international context.
DOI: 10.5281/zenodo.14904163
title: Survey - Quality of Life Related to the Frequency of Visits to Natural Areas in Warsaw agglomeration

description: The database contains the results of a survey conducted among a sample of 1000 people living in the Warsaw agglomeration. The study focused releation between indicators of quality of life, such as social, economic,infrastructural, and environmental factors, influence the frequency of visits to urban green areas and forest areas.
DOI: 10.5281/zenodo.14904164
title: Survey - Quality of Life Related to the Frequency of Visits to Natural Areas in Warsaw agglomeration

description: The database contains the results of a survey conducted among a sample of 1000 people living in the Warsaw agglomeration. The study focused releation between indicators of quality of life, such as social, economic,infrastructural, and environmental factors, influence the frequency of visits to urban green areas and forest areas.
DOI: 10.5281/zenodo.14893004
title: Data supplement: Detection of Drainage Ditches from LiDAR DTM Using U-Net and Transfer Learning

description: Data supplement: Detection of Drainage Ditches from LiDAR DTM Using U-Net and Transfer Learning

Holger Virro, Alexander Kmoch, William Lidberg, Wai Tik Chan, Evelyn Uuemaa

Accurate mapping of ditches is essential for effective hydrological modeling and land management. Traditional methods, such as manual digitization or threshold-based extraction, utilize LiDAR-derived digital terrain model (DTM) data but are labor-intensive and impractical to apply for large-scale applications. Deep learning offers a promising alternative but requires extensive labeled data, often unavailable. To address this, we developed a transfer learning approach using a U-Net model pre-trained on a large high-quality Swedish dataset and fine-tuned on a smaller localized Estonian dataset. The model uses a single-band LiDAR DTM raster as input, minimizing preprocessing. We identified the optimal model configuration by systematically testing kernel sizes and data augmentation. The best fine-tuned model achieved an overall F1 score of 0.766, demonstrating its effectiveness in detecting drainage ditches in training data-scarce regions. Performance varied by land use, with higher accuracy in peatlands (F1=0.822) than in forests (F1=0.752) and arable land (F1=0.779). These findings underscore the model's suitability for large-scale ditch mapping and its adaptability to different landscapes.
DOI: 10.5281/zenodo.14893003
title: Data supplement: Detection of Drainage Ditches from LiDAR DTM Using U-Net and Transfer Learning

description: Data supplement: Detection of Drainage Ditches from LiDAR DTM Using U-Net and Transfer Learning

Holger Virro, Alexander Kmoch, William Lidberg, Wai Tik Chan, Evelyn Uuemaa

Accurate mapping of ditches is essential for effective hydrological modeling and land management. Traditional methods, such as manual digitization or threshold-based extraction, utilize LiDAR-derived digital terrain model (DTM) data but are labor-intensive and impractical to apply for large-scale applications. Deep learning offers a promising alternative but requires extensive labeled data, often unavailable. To address this, we developed a transfer learning approach using a U-Net model pre-trained on a large high-quality Swedish dataset and fine-tuned on a smaller localized Estonian dataset. The model uses a single-band LiDAR DTM raster as input, minimizing preprocessing. We identified the optimal model configuration by systematically testing kernel sizes and data augmentation. The best fine-tuned model achieved an overall F1 score of 0.766, demonstrating its effectiveness in detecting drainage ditches in training data-scarce regions. Performance varied by land use, with higher accuracy in peatlands (F1=0.822) than in forests (F1=0.752) and arable land (F1=0.779). These findings underscore the model's suitability for large-scale ditch mapping and its adaptability to different landscapes.
DOI: 10.5281/zenodo.14904165
title: AMPERE Summary Plot Movies (2015-May)

description: 2015-May AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5281/zenodo.14904166
title: AMPERE Summary Plot Movies (2015-May)

description: 2015-May AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.6084/m9.figshare.28448441.v3
title: Monthly_average_for_each_year.7z

description: 该数据集基于多颗卫星 NO<sub>2</sub> 柱浓度数据的融合，结合气象再分析数据、人类活动数据等地理相关变量，利用机器学习模型生成<sub>2</sub> 2014 年至 2020 年中国大陆月平均近地表 NO 2 浓度及其分布。我国近地表 NO 2 月平均浓度<sub>2</sub>为 18.06±5.75μg/m<sup>3</sup>，近地表 NO<sub>2</sub> 浓度和分布随时间变化显著。总体而言，全国近地表 NO<sub>2</sub>主要分布在经济发展水平高、人口密集的地区，省会城市间距离较近，在空间上呈斑块状分布。
DOI: 10.4121/aa2f81d8-d8b2-4deb-99af-ef5a431de10f
title: Data underlying the project: Circular and Adaptable Building Transformation: Reconceptualization, Practice Exploration, Framework Co-Development, and Implementation

description: The data are organized on the basis of 3 datasets from 3 out of 4 studies (papers) from the research project. These datasets are based on empirical data collected through qualitative research methods. The data of the first study is literature material which is available online.As explained and shown in the README file, the three datasets are organized under three main categories, corresponding to the related study number, whereas the file name explains the content
DOI: 10.4121/aa2f81d8-d8b2-4deb-99af-ef5a431de10f.v1
title: Data underlying the project: Circular and Adaptable Building Transformation: Reconceptualization, Practice Exploration, Framework Co-Development, and Implementation

description: The data are organized on the basis of 3 datasets from 3 out of 4 studies (papers) from the research project. These datasets are based on empirical data collected through qualitative research methods. The data of the first study is literature material which is available online.As explained and shown in the README file, the three datasets are organized under three main categories, corresponding to the related study number, whereas the file name explains the content
DOI: 10.57707/fmi-b2share.0ab5540484e0492e831e2ea37b3f0f8e
title: Data for manuscript "Extreme cold days and spells in Northern Europe at 0.5-2.0°C global warming levels" by Ruosteenoja and Jylhä

description: Projections for cold days and spells in Northern Europe at global warming levels of 0.5-2.0°C are derived from bias-corrected output data from 60 runs performed with 25 global climate models. The threshold temperature of a cold day is defined as the 10th percentile of December-February daily mean temperatures at the 0.5°C warming level. For a description of the individual files, see the document "data_description_cold_days_and_spells.txt".
DOI: 10.5281/zenodo.14904218
title: AMPERE Summary Plot Movies (2015-Jun)

description: 2015-Jun AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5281/zenodo.14904219
title: AMPERE Summary Plot Movies (2015-Jun)

description: 2015-Jun AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5281/zenodo.13321535
title: TreatmentBank

description: TreatmentBank (TB) is a service provided by the Swiss Plazi GmbH to liberate data from within scholarly publications, and convert, enhance, link, store, and disseminate it as Findable, Accessible, Interoperable and Reusable (FAIR) data. These data include taxonomic treatments, treatment citations, figures, tables, material citations and bibliographic reference. http://plazi.org/treatmentbank/

Attribute records for species described in TreatmentBank are extracted using the PenSoft Annotator 
https://annotator.pensoft.net/ and selected ontologies from the OBOFoundry 
http://obofoundry.org/ as well as selected terms from 
https://www.geonames.org/ and 
https://www.wikidata.org/.
DOI: 10.5281/zenodo.14904238
title: TreatmentBank

description: TreatmentBank (TB) is a service provided by the Swiss Plazi GmbH to liberate data from within scholarly publications, and convert, enhance, link, store, and disseminate it as Findable, Accessible, Interoperable and Reusable (FAIR) data. These data include taxonomic treatments, treatment citations, figures, tables, material citations and bibliographic reference. http://plazi.org/treatmentbank/

Attribute records for species described in TreatmentBank are extracted using the PenSoft Annotator 
https://annotator.pensoft.net/ and selected ontologies from the OBOFoundry 
http://obofoundry.org/ as well as selected terms from 
https://www.geonames.org/ and 
https://www.wikidata.org/.
DOI: 10.6084/m9.figshare.28456967
title: <b>Loess Hilly Region n</b><b>itrogen and phosphorus stoichiometry</b>

description: Stoichiometric data of the Loess Plateau region
DOI: 10.6084/m9.figshare.28456967.v1
title: <b>Loess Hilly Region n</b><b>itrogen and phosphorus stoichiometry</b>

description: Stoichiometric data of the Loess Plateau region
DOI: 10.34740/kaggle/dsv/10811394
title: cypto_data
DOI: 10.6084/m9.figshare.28457003
title: <b><i>Syzygium samarangense</i></b>

description: <b><i>Syzygium samarangense</i></b> Genome
DOI: 10.5281/zenodo.14904261
title: iCare quantitative CT shows a higher detection rate of hip osteoporosis compared with Mindways quantitative CT
DOI: 10.4121/f3bf339d-9be2-4afe-82b5-3e3c4c1d0e15
title: A Study on Impact-Aware Aerial Robots Colliding with the Environment at non-zero Speed

description: This dataset studies impact between an aerial robot and an object at different velocities-at-impact. The purpose of the experiments is to assess the behaviour of an underactuated UAV under non-zero velocity impacts at low altitude, hence, with limited recovery space. The experiments consider both the impact against a block and the impact against a grasped object, which thus affects the inertia of the UAV after the impact.
DOI: 10.4121/f3bf339d-9be2-4afe-82b5-3e3c4c1d0e15.v1
title: A Study on Impact-Aware Aerial Robots Colliding with the Environment at non-zero Speed

description: This dataset studies impact between an aerial robot and an object at different velocities-at-impact. The purpose of the experiments is to assess the behaviour of an underactuated UAV under non-zero velocity impacts at low altitude, hence, with limited recovery space. The experiments consider both the impact against a block and the impact against a grasped object, which thus affects the inertia of the UAV after the impact.
DOI: 10.6084/m9.figshare.28418309.v1
title: 组蛋白修饰 Chrom 的 <i>Mulinia lateralis</i>

description: <br>双壳类软体动物;剪切&标;;组蛋白修饰;表观遗传调控;胚胎发育
DOI: 10.5281/zenodo.14904239
title: AMPERE Summary Plot Movies (2015-Jul)

description: 2015-Jul AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.5281/zenodo.14904240
title: AMPERE Summary Plot Movies (2015-Jul)

description: 2015-Jul AMPERE summary plot movies. See included zenodo_summary_plot_movie_description document for details.
DOI: 10.6084/m9.figshare.25472545
title: Measurement Report: Cloud condensation nuclei (CCN) activity in the South China Sea from shipborne observations during summer and winter of 2021: seasonal variation and anthropogenic influence.

description: The field measurement data in SCS in summer and winter in 2021. The data file 202105SCS.csv and the 202112SCS.csv are the verfied files which can be used.
DOI: 10.6084/m9.figshare.25472545.v1
title: Data

description: The field measurement data in SCS in summer and winter in 2021.
DOI: 10.6084/m9.figshare.28430936
title: CARTNAT SPATIAL DATA ARCHIVE

description: CARTNAT spatial data layers
DOI: 10.6084/m9.figshare.28430936.v1
title: CARTNAT SPATIAL DATA ARCHIVE

description: CARTNAT spatial data layers
DOI: 10.5281/zenodo.14904288
title: Khaadi Fashion Dataset

description: Description:







This dataset offers a detailed collection of Khaadi Fashion Dataset line, providing a wide variety of high-quality images and metadata of clothing items such as dresses, shirts, and trousers. It is a powerful resource for exploring fashion trends, customer preferences, and product analysis.

 







Download Dataset

 









Applications in Fashion Industry

This dataset is ideal for applications in retail analytics, trend identification, and predictive modeling. It supports tasks like developing personalized recommendation systems, inventory management, and understanding customer buying behavior. Businesses can utilize it to optimize their fashion offerings, enhance customer satisfaction, and increase sales by aligning with trends and preferences.

Machine Learning Applications

This dataset supports various machine learning and AI models, especially in the areas of image classification, visual search engines, and style-based recommendations. It can be used for training convolutional neural networks (CNNs) to recognize patterns in clothing styles, materials, and other relevant features, helping to build automated systems that assist in inventory categorization, demand prediction, and customer recommendations.

Conclusion

The Khaadi Fashion Dataset is a versatile and powerful resource for both the fashion and tech industries. Whether you’re a retailer looking to enhance your customer offerings or a machine learning practitioner aiming to build robust models, this dataset equips you with the essential data for an in-depth analysis of fashion products.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14904289
title: Khaadi Fashion Dataset

description: Description:







This dataset offers a detailed collection of Khaadi Fashion Dataset line, providing a wide variety of high-quality images and metadata of clothing items such as dresses, shirts, and trousers. It is a powerful resource for exploring fashion trends, customer preferences, and product analysis.

 







Download Dataset

 









Applications in Fashion Industry

This dataset is ideal for applications in retail analytics, trend identification, and predictive modeling. It supports tasks like developing personalized recommendation systems, inventory management, and understanding customer buying behavior. Businesses can utilize it to optimize their fashion offerings, enhance customer satisfaction, and increase sales by aligning with trends and preferences.

Machine Learning Applications

This dataset supports various machine learning and AI models, especially in the areas of image classification, visual search engines, and style-based recommendations. It can be used for training convolutional neural networks (CNNs) to recognize patterns in clothing styles, materials, and other relevant features, helping to build automated systems that assist in inventory categorization, demand prediction, and customer recommendations.

Conclusion

The Khaadi Fashion Dataset is a versatile and powerful resource for both the fashion and tech industries. Whether you’re a retailer looking to enhance your customer offerings or a machine learning practitioner aiming to build robust models, this dataset equips you with the essential data for an in-depth analysis of fashion products.

This dataset is sourced from Kaggle.