cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24961131.v1
title: Deliberative Democracy in the British Parliament: Data

description: Accompanying dataset for the research article titled: '<b>Deliberative Democracy in the British Parliament: Assessing the quality of the Health and Social Care Act 2012 parliamentary debates'.</b>
DOI: 10.6084/m9.figshare.24961131
title: Deliberative Democracy in the British Parliament: Data

description: Accompanying dataset for the research article titled: '<b>Deliberative Democracy in the British Parliament: Assessing the quality of the Health and Social Care Act 2012 parliamentary debates'.</b>
DOI: 10.5281/zenodo.10469672
title: ABOships-PLUS

description: 08.01.2024 Updated the annotations to be the correct ones.

 

ABOships-PLUS is an improved iteration of the original ABOships dataset. It includes 9,880 images capturing maritime scenes, showcasing various types of maritime objects such as powerboats, ships, sailboats, and stationary objects. Detailed category definitions and images can be found in the associated reference paper. In total, ABOships-PLUS contains 33,227 annotated objects across these categories, including four types of ships.

Several key changes and improvements have been made to ABOships-PLUS:



Object Size Filtering: In ABOships-PLUS, a filtering process was applied to exclude very small objects, specifically those with an occupied pixel area less than 16^2x16^2 pixels. This filtering ensures that the dataset primarily consists of more discernible maritime objects, contributing to improved data quality.

Superclass Aggregation: A notable transformation in ABOships-PLUS is the grouping of objects into four superclasses based on their distinct visual characteristics. This superclass aggregation facilitates the use of both transfer learning and learning from scratch, making the dataset more versatile for various machine learning applications.

Semantic Relevance: The categorization into superclasses in ABOships-PLUS was guided by semantic relevance, with human supervision. The objective was to create more meaningful superclasses, both from a semantic and visual perspective, enhancing the dataset's utility for maritime object detection research.

Format Transition: A significant change occurred in the data format. ABOships-PLUS adopts the COCO format for object detection: https://cocodataset.org/#format-data. This format transition enhances compatibility with a broader range of machine learning frameworks and tools, in contrast to the original ABOships dataset, which used CSV format.


To create ABOships-PLUS, images were extracted from videos recorded in MPEG format, with a resolution of 720p at 15 frames per second (FPS). An image was extracted every 15 seconds, equivalent to every 225 frames, from videos filmed in the Finnish Archipelago using a camera attached to a moving watercraft known as a waterbus or "vesibussi" in Finnish.

The distribution of labels within ABOships-PLUS is as follows: powerboat (21.8%), ship (46.0%), sailboat (24.2%), and stationary objects (8.1%). These changes aim to enhance the dataset's usability for maritime object detection research and applications.

Reference article: https://doi.org/10.3390/jmse11091638
DOI: 10.5281/zenodo.8383204
title: ABOships-PLUS

description: 08.01.2024 Updated the annotations to be the correct ones.

 

ABOships-PLUS is an improved iteration of the original ABOships dataset. It includes 9,880 images capturing maritime scenes, showcasing various types of maritime objects such as powerboats, ships, sailboats, and stationary objects. Detailed category definitions and images can be found in the associated reference paper. In total, ABOships-PLUS contains 33,227 annotated objects across these categories, including four types of ships.

Several key changes and improvements have been made to ABOships-PLUS:



Object Size Filtering: In ABOships-PLUS, a filtering process was applied to exclude very small objects, specifically those with an occupied pixel area less than 16^2x16^2 pixels. This filtering ensures that the dataset primarily consists of more discernible maritime objects, contributing to improved data quality.

Superclass Aggregation: A notable transformation in ABOships-PLUS is the grouping of objects into four superclasses based on their distinct visual characteristics. This superclass aggregation facilitates the use of both transfer learning and learning from scratch, making the dataset more versatile for various machine learning applications.

Semantic Relevance: The categorization into superclasses in ABOships-PLUS was guided by semantic relevance, with human supervision. The objective was to create more meaningful superclasses, both from a semantic and visual perspective, enhancing the dataset's utility for maritime object detection research.

Format Transition: A significant change occurred in the data format. ABOships-PLUS adopts the COCO format for object detection: https://cocodataset.org/#format-data. This format transition enhances compatibility with a broader range of machine learning frameworks and tools, in contrast to the original ABOships dataset, which used CSV format.


To create ABOships-PLUS, images were extracted from videos recorded in MPEG format, with a resolution of 720p at 15 frames per second (FPS). An image was extracted every 15 seconds, equivalent to every 225 frames, from videos filmed in the Finnish Archipelago using a camera attached to a moving watercraft known as a waterbus or "vesibussi" in Finnish.

The distribution of labels within ABOships-PLUS is as follows: powerboat (21.8%), ship (46.0%), sailboat (24.2%), and stationary objects (8.1%). These changes aim to enhance the dataset's usability for maritime object detection research and applications.

Reference article: https://doi.org/10.3390/jmse11091638
DOI: 10.5281/zenodo.8383205
title: ABOships-PLUS

description: ABOships-PLUS is an improved iteration of the original ABOships dataset. It includes 9,880 images capturing maritime scenes, showcasing various types of maritime objects such as powerboats, ships, sailboats, and stationary objects. Detailed category definitions and images can be found in the associated reference paper. In total, ABOships-PLUS contains 33,227 annotated objects across these categories, including four types of ships. Several key changes and improvements have been made to ABOships-PLUS: Object Size Filtering: In ABOships-PLUS, a filtering process was applied to exclude very small objects, specifically those with an occupied pixel area less than 16^2x16^2 pixels. This filtering ensures that the dataset primarily consists of more discernible maritime objects, contributing to improved data quality. Superclass Aggregation: A notable transformation in ABOships-PLUS is the grouping of objects into four superclasses based on their distinct visual characteristics. This superclass aggregation facilitates the use of both transfer learning and learning from scratch, making the dataset more versatile for various machine learning applications. Semantic Relevance: The categorization into superclasses in ABOships-PLUS was guided by semantic relevance, with human supervision. The objective was to create more meaningful superclasses, both from a semantic and visual perspective, enhancing the dataset's utility for maritime object detection research. Format Transition: A significant change occurred in the data format. ABOships-PLUS adopts the COCO format for object detection: https://cocodataset.org/#format-data. This format transition enhances compatibility with a broader range of machine learning frameworks and tools, in contrast to the original ABOships dataset, which used CSV format. To create ABOships-PLUS, images were extracted from videos recorded in MPEG format, with a resolution of 720p at 15 frames per second (FPS). An image was extracted every 15 seconds, equivalent to every 225 frames, from videos filmed in the Finnish Archipelago using a camera attached to a moving watercraft known as a waterbus or "vesibussi" in Finnish. The distribution of labels within ABOships-PLUS is as follows: powerboat (21.8%), ship (46.0%), sailboat (24.2%), and stationary objects (8.1%). These changes aim to enhance the dataset's usability for maritime object detection research and applications. Reference article: https://doi.org/10.3390/jmse11091638
DOI: 10.5281/zenodo.10469579
title: Citizens living in localities close to a petrochemical complex in the north of Tarragona

description: This dataset is made up by the answers of citizens to a questionnaire with items representing the constructs of the research model. The questionnaire was addressed to citizens who live in localities close to the North of Tarragona petrochemical site. 390 valid responses were obtained through simple random sampling.

Citizens manifested their opinions about risk perception, benefit perception or their legitimacy they confer to petrochemical corporations or local environmental organisations that represents community members interests.
DOI: 10.5281/zenodo.10469580
title: Citizens living in localities close to a petrochemical complex in the north of Tarragona

description: This dataset is made up by the answers of citizens to a questionnaire with items representing the constructs of the research model. The questionnaire was addressed to citizens who live in localities close to the North of Tarragona petrochemical site. 390 valid responses were obtained through simple random sampling.

Citizens manifested their opinions about risk perception, benefit perception or their legitimacy they confer to petrochemical corporations or local environmental organisations that represents community members interests.
DOI: 10.5281/zenodo.10469685
title: Citizens living in localities close to a petrochemical complex in the north of Tarragona

description: This dataset is made up by the answers of citizens to a questionnaire with items representing the constructs of the research model. The questionnaire was addressed to citizens who live in localities close to the North of Tarragona petrochemical site. 390 valid responses were obtained through simple random sampling.

Citizens manifested their opinions about risk perception, benefit perception or their legitimacy they confer to petrochemical corporations or local environmental organisations that represents community members interests.
DOI: 10.5281/zenodo.10468445
title: Cardiorespiratory and metabolic consequences of detraining in endurance athletes

description: Background: A training program can stimulate physiological, anatomical, and performance adaptations, but these improvements can be partially or entirely reversed due to the cessation of habitual physical activity resulting from illness, injury, or other influencing factors.

Purpose: To investigate the effects of detraining on cardiorespiratory, metabolic, hormonal, muscular adaptations, as well as short-term and long-term performance changes in endurance athletes.

Methods: Eligible studies were sourced from databases and the library up until July 2023. Included studies considered endurance athletes as subjects and reported on detraining duration.

Results: Total cessation of training leads to a decrease in VO2max due to reductions in both blood and plasma volume. Cardiac changes include decreases in left ventricular mass, size, and thickness, along with an increase in heart rate and blood pressure, ultimately resulting in reduced cardiac output and impaired performance. Metabolically, there are declines in lactate threshold and muscle glycogen, increased body weight, altered respiratory exchange ratio, and changes in power parameters. In the short term, there is a decrease in insulin sensitivity, while glucagon, growth hormone, and cortisol levels remain unchanged. Skeletal muscle experiences reductions in arterial-venous oxygen difference and glucose transporter-4. Implementing a partial reduction in training may help mitigate drastic losses in physiological and performance parameters, a consideration when transitioning between training seasons.

Conclusion: There is a dearth of data investigating the detraining effects of training reduction/cessation among endurance athletes. Delving deeper into this topic may be useful for professionals and researchers to identify the optimal strategies to minimize these effects.
DOI: 10.5281/zenodo.10468446
title: Cardiorespiratory and metabolic consequences of detraining in endurance athletes

description: Background: A training program can stimulate physiological, anatomical, and performance adaptations, but these improvements can be partially or entirely reversed due to the cessation of habitual physical activity resulting from illness, injury, or other influencing factors.

Purpose: To investigate the effects of detraining on cardiorespiratory, metabolic, hormonal, muscular adaptations, as well as short-term and long-term performance changes in endurance athletes.

Methods: Eligible studies were sourced from databases and the library up until July 2023. Included studies considered endurance athletes as subjects and reported on detraining duration.

Results: Total cessation of training leads to a decrease in VO2max due to reductions in both blood and plasma volume. Cardiac changes include decreases in left ventricular mass, size, and thickness, along with an increase in heart rate and blood pressure, ultimately resulting in reduced cardiac output and impaired performance. Metabolically, there are declines in lactate threshold and muscle glycogen, increased body weight, altered respiratory exchange ratio, and changes in power parameters. In the short term, there is a decrease in insulin sensitivity, while glucagon, growth hormone, and cortisol levels remain unchanged. Skeletal muscle experiences reductions in arterial-venous oxygen difference and glucose transporter-4. Implementing a partial reduction in training may help mitigate drastic losses in physiological and performance parameters, a consideration when transitioning between training seasons.

Conclusion: There is a dearth of data investigating the detraining effects of training reduction/cessation among endurance athletes. Delving deeper into this topic may be useful for professionals and researchers to identify the optimal strategies to minimize these effects.
DOI: 10.34740/kaggle/dsv/7362471
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.5141574
title: Datasets to: Measurement report: Introduction to the HyICE-2018 campaign for the measurements of ice nucleating particles in the boreal forest of Hyytiälä

description: This repository contains the datasets used in the study 'Measurement report: Introduction to the HyICE-2018 campaign for the measurements of ice nucleating particles in the boreal forest of Hyytiälä'. Detailed information and technical aspects of the data can be found in the publication.

Update (version 2 - January 2024): The repository was updated and now contains the complete HyICE-2018 datasets for the instruments uL-NIPI and PINE.
DOI: 10.5281/zenodo.5141575
title: Datasets to: Measurement report: Introduction to the HyICE-2018 campaign for the measurements of ice nucleating particles in the boreal forest of Hyytiälä

description: This repository contains the datasets used in the study 'Measurement report: Introduction to the HyICE-2018 campaign for the measurements of ice nucleating particles in the boreal forest of Hyytiälä'. Detailed information and technical aspects of the data can be found in the publication.
DOI: 10.6084/m9.figshare.24960981
title: All.Unigene.annot.xlsx

description: Transcriptome annotation information
DOI: 10.6084/m9.figshare.24960981.v1
title: All.Unigene.annot.xlsx

description: Transcriptome annotation information
DOI: 10.6084/m9.figshare.24960972
title: Unigene.fa

description: Transcriptome sequence information of Angelica sinensis
DOI: 10.6084/m9.figshare.24960972.v1
title: Unigene.fa

description: Transcriptome sequence information of Angelica sinensis
DOI: 10.5281/zenodo.10462225
title: [Data] Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance

description: The proposed Deep Learning (DL) model in this study was trained using signals obtained from a comprehensive time-synced sensing system, incorporating four sensors: back reflection (BR), Visible, Infra-Red (IR), and structure-borne Acoustic Emission (AE). The dataset comprises time-synced signals corresponding to each layer produced by Laser Powder Bed Fusion (LPBF) of SS-316L, capturing three distinct built regimes (Lack of Fusion (LoF), conduction mode, and Keyhole) across nine different parameter sets. Each parameter set uniquely generates one regime, resulting in three parameter sets associated with each regime. These signals from the four sensors are then organized into four distinct running windows (w1, w2, w3, and w4). The durations of these windows are 0.83, 1.65, 2.5, and 3.30 ms, translating to 2500, 5000, 7500, and 10000 data points, respectively, if sampled at 3 MHz. These windows, of varying lengths, are computed across all three processing regimes without overlaps. The naming convention for the .npy file within the .zip file follows this format: channel[a]_[b], where 'a' represents the source channel (0 for back reflection, 1 for Visible, 2 for Infra-Red (IR), and 3 for structure-borne Acoustic Emission (AE)), and 'b' indicates the number of data points in each window (2500, 5000, 7500, and 10000). The dataset is organized based on window length and further grouped by the four channels. Consequently, four datasets are created, each corresponding to one of the four window lengths, for the training of a Convolutional Neural Network (CNN). The operando X-ray imaging analysis ensured that the regimes correlated with the defined process parameters.
DOI: 10.5281/zenodo.10462226
title: [Data] Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance

description: The proposed Deep Learning (DL) model in this study was trained using signals obtained from a comprehensive time-synced sensing system, incorporating four sensors: back reflection (BR), Visible, Infra-Red (IR), and structure-borne Acoustic Emission (AE). The dataset comprises time-synced signals corresponding to each layer produced by Laser Powder Bed Fusion (LPBF) of SS-316L, capturing three distinct built regimes (Lack of Fusion (LoF), conduction mode, and Keyhole) across nine different parameter sets. Each parameter set uniquely generates one regime, resulting in three parameter sets associated with each regime. These signals from the four sensors are then organized into four distinct running windows (w1, w2, w3, and w4). The durations of these windows are 0.83, 1.65, 2.5, and 3.30 ms, translating to 2500, 5000, 7500, and 10000 data points, respectively, if sampled at 3 MHz. These windows, of varying lengths, are computed across all three processing regimes without overlaps. The naming convention for the .npy file within the .zip file follows this format: channel[a]_[b], where 'a' represents the source channel (0 for back reflection, 1 for Visible, 2 for Infra-Red (IR), and 3 for structure-borne Acoustic Emission (AE)), and 'b' indicates the number of data points in each window (2500, 5000, 7500, and 10000). The dataset is organized based on window length and further grouped by the four channels. Consequently, four datasets are created, each corresponding to one of the four window lengths, for the training of a Convolutional Neural Network (CNN). The operando X-ray imaging analysis ensured that the regimes correlated with the defined process parameters.
DOI: 10.6084/m9.figshare.24960996
title: unigene.fa.transdecoder.cds

description: Predicting CDS sequences based on transcriptome information of Angelica sinensis
DOI: 10.6084/m9.figshare.24961002
title: unigene.fa.transdecoder.pep

description: Protein sequence prediction based on Angelica sinensis transcriptome information
DOI: 10.6084/m9.figshare.24961002.v1
title: unigene.fa.transdecoder.pep

description: Protein sequence prediction based on Angelica sinensis transcriptome information
DOI: 10.34740/kaggle/dsv/7362507
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.10454500
title: Data for the social media study of sustainable construction communication

description: We present raw data collected in the social media study for the article “What type of social media posts about sustainable construction is better for audience engagement?”. The study was conducted in 2022 and consisted of two phases and one shorter additional phase in 2023. The first phase took place from 3 May 2022 until 9 August 2022, the second phase took pace from 30 August 2022, until 6 December 2022, and the additional phase took place from 21 November 2023, until 12 December 2023. 

The data was collected by one researcher weekly. The social media post was posted every Tuesday at 10:00 pm (CET), and monitored every Thursday at 10:00 pm (CET).

The collected data is social media viewing and impressions, engagement and likes information. It presents comparison of impressions, engagement and likes collected on three different social media channels (Facebook, Twitter (X), LinkedIn) in two phases and one additional phase of posts. Posts were of three different types – written type, image type showing equipment, image type showing people. 

The study was done in the framework of the project J4-3087 Engineered wood composites with enhanced impact sound insulation performance to improve human well being financed by Slovenian Research and Innovation Agency and Austrian Science Fund.
DOI: 10.5281/zenodo.10454501
title: Data for the social media study of sustainable construction communication

description: We present raw data collected in the social media study for the article “What type of social media posts about sustainable construction is better for audience engagement?”. The study was conducted in 2022 and consisted of two phases and one shorter additional phase in 2023. The first phase took place from 3 May 2022 until 9 August 2022, the second phase took pace from 30 August 2022, until 6 December 2022, and the additional phase took place from 21 November 2023, until 12 December 2023. 

The data was collected by one researcher weekly. The social media post was posted every Tuesday at 10:00 pm (CET), and monitored every Thursday at 10:00 pm (CET).

The collected data is social media viewing and impressions, engagement and likes information. It presents comparison of impressions, engagement and likes collected on three different social media channels (Facebook, Twitter (X), LinkedIn) in two phases and one additional phase of posts. Posts were of three different types – written type, image type showing equipment, image type showing people. 

The study was done in the framework of the project J4-3087 Engineered wood composites with enhanced impact sound insulation performance to improve human well being financed by Slovenian Research and Innovation Agency and Austrian Science Fund.
DOI: 10.5281/zenodo.10469915
title: Barium isotope variation during fluid-rock interaction at forearc depths: evidence from the high-pressure fluid-metasomatized rocks in the Eastern Alps

description: The supporting information presents the analytical methods of whole-rock major-trace element compositions and Mg–Fe isotope compositions , as well as whole-rock major-trace elements and Fe–Mg–Sr isotope data.
DOI: 10.5281/zenodo.10469916
title: Barium isotope variation during fluid-rock interaction at forearc depths: evidence from the high-pressure fluid-metasomatized rocks in the Eastern Alps

description: The supporting information presents the analytical methods of whole-rock major-trace element compositions and Mg–Fe isotope compositions , as well as whole-rock major-trace elements and Fe–Mg–Sr isotope data.
DOI: 10.34740/kaggle/dsv/7362481
title: Politifact's truth-o-meter Dataset