Pub Date : 2024-09-21DOI: 10.1016/j.dib.2024.110956
Fault detection and diagnosis (FDD) in Air Handling Units (AHUs) ensure building functions such as energy efficiency and occupant comfort by quickly identifying and diagnosing faults. Combining deep learning with FDD has demonstrated high generalization ability in this field. To develop deep learning models, this research constructed a dataset sourced from real data collected from a large-scale office in South Korea. The raw AHU data were extracted from the Building Management System (BMS) at 1-h intervals, spanning from November 2023 to May 2024. The dataset was partially labeled by annotation experts, categorizing the data into six types: normal condition, supply fan fault, total heating pump fault, return air temperature sensor fault, supply air Temperature sensor fault, and valve position fault. Additionally, semi-supervised learning methods were applied as an application example using this constructed dataset. The main contributions of this dataset to the field are twofold. First, it represents a unique dataset sourced from the real operational data of a large-scale office, which is currently non-existent in this domain. Second, the dataset's expert labeling adds significant value by ensuring accurate fault classification. Therefore, we hope that this dataset will encourage the development of robust FDD techniques that are more suitable for real-world applications.
{"title":"A semi-labelled dataset for fault detection in air handling units from a large-scale office","authors":"","doi":"10.1016/j.dib.2024.110956","DOIUrl":"10.1016/j.dib.2024.110956","url":null,"abstract":"<div><div>Fault detection and diagnosis (FDD) in Air Handling Units (AHUs) ensure building functions such as energy efficiency and occupant comfort by quickly identifying and diagnosing faults. Combining deep learning with FDD has demonstrated high generalization ability in this field. To develop deep learning models, this research constructed a dataset sourced from real data collected from a large-scale office in South Korea. The raw AHU data were extracted from the Building Management System (BMS) at 1-h intervals, spanning from November 2023 to May 2024. The dataset was partially labeled by annotation experts, categorizing the data into six types: normal condition, supply fan fault, total heating pump fault, return air temperature sensor fault, supply air Temperature sensor fault, and valve position fault. Additionally, semi-supervised learning methods were applied as an application example using this constructed dataset. The main contributions of this dataset to the field are twofold. First, it represents a unique dataset sourced from the real operational data of a large-scale office, which is currently non-existent in this domain. Second, the dataset's expert labeling adds significant value by ensuring accurate fault classification. Therefore, we hope that this dataset will encourage the development of robust FDD techniques that are more suitable for real-world applications.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1016/j.dib.2024.110943
This dataset comprises survey data from South African additive manufacturing (AM) enterprises, representing approximately 80% of the industry. The survey was designed using innovation indicators from the technology innovation system (TIS) framework to explore the dynamics within South African AM enterprises. Six TIS experts and 2AM industry specialists validated the survey's comprehensiveness and relevance. A statistician reviewed the data collection process to ensure it was suitable for robust statistical analysis. Managers of South African AM enterprises were invited to complete the survey online, and the responses were systematically collected and stored. This dataset is a valuable resource for AM researchers and practitioners, facilitating market gap analysis concerning current materials and services and offering insights for future AM applications.
该数据集包含来自南非增材制造（AM）企业的调查数据，约占该行业的 80%。调查使用技术创新系统（TIS）框架中的创新指标进行设计，以探索南非 AM 企业内部的动态。六位技术创新体系专家和 2 位 AM 行业专家对调查的全面性和相关性进行了验证。一位统计学家对数据收集过程进行了审查，以确保其适合进行可靠的统计分析。南非 AM 企业的管理人员应邀在线完成了调查，并系统地收集和储存了答复。该数据集是 AM 研究人员和从业人员的宝贵资源，有助于分析当前材料和服务的市场差距，并为 AM 的未来应用提供见解。
{"title":"Innovation system functions: Survey data of additive manufacturing enterprises in South Africa","authors":"","doi":"10.1016/j.dib.2024.110943","DOIUrl":"10.1016/j.dib.2024.110943","url":null,"abstract":"<div><div>This dataset comprises survey data from South African additive manufacturing (AM) enterprises, representing approximately 80% of the industry. The survey was designed using innovation indicators from the technology innovation system (TIS) framework to explore the dynamics within South African AM enterprises. Six TIS experts and 2AM industry specialists validated the survey's comprehensiveness and relevance. A statistician reviewed the data collection process to ensure it was suitable for robust statistical analysis. Managers of South African AM enterprises were invited to complete the survey online, and the responses were systematically collected and stored. This dataset is a valuable resource for AM researchers and practitioners, facilitating market gap analysis concerning current materials and services and offering insights for future AM applications.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1016/j.dib.2024.110959
In this data article, the Entropy-based TOPSIS technique is employed to assess the cash flow-based financial performance of companies. The study encompasses data from companies listed on the Borsa Istanbul (İstanbul Stock Exchange) and included in the BIST Sustainability 25 Index between 2018 and 2022. The performance metrics considered in the dataset are grouped into categories including liquidity, operational efficiency, financial structure, and profitability ratios. The dataset is derived from company balance sheets, income statements, and cash flow statements.
{"title":"Data on the financial performance of companies on BIST Sustainability 25 Index: An Entropy-based TOPSIS approach","authors":"","doi":"10.1016/j.dib.2024.110959","DOIUrl":"10.1016/j.dib.2024.110959","url":null,"abstract":"<div><div>In this data article, the Entropy-based TOPSIS technique is employed to assess the cash flow-based financial performance of companies. The study encompasses data from companies listed on the Borsa Istanbul (İstanbul Stock Exchange) and included in the BIST Sustainability 25 Index between 2018 and 2022. The performance metrics considered in the dataset are grouped into categories including liquidity, operational efficiency, financial structure, and profitability ratios. The dataset is derived from company balance sheets, income statements, and cash flow statements.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352340924009211/pdfft?md5=dc102832543f9abf5d58ffb9c7b403fc&pid=1-s2.0-S2352340924009211-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1016/j.dib.2024.110960
One of the most striking topics in Artificial Intelligence (AI) is Image captioning that aims to integrate computer vision and natural language processing to create descriptions for each image. In this paper, we propose a new dataset designed specifically for image captioning in gingivitis diagnosis using deep learning. It includes 1,096 high-resolution intraoral images of 12 anterior teeth and surrounding gingival tissue that were collected under controlled conditions with professional-grade photography equipment. Each image features detailed labels and descriptive captions. The labeling process involved three periodontists with over ten years of experience who assigned Modified Gingival Index (MGI) scores to each tooth in the images, achieving high inter-rater reliability through a rigorous calibration process. Captions were then created by the same periodontists, offering diverse descriptions of gingivitis severity and locations. The dataset is systematically organized into training, validation, and testing subsets for systematic accessibility. This dataset supports the development of advanced image captioning algorithms and is a valuable educational resource for integrating real-world data into dental research and curriculum.
{"title":"A dental intraoral image dataset of gingivitis for image captioning","authors":"","doi":"10.1016/j.dib.2024.110960","DOIUrl":"10.1016/j.dib.2024.110960","url":null,"abstract":"<div><div>One of the most striking topics in Artificial Intelligence (AI) is Image captioning that aims to integrate computer vision and natural language processing to create descriptions for each image. In this paper, we propose a new dataset designed specifically for image captioning in gingivitis diagnosis using deep learning. It includes 1,096 high-resolution intraoral images of 12 anterior teeth and surrounding gingival tissue that were collected under controlled conditions with professional-grade photography equipment. Each image features detailed labels and descriptive captions. The labeling process involved three periodontists with over ten years of experience who assigned Modified Gingival Index (MGI) scores to each tooth in the images, achieving high inter-rater reliability through a rigorous calibration process. Captions were then created by the same periodontists, offering diverse descriptions of gingivitis severity and locations. The dataset is systematically organized into training, validation, and testing subsets for systematic accessibility. This dataset supports the development of advanced image captioning algorithms and is a valuable educational resource for integrating real-world data into dental research and curriculum.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352340924009223/pdfft?md5=f01ff43bb621297b870041fdf2b81d30&pid=1-s2.0-S2352340924009223-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1016/j.dib.2024.110957
The article presents a processed dataset from amplicon sequencing of the V4 region of the 16S rRNA gene to recover bacterial and archaeal taxa from the caeca of multiple chicken breeds of Pakistan. These include chicken breeds commonly raised at commercial level, Naked Neck, Black Australorp, Rhode Island Red, White Layer, and Broiler. All the breeds were challenged with Newcastle Disease Virus (NDV), with vaccination against the disease also explored. This resulted in samples belonging to four treatment groups as: Control; Vaccinated; Vaccinated and Challenged; and Non-vaccinated and Challenged. These were raised on an antibiotic free diet in a semi-controlled farming setup. 16S rRNA gene amplicon sequencing of caecal DNA from day old and mature chicken samples (22 weeks for Naked Neck, Black Australorp, Rhode Island Red and White Layer; 8 weeks for Broiler) of the four groups was performed. The paired-end reads from all the samples were quality trimmed, error corrected, and overlapped, on which unique Operational Taxonomic Units (OTUs) were obtained at 99 % similarity. Using predictive modelling, the MetaCyc functional pathways, as well as KEGG orthologs were also recovered. The generated data may be used to explore microbial interactions in gastrointestinal tract with respect to NDV vaccination and infection, together with increased understanding of chicken health and productivity.
{"title":"Dataset of 16S rRNA gene sequences of 111 healthy and Newcastle disease infected caecal samples from multiple chicken breeds of Pakistan","authors":"","doi":"10.1016/j.dib.2024.110957","DOIUrl":"10.1016/j.dib.2024.110957","url":null,"abstract":"<div><div>The article presents a processed dataset from amplicon sequencing of the V4 region of the 16S rRNA gene to recover bacterial and archaeal taxa from the caeca of multiple chicken breeds of Pakistan. These include chicken breeds commonly raised at commercial level, Naked Neck, Black Australorp, Rhode Island Red, White Layer, and Broiler. All the breeds were challenged with Newcastle Disease Virus (NDV), with vaccination against the disease also explored. This resulted in samples belonging to four treatment groups as: Control; Vaccinated; Vaccinated and Challenged; and Non-vaccinated and Challenged. These were raised on an antibiotic free diet in a semi-controlled farming setup. 16S rRNA gene amplicon sequencing of caecal DNA from day old and mature chicken samples (22 weeks for Naked Neck, Black Australorp, Rhode Island Red and White Layer; 8 weeks for Broiler) of the four groups was performed. The paired-end reads from all the samples were quality trimmed, error corrected, and overlapped, on which unique Operational Taxonomic Units (OTUs) were obtained at 99 % similarity. Using predictive modelling, the MetaCyc functional pathways, as well as KEGG orthologs were also recovered. The generated data may be used to explore microbial interactions in gastrointestinal tract with respect to NDV vaccination and infection, together with increased understanding of chicken health and productivity.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352340924009193/pdfft?md5=43209103587f441093dce9cc9377985f&pid=1-s2.0-S2352340924009193-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1016/j.dib.2024.110939
Thermal properties play a critical role in the compost used as a soil amendment for different agricultural applications especially for green roof buildings. Despite this importance, there remains insufficient information on thermal conductivity of composted olive cake (COC), K, and how it is influenced by bulk its density and water content. This shows how thermal conductivity (K) is affected by these two parameters and the potential use of COC as cheap padding in geothermal heat storage and green roof building applications. Thermal conductivities of 30 samples of (COC) were measured experimentally at different moisture contents and bulk densities using a hot wire technique. The results revealed that thermal conductivity exhibits a linear increase as both bulk density and water content increased. It increased from 0.10 to 0.60 W/(m K) at saturation levels ranging from dry to 90 %. The highest thermal conductivity of 0.60 W/m K was revealed at a water content of 90 %. Therefore, (COC) might be used as an inexpensive padding in geothermal heat storage applications and as an eco-friendly insulation pad in green- roof buildings, leading to passive energy savings. Overall, the study provides important insights into the thermal properties of COC and its potential as a sustainable insulation material.
{"title":"Dataset on thermal conductivity of composted olive cake (COC)","authors":"","doi":"10.1016/j.dib.2024.110939","DOIUrl":"10.1016/j.dib.2024.110939","url":null,"abstract":"<div><div>Thermal properties play a critical role in the compost used as a soil amendment for different agricultural applications especially for green roof buildings. Despite this importance, there remains insufficient information on thermal conductivity of composted olive cake (COC), K, and how it is influenced by bulk its density and water content. This shows how thermal conductivity (K) is affected by these two parameters and the potential use of COC as cheap padding in geothermal heat storage and green roof building applications. Thermal conductivities of 30 samples of (COC) were measured experimentally at different moisture contents and bulk densities using a hot wire technique. The results revealed that thermal conductivity exhibits a linear increase as both bulk density and water content increased. It increased from 0.10 to 0.60 W/(m K) at saturation levels ranging from dry to 90 %. The highest thermal conductivity of 0.60 W/m K was revealed at a water content of 90 %. Therefore, (COC) might be used as an inexpensive padding in geothermal heat storage applications and as an eco-friendly insulation pad in green- roof buildings, leading to passive energy savings. Overall, the study provides important insights into the thermal properties of COC and its potential as a sustainable insulation material.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1016/j.dib.2024.110958
This work contains a water sorption isotherms dataset obtained on dried parchment coffee beans processed by wet and semi-dry postharvest methods and their mid-infrared spectral data. The experimental data of water sorption isotherms were determined using the Dynamic Dewpoint Isotherm (DDI) method. The measurements were taken in a water activity range of 0.1 to 0.85 and at 25, 35, and 45 °C temperatures. To spectrally characterize the dried parchment coffee beans processed by wet and semi-dry postharvest methods, the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy technique was used. The dataset comprises Excel files with the experimental data acquired for the dried parchment coffee beans processed by wet and semi-dry postharvest methods and the experimental conditions assessed. This dataset serves as a reliable and valuable tool for researchers, coffee producers, and decision-makers to be used as the basis for mathematically computing relevant parameters related to the coffee shelf life and hygroscopic behavior, as well as to develop suitable packaging materials/containers to maximize the quality of coffee beans in terms of sensory flavors and moisture stability. Furthermore, the experimental data provide a reliable tool for optimizing the coffee storage process and gaining insights into the water-sorption process.
{"title":"Water sorption isotherms and mid-infrared spectra of dried parchment coffee beans (Coffee arabica L.) processed by wet and semi-dry postharvest methods. A dataset for estimating water sorption and thermodynamic properties.","authors":"","doi":"10.1016/j.dib.2024.110958","DOIUrl":"10.1016/j.dib.2024.110958","url":null,"abstract":"<div><div>This work contains a water sorption isotherms dataset obtained on dried parchment coffee beans processed by wet and semi-dry postharvest methods and their mid-infrared spectral data. The experimental data of water sorption isotherms were determined using the Dynamic Dewpoint Isotherm (DDI) method. The measurements were taken in a water activity range of 0.1 to 0.85 and at 25, 35, and 45 °C temperatures. To spectrally characterize the dried parchment coffee beans processed by wet and semi-dry postharvest methods, the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy technique was used. The dataset comprises Excel files with the experimental data acquired for the dried parchment coffee beans processed by wet and semi-dry postharvest methods and the experimental conditions assessed. This dataset serves as a reliable and valuable tool for researchers, coffee producers, and decision-makers to be used as the basis for mathematically computing relevant parameters related to the coffee shelf life and hygroscopic behavior, as well as to develop suitable packaging materials/containers to maximize the quality of coffee beans in terms of sensory flavors and moisture stability. Furthermore, the experimental data provide a reliable tool for optimizing the coffee storage process and gaining insights into the water-sorption process.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1016/j.dib.2024.110949
Keyboard acoustic recognition is a pivotal area within cybersecurity and human-computer interaction, where the identification and analysis of keyboard sounds are used to enhance security measures. The performance of acoustic-based security systems can be influenced by factors such as the platform used, typing style, and environmental noise. To address these variations and provide a comprehensive resource, we present the Multi-Keyboard Acoustic (MKA) Datasets. These extensive datasets, meticulously gathered by a team in the Computer Science Department at the University of Halabja, include recordings from six widely-used platforms: HP, Lenovo, MSI, Mac, Messenger, and Zoom. The MKA datasets have structured data for each platform, including raw recordings, segmented sound files, and matrices derived from these sounds. They can be used by researchers in keylogging detection, cybersecurity, and other fields related to acoustic emanation attacks on keyboards. Moreover, the datasets capture the intricacies of typing behaviour with both hands and all ten fingers by carefully segmenting and pre-processing the data using the Praat tool, thus ensuring high-quality and dependable data. This comprehensive approach allows researchers to explore various aspects of keyboard sound recognition, contributing to the development of robust recognition algorithms and enhanced security measures. The MKA Datasets stand as one of the largest and most detailed datasets in this domain, offering significant potential for advancing research and improving defences against acoustic-based threats.
{"title":"Multi-datasets for different keyboard key sound recognition","authors":"","doi":"10.1016/j.dib.2024.110949","DOIUrl":"10.1016/j.dib.2024.110949","url":null,"abstract":"<div><div>Keyboard acoustic recognition is a pivotal area within cybersecurity and human-computer interaction, where the identification and analysis of keyboard sounds are used to enhance security measures. The performance of acoustic-based security systems can be influenced by factors such as the platform used, typing style, and environmental noise. To address these variations and provide a comprehensive resource, we present the Multi-Keyboard Acoustic (MKA) Datasets. These extensive datasets, meticulously gathered by a team in the Computer Science Department at the University of Halabja, include recordings from six widely-used platforms: HP, Lenovo, MSI, Mac, Messenger, and Zoom. The MKA datasets have structured data for each platform, including raw recordings, segmented sound files, and matrices derived from these sounds. They can be used by researchers in keylogging detection, cybersecurity, and other fields related to acoustic emanation attacks on keyboards. Moreover, the datasets capture the intricacies of typing behaviour with both hands and all ten fingers by carefully segmenting and pre-processing the data using the Praat tool, thus ensuring high-quality and dependable data. This comprehensive approach allows researchers to explore various aspects of keyboard sound recognition, contributing to the development of robust recognition algorithms and enhanced security measures. The MKA Datasets stand as one of the largest and most detailed datasets in this domain, offering significant potential for advancing research and improving defences against acoustic-based threats.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352340924009090/pdfft?md5=946e747027631a229faaaa7cdf2abc37&pid=1-s2.0-S2352340924009090-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1016/j.dib.2024.110955
Structural complexity refers to the three-dimensional arrangement and variability of both biotic and abiotic components of an ecosystem. Metrics that characterize structural complexity are often used to manage various aspects of ecosystem function, such as light transmittance, wildlife habitat, and biological diversity. Additionally, these metrics aid in evaluating resilience to disturbance events, including hurricanes, bark-beetle outbreaks, and wildfire. Recent advances in wildland fire modelling have facilitated the integration of forest structural complexity metrics into the QUIC-Fire model, enabling real-time prediction of fire spread and behaviour by simulating interactions between fire, weather, topography, and forest structure. While QUIC-Fire is designed to be highly adaptable, model performance depends on the availability and accuracy of local data inputs. Expanding the model's usability across different regions can be facilitated by the availability of more comprehensive and high-quality data. Thus, the primary goal behind the data products we developed was to establish a basis for collaborative research across various disciplines, particularly within the focal areas of the Southern Research Station, such as forestry, wildland fire, hydrology, soil science, and cultural resources at Bent Creek, Coweeta, Escambia, and Hitchiti Experimental Forests (EFs).
Airborne laser scanning (ALS) was used to collect point-cloud data for each EF during the leaf-off season to minimize interference from foliage. Subsequent processing of the raw lidar data involved outlier detection and filtering, ground and non-ground classification, and the computation of a variety of metrics representing various aspects of topography and forest structure at both the pixel-level and the tree-level. Pixel-level topographic data products include: digital elevation model (DEM), slope, aspect, topographic position index (TPI), topographic roughness index (TRI), roughness, and flow direction. Forest structural-complexity metrics include canopy height, foliar height diversity (FHD), vertical distribution ratio (VDR), canopy rugosity, crown relief ratio (CRR), understory complexity index (UCI), vertical complexity index (VCI), canopy cover, mean vegetation height, and the standard deviation of vegetation height. Tree-level data products were computed from the point cloud using multiple algorithms to perform individual tree detection (ITD) and individual tree segmentation (ITS). The datasets have been harmonized and are openly accessible through the USDA Forest Service Research Data Archive.
{"title":"Lidar-derived structural-complexity data across four experimental forests","authors":"","doi":"10.1016/j.dib.2024.110955","DOIUrl":"10.1016/j.dib.2024.110955","url":null,"abstract":"<div><div>Structural complexity refers to the three-dimensional arrangement and variability of both biotic and abiotic components of an ecosystem. Metrics that characterize structural complexity are often used to manage various aspects of ecosystem function, such as light transmittance, wildlife habitat, and biological diversity. Additionally, these metrics aid in evaluating resilience to disturbance events, including hurricanes, bark-beetle outbreaks, and wildfire. Recent advances in wildland fire modelling have facilitated the integration of forest structural complexity metrics into the QUIC-Fire model, enabling real-time prediction of fire spread and behaviour by simulating interactions between fire, weather, topography, and forest structure. While QUIC-Fire is designed to be highly adaptable, model performance depends on the availability and accuracy of local data inputs. Expanding the model's usability across different regions can be facilitated by the availability of more comprehensive and high-quality data. Thus, the primary goal behind the data products we developed was to establish a basis for collaborative research across various disciplines, particularly within the focal areas of the Southern Research Station, such as forestry, wildland fire, hydrology, soil science, and cultural resources at Bent Creek, Coweeta, Escambia, and Hitchiti Experimental Forests (EFs).</div><div>Airborne laser scanning (ALS) was used to collect point-cloud data for each EF during the leaf-off season to minimize interference from foliage. Subsequent processing of the raw lidar data involved outlier detection and filtering, ground and non-ground classification, and the computation of a variety of metrics representing various aspects of topography and forest structure at both the pixel-level and the tree-level. Pixel-level topographic data products include: digital elevation model (DEM), slope, aspect, topographic position index (TPI), topographic roughness index (TRI), roughness, and flow direction. Forest structural-complexity metrics include canopy height, foliar height diversity (FHD), vertical distribution ratio (VDR), canopy rugosity, crown relief ratio (CRR), understory complexity index (UCI), vertical complexity index (VCI), canopy cover, mean vegetation height, and the standard deviation of vegetation height. Tree-level data products were computed from the point cloud using multiple algorithms to perform individual tree detection (ITD) and individual tree segmentation (ITS). The datasets have been harmonized and are openly accessible through the USDA Forest Service Research Data Archive.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1016/j.dib.2024.110953
The earthquake in Ecuador on 16 April 2016 generated large volumes of debris and waste. This dataset contains data on recovered and reused disaster materials. Data were collected through a census survey of the scrap dealers of earthquake 2016 debris and waste (n = 62). This dataset was compiled to demonstrate how earthquake waste was generated during the 2016 earthquake and compare it with the pre-disaster period 2015 and the 2019 current when the data were collected. The recovered disaster materials include plastic, metal, cardboard, paper, glass, other recyclable materials, and reused materials. Likewise, the database allows us to observe the time response of medium- and small-sized scrap businesses as scrap dealers engage in the commercial transaction of disaster materials, and this dataset shows the process phases of recovering disaster waste. In addition, the dataset includes profit perceptions and factual earnings from scrap businesses after an earthquake. Considering the significant volume of waste and debris generated, this database can provide useful data for evaluating disaster waste management as an important task in post-disaster recovery.
{"title":"Dataset on recovered waste post-earthquake 2016 in Manabi Province, Ecuador for recycling and reuse","authors":"","doi":"10.1016/j.dib.2024.110953","DOIUrl":"10.1016/j.dib.2024.110953","url":null,"abstract":"<div><div>The earthquake in Ecuador on 16 April 2016 generated large volumes of debris and waste. This dataset contains data on recovered and reused disaster materials. Data were collected through a census survey of the scrap dealers of earthquake 2016 debris and waste (<em>n</em> = 62). This dataset was compiled to demonstrate how earthquake waste was generated during the 2016 earthquake and compare it with the pre-disaster period 2015 and the 2019 current when the data were collected. The recovered disaster materials include plastic, metal, cardboard, paper, glass, other recyclable materials, and reused materials. Likewise, the database allows us to observe the time response of medium- and small-sized scrap businesses as scrap dealers engage in the commercial transaction of disaster materials, and this dataset shows the process phases of recovering disaster waste. In addition, the dataset includes profit perceptions and factual earnings from scrap businesses after an earthquake. Considering the significant volume of waste and debris generated, this database can provide useful data for evaluating disaster waste management as an important task in post-disaster recovery.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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