Data in Brief最新文献

{"title":"EAHE-D: A dataset for modeling and performance evaluation of earth to air heat exchangers.","authors":"Youssef Alidrissi, Marouane Wakil, Mehdi Najib, Samir Idrissi Kaitouni, Mohamed Oualid Mghazli, Mohamed Bakhouya","doi":"10.1016/j.dib.2024.111110","DOIUrl":"10.1016/j.dib.2024.111110","url":null,"abstract":"<p><p>Integrating natural ventilation with Earth-to-Air Heat Exchangers (EAHE) is an innovative approach that effectively combines the benefits of both systems to enhance indoor environments while reducing energy consumption and operational costs. Many studies focus on reducing reliance on conventional heating, ventilation, and air conditioning systems by optimizing the use of EAHE and natural ventilation in buildings to improve occupant comfort. However, these studies require actual datasets for systems' design and optimization. This work aims to provide such a dataset using a deployed EAHE with in-situ measurements. The dataset is available in its original form, allowing users to perform data preprocessing according to their specific needs. Additionally, we offer a processed version of the dataset, which can be used to further investigate the effectiveness of EAHE deployment in buildings and its integration with other passive and active systems, aiming to enhance energy efficiency while maintaining occupant comfort.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111110"},"PeriodicalIF":1.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779578","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}

{"title":"PaveDistress: A comprehensive dataset of pavement distresses detection.","authors":"Zhen Liu, Wenxiu Wu, Xingyu Gu, Bingyan Cui","doi":"10.1016/j.dib.2024.111111","DOIUrl":"10.1016/j.dib.2024.111111","url":null,"abstract":"<p><p>The PaveDistress dataset contains high-resolution images of road surface distresses, including cracks, repairs, potholes, and background images without defects. The data were collected using a specialized pavement inspection vehicle along the S315 highway in China. The vehicle was equipped with a Basler raL2048-80km line scan camera and infrared laser-assisted lighting, capturing images at 1mm intervals with a resolution of 3854 × 2065 pixels. The images were taken every 2 meters across various lighting conditions, including daylight, dusk, and in challenging environments such as tunnels and cloudy weather. The dataset is organized into distinct categories, covering transverse cracks, longitudinal cracks, map cracks, and more, enabling detailed categorization of pavement distresses. Each image represents a real-world road coverage area of 3.9m × 2.1m, allowing for accurate measurements of defect dimensions. This dataset supports the development of deep learning models for non-destructive detection of road defects, providing valuable resources for civil engineering research and practical applications in road maintenance systems. The dataset can be reused for tasks such as image classification, object detection, and segmentation, enabling researchers to create advanced machine learning models for road distress detection and assessment. By providing high-quality, diverse images, the PaveDistress dataset offers significant potential for research in automated pavement condition monitoring and management systems.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111111"},"PeriodicalIF":1.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779585","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}

{"title":"A replicated, whole-insect transcriptomic dataset for <i>Nezara viridula</i> (southern green stink bug) spanning developmental stadia and sexes.","authors":"Michael E Sparks, Daniel Kuhar, Donald C Weber, Dawn E Gundersen-Rindal","doi":"10.1016/j.dib.2024.111106","DOIUrl":"10.1016/j.dib.2024.111106","url":null,"abstract":"<p><p>The southern green stink bug, <i>Nezara viridula</i> (Hemiptera: Pentatomidae), is a serious agricultural pest insect causing economic damage in the western hemisphere. Although transcriptome resources exist for adults of this species at both midgut-only and whole-insect levels, data for juvenile developmental stadia are not currently available. Sequence data reported in this study close this gap, providing a substantial increase of extrinsic evidence for use in improving gene annotations in the southern green stink bug genome project, for example. Analysis of these data identify 21,380 putative transcripts associated with differentially expressed genes, providing a list of potential targets for downstream experimental characterization of gene function.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111106"},"PeriodicalIF":1.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779662","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}

{"title":"An acoustic dataset for surface roughness estimation in milling process.","authors":"N R Sakthivel, Josmin Cherian, Binoy B Nair, Abburu Sahasransu, L N V Pratap Aratipamula, Singamsetty Anish Gupta","doi":"10.1016/j.dib.2024.111108","DOIUrl":"10.1016/j.dib.2024.111108","url":null,"abstract":"<p><p>Machining process involves numerous variables that can influence the desired outcomes, with surface roughness being a critical quality index for machined products. Surface roughness is often a technical requirement for mechanical products as it can lead to chatter and impact the functional performance of parts, especially those in contact with other materials. Therefore, predicting surface roughness is essential. This dataset comprises 7444 audio files containing acoustic signal samples recorded using a 44.1 kHz microphone during the milling of mild steel with a tungsten carbide tool on a BFW YF1 vertical milling machine. Various combinations of speed, feed and depth of cut were used, and surface roughness values measured using a Carl Zeiss E-35B profile-meter are provided for each combination. Additionally, an example workflow indicating the possible use of the data to estimate the surface roughness from the acoustic signals is presented. This dataset is the first publicly available resource for surface roughness measurement using sound signals in milling, offering significant potential for reuse in related research and applications.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111108"},"PeriodicalIF":1.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779672","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}

{"title":"Abundance and composition data of microbiomes in agricultural biogas plants of Lower Saxony, Germany, with variation in organic substrates, process parameters and nutrients","authors":"Sascha M.B. Krause ,&nbsp;Rui Wang ,&nbsp;Anja B. Dohrmann ,&nbsp;Meike Walz ,&nbsp;Achim Loewen ,&nbsp;Christoph C. Tebbe","doi":"10.1016/j.dib.2024.111095","DOIUrl":"10.1016/j.dib.2024.111095","url":null,"abstract":"<div><div>This article presents high-throughput DNA sequencing, quantitative PCR data of microbial communities, and process parameters as recovered from eight biogas plants (BPs) located in Lower Saxony, Germany. Samples were collected from both the main (MD) and secondary digesters (SD). Additionally, for 4 BPs, samples were also obtained from the residue digester storage (RDS). Different BPs employed various types of substrates originating from cattle manure, chicken manure, pig manure, or renewable resources. Information on physico-chemical process parameters and concentrations of macro- and micro-nutrients in the BPs is provided. Total DNA from all samples were extracted using a phenol-chloroform-based method. To determine the abundance of bacteria and archaea, their 16S rRNA genes were quantified by real-time PCR (qPCR), and to characterize their community composition, paired-end DNA sequence reads were generated from PCR amplicons with Illumina MiSeq. All statistical analyses were performed in R to explore the microbial diversity, abundance, and community structure among different BPs and digesters (MD, SD, RDS). The presence and distribution of the major bacterial and archaeal phyla indicated for each BP unique and diverse microbial communities with typically higher bacterial than archaeal abundances.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111095"},"PeriodicalIF":1.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707123","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}

{"title":"Dataset with updated ozone depletion characterization factors for life cycle impact assessment.","authors":"Anne E M van den Oever, Stefano Puricelli, Daniele Costa, Nils Thonemann, Maeva Lavigne Philippot, Maarten Messagie","doi":"10.1016/j.dib.2024.111103","DOIUrl":"10.1016/j.dib.2024.111103","url":null,"abstract":"<p><p>This dataset provides the latest characterization factors for ozone depletion based on the most recent ozone depletion potentials from the 2022 World Meteorological Organization (WMO) scientific assessment. These characterization factors can be used in life cycle assessment (LCA) to convert emissions of ozone-depleting substances to the common unit of the ozone depletion impact category, measured in kg CFC-11-eq. The dataset is formatted for easy import into LCA software such as Brightway, the Activity Browser, and SimaPro. The characterization factors are provided for both 100-year and infinite time horizons. The dataset addresses the current limitations of life cycle impact assessment (LCIA) methods, which are outdated and lack comprehensive substance coverage, by including 318 substances reported in the latest WMO assessment. This update ensures relevance for current ozone depletion assessment, including substances banned but still in use, very short-lived substances, and N₂O. The methodology for updating and converting characterization factors is provided, supporting future updates in line with new scientific assessments. The dataset aims to enhance the accuracy and comprehensiveness of ozone depletion impact assessments in LCA studies.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111103"},"PeriodicalIF":1.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779674","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}

{"title":"Dataset on soil nematode abundance and composition from invaded and non-invaded grassland and forest ecosystems in Europe","authors":"Andrea Čerevková ,&nbsp;Volodimir Sarabeev ,&nbsp;Marek Renčo","doi":"10.1016/j.dib.2024.111098","DOIUrl":"10.1016/j.dib.2024.111098","url":null,"abstract":"<div><div>The dataset presents comprehensive information on soil nematode genera distribution in ecosystems across Slovakia, Poland, Lithuania, and Russia. Data were collected from invaded plots by invasive plants and non-invaded plots from grasslands, deciduous forests, and coniferous forest ecosystems in diverse geographical regions. Invasive plant species included in this dataset are <em>Asclepias syriaca, Fallopia japonica, Heracleum mantegazzianum, H. sosnowskyi, Impatiens parviflora</em> and <em>Solidago gigantea.</em> The soil properties such as pH, moisture content, carbon, and nitrogen levels were recorded, providing comprehensive information on soil conditions. The data collection process involved standardized soil sampling techniques across all sites, ensuring consistency and comparability. The dataset offers valuable insights into soil nematode biodiversity dynamics in response to plant species invasions in European ecosystems. Nematode genera were classified according to feeding types and colonizer-persister class. Researchers interested in soil ecology, biodiversity conservation, and invasive species management can use this dataset for various purposes. Potential reuses include comparative analyses of nematode community composition, ecological modelling to predict invasive species impacts and assessments of ecosystem health and resilience.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111098"},"PeriodicalIF":1.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707126","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}

{"title":"Salient object detection dataset with adversarial attacks for genetic programming and neural networks.","authors":"Matthieu Olague, Gustavo Olague, Roberto Pineda, Gerardo Ibarra-Vazquez","doi":"10.1016/j.dib.2024.111043","DOIUrl":"10.1016/j.dib.2024.111043","url":null,"abstract":"<p><p>Machine learning is central to mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial feature extraction, it has an end-to-end paradigm from input to output, reaching outstandingly accurate results. However, security concerns about its robustness to malicious and imperceptible perturbations have drawn attention since humans or machines can change the predictions of programs entirely. Salient object detection is a research area where deep convolutional neural networks have proven effective but whose trustworthiness represents a significant issue requiring analysis and solutions to hackers' attacks. This dataset is an image repository containing five different image databases to evaluate adversarial robustness by introducing 12 adversarial examples, each leveraging a known adversarial attack or noise perturbation. The dataset comprises 56,387 digital images, resulting from applying adversarial examples on subsets of four standard databases (i.e., FT, PASCAL-S, ImgSal, DUTS) and a fifth database (SNPL) portraying a real-world visual attention problem of a shorebird called the snowy plover. We include original and rescaled images from the five databases used with the adversarial examples as part of this dataset for easy access and distribution.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111043"},"PeriodicalIF":1.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11647110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142834365","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}

{"title":"High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forests","authors":"Tomáš Hanousek ,&nbsp;Terézia Slanináková ,&nbsp;Tomáš Rebok ,&nbsp;Růžena Janoutová","doi":"10.1016/j.dib.2024.111105","DOIUrl":"10.1016/j.dib.2024.111105","url":null,"abstract":"<div><div>Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests.</div><div>The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT.</div><div>The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111105"},"PeriodicalIF":1.0,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650681","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}

{"title":"Radio frequency-based human activity dataset collected using ESP32 microcontroller in line-of-sight and non-line-of-sight indoor experiment setups.","authors":"Zhe-Yu Lim, Lee-Yeng Ong, Meng-Chew Leow","doi":"10.1016/j.dib.2024.111101","DOIUrl":"10.1016/j.dib.2024.111101","url":null,"abstract":"<p><p>This study presents the \"ESP32 Dataset,\" a dataset of radio frequency (RF) data intended for human activity detection. This dataset comprises 10 activities carried out by 8 volunteers in three different indoor floor plan experiment setups. Line-of-sight (LOS) scenarios are represented by the first two experiment setups, and non-line-of-sight (NLOS) scenarios are simulated in the third experiment setup. For every activity, the volunteers performed 20 trials, hence there were 1,600 recorded trials overall per experiment setup in the sample (8 people × 10 activities × 20 trials) . In order to obtain the Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) values from the recorded transmissions, the D-Link AX3000 router and ESP32 microcontroller were used as the transmitter (Tx) and receiver (Rx) in the data collection process. This collection is an invaluable resource for academics and practitioners in the field of human activity detection since it offers rich and diversified RF data across a wide range of experiment setups and activities. In contrast to other datasets with different hardware configurations, this dataset records one RSSI value and fifty-two CSI subcarriers using the ESP-CSI Tool RF data capture tool. The number of RSSI and CSI signals, specific to the ESP32 hardware, allows for the exploration of resource-efficient activity detection algorithms, which is crucial for Internet of Things (IoT) applications where low-power and cost-effective solutions are required. This dataset is particularly valuable because it reflects the constraints and capabilities of the widely used ESP32 microcontrollers, making it highly relevant for developing and testing new algorithms tailored to IoT environments. The availability of this dataset enables the development and evaluation of activity detection algorithms and methodologies, enhancing the potential for improved experimental setups in IoT applications.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111101"},"PeriodicalIF":1.0,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779600","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}