Data in Brief最新文献

{"title":"Data on Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture","authors":"Jeanine Ammann ,&nbsp;Nadja El Benni ,&nbsp;Sandie Masson ,&nbsp;Rita Saleh","doi":"10.1016/j.dib.2024.111212","DOIUrl":"10.1016/j.dib.2024.111212","url":null,"abstract":"<div><div>This article describes data from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained 485 respondents. In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then randomly assigned to one of five experiment groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects (e.g. perception of economic, environmental and social sustainability). Finally, respondents answered questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111212"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730271/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983029","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":"BDMANGO: An image dataset for identifying the variety of mango based on the mango leaves","authors":"Mohammad Manzurul Islam,&nbsp;Md. Jubayer Ahmed,&nbsp;Mahmud Bin Shafi,&nbsp;Aritra Das,&nbsp;Md. Rakibul Hasan,&nbsp;Abdullah Al Rafi,&nbsp;Mohammad Rifat Ahmmad Rashid,&nbsp;Nishat Tasnim Niloy,&nbsp;Md. Sawkat Ali,&nbsp;Abdullahi Chowdhury,&nbsp;Ahmed Abdal Shafi Rasel","doi":"10.1016/j.dib.2024.111241","DOIUrl":"10.1016/j.dib.2024.111241","url":null,"abstract":"<div><div>In the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. To address the challenges of identifying different mango leaf types and recognizing the diverse and unique characteristics of mango varieties in Bangladesh, a comprehensive and publicly accessible dataset titled “BDMANGO” has been created. This dataset includes images essential for research, featuring six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar, which were collected from different locations. The images were captured using the rear cameras of a Google Pixel 6a and an iPhone XR and were stored in 640 × 480 pixels resolution. Both sides of each mango leaf were photographed against white background to accurately reflect real-world scenarios in mango cultivation fields. The white background was specifically chosen to remove noise in image sample, allowing for accurate feature extraction by machine learning algorithms. This will ensure the trained model's efficacy in identifying a specific mango leaf while implemented alongside any segmentation algorithm. Additionally, image augmentation techniques such as rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming were applied to expand the dataset from 837 original images to a total of 6696 images (837 original image and 5859 augmented images). This expansion significantly enhances the dataset's utility for training, testing, and validating machine learning models designed for classifying mango leaf varieties, thereby supporting research efforts in this domain.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111241"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11748707/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001428","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":"Diversity of the non-targeted metabolomic data across various varieties of Cloves (Syzygium spp.)","authors":"Jakty Kusuma ,&nbsp;Analianasari ,&nbsp;Anung Wahyudi ,&nbsp;Muhammad Khalid Abdullah ,&nbsp;Ahmad Zainul Hasan ,&nbsp;Imam Asrowardi ,&nbsp;Fitriani ,&nbsp;Muhammad Tahir","doi":"10.1016/j.dib.2024.111237","DOIUrl":"10.1016/j.dib.2024.111237","url":null,"abstract":"<div><div>Cloves (<em>Syzygium aromaticum</em>), a tree in the Myrtaceae family, are indigenous to the Maluku Islands in Indonesia and are widely utilized as a spice. Essential oils are commonly extracted from clove leaves, flower buds, and stalks. However, due to supply constraints, other clove species, notably <em>Syzygium obtusifolium</em>, are sometimes used as substitutes, leading to lower-grade essential oils. Here, we employed a non-targeted mass-spectrometry-based metabolomics approach to characterize the metabolic profiles of leaves from ten clove varieties, including <em>S. obtusifolium</em>. We identified and quantified 427 metabolites across various metabolic pathways. The metabolomics data for all samples are publicly available at the Figshare repository under 10.6084/m9.figshare.27212016. The data can be accessed directly at <span><span>https://figshare.com/s/f4a40b7903b6a946b203</span><svg><path></path></svg></span><em>.</em></div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111237"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11748708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001659","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":"MBI-KG: A knowledge graph of structured and linked economic research data extracted from the 1937 book “Die Maschinen-Industrie im Deutschen Reich”","authors":"Renat Shigapov ,&nbsp;Thomas Schmidt ,&nbsp;Jan Kamlah ,&nbsp;Irene Schumm ,&nbsp;Jochen Streb ,&nbsp;Sibylle Lehmann-Hasemeyer","doi":"10.1016/j.dib.2024.111238","DOIUrl":"10.1016/j.dib.2024.111238","url":null,"abstract":"<div><div>The MaschinenBauIndustrie Knowledge Graph (MBI-KG) is a structured and semantically enriched dataset extracted from the 1937 publication “Die Maschinen-Industrie im Deutschen Reich” (The Machinery Industry in the German Reich), published by the “Wirtschaftsgruppe Maschinenbau” and edited by Herbert Patschan. This historical source offers data on German companies within the mechanical engineering industry during the pre-World War II era.</div><div>The book was digitized, and Optical Character Recognition (OCR) was applied to extract text. The unstructured extracted data was then structured and semantically enriched to enable data integration and reuse. The semantically enriched data was uploaded into an open-source knowledge-graph software. The resulting knowledge graph includes detailed information about companies, individuals, and administrative entities relevant to the German mechanical engineering industry. The data is accessible through various means, including a SPARQL endpoint, an API, advanced search functionalities, a reconciliation API, and bulk files. Each entity in the knowledge graph can be exported in multiple formats, such as CSV, RDF (ttl), JSON, and NDJSON, ensuring compatibility with diverse research tools and platforms.</div><div>This dataset can be reused in various research domains, including economic history, data science, and digital humanities. By providing machine-readable, structured data from a crucial historical period, the MBI-KG facilitates novel analyses and insights into the economic and industrial landscape of early 20th-century Germany. The dataset's interoperability with other data sources and its alignment with FAIR principles further enhance its value for interdisciplinary research and long-term preservation.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111238"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001712","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":"Balkan Peace Index decision EXpert model and data","authors":"Nemanja Džuverović ,&nbsp;Sandro Radovanović ,&nbsp;Goran Tepšić ,&nbsp;Đorđe Krivokapić","doi":"10.1016/j.dib.2024.111181","DOIUrl":"10.1016/j.dib.2024.111181","url":null,"abstract":"<div><div>This paper details the data collection process, dataset, and reuse potential of the Balkan Peace Index, a model designed to evaluate the levels of peacefulness in the Western Balkans. Data was gathered in phases: initially, a team of local experts conducted on-ground data collection, interviews, and focus groups, as well as using external international databases describing different notions of peace. This data was then processed and classified on a predefined scale by another team of experts using the Decision EXpert model. The BPI model incorporates both quantitative and qualitative data, reflecting the local context. The comprehensive dataset is stored in the Mendeley Data repository and offers significant reuse potential for further research, policy-making, and sensitivity analysis. This open-access resource aims to provide actionable insights for improving peace levels and preventing potential deterioration in the region.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111181"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982987","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 dataset of annotated free comments on the sensory perception of madeleines for benchmarking text mining techniques","authors":"Michel Visalli ,&nbsp;Ronan Symoneaux ,&nbsp;Cécile Mursic ,&nbsp;Margaux Touret ,&nbsp;Flore Lourtioux ,&nbsp;Kipédène Coulibaly ,&nbsp;Benjamin Mahieu","doi":"10.1016/j.dib.2024.111250","DOIUrl":"10.1016/j.dib.2024.111250","url":null,"abstract":"<div><div>This dataset was created to investigate the impact of data collection modes and pre-processing techniques on the quality of free comment data related to consumers' sensory perceptions. A total of 200 consumers were recruited and divided into two groups of 100. Each group evaluated six madeleine samples (five distinct samples and one replicate) in a sensory analysis laboratory, using different free comment data collection modes. Consumers in the first group provided only words or short expressions, while those in the second group used complete sentences. Additionally, participants reported their liking for each sample.</div><div>The collected data provided valuable insights into the effectiveness of the free comment method in sensory evaluation of food products. They emphasized the importance of data pre-processing and demonstrated how the chosen techniques can impact the quality of the results. The dataset is based on real-world consumer data, showcasing how individuals naturally express their subjective perceptions. It features descriptions that reflect authentic consumer language, including informal expressions, incorrect phrasing, spelling errors, and unstructured sentences. This raw textual data has been annotated and translated into English. The dataset can therefore be repurposed to assess and compare the performance of different text mining, natural language processing and sentiment analysis algorithms in both French and English, as well as to drive innovations in AI tools for sensory and consumer research.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111250"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001833","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 of microscale atmospheric flow and pollutant concentration large-eddy simulations for varying mesoscale meteorological forcing in an idealized urban environment","authors":"Eliott Lumet,&nbsp;Thomas Jaravel,&nbsp;Mélanie C. Rochoux","doi":"10.1016/j.dib.2025.111285","DOIUrl":"10.1016/j.dib.2025.111285","url":null,"abstract":"&lt;div&gt;&lt;div&gt;By 2050, two-thirds of the world's population will live in urban areas under climate change, exacerbating the environmental and public health risks associated with poor air quality and urban heat island effects. Assessing these risks requires the development of microscale meteorological models that quickly and accurately predict wind velocity and pollutant concentration with high resolution, as the heterogeneity of urban environments leads to complex wind patterns and strong pollutant concentration gradients. Computational Fluid Dynamics (CFD) has emerged as a powerful tool to address this challenge by providing obstacle-resolved flow and dispersion predictions. However, CFD models are very expensive and require intensive computing resources, which can hinder their systematic use in practical engineering applications. They are also subject to significant uncertainties, particularly those arising from the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, some of which are aleatory and thereby irreducible. Given these issues, the construction of CFD datasets that account for uncertainty would be an interesting avenue of research for microscale atmospheric science.&lt;/div&gt;&lt;div&gt;In this context, we present the PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset, which consists of 200 large-eddy simulations (LES) characterizing the complex interactions between the turbulent airflow, the tracer dispersion, and an idealized urban environment. These simulations reproduce the canonical MUST dispersion field campaign while perturbing the model's mesoscale meteorological forcing parameters. PPMLES includes time series at human height within the built environment to track wind velocity and pollutant release and dispersion over time. PPMLES also includes complete 3-D fields of first- and second-order temporal statistics of the wind velocity and pollutant concentration, with a sub-metric resolution. The uncertainty of the fields induced by the internal variability of the atmospheric boundary layer is also provided. The computation of PPMLES required significant resources, consuming 6 million CPU core hours, equivalent to the emission of approximately 10 tCO2eq of greenhouse gases. This significant computational effort and associated carbon footprint motivates the sharing of the data generated.&lt;/div&gt;&lt;div&gt;The added value of the PPMLES dataset is twofold. First, the perturbed-parameter ensemble of LES enables to quantify and understand the effects of the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, which has been identified as a major challenge in predicting atmospheric flow and pollutant dispersion in urban environments. Secondly, PPMLES reference data can be used to benchmark models of different levels of complexity, and to extract key information about the physical processes involved to inform more operational modeling approaches, ","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111285"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143131300","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":"Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchards","authors":"Virginia Maß ,&nbsp;Pendar Alirezazadeh ,&nbsp;Johannes Seidl-Schulz ,&nbsp;Matthias Leipnitz ,&nbsp;Eric Fritzsche ,&nbsp;Rasheed Ali Adam Ibraheem ,&nbsp;Martin Geyer ,&nbsp;Michael Pflanz ,&nbsp;Stefanie Reim","doi":"10.1016/j.dib.2025.111271","DOIUrl":"10.1016/j.dib.2025.111271","url":null,"abstract":"<div><div>The evaluation of fruit genetic resources regarding a resistance to pathogens is an essential basis for subsequent selection in fruit breeding. Both genetic analysis and phenotyping of defined traits are important tools and provide decision data in the evaluation process. However, the phenotyping of plants is often carried out ‘by hand’ and remains the bottleneck in fruit breeding and fruit growing. The development of a digital and UAV (unmanned aerial vehicle)-based phenotyping method for the assessment of genotype-specific susceptibility or resistance against diseases in orchards would significantly increase the efficiency of plant breeding. In this framework, a workflow for drone-based monitoring of pathogens in orchards was developed using the European pear rust (<em>Gymnosporangium sabinae</em>) as model pathogen. Pear rust is widespread in orchards and causes conspicuous, clearly visible, yellow to orange-colored disease symptoms.</div><div>In this paper, we provide a dataset with expert-annotated high-resolution RGB images with pear rust symptoms. For data collection, ten UAV-flight campaigns were realized between 2021 and 2023 under various weather conditions and with different flight parameters in the experimental orchard of the Julius Kühn-Institute for Breeding Research on Fruit Crops in Dresden-Pillnitz (Germany). 1394 images were captured of different pear genotypes, including varieties, wild species and progeny from breeding. The dataset contains manually labelled images with a size of 768 × 768 pixels of leaves infected with pear rust at different stages of development, labelled as class GYMNSA, as well as background images without symptoms. Each leaf with pear rust symptoms was annotated with the drawing method by two points (bounding boxes) using the Computer Vision Annotation Tool (CVAT, v1.1.0) [1] and presented in YOLO 1.1 file format (.txt files). A total of 584 annotated images and 162 background images, organized into a training and validation set, are included in the GYMNSA dataset. This GYMNSA dataset can be used as a resource for researchers and developers working on drone-based plant disease monitoring systems.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111271"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143078893","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":"Sediment flow connectivity index data for the Apulia region (Italy): An open-source geodatabase and the innovative CONNECTOSED WebGIS platform","authors":"Alok Kushabaha ,&nbsp;Domenico Capolongo ,&nbsp;Giovanni Scicchitano ,&nbsp;Floriana Rizzo ,&nbsp;Marina Zingaro","doi":"10.1016/j.dib.2024.111210","DOIUrl":"10.1016/j.dib.2024.111210","url":null,"abstract":"<div><div>An open-source geodatabase and its associate WebGIS platform (CONNECTOSED) were developed to collect and utilize data for the Sediment Flow Connectivity Index (SfCI) for the Apulia region of southern Italy. Maps depicting sediment mobility and connectivity across the hydrographic basins of the Apulia region were generated and stored in the geodatabase. This geodatabase is organized into folders containing data in TIFF, shapefile, Jpeg and Pdf formats, including input variables (digital elevation model, land cover map, rainfall map, and soil units dataset for each hydrographic basin), classification graphs (ranking of variable values), dimensionless index maps (slope, ruggedness, rainfall, land cover, and soil stability) and key products (maps of sediment mobility, SfCI, and applied SfCI). The geodatabase maintains the mapping methodology underlying the SfCI algorithm by integrating various Earth datasets from multiple sources through ArcMap™, QGIS® and Matlab® software. This approach aligns surface characteristics with driving forces to describe the spatial variability of sediment pathways and identify hotspot areas. The availability of both input and processed data enables the computation and continuous updating of this applied geomorphological indicator, which is useful for assessing susceptibility to rapid Earth surface changes related to multi-hazard exposure. The geodatabase and the CONNECTOSED platform are valuable tools for researchers and stakeholders involved in land monitoring. The geodatabase and the CONNECTOSED platform are essential tools for researchers, policymakers, and stakeholders involved in land monitoring and environmental management. These tools provide open access to extensive datasets and detailed descriptions of surface dynamics, establishing connections between the causes and effects of extreme phenomena, such as floods, landslides, fires, soil pollution. This integration allows users to combine various forms of environmental data, a capability that is vital for enhancing scientific knowledge, supporting the development of insights, and fostering more informed, evidence-based decision-making in land use planning, conservation efforts, and sustainability initiatives.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111210"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982990","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":"Using microtremor data to obtain dynamic properties of soils in the Veracruz-Boca del Rio metropolitan area","authors":"José E. Barradas-Hernández,&nbsp;Sergio Márquez-Domínguez,&nbsp;Franco Antonio Carpio-Santamaria,&nbsp;Alejandro Vargas-Colorado,&nbsp;Abigail Zamora-Hernández,&nbsp;Roberto Rivera-Baizabal","doi":"10.1016/j.dib.2024.111196","DOIUrl":"10.1016/j.dib.2024.111196","url":null,"abstract":"<div><div>The data presented here are the result of microtremor measurements at 44 points in three different soil types classified according to their fundamental vibration frequencies, on the metropolitan area of Veracruz-Boca del Río, Mexico. These Data are raw and was obtained using a GÜRALP 6TD model broadband orthogonal triaxial seismometer with an integrated 24-bit digitizer with a minimum recording time of 30 min and a recording rate of 100 samples per second (sps). The microtremor records were used to construct the H/V spectral ratios using the method of Nakamura. These H/V spectral ratios are a good approximation of the transfer function between the vibration waves in the sediment and the rigid stratum. Therefore, they can be used to construct seismic microzonation maps, seismic intensity maps and spectra for designing seismic resistant structures. One-dimensional stratigraphic soil models were obtained by processing the H/V spectral ratios. The relevant data from these models are layer thickness, primary wave velocities (Vp), secondary wave velocities (Vs) and density. These models represent a mathematical approximation of the soil structure that can be used to dynamically classify it according to Mexican technical codes.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111196"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930934","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}