Pub Date : 2025-12-03DOI: 10.1016/j.dib.2025.112355
Julian Quandt, Jan Paul Lindner, Nico Mumm, Barbara von Hippel
Life‑cycle impact assessment (LCIA) is progressively expanding to address biodiversity impacts, but spatial heterogeneity continues to dominate the associated uncertainty. To account for this, the Ecoregion Factor (EF)—a dimensionless weighting coefficient—modulates biodiversity‑impact scores according to the ecological richness of the ecoregion in which a land‑use takes place. Since the initial publication, the underlying spatial datasets have been superseded by more recent releases. Consequently, we recomputed the EF using the latest ecoregional delineation and the most recent global layers for grasslands, forests and wetlands. To facilitate integration with LCIA frameworks that operate at the national level, the updated EF values were aggregated to country‑level averages by calculating an area‑weighted mean of the normalised EF across all ecoregions intersecting each nation’s borders. This yields a robust geographic adjustment factor that preserves the spatial nuance of ecoregional biodiversity while making biodiversity impact assessments feasible even when inventory data are available only at the country scale. By providing an up‑to‑date, geographically calibrated weighting coefficient, the revised EF enhances the spatial granularity of biodiversity‑focused LCIA results.
{"title":"Updating the global weighting factor for biodiversity impact assessment in LCA","authors":"Julian Quandt, Jan Paul Lindner, Nico Mumm, Barbara von Hippel","doi":"10.1016/j.dib.2025.112355","DOIUrl":"10.1016/j.dib.2025.112355","url":null,"abstract":"<div><div>Life‑cycle impact assessment (LCIA) is progressively expanding to address biodiversity impacts, but spatial heterogeneity continues to dominate the associated uncertainty. To account for this, the Ecoregion Factor (EF)—a dimensionless weighting coefficient—modulates biodiversity‑impact scores according to the ecological richness of the ecoregion in which a land‑use takes place. Since the initial publication, the underlying spatial datasets have been superseded by more recent releases. Consequently, we recomputed the EF using the latest ecoregional delineation and the most recent global layers for grasslands, forests and wetlands. To facilitate integration with LCIA frameworks that operate at the national level, the updated EF values were aggregated to country‑level averages by calculating an area‑weighted mean of the normalised EF across all ecoregions intersecting each nation’s borders. This yields a robust geographic adjustment factor that preserves the spatial nuance of ecoregional biodiversity while making biodiversity impact assessments feasible even when inventory data are available only at the country scale. By providing an up‑to‑date, geographically calibrated weighting coefficient, the revised EF enhances the spatial granularity of biodiversity‑focused LCIA results.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112355"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.dib.2025.112351
Aina Mampionona Rakotoarivelo , Noëlson Noah Randrianantenaina
Madagascar’s economy relies heavily on agriculture, which employs around 80% of the population and contributes approximately 23% to the national GDP. This sector also produces a large amount of agricultural waste that is poorly managed. It is essential to assess the potential of this agricultural waste in order to inform policy makers and integrate it into the energy sector. In this context, the biomass database is an essential tool for assessing the energy potential of agricultural residues, promoting environmentally sustainable energy development, and reducing dependence on conventional energy sources. The database was developed to assess agricultural products as potential energy resources, highlighting the contribution of different crops to overall energy production. It provides a framework for identifying effective methods for the collection, management, and use of residues, thereby helping policymakers to set priorities, optimize the use of residues, and improve Madagascar’s energy security.
{"title":"Biomass database for energy conversion in Madagascar","authors":"Aina Mampionona Rakotoarivelo , Noëlson Noah Randrianantenaina","doi":"10.1016/j.dib.2025.112351","DOIUrl":"10.1016/j.dib.2025.112351","url":null,"abstract":"<div><div>Madagascar’s economy relies heavily on agriculture, which employs around 80% of the population and contributes approximately 23% to the national GDP. This sector also produces a large amount of agricultural waste that is poorly managed. It is essential to assess the potential of this agricultural waste in order to inform policy makers and integrate it into the energy sector. In this context, the biomass database is an essential tool for assessing the energy potential of agricultural residues, promoting environmentally sustainable energy development, and reducing dependence on conventional energy sources. The database was developed to assess agricultural products as potential energy resources, highlighting the contribution of different crops to overall energy production. It provides a framework for identifying effective methods for the collection, management, and use of residues, thereby helping policymakers to set priorities, optimize the use of residues, and improve Madagascar’s energy security.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112351"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.dib.2025.112353
Camila Vicioso , Hannah L. Terry , Ava G. Neijna , Sabrina M. Strickland
This dataset provides a comprehensive collection and classification of publicly available online questions and linked websites related to matrix-induced autologous chondrocyte implantation (MACI), an implant that can be utilized by orthopaedic surgeons for patients requiring knee cartilage restoration. Eight MACI-related search terms were entered individually into a history-cleared Google Chrome browser in incognito mode to minimize personalization bias. For each term, the “People Also Ask” feature was expanded to retrieve approximately 200 question-website pairs, yielding a total of 1620 entries that were compiled and screened for relevance. The final dataset includes 1107 unique, relevant question–website pairs organized in a spreadsheet containing variables for search term, question text, linked website, website source type, Rothwell classification (Fact, Policy, or Value) and subcategories, JAMA Benchmark Criteria component scores, total JAMA credibility score, and thematic grouping based on question content and author consensus. Each entry was rated independently by two reviewers, with discrepancies resolved by the primary author using an Excel-based verification process. Descriptive statistics and logistic regression were performed in Python (statsmodels, SciPy). The dataset is accompanied by materials outlining classification frameworks, frequently repeated questions, and commonly linked websites. By documenting how patients search for and encounter information on a popular cartilage restoration option, this dataset provides a model for evaluating digital health resources and developing accurate, accessible educational content for patients and clinicians across medical disciplines.
{"title":"Dataset on patient education and digital information quality in knee cartilage restoration with matrix-induced autologous chondrocyte implantation (MACI)","authors":"Camila Vicioso , Hannah L. Terry , Ava G. Neijna , Sabrina M. Strickland","doi":"10.1016/j.dib.2025.112353","DOIUrl":"10.1016/j.dib.2025.112353","url":null,"abstract":"<div><div>This dataset provides a comprehensive collection and classification of publicly available online questions and linked websites related to matrix-induced autologous chondrocyte implantation (MACI), an implant that can be utilized by orthopaedic surgeons for patients requiring knee cartilage restoration. Eight MACI-related search terms were entered individually into a history-cleared Google Chrome browser in incognito mode to minimize personalization bias. For each term, the “People Also Ask” feature was expanded to retrieve approximately 200 question-website pairs, yielding a total of 1620 entries that were compiled and screened for relevance. The final dataset includes 1107 unique, relevant question–website pairs organized in a spreadsheet containing variables for search term, question text, linked website, website source type, Rothwell classification (Fact, Policy, or Value) and subcategories, JAMA Benchmark Criteria component scores, total JAMA credibility score, and thematic grouping based on question content and author consensus. Each entry was rated independently by two reviewers, with discrepancies resolved by the primary author using an Excel-based verification process. Descriptive statistics and logistic regression were performed in Python (statsmodels, SciPy). The dataset is accompanied by materials outlining classification frameworks, frequently repeated questions, and commonly linked websites. By documenting how patients search for and encounter information on a popular cartilage restoration option, this dataset provides a model for evaluating digital health resources and developing accurate, accessible educational content for patients and clinicians across medical disciplines.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112353"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.dib.2025.112354
Sayyad Alizadeh , Hamed Shamsi
We present SapBark-64, a curated dataset of 5742 close-range bark images from 64 fruit-tree sapling classes (species/cultivar). Images were acquired in situ at three commercial nurseries in Trabzon (Türkiye) in 2025, targeting 1–2-year saplings routinely traded in nurseries. Photographs were captured with an iPhone 16 Pro Max at approximately 10 cm from the trunk under near-uniform illumination, using a white background to occlude scene clutter and preserve fine-scale morphology. For each class, a nursery label photo was recorded to support ground truth, and class-level characteristics were collected at the time of recording under expert supervision.
The repository is organized as two parallel image folders plus a structured metadata workbook: (i) raw images (JPG) and (ii) background-removed images (WebP) that mirror the same 64 class folders named by species/cultivar, enabling one-to-one pairing across versions; and (iii) an Excel (XLSX) metadata file list- ing standardized fields (family, scientific/common name, cultivar/variety, sapling height, trunk diameter, best planting season, growth rate, fruit-bearing age, average yield, production region, propagation method). This organization facilitates fine- grained identification and retrieval tasks and supports trait-conditioned analyses linking visual texture to horticultural attributes.
The dataset is publicly available in an open repository under a permissive license; acquisition conditions, directory layout, and the metadata schema are documented to enable unambiguous reuse.
我们展示了SapBark-64，这是一个精选的数据集，包含来自64个果树幼树类别（物种/栽培）的5742张近距离树皮图像。图像于2025年在Trabzon （t rkiye）的三个商业苗圃就地获取，目标是苗圃常规交易的1 - 2年树苗。照片是用iPhone 16 Pro Max在距离树干约10厘米的地方在近乎均匀的照明下拍摄的，使用白色背景遮挡场景杂乱并保持精细尺度的形态。对于每个班级，记录一张托儿所标签照片以支持地面事实，并在专家监督下收集记录时的班级水平特征。该数据库被组织为两个并行的图像文件夹和一个结构化的元数据工作簿：(i)原始图像（JPG）和（ii）删除背景的图像（WebP），它们镜像相同的64个分类文件夹，以物种/栽培命名，实现版本间的一对一配对；（iii）列出标准化田的Excel （XLSX）元数据文件（科、学名/通用名、栽培/品种、树苗高度、树干直径、最佳种植季节、生长速度、结果年龄、平均产量、生产区域、繁殖方法）。该组织促进了细粒度的识别和检索任务，并支持将视觉纹理与园艺属性联系起来的特征条件分析。数据集在一个开放的存储库中是公开的，在许可的许可下；记录了获取条件、目录布局和元数据模式，以实现明确的重用。
{"title":"SapBark-64: A dataset of bark images for 64 fruit-tree sapling classes","authors":"Sayyad Alizadeh , Hamed Shamsi","doi":"10.1016/j.dib.2025.112354","DOIUrl":"10.1016/j.dib.2025.112354","url":null,"abstract":"<div><div>We present SapBark-64, a curated dataset of 5742 close-range bark images from 64 fruit-tree sapling classes (species/cultivar). Images were acquired in situ at three commercial nurseries in Trabzon (Türkiye) in 2025, targeting 1–2-year saplings routinely traded in nurseries. Photographs were captured with an iPhone 16 Pro Max at approximately 10 cm from the trunk under near-uniform illumination, using a white background to occlude scene clutter and preserve fine-scale morphology. For each class, a nursery label photo was recorded to support ground truth, and class-level characteristics were collected at the time of recording under expert supervision.</div><div>The repository is organized as two parallel image folders plus a structured metadata workbook: (i) raw images (JPG) and (ii) background-removed images (WebP) that mirror the same 64 class folders named by species/cultivar, enabling one-to-one pairing across versions; and (iii) an Excel (XLSX) metadata file list- ing standardized fields (family, scientific/common name, cultivar/variety, sapling height, trunk diameter, best planting season, growth rate, fruit-bearing age, average yield, production region, propagation method). This organization facilitates fine- grained identification and retrieval tasks and supports trait-conditioned analyses linking visual texture to horticultural attributes.</div><div>The dataset is publicly available in an open repository under a permissive license; acquisition conditions, directory layout, and the metadata schema are documented to enable unambiguous reuse.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112354"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces a multi-part dataset designed to support the development of artificial intelligence models for banana bunch detection and harvesting decision-making. The dataset includes images collected from four different fields in Madeira Island, Portugal, under varying environmental conditions. It is divided into three core subsets, namely, a detection dataset annotated using You Only Look Once (YOLO) format (2179 images labelled for bunch and flower bud detection), a harvesting classification dataset labelled by expert teams as “Cut”“ and “Keep” (2685 images, with 1143 labelled as “Cut” and 1542 as “Keep”) and an expert opinion dataset where images were classified by human experts into three decision categories: “Cut now”, “Keep for next cut” and “Wait more than three weeks” (400 images, with 100 samples evaluated by each of four expert cutters, capturing all three decision categories) These datasets enable the creation and benchmarking of computer vision models and allow for expert consensus analysis.
本研究引入了一个多部分数据集，旨在支持香蕉束检测和收获决策的人工智能模型的开发。该数据集包括在不同环境条件下从葡萄牙马德拉岛四个不同领域收集的图像。它分为三个核心子集，即使用You Only Look Once （YOLO）格式注释的检测数据集（2179张图像标记为束和花蕾检测），由专家团队标记为“Cut”和“Keep”的收获分类数据集（2685张图像，其中1143张标记为“Cut”，1542张标记为“Keep”）和专家意见数据集，其中图像被人类专家分类为三个决策类别：“现在切割”，“保留下一次切割”和“等待超过三周”（400张图像，由四位专家切割师分别评估100个样本，捕获所有三个决策类别）这些数据集可以创建和基准计算机视觉模型，并允许专家共识分析。
{"title":"A multi-stage dataset for banana bunch detection and harvesting decision support","authors":"Preety Baglat , Fábio Mendonça , Sheikh Shanawaz Mostafa , Fernando Morgado-Dias","doi":"10.1016/j.dib.2025.112337","DOIUrl":"10.1016/j.dib.2025.112337","url":null,"abstract":"<div><div>This study introduces a multi-part dataset designed to support the development of artificial intelligence models for banana bunch detection and harvesting decision-making. The dataset includes images collected from four different fields in Madeira Island, Portugal, under varying environmental conditions. It is divided into three core subsets, namely, a detection dataset annotated using You Only Look Once (YOLO) format (2179 images labelled for bunch and flower bud detection), a harvesting classification dataset labelled by expert teams as “Cut”“ and “Keep” (2685 images, with 1143 labelled as “Cut” and 1542 as “Keep”) and an expert opinion dataset where images were classified by human experts into three decision categories: “Cut now”, “Keep for next cut” and “Wait more than three weeks” (400 images, with 100 samples evaluated by each of four expert cutters, capturing all three decision categories) These datasets enable the creation and benchmarking of computer vision models and allow for expert consensus analysis.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112337"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.dib.2025.112330
Javier Fernández-Macho
The dataset compiles time series of Sustainable Development Goals (SDG) indicators for NUTS2 regions across EU Member States, candidate countries, and EFTA members from 1980 to 2024. To support multivariate analyses—e.g. with methods based on Data Envelopment Analysis (DEA), which requires complete data—extensive filtering and imputation were performed. Missing values were addressed through temporal and geographical methods, followed by either a conservative or a neutral final imputation to ensure dataset completeness. The processed dataset may also support broader applications, such as econometric modeling, policy evaluation, or index construction, which require a complete set of data with no missing values.
{"title":"A time-series dataset on sustainable development Goals compliance in Europe","authors":"Javier Fernández-Macho","doi":"10.1016/j.dib.2025.112330","DOIUrl":"10.1016/j.dib.2025.112330","url":null,"abstract":"<div><div>The dataset compiles time series of Sustainable Development Goals (SDG) indicators for NUTS2 regions across EU Member States, candidate countries, and EFTA members from 1980 to 2024. To support multivariate analyses—<em>e.g.</em> with methods based on Data Envelopment Analysis (DEA), which requires complete data—extensive filtering and imputation were performed. Missing values were addressed through temporal and geographical methods, followed by either a conservative or a neutral final imputation to ensure dataset completeness. The processed dataset may also support broader applications, such as econometric modeling, policy evaluation, or index construction, which require a complete set of data with no missing values.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112330"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.dib.2025.112352
Talal S. Salih , Zeyad T. Al-Rrassam , Muhammad A. Muhammad , Hozan R. Ziwar , Mohammed M. Mohammed
Enterococcus faecalis is a common pathogen associated with urinary tract infections (UTIs) worldwide. Here we present the draft genome sequence of E. faecalis strain HMTZ24, isolated from the urine of a female patient in Mosul, Iraq. Whole-genome sequencing was performed on the Illumina NovaSeq 6000 platform. The assembled genome is 2,623,745 base pairs (bp) in length distributed across 128 contigs, with an N50 of 41,811 bp and a GC content of 37.73%. Annotation revealed 2,510 coding sequences (CDSs), 50 tRNAs, and 5 rRNA genes. Phylogenomic taxonomy analysis indicated that strain HMTZ24 is closely related to E. faecalis NBRC 100480 (=ATCC 19433) with a digital DNA-DNA hybridisation (dDDH) value of 92.9 % and an average nucleotide identity (ANI) of 99.16 %. Multilocus sequence typing (MLST) assigned the HMTZ24 strain to sequence type 28 (ST28). The genome harbors six antimicrobial resistance genes confirming resistance to nalidixic acid, ciprofloxacin, chloramphenicol, erythromycin, rifampin, trimethoprim, lincomycin, clindamycin, tetracycline, and vancomycin. Two mobile genetic elements (MGEs) including Tn6009 and ISLgar5, and 14 virulence factor genes including ebpA, ebpB, ebpC, ace, strA, espfs, cad, camE, cCf10, cOB1, gelE, tpx, efaA, and ElrA were also identified. The dataset provides a valuable genomic resource for comparative analyses of E. faecalis strains, supporting studies on antimicrobial resistance, virulence factors and regional epidemiology. The draft genome sequence of strain HMTZ24 has been deposited in NCBI under the accession number JBISBO000000000.1.
{"title":"Genome sequence data of multidrug-resistant Enterococcus faecalis HMTZ24 carrying multiple virulence factors, isolated from a urinary tract infection in Mosul, Iraq","authors":"Talal S. Salih , Zeyad T. Al-Rrassam , Muhammad A. Muhammad , Hozan R. Ziwar , Mohammed M. Mohammed","doi":"10.1016/j.dib.2025.112352","DOIUrl":"10.1016/j.dib.2025.112352","url":null,"abstract":"<div><div><em>Enterococcus faecalis</em> is a common pathogen associated with urinary tract infections (UTIs) worldwide. Here we present the draft genome sequence of <em>E. faecalis</em> strain HMTZ24, isolated from the urine of a female patient in Mosul, Iraq. Whole-genome sequencing was performed on the Illumina NovaSeq 6000 platform. The assembled genome is 2,623,745 base pairs (bp) in length distributed across 128 contigs, with an N50 of 41,811 bp and a GC content of 37.73%. Annotation revealed 2,510 coding sequences (CDSs), 50 tRNAs, and 5 rRNA genes. Phylogenomic taxonomy analysis indicated that strain HMTZ24 is closely related to <em>E. faecalis</em> NBRC 100480 (=ATCC 19433) with a digital DNA-DNA hybridisation (dDDH) value of 92.9 % and an average nucleotide identity (ANI) of 99.16 %. Multilocus sequence typing (MLST) assigned the HMTZ24 strain to sequence type 28 (ST28). The genome harbors six antimicrobial resistance genes confirming resistance to nalidixic acid, ciprofloxacin, chloramphenicol, erythromycin, rifampin, trimethoprim, lincomycin, clindamycin, tetracycline, and vancomycin. Two mobile genetic elements (MGEs) including Tn6009 and ISLgar5, and 14 virulence factor genes including <em>ebpA, ebpB, ebpC, ace, strA, espfs, cad, camE, cCf10, cOB1, gelE, tpx, efaA</em>, and <em>ElrA</em> were also identified. The dataset provides a valuable genomic resource for comparative analyses of <em>E. faecalis</em> strains, supporting studies on antimicrobial resistance, virulence factors and regional epidemiology. The draft genome sequence of strain HMTZ24 has been deposited in NCBI under the accession number JBISBO000000000.1.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112352"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.dib.2025.112336
Tong Wang, Jiming Ma, Yongling Fu
The constant-pressure variable-displacement piston pumps on aircraft are typically driven by the engine, providing energy and power for the aircraft's hydraulic systems and fuel servo systems. Due to the varying engine speeds and fluctuating loads in the hydraulic and fuel systems, aviation piston pumps must also operate across a wide range of rotational speeds and load flow rate conditions. The pressure, flow rate, temperature, and vibration signals at different ports of a piston pump are critical indicators reflecting its operational health. Utilizing these limited metrics for fault diagnosis of piston pumps under multiple working conditions is a research field of significant importance and considerable challenge. Based on the operational characteristics of aviation piston pumps, this paper designs a feasible standardized test procedure and introduces a dataset comprising four types of signals—pressure, flow rate, temperature, and vibration—collected during the standardized tests. The dataset includes test data from the standard pump under normal conditions, as well as data collected under three typical fault conditions: wear of the servo piston, wear of the regulation valve, and wear of the swashplate bearing bush. Based on this dataset, researchers can validate the effectiveness of proposed fault diagnosis and health evaluation methods.
{"title":"Multi-mode fault dataset for aviation piston pump based on standard test procedure","authors":"Tong Wang, Jiming Ma, Yongling Fu","doi":"10.1016/j.dib.2025.112336","DOIUrl":"10.1016/j.dib.2025.112336","url":null,"abstract":"<div><div>The constant-pressure variable-displacement piston pumps on aircraft are typically driven by the engine, providing energy and power for the aircraft's hydraulic systems and fuel servo systems. Due to the varying engine speeds and fluctuating loads in the hydraulic and fuel systems, aviation piston pumps must also operate across a wide range of rotational speeds and load flow rate conditions. The pressure, flow rate, temperature, and vibration signals at different ports of a piston pump are critical indicators reflecting its operational health. Utilizing these limited metrics for fault diagnosis of piston pumps under multiple working conditions is a research field of significant importance and considerable challenge. Based on the operational characteristics of aviation piston pumps, this paper designs a feasible standardized test procedure and introduces a dataset comprising four types of signals—pressure, flow rate, temperature, and vibration—collected during the standardized tests. The dataset includes test data from the standard pump under normal conditions, as well as data collected under three typical fault conditions: wear of the servo piston, wear of the regulation valve, and wear of the swashplate bearing bush. Based on this dataset, researchers can validate the effectiveness of proposed fault diagnosis and health evaluation methods.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112336"},"PeriodicalIF":1.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145787984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.dib.2025.112356
Marco La Salandra, Rosa Colacicco, Pierfrancesco Dellino, Domenico Capolongo
Understanding and predicting river dynamics requires frequent and systematic observations from imaging sensors deployed in situ or on different platforms. In this context, machine learning methods are increasingly applied to pixel-based classification and image segmentation, enabling innovative approaches for spatiotemporal mapping of dynamic fluvial environments. These methods often rely on large, multitemporal, and well-annotated datasets to ensure robust training and validation. We present RivAIrSet, a dataset of 7630 high-resolution RGB images collected by Unmanned Aerial Vehicle (UAV) along a reach of the Basento River (Southern Italy), with corresponding annotations of river water areas. The images were acquired during multitemporal surveys under varying hydrological and meteorological conditions, capturing different flow regimes. Overall, the dataset provides a valuable resource for fluvial research, supporting advances in machine learning–based river water segmentation, enabling the calibration and validation of hydrological and hydraulic models, and fostering the development of intelligent systems for monitoring fluvial environments. RivAIrSet is available in the UAVRiverMonitoring community, an open repository we created to promote data sharing, enable comparative studies, and drive new research on UAV-based river monitoring.
{"title":"RivAIrSet: A multitemporal high-resolution UAV imagery dataset for machine learning-based river water segmentation","authors":"Marco La Salandra, Rosa Colacicco, Pierfrancesco Dellino, Domenico Capolongo","doi":"10.1016/j.dib.2025.112356","DOIUrl":"10.1016/j.dib.2025.112356","url":null,"abstract":"<div><div>Understanding and predicting river dynamics requires frequent and systematic observations from imaging sensors deployed in situ or on different platforms. In this context, machine learning methods are increasingly applied to pixel-based classification and image segmentation, enabling innovative approaches for spatiotemporal mapping of dynamic fluvial environments. These methods often rely on large, multitemporal, and well-annotated datasets to ensure robust training and validation. We present RivAIrSet, a dataset of 7630 high-resolution RGB images collected by Unmanned Aerial Vehicle (UAV) along a reach of the Basento River (Southern Italy), with corresponding annotations of river water areas. The images were acquired during multitemporal surveys under varying hydrological and meteorological conditions, capturing different flow regimes. Overall, the dataset provides a valuable resource for fluvial research, supporting advances in machine learning–based river water segmentation, enabling the calibration and validation of hydrological and hydraulic models, and fostering the development of intelligent systems for monitoring fluvial environments. RivAIrSet is available in the UAVRiverMonitoring community, an open repository we created to promote data sharing, enable comparative studies, and drive new research on UAV-based river monitoring.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112356"},"PeriodicalIF":1.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.dib.2025.112345
Yingjie Ma , Shuhan Wang , Jiale Ding , Yingpai Liu , Yonghui Wang , Zongxiao Zhang
This dataset comprises molecular ecological networks (MENs) and associated microbial community data from surface soils (0–5 cm depth) across nine Chinese estuarine wetlands: Liaohe Estuary (Liaoning province), Haihe Estuary (Tianjin Municipality), Yellow River Estuary (Shandong province), Sheyanghe Estuary (Jiangsu province), Changjiang Estuary (Shanghai Municipality), Oujiang Estuary (Zhejiang province), Jiulongjiang Estuary (Fujian province), Zhujiang Estuary (Guangdong province), and Beibuwan Gulf (Guangxi province). Twenty-nine composite samples (15–20 subsamples/site) were collected during August-September 2019.
Data include: Bacterial (16S V4-V5) and fungal (ITS1) OTU tables clustered at 97 % similarity (SILVA/UNITE databases; NCBI SRA PRJNA755846); Global and regional MEN adjacency matrices (Northern/Eastern/Southern China Sea groupings); Network topology indices (node/edge counts, average degree, modularity); Node-level metrics (betweenness centrality, clustering coefficient); Identified "Broker'' OTUs based on network indexes for individual nodes.
MENs were constructed using the Molecular Ecological Network Analysis Pipeline (MENAP, http://ieg2.ou.edu/MENA/) with Pearson correlations of log-transformed OTU abundances. Only OTUs present in >50 % of samples per group were included, with significance thresholds determined via Random Matrix Theory.
{"title":"Dataset on soil microbial community composition based on 16S rRNA gene OTUs from estuarine wetlands in China","authors":"Yingjie Ma , Shuhan Wang , Jiale Ding , Yingpai Liu , Yonghui Wang , Zongxiao Zhang","doi":"10.1016/j.dib.2025.112345","DOIUrl":"10.1016/j.dib.2025.112345","url":null,"abstract":"<div><div>This dataset comprises molecular ecological networks (MENs) and associated microbial community data from surface soils (0–5 cm depth) across nine Chinese estuarine wetlands: Liaohe Estuary (Liaoning province), Haihe Estuary (Tianjin Municipality), Yellow River Estuary (Shandong province), Sheyanghe Estuary (Jiangsu province), Changjiang Estuary (Shanghai Municipality), Oujiang Estuary (Zhejiang province), Jiulongjiang Estuary (Fujian province), Zhujiang Estuary (Guangdong province), and Beibuwan Gulf (Guangxi province). Twenty-nine composite samples (15–20 subsamples/site) were collected during August-September 2019.</div><div>Data include: Bacterial (16S V4-V5) and fungal (ITS1) OTU tables clustered at 97 % similarity (SILVA/UNITE databases; NCBI SRA PRJNA755846); Global and regional MEN adjacency matrices (Northern/Eastern/Southern China Sea groupings); Network topology indices (node/edge counts, average degree, modularity); Node-level metrics (betweenness centrality, clustering coefficient); Identified \"Broker'' OTUs based on network indexes for individual nodes.</div><div>MENs were constructed using the Molecular Ecological Network Analysis Pipeline (MENAP, <span><span>http://ieg2.ou.edu/MENA/</span><svg><path></path></svg></span>) with Pearson correlations of log-transformed OTU abundances. Only OTUs present in >50 % of samples per group were included, with significance thresholds determined via Random Matrix Theory.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"64 ","pages":"Article 112345"},"PeriodicalIF":1.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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