Data in Brief最新文献

{"title":"Multi-analytical dataset on Lekhaniya Mahakashaya: HRLC-MS/MS Orbitrap profiling, HPTLC fingerprinting with marker estimation, and FTIR spectroscopy","authors":"Narayan Singh,&nbsp;Anjali Upadhyay,&nbsp;Debajyoti Chakraborty,&nbsp;Girimalla Patil,&nbsp;Pramod Yadav,&nbsp;Galib R,&nbsp;Pradeep Kumar Prajapati","doi":"10.1016/j.dib.2026.112464","DOIUrl":"10.1016/j.dib.2026.112464","url":null,"abstract":"<div><div>This dataset provides a comprehensive, multidimensional phytochemical characterization of <em>Lekhniya Mahakashaya</em> (LMK), a classical Ayurvedic formulation used for the Treatment of obesity and metabolic disorders. Three complementary analytical platforms were employed: High-Resolution Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (HRLC-MS/MS) Orbitrap, High-Performance Thin Layer Chromatography (HPTLC), and Fourier-Transform Infrared (FTIR) spectroscopy. For HRLC-MS/MS analysis, Hydroalcoholic extracts of LMK were prepared and analysed in both Positive and negative ionisation modes using an Orbitrap mass spectrometer. The dataset includes 2034 metabolomics-identified compounds: 1712 in positive ion mode and 322 in negative ion mode, with detailed retention times, molecular weights, and fragmentation patterns, suitable for compound annotation, metabolite networking, and cheminformatics-based correlation studies. HPTLC fingerprinting was performed using methanolic extracts (2–10 µL) on silica gel 60 F₂₅₄ plates, which yielded 7–8 reproducible peaks across the Rf range 0.12–0.89 under 254 nm, 366 nm, and 540 nm, confirming LMK’s polyherbal complexity. Marker-based quantification revealed that berberine (0.24 % w/w) and curcumin (0.31 % w/w) were performed using validated HPTLC protocols, and calibration curves are included for reproducibility. FTIR Spectroscopic data encompass 19 absorption peaks (3278–0468 cm⁻¹), representing hydroxyl, aliphatic, unsaturated, sulfur-, nitrogen-, and halogen-containing functional groups, which highlights LMK’s diverse phytochemical matrix. This dataset is structured for pharmacological exploration, quality control, and phytochemical standardisation of LMK and associated Ayurvedic formulations. This dataset is a reference resource. Additionally, the dataset can be used for molecular docking validation, network pharmacology mapping, metabolomics comparisons, and future drug discovery. To promote transparency, encourage computational or experimental reuse, and support integrative research on traditional medicine, all raw chromatograms, spectrum files, and processed data tables are made available in widely accessible formats.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112464"},"PeriodicalIF":1.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075124","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}

{"title":"Dataset on the performance of a photovoltaic solar water pump in coffee plantations using response surface methodology (RSM)","authors":"Nopparat Suriyachai,&nbsp;Torpong Kreetachat,&nbsp;Saksit Imman","doi":"10.1016/j.dib.2026.112467","DOIUrl":"10.1016/j.dib.2026.112467","url":null,"abstract":"<div><div>This dataset presents experimental data on the performance of a photovoltaic (PV) solar-powered water pumping system installed in a coffee plantation in Chiang Mai province, Thailand. The system performance was evaluated through controlled experiments using response surface methodology (RSM). Three independent variables were systematically varied: solar irradiance (300–900 W/m²), panel inclination (15–35°), and panel surface temperature (30–60°C). A total of 15 experimental runs were conducted, and the pumping efficiency (%) was recorded under each condition. Statistical analyses, including analysis of variance (ANOVA) and regression modeling, were applied to evaluate the effects of the individual variables and their interactions on system performance. The dataset includes raw and processed measurements, regression coefficients, and response surface parameters, enabling replication and further analysis. Perturbation plots, 3D surface plots, and contour plots provide detailed visualizations of the relationships between environmental factors and system efficiency. The optimal operating conditions were identified at a solar irradiance of 600 W/m², a panel inclination of 25°, and a panel surface temperature of 45°C, corresponding to a predicted maximum efficiency of 76.3–77.0%.</div><div>This dataset can be reused for designing optimized solar water pumping systems, validating predictive models, and comparing system performance under different environmental conditions or geographic locations. It also serves as a reference for researchers in renewable energy system optimization and agricultural water management. The data provide high-resolution, experimentally validated information on the combined effects of solar irradiance, panel inclination, and panel surface temperature on PV water pumping efficiency. Unlike previous studies, it includes detailed quantitative analysis specific to coffee-growing regions in Northern Thailand, along with regression models and visualizations that can guide both experimental replication and predictive modeling under similar climatic and agricultural conditions</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112467"},"PeriodicalIF":1.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036170","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}

{"title":"The Cadenza lyric intelligibility prediction (CLIP) dataset","authors":"Gerardo Roa-Dabike ,&nbsp;Trevor J. Cox ,&nbsp;Jon P. Barker ,&nbsp;Bruno M. Fazenda ,&nbsp;Simone Graetzer ,&nbsp;Rebecca R. Vos ,&nbsp;Michael A. Akeroyd ,&nbsp;Jennifer Firth ,&nbsp;William M. Whitmer ,&nbsp;Scott Bannister ,&nbsp;Alinka Greasley","doi":"10.1016/j.dib.2026.112466","DOIUrl":"10.1016/j.dib.2026.112466","url":null,"abstract":"<div><div>This paper presents CLIP, a dataset of 11,072 popular western music signals sourced from independent artists, accompanied by ground truth lyrics, and lyric intelligibility scores from listening tests. The dataset is designed to facilitate music information retrieval (MIR) research using machine learning. It was created to allow the development of algorithms to predict lyric intelligibility for the Cadenza ICASSP 2026 Signal Processing Grand Challenge. Currently, it is the only publicly available large-scale dataset for such a task. The music was sourced from the Free Music Archive (FMA) dataset and is unlikely to be familiar to listeners. We excluded tracks whose license did not allow derivative works and those that did not have English singing. Ground truth transcriptions were generated by seven native English speakers, resulting in 3700 excerpts of 5 to 10 words each from 1452 different songs. A hearing loss simulation was also applied to the stereo audio. This resulted in 11,100 music signals with no, mild or moderate hearing loss. This was done so more diverse hearing is represented in the dataset. Human transcriptions were then collected via an online listening experiment. Participants self-reported as having normal-hearing and being native English speakers. They listened to each music signal twice before transcribing each line. Final intelligibility scores were the ratio of matching words between the listening test responses and the ground truth transcriptions. The final dataset consists of audio, ground truth lyrics, intelligibility scores and associated metadata.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112466"},"PeriodicalIF":1.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036497","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}

{"title":"Long-term management reporting credibility data from an accounting experiment","authors":"Eric Gooden,&nbsp;Nicole Holden","doi":"10.1016/j.dib.2026.112470","DOIUrl":"10.1016/j.dib.2026.112470","url":null,"abstract":"<div><div>The data reports the results of an experiment used to examine whether short-horizon investors differ from long-horizon investors in their sensitivity to truthful disclosure regarding negative earnings news. We used an experimental methodology to isolate the impact of Investment Horizon and Forthcomingness on investors' long-term management credibility assessments. Specifically, participants assumed the role of either a long-horizon current investor (already owned shares) or a short-horizon prospective investor (contemplating an investment decision) in a fictional company. Participants were then given identical information regarding the firm and asked to make an initial credibility assessment. Subsequently, participants either received forthcoming disclosure from company management regarding negative earnings news (Forthcomingness) or the participant did not receive disclosure. Short-horizon investors then made an investment decision regarding an investment position in the firm, and all participants received negative earnings news regarding the firm. Participants returned two-weeks later to make final credibility assessments of company management as part of the post-experimental questionnaire.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112470"},"PeriodicalIF":1.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075119","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}

{"title":"Survey data on digital competence assessment among pre-service teachers in Vietnam","authors":"Ha-Nam Nguyen ,&nbsp;Hoai-Nam Nguyen ,&nbsp;Thi-Thu Ngo","doi":"10.1016/j.dib.2026.112465","DOIUrl":"10.1016/j.dib.2026.112465","url":null,"abstract":"<div><div>In the context of digital transformation in education, digital competence is one of the significant essential requirements for future teachers. This study surveyed and analyzed the digital competence structure of 1439 pre-service teachers in different regions of Viet Nam. We utilized a self-assessment questionnaire based on the Digital Kids Asia-Pacific (DKAP) framework, with references to TPACK (Technological Pedagogical Content Knowledge), DigComp (Digital Competence Framework for Citizens), and DigCompEdu frameworks. The dataset provided detailed information on each participant’s self-evaluated digital proficiency in five categories, along with demographic variables such as gender and subject specialization. The core of this data file locates itself in such potential to inform teacher training programs and educational policy by offering evidence on prowess and weakness in future teachers’ digital competence.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112465"},"PeriodicalIF":1.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075120","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}

{"title":"A Sentinel-1 SAR imagery dataset for airstrips detection and segmentation in the Brazilian Amazon Rainforest","authors":"Leandro da Silva Gomes ,&nbsp;Gustavo Henrique de Queiroz Stabile ,&nbsp;Tahisa Neitzel Kuck ,&nbsp;Felipe Augusto Pereira de Figueiredo ,&nbsp;Elcio Hideiti Shiguemori ,&nbsp;Dimas Irion Alves","doi":"10.1016/j.dib.2026.112472","DOIUrl":"10.1016/j.dib.2026.112472","url":null,"abstract":"<div><div>The Brazilian Amazon Rainforest holds a large ecological and economic importance and is considered one of the most biodiverse regions on the planet. The region faces numerous challenges from illegal human activities that threaten its sustainability and well-being, which are often supported by the construction of unauthorized airstrips. Additionally, due to its persistent cloud cover, which often hinders monitoring with optical satellites, Synthetic Aperture Radar (SAR) imagery provides a crucial alternative for the region surveillance. Thus, this dataset was developed to support the training and evaluation of machine learning techniques, including deep learning models for detecting and segmenting airstrips in the Brazilian Amazon Rainforest using SAR imagery. The dataset comprises images from the Sentinel-1 satellite, acquired primarily between 2021 and 2024, covering 1040 locations of known airstrips sourced from the MapBiomas project (published in 2023, based on 2021 reference data). For the change detection task, historical “before” images were selected from the period between 2014 and 2021 to capture the pre-construction state. The data is structured to support three distinct machine learning tasks: object detection (e.g., YOLOv8), semantic segmentation (e.g., U-Net), and change detection. For each task, specific images and annotations are provided. Additionally, geospatial files (Shapefile, GeoPackage) are included to facilitate the integration and visualization of the dataset in a GIS environment. The data is valuable for researchers in remote sensing, computer vision, environmental monitoring, security and defense, enabling the development of automated systems to monitor irregular activities in remote forest regions. The dataset is available at a Mendeley Data repository: <span><span>https://data.mendeley.com/datasets/x7rn78ymtn/1</span><svg><path></path></svg></span></div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112472"},"PeriodicalIF":1.4,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036171","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}

{"title":"Visualizing archaeobotanical data: A comprehensive photographic record of desiccated plant remains from an early modern context at Santi Quattro Coronati, Rome","authors":"Claudia Moricca ,&nbsp;Rachele Nicolini ,&nbsp;Lucrezia Masci ,&nbsp;Lia Barelli ,&nbsp;Simona Morretta ,&nbsp;Raffaele Pugliese ,&nbsp;Laura Sadori","doi":"10.1016/j.dib.2026.112468","DOIUrl":"10.1016/j.dib.2026.112468","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The “Santi Quattro Coronati – archaeobotanical plates” dataset presents a comprehensive photographic collection of carpological remains recovered from a pit in the complex of Santi Quattro Coronati (Rome, Italy). The deposit, dated between the late 15th and the mid-16th century, yielded a diverse assemblage of desiccated plant remains. The dataset is novel in that it provides the complete photographic documentation of all identified taxa from a single Early Modern archaeological context, a chronological phase that remains underrepresented in Italian archaeobotanical research.&lt;/div&gt;&lt;div&gt;The photographic documentation focuses on a representative sample of each taxon identified in the archaeobotanical analysis, with particular attention to the best-preserved specimens. When multiple plant parts of the same taxon were present, all were included. The dataset also includes fragile and rarely illustrated plant parts, such as cereal rachis fragments, tunics and basal plates of onion and garlic, grapevine tendrils and legume seed coats. These are often excluded from reference atlases due to their low archaeological survivability and the consequent scarcity of well-preserved comparative specimens.&lt;/div&gt;&lt;div&gt;High-resolution images were acquired using a Leica MC205C stereomicroscope equipped with a Leica IC80HD camera and the Leica Application Suite v.4.5.0 software. Illumination was provided by the Leica LED5000 HDI™ dome system, ensuring constant, diffuse light conditions. A column of images was captured for each specimen and processed with Helicon Focus v.7.0.1 Pro through focus stacking to obtain a single fully focused image. Depending on specimen size and complexity, between 9 and 127 photographs were used per perspective. Larger samples, unsuitable for microscopic observation, were photographed using a Canon digital camera under controlled illumination. Post-processing was performed with GIMP, applying standard tools for background cleaning and masking. Each final plate includes a scale bar for size reference.&lt;/div&gt;&lt;div&gt;The dataset is organized alphabetically by plant family and taxon. For each taxon, one or more plates are provided, displaying specimens from one to three perspectives to represent their 3D morphology. Nomenclature follows the taxonomy used in the original publication of the assemblage and has been updated according to the most recent checklist of the Italian vascular flora. A metadata .xls file is provided to facilitate consultation, reuse, comparison and integration with other archaeobotanical datasets.&lt;/div&gt;&lt;div&gt;This dataset offers a well-documented comparative visual reference for species/genus identification and for assessing the preservation state and morphological integrity of desiccated archaeobotanical remains. Offering detailed photographic records of New World plant taxa previously identified in this context, the study enhances accessibility and understanding of these materials through visual reference. Despite bein","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112468"},"PeriodicalIF":1.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036501","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}

{"title":"A dataset of acoustics emissions recordings of woodboring insects in wood and cultural objects, context images and remarks","authors":"Tom Marti ,&nbsp;Cécile Costa ,&nbsp;Emmanuel de Salis ,&nbsp;Laura Brambilla ,&nbsp;Stefano Carrino","doi":"10.1016/j.dib.2026.112461","DOIUrl":"10.1016/j.dib.2026.112461","url":null,"abstract":"<div><div>This dataset presents acoustic emission (AE) recordings collected from woodboring insect-infested and non-infested wood samples and cultural heritage objects. Data acquisition was conducted across four institutions: Haute École Arc (HE-Arc), Switzerland; Canadian Museum of History (CMH), Canada; National Gallery of Canada (NGC), Canada; and Musée National de l'Automobile (MNA), France; from April to July 2025.</div><div>The recordings were captured using Vallen VS900-M sensors with AEP5 preamplifiers set to 34dB gain and AMSY-6 4-channel chassis, employing continuous acoustic emission monitoring at 2 MHz sampling rate. Each experiment utilized three sensors positioned on test objects and one reference sensor facing up to record ambient noise conditions. The dataset comprises approximately 440.9 hours of recordings distributed across the four collection sites.</div><div>The dataset includes four main components: raw Vallen AE database files (.tradb format), processed statistical data exported as CSV files, contextual images documenting setups and sensor placements, and Python script for statistical data processing. Each experiment is documented with duration, material specifications, coupling methods (renaissance wax, cyclododecane, or mechanical fastening), environmental conditions, and infestation labels.</div><div>The dataset's structure enables multiple research applications. The time-series statistical features and binary classification labels (infested/non-infested) provide a foundation for supervised machine learning model development. The diverse experimental conditions across four geographic locations, varying coupling methods, and different ambient environments offer opportunities to evaluate model generalization and robustness. Reference sensor recordings captured simultaneously with each experiment allow for ambient noise characterization studies and development of noise filtering methodologies. The combination of raw acoustic data and contextual documentation makes this dataset suitable for comparative studies of different signal processing approaches and feature extraction techniques in acoustic emission analysis for heritage conservation applications.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112461"},"PeriodicalIF":1.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036169","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}

{"title":"BanglaMUSE: A multimodal Bangla sentiment dataset of text–audio pairs for speech and sentiment analysis","authors":"Md. Darun Nayeem ,&nbsp;Zarin Rafa ,&nbsp;Tasnuva Tasnim Nova ,&nbsp;Yasin Rahman ,&nbsp;Abdul Mumeet Pathan ,&nbsp;Md. Masudul Islam","doi":"10.1016/j.dib.2026.112458","DOIUrl":"10.1016/j.dib.2026.112458","url":null,"abstract":"<div><div>This article describes a publicly available multimodal Bangla sentiment dataset designed to support research in speech processing, sentiment analysis, and low-resource language modeling. The dataset comprises two synchronized modalities: sentiment-annotated Bangla text and corresponding speech recordings. It contains 1,000 manually curated Bangla sentences evenly distributed across positive and negative sentiment classes, alongside 4,000 aligned audio recordings produced by four native speakers. Each sentence is recorded independently by all speakers to ensure speaker diversity while maintaining consistent textual content. The text component reflects natural, everyday Bangla language usage and is structured to facilitate sentiment classification and linguistic analysis. The audio recordings were collected under controlled yet realistic acoustic conditions using multiple recording devices, introducing natural variability relevant for real-world speech applications. All samples underwent manual quality verification to ensure accurate text–audio alignment and to remove noisy or duplicated recordings. The dataset is suitable for a wide range of applications, including multimodal sentiment classification, sentiment-aware speech recognition, audio–text alignment, and benchmarking of multimodal learning approaches for low-resource languages. Its modular structure allows straightforward extension with additional speakers, dialects, or sentiment categories. By providing aligned textual and speech data for Bangla, this dataset contributes a valuable resource to the research community and supports broader efforts toward linguistic diversity in artificial intelligence.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112458"},"PeriodicalIF":1.4,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075154","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}

{"title":"Deep-sea image dataset for organism detection","authors":"Takaki Nishio,&nbsp;Yuki Kawae","doi":"10.1016/j.dib.2026.112462","DOIUrl":"10.1016/j.dib.2026.112462","url":null,"abstract":"<div><div>The conservation of marine resources and the mitigation of marine pollution require strengthened knowledge of marine biodiversity, particularly in the deep sea. Videos and images are valuable for documenting the distribution of deep-sea organisms, but manual processing is labor-intensive and variable, emphasizing the need for automated methods. To address this, the J-EDI Organism Detection Dataset (JODD) is introduced. This dataset comprises 8151 images and 15,621 bounding boxes annotated in the Common Objects in Context (COCO) format. The images were captured during deep-sea surveys conducted by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) between 1984 and 2021, using remotely operated vehicles (ROVs) and human-occupied vehicles (HOVs). All images were derived from publicly available videos in JAMSTEC’s E-library of Deep-sea Images (J-EDI). The dataset includes 20 object categories—19 biological groups and one machine category—providing a reusable resource for developing and benchmarking machine learning models for the automatic detection of deep-sea organisms.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112462"},"PeriodicalIF":1.4,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976670","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}