Scientific Data最新文献_第9页

DNA Barcoding of Teleost Fishes from North Luzon, Philippines: A Dataset for Ichthyofaunal Diversity Assessment. 菲律宾北吕宋岛硬骨鱼的DNA条形码：鱼类物种多样性评估数据集。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-26 DOI: 10.1038/s41597-025-05758-3

Al C Dimaquibo, Wei-Cheng Jhuang, Wen-Chien Huang, Angel B Encarnacion, Melanie C Villarao, Romina V Yutuc, Te-Yu Liao

{"title":"DNA Barcoding of Teleost Fishes from North Luzon, Philippines: A Dataset for Ichthyofaunal Diversity Assessment.","authors":"Al C Dimaquibo, Wei-Cheng Jhuang, Wen-Chien Huang, Angel B Encarnacion, Melanie C Villarao, Romina V Yutuc, Te-Yu Liao","doi":"10.1038/s41597-025-05758-3","DOIUrl":"10.1038/s41597-025-05758-3","url":null,"abstract":"Reliable identification of teleost fishes is essential for understanding their biology and conserving biodiversity. To support this, a comprehensive DNA barcode reference library was developed for Aurora, Cagayan, and Zambales, Luzon, Philippines. A total of 1,513 specimens were collected from 27 sampling sites, including fish markets, landing areas, and freshwater habitats, and analyzed using COI (707 sequences) and 12S rRNA gene (343 sequences) markers. The dataset identified 323 fish species across 187 genera, 83 families, and 37 orders, including nine newly recorded species for the Philippines and nine deep-sea species (>200 meters). Additionally, 29 newly barcoded taxa were deposited in GenBank, with sequences available for COI, 12S rRNA gene, or both. The expansion of the 12S rRNA gene sequence library enhances its utility as an alternative genetic tool, particularly for environmental DNA (eDNA) studies. This reference database serves as a valuable resource for species identification, biodiversity assessments, and sustainable fisheries management in North Luzon, Philippines.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1571"},"PeriodicalIF":6.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamical reconstruction of Southern Ocean and Antarctic climate variability since 1700. 1700年以来南大洋和南极气候变率的动力重建。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-26 DOI: 10.1038/s41597-025-05808-w

Quentin Dalaiden, Hugues Goosse, Paul R Holland, Antoine Barthelemy

{"title":"Dynamical reconstruction of Southern Ocean and Antarctic climate variability since 1700.","authors":"Quentin Dalaiden, Hugues Goosse, Paul R Holland, Antoine Barthelemy","doi":"10.1038/s41597-025-05808-w","DOIUrl":"10.1038/s41597-025-05808-w","url":null,"abstract":"Understanding long-term climate variability in the high latitudes of the Southern Hemisphere is critical due to the key role of the Southern Ocean in the global climate system. However, sparse observations (in space and time) coupled with strong internal variability limit our ability to interpret the origin of recent changes, and their longer-term context. Here we present a dynamically consistent reconstruction of the Antarctic atmosphere and Southern Ocean from 1700 to 2023. We first use data assimilation (DA)-based Antarctic atmospheric reanalyses that combine instrumental observations (1958-2023) and paleoclimate proxies (1700-2000) with Earth System Models to reconstruct key surface climate fields. We then drive a global ocean-sea-ice model with this atmospheric reanalysis to simulate historical ocean conditions, including temperature, salinity, currents, and sea-ice-related variables at 1° resolution. This reconstruction provides the first long-term physically consistent dataset of Antarctic atmosphere-ocean variability, suitable for studying low-frequency climate variability, evaluating climate models, and potentially driving regional atmospheric and ocean models as well as ice sheet models.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1574"},"PeriodicalIF":6.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12474946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection. 矽肺检测的带注释的基准CXR数据集。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-26 DOI: 10.1038/s41597-025-05595-4

Yasmeena Akhter, Rishabh Ranjan, Mayank Vatsa, Richa Singh, Santanu Chaudhury, Anjali Agrawal, Shruti Aggarwal, Arjun Kalyanpur, Anurita Menon

{"title":"Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection.","authors":"Yasmeena Akhter, Rishabh Ranjan, Mayank Vatsa, Richa Singh, Santanu Chaudhury, Anjali Agrawal, Shruti Aggarwal, Arjun Kalyanpur, Anurita Menon","doi":"10.1038/s41597-025-05595-4","DOIUrl":"10.1038/s41597-025-05595-4","url":null,"abstract":"This research introduces a unique dataset targeting Silicosis, a significant global occupational lung disease, and a member of the Pneumoconiosis family. Addressing the challenges in healthcare data collection and the need for expert annotation, this dataset aims to aid AI algorithms in medical applications. The comprehensive dataset includes not only Silicosis cases but also related conditions, such as tuberculosis and silicotuberculosis, alongside healthy lung images, addressing the diagnostic complexity due to symptom overlap. As the first public dataset of its kind, it offers detailed annotations for lung and disease region segmentation, as well as disease prediction, provided by multiple radiologists. Baseline experiments and findings demonstrate that current AI models have limited predictive accuracy for these disease classes, emphasizing the critical need for dedicated research. It is our assertion that the proposed Silicodata can be a key dataset in designing automated Silicosis detection tools and addressing challenges associated with small sample sizes in medical AI research.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1559"},"PeriodicalIF":6.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12474939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wordsworth: A generative word dataset for comparison of speech representations in humans and neural networks. 华兹华斯：一个用于比较人类和神经网络的语音表示的生成词数据集。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-26 DOI: 10.1038/s41597-025-05769-0

Yunkai Zhu, Cameron Grier, Amy Garcia, Dylan Pearson, Christian Gibson, Odelia Schwartz, Andrew R Dykstra

{"title":"Wordsworth: A generative word dataset for comparison of speech representations in humans and neural networks.","authors":"Yunkai Zhu, Cameron Grier, Amy Garcia, Dylan Pearson, Christian Gibson, Odelia Schwartz, Andrew R Dykstra","doi":"10.1038/s41597-025-05769-0","DOIUrl":"10.1038/s41597-025-05769-0","url":null,"abstract":"Speech perception is fundamental for human communication, but its neural basis is not well understood. Furthermore, while modern neural networks (NNs) can accurately recognize speech, whether they effectively model human speech processing remains unclear. Here, we introduce Wordsworth, a dataset designed to facilitate comparisons of speech representations between artificial and biological NNs. We synthesised 1,200 tokens for each of 84 monosyllabic words while controlling for acoustic parameters such as amplitude, duration, and background noise, thus encouraging the use of phonetic features known to be important for speech perception. Human listening experiments showed that Wordsworth tokens are intelligible. Additional experiments using convolutional NNs showed (i) that Wordsworth tokens were recognizable and (ii) that error patterns could be at least partially explained by acoustic phonetics. The control with which tokens were created permits end users to manipulate them in whatever ways might be useful for their purposes. Finally, a subset of tokens specifically for human neuroscience experiments was also created, with precise and known distributions of amplitude, onset, and offset times.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1572"},"PeriodicalIF":6.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dataset on bus mobility and environmental indicators from Rio de Janeiro. 巴西巴西的公交机动性和环境指标数据集。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-26 DOI: 10.1038/s41597-025-05755-6

Diego Carvalho, Vinícius Vancellote, Pablo Moreira Casais, João Luiz Carabetta, Bruno Almeida, Fabio Porto, Eduardo Mendes, Renato Rocha Souza, Douglas de O Cardoso, Peter Fernandes Wanke, Rafael Barbastefano, Rafaelli Coutinho, Diego Brandão, Miguel Diaz-Cacho, André Mendes, Eduardo Ogasawara

{"title":"Dataset on bus mobility and environmental indicators from Rio de Janeiro.","authors":"Diego Carvalho, Vinícius Vancellote, Pablo Moreira Casais, João Luiz Carabetta, Bruno Almeida, Fabio Porto, Eduardo Mendes, Renato Rocha Souza, Douglas de O Cardoso, Peter Fernandes Wanke, Rafael Barbastefano, Rafaelli Coutinho, Diego Brandão, Miguel Diaz-Cacho, André Mendes, Eduardo Ogasawara","doi":"10.1038/s41597-025-05755-6","DOIUrl":"10.1038/s41597-025-05755-6","url":null,"abstract":"The quality of public transport is essential when considering urban mobility in large cities. Several factors, such as the increase in urban population, rain, and traffic events, can impact mobility, causing congestion. Addressing this issue is essential for the population and is part of the UN's 2030 Agenda for Sustainable Development goals. Integrating data from different sources is crucial to understanding and planning urban traffic. This work aims to provide a dataset with spatiotemporal information on the mobility of municipal buses, including the estimated emission of polluting gases and the rainfall volume in Rio de Janeiro from 2014 to 2023. Its format facilitates integration with other Rio de Janeiro City Hall datasets, enabling the increase and deepening of the analyses. This work is the first to combine data from bus observation with positional information on neighborhoods and rainfall regions, rainfall volumes, and pollutant gas emissions. Thus, its availability opens opportunities for research topics involving public transport associated with environmental indicators and data science with time series studies and positional data.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1569"},"PeriodicalIF":6.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A global dataset for steel aluminum and cement in-use stocks at 500 m gridded level 2000-2019. 2000-2019年全球500米网格级钢铝和水泥在用库存数据集。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-26 DOI: 10.1038/s41597-025-05618-0

Kun Sun, Qiaoxuan Li, Menglin Dai, Hongwei Guo, Qiance Liu, Jinchao Song, Zuoqi Chen, Srinivasa Raghavendra Bhuvan Gummidi, Wu Chen, Bailang Yu, Gang Liu

{"title":"A global dataset for steel aluminum and cement in-use stocks at 500 m gridded level 2000-2019.","authors":"Kun Sun, Qiaoxuan Li, Menglin Dai, Hongwei Guo, Qiance Liu, Jinchao Song, Zuoqi Chen, Srinivasa Raghavendra Bhuvan Gummidi, Wu Chen, Bailang Yu, Gang Liu","doi":"10.1038/s41597-025-05618-0","DOIUrl":"10.1038/s41597-025-05618-0","url":null,"abstract":"The anthropogenic material in-use stocks accumulated in products, buildings, and infrastructure are essential for satisfying basic human demands and ensuring well-being. They drive global resource demand and environmental impacts while representing valuable resource reservoirs for potential recycling through urban mining. A high-resolution understanding of global material in-use stocks was achieved by integrating reconciled night-time light imageries with national stock data on primary construction materials, including steel, aluminum, and cement. The integration enabled the estimates of global stocks from 2000 to 2019 at a 500 × 500 m grid resolution. The updated dataset mitigated saturation and blooming effects in prior satellite data compared to previous datasets, offering refined temporal and geographical representations despite some regional variations. The refined results systematically elucidate the spatiotemporal dynamics of material accumulation worldwide, highlighting distribution discrepancies between and within cities. The comprehensive database serves as a helpful resource for supporting waste management, circular economy, spatial planning, urban sustainability, and climate change mitigation efforts across various geographical scales.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1560"},"PeriodicalIF":6.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction. TrialBench：用于临床试验预测的多模态ai就绪数据集。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-26 DOI: 10.1038/s41597-025-05680-8

Jintai Chen, Yaojun Hu, Mingchen Cai, Yingzhou Lu, Yue Wang, Xu Cao, Miao Lin, Hongxia Xu, Jian Wu, Xiao Cao, Jimeng Sun, Yuqiang Li, Lucas Glass, Kexin Huang, Marinka Zitnik, Tianfan Fu

{"title":"TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction.","authors":"Jintai Chen, Yaojun Hu, Mingchen Cai, Yingzhou Lu, Yue Wang, Xu Cao, Miao Lin, Hongxia Xu, Jian Wu, Xiao Cao, Jimeng Sun, Yuqiang Li, Lucas Glass, Kexin Huang, Marinka Zitnik, Tianfan Fu","doi":"10.1038/s41597-025-05680-8","DOIUrl":"10.1038/s41597-025-05680-8","url":null,"abstract":"Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate/event, serious adverse event, mortality event, trial approval outcome, trial failure reason, drug dose, and design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1564"},"PeriodicalIF":6.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping the "Ghost Fleet of Mallows Bay", Maryland with drone-based remote sensing. 测绘“幽灵舰队的马洛湾”，马里兰州与无人机遥感。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-25 DOI: 10.1038/s41597-025-05635-z

Elizabeth C White, Alexander C Seymour, Julian Dale, Everette Newton, David W Johnston

{"title":"Mapping the \"Ghost Fleet of Mallows Bay\", Maryland with drone-based remote sensing.","authors":"Elizabeth C White, Alexander C Seymour, Julian Dale, Everette Newton, David W Johnston","doi":"10.1038/s41597-025-05635-z","DOIUrl":"10.1038/s41597-025-05635-z","url":null,"abstract":"Shipwrecks hold significant historical, archaeological, and ecological value. In this dataset, we present two high-resolution (~0.60 cm & 3.0 cm GSD) orthomosaics and associated data that accurately maps the so-called \"Ghost Fleet of Mallows Bay\", a prominent shipwreck assemblage near the eastern banks of the Potomac River, Maryland, USA. Using unoccupied aircraft systems (UAS), we conducted aerial surveys at regional and individual wreck scales, imaging all 147 wrecks in the bay. Through structure-from-motion photogrammetric processing, we generated the highest-resolution georeferenced mosaics currently available for Mallows Bay. We used the regional orthomosaic to vectorize individual wrecks, with the resulting polygons linked to archaeological records from the Maryland Historic Trust. These data establish a baseline for the shipwreck-associated ecological, archaeological, and cultural resources at Mallows Bay. The orthomosaics and associated outputs suit various applications, including image analysis and habitat mapping. The digital spatial records of individual wrecks support field research efforts and aid in monitoring the evolution of shipwrecks over time.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1547"},"PeriodicalIF":6.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12464171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145150715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CGMacros: a pilot scientific dataset for personalized nutrition and diet monitoring. CGMacros：用于个性化营养和饮食监测的试点科学数据集。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-25 DOI: 10.1038/s41597-025-05851-7

Anurag Das, David Kerr, Namino Glantz, Wendy Bevier, Rony Santiago, Ricardo Gutierrez-Osuna, Bobak J Mortazavi

{"title":"CGMacros: a pilot scientific dataset for personalized nutrition and diet monitoring.","authors":"Anurag Das, David Kerr, Namino Glantz, Wendy Bevier, Rony Santiago, Ricardo Gutierrez-Osuna, Bobak J Mortazavi","doi":"10.1038/s41597-025-05851-7","DOIUrl":"10.1038/s41597-025-05851-7","url":null,"abstract":"Tracking food intake is key to using nutrition to prevent or manage common diseases including type 2 diabetes (T2D) and obesity. Several datasets are publicly available to promote research in diet monitoring, but generally contain data from a limited set of sensors (e.g., accelerometry, food images), which limits their application to specific use cases such as activity recognition or image recognition. Also lacking are publicly available datasets with food macronutrients and their associated continuous glucose measurements; datasets containing such rich information are proprietary. To address this gap, we present CGMacros, a dataset containing multimodal information from an activity tracker, two continuous glucose monitors (CGM), food macronutrients, and food photographs, as well as anonymized participant demographics, anthropometric measurements and health parameters from blood analyses and gut microbiome profiles. CGMacros contains data for 45 participants (15 healthy, 16 pre-diabetes, 14 T2D) who consumed meals with varying and known macronutrient compositions in a free-living setting for ten consecutive days. To our knowledge, this is the first database of its kind to be made publicly available. CGMacros, and larger publicly available datasets that we hope may follow, are essential to democratize academic research in personalized nutrition and algorithmic approaches to automated diet monitoring.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1557"},"PeriodicalIF":6.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145150608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The FOod Commodity composition for Waste qUantification and valorization opportunitieS (FOWCUS) Dataset. 食品商品构成的废物量化和价值增值机会（FOWCUS）数据集。

IF 6.9 2区综合性期刊

Scientific Data Pub Date : 2025-09-25 DOI: 10.1038/s41597-025-05629-x

Antoine Coudard, Tom Szabo-Hemmings, Mona Honorine Delval, Sowmya Marriyapillai Ravisandiran, José Manuel Mogollón

{"title":"The FOod Commodity composition for Waste qUantification and valorization opportunitieS (FOWCUS) Dataset.","authors":"Antoine Coudard, Tom Szabo-Hemmings, Mona Honorine Delval, Sowmya Marriyapillai Ravisandiran, José Manuel Mogollón","doi":"10.1038/s41597-025-05629-x","DOIUrl":"10.1038/s41597-025-05629-x","url":null,"abstract":"This study introduces FOWCUS, a novel dataset detailing the composition of about 280 food commodities, categorized into various groups including vegetables, fruits, nuts, eggs, livestock, seafood, cereals, sugar, vegetable oils, stimulants, pulses, and root vegetables. Unlike previous works focusing on the chemical composition of food products, this dataset extends to quantitatively capture the amounts of products and by-products that could potentially contribute to the generation of avoidable, potentially avoidable, and unavoidable food waste across the food supply chain. By providing a detailed breakdown of the different components of food products, this dataset aids in identifying potential revalorization opportunities for unavoidable food waste via, for instance, feedstocks for biochemical, biomaterial, and bioenergy applications. It also supports the quantification of food waste generation across the food supply chain, which is crucial for food waste studies and the monitoring of food waste reduction targets at the national and international levels. Indexed according to the FAOSTAT commodity typology, it serves as a valuable resource for researchers and policymakers in managing food waste and valorizing by-products.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1553"},"PeriodicalIF":6.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145150711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0