Data in Brief最新文献_第10页

A comprehensive voice dataset for Hindko digit recognition 一个全面的语音数据集欣德科数字识别。

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Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2024.111220

Tanveer Ahmed , Maqbool Khan , Khalil Khan , Ikram Syed , Syed Sajid Ullah

{"title":"A comprehensive voice dataset for Hindko digit recognition","authors":"Tanveer Ahmed , Maqbool Khan , Khalil Khan , Ikram Syed , Syed Sajid Ullah","doi":"10.1016/j.dib.2024.111220","DOIUrl":"10.1016/j.dib.2024.111220","url":null,"abstract":"<div><div>Hindko is a language primarily spoken in Northwestern areas of Pakistan. Approximately eight million people speak the Hindko language. According to its native speakers, it is 7<sup>th</sup> largest language of Pakistan and 2<sup>nd</sup> largest language of Khyber Pakhtunkhwa. The Hazara region is the cultural hub of Hindko language. About 80% of the population in districts like Haripur, Abbotabad and Mansehra speak Hindko. The spoken content of Hindko covers a wide range of subjects, including religion, education, poetry, politics, theater, and more. Despite all this, Hindko lacks a voice recognition system that could enhance accessibility, preserve the language, and promote digital inclusion for its speakers. This paper presents a voice recognition dataset that consists of 17,597 voice samples, and is accessible to the public for academic and research purposes. The dataset consists of 20 Hindko digits ranging from 1 to 20 and all the voice samples are taken from the students and staff and faculty of Pak-Austria Fachhochschule Institute of Applied Science and Technology.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111220"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142982943","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}

引用次数: 0

Building a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometer 使用手持近红外分光光度计建立近红外（NIR）土壤光谱数据集和预测机器学习模型。

IF 1

Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2024.111229

Colleen Partida , Jose Lucas Safanelli , Sadia Mannan Mitu , Mohammad Omar Faruk Murad , Yufeng Ge , Richard Ferguson , Keith Shepherd , Jonathan Sanderman

{"title":"Building a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometer","authors":"Colleen Partida , Jose Lucas Safanelli , Sadia Mannan Mitu , Mohammad Omar Faruk Murad , Yufeng Ge , Richard Ferguson , Keith Shepherd , Jonathan Sanderman","doi":"10.1016/j.dib.2024.111229","DOIUrl":"10.1016/j.dib.2024.111229","url":null,"abstract":"<div><div>This near-infrared spectral dataset consists of 2,106 diverse mineral soil samples scanned, on average, on six different units of the same low-cost commercially available handheld spectrophotometer. Most soil samples were selected from the USDA NRCS National Soil Survey Center-Kellogg Soil Survey Laboratory (NSSC-KSSL) soil archives to represent the diversity of mineral soils (0–30 cm) found in the United States, while 90 samples were selected from Ghana, Kenya, and Nigeria to represent available African soils in the same archive. All scanning was performed on dried and sieved (<2 mm) soil samples. Machine learning predictive models were developed for soil organic carbon (SOC), pH, bulk density (BD), carbonate (CaCO3), exchangeable potassium (Ex. K), sand, silt, and clay content from their spectra in the R programming language using most of this dataset (1,976 US soils) and are included in this data release. Two model types, Cubist and partial least squares regression (PLSR) were developed using two strategies: (1) using an average of the spectral scans across devices for each sample and, (2) using the replicate spectral scans across devices for each sample. We present the internal performance of these models here. The dry spectra and Cubist models for these soil properties are available for download from <span><span>10.5281/zenodo.7586621</span><svg><path></path></svg></span>. An example of detailed code used to produce these models is hosted at the Open Soil Spectral Library, a free service of the Soil Spectroscopy for the Global Good Network (<span><span>soilspectroscopy.org</span><svg><path></path></svg></span>), enabling broad use of these data for multiple soil monitoring applications.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111229"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11731769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983005","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}

引用次数: 0

The first engkabang jantong (Rubroshorea macrophylla) genome survey data 第一个大叶紫檀（Rubroshorea macrophylla）基因组调查数据。

IF 1

Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2024.111248

Hung Hui Chung , Asmeralda Ai Leen Soh , Melinda Mei Lin Lau , Han Ming Gan , Siong Fong Sim , Leonard Whye Kit Lim

{"title":"The first engkabang jantong (Rubroshorea macrophylla) genome survey data","authors":"Hung Hui Chung , Asmeralda Ai Leen Soh , Melinda Mei Lin Lau , Han Ming Gan , Siong Fong Sim , Leonard Whye Kit Lim","doi":"10.1016/j.dib.2024.111248","DOIUrl":"10.1016/j.dib.2024.111248","url":null,"abstract":"<div><div>The engkabang jantong (<em>Rubroshorea macrophylla</em>) is one of the most indispensable tree species for reforestation due to its high survival rate and rapid growth rate. Due to relatively low genetic interest of this tree species, its genomic landscape has since faced scarcity, impeding our further elucidation on genes that are involved in expressing its aforementioned superior properties. In this study, we performed genome survey and microsatellite analysis of engkabang jantong. Based on the results, the estimated genome size of this species is 312,071,515 bp with 18.43 % repeated sequences and 1.16 % heterozygosity. BUSCO analysis unearthed that 83.5 % of the contigs are single-copy genes whereas 12.7 % of them are duplicated. Only 2.8 % and 1 % of them are fragmented and missing respectively. The short-read sequencing results obtained from the Illumina platform in this study will be essential to complement the Nanopore long-read sequencing results in hybrid genome assembly endeavors in the near future.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111248"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001739","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}

引用次数: 0

Ichnologic and sedimentologic datasets from the Ediacaran–Cambrian Chapel island formation, Newfoundland, Canada

IF 1

Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2024.111258

Romain Gougeon , M. Gabriela Mángano , Luis A. Buatois , Guy M. Narbonne , Brittany A. Laing , Maximiliano Paz

{"title":"Ichnologic and sedimentologic datasets from the Ediacaran–Cambrian Chapel island formation, Newfoundland, Canada","authors":"Romain Gougeon , M. Gabriela Mángano , Luis A. Buatois , Guy M. Narbonne , Brittany A. Laing , Maximiliano Paz","doi":"10.1016/j.dib.2024.111258","DOIUrl":"10.1016/j.dib.2024.111258","url":null,"abstract":"<div><div>Extensive ichnologic and sedimentologic datasets were gathered from six localities (Fortune Head, Fortune North, Grand Bank Head, Lewin's Cove, Little Dantzic Cove, and Point May) of the Ediacaran–Cambrian Chapel Island Formation at Burin Peninsula, southeastern Newfoundland, eastern Canada. 1708.2 m of sedimentary strata were logged at a centimeter scale (1:40) using a Jacob staff, in addition to 11.08 m of strata reported at a millimeter scale (1:1.67). Sedimentary logs focus on: (1) bed geometry; (2) bed thickness; (3) bed grain size; (4) sandstone/ mudstone ratio; and (5) sedimentary structures. For each log, trace-fossil datasets were reported, consisting of: (1) bioturbation intensity in cross-section (1596 data points); (2) bed surface bioturbation intensity (1481 data points); (3) stratigraphic position of individual trace fossils; (4) trace-fossil width (3164 data points); (5) trace-fossil depth (1539 data points); and (6) ichnotaxonomic classification (3510 trace fossils identified at ichnospecies rank). The datasets are of importance to researchers interested in the palaeoecological signals depicted in this classic Ediacaran–Cambrian succession or in the compilation of worldwide data for deciphering macro-evolutionary trends in early animal life.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111258"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028014","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}

引用次数: 0

Ghadeer-speech-crowd-corpus: Speech dataset

IF 1

Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2024.111201

Ghadeer Qasim Ali, Husam Ali Abdulmohsin

{"title":"Ghadeer-speech-crowd-corpus: Speech dataset","authors":"Ghadeer Qasim Ali, Husam Ali Abdulmohsin","doi":"10.1016/j.dib.2024.111201","DOIUrl":"10.1016/j.dib.2024.111201","url":null,"abstract":"<div><div>The availability of raw data is a considerable challenge across most branches of science. In the absence of data, neither experiments can be conducted nor development can be undertaken. Despite their importance, raw data are still lacking across many scientific fields. A literature survey conducted at the beginning of our study indicated a significant lack of Arabic speech datasets. Therefore, this study aims to address this problem by proposing a new Arabic and English dataset called Ghadeer-Speech-Crowd-Corpus. This dataset was designed to target more than one branch of speech-processing applications, such as crowd speaker identification, speech synthesis (text-to-speech), and speech recognition (speech-to-text). Speech samples were recorded over three months from 210 Iraqi Arab citizens living in different parts of Iraq and included more than one accent. The proposed dataset was fully balanced with respect to sex and recordings (same number of Arabic and English recordings). Additionally, it is a mono dataset and contains 15,626 audio samples recorded at a sampling rate of 44,100 Hz, 16-bit depth, and bit rate of 705.6 kb/s. The recordings were conducted at the Academy for Media Training of the College of Media, University of Baghdad.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111201"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11754659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028178","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}

引用次数: 0

A comprehensive dataset of above-ground forest biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya

IF 1

Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2024.111262

Syed Danish Rafiq Kashani, Faisal Zahoor Jan, Imtiyaz Ahmad Bhat, Nadeem Ahmad Najar, Irfan Rashid

{"title":"A comprehensive dataset of above-ground forest biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya","authors":"Syed Danish Rafiq Kashani, Faisal Zahoor Jan, Imtiyaz Ahmad Bhat, Nadeem Ahmad Najar, Irfan Rashid","doi":"10.1016/j.dib.2024.111262","DOIUrl":"10.1016/j.dib.2024.111262","url":null,"abstract":"<div><div>Accurate estimates of forest dynamics and above-ground forest biomass for the topographically challenging Himalaya are crucial for understanding carbon storage potential, assessing ecosystem services, and guiding conservation efforts in response to climate change. This dataset provides a manually delineated multi-temporal forest inventory and a comprehensive record of above-ground biomass (AGB) across the Kashmir Himalaya, generated from field observations, advanced remote sensing and machine learning. Data were collected and generated through remote sensing techniques and extensive in-situ measurements of 6220 trees (n=275 plots), including tree diameter at breast height, species composition, and tree density to map forest area and model AGB across varied terrain. The dataset captures major forest types and species-specific AGB variation influenced by elevation, slope, and aspect. Additionally, newly developed species-specific allometric models, improved through the integration of normalized difference vegetation index (NDVI) and topographical augmentation are provided to improve AGB estimation accuracy. This dataset serves as a crucial resource for forest management, carbon monitoring, and ecological modeling, with broad applications in regional conservation strategies, biodiversity planning, and climate policy development in mountainous ecosystems.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111262"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064329","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}

引用次数: 0

Comprehensive collection of uniaxial stress-strain data for rubberized concrete 橡胶混凝土单轴应力-应变数据的综合收集。

IF 1

Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2024.111189

Abdulaziz Alsaif

引用次数: 0

An instruction dataset for extracting quantum cascade laser properties from scientific text 从科学文本中提取量子级联激光特性的指令数据集。

IF 1

Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2024.111255

Deperias Kerre , Anne Laurent , Kenneth Maussang , Dickson Owuor

{"title":"An instruction dataset for extracting quantum cascade laser properties from scientific text","authors":"Deperias Kerre , Anne Laurent , Kenneth Maussang , Dickson Owuor","doi":"10.1016/j.dib.2024.111255","DOIUrl":"10.1016/j.dib.2024.111255","url":null,"abstract":"<div><div>Quantum Cascade Lasers (QCL) are promising semiconductor lasers, compact and powerful, but of complex design. Availability of structured data of the QCL properties can support data mining activities that seek to understand the relationship between these properties, for instance between the design and performance features. The main open source of QCL data is in scientific text which in most cases is usually unstructured. One of the ways to extract and organize this data is by utilizing Information Extraction techniques. These techniques can accelerate the process of curating QCL properties data from scientific articles for further analysis. One of the main challenges in developing machine learning algorithms for extraction of QCL properties from text is lack of quality training data for these algorithms. Large Language Models (LLMs) have demonstrated great capabilities in materials property extraction from text. They however experience challenges with domain specific properties, for instance the heterostructure and design types in the QCL domain hence for adaptation. In this paper, we present an original instruction dataset for training and evaluation of LLMs for QCL properties extraction from text. The data is generated by augmenting sample sentences from scientific articles with GPT-3.5 instruct with a few shot strategy. The dataset then is manually annotated with the help of QCL experts and is composed of 1300 rows of training examples consisting of an Instruction, Input Text and the Output.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111255"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001213","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}

引用次数: 0

A global gross primary productivity of sunlit and shaded canopies dataset from 2002 to 2020 via embedding random forest into two-leaf light use efficiency model

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Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2025.111298

Zhilong Li , Ziti Jiao , Ge Gao , Jing Guo , Chenxia Wang , Sizhe Chen , Zheyou Tan

{"title":"A global gross primary productivity of sunlit and shaded canopies dataset from 2002 to 2020 via embedding random forest into two-leaf light use efficiency model","authors":"Zhilong Li , Ziti Jiao , Ge Gao , Jing Guo , Chenxia Wang , Sizhe Chen , Zheyou Tan","doi":"10.1016/j.dib.2025.111298","DOIUrl":"10.1016/j.dib.2025.111298","url":null,"abstract":"<div><div>Gross primary productivity (GPP) is crucial for understanding the carbon cycle and maintaining ecosystem balance under climate change. We attempt to generate a long-term global dataset for GPP of sunlit (GPP<sub>su</sub>) and shaded leaves (GPP<sub>sh</sub>) by a hybrid model combining the random forest (RF) submodule with the two-leaf light use efficiency (TL-LUE) model. First, the TL-LUE model was optimized by considering the seasonal differences in the clumping index on a global scale (TL-CLUE). Then, we used the RF technique to integrate various environmental stress factors, including meteorological factors, hydrological variables, soil properties, and elevation, which originate from the NASA MERRA-2 dataset, ISRIC soil Grids, and USGS data center. Furthermore, the RF submodule was embedded into the TL-CLUE model to construct the hybrid model (TL-CRF), which was trained and evaluated based on global eddy covariance (EC) site data from the AmeriFlux and FLUXNET2015 datasets. We produced a global GPP, GPP<sub>su</sub>, and GPP<sub>sh</sub> dataset with a spatial resolution of 0.05 × 0.05° over 2002–2020 by the TL-CRF model driven by the LP DACC leaf area index and land cover, NASA MERRA-2 incoming shortwave solar radiation, and the above environmental variables. This GPP product provides a data basis for improving our understanding of the dynamics of global vegetation productivity and its interactions with the changes in environmental conditions<em>.</em></div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111298"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11786690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143078891","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}

引用次数: 0

Dataset on travellers’ acceptance of border control technologies: Insights from METICOS pilot trials

IF 1

Data in Brief Pub Date : 2025-02-01 DOI: 10.1016/j.dib.2025.111278

Sarang Shaikh, Sule Yildirim Yayilgan, Erjon Zoto, Mohamed Abomhara

{"title":"Dataset on travellers’ acceptance of border control technologies: Insights from METICOS pilot trials","authors":"Sarang Shaikh, Sule Yildirim Yayilgan, Erjon Zoto, Mohamed Abomhara","doi":"10.1016/j.dib.2025.111278","DOIUrl":"10.1016/j.dib.2025.111278","url":null,"abstract":"<div><div>This paper presents a dataset from the METICOS<span><span><sup>1</sup></span></span> project pilot trials related to the acceptance of border control technologies. There was total five pilots held at Tallin Airport, Athens International Airport, Larnaca International Airport, Border Crossing Point Moravita Land Border, and Vienna International Airport. The dataset consists of the data collected using an online questionnaire (survey) to assess travellers’ technology acceptance in terms of their demographics, profiles, and user perceptions along with operational information of border control technologies during their use. The questionnaire items together with their responses are paired samples collected after the travellers use the technology at the METICOS project pilot trials held in five different locations (i.e. four airports, and one land border) during the period of January to August 2023. The ABC-gates were used to assess technology acceptance during all the pilot trials provided by pilot partner of the METICOS. The total size of the dataset is 147 instances and is well-suited for quantitative analysis of assessing technology acceptance indicators for border control technologies. This information can aid policymakers and border control authorities in enhancing the acceptance and the use of these technologies at various border crossing points across Europe.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"58 ","pages":"Article 111278"},"PeriodicalIF":1.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143078912","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}

引用次数: 0