Data in Brief最新文献

{"title":"Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks","authors":"Ali Nirabi,&nbsp;Faridah Abd Rahman,&nbsp;Mohamed Hadi Habaebi,&nbsp;Khairul Azami Sidek,&nbsp;Siti Yusoff","doi":"10.1016/j.dib.2025.111477","DOIUrl":"10.1016/j.dib.2025.111477","url":null,"abstract":"<div><div>This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recordings designed to unravel mental stress patterns through the perspective of cognitive load. The dataset incorporates EEG signals obtained from 15 subjects, with a gender distribution of 8 females and 7 males, and a mean age of 21.5 years [<span><span>1</span></span>]. Recordings were collected during the subjects' engagement in diverse tasks, including the Stroop color-word test and arithmetic problem-solving tasks. The recordings are categorized into four classes representing varying levels of induced mental stress: normal, low, mid, and high. Each task was performed for a duration of 10–20 s, and three trials were conducted for comprehensive data collection. Employing an OpenBCI device with an 8-channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress.</div><div>The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. This study's foundation is crucial for advancing stress classification research, with significant implications for cognitive function and well-being.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111477"},"PeriodicalIF":1.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nature's best vs. bruised: A veggie edibility evaluation database","authors":"Bidisha Samanta ,&nbsp;Sriparna Banerjee ,&nbsp;Ranadhir Das ,&nbsp;Sheli Sinha Chaudhuri ,&nbsp;Khalifa Djemal ,&nbsp;Amir Ali Feiz","doi":"10.1016/j.dib.2025.111483","DOIUrl":"10.1016/j.dib.2025.111483","url":null,"abstract":"<div><div>In the realm of evaluating vegetable freshness, automated methods that assess external morphology, texture, and colour have emerged as efficient and cost-effective tools. These methods play a crucial role in sorting high-quality vegetables for both export and local consumption, significantly impacting the revenue of the food industry worldwide. Researchers have recognized the importance of this area, leading to the development of various automated techniques, particularly leveraging advanced deep learning technologies to categorize vegetables into specific classes. However, the effectiveness of these methods heavily relies on the databases used for training and validation, posing a challenge due to the lack of suitable datasets.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111483"},"PeriodicalIF":1.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset on indoor and outdoor PM2.5 concentrations at two residences using low-cost sensors in the Rio Grande Valley Region of South Texas","authors":"Kabir Bahadur Shah ,&nbsp;Sai Deepak Pinakana ,&nbsp;Mkhitar Hobosyan ,&nbsp;Armando Montes ,&nbsp;Amit U. Raysoni","doi":"10.1016/j.dib.2025.111492","DOIUrl":"10.1016/j.dib.2025.111492","url":null,"abstract":"<div><div>Fine particulate matter (PM_2.5) is one of the criteria air pollutants associated with adverse respiratory and cardiovascular health effects. This dataset contains hourly and daily averaged measurements of PM_2.5 concentrations (µg/m³) collected from two residential homes in the Rio Grande Valley (RGV) Region of South Texas, using PurpleAir-II-SD low-cost sensor. In addition, the dataset also includes temperature (ºC) and relative humidity (%) measured by the sensors. The data was collected for one month during the summer season from indoor (kitchen) and outdoor (front yard) environments at two locations: Home-1 in Weslaco City from June 16, 2024, to July 16, 2024, and Home-2 in Mission City from June 22, 2024, to July 16, 2024. Furthermore, the resultant wind speed and wind direction data were obtained from nearby Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Station (CAMS) site, providing a better understanding of meteorological influences on air quality.</div><div>The raw PM_2.5 data were corrected using Barkjohn's US-wide correction equation to increase the accuracy of the measurements and are presented in the datasets. The dataset is available in three CSV files: one for Home-1, one for Home-2, and one for wind data (Wind Rose) from the TCEQ stations. This dataset has significant potential for reuse by researchers interested in air quality monitoring, exposure assessment, and the health impacts of PM_2.5. Furthermore, the study highlights the effectiveness of using low-cost sensors for continuous air quality monitoring in residential settings, thereby contributing to the growing body of air quality literature emanating from the U.S.-Mexico border region.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111492"},"PeriodicalIF":1.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems","authors":"Arjun Upadhyay ,&nbsp;Sunil G. C ,&nbsp;Maria Villamil Mahecha ,&nbsp;Joseph Mettler ,&nbsp;Kirk Howatt ,&nbsp;William Aderholdt ,&nbsp;Michael Ostlie ,&nbsp;Xin Sun","doi":"10.1016/j.dib.2025.111486","DOIUrl":"10.1016/j.dib.2025.111486","url":null,"abstract":"<div><div>Effective weed management is crucial for maintaining optimal crop growth and achieve higher yield. Recent advancement in robotic technologies and advanced deep learning (DL) models is shaping the future of robotic weed control systems. However, DL models for weed identification requires substantial amount of data collected in natural field conditions. This article presents red, green, and blue (RGB) datasets for multiple weed species found across different crop production systems. DL models require sophisticated datasets for training the model to achieve high object detection accuracy. To achieve this, a real field dataset was collected under diverse environmental conditions to mimic the natural environment and exhibits the variability in datasets. This aims to improve the accuracy of deep learning models for real time weed identification in precision agriculture. The dataset presented in this article was collected using Canon RGB camera, mounted on the front of remote-controlled robotic platform. This dataset comprises 1120 labelled images presenting five species of weeds and eight different crop species. This resource can be utilized by researchers, educators, and students in developing DL models for weed identification. The dataset can be further enriched by combining it with other relevant weed-crop datasets to create more diverse and robust datasets. This will enhance the capabilities of DL algorithms to be integrated with robotic weed control platforms for precision weed management.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111486"},"PeriodicalIF":1.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Release kinetics, static and dynamic water contact angles and FTIR data for tissue inhibitor of matrix metalloprotease-1 (TIMP-1) incorporated in electrospun random DegraPol® fibers and TIMP-1 impact on tenocytes and adipose-derived stem cells proliferation and gene expression data","authors":"Julia Rieber,&nbsp;Roger Khalid Niederhauser,&nbsp;Pietro Giovanoli,&nbsp;Johanna Buschmann","doi":"10.1016/j.dib.2025.111468","DOIUrl":"10.1016/j.dib.2025.111468","url":null,"abstract":"<div><div>A first data set refers to tissue inhibitor of matrix metalloprotease-1 (TIMP-1) protein inclusion into a DegraPol® fibres utilizing emulsion electrospinning and the characterization of the random fibre mesh. Specifically, the release kinetics of the protein from the mesh was studied over 7 days. Moreover, the static and the dynamic water contact angles were determined. Finally, we assessed Fourier-Transformed Infrared Spectra (FTIR spectra) for DegraPol® with and without TIMP-1.</div><div>A second data set represents proliferation data obtained with the Alamar Blue Assay, applied on rabbit Achilles tenocytes and rabbit adipose-derived stem cells, when stimulated <em>in vitro</em> with 1, 10, and 100 ng/mL TIMP-1 supplementation compared to the corresponding cell culture without TIMP-1 (control). Furthermore, qPCR was performed and <em>collagen I, ki67, tenomodulin</em> and <em>alkaline phosphatase</em> gene expression data are presented for both cell types <em>in vitro</em> stimulated with 1, 10, and 100 ng/mL TIMP-1 supplementation, respectively, and data are presented as manifold induction compared to a TIMP-1-free cell culture medium (control).</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111468"},"PeriodicalIF":1.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Datasets for sustainability reporting index of agricultural & plantation companies in Malaysia","authors":"Nurfara Hasyikin Hanapi,&nbsp;Waleed M. Alahdal,&nbsp;Hafiza Aishah Hashim,&nbsp;Zalailah Salleh,&nbsp;Roshaiza Taha,&nbsp;Shayuti Mohamed Adnan","doi":"10.1016/j.dib.2025.111491","DOIUrl":"10.1016/j.dib.2025.111491","url":null,"abstract":"<div><div>This article provides extensive information about how firms adhere to sustainability reporting guidelines set by Bursa Malaysia Sustainability Reporting Guideline (SRG) 2015 and Global Reporting Initiative (GRI) Sustainability Reporting Standard 2021. This research contains panel data for 56 Malaysian listed firms (agriculture and plantation sector) in Bursa Malaysia. This dataset was collected from 2016 to 2023. Information provided was obtained from the sustainability reports and annual reports of each firm, which had been downloaded from Bursa Malaysia website.</div><div>The researcher can use the data effectively to create and calculate a sustainability reporting index that involves seventy-five sustainability reporting disclosure item and is comprised of three weighted sub-indices. The initial sub-index, “Economic Disclosure” consists of 8 items. The “Environmental Disclosure” sub-index consists of 31 items. The final sub-category, “Social Disclosure” consists of 36 items. Therefore, the researchers can assess companies using an overall index score provided by the unweighted sustainability reporting index.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111491"},"PeriodicalIF":1.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset: Compositional analysis and hydrothermal liquefaction of a high-ash microalgae biofilm","authors":"Jacob D. Watkins,&nbsp;Hamza Abdellaoui,&nbsp;Elise Barton,&nbsp;Clayton Lords,&nbsp;Ronald C. Sims","doi":"10.1016/j.dib.2025.111490","DOIUrl":"10.1016/j.dib.2025.111490","url":null,"abstract":"<div><div>This dataset contains biochemical composition data and hydrothermal liquefaction (HTL) yield results for a high-ash microalgae biofilm which was cultivated in effluent from a mesophilic anaerobic digester using polyethylene rotating algae biofilm reactors (RABRs). These data were originally collected for use in a techno-economic analysis of biocrude, biodiesel, and bioplastic production from algae that was cultivated using RABRs for municipal wastewater reclamation.</div><div>Biochemical data for the microalgae biomass includes bulk protein, measured both using the Bradford protein assay and by multiplying total N; carbohydrate content, measured using a 3-methyl-2-benzothiazolinone hydrazone / dithiothreitol (MBTH/DTT) assay; total lipid content, measured using a sulpho-phospho-vanillin method; hexane-extractable lipid content, measured by mass difference after extraction with methanol and hexane; ash content, measured by mass difference after incineration at 550°C; moisture content of the harvested biofilm slurry, measured by mass difference after drying at 60°C, mineral composition, measured using an inductively-coupled plasma spectrophotometer; higher heating value, measured using a bomb calorimeter; and CHNS-O elemental composition, measured using an elemental analyser.</div><div>Data reported for the HTL product phases include mass yields for each phase (solid, aqueous, biocrude, gas); higher heating value of the biocrude phase, measured using a bomb calorimeter; elemental composition of the biocrude phase, measured using an elemental analyzer; and chemical properties of the aqueous phase, including pH, chemical oxygen demand (HACH method 8000), total nitrogen (HACH method 10,208), total ammonia (HACH method 10,301), total phosphorus (HACH method 10,209/10,210), and total organic carbon (HACH method 10,267).</div><div>Currently, the effects of ash composition and HTL heating rate on biocrude yields and on N and P partitioning into biocrude, aqueous, and solid phases are not clearly defined. Models used to predict biocrude yields after HTL of microalgae are commonly trained using data collected from numerous studies. This dataset contains the feedstock composition data and ramp rate data necessary to help define the effects of ash content on biocrude yields after HTL and can be reused to help train yield-prediction models for the HTL of microalgae and other feedstocks.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111490"},"PeriodicalIF":1.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Banana bunch image and video dataset for variety classification and grading","authors":"D.S. Guru,&nbsp;Saritha N","doi":"10.1016/j.dib.2025.111478","DOIUrl":"10.1016/j.dib.2025.111478","url":null,"abstract":"<div><div>Banana, a major commercial fruit crop, holds high nutritional value and widespread consumption [<span><span>[4]</span></span>, <span><span>[8]</span></span>,<span><span>10</span></span>]. The global banana market valued at USD 140.83 billion in 2024 is projected to reach USD 147.74 billion by 2030. Accurate variety identification and quality grading are crucial for marketing, pricing, and operational efficiency in food processing industries [<span><span>9</span></span>]. As wholesalers and food processing industries process bananas in bunches (not individual fruit levels) , our bunch-level dataset offers a more accurate assessment by capturing bunch-level characteristics, which are vital for grading. Existing datasets, such as [<span><span>1</span></span>,<span><span>6</span></span>], focus on individual bananas or have limited bunch-level data, highlighting the lack of large-scale bunch datasets. This dataset fills the gap by providing bunch-level images and videos of three widely consumed banana varieties-Elakki-bale, Pachbale, and Rasbale, from Mysuru, South Karnataka, India, serving as a valuable resource for food processing industries. Our dataset supports training machine learning models for bunch-level variety classification and grading of bananas and serves as a resource for research and education.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111478"},"PeriodicalIF":1.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GrapeSLAM: UAV-based monocular visual dataset for SLAM, SfM and 3D reconstruction with trajectories under challenging illumination conditions","authors":"Kaiwen Wang ,&nbsp;Sergio Vélez ,&nbsp;Lammert Kooistra ,&nbsp;Wensheng Wang ,&nbsp;João Valente","doi":"10.1016/j.dib.2025.111495","DOIUrl":"10.1016/j.dib.2025.111495","url":null,"abstract":"<div><div>SLAM (Simultaneous Localization and Mapping) is an efficient method for robot to percept surrendings and make decisions, especially for robots in agricultural scenarios. Perception and path planning in an automatic way is crucial for precision agriculture. However, there are limited public datasets to implement and develop robotic algorithms for agricultural environments. Therefore, we collected dataset “GrapeSLAM”. The ``GrapeSLAM'' dataset comprises video data collected from vineyards to support agricultural robotics research. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing videos under different illumination conditions, and (2) trajectories of the UAV during each flight collected by RTK and IMU. The UAV used was Phantom 4 RTK, equipped with a high resolution camera, flying at around 1 to 3 meters above ground level.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111495"},"PeriodicalIF":1.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GLD-IPOR Malawi COVID-19 panel survey dataset","authors":"Erica Ann Metheney,&nbsp;Victor Saidi Phiri,&nbsp;Samuel Tafesse Wakuma","doi":"10.1016/j.dib.2025.111485","DOIUrl":"10.1016/j.dib.2025.111485","url":null,"abstract":"<div><div>March 2020 marked a critical juncture for Malawi as the nation confirmed its initial COVID-19 cases. In response, the government imposed stringent measures including travel restrictions, bans on large gatherings, and the creation of emergency management committees to mitigate the spread of the virus. Concurrently, Malawi navigated a significant political event—the June 2020 presidential election—following the annulment of the previous year's election results.</div><div>To document the range of responses by Malawian citizens to these COVID-19 containment strategies and how their livelihoods and political engagement were affected, a three-wave survey was conducted. Captured in the GLD-IPOR Malawi COVID-19 Panel Survey Dataset, this effort provides detailed insights into public knowledge and perceptions of COVID-19, socio-economic and health vulnerabilities prompted by the pandemic, citizens' adherence to public health directives, and engagement during this crisis. Notably, the survey waves were aligned with the June 23, 2020, presidential electoral cycle: wave one occurred during the pre-election period, and wave two immediately followed the election. The dataset, which includes 13,696 observations collected over all three rounds offers a comprehensive understanding of local variation in responses – particularly in terms of social stigmatization, enforcement of containment measures, and political participation – during a period marked by both public health challenges and political transitions.</div><div>This dataset can be leveraged to offer actionable insights and opportunities for examining resilience and vulnerabilities during different stages of health crises. Furthermore, merging this dataset with the Local Governance Performance Index (LGPI) survey datasets from 2016 and 2019, or the forthcoming project ``Survive, Thrive, or Deprive? Drivers and Outcomes of Resilience During the COVID-19,'' can facilitate a detailed examination of governance and development issues before, during, and after the pandemic.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111485"},"PeriodicalIF":1.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696171","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}