Data in Brief最新文献

{"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}

{"title":"Exploring brain health knowledge and practices in young adults in Cuba: Dataset","authors":"Yunier Broche-Pérez ,&nbsp;Diego D. Díaz-Guerra ,&nbsp;Marena de la C. Hernández-Lugo ,&nbsp;Zoylen Fernández-Fleites ,&nbsp;Carlos Ramos-Galarza","doi":"10.1016/j.dib.2025.111479","DOIUrl":"10.1016/j.dib.2025.111479","url":null,"abstract":"<div><div>Understanding brain health is crucial, particularly in the context of emerging public health challenges. In this scenario, this dataset aims to provide insights into Cuban youth's perceptions and awareness of brain health. The database includes information from 1, 049 Cuban participants aged between 18 and 45 years. The database is the result of a cross-sectional study conducted online, between June 30 and December 12, 2022. To explore conceptions of brain health among the Cuban population, the study utilized ``The Global Brain Health Survey''. The primary aim of this survey is to examine attitudes toward brain health and to identify factors that may encourage individuals to adopt brain health practices. By identifying knowledge gaps and misconceptions, this study highlights opportunities for improving public health initiatives tailored to this demographic. Understanding the perceptions of young people is critical, as many brain disorders develop long before clinical symptoms appear. The findings aim to inform strategies that enhance awareness and prevention efforts, ultimately contributing to better brain health outcomes among Cuban youth.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111479"},"PeriodicalIF":1.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748364","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":"ActBeCalf: Accelerometer-based multivariate time-series dataset for calf behavior classification","authors":"Oshana Iddi Dissanayake ,&nbsp;Sarah E. McPherson ,&nbsp;Joseph Allyndrée ,&nbsp;Emer Kennedy ,&nbsp;Pádraig Cunningham ,&nbsp;Lucile Riaboff","doi":"10.1016/j.dib.2025.111462","DOIUrl":"10.1016/j.dib.2025.111462","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Getting new insights on pre-weaned calf behavioral adaptation to routine challenges (transport, group relocation, etc.) and diseases (respiratory diseases, diarrhea, etc.) is a promising way to improve calf welfare in dairy farms. A classic approach to automatically monitoring behavior is to equip animals with accelerometers attached to neck collars and to develop machine learning models from accelerometer time series. However, this accelerometer time series data must be aligned with labels describing behaviors (gold standard) to be used for model development. Obtaining these labels requires annotating behaviors from direct observations or videos, which is highly time-consuming and labor-intensive. In addition, accurate alignment between accelerometer data and behaviors is always challenging due to time synchronization issues. However, the procedure used for the timestamp alignment has not been described in other studies. We introduce &lt;strong&gt;ActBeCalf&lt;/strong&gt;: &lt;em&gt;Accelerometer Time-Series Dataset for Calf Behavior classification&lt;/em&gt; to address this challenge. Thirty pre-weaned dairy calves (Holstein Friesian and Jersey) housed in 4 group pens at Teagasc Moorepark Research Farm (Fermoy, Co. Cork, Ireland) were equipped with a 3D-accelerometer sensor (sampling rate: 25 Hz) attached to a neck-collar from one week of birth for 13 weeks. The calves were simultaneously filmed with a high-up camera in each pen. Every 15 days, accelerometers were removed from the collars to recharge the battery, collect the data, and reattached to the neck collar again. At the end of the trial, behaviors were manually annotated from the videos using the Behavioral Observation Research Interactive Software (BORIS) by three observers (Cohen's Kappa = 0.72 ± 0.01) using an ethogram with 23 pre-weaned dairy calves' behaviors. Videos were carefully selected to annotate calves’ behaviors. Observations were synchronized with the accelerometer timestamps using an external clock and aligned to the corresponding accelerometer time series. Thereby, ActBeCalf contains 27.4 h of accelerometer data from 30 pre-weaned calves (age 23.7 ± 10.7 days) aligned adequately with calf behaviors. The dataset includes the main behaviors of the calf time-budget, like lying, standing, walking, and running, as well as less prominent behaviors, such as sniffing, scratching, social interaction, and grooming. The reliability of ActBeCalf was validated by developing two machine learning models designed to classify behaviors into two and four classes, respectively. Good predictive performance was achieved for both models (balanced accuracy: 92 % and 84 %, respectively), thereby confirming ActBeCalf's reliability for model development in the field. The code utilized for the classification is publicly available in the dataset repository. ActBeCalf is a comprehensive, ready-to-use dataset, ideal for advancing research in two key areas: classifying pre-weaned calf behavior to support animal wel","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111462"},"PeriodicalIF":1.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725546","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":"A dataset of yearly probabilistic crop type maps for the EU from 1990 to 2018","authors":"Josef Baumert,&nbsp;Thomas Heckelei,&nbsp;Hugo Storm","doi":"10.1016/j.dib.2025.111472","DOIUrl":"10.1016/j.dib.2025.111472","url":null,"abstract":"<div><div>We provide an ensemble of probabilistic crop type maps for the entire European Union, mapping the shares of 25 different crop types at 1km resolution for all years from 1990 to 2018. We generate the maps using a recently developed approach based on a model of the data generating process from field- to region: essentially, we link knowledge about which crops farmers are more likely to grow under certain environmental conditions with data on regional crop acreages. Consequently, the resulting crop share estimates are consistent with regional crop area statistics while considering spatial heterogeneity. To reflect estimation uncertainty of the provided crop shares, we sample 100 maps per year and EU country, each being coherent with regional or national information. This ensemble of maps allows users to sample from potentially different but similarly likely spatial crop type distributions and thereby adequately reflect uncertainty in their applications, for example, in an environmental model. We additionally provide maps with only the most likely crop shares for users mainly interested in point estimates. The maps provide researchers, policy makers, and other stakeholders information to evaluate the impact of changing political, economic, and environmental conditions over three decades on agricultural production in the European Union.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111472"},"PeriodicalIF":1.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696151","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":"Proliferation and gene expression data of rabbit Achilles tenocyte in vitro culture in the presence of high-molecular weight hyaluronic acid (1.01–1.8 MDa)","authors":"Iris Miescher ,&nbsp;Petra Wolint ,&nbsp;Christine Opelz ,&nbsp;Jess G. Snedeker ,&nbsp;Pietro Giovanoli ,&nbsp;Maurizio Calcagni ,&nbsp;Johanna Buschmann","doi":"10.1016/j.dib.2025.111459","DOIUrl":"10.1016/j.dib.2025.111459","url":null,"abstract":"<div><div>Hyaluronic acid (HA) is a natural bio macromolecule and a good bio lubricant. As it is a predominant component of the synovial fluid, the aim was to explore the effects HA on rabbit Achilles tenocytes in vitro. The data set presents proliferation assessed by the Alamar Blue® assay; and gene expression of tenocytes that were cultured either with or without supplementation of high-molecular weight HA in a concentration of 1.4 mg/mL. There was a daily medium exchange with fresh HA supplementation. On days 3, 7 and 14 the following target genes were analysed and manifold induction was calculated against the control (no HA): Biglycan, Decorin, Aggrecan, Collagen 1A1, Collagen 1A2, Collagen 3, LOX, IL-6, TNF-α, PAR-2, ALOX-15, Tenascin-C, Tenomodulin, Mohawk, α-SMA, TIMP-1, MMP-2, MMP-9, and ki67. These data might be interesting for researchers working in the tendon field.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111459"},"PeriodicalIF":1.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683841","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":"Photophysiology data from smooth cordgrass (Sporobolus alterniflorus) measured in a North American saltmarsh using pulse amplitude modulated (PAM) fluorescence","authors":"Daniel Conrad Ogilvie Thornton","doi":"10.1016/j.dib.2025.111458","DOIUrl":"10.1016/j.dib.2025.111458","url":null,"abstract":"<div><div>Coastal saltmarshes play an important role as an interface between terrestrial and marine environments. <em>Sporobolus alterniflorus</em> (smooth cordgrass) occurs naturally along the east coast of North America, from Texas to Quebec, where it often forms extensive monospecific stands. <em>Sporobolus alterniflorus</em> is highly productive and is often the dominant plant in terms of biomass. This data set presents variable chlorophyll fluorescence measurements made <em>in situ</em> from the leaves of <em>Sporobolus alterniflorus</em> growing in a tidal saltmarsh ecosystem (North Inlet, South Carolina, United states). Measurements were made using pulse amplitude modulated (PAM) fluorescence. The data include raw measurements of variable fluorescence (<em>F_t</em>) and maximum fluorescence (<em>F’_m</em>) made at 12 different actinic photosynthetic photon flux densities (PPFD). These data were used to calculate the quantum yield of photosystem II <em>(Φ_PSII</em>) and estimate electron transport rates (ETRs). Rapid light curves (RLCs) were fitted to the ETRs to parametrize the relationship between ETR and PPFD in <em>S. alterniflorus</em> under different environmental conditions. Measurements were made from <em>S. alterniflorus</em> culms growing at different positions on the shore and at different times of the day. These data provide a resource for researchers interested in the photophysiology and photosynthesis of <em>Sporobolus alterniflorus</em>, and saltmarsh ecology and management.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111458"},"PeriodicalIF":1.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696161","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}