Pub Date : 2026-01-22DOI: 10.1016/j.dib.2026.112501
Marcello Abbondio , Alessandro Tanca , Rosangela Sau , Giovanna Pira , Alessandra Errigo , Roberto Manetti , Giovanni Mario Pes , Stefano Bibbò , Maria Pina Dore , Sergio Uzzau
This dataset provides the fecal metaproteome profiles of 28 celiac disease patients on a gluten-free diet, distinguished by the presence or absence of co-occurring autoimmune conditions. The resource includes raw liquid chromatography-tandem mass spectrometry (LC-MS/MS) files, database search results, protein/peptide identification outputs, and taxonomic/functional annotation outputs, along with comprehensive anthropometric, clinical, and dietary metadata for each patient. The identified proteins originate from microbial, human, and plant sources, consistent with the multi-database search strategy used. This collection is designed for reuse in meta-analyses and integrative studies exploring functional changes in the gut microbiome related to auto-immune status and dietary variables. The complete dataset is available via the ProteomeXchange Consortium with the identifier PXD069517.
{"title":"A human fecal metaproteomic dataset from celiac disease patients on gluten-free diet with or without poly-autoimmunity","authors":"Marcello Abbondio , Alessandro Tanca , Rosangela Sau , Giovanna Pira , Alessandra Errigo , Roberto Manetti , Giovanni Mario Pes , Stefano Bibbò , Maria Pina Dore , Sergio Uzzau","doi":"10.1016/j.dib.2026.112501","DOIUrl":"10.1016/j.dib.2026.112501","url":null,"abstract":"<div><div>This dataset provides the fecal metaproteome profiles of 28 celiac disease patients on a gluten-free diet, distinguished by the presence or absence of co-occurring autoimmune conditions. The resource includes raw liquid chromatography-tandem mass spectrometry (LC-MS/MS) files, database search results, protein/peptide identification outputs, and taxonomic/functional annotation outputs, along with comprehensive anthropometric, clinical, and dietary metadata for each patient. The identified proteins originate from microbial, human, and plant sources, consistent with the multi-database search strategy used. This collection is designed for reuse in meta-analyses and integrative studies exploring functional changes in the gut microbiome related to auto-immune status and dietary variables. The complete dataset is available via the ProteomeXchange Consortium with the identifier PXD069517.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112501"},"PeriodicalIF":1.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.dib.2026.112481
Paulo Moreno-Meynard
This dataset documents the spatially explicit quantification of multiple ecosystem functions across 12 mountain headwater catchments in the Aysén Region of Chilean Patagonia. Designed to capture landscape variability, the observational framework employs a paired-catchment approach, comparing basins with different degrees of anthropogenic disturbance across two forest types: deciduous and evergreen. Each catchment is treated as an integrated landscape unit, with cluster-based field measurements capturing fine-scale variation in vegetation structure, biomass, soil conditions, and species richness.
The field inventory integrates and adapts methodologies from several national and international forest monitoring frameworks. Its core structure is based on Chile’s Continuous National Forest Inventory, but also incorporates sampling concepts and measurement protocols inspired by the Swiss National Forest Inventory (LFI), the U.S. Forest Inventory and Analysis (FIA) program, and long-term ecological monitoring plots used in New Zealand. This hybrid design ensures multidimensional assessment of ecosystem functions while enhancing cross-regional comparability.
The sampling design addresses ecosystem functions across four service categories: provisioning (sawlog and firewood volume), regulating (carbon stocks in trees, shrubs, and deadwood, and decadal sequestration rates), supporting (soil formation and erosion proxies, plus nutrient concentrations), and biodiversity maintenance (vascular plant and epiphyte).
This dataset supports ecological synthesis, spatial modeling, and integration into broader assessments of ecosystem services and land-use impacts under changing environmental conditions.
{"title":"Monitoring ecosystem functions in mountain catchments of chilean patagonia: A cluster-based dataset","authors":"Paulo Moreno-Meynard","doi":"10.1016/j.dib.2026.112481","DOIUrl":"10.1016/j.dib.2026.112481","url":null,"abstract":"<div><div>This dataset documents the spatially explicit quantification of multiple ecosystem functions across 12 mountain headwater catchments in the Aysén Region of Chilean Patagonia. Designed to capture landscape variability, the observational framework employs a paired-catchment approach, comparing basins with different degrees of anthropogenic disturbance across two forest types: deciduous and evergreen. Each catchment is treated as an integrated landscape unit, with cluster-based field measurements capturing fine-scale variation in vegetation structure, biomass, soil conditions, and species richness.</div><div>The field inventory integrates and adapts methodologies from several national and international forest monitoring frameworks. Its core structure is based on Chile’s Continuous National Forest Inventory, but also incorporates sampling concepts and measurement protocols inspired by the Swiss National Forest Inventory (LFI), the U.S. Forest Inventory and Analysis (FIA) program, and long-term ecological monitoring plots used in New Zealand. This hybrid design ensures multidimensional assessment of ecosystem functions while enhancing cross-regional comparability.</div><div>The sampling design addresses ecosystem functions across four service categories: provisioning (sawlog and firewood volume), regulating (carbon stocks in trees, shrubs, and deadwood, and decadal sequestration rates), supporting (soil formation and erosion proxies, plus nutrient concentrations), and biodiversity maintenance (vascular plant and epiphyte).</div><div>This dataset supports ecological synthesis, spatial modeling, and integration into broader assessments of ecosystem services and land-use impacts under changing environmental conditions.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112481"},"PeriodicalIF":1.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper proposes DroSev, a dataset for drone problem identification and severity estimation. The collection of drone flight log messages was acquired from publicly accessible sources on Mendeley Data and AirData. This dataset consists of two subtasks: binary problem identification and multiclass problem severity classification. The former task used only the collection of log messages from Mendeley Data, and the latter task used the merged collection of log messages from both sources. Each subtask has a train and test split with an 80:20 ratio generated with stratified sampling. Further syntactical characteristics are reported and summarized.
{"title":"Dataset for drone problem identification and severity estimation","authors":"Swardiantara Silalahi, Tohari Ahmad, Hudan Studiawan","doi":"10.1016/j.dib.2026.112494","DOIUrl":"10.1016/j.dib.2026.112494","url":null,"abstract":"<div><div>The paper proposes DroSev, a dataset for drone problem identification and severity estimation. The collection of drone flight log messages was acquired from publicly accessible sources on Mendeley Data and AirData. This dataset consists of two subtasks: binary problem identification and multiclass problem severity classification. The former task used only the collection of log messages from Mendeley Data, and the latter task used the merged collection of log messages from both sources. Each subtask has a train and test split with an 80:20 ratio generated with stratified sampling. Further syntactical characteristics are reported and summarized.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112494"},"PeriodicalIF":1.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.dib.2026.112491
Waseem Shariff , Timothy Hanley , Maciej Stec , Hossein Javidnia , Peter Corcoran
This article presents a C3I-SynMicrosaccade dataset: a synthetic microsaccade dataset designed to enable event-based modelling and classification of microsaccadic eye movements. Using Blender, we generated high-resolution RGB sequences of microsaccades, characterized by small, transient eye rotations around a fixed head pose. Each microsaccade follows a horizontal-boomerang-like trajectory, simulating the natural back-and-forth displacement of the eye during visual fixation. Seven distinct angular classes, ranging from 0.5° to 2.0°, capture varying motion amplitudes while maintaining consistent scene, lighting, and texture conditions. The rendered RGB frames were converted into event-based data streams using the v2e simulator, which replicates the asynchronous behaviour of neuromorphic vision sensors. Temporal durations and event counts were carefully controlled and resampled to ensure class balance and eliminate bias toward motion magnitude. The resulting dataset comprises 175,000 event sequences (87,500 per eye), providing a large-scale, balanced foundation for microsaccade recognition, neuromorphic vision research, and synthetic-to-real transfer learning. This work offers a controlled, reproducible framework for studying fixational eye movements and evaluating event-based algorithms under fine motion dynamics.
{"title":"C3I-SynMicrosaccade: A pipeline and dataset for microsaccade recognition using neuromorphic event camera streams","authors":"Waseem Shariff , Timothy Hanley , Maciej Stec , Hossein Javidnia , Peter Corcoran","doi":"10.1016/j.dib.2026.112491","DOIUrl":"10.1016/j.dib.2026.112491","url":null,"abstract":"<div><div>This article presents a C3I-SynMicrosaccade dataset: a synthetic microsaccade dataset designed to enable event-based modelling and classification of microsaccadic eye movements. Using Blender, we generated high-resolution RGB sequences of microsaccades, characterized by small, transient eye rotations around a fixed head pose. Each microsaccade follows a horizontal-boomerang-like trajectory, simulating the natural back-and-forth displacement of the eye during visual fixation. Seven distinct angular classes, ranging from 0.5° to 2.0°, capture varying motion amplitudes while maintaining consistent scene, lighting, and texture conditions. The rendered RGB frames were converted into event-based data streams using the v2e simulator, which replicates the asynchronous behaviour of neuromorphic vision sensors. Temporal durations and event counts were carefully controlled and resampled to ensure class balance and eliminate bias toward motion magnitude. The resulting dataset comprises 175,000 event sequences (87,500 per eye), providing a large-scale, balanced foundation for microsaccade recognition, neuromorphic vision research, and synthetic-to-real transfer learning. This work offers a controlled, reproducible framework for studying fixational eye movements and evaluating event-based algorithms under fine motion dynamics.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112491"},"PeriodicalIF":1.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.dib.2026.112497
Niels Souverijns , Dirk Lauwaet , Quentin Lejeune , Chahan M. Kropf , Kam Lam Yeung , Shruti Nath , Carl F. Schleussner
Cities worldwide are increasingly facing the challenges of heat stress, a problem expected to worsen with ongoing climate change. The lack of detailed, city-specific data hinders effective response measures and limits the adaptive capacity of urban populations. In this data descriptor, we introduce a comprehensive database providing climate and heat stress information for 142 cities globally, covering the present and extending projections up to 2100 across three distinct climate scenarios, including two overshoot scenarios. This dataset includes 34 heat stress indicators at a spatial resolution of 100 meters, offering a unique database to identify vulnerable areas and deepen the understanding of urban heat risks. The data is presented through an accessible, user-friendly dashboard, enabling policymakers, researchers, and city planners, as well as non-experts, to easily visualise and interpret the findings, supporting more informed decision-making and urban adaptation strategies.
{"title":"100 m climate and heat stress data up to 2100 for 142 cities around the globe","authors":"Niels Souverijns , Dirk Lauwaet , Quentin Lejeune , Chahan M. Kropf , Kam Lam Yeung , Shruti Nath , Carl F. Schleussner","doi":"10.1016/j.dib.2026.112497","DOIUrl":"10.1016/j.dib.2026.112497","url":null,"abstract":"<div><div>Cities worldwide are increasingly facing the challenges of heat stress, a problem expected to worsen with ongoing climate change. The lack of detailed, city-specific data hinders effective response measures and limits the adaptive capacity of urban populations. In this data descriptor, we introduce a comprehensive database providing climate and heat stress information for 142 cities globally, covering the present and extending projections up to 2100 across three distinct climate scenarios, including two overshoot scenarios. This dataset includes 34 heat stress indicators at a spatial resolution of 100 meters, offering a unique database to identify vulnerable areas and deepen the understanding of urban heat risks. The data is presented through an accessible, user-friendly dashboard, enabling policymakers, researchers, and city planners, as well as non-experts, to easily visualise and interpret the findings, supporting more informed decision-making and urban adaptation strategies.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112497"},"PeriodicalIF":1.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.dib.2026.112487
Viktor Peterson
Impact-loaded reinforced concrete beams often fail in shear. This becomes relevant for shelter design against ballistics or fragment impact, for instance. An experimental campaign was conducted to study the different types of shear failure and governing parameters. Eighteen reinforced concrete beams were tested by a 70 kg steel striker dropped from a 2.4 m height. The beams were loaded at different positions from the support with different amounts of transverse reinforcement. The beams were of reduced scale with a length of 0.80 m and a square 0.15 m × 0.15 m cross-section. The drop weight tests were monitored with shock accelerometers on the striker and beam centre, load cells under the supports measuring reaction forces, and a high-speed camera (HSC). High-speed camera measurements were recorded orthogonal to the surface with the aim of performing high-quality digital image correlation (DIC) analyses. The beams and striker were painted with a speckled pattern prior to testing for the DIC analyses. Camera recordings were conducted with a 1024 × 512 px resolution and 6 kHz sampling, resulting in a time resolution of about 0.17 ms. Accelerometer and load cell measurements were sampled at 19.2 kHz. The accelerometer on the striker was used to approximate the impact force, and beam acceleration can be used to synchronize the camera and DAQ recordings. The data may be used to calibrate finite element models, study the impact response of beams, or develop new mechanical models.
{"title":"Dataset of high-speed camera measurements from impact-tested reinforced concrete beams","authors":"Viktor Peterson","doi":"10.1016/j.dib.2026.112487","DOIUrl":"10.1016/j.dib.2026.112487","url":null,"abstract":"<div><div>Impact-loaded reinforced concrete beams often fail in shear. This becomes relevant for shelter design against ballistics or fragment impact, for instance. An experimental campaign was conducted to study the different types of shear failure and governing parameters. Eighteen reinforced concrete beams were tested by a 70 kg steel striker dropped from a 2.4 m height. The beams were loaded at different positions from the support with different amounts of transverse reinforcement. The beams were of reduced scale with a length of 0.80 m and a square 0.15 m × 0.15 m cross-section. The drop weight tests were monitored with shock accelerometers on the striker and beam centre, load cells under the supports measuring reaction forces, and a high-speed camera (HSC). High-speed camera measurements were recorded orthogonal to the surface with the aim of performing high-quality digital image correlation (DIC) analyses. The beams and striker were painted with a speckled pattern prior to testing for the DIC analyses. Camera recordings were conducted with a 1024 × 512 px resolution and 6 kHz sampling, resulting in a time resolution of about 0.17 ms. Accelerometer and load cell measurements were sampled at 19.2 kHz. The accelerometer on the striker was used to approximate the impact force, and beam acceleration can be used to synchronize the camera and DAQ recordings. The data may be used to calibrate finite element models, study the impact response of beams, or develop new mechanical models.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112487"},"PeriodicalIF":1.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.dib.2026.112486
Italo Aldo Campodonico-Avendano , Silvia Erba , Panayiotis Papadopoulos , Salvatore Carlucci , Antonio Luparelli , Amedeo Ingrosso , Greta Tresoldi , Muhammad Salman Shahid , Frederic Wurtz , Benoit Delinchant , Per Martin Leinan , Stefano Cera , Peter Riederer , Runar Solli , Amin Moazami , Mohammadreza Aghaei
Indoor Environmental Quality directly affects public health, productivity, and well-being, while also playing a vital role in developing climate-neutral, energy-efficient, and resilient buildings. This paper presents a comprehensive dataset of indoor environmental parameters that affect thermal comfort, indoor air quality, and visual comfort, which was created under the European Union’s Horizon 2020 Project Collective Intelligence for Energy Flexibility. The dataset comprises high-resolution measurements of carbon dioxide, pollutants, volatile organic compounds, air temperature, relative humidity, and illuminance on a horizontal plane, collected over a two-year period at 1-minute intervals. Data were gathered from 14 pilot buildings across four European climates: Cyprus, France, Italy, and Norway, covering diverse building types such as schools, medical centres, sports arenas, residential complexes, universities, and elder care facilities, representing about 40 % of common European building categories. Sensors were installed in specific thermal zones within each building to monitor environmental conditions. All data is organized by building and zone and supplemented with standardized Brick metadata to ensure interoperability. This comprehensive dataset, with its broad geographic coverage, variety of building types, long-term high-frequency measurements, and multimodal data, provides a valuable resource for comparative IEQ research, cross-domain modelling, and integrated assessments of comfort, ventilation, and daylighting across different climates and operational settings and is available upon request under a non-disclosure agreement provided by the consortium.
{"title":"COLLECTiEF dataset: A high-resolution indoor environmental dataset from European buildings across diverse climates supporting thermal, air-quality, and visual-comfort assessments","authors":"Italo Aldo Campodonico-Avendano , Silvia Erba , Panayiotis Papadopoulos , Salvatore Carlucci , Antonio Luparelli , Amedeo Ingrosso , Greta Tresoldi , Muhammad Salman Shahid , Frederic Wurtz , Benoit Delinchant , Per Martin Leinan , Stefano Cera , Peter Riederer , Runar Solli , Amin Moazami , Mohammadreza Aghaei","doi":"10.1016/j.dib.2026.112486","DOIUrl":"10.1016/j.dib.2026.112486","url":null,"abstract":"<div><div>Indoor Environmental Quality directly affects public health, productivity, and well-being, while also playing a vital role in developing climate-neutral, energy-efficient, and resilient buildings. This paper presents a comprehensive dataset of indoor environmental parameters that affect thermal comfort, indoor air quality, and visual comfort, which was created under the European Union’s Horizon 2020 Project <em>Collective Intelligence for Energy Flexibility</em>. The dataset comprises high-resolution measurements of carbon dioxide, pollutants, volatile organic compounds, air temperature, relative humidity, and illuminance on a horizontal plane, collected over a two-year period at 1-minute intervals. Data were gathered from 14 pilot buildings across four European climates: Cyprus, France, Italy, and Norway, covering diverse building types such as schools, medical centres, sports arenas, residential complexes, universities, and elder care facilities, representing about 40 % of common European building categories. Sensors were installed in specific thermal zones within each building to monitor environmental conditions. All data is organized by building and zone and supplemented with standardized Brick metadata to ensure interoperability. This comprehensive dataset, with its broad geographic coverage, variety of building types, long-term high-frequency measurements, and multimodal data, provides a valuable resource for comparative IEQ research, cross-domain modelling, and integrated assessments of comfort, ventilation, and daylighting across different climates and operational settings and is available upon request under a non-disclosure agreement provided by the consortium.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112486"},"PeriodicalIF":1.4,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.dib.2026.112495
Tri Lathif Mardi Suryanto , Aji Prasetya Wibawa , Hariyono , Andrew Nafalski , Gulsun Kurubacak Çakır
This dataset presents a valuable compilation of question–answer (QA) pairs derived from cultural texts and sources related to Durga mythology. A total of 21,395 QA pairs, encompassing textual materials such as scriptures, ritual narratives, temple inscriptions, and traditional storytelling records. Each entry includes the source reference, question, and corresponding answer, provided in a structured format compatible with Excel for seamless integration into downstream natural language processing (NLP) tasks. Data collection involved manual curation and annotation by domain experts, followed by preprocessing steps including text normalization, duplication removal, and verification of factual and contextual accuracy. The dataset is designed to support generative QA models, culturally aware chatbots, and digital preservation of heritage knowledge. It is particularly valuable for research in AI-driven cultural applications, educational tools, and digital humanities initiatives aiming to bridge traditional knowledge with computational methods. Researchers and practitioners may utilize the dataset for training generative models, creating interactive educational platforms, developing culturally sensitive AI agents, and supporting comparative studies in cross-cultural heritage. This openly accessible resource adheres to ethical standards, with proper attribution to source materials, and provides a foundational asset for both academic research and applied development in culturally informed artificial intelligence.
{"title":"Generated cultural heritage question–answer dataset: Durga in multi-dimensional perspectives","authors":"Tri Lathif Mardi Suryanto , Aji Prasetya Wibawa , Hariyono , Andrew Nafalski , Gulsun Kurubacak Çakır","doi":"10.1016/j.dib.2026.112495","DOIUrl":"10.1016/j.dib.2026.112495","url":null,"abstract":"<div><div>This dataset presents a valuable compilation of question–answer (QA) pairs derived from cultural texts and sources related to Durga mythology. A total of 21,395 QA pairs, encompassing textual materials such as scriptures, ritual narratives, temple inscriptions, and traditional storytelling records. Each entry includes the source reference, question, and corresponding answer, provided in a structured format compatible with Excel for seamless integration into downstream natural language processing (NLP) tasks. Data collection involved manual curation and annotation by domain experts, followed by preprocessing steps including text normalization, duplication removal, and verification of factual and contextual accuracy. The dataset is designed to support generative QA models, culturally aware chatbots, and digital preservation of heritage knowledge. It is particularly valuable for research in AI-driven cultural applications, educational tools, and digital humanities initiatives aiming to bridge traditional knowledge with computational methods. Researchers and practitioners may utilize the dataset for training generative models, creating interactive educational platforms, developing culturally sensitive AI agents, and supporting comparative studies in cross-cultural heritage. This openly accessible resource adheres to ethical standards, with proper attribution to source materials, and provides a foundational asset for both academic research and applied development in culturally informed artificial intelligence.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112495"},"PeriodicalIF":1.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.dib.2026.112485
Enzo Komatz , Marion Andritz , Christoph Markowitsch
This data article presents a dataset of miniplant-scale reverse water-gas shift (rWGS) experiments conducted in a heated fixed-bed reactor under systematically varied operating conditions. The dataset contains processed measurements including reactor temperature, molar fractions of CO2, CO, H2, CH4, and derived quantities such as CO2 conversion and CO selectivity. The experiments cover a wide parameter space, including gas hourly space velocities of 8000, 14,000 and 20,000 h-1 with temperatures between 550 and 950 °C (increment of 50 K), and H2:CO2 feed ratios of 2:1, 2.5:1 and 3:1.
The dataset presents the steady-state values and links to the reproductible data processing step, based on a prior study, enabling Fairness of all steps from the initial measurements to the final processed variables. The processing workflow includes calibration of gas analysis signals, smoothing, dry-gas calculation, and uncertainty estimation.
These data provide value for validating mechanistic kinetic models, benchmarking computational fluid dynamics (CFD) reactor simulations, training machine learning models including physics-informed machine learning frameworks, and supporting thermodynamic model assessments. All raw and processed data are made publicly available in a long-term repository, ensuring FAIR access and enabling reuse by the scientific community.
{"title":"Experimental dataset of the reverse water-gas shift reaction in a fixed-bed reactor setup under varying reactor conditions","authors":"Enzo Komatz , Marion Andritz , Christoph Markowitsch","doi":"10.1016/j.dib.2026.112485","DOIUrl":"10.1016/j.dib.2026.112485","url":null,"abstract":"<div><div>This data article presents a dataset of miniplant-scale reverse water-gas shift (rWGS) experiments conducted in a heated fixed-bed reactor under systematically varied operating conditions. The dataset contains processed measurements including reactor temperature, molar fractions of CO<sub>2</sub>, CO, H<sub>2</sub>, CH<sub>4</sub>, and derived quantities such as CO<sub>2</sub> conversion and CO selectivity. The experiments cover a wide parameter space, including gas hourly space velocities of 8000, 14,000 and 20,000 h<sup>-1</sup> with temperatures between 550 and 950 °C (increment of 50 K), and H<sub>2</sub>:CO<sub>2</sub> feed ratios of 2:1, 2.5:1 and 3:1.</div><div>The dataset presents the steady-state values and links to the reproductible data processing step, based on a prior study, enabling Fairness of all steps from the initial measurements to the final processed variables. The processing workflow includes calibration of gas analysis signals, smoothing, dry-gas calculation, and uncertainty estimation.</div><div>These data provide value for validating mechanistic kinetic models, benchmarking computational fluid dynamics (CFD) reactor simulations, training machine learning models including physics-informed machine learning frameworks, and supporting thermodynamic model assessments. All raw and processed data are made publicly available in a long-term repository, ensuring FAIR access and enabling reuse by the scientific community.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112485"},"PeriodicalIF":1.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.dib.2026.112492
Md. Famidul Islam Pranto, Md. Rifatul Islam, Md. Ali Akbor, Nabonita Ghosh, Md. Rahatun Alam, Sudipto Chaki, Md. Masudul Islam
We present ASL-HG, a comprehensive American Sign Language (ASL) image dataset designed to advance gesture recognition and assistive technologies. The collection contains 36,000 static images across 36 classes, covering the full English alphabet (A–Z) and digits (0–9). Data were captured from 10 volunteers in Mirpur, Dhaka, Bangladesh, with each participant contributing 100 samples per class, ensuring a balanced distribution across subjects, genders, and skin tones. Unlike many existing ASL datasets, ASL-HG explicitly distinguishes between the letter “O” and the digit “0″ by including the standard two-handed ASL “zero” sign used in practical alphanumeric communication. The dataset is released in two complementary forms: raw images with natural indoor and outdoor backgrounds, and a MediaPipe-processed version with hand-segmented crops and predefined 80–20 train–test splits. This design supports both custom pre-processing and immediate model training. ASL-HG is intended to serve as a benchmark resource for developing robust and fair ASL recognition systems, reducing communication barriers for deaf and speech-impaired users, and enabling broader research in gesture-based human–computer interaction.
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