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IndoHotelABSA is a large-scale benchmark for aspect-based sentiment analysis (ABSA) of Indonesian hotel reviews. It contains 14,988 unique, deduplicated Indonesian reviews collected from the Google Places API across 55 cities spanning all six major regions of Indonesia (Sumatra; Java; Bali & Nusa Tenggara; Kalimantan; Sulawesi; Maluku & Papua). All personally identifying metadata were removed at collection; only review text, star rating, hotel name, and city are retained. A 3,000-review, class-balanced sample was annotated for seven CRM-meaningful aspect categories (Location; Cleanliness; Service/Staff; Room & Facilities; Price/Value; Food & Beverage; Public Facilities), each with three-way polarity (positive/negative/neutral), using a two-tier, LLM-assisted, human-verified protocol. A 500-review gold subset was independently verified by three annotators at almost-perfect agreement (Fleiss' kappa = 0.894; 92.1% of LLM drafts retained, 7.9% corrected), yielding 1,506 gold aspect annotations. The release distinguishes LLM-drafted (silver) from human-verified (gold) labels and provides fixed train/validation/test splits (2,480 silver train; 100 gold validation; 400 gold test). All reported evaluations in the accompanying article use the human-verified gold labels. Version 2: licence now stated per component (annotations CC BY 4.0, review text redistributed as third-party content, not relicensed). In-text PII scrubbing applied. Per-annotator label files, adjudication record, gold subset identifiers, corpus id mapping, blinded re-annotation, and the 20 excluded records added. Documentation corrected so that "human-verified" refers only to the 500-review gold subset.

This dataset supports a study evaluating whether iron sand from Pantai Panjang (Panjang Beach), Bengkulu, Indonesia, can serve as a low-cost, locally sourced zero-valent iron (ZVI) precursor for groundwater remediation. The underlying hypothesis is that a reagent-free thermal processing route can modify the mineralogy of iron sand without raising its reactive iron (Fe) content above the pre-heating baseline, thereby preserving the composition required for a usable ZVI precursor. Data were gathered experimentally from eight sand samples collected by proportional random sampling along the beach to capture spatial variation in iron content. Each sample was dried, weighed to a 50 g target, magnetically separated, and heated at two power levels (300 W, 300–420 °C; 600 W, 430–500 °C). Mass was recorded at four stages: after drying, after magnetic separation, after 300 W heating, and after 600 W heating. Elemental composition—iron (Fe), the balance fraction (Bal = Si + Al), titanium (Ti), and 14 trace elements—was measured by X-ray fluorescence (XRF) before heating and after heating at 350 °C and 450 °C. The dataset comprises: (1) X-ray fluorescence (XRF) elemental composition results before and after thermal processing, (2) sample mass measurements across all processing stages, (3) list of required research equipment, and (4) documentation of the experimental procedure. Notable findings: heating produced a consistent inverse relationship between Fe and Bal—Fe declined as Si + Al increased—while Ti remained stable (4.2–6.4%) and all trace elements stayed below 1%. Samples 3, 4, 5, and 8 showed a clean monotonic Fe decline; Samples 1, 2, and 6 rebounded slightly at 450 °C but stayed below their pre-heating Fe. Because post-heating Fe did not exceed the pre-heating baseline, Samples 1–6 and 8 retained a usable precursor composition; only Sample 7, whose Fe rose above baseline, was excluded. How to interpret and use the data: pre-heating Fe indicates starting feedstock quality, while post-heating values show how thermal conditions redistribute Fe relative to the silicate matrix. A sample is treated as a viable ZVI precursor when its post-heating Fe remains at or below the pre-heating level. Researchers can use the data to compare thermal processing conditions, assess the spatial heterogeneity of coastal iron sand, benchmark XRF-based screening of ZVI feedstocks, or design follow-up work (e.g., XRD/SEM phase analysis and direct ZVI reactivity testing). The data are descriptive: no statistical software was applied, so values should be read as comparative measurements across stages and heating conditions rather than as inferential statistics.

This dataset was generated from open-ended questionnaires and interviews aimed at identifying the definitions and factors of intercultural skills according to Javanese students (as local students) and Sumbanese students (as migrant students). The open-ended questionnaire was completed by 228 Sumbanese students and 35 Javanese students from February 19, 2026, to April 12, 2026. Meanwhile, the semi-structured interviews involved four students from each group from April 13, 2026, to May 10, 2026. Note. The source of questionnaire responses, interview conversations, and observational notes cited in our article is formatted as PS[Sumbanese participant number], PJ[Javanese participant number], and Obs.Lap[observation date].

📌 Steps to Reproduce This dataset contains a collection of primary chest X-ray images acquired from Epic Chittagong, Bangladesh. The dataset is designed for the study and development of deep learning and machine learning models for pneumonia detection and classification. This dataset contains 3,355 primary chest X-ray images collected from Epic Chittagong, Bangladesh, categorized into two classes: (1) Normal (2) Pneumonia 📊 Dataset Composition --------------------------------- Training Data : => Normal: 321 images => Pneumonia: 321 images => Total Training Samples: 642 Testing Data : ------------------- Normal: 1,363 images => Pneumonia: 1,350 images => Total Testing Samples: 2,713 👉 Grand Total: 3,355 X-ray images 📂 Folder Structure : ------------------------- /Chest_Xray_EpicChittagong_Dataset/ ├── train/ │ ├── Normal/ │ └── Pneumonia/ ├── test/ │ ├── Normal/ │ └── Pneumonia/ 📷 Image Details : ------------------------ Format: JPEG / PNG Modality: Chest X-ray (CXR) Color: Grayscale Source: Epic Chittagong, Bangladesh 2025 Status: Primary dataset (raw and unprocessed) 🧪 Applications : --------------------- => Pneumonia vs. Normal chest X-ray classification => Deep learning model training (CNN, transfer learning) => Benchmarking medical imaging algorithms => Computer-aided diagnosis (CAD) => Radiology research and teaching 📬 Contact : ------------------ For questions or collaboration Email: hiramdirfanulkabir@gmail.com 🎓 Department of Computer Science and Engineering 🏛️ Institutions : -------------------- Epic Chittagong, Bangladesh National Institute of Textile Engineering and Research University of Dhaka 📚 Categories : ---------------------- Computer Science, Radiology, Health Sciences, Artificial Intelligence, Computer Vision, Medical Imaging, Pneumonia, Chest X-ray, Deep Learning, Machine Learning

Hypoxia has become a global marine environmental issue under climate warming. This study focuses on Core YEC1701 (upper 100 cm) from hypoxic area off the Changjiang Estuary, applying 210Pb and 137Cs dating together with lithology, grain size, benthic foraminifera and elemental geochemistry to reveal paleoenvironmental changes and hypoxic history since 1950. Notable findings are as follows: 1. The age model in the upper 100 cm of Core YEC1701 reveals two different sedimentation stages in the Changjiang delta front since 1929: a low sedimentation rate (0.53 cm/yr) from 1929 to 1976, and a high sedimentation rate (1.62 cm/yr) after 1976. 2. The core location has been a delta-front environment since 1950. 3. Hyaline benthic foraminifera (87.06%) dominate the overall assemblage, while the percentages of agglutinated (4.54%) and porcelaneous benthic foraminifera (8.40%) are relatively low. The averages of abundance, number of species and Shannon-Wiener index (H′) are 4182 individuals/40 g, 47 and 3.41, respectively. Based on quantitative analysis, there are a total of 16 dominant species with an average relative abundance of >2% for the overall assemblage. 4. TN contents range from 0.09% to 0.12% (average 0.10%), and TOC contents range from 0.40% to 0.58% (average 0.50%). C/N ratios range from 4.48 to 5.29 (average 4.92). δ13C values range from –23.93‰ to –23.15‰ (average –23.51‰). 5. Bottom-water hypoxia off the Changjiang Estuary has been persistent throughout the study period. From 1950 to 1980, hypoxia was weak and declining; after 1980, hypoxia was severe and intensifying. The details for sample collection are as follows: Core YEC1701 was collected using a gravity corer during a summer cruise by the State Key Laboratory of Marine Geology (Tongji University) in August, 2017, and its total length is 405 cm. The coring site (30°57′3.062″N, 122°44′19.792″E) was located in the muddy delta front and adjacent to the modern core hypoxic area (Li et al., 2002), and the water depth was 22.4 m. The core was split lengthwise in the laboratory: one half was sealed with the preservative film and stored at 4℃ for archiving, and the other (working half) was carefully smoothed with a stainless steel knife to expose sedimentary structures. The working half was photographed, described lithologically, and scanned non-destructively by an X-ray fluorescence (XRF) core scanner before subsampling. Subsamples were collected at 1 cm intervals using a stainless steel cutter, sealed in plastic bags, and stored at 4℃ for subsequent analyses. Note: All the measurements were conducted at the State Key Laboratory of Marine Geology.

This data set contains raw data, and the results of CFA and EFA statistical analysis with JASP to examine the environmental concerns of prospective teachers.

This is the distribution of brGMGTs in Sihailongwan Maar Lake in northeastern China.

This dataset contains the literature-derived database and R code supporting the systematic review of methods used to quantify belowground biomass stock and productivity across tropical Andean ecosystems. The repository includes the original data-extraction database, the analysis-ready dataset used for statistical analyses, R scripts for data processing and generalized linear models, and derived statistical tables supporting the figures presented in the manuscript.

Accompanying Dataset for Lee, B.J. & Jeffery, D.C. (2026)

This dataset and code package supports the manuscript “Field morphometric fingerprint reveals the limits of a reduced stochastic-threshold model of honeycomb weathering”. It contains analysis-ready morphometric measurements for 819 field cavities, OFAT and three-level full-factorial experimental designs and summaries, run-level metrics for 1,350 factorial simulations, source data for Figures 5 and 7, bootstrap and sensitivity-analysis outputs, four computational scripts, software-environment information, file and checksum manifests, and Supplementary Tables S5–S11. The field observations represent one sandstone surface in South China. The bootstrap analyses quantify within-surface sampling variability and do not constitute external validation. The nondimensional model parameters and the identified relatively low-mismatch region should not be interpreted as calibrated physical parameter estimates.