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BanglaKBase is a structured affective commonsense knowledge base developed specifically for Bengali concepts. Each entry within the resource is represented as a key in a dictionary, linked to a set of sentiment-related attributes. These include a primary emotion and a secondary emotion (e.g., #anger, #disgust), which together reflect both the dominant and supporting affective tones associated with the concept. In addition, a polarity label (positive or negative) indicates the general sentiment orientation, while a polarity score—ranging from -1 (strongly negative) to +1 (strongly positive)—quantifies the intensity of sentiment. To capture deeper contextual meaning, each concept is further enriched with five semantically related Bengali terms. These associated terms provide a broader view of the concept’s affective landscape and enhance its usability in computational analysis. For example, the concept ‘অনেক বেশি চরবি’ (excessively overweight) is labeled with strong negative emotions (#anger and #disgust), carries a polarity score of -0.97, and is linked to semantically related terms such as ‘মোটা’, ‘বিচ্ছিরী’, and ‘ভারি’, all reinforcing its negative emotional tone. The full knowledge base includes 30,000 annotated Bengali concepts. Among these, 15,126 are marked as positive and 12,492 as negative. Beyond polarity, concepts span a wide array of emotion categories including sadness (7,862), disgust (8,933), joy (8,926), surprise (4,570), anger (6,139), fear (3,132), admiration (10,306), and interest (7,208). These emotional dimensions enable nuanced affective analysis of text in Bengali and support a variety of tasks in sentiment analysis and emotion-aware computing. Illustrative entries further demonstrate the granularity and contextual depth captured by BanglaKBase. For instance, the term ‘একটু’ is classified as negative with a polarity score of -0.79 and is linked to contextually relevant terms such as ‘অন্তত’, ‘ছোট’, and ‘ক্ষতিকারক’. In contrast, a similar concept like ‘একটু ক্ষুধার্ত’ is categorized as positive with a score of 0.65, and is connected to expressions indicating satisfaction or hunger relief. Such examples underscore how the resource integrates sentiment and semantics to model affective meaning more comprehensively. Overall, BanglaKBase serves as a valuable tool for sentiment classification, emotion mining, and natural language understanding in Bengali, supporting both computational research and practical applications in affective computing.

This dataset contains images of Ziziphus mauritiana (jujube) categorized into Healthy and Bruised.The dataset is curated for bruise detection and classification using computer vision techniques. It includes both original and augmented images, making it suitable for deep learning applications. Dataset Composition: Original Dataset: Healthy: 732 images Bruised: 732 images Augmented Dataset: Healthy: 2,928 images Bruised: 2,928 images Total Images: 7,320 File Format: JPG/PNG (Specify based on your dataset) Potential Applications: Computer vision-based bruise detection Quality assessment of jujube fruits Deep learning model training for agricultural product classification

This replication package includes the necessary code and data to generate the Tables and Figures in the "Why Does Options Market Information Predict Stock Returns?" paper by Dmitriy Muravyev, Neil D. Pearson, and Joshua M. Pollet, forthcoming in the Journal of Financial Economics. Please read README.txt for details. Abstract: Several influential studies show that transformations of implied volatilities calculated from options prices predict stock returns. This predictability is puzzling because market participants readily observe options prices. We find that this predictability is consistent with implied volatilities reflecting stock borrow fees that are known to predict stock returns. We derive a formula relating the option-implied volatility spread to the borrow fee. Motivated by this relation, we show that the return predictability from implied volatility spread and skew decreases by at least two-thirds if high-fee stocks are excluded. The patterns for other predictors computed from option implied volatilities are similar.

These data were collected for a study on the impact of diversity, equity, and inclusion (DEI) on employee outcomes.

Excel-file with all the data for figures 1-2 in the main text and figure 3-11 in the supplementary materials

The dataset contains information used for flood risk assessment (FRA) along 58 km of the Niger River upstream of the city of Niamey. We hypothesised that a more detailed definition of assets exposed to flooding than that provided by standard flood risk assessments (FRA) developed in low- and medium-income economies would facilitate risk management. The dataset comprises five shapefiles, two data tables, and two tables of information related to the reference FRAs in low- and middle-income economies, published between 2016 and 2025. The shape files contain: (i) Crops, (ii) Buildings, (iii) Guinean floodplain with 2, 5, 10, 30, 100 years of return period, and (iv) Local floodplain with 2, 5, 10, 30, 100 years of return period. The data tables concern: (i) Sixty-three flood-prone settlements according to the return period and floodable buildings, and (ii) flood risk in monetary terms (EUR) according to the probability of occurrence. The information tables concern: (i) forty river FRAs in low- to middle-income economies, 2016-2025, and (ii) risk mapping implications based on forty river FRAs in low- to middle-income economies, 2016-2025.

Gut microbiota has been reported to play an important role in weaned piglets. Our previous study demonstrated a notable enrichment of intestinal Prevotella in weaned piglets experiencing diarrhea, although the mechanisms are not clear. In this study, we elucidated the mechanism of Prevotella copri (P. copri) in causing diarrhea, and the protective effect by protocatechuic acid (PCA). A total of 108 healthy Duroc × Landrace × Yorkshire weaned piglets, aged 21 d, were randomly assigned into 3 groups, with 6 replicates and 6 piglets per replicate. The piglets were fed a basal diet, a basal diet containing 1.0 × 108 CFU/kg P. copri or 1.0 × 108 CFU/kg P. copri + 400 mg/kg PCA for 28 d. Results showed that P. copri decreased the final body weight and average daily gain (ADG), while increased the feed-to-gain ratio (F/G) , with increased serum levels of interleukin (IL) -2 and IL-8 in piglets (P < 0.05), and reduced the expression of intestinal tight junction protein (P < 0.05). In vitro experiments indicated that argininosuccinic acid, indole-3-aldehyde, and N-acetylpurine are the primary virulence factors of P. copri in inducing inflammation. PCA suppressed inflammatory cytokines both in vivo and in vitro (P < 0.05), and enhanced the expression of tight junction proteins in piglets (P < 0.05). In conclusion, P. copri can reduce the growth performance of weaned piglets by inducing intestinal inflammation, while argininosuccinic acid, indole-3-aldehyde, and N-acetylpurine are the primary virulence factors, and PCA has shown a protective effect.

Ảnh khảo sát phục vụ cho bài nghiên cứu về hành vi người tiêu dùng thương hiệu Dermarium

The data used in Formal and Informal Debt in China: Evidence from the 2014 Hukou Reform are sourced from the China Household Finance Survey (CHFS). The authors accessed the CHFS data through the secure CHFS Data Platform. Under the platform’s terms of use, they are not permitted to export or share the data. However, interested researchers can apply for access directly via the CHFS official website (https://chfs.swufe.edu.cn/). Below, we provide a detailed and replicable guide in data and methodology used in this study.

Stata and R scripts for replicating the analysis in: "The 'Acting Native' Hypothesis: Evidence from Classrooms in Four European Countries". Datasets are confidential and must be obtained separately in order to run the scripts.