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Abstract Integrating bimetallic sulfides into metal-organic frameworks (MOFs) crystal materials to develop MOF-based composites was an effective strategy to enhance the electrochemical performance of MOFs. Herein, bimetallic MOF zinc/cobalt-porphyrin (ZnCo-TCPP) nanosheets were firstly prepared using a facile surfactant-assisted method, and then ZnCoS/ZnCo-TCPP was synthesized by in-situ sulfidation of ZnCo-TCPP with thioacetamide (TAA) under different vulcanization times. Subsequently, a novel dopamine (DA) sensing method was established based on these materials. The morphologies, compositions and structures of these compounds were characterized by Transmission electron microscopy, X-ray photoelectron spectroscopy, X-ray diffraction, and N2 adsorption/desorption isotherm. The characterization results indicated that ZnCoS with a size of about 5 nm were uniformly dispersed on the surface of ZnCo-TCPP without agglomeration. The electrochemical results demonstrated that ZnCoS/ZnCo-TCPP had the highest current response to DA oxidation with the vulcanization time of 3 h. The linear range of the sensor based on ZnCoS/ZnCo-TCPP was 0.01-327.8 μM, the detection limit was determined to be 3 nM (S/N＝3), and the sensitivity was 379.3 μA mM−1 cm−2. Compared with analogous types DA sensors reported in previous literature, the linear range of this sensor had been expanded by at least an order of magnitude, and the detection limit had been reduced by at least an order of magnitude. Furthermore, the fabricated sensor was used to detect DA in human serum with the recoveries ranged from 96.26% to 103.6%. The acceptable recoveries rate proved that the sensor had the potential for the efficient determination of DA. The proposed strategy provided a discriminative and sensitive analytical platform for DA clinical diagnostics and drug screening. Keywords: Electrochemical sensing, Bimetallic MOF, Bimetallic sulfides, Dopamine.

Hipotesis penelitian: Strategi komunikasi krisis berbasis komunikasi Islam khususnya delegated spokesperson/borrowed credibility (delegasi ke otoritas eksternal) dan religious routine as relational infrastructure (pengajian rutin), efektif memulihkan citra dan menstabilkan krisis pascakekerasan seksual di pesantren, meskipun tanpa permintaan maaf institusional. Data: Kualitatif studi kasus tunggal (16 informan: pimpinan pesantren, guru, aparat desa/kecamatan, wali santri, masyarakat). Wawancara mendalam, observasi non-partisipan (Mei–Desember 2025), analisis dokumen. Data pendukung: foto struktur organisasi, tata tertib, visi-misi, hasil wawancara dengan ustaz, camat, pimpinan ponpes. Temuan utama: Penurunan santri 56,3% (224→98). Reaksi publik: bom ikan, ancaman bakar. Strategi sukses: delegasi komunikasi ke camat, kemenag, polisi, bupati; pengajian 2x/bulan sebagai ruang pemulihan kepercayaan. Celah akuntabilitas: tidak ada permintaan maaf resmi dari pesantren. Interpretasi & penggunaan: Model krisis pesantren perlu borrowed credibility dan ritual religio-sosial yang dikenali komunitas. Ketiadaan mortification membatasi pemulihan penuh → rekomendasi untuk memasukkan pengakuan dan dukungan korban. Data dapat dipakai untuk menyusun protokol komunikasi krisis bagi lembaga pendidikan Islam di era pascakebenaran dan populisme digital.

This is the data for the paper "Inflation Co-Movement in the Euro Zone: Headline versus Core, Trends versus Cycles, and the Impact of Dent Crises

In this data set, there are closing prices of the Nepal Stock Exchange (NEPSE) Index over a consistent span of twenty years from April 2004 to April 2024. This NEPSE Index is the leading benchmark of the equity market in Nepal and is the only national representative of the stock market performance indicator in the country. This data set has been constructed to study the dynamics of NEPSE volatility, cross-border financial contagion with the countries in its neighbourhood, and the long-term behavior of a frontier stock market. In each row of the data set, there is one day of trading along with its NEPSE closing index. The NEPSE is captured through this data during the occurrence of many economic events such as the global financial crisis of 2008, Nepal Earthquake in 2015, trade embargo of 2015-16, and coronavirus pandemic of 2020.

The dataset contains anonymized three-wave matched employee–coworker survey data used in the study “Admiration or perceived threat? How coworkers’ AI literacy shapes employees’ knowledge hiding.” It includes variables related to coworkers’ AI literacy, perceived threat, admiration, performance-prove goal orientation, knowledge hiding behavior, and relevant control variables.

The original Western Blot results in the paper, and the results of a single independent experiment are presented on the same membrane by cutting or stripping.

The data used in the empirical analysis to examine volatility regime switching between the National Stock Exchange (NSE) of India and Nepal Stock Exchange (NEPSE) market is this working data file. This file includes 1,018 weekly observations from April 2004 to April 2024, where each observation comprises the weekly closing value of the NSE and NEPSE stock exchanges, along with the weekly logarithmic return on the continuous compounding basis. The purpose of choosing the weekly data series is that, by aggregating to a weekly level, the difference in trading days between the two stock exchanges would be equalised as well as the microstructure effect present in daily series would be overcome.

ChattogramSent is a pioneering, high-quality, and manually curated sentiment analysis dataset for the Chattogram dialect (Chittangga), a major underrepresented oral language spoken in southeastern Bangladesh. This corpus marks the first significant effort to create a digital benchmark for this dialect, which traditionally lacks a standardized writing system and high-quality computational resources. Developed entirely by a team of native researchers, the dataset bridges the gap between oral tradition and modern NLP by providing a meticulously cleaned and phonetically transcribed corpus in Bengali script. Data Composition and Scale The dataset comprises 7,052 unique samples, each manually annotated for sentiment and verified for linguistic accuracy. Unlike automated datasets, every entry in this corpus has been reviewed by native speakers to ensure cultural and contextual relevance. 1. Sentiment Distribution (Class Balance) The dataset is categorized into three distinct sentiment classes: Neutral: 4,287 samples (The primary baseline for objective dialectal speech) Negative: 1,600 samples (Capturing regional expressions of dissatisfaction or criticism) Positive: 1,165 samples (Reflecting appreciative and affirmative dialectal nuances) 2. Source of Data (Multi-Domain Coverage) To ensure the model's robustness, data was harvested from three authentic domains: Drama (3,292 samples): Scripts from regional Chittagonian dramas, rich in idiomatic expressions and emotional depth. Conversation (2,568 samples): Real-world everyday dialogues capturing the natural flow of the dialect. Social Media (1,192 samples): Modern digital interactions, providing insights into how the dialect is adapted for social platforms. Key Highlights for Researchers First of its Kind: The first comprehensive benchmark for sentiment analysis in the Chattogram dialect. Native-Led Annotation: 100% manual annotation by native experts, eliminating the errors common in machine-translated or non-native datasets. Multi-Domain Diversity: Includes data from entertainment (drama), social media, and interpersonal speech, making it ideal for training versatile NLP models. Phonetic Accuracy: Provides a standardized phonetic transcription in Bengali script, essential for training speech-to-text and sentiment classifiers. Potential Use Cases This dataset serves as a foundational resource for: Developing Sentiment Classifiers for low-resource regional languages. Fine-tuning Transformer-based models (like BERT or RoBERTa) for dialectal understanding. Linguistic Research into the emotional semantics of the Chattogram dialect. Enhancing Multilingual AI systems to support regional Bangladeshi languages.

This dataset contains 24,103 Google Play user reviews collected from 21 connected-car / automotive OEM companion applications between 2018 and 2026. Each record represents one app review and includes metadata such as the app or brand name, Google Play URL, package ID, reviewer information, review text, star rating, thumbs-up count, review date, app version, and language. The dataset is primarily textual and rating-based, making it suitable for empirical studies on mobile app user experience, complaint detection, sentiment analysis, service quality issues, dissatisfaction monitoring, and comparative analysis of connected-car digital services across automotive brands.

Hand-coded proposal-tracking dataset of 493 Parliament and Council contributions on the digital package recitals (Data Governance Act, Data Act, Digital Markets Act, Digital Services Act, and Artificial Intelligence Act).