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BANTEN is a manually curated parallel Banglish–English dataset comprising 14,000 sentence pairs collected from publicly available online sources, including newspapers, Facebook posts and comments, YouTube comments, daily conversations, and blogs. Each instance contains a Banglish sentence written in Roman script and its corresponding human-translated English sentence. The dataset was developed through data collection, filtering, cleaning, manual translation, and expert validation. It is intended to support research in Banglish-to-English machine translation, code-mixed language processing, transliteration, and low-resource natural language processing.

This dataset includes raw data and raw figures from the manuscript "Clinical Insights into PEDV G2C Infection: Intestinal Barrier Dysfunction and Gut Microbiota Dysbiosis in Naturally Infected Piglets"

引物数据

This dataset contains anonymised survey responses from 302 Bangladeshi current and former viewers of Turkish television dramas. Data were collected through a bilingual Bangla–Turkish online questionnaire between 25 October and 15 November 2025 using targeted convenience and self-selection sampling. The workbook contains 51 analysis-ready variables covering viewing patterns, motivations, platform and genre preferences, cultural similarity, language learning, viewer interaction, tourism interest, and interest in Turkish history and culture. It also includes a detailed codebook, 12 exploratory association tests, and 72 validation checks. Names and identities were not collected, while timestamps and open-text responses were removed. The dataset supports research on Turkish television dramas, cultural proximity, audience reception, and perceived soft-power outcomes in Bangladesh.

This dataset accompanies a study testing whether nucleus accumbens dopamine (DA) acts as an action‑contingent reinforcement signal shaping juvenile social play in a sex‑divergent manner, and whether Shank3 deficiency disrupts this signal. We collected multimodal data from wild‑type (WT) and Shank3+/- (PND 35‑85) during same‑sex dyadic interactions. A nine‑camera volumetric system (120 fps) with segmentation‑first instance segmentation (Mask R‑CNN/YOLOv8) and DANNCE tracked 14 body keypoints. The Social‑Seq pipeline derived 32 kinematic/interaction features, segmented interactions into 800‑ms clips, and used Seq2seq RNN with perspective‑invariant active learning to generate 36 behavioral syllables (e.g., sniffing, chasing, pouncing, pinning, rearing). Simultaneously, fiber photometry recorded NAc DA dynamics via GRAB‑DA3m. In Shank3+/- males, a real‑time closed‑loop system (266‑ms latency) delivered 40‑Hz optogenetic stimulation to VTA‑NAc projections specifically upon proactive play initiation over 8 training days, followed by 10 stimulation‑free days. Key findings: In WT males, proactive play (pouncing, pinning) evoked DA surges, while forced submission suppressed DA. In WT females, DA increased during evasion and rearing but not contact‑heavy play. Shank3 mutants showed blunted DA during sniffing/chasing and, critically, a sign‑inverted DA response during pouncing , while solitary rearing produced exaggerated DA only in mutant males . A multi‑agent reinforcement learning model parameterized with empirical DA amplitudes reproduced mutant phenotypes (preserved sniffing, reduced play, disrupted transitions). Closed‑loop DA during play increased targeted play duration persistently and reduced non‑social leaving, establishing causal sufficiency. Data include raw 3D keypoint coordinates, behavioral syllable labels, synchronized DA ΔF/F traces, optogenetic timestamps, and summary metrics (frequencies, durations, transition matrices). Researchers can use these to replicate the ethogram, perform neural‑behavior alignment, parameterize computational models, or evaluate intervention outcomes. Key considerations: sex must be treated as a covariate; behavioral labels have quantitative operational definitions; the Shank3 model (exon 11‑21) is specific to Phelan‑McDermid syndrome; and optogenetic results demonstrate sufficiency, not exclusivity, of DA. This multi‑scale resource supports studies of developmental social reward and autism‑related motivation deficits.

This dataset is a meticulously re-annotated instance segmentation version of the original TrashNet dataset [1], which was initially created for image classification tasks. While the original dataset provided 2,527 images across six waste categories, it lacked the precise pixel-level boundaries essential for robotic manipulation and automated sorting systems. Our re-annotation transforms this classification dataset into a high-quality instance segmentation benchmark, providing polygon masks suitable for training models that require geometric precision, such as those used in robotic grasping applications. Dataset Characteristics Total Images: 2,527 high-resolution RGB images Classes: 6 recyclable material categories Annotation Format: YOLO segmentation format (polygon masks with .txt files) Average Vertices per Polygon: 24.3 Annotation Quality: Manual verification ensuring IoU > 0.92 between annotated polygons and visible object boundaries Split Distribution: Training: 1,765 images (~70%) Validation: 504 images (~20%) Test: 258 images (~10%) Class Distribution Class Number of Images Glass 502 Paper 600 Cardboard 405 Plastic 467 Metal 415 Trash 138 Total 2,527 Key Features Instance Segmentation Annotations: Unlike the original classification labels, each object is annotated with a detailed polygon mask, enabling pixel-level segmentation. Geometric Precision: With an average of 24.3 vertices per polygon and IoU > 0.92, the annotations are suitable for training high-precision segmentation models. YOLO-Compatible Format: Annotations are provided in YOLO polygon segmentation format, with a data.yaml configuration file for seamless integration with YOLO-based frameworks (YOLOv8, YOLO11, etc.). Balanced Splits: Careful partitioning ensures balanced class distribution across training, validation, and test sets. Motivation The original TrashNet dataset [1] was designed for classification and bounding box detection, which do not provide the spatial precision required for tasks like robotic grasping or automated pneumatic sorting. This re-annotation addresses that gap by providing high-quality polygon masks that capture the exact contours of waste objects, even for challenging materials such as transparent glass and crumpled plastics. Applications Training instance segmentation models for automated waste sorting Developing robotic grasping systems for recycling facilities Evaluating attention mechanisms for visually ambiguous materials Benchmarking segmentation algorithms on recyclable waste streams Citation If you use this dataset in your research, please cite: Rohit, N.-U.-H., Tabassum, F., & Raihan, A. (2026). Re‑annotated and Segmented TrashNet Dataset for Waste Classification in YOLO Format [Data set]. Mendeley Data. https://doi.org/[YOUR-DOI] Additionally, please cite the original TrashNet dataset: [1] G. Thung and M. Yang. "Classification of Trash for Recyclability Status." CS229 Project Report, Stanford University, 2016.

These raw data are associated with the novel small molecule STAT3 inhibitors named SIMMPYRA1 and 2 on their anti-tumor role against triple-negative breast cancer.

This dataset was generated in the following publication: Stengl C, Mooshammer C, Mahnke J, Runz A, Vedelago J, A review on radiological properties of fused deposition modelling material for three-dimensional printing in proton and light ion beam therapy. Physics and Imaging in Radiation Oncology 2026;40:101042. https://doi.org/10.1016/j.phro.2026.101042. Excel file with 17 3D printing fused deposition modeling (FDM) material classes and a total of 70 different materials describing the 3D printing parameters such as vendor, chemical composition, density, 3D printer model, slicer software, extrusion factor, extrusion temperature, bed temperature, printing speed and maximum layer thickness, infill and pattern. For each of these parameters, the resulting radiological properties are given, including printed density, CT number at 80 kV, 100 kV and 140 kV, relative electron density and relative stopping power. The full table is given in the file "2026-FDM Lookup Table.xlsx". To simplify the tables, ABS, PLA and other filaments are separated additionally. How to read and how to use the lookup tables: Material names are given in column A. The largest material classes ABS and PLA have distinct colours (ABS = purple), (PLA = red). The other materials classes are dark grey. For the materials with some modifications such as additives the hue of the color is reduced to a lighter purple, red or grey. Uncertainty from each publication and entry is given as () brackets, while the literature is given with [] brackets. The reference source given in brackets is the same numbering as for the Reference section of the mentioned publication.

Physicochemical parameters, including pH, total dissolved solids, chloride, electrical conductivity, total hardness, sodium, potassium, calcium, magnesium, bicarbonate, carbonate, nitrate, phosphate, fluoride, and sulphate of water from bore wells and tube wells on agricultural farms during two seasons viz. pre-monsoon and post-monsoonwere analyzed across different blocks in the Karnal district, India.

The "Public Perception about KOL Electric Vehicle" survey is a structured quantitative measurement instrument designed to evaluate the persuasive impact of Key Opinion Leaders (KOLs) and automotive reviewers on Electric Vehicle (EV) adoption among urban consumers in DKI Jakarta. Grounded in the Source Credibility Model and the Elaboration Likelihood Model (ELM), the questionnaire measures how specific credibility attributes of influencers mitigate perceived risk anxieties and foster consumer purchase intentions toward green automotive technology.