Omkar2118/datasets huggingface dataset:
Calibration data
Full name: Observations for intensive stations in the Finnish Archipelago Sea 2006-2015

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14028484.svg)](https://doi.org/10.5281/zenodo.14028484)
Data used for calibrating Finnish Coastal nutrient load model (FICOS). See the Zenodo repository in the badge above for details.

Introducing paper: Bayesian Calibration and Uncertainty Quantification for a Large Nutrient Load Impact Model
hdf5 files for FICOS
Full name: Hdf5 input files for FICOS simulator

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14028433.svg)](https://doi.org/10.5281/zenodo.14028433)
Nutrient loadings and boundary conditions required by the FICOS model. See the Zenodo repository for details (link in the badge) above.

Introducing paper: Bayesian Calibration and Uncertainty Quantification for a Large Nutrient Load Impact Model
PhD
Full name: PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset

Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hallucination evaluation Dataset (PhD) for objective VHE at a large scale. The essence of VHE is to ask an MLLM questions about specific images to assess its susceptibility to hallucination. Depending on what to ask (objects, attributes, sentiment, etc.) and how the questions are asked, we structure PhD along two dimensions, i.e., task and mode. Five visual recognition tasks, ranging from low-level (object / attribute recognition) to middle-level (sentiment / position recognition and counting), are considered. Besides a normal visual QA mode, which we term PhD-base, PhD also asks questions with inaccurate context (PhD-iac) or with incorrect context (PhD-icc), or with AI-generated counter common sense images (PhD-ccs). We construct PhD by a ChatGPT-assisted semi-automated pipeline, encompassing four pivotal modules: task-specific hallucinatory item (hitem) selection, hitem-embedded question generation, inaccurate / incorrect context generation, and counter-common-sense (CCS) image generation. With over 14k daily images, 750 CCS images and 102k VQA triplets in total, PhD reveals considerable variability in MLLMs' performance across various modes and tasks, offering valuable insights into the nature of hallucination. As such, PhD stands as a potent tool not only for VHE but may also play a significant role in the refinement of MLLMs.

Introducing paper: PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
CartoMark

A large-scale benchmark dataset involving well-labelled datasets to employ the state-of-the-art machine intelligence technologies for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring.  Furthermore, these well-labelled datasets would facilitate map feature detection, map pattern recognition and map content retrieval. We hope our efforts would provide well-labelled data resources for advancing the ability to recognize and discover valuable map content.

Introducing paper: CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence