Omkar2118/datasets huggingface dataset:
U-DIADS-Bib

U-DIADS-Bib is a proprietary dataset developed through the collaboration of computer scientists and humanities at the University of Udine. It is composed of 200 images, 50 for each of the 4 different manuscripts that characterize it. These handwritten books were selected in collaboration with humanist partners considering both the complexity of their layout and the presence of significant and semantically distinguishable elements. In particular, the images of the four manuscripts were collected from the digital library Gallica. All manuscripts are Latin and Syriac Bibles published between the 6th and 12th centuries A.D.

Introducing paper: U-DIADS-Bib: a full and few-shot pixel-precise dataset for document layout analysis of ancient manuscripts
Cybench

Language Model (LM) agents for cybersecurity that are capable of autonomously identifying vulnerabilities and executing exploits have potential to cause real-world impact. Policymakers, model providers, and researchers in the AI and cybersecurity communities are interested in quantifying the capabilities of such agents to help mitigate cyberrisk and investigate opportunities for penetration testing. Toward that end, we introduce Cybench, a framework for specifying cybersecurity tasks and evaluating agents on those tasks. We include 40 professional-level Capture the Flag (CTF) tasks from 4 distinct CTF competitions, chosen to be recent, meaningful, and spanning a wide range of difficulties. Each task includes its own description, starter files, and is initialized in an environment where an agent can execute commands and observe outputs. Since many tasks are beyond the capabilities of existing LM agents, we introduce subtasks for each task, which break down a task into intermediary steps for a more detailed evaluation. To evaluate agent capabilities, we construct a cybersecurity agent and evaluate 8 models: GPT-4o, OpenAI o1-preview, Claude 3 Opus, Claude 3.5 Sonnet, Mixtral 8x22b Instruct, Gemini 1.5 Pro, Llama 3 70B Chat, and Llama 3.1 405B Instruct. For the top performing models (GPT-4o and Claude 3.5 Sonnet), we further investigate performance across 4 agent scaffolds (structed bash, action-only, pseudoterminal, and web search). Without subtask guidance, agents leveraging Claude 3.5 Sonnet, GPT-4o, OpenAI o1-preview, and Claude 3 Opus successfully solved complete tasks that took human teams up to 11 minutes to solve. In comparison, the most difficult task took human teams 24 hours and 54 minutes to solve. All code and data are publicly available at https://cybench.github.io/.

Introducing paper: Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models
MLFF-distill data
Full name: Hessian Labels and Specialized Subdatasets for Distilling  ML Force Fields

Contains the datasets and distillation labels which were used in our paper  Amin, I., Raja, S., Krishnapriyan, A.S. (2024). Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians. Accepted to ICLR 2025. arXiv:2501.09009.

Introducing paper: Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
EMMA
Full name: An Enhanced MultiModal ReAsoning Benchmark

We introduce EMMA (Enhanced MultiModal reAsoning), a benchmark targeting organic multimodal reasoning across mathematics, physics, chemistry, and coding. EMMA tasks demand advanced cross-modal reasoning that cannot be solved by thinking separately in each modality, offering an enhanced test suite for MLLMs' reasoning capabilities.

EMMA is composed of 2,788 problems, of which 1,796 are newly constructed, across four domains. Within each subject, we further provide fine-grained labels for each question based on the specific skills it measures.
ADP Dataset

The ADP dataset consists of over 200,000 experimental crystal structures curated from the Cambridge Structural Database (CSD). It focuses on Anisotropic Displacement Parameters (ADPs), which describe atomic thermal vibrations within crystal lattices. ADPs provide insights into material properties such as thermal motion, heat capacity, vibrational entropy, and thermal expansion.

The dataset was curated with strict filtering criteria: it includes only non-polymeric crystal structures with 3D atomic coordinates and anisotropic thermal displacements for all non-hydrogen atoms. Structures with high disorder, missing temperature data, or poor-quality diffraction data (e.g., R-factor > 5%) were excluded. It covers a wide temperature range (2K to 573K), but most data points fall within the 100K–300K range, which is common in crystallographic experiments.

 It helps reduce computational costs compared to traditional Density Functional Theory (DFT) calculations, which are computationally expensive. The dataset’s diversity in elemental composition and thermal conditions makes it valuable for advancing machine learning models in crystallography and materials science.s

Introducing paper: A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects
Full name: PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects

PoTATO is a dataset designed to enhance the detection of floating plastic waste in aquatic environments by leveraging polarimetric imaging. It comprises 12,380 labeled images of plastic bottles, each enriched with detailed polarimetric information. The dataset aims to improve object detection accuracy under challenging outdoor lighting conditions and water surface reflections. By providing raw image data, PoTATO offers researchers the opportunity to explore novel approaches and advance state-of-the-art object detection algorithms. The dataset and associated code are publicly available

Introducing paper: PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects