Omkar2118/datasets huggingface dataset:
TOMG-Bench
Full name: Text-based Open Molecule Generation Benchmark

In this paper, we propose Text-based Open Molecule Generation Benchmark (TOMG-Bench), the first benchmark to evaluate the open-domain molecule generation capability of LLMs. TOMG-Bench encompasses a dataset of three major tasks: molecule editing (MolEdit), molecule optimization (MolOpt), and customized molecule generation (MolCustom). Each task further contains three subtasks, with each subtask comprising 5,000 test samples. Given the inherent complexity of open molecule generation, we have also developed an automated evaluation system that helps measure both the quality and the accuracy of the generated molecules. Our comprehensive benchmarking of 25 LLMs reveals the current limitations and potential areas for improvement in text-guided molecule discovery. Furthermore, with the assistance of OpenMolIns, a specialized instruction tuning dataset proposed for solving challenges raised by TOMG-Bench, Llama3.1-8B could outperform all the open-source general LLMs, even surpassing GPT-3.5-turbo by 46.5\% on TOMG-Bench.

Introducing paper: TOMG-Bench: Evaluating LLMs on Text-based Open Molecule Generation
Amazon Sports
Full name: Amazon Sports 5-core

Amazon Sports (Amazon Sports 5-core)

Introducing paper: Image-based Recommendations on Styles and Substitutes
Amazon Clothing
Full name: Amazon Clothing 5-core

Amazon Clothing (Amazon Clothing 5-core)

Introducing paper: Image-based Recommendations on Styles and Substitutes
CTU Relational
Full name: The CTU Prague Relational Learning Repository

The CTU Relational Learning Repository offers relational database datasets to the machine learning community. It currently hosts 148 SQL databases on a public MySQL server. A searchable meta-database provides key metadata, such as the number of tables, rows, columns, and self-relationships within each database.

Introducing paper: The CTU Prague Relational Learning Repository
Reflectance_Refractive index_Simple

featureinput.npy 
Reflectance of 5 structures. Each row is 5 reflectance of a target material in 5 structures. Each row has 4000 values, representing 5 pairs of 800 values. For example, in the first 800 values, the first 400 is reflectance measured with target material on structure n, and the second 400 is reflectance measured without target material on structure n. 

featureoutput.npy
Refractive index of materials. Each row is a material. Each row has 800 values; the first 400 is n, and the second 400 is k. The wavelength range is 400-800nm.

Introducing paper: Measuring complex refractive index through deeplearning-enabled optical reflectometry
MS-HAB-Demonstrations
Full name: ManiSkill-HAB Demonstration Datasets

Whole-body, low-level control/manipulation demonstration dataset for ManiSkill-HAB. Demonstrations are organized by task-subtask-object. All demos use RGBD (128x128) and state. JSON files store metadata (tincluding even labels and success/failure mode), while HDF5 files store demonstration data.

Introducing paper: ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks