Omkar2118/datasets huggingface dataset:
Situation Puzzle

LLMs' lateral thinking capabilities remain under-explored and challenging to measure due to the complexity of assessing creative thought processes and the scarcity of relevant data. To address these challenges, we introduce **SPLAT**, a benchmark leveraging **S**ituation **P**uzzles to evaluate and elicit **LA**teral **T**hinking of LLMs. This benchmark, containing 975 graded situation puzzles across three difficulty levels.

Introducing paper: Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles
MultiOFF

Introudced from Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text
CIC IoMT dataset 2024

The **CICIoMT2024 dataset** is a comprehensive dataset designed for cybersecurity research focused on the Internet of Medical Things (IoMT). Developed by the Canadian Institute for Cybersecurity, it simulates realistic IoMT network traffic, representing the diverse and evolving threats faced by connected healthcare devices. The dataset comprises network traffic data from various IoMT devices, with labeled instances for 18 distinct types of cyberattacks, alongside benign traffic data. 

Each cyberattack type is meticulously crafted to reflect common and advanced threats, such as Distributed Denial of Service (DDoS), ransomware, man-in-the-middle attacks, and malware injections. With a balanced mix of attacks and benign instances, CICIoMT2024 enables robust training and testing for various cybersecurity models. 

The CICIoMT2024 dataset’s detailed packet-level information and multi-class labels make it ideal for developing and evaluating machine learning and deep learning algorithms. Its design emphasizes real-world application, making it a valuable resource for researchers aiming to enhance IoMT security, protect patient data, and ensure the resilience of medical networks against cyber threats.
CICIoMT2024 dataset

The **CICIoMT2024 dataset** is a comprehensive dataset designed for cybersecurity research focused on the Internet of Medical Things (IoMT). Developed by the Canadian Institute for Cybersecurity, it simulates realistic IoMT network traffic, representing the diverse and evolving threats faced by connected healthcare devices. The dataset comprises network traffic data from various IoMT devices, with labeled instances for 18 distinct types of cyberattacks, alongside benign traffic data. 

Each cyberattack type is meticulously crafted to reflect common and advanced threats, such as Distributed Denial of Service (DDoS), ransomware, man-in-the-middle attacks, and malware injections. With a balanced mix of attacks and benign instances, CICIoMT2024 enables robust training and testing for various cybersecurity models. 

The CICIoMT2024 dataset’s detailed packet-level information and multi-class labels make it ideal for developing and evaluating machine learning and deep learning algorithms. Its design emphasizes real-world application, making it a valuable resource for researchers aiming to enhance IoMT security, protect patient data, and ensure the resilience of medical networks against cyber threats.
replication archive
Full name: replication archive

County-level and municipal-level data on private election administration grant receipt, census data, and election outcomes all in tabular form

Introducing paper: Did Private Election Administration Funding Advantage Democrats in 2020?
LeukemiaAttri

The LeukemiaAttri dataset is a large-scale, multi-domain collection of microscopy images derived from leukemia patient samples, enriched with detailed morphological information. This dataset comprises a total of 28.9K images (2.4K × 2 × 3 × 2), which were captured using both low-cost and high-cost microscopes at three different resolutions: 10x, 40x, and 100x, utilizing various cameras.
In addition to providing location annotations for each white blood cell (WBC), the dataset includes comprehensive morphological attributes for every WBC, enhancing its utility for research and analysis in the field.



| Sr. # | Dataset Name  | Microscope    | Resolution | Camera         | Image Count | WBC Count | Artifacts Count |
|-------|---------------|---------------|------------|----------------|-------------|-----------|-----------------|
| 1     | H_100x_C1    | Olympus CX23  | 100x       | Honor 9X Lite  | 2.4k       | 7.8k      | 2.5k            |
| 2     | H_100x_C2    | Olympus CX23  | 100x       | HD1500T        | 2.4k       | 7.8k      | 2.5k            |
| 3     | H_40x_C1     | Olympus CX23  | 40x        | Honor 9X Lite  | 2.4k       | 7.8k      | 2.5k            |
| 4     | H_40x_C2     | Olympus CX23  | 40x        | HD1500T        | 2.4k       | 7.8k      | 2.5k            |
| 5     | H_10x_C1     | Olympus CX23  | 10x        | Honor 9X Lite  | 2.4k       | 7.8k      | 2.5k            |
| 6     | H_10x_C2     | Olympus CX23  | 10x        | HD1500T        | 2.4k       | 7.8k      | 2.5k            |
| 7     | L_100x_C1    | XSZ_107BN     | 100x       | Honor 9X Lite  | 2.4k       | 4.8k      | 1.1k            |
| 8     | L_100x_C2    | XSZ_107BN     | 100x       | ZZCAT-5MP      | 2.4k       | 4.8k      | 1.1k            |
| 9     | L_40x_C1     | XSZ_107BN     | 40x        | Honor 9X Lite  | 2.4k       | 7.8k      | 2.5k            |
| 10    | L_40x_C2     | XSZ_107BN     | 40x        | ZZCAT-5MP      | 2.4k       | 7.8k      | 2.5k            |
| 11    | L_10x_C1     | XSZ_107BN     | 10x        | Honor 9X Lite  | 2.4k       | 7.8k      | 2.5k            |
| 12    | L_10x_C2     | XSZ_107BN     | 10x        |  ZZCAT-5MP    | 2.4k       | 7.8k      | 2.5k            |
|       | **Total Count** |               |            |                | **29k**     | **88.2k**  | **27.8k**       |

Introducing paper: A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability