Omkar2118/datasets huggingface dataset:
AMOS

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. Information can be found at https://amos22. grand-challenge. org.

Introducing paper: AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
MICCAI iSEG-2017

The MICCAI iSEG dataset was described in "https://iseg2017.web.unc.edu/how-to-cite/",  that has a total of 10 images, including T1-1 through T1-10, T2-1 through T2-10, and a
ground truth for the training set. And there are 13 images, T-11 through T-23, including T-11 through T-23 for test set.
Famous Keyword Twitter Replies

The **"Famous Keyword Twitter Replies Dataset"** is a comprehensive collection of Twitter data that focuses on popular keywords and their associated replies. This dataset contains five essential columns that provide valuable insights into the Twitter conversation dynamics:

1. **Keyword:** This column represents the specific keyword or topic of interest that generated the original tweet. It helps identify the context or subject matter around which the conversation revolves.

2. **Main_tweet:** The main_tweet column contains the original tweet related to the keyword. It serves as the starting point or focal point of the conversation and often provides essential information or opinions on the given topic.

3. **Main_likes:** This column provides the number of likes received by the main_tweet. Likes serve as a measure of engagement and indicate the level of popularity or resonance of the original tweet within the Twitter community.

4. **Reply:** The reply column consists of the replies or responses to the main_tweet. These replies may include comments, opinions, additional information, or discussions related to the keyword or the original tweet itself. The replies help capture the diverse perspectives and conversations that emerge in response to the main_tweet.

5. **Reply_likes:** This column records the number of likes received by each reply. Similar to the main_likes column, the reply_likes column measures the level of engagement and popularity of individual replies. It enables the identification of particularly noteworthy or well-received replies within the dataset.

By analyzing this "Famous Keyword Twitter Replies Dataset," researchers, analysts, and data scientists can gain valuable insights into how popular keywords spark discussions on Twitter and how these discussions evolve through replies. 

The dataset's information on likes allows for the evaluation of tweet and reply popularity, helping to identify influential or impactful content. 

This dataset serves as a valuable resource for various applications, including sentiment analysis, trend identification, opinion mining, and understanding social media dynamics.

&gt; Number of tweets for each pairs of tweet and reply

**Total has 17255 pairs of tweet/reply**
WeiboPolls

### Dataset Description

The dataset described in the provided text is focused on social media polls collected from Weibo, a popular Chinese microblogging platform. The dataset aims to empirically study social media polls and analyze user engagement patterns.

### Characteristics of the Dataset

- Size: The dataset consists of 20,252 polls collected from 1,860 users on Weibo.
- Data Collection: The polls were obtained by sampling Weibo posts containing polls and examining the posting history of the authors. The dataset also includes comments on each post.
- Sparsity: The dataset faces the challenge of sparse distribution of polls on Weibo, as less than 0.1% of the randomly gathered posts contained polls.
- Content: The dataset includes user-generated polls with questions, answer choices, and corresponding votes. The polls often incorporate trendy hashtags to attract user attention and cover various topics, including social events, public emergencies (such as the COVID-19 outbreak), entertainment topics (celebrities, TV shows), and more.

### Motivations and Summary

The motivation behind collecting this dataset is to explore social media polls on Weibo and analyze user engagement patterns. The study aims to understand how users interact with polls, the influence of polls on user engagement, and the types of topics that are more likely to contain polls.

The dataset provides insights into user behavior on Weibo by examining factors such as the length of posts, comments, questions, and answers. It also highlights the preference for voting over commenting as a means of expressing opinions. The dataset's analysis suggests that posts with polls tend to attract more comments, likes, and reposts compared to posts without polls.

### Potential Use Cases

This dataset can be useful for various research and practical applications, such as:

- Social Media Analysis: Researchers can analyze the characteristics and dynamics of social media polls, understanding how they influence user engagement and the types of topics that attract poll creation.
- User Engagement Studies: The dataset allows for the exploration of user behavior and preferences when it comes to interacting with polls, providing insights into the factors that drive user engagement on social media platforms.
- Trend Analysis: By examining the hashtags associated with polls, the dataset can contribute to understanding social events, public emergencies, and entertainment trends on Weibo.
- Marketing and Advertising: The dataset can assist marketers and advertisers in understanding user preferences and interests, enabling them to create targeted campaigns based on the popular topics identified in the dataset.
Please note that the actual contents and specific applications of the dataset may vary based on further analysis and research conducted by users.

Introducing paper: Engage the Public: Poll Question Generation for Social Media Posts
PTCGA200
Full name: Patch TCGA in 200 microns by 512 px

**PTCGA200** is a public pathological H&E image datasets from Patch TCGA in 200 microns by 512 px.

Introducing paper: Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks
PCam200
Full name: Patch Camelyon in 200 microns by 512 px

**PCam200** is a public pathological H&E image dataset from Patch Camelyon in 200 microns by 512 px made in the same manner from Camelyon2016 challenge dataset.

Introducing paper: Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks
SegPANDA200
Full name: Segmentation task on PANDA challenge in 200 microns by 512px

**SegPANDA200** is a public pathological H&E image dataset from segmentation task on PANDA challenge in 200 microns by 512px made in the same manner from PANDA challenge dataset .

Introducing paper: Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarks
Immobilized fluorescently stained zebrafish through the eXtended Field of view Light Field Microscope 2D-3D dataset

This dataset comprises three immobilized fluorescently stained zebrafish imaged through the eXtended Field of view Light Field Microscope (XLFM, also known as Fourier Light Field Microscope). The images were preprocessed with the SLNet, which extracts the sparse signals from the images (a.k.a. the neural activity).

If you intend to use this with Pytorch, you can find a data loader and working source code to load and train networks here.

This dataset is part of the publication: Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows.

Introducing paper: Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows
Top-N Recommendation Runs

We ran 21 recommender systems on three datasets (BeerAdvocate, LibraryThing and MovieLens 1M). The output of these recommenders was evaluated using rec_eval tool. We also measured statistically significant improvements using permutation test. The output of both tools can be found in data.
TREC Ad Hoc Retrieval Runs (2020)

TREC Submissions for all Ad Hoc Retrieval runs. 

core (2017-2018)
deep-docs (2019-2020)
deep-pass (2019-2020)
web (2009-2014)
robust (2004)
ChinaOpen-1k

ChinaOpen is a new video dataset targeted at open-world multimodal learning, with raw data gathered from Bilibili, a popular Chinese video-sharing website. The dataset has a large webly annotated training set of videos (associated with user-generated titles and tags) and a smaller manually annotated test set of videos (with manually checked user titles / tags, manually written captions, and manual labels describing what visual objects / actions / scenes shown in the visual content).

Source: [https://arxiv.org/abs/2305.05880](https://arxiv.org/abs/2305.05880)