Omkar2118/datasets huggingface dataset:
Clinical Admission Notes from MIMIC-III

This dataset is created from **MIMIC-III** ([Medical Information Mart for Intensive Care III](https://paperswithcode.com/dataset/mimic-iii)) and contains simulated patient admission notes. The clinical notes contain information about a patient at **admission time** to the ICU and are labelled for four outcome prediction tasks: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay.

To obtain the data one first has to [gain access](https://mimic.physionet.org/gettingstarted/access/) to the MIMIC-III dataset and then run the scripts introduced in the linked repository.

Introducing paper: Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
IBC
Full name: Individual Brain Charting

The Individual Brain Charting (IBC) project aims at providing a new generation of functional-brain atlases. To map cognitive mechanisms in a fine scale, task-fMRI data at high-spatial-resolution are being acquired on a fixed cohort of 12 participants, while performing many different tasks. These data—free from both inter-subject and inter-site variability—are publicly available as means to support the investigation of functional segregation and connectivity as well as individual variability with a view to establishing a better link between brain systems and behavior.

** What’s special about the IBC dataset?**

* Taskwise dataset: spanning the cognitive spectrum within subject
* Fixed cohort over a 10-year span to minimize inter-subject variability
* Fixed experimental setting to minimize inter-site variability
* Multimodal: fMRI (task-based and resting state), DWI, structural

** Main characteristics **

* 12 healthy participants (aged 27-40 at the time of recruitment)
* Spatial resolution: 1.5mm (isotropic); Temporal resolution: 2s
* Scanner: Siemens 3T Magnetom Prisma; Coil: 64-channel
* 50 acquisitions per participant upon completion of the dataset in 2022

Introducing paper: An empirical evaluation of functional alignment using inter-subject decoding
MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge

Under Institutional Review Board (IRB) supervision, 50 abdomen CT scans of were randomly selected from a combination of an ongoing colorectal cancer chemotherapy trial, and a retrospective ventral hernia study. The 50 scans were captured during portal venous contrast phase with variable volume sizes (512 x 512 x 85 - 512 x 512 x 198) and field of views (approx. 280 x 280 x 280 mm3 - 500 x 500 x 650 mm3). The in-plane resolution varies from 0.54 x 0.54 mm2 to 0.98 x 0.98 mm2, while the slice thickness ranges from 2.5 mm to 5.0 mm. The standard registration data was generated by NiftyReg.

Source: [MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge](https://www.synapse.org/#!Synapse:syn3193805/wiki/217789)

Image source: [MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge](https://www.synapse.org/#!Synapse:syn3193805/wiki/217789)
Alchemy

The DeepMind Alchemy environment is a meta-reinforcement learning benchmark that presents tasks sampled from a task distribution with deep underlying structure. It was created to test for the ability of agents to reason and plan via latent state inference, as well as useful exploration and experimentation. 

Alchemy is a single-player video game, implemented in Unity. The player sees a first-person view of a table with a number of objects on it, including a set of colored stones, a set of dishes containing colored potions, and a central cauldron. Stones have different point values, and points are collected when stones are added to the cauldron. By dipping stones into the potions, the player can transform the stones’ appearance, and thus their value, increasing the number of points that can be won.

Source: [dm_alchemy: DeepMind Alchemy environment](https://github.com/deepmind/dm_alchemy)

Image Source: [dm_alchemy: DeepMind Alchemy environment](https://github.com/deepmind/dm_alchemy)

Introducing paper: Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
Biase et al

Source: [Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing](https://pubmed.ncbi.nlm.nih.gov/25096407/)
Goolam et al

Source: [Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4819611/)
Yan et al

Source: [Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells](https://pubmed.ncbi.nlm.nih.gov/23934149/)
Deng et al

Source: [Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells](https://pubmed.ncbi.nlm.nih.gov/24408435/)
Pollen et al

TPM values together with cell type annotations that were obtained from Alex Pollen on 15/10/15

Source: [Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex](https://www.nature.com/articles/nbt.2967)
Treutlein et al

Source: [Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq](https://pubmed.ncbi.nlm.nih.gov/24739965/)
Synthetic Rain Datasets

The Synthetic Rain Datasets consists of 13,712 clean-rain image pairs gathered from multiple datasets (Rain14000, Rain1800, Rain800, Rain12). With a single trained model, evaluation could be performed on various test sets, including Rain100H, Rain100L, Test100, Test2800, and Test1200.

PSNR and SSIM are computed on Y-channel in YCbCr color space.

Introducing paper: Multi-Scale Progressive Fusion Network for Single Image Deraining
The Contextual TV Dataset
Full name: CTV

Using the Experience-Sampling Method (ESM), participants are asked to report TV consumption multiple times each day for a five week period. Through self-reported data, authors decrease uncertainty of exposure to content, and allow collection of non-trivial information, such as how much attention is paid to the TV. The data is structured to accommodate quantitative analyses, e.g. in the CARS community, and is publicly available under the name **Contextual TV (CTV)** dataset.

Source: [Kristoffersen et al.](https://arxiv.org/pdf/1808.00337v2.pdf)

Image source: [Kristoffersen et al.](https://arxiv.org/pdf/1808.00337v2.pdf)

Introducing paper: The Importance of Context When Recommending TV Content: Dataset and Algorithms
twitter politicians data

Dataset based on Twitter usernames of American politicians. Data extracted from Wikidata.

The same politician can appear several times: if he has different pseudonyms on Twitter or Instagram, if he has been in several parties, or if several Twitter account IDs are associated with him. But the data is sorted in ascending order by name, so it is visible

Introducing paper: SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset
3D Platelet EM
Full name: Platelet Electron Microscopy

The platelet-em dataset contains two 3D scanning electron microscope (EM) images of human platelets, as well as instance and semantic segmentations of those two image volumes.
This data has been reviewed by NIBIB, contains no PII or PHI, and is cleared for public release. All files use a multipage uint16 TIF format. A 3D image with size [Z, X, Y] is saved as Z pages of size [X, Y]. Image voxels are approximately 40x10x10 nm

Introducing paper: Dense cellular segmentation for EM using 2D–3D neural network ensembles
Sintel 4D LFV
Full name: Sintel 4D Light Field Video Dataset

A medium-scale synthetic 4D Light Field video dataset for depth (disparity) estimation. From the open-source movie Sintel. The dataset consists of 24 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20–50 frames, and has ground-truth disparity values, so that can be used for training deep learning-based methods. Each scene was rendered with a clean pass after modifying the production file of Sintel with reference to the MPI Sintel dataset.

Introducing paper: Depth estimation from 4D light field videos