Omkar2118/datasets huggingface dataset:
Cinescale
Full name: CineScale: A dataset of cinematic shot scale in movies

We provide a database containing shot scale annotations (i.e., the apparent distance of the camera from the subject of a filmed scene) for more than 792,000 image frames. Frames belong to 124 full movies from the entire filmographies by 6 important directors: Martin Scorsese, Jean-Luc Godard, Béla Tarr, Federico Fellini, Michelangelo Antonioni, and Ingmar Bergman. Each frame, extracted from videos at 1 frame per second, is annotated on the following scale categories: Extreme Close Up (ECU), Close Up (CU), Medium Close Up (MCU), Medium Shot (MS), Medium Long Shot (MLS), Long Shot (LS), Extreme Long Shot (ELS), Foreground Shot (FS), and Insert Shots (IS). Two independent coders annotated all frames from the 124 movies, whilst a third one checked their coding and made decisions in cases of disagreement. The CineScale database enables AI-driven interpretation of shot scale data and opens to a large set of research activities related to the automatic visual analysis of cinematic material, such as the automatic recognition of the director’s style, or the unfolding of the relationship between shot scale and the viewers’ emotional experience. To these purposes, we also provide the model and the code for building a Convolutional Neural Network (CNN) architecture for automated shot scale recognition. All this material is provided through the project website, where video frames can also be requested to authors, for research purposes under fair use.

Introducing paper: CineScale: A dataset of cinematic shot scale in movies
BANKEX
Full name: BANKEX

Contains stock market closing prices of ten financial institutions. Closing Price in Indian Rupee (INR). Daily samples retrieved between 12 July 2005 and 3 November 2017. All time series with 3 032 samples.

Introducing paper: An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series Forecasting
CIPM evaluation data
Full name: Evaluation data for  Continuous Integration of Performance Models (CIPM)

The main goal of the Continuous Integration of Performance Models (CIPM) is to enable an accurate architecture-based performance prediction at each point of the systems development life cycle. For this goal, the CIPM approach continuously updates the architecture-level performance models of a software system according to observed changes at development time and at operation time. 

The linked webpage provides data on experiments with CIPM published in several papers.
SST-5

The SST-5, also known as the Stanford Sentiment Treebank with 5 labels, is a dataset used for sentiment analysis. The SST-5 dataset consists of 11,855 single sentences extracted from movie reviews¹. It includes a total of 215,154 unique phrases from parse trees, each annotated by 3 human judges¹. Each phrase is labeled as either negative, somewhat negative, neutral, somewhat positive, or positive. This is why it's referred to as SST-5 or SST fine-grained.

Introducing paper: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank