Omkar2118/datasets huggingface dataset:
RKI and DIVI COVID-19 Data combined

This database consists of two main components; data on COVID-19 infections and data on the ICU occupancy of COVID-19 patients. The infections and the ICU occupancy are collected by the German health care departments, recorded by the Robert Koch Institute (2021) (RKI), the German federal government agency and scientific institute responsible for health reporting and disease control.

Introducing paper: The Skellam Distribution revisited -Estimating the unobserved incoming and outgoing ICU COVID-19 patients on a regional level in Germany
HighwayPavementCrackDetection

1. The image comes from the CCD camera of the highway measurement vehicle. 
2. Cracks and sealed cracks have been labeled. 
3. The form of labels is different from traditional block annotations, but uses redundant and dense annotation boxes. 
4. Some of the data is manually annotated, while others are model generated annotations that have undergone careful manual inspection.

Introducing paper: An Efficient Method for Detecting Asphalt Pavement Cracks and Sealed Cracks Based on a Deep Data-Driven Model
mOKB6

Multilingual Open Knowledge Base Completion benchmark in 6 languages: English, Hindi, Telugu, Spanish, Portuguese, and Chinese.

Introducing paper: mOKB6: A Multilingual Open Knowledge Base Completion Benchmark
Krapivin

A dataset for benchmarking keyphrase extraction and generation techniques from long document English scientific papers. The dataset has high quality and consists of 2,000 scientific papers from the Computer Science domain published by ACM. Each paper has its keyphrases assigned by the authors and verified by the reviewers. Different parts of papers, such as title and abstract, are separated, enabling extraction based on the part of an article's text. The content of each paper is converted from PDF to plain text. The pieces of formulae, tables, figures and LaTeX mark up were removed automatically. Link: https://huggingface.co/datasets/midas/krapivin
NUS

The dataset was constructed by first finding suitable publications and then collecting keyphrases from manual annotators. Google SOAP API was used to find documents using variants of the query “keywords general terms filetype:pdf”. Over 250 of these PDF documents were downloaded for further processing. Documents were then manually restricted to scientific conference papers, with a length range of 4-12 pages. The PDF documents were then converted to plain text using the PDF995 software suite (as it handled two-columned text better than other programs tried). At the end of this process, 211 documents in plain text format were selected which were converted successfully without problems. The authors then recruited student volunteers from our department to participate in manual keyphrase assignments. Each volunteer was given three PDF files (with author-assigned keyphrases hidden) to assign keyphrases to.