Omkar2118/datasets huggingface dataset:
SAIL 2017
Full name: Sentiment Analysis for Indian Languages

India is a linguistic area with one of the longest histories of contact, influence, use, teaching and learning of English-in-diaspora in the world (Kachru and Nelson, 2006). Thus, a huge number of Indians active on the internet are able in English communication to some degree. India also enjoys huge diversity in language. Apart from Hindi, it has several regional languages that are the primary tongue of people native to the region. This is to the extent that social media including Facebook, WhatsApp, Twitter, etc. contain more than one language, and such phenomena are called code-mixing and code-switching. On the other side, the evolution of sentiments from such social media texts have also created many new opportunities for information access and language technology, but also many new challenges, making it one of the prime present-day research areas. Sentiment analysis in code-mixed data has several real-life applications in opinion mining from social media campaign to feedback analysis.

Linguistic processing of such social media dataset and its sentiment analysis is a difficult task. Till date, most of the experiments have been performed on identifying the languages (Bali et al., 2014; Das and Gamback, 2014), parts-of-speech tagging (Ghosh et al., 2016), etc. Few tasks also have been started on the sentiment analysis of code-mixed data such as Hindi-English (Joshi et al., 2016). Therefore, we believe that it is the best place to bring more research attention towards developing language technologies for identifying sentiments from Indian social media texts.

Main goal of this task is to identify the sentence level sentiment polarity of the code-mixed dataset of Indian languages pairs (Hi-En, Ben-Hi-En) collected from Twitter, Facebook, and WhatsApp. Each of the sentences is annotated with language information as well as polarity at the sentence level. The participants will be provided development, training and test dataset.
Kepler Exoplanet Search Results
Full name: Kepler Exoplanet Search Results

Context
The Kepler Space Observatory is a NASA-build satellite that was launched in 2009. The telescope is dedicated to searching for exoplanets in star systems besides our own, with the ultimate goal of possibly finding other habitable planets besides our own. The original mission ended in 2013 due to mechanical failures, but the telescope has nevertheless been functional since 2014 on a "K2" extended mission.

Kepler had verified 1284 new exoplanets as of May 2016. As of October 2017 there are over 3000 confirmed exoplanets total (using all detection methods, including ground-based ones). The telescope is still active and continues to collect new data on its extended mission.

Content
This dataset is a cumulative record of all observed Kepler "objects of interest" — basically, all of the approximately 10,000 exoplanet candidates Kepler has taken observations on.

This dataset has an extensive data dictionary, which can be accessed here. Highlightable columns of note are:

kepoi_name: A KOI is a target identified by the Kepler Project that displays at least one transit-like sequence within Kepler time-series photometry that appears to be of astrophysical origin and initially consistent with a planetary transit hypothesis
kepler_name: [These names] are intended to clearly indicate a class of objects that have been confirmed or validated as planets—a step up from the planet candidate designation.
koi_disposition: The disposition in the literature towards this exoplanet candidate. One of CANDIDATE, FALSE POSITIVE, NOT DISPOSITIONED or CONFIRMED.
koi_pdisposition: The disposition Kepler data analysis has towards this exoplanet candidate. One of FALSE POSITIVE, NOT DISPOSITIONED, and CANDIDATE.
koi_score: A value between 0 and 1 that indicates the confidence in the KOI disposition. For CANDIDATEs, a higher value indicates more confidence in its disposition, while for FALSE POSITIVEs, a higher value indicates less confidence in that disposition.
Acknowledgements
This dataset was published as-is by NASA. You can access the original table here. More data from the Kepler mission is available from the same source here.

Inspiration
How often are exoplanets confirmed in the existing literature disconfirmed by measurements from Kepler? How about the other way round?
What general characteristics about exoplanets (that we can find) can you derive from this dataset?
What exoplanets get assigned names in the literature? What is the distribution of confidence scores?