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# System prepended metadata

title: Dataset Collection

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# Dataset Collection

I have found a very interesting report:

https://ourworldindata.org/ukraine-russia-food

Good for our reference.



## Film

### Netflix Original Films & IMDB Scores

https://www.kaggle.com/datasets/luiscorter/netflix-original-films-imdb-scores

* title
* Genre
* date
* runtime
* score
* language

Insights:

1. Which 3 languages are the most used language except English
2. which 3 languages have the highest mean score
3. which 3 genres have the highest mean score
4. which year has the largest number of films released



### Imdb Most popular Films and series

https://www.kaggle.com/datasets/mazenramadan/imdb-most-popular-films-and-series

* name
* date
* rate
* votes
* genre
* duration
* type
* certificate
* episode
* nudity



### The Oscar Award 1927 - 2020

https://www.kaggle.com/datasets/unanimad/the-oscar-award

* File_screened_year
* year_ceremony
* ceremony
* category
* name(nomination)
* winner
* film's title



## Covid

### COVID-19 World Vaccination Progress

https://www.kaggle.com/datasets/gpreda/covid-world-vaccination-progress

* country
* country ISO Code
* Date
* Total number of vaccinations
* Total number of people vaccinated
* total number of people fully vaccinated
* daily vaccinations
* total vaccinations per hundred
* Number of vaccinations per day
* Vaccines used in the country

Question:

1. Which country is using what vaccine?
2. In which country the vaccination programme is more advanced?
3. Where are vaccinated more people per day? But in terms of percent from entire population ?
4. which country has the lowest rate of vaccination



### COVID-19 Deaths Dataset

https://www.kaggle.com/datasets/dhruvildave/covid19-deaths-dataset?select=all_weekly_excess_deaths.csv

* country
* region
* start_date
* end_date
* year
* week_no
* population
* total_deaths

Question:

1. Does the vaccination impact the rate of deaths?
2. In which country, the whole death rate is the highest?



## NFT

dataset: https://www.kaggle.com/datasets/simiotic/ethereum-nfts

~2GB compressed sqlite file

terminologies:

* NFT: https://en.wikipedia.org/wiki/Non-fungible_token
* NFT Collection: NFT address (e.g. https://opensea.io/collection/the-doge-pound)
* NFT Item: A specific item in NFT collection; identified by [nft_address + token_id] (e.g. https://opensea.io/assets/0xf4ee95274741437636e748ddac70818b4ed7d043/1828)
* Owner address: ETH account address

tables:

* nfts
    * the address of collections
* transfers
    * [nft_address + token_id] identifies item
    * from addr, to addr
* current_market_values
    * [nft_address + token_id] identifies item
    * market_value shows the value
* current_owners
    * [nft_address + token_id] identifies item
    * owner shows the owner address

Tasks:

1. What's the most valuable NFT collection
    * Each NFT collection total value
2. What's the most valuable NFT item
3. Which owner address is the richest
4. Which owner address owns the most items
5. Which NFT collection trade the most frequently

## Shakespeare plays

https://www.kaggle.com/datasets/kingburrito666/shakespeare-plays

* play name
* character (speaker)
* line

Tasks:

1. Who has the most lines
2. Which play has the most lines
3. Sentiment analysis for each play (positive line / total line count, etc)
4. Sentiment analysis for each character
5. Stopword / total word count of each play
