updated examples repo for directory consistency

csvParquet_crypto/README.md → CsvParquetCrypto/README.md

@@ -1,6 +1,6 @@
 # BTC and ETH sample data
 
-This folder contains sample data files for Bitcoin and Ethereum in CSV and Parquet format on different days.
+This folder contains sample data files for Bitcoin and Ethereum in CSV and Parquet format on different days taken from [CoinGecko](https://www.coingecko.com/).
 
 ## Table of contents
 

DeNiro/README.md

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 # Robert De Niro movie data
 
-This folder contains three files, each containing information about Robert De Niro's movies up until 2016.
+This folder contains three files, each containing information about [Robert De Niro's movies](https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html) up until 2016.
 
 ## Table of contents
 

fit/README.md → Fit/README.md

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 # Fit file as available from Strava
 
-Pull your own data from your Strava account. Visualize, manipulate, and combine your data with other sources, all while maintaining full ownership and control of the data in your own environment.
+Pull your own data from your [Strava](https://www.strava.com/) account. Visualize, manipulate, and combine your data with other sources, all while maintaining full ownership and control of the data in your own environment.
 
 ## Table of contents
 

gsod/README.md → GSOD/README.md

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 # Weather Observation Data
 
-This directory contains daily weather observations from 19 different locations around the world. The observations start in the 1940s and are continuous to the present day. In total, there are 57,330 observations.
+This directory contains [daily weather observations](https://catalog.data.gov/dataset/global-surface-summary-of-the-day-gsod) from 19 different locations around the world. The observations start in the 1940s and are continuous to the present day. In total, there are 57,330 observations.
 
 The samples come from the NOAA Global Surface Summary of the Day (GSOD) collection.
 

iris/README.md → Iris/README.md

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 # Iris Data
 
-This sample contains the observations about iris sepal and petal length from R. A. Fisher's classic 1936 paper, "The Use of Multiple Measurements in Taxonomic Problems". The paper describes categorizing plant varieties by using observable metrics of plants. The data is often used to demonstrate machine learning categorization algorithms.
+This sample contains the observations about [iris sepal and petal length](https://archive.ics.uci.edu/ml/datasets/iris) from R. A. Fisher's classic 1936 paper, "The Use of Multiple Measurements in Taxonomic Problems". The paper describes categorizing plant varieties by using observable metrics of plants. The data is often used to demonstrate machine learning categorization algorithms.
 
 There are 150 observations in the data set, each with a class identifying a species of iris flower upon which the observations were made.