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I'm not aware of any such existing datasets, but you can make your own layer in either QGIS or Arc. Search for "create fishnets" in GIS Stack Exchange. BTW, a worldwide layer at your granularity will be immense, approaching 4 billion(!) polygons, which probably explains why you didn't find one. You may want to create such a layer at a regional scale ...


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Railroads and the Making of Modern America has links to the following downloadable historical US railroad data. I didn't investigate the actual geospatial extent of these railroad datasets, so I can't promise they have Colorado railroads. Here's a wayback machine link in case the original site goes down. Download Data Historical GIS: Depots of the ...


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iNaturalist has worldwide observations of biological organisms. You can do a bulk download here. This data is not strictly "open", because login is required. However it is free to register. Use this query to download observations of Suborder Anisoptera (Dragonflies): quality_grade=any&identifications=any&taxon_id=47927 There are currently 357100 ...


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You can create your own from these lists: List of business and finance abbreviations on Wikipedia Business Abbreviations on All Acronyms Business Industry Search on fullformgo


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The links may help. The first is to the Douban Book Rating Dataset. It is the largest book review website in China. The second is a collection of Amazon ratings the was web scraped already by a user on Kaggle. Those are all I know of. Hope they help. http://opendata.pku.edu.cn/dataset.xhtml?persistentId=doi:10.18170/DVN/LA9GRH https://www.kaggle.com/...


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If you look at the XML for that podcast you'll see that it is structured like this channel stream description item item description item item description … where the items are the individual episodes and the item description includes a whole lot of tags containing metadata about each episode. One of the metadata is the description tag which ...


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I have posted a collection of several text datasets related to news here. They contain 50+ mb of text each in an easily parsable format. https://www.kaggle.com/therohk/datasets


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Check out the ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction: https://rrc.cvc.uab.es/?ch=13 You have to make a free account and then you can access the dataset, approximately 1000 images of scanned receipts.


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