For automated processing, by far the best alternative is a database dump. They might be a little bit old, but hopefully that won't be a problem.
You can select database dumps by language and wiki, with or without edit history.
At the time of writing, the most recent enwikisource dump is from 26 December 2015. You can also find dumps for dewikisource, ...
This resource has a link to a bunch of online and off line databases for handwritten letters.
This offline database seems to be a big one, hosted in Japan by Electrotechnical Laboratory has 1.2 million samples from Chinese, Japanese and other languages.
Nakagawa Labs has another online database, but it's not openly available.
Project Gutenberg offers 57.000 free books, available in different formats. Among them, utf-8 encoded plain text with minimal formatting.
The NLTK comes with access to a range of corpora. Among them, a selection from Project Gutenberg, and a chat corpus (if you are looking for more colloquial use of English). Beware of the varying licenses that apply.
US City Open Data Census results for Service Requests should have what you need. And there's always Open 311, though I'm pretty sure its implementers will overlap with the census results.
There is also Open Referrals, an initiative developing common standards and open platforms for the sharing of community resource directory data — i.e., information about ...
Some Corpora in the NLTK have a method sents() that returns each sentence as a list of words, for example the Brown corpus:
nltk.download() # this opens a GUI to download all corpora needed
from nltk.corpus import brown
sentences = brown.sents()
OK, this one is a stretch... but instead of comparing text with errors to the corrected text, you can compare original text against incorrect transcriptions.
Reuters Transcribed Subset and data set
This data was created by selecting 20 files each from the 10 largest classes in the Reuters-21578 collection. The files were read out by 3 Indian speakers and ...
If you are just interested in two dozen text I think it is easiest just to request for the raw wikitext, for instance for the 'The Metal Pig' page:
This query returns the raw wikitext, i.e., with unexpanded templates and category links.
There is a blog that lists political blogs based in the US:
Categories (count of listed blogs):
Non/Bi-Partisan or Independent/Moderate (11)
Region Specific (1)
Issue Specific (2)
The list is originally from 2005, but many ...
Probably not formatted much better than what you've already found (I haven't checked the HTML), but UC-Santa Barbara's American Presidency Project has them as well: http://www.presidency.ucsb.edu/inaugurals.php
There are 1,108,558 English Wikipedia articles that have location information. Location information is specified by Template:Coord in Wikipedia and can be easily extracted from the text.
I guess this might be the largest free dataset that you can get with your requirements.
I think The British National Corpus could do the job. It's a large and free corpus labeled at document, sentence and paragraph level (but also word level, so you'll have a lot of useless xml tags to clean).
It's a big file. The best, for testing, is probably to download the Baby edition (less than 200 Mb once uncompressed). As you can see in the sample ...
Here is a general list of data repositories:
Towards Data Science Repository
15 Best Chatbot Datasets for Machine Learning
Artificial Intelligence Wiki - Open Data Sets
Reddit - Machine Learning - Datasets of One to One Conversations
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
Searching for "clinical science reports" (CSR) on Google returns various websites from pharmaceutical companies, which publish summary reports. Maybe these are enough for your research.
As this article points out only recently did the European Medicines Agency start publishing CSRs, the Food and Drug administration currently does not.
You can also contact ...
You can also use Text8 corpus of Wikipedia text as mentioned in the research paper Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics. I recently worked on a research work and have the dataset you are looking. However you need to wait till 30th March 2017 to have hands on this dataset.