Is there any mapping between the two?
There exist miscellaneous services, e. g. this one.
However, I guess you are looking for a whole dataset, according to the scope of this site.
On this page, I have found the following links:
2002 NAICS to 1987 SIC
1987 SIC to 2002 NAICS
The page also contains mappings between different versions of NAICS. Thus, one ...
Not sure what you're trying to do so this could be totally out there, but what about finance data like stocks?
Objects m are unique company stock symbols like GOOG
Classifications are the stock price trend at every sequential data point, positive or negative, represented as a boolean 1 or 0.
Observations n are the trends at discrete points in time
Not sure ...
Check out enigma.io's 'Public Data Explorer' and search for business license permits. This will result in a long list of locations that you can the use the service's filtering feature to hone in on a small enough area to provide a dataset that is manageable and not massive in size. After doing that, you'll have the address data of license permit issuances ...
Code for America runs the NAICS (North American Industry Classification System) API; NAICS is used by Canada, Mexico and the US to classify businesses under differenty industries.
Apiary runs their own NAICS API as well.
There is a cited reference paper in the link you provide
Campos, M.M., Milenova, B.L.
"O-Cluster: Scalable Clustering of Large High Dimensional Data Sets"
Unfortunately, the paper is behind a paywall and the link from Oracle goes to linkrot. I checked a few dates on the wayback machine and they also are no good.
Perhaps of interest, this algorithm is also ...
Yes. Check out http://xpresso.abzooba.com/XpressoOnWeb/.
They have built up so many domains already (health care - armamentarium) is one of them. They have fixed aspects (usually 5-6) for every domain. and the engine does aspect based sentiment analysis. Try out a few sample sentences in that link.
Note: You'll have to create an account, but it is open ...
I would do a google search "road signs India". I found this Wikipedia article had some clear pictures with descriptions in English. If you want Hindi or other language translation, I suspect a google image search will help.
How about bike share data?
https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset (currently the UCI website is down for me - 4/9/17)
Each station (m) has multiple observations about how many bikes are checked out, and it has features like temperature, humidity, season, whether it's a holiday, etc.
The Internet Archive's Wayback Machine has indexed the text of Nazi and white supremacist message boards, such as Stormfront and VNN. You can download the WARC's (snapshot compilations of the websites, with all their pages and assets) from those sites and then scrape the text.
Two standards are big in the USA, but there may be many others
North American Industry Classification System (NAICS)
Standard Industrial Classification (SIC) (older US standard)
You can read about these two standards here.
Also, internationally there is the International Standard Industrial Classification of All Economic Activities (ISIC) a production by ...
The UN Global Code 001 and the Global Name World are being used as delimiters for a taxonomy; it does seem very redundant, as well as implied, however there probably is a good reason for it.
Toggling the Geographic Regions closed is what made this makes sense to me:
Geographic Regions Toggled Open:
Geographic Regions Toggled Closed:
1B rows is easy to reach. 10K labels can be achieved with concatenations.
For example, take Wikipedia for 100 languages, and split it into rows - by sentence, by n-gram
You now have enough rows, but still only 100 labels.
Then run some lib over each, and concatenate the output to the labels, yielding a new set of labels.
The lib could be a parsing lib ...
Voter rolls would be a fantastic place to look, the North Carolina State Board of Elections & Ethics Enforcement provides this data:
Voter Rolls Download
Voter Data Format
The North Carolina database includes race, ethnicity, and gender. It would be a good place for your classifier to train.
You could start with a benchmark using the MNIST dataset. There are many tutorials and articles written on training a CNN for digit recognition. You might also want to consider preprocessing your color-blindness charts input data (e.g. converting to grayscale, enhancing digit contrast).
A simple answer:
As stated in their last new from the 2017 challenge webpage:
Jul 26, 2017: We are passing the baton to Kaggle. From now on, all
three challenges(LOC-CLS, DET, VID) will be hosted on Kaggle!
it's now hosted on kaggle directly:
I found it here: https://www.dnb.com/utility-pages/dnb-demographic-firmographic-code-tables.html:
There are 2,4,6,8-digit SIC, Local Activity Type, and NAICS.
Here is a link to a dataset of businesses from Paducah, KY, which include a SIC designation.
I'm not exactly sure what's going on with these industry codes. I looked into it briefly and since it's a six-digit code I'm wondering if it's not reflecting the NAICS 2017 revision or some other weird industry code. I didn't name the columns so I'm not sure what ...
Berkeley DeepDrive currently hosts the world's largest self-driving dataset, with 100,000 video sequences.
However, you should bear in mind that if you wish to download all of the data (including the videos), then the total file size will be over 1000 GB. If you are just looking to download the Info part of the dataset, then that will be just under 4 GB.
We are currently developing Veleda, an open source, open data platform for hydroponics. We have a live data view of the state in our greenhouses. We develop the software and hardware (ESP8266) behind it and are in the process of extending it with additional parameters.
Parameters for which we are building low-cost sensors range from air and water ...
There is a data set about credit card fraud on Kaggle.
The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all ...
Yelp holds a competition to find trends in its data. According to the website it contains 4.1 million reviews. Reviews include text and a rating (and other values).
You can find the dataset (and more information) here: https://www.yelp.com/dataset_challenge
The fields in each review object:
It..depends. Depends on who you ask. NAICS is good. TBRC (Thomson Reuters Business Classification) will offer a somewhat different view of what company is in what industry (and, to get more meta, what is an industry? What is a sector? What delineation do we want? etc).
If you're only after publicly traded companies, then stock exchange APIs would be an easy ...
I have the feeling that you'll be better off getting the company's industry directly (preferably classified). Given the region, I can point you to the Unigraph's API, here is a query returning the sic code for Microsoft. Data is available on publicly traded companies as reported to SEC Edgar.
As @jan-doggen correctly pointed out it depends on the ...
Have a look at this website, it contains various databases used for machine learning algorithms: https://archive.ics.uci.edu/ml/
Also, there's a data analysis competition platform with a lot of datasets: https://www.kaggle.com/
QB4OLAP - Business Intelligence over Linked Data.
FIBO - Financial Industry Business Ontology
XBRL - eXtensible Business Reporting Language
Always helpful to peruse LOV - Linked Open Vocabularies.