A database of S. cerevisiae H/ACA and C/D box snoRNAs, useful for research on rRNA nucleotide modifications in the ribosome, especially those created by small nucleolar RNA:protein complexes (snoRNPs). The interactive service enables a user to visualize the positions of pseudouridines, 2'-O-methylations, and base methylations in three-dimensional space in the ribosome and also in linear and secondary structure formats of ribosomal RNA. The tools provide additional perspective on where the modifications occur relative to functional regions within the rRNA and relative to other nearby modifications. This package of tools is presented as a major enhancement of an existing but significantly upgraded yeast snoRNA database. The other key features of the enhanced database include details of the base pairing of snoRNAs with target RNAs, genomic organization of the yeast snoRNA genes, and information on corresponding snoRNAs and modifications in other model organisms.

Database of maps showing the sites of modified rRNA nucleotides. Access to the rRNA sequences, secondary structures both with modification sites indicated, 3D modification maps and the supporting tables of equivalent nucleotides for rRNA from model organisms including yeast, arabidopsis, e. coli and human is provided. This database complements the Yeast snoRNA Database at UMass-Amherst and relies on linking to some content from that database, as well as to others by colleagues in related fields. Therefore, please be very cognizant as to the source when citing information obtained herein. Locations of modified rRNA nucleotides within the 3D structure of the ribosome.

US Adult Census data relating income to social factors such as Age, Education, race etc.

The Us Adult income dataset was extracted by Barry Becker from the 1994 US Census Database. The data set consists of anonymous information such as occupation, age, native country, race, capital gain, capital loss, education, work class and more. Each row is labelled as either having a salary greater than ">50K" or "<=50K".

This Data set is split into two CSV files, named adult-training.txt and adult-test.txt.

The goal here is to train a binary classifier on the training dataset to predict the column income_bracket which has two possible values ">50K" and "<=50K" and evaluate the accuracy of the classifier with the test dataset.

Note that the dataset is made up of categorical and continuous features. It also contains missing values The categorical columns are: workclass, education, marital_status, occupation, relationship, race, gender, native_country

The continuous columns are: age, education_num, capital_gain, capital_loss, hours_per_week

This Dataset was obtained from the UCI repository, it can be found on

https://archive.ics.uci.edu/ml/datasets/census+income, http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/

USAGE This dataset is well suited to developing and testing wide linear classifiers, deep neutral network classifiers and a combination of both. For more info on Combined Deep and Wide Model classifiers, refer to the Research Paper by Google https://arxiv.org/abs/1606.07792

Refer to this kernel for sample usage : https://www.kaggle.com/johnolafenwa/wage-prediction

Complete Tutorial is available from http://johnolafenwa.blogspot.com.ng/2017/07/machine-learning-tutorial-1-wage.html?m=1

Context Labeled Faces in the Wild (LFW) is a database of face photographs designed for studying the problem of unconstrained face recognition. This database was created and maintained by researchers at the University of Massachusetts, Amherst (specific references are in Acknowledgments section). 13,233 images of 5,749 people were detected and centered by the Viola Jones face detector and collected from the web. 1,680 of the people pictured have two or more distinct photos in the dataset. The original database contains four different sets of LFW images and also three different types of "aligned" images. According to the researchers, deep-funneled images produced superior results for most face verification algorithms compared to the other image types. Hence, the dataset uploaded here is the deep-funneled version.

Content There are 11 files in this dataset. lfw-deepfunneled.zip is the file containing the images. All other 10 files are relevant metadata that may help you in forming your training and testing sets for your model. There are two sections below to help you navigate the files better. The first section provides information specifically pertaining to the images. The second section explains the content of each metadata file.

Image information:

• Image file format: Each image is available as "lfw/name/name_xxxx.jpg" where "xxxx" is the image number padded to four characters with leading zeroes. For example, the 10th George_W_Bush image can be found as "lfw/George_W_Bush/George_W_Bush_0010.jpg"
• Image dimensions: Each image is a 250x250 jpg, detected and centered using the openCV implementation of Viola-Jones face detector. The cropping region returned by the detector was then automatically enlarged by a factor of 2.2 in each dimension to capture more of the head and then scaled to a uniform size.

Metadata information:

• lfwallnames.csv: Contains all names of each face in the dataset along with number of images each face has.
• lfwreadme.csv: Comprehensive readme file found on the original database. If there is any information you are missing here or are looking for additional resources you will probably find it in this file. It explains how each .csv file comes into play when forming training and testing models, as well as column metadata information for figuring out what the .csv is talking about. The original website also gives recommendations on training/testing splits and comparison benchmarks.

There are two recommended configurations for developing training and testing sets (pairs vs people). Depending on which route you choose, you will use the following .csv files:

• pairs.csv: Contains randomly generated splits for 10-fold cross validation specifically for pairs. Use this for the image restricted configuration when forming training sets (refer to readme). There are 10 total sets; 5 sets contain 300 matched pairs, the other 5 sets contain 300 mismatched pairs.
• people.csv: Contains randomly generated splits for 10-fold cross validation specifically for individual faces. Use this for the unrestricted configuration when forming training sets (refer to readme). There are 10 total sets, each with a different amount of people; Set 1: 601. Set 2: 555. Set 3: 552. Set 4: 560. Set 5: 567. Set 6: 527. Set 7: 597. Set 8: 601. Set 9: 580. Set 10: 609.
• matchpairsDevTest.csv: Use this testing set if you decide to go with the pairs configuration. Contains 500 matched pairs of faces for testing set.
• matchpairsDevTrain.csv: Use this training set if you decide to go with the pairs configuration. Contains 1100 matched pairs of faces for training set.
• mismatchpairsDevTest.csv: Use this testing set if you decide to go with the pairs configuration. Contains 500 - mismatched pairs of faces for testing set.
• mismatchpairsDevTrain.csv: Use this training set f you decide to go with the pairs configuration. Contains 1100 mismatched pairs of faces for training set.
• peopleDevTest.csv: Use this testing test if you decide to go with the people configuration. Contains 1711 people and 3708 images.
• peopleDevTrain.csv: Use this training set if you decide to go with the people configuration. Contains 4038 people and 9525 images.

Acknowledgements All data and metadata were originally found on http://vis-www.cs.umass.edu/lfw/. Please visit the site for other data versions including original, non-aligned data as well as more information on errata and training/testing model resources.

A big thank you and kudos to the creators of this dataset and relevant research:

Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.

Specifically for the deep-funneled version of the image data:

Gary B....

This is a compilation of spectacled bear records in Peru. Records included direct observations, indirect observations (footprints, food remains, bear beds and scats) and camera trap photographs from published, grey literature, and environmental impact studies. Source, type of record, coordinates, data and other relevant information from the records is included in each record.

Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.

Welcome to Labeled Faces in the Wild, a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector. More details can be found in the technical report below. Information: 13233 images 5749 people 1680 people with two or more images Citation: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments Gary B. Huang and Manu Ramesh and Tamara Berg and Erik Learned-Miller University of Massachusetts, Amherst - 2007

Labeled Faces in the Wild, is a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector. More details can be found in the technical report below.