Data We Have:

name	# obs	# dim	feature	idm	label	note
SN4	579 (root) 174 (branch 2)	.	SN4rootfeat.txt	SN4rootdist.txt SN4branchdist.txt	SN4labelR.txt SN4mclustR.txt SN4labelB2.txt SN4mclustB2txt	2: "Astronomy" 6: "Mathematics" 7: "Medicine" 8: "Physics" See SN4.pdf
tiger	1600	.	.	rfidm_C rfidm_I	tiger-label.txt	1: "animal" 2: "baseball" 3: "golf" 4: "rebel" 5: "rugby" 6: "else"
lddmm	101	.	.	see README	see README	README
hyperspectral	14748	126	Hymapfeat.txt	.	Hymaplabel.txt 0: "runway" 1: "pine" 2: "oak" 3: "grass" 4: "water" 5: "scrub" 6: "swamp"	See this paper for details about this data!
artificial nose	1112 obs with 60 time series	19x2 sensors	unsmoothed data smoothed data (~20MB), (1112 x 38 x 60) (use R's "dget" command to read in.)	unsmootheddist.txt.gz smootheddist.txt.gz (~10MB), (1112 x 1112)	2classlabel.txt (TCE v noTCE) subconcent.txt (see this) subclasslabel.txt (19 odorants)	nose.tar.gz (~10MB) data.r nose-pami-2001.pdf
face	200	100	datc.txt datu.txt	.	clc.txt clu.txt	200 per observations (8 for each of the 25 classes) collected under two different conditions
challenge	2000	6	challenge-train.txt challenge-test.txt	.	the first 1000 are class 0 the last 1000 are class 1	.
UMD Enron	666	.	.	.	6 classes 32 classes	See here
Wiki	1382	.	English Algebraic Geometry WCH French Algebraic Geometry WCH See here for more detail	GE, GF TE, TF See here for more detail Ming's Data	6 classes: 1. people, 2. places, 3. dates, 4. things, 5. math things, 6. categories	agdata.tar See here agxml.tgz See here
HHMI	1019	1080: 180 time steps (90 seconds) * 6 sensors	features-splined-1019x1080.txt	dist-weighted-1019-Z23.txt mdsf-1019x40.txt	Z2-1019-filenames.txt (There are 11 non-GMR's and 2 pBD's.)	.

prepared by Youngser Park (youngser at jhu.edu), 18 Jan 2013