Professional Development Short Course On:
Exploring Data: Accessing, Understanding
and Visualizing Data To Gain Insight.
Instructor:
Ted Meyer
Dr. Brand Fortner
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ATI's Exploring Data: Visualization course http://www.aticourses.com/data_presentation_and_visualization.htm
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Exploring Data: Visualization
Summary
Visualization of data has become a mainstay in SEE CURRENT SCHEDULE
everyday life. Whether reading the newspaper or
presenting viewgraphs to the board of directors, FOR THE LATEST DATES
professionals are expected to be able to interpret
and apply basic visualization techniques. Technical
http://www.ATIcourses.com/schedule.htm
workers, engineers and scientists, need to have an
even greater understanding of visualization
techniques and methods. In general, though, the
basic concepts of understanding the purposes of Course Outline
visualization, the building block concepts of visual
perception, and the processes and methods for 1. Overview.
creating good visualizations are not required even • Why Visualization? – The Purposes for Visualization: Evaluation,
in most technical degree programs. This course Exploration, Presentation.
provides a “Visualization in a Nutshell” overview 2. Basics of Data.
that provides the building blocks necessary for • Data Elements – Values, Locations, Data Types, Dimensionality ensuring
effective use of visualization. a successful mission.
• Data Structures – Tables, Arrays, Volumes.
• Data – Univariate, Bivariate, Multi-variate.
Instructors • Data Relations – Linked Tables.
Ted Meyer has worked with the National • Data Systems.
Geospatial-Intelligence Agency (NGA), NASA, • Metadata – Vs. Data, Types, Purpose.
and the US Army and Marine Corps to develop 3. Visualization.
systems that interact with and provide data access • Purposes – Evaluation, Exploration, Presentation.
to users. At the MITRE Corporation and Fortner • Editorializing – Decision Support.
Software he has lead efforts to build tools to • Basics – Textons, Perceptual Grouping.
provide users improved access and better insight • Visualizing Column Data – Plotting Methods.
into data. Mr. Meyer was the Information Architect • Visualizing Grids – Images, Aspects of Images, Multi-Spectral Data
for NASA’s groundbreaking Earth Science Data Manipulation, Analysis, Resolution, Intepolation.
and Information System Project where he helped • Color – Perception, Models, Computers and Methods.
to design and implement the data architecture for • Visualizing Volumes – Transparency, Isosurfaces.
EOSDIS.
• Visualizing Relations – Entity-Relations & Graphs.
Dr. Brand Fortner, an astrophysicist by
• Visualizing Polygons – Wireframes, Rendering, Shading.
training, has founded two scientific visualization
• Visualizing the World – Basic Projections, Global, Locart.
companies (Spyglass, Inc., Fortner Software
ENGINEERING
LLC.), and has written two books on visualization • N-dimensional Data – Perceiving Many Dimensions.
(The Data Handbook and Number by Colors, with • Exploration Basics – Linking, Perspective and Interaction.
Ted Meyer). Besides his own companies, Dr. • Mixing Methods to Show Relationships.
Fortner has held positions at the NCSA, NASA • Manipulating Viewpoint – Animation, Brushing, Probes.
(where he lead the HDF-EOS team), and at • Highlights for Improving Presentation Visualizations – Color,
JHU/APL (chief scientist, intelligence exploitation Grouping, Labeling, Clutter.
group). He currently is research professor in the 5. Data Access – Standards and Tools.
department of physics, North Carolina State • Data Standards – Overview, Purpose, Why Use?
University. • Overview of Popular Standards.
• Grid/Image Standards – DTED, NITF, SDTS.
• Science Standards.
What You Will Learn • SQL and Databases.
• Decision support techniques: which type of • Metadata – PVL, XML.
visualization is appropriate. 6. Tools for Visualization.
• Appropriate visualization techniques for the • APIs & Libraries.
spectrum of data types. • Development Enviroments.
• Cross-discipline visualization methods and CLI
“tricks”.
Graphical
• Leveraging color in visualizations.
• Applications.
• Use of data standards and tools. • Which Tool?
• Capabilities of visualization tools. • User Interfaces.
This course is intended to provide a survey of 7. A Survey of Data Tools.
information and techniques to students, giving them
• Commercial.
the basics needed to improve the ways they
understand, access, and explore data. • Shareware & Freeware.
44 – Vol. 93 Register online at www.ATIcourses.com or call ATI at 888.501.2100 or 410.956.8805

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NASA’s
Data Pyramid

Universe of Data
• Data Elements
• Data Structures (Objects)
• Data Collections
• Data Systems
• Metadata

When Dealing with Data
• How are the numbers stored?
• How is the data Organized
• What is the dimensionality of the data?
• Is the data on a grid?
• What is the best way to analyze the data?

Data Characteristics
• Numeric, symbolic (or mix)
• Scalar, vector, or complex structure
• Various units
• Discrete or continuous
• Spatial, quantity, category, temporal, relational, structural
• Accurate or approximate
• Dense or sparce
• Ordered or non-ordered
• Disjoint or overlapping
• Binary, enumerated, multilevel
• Independent or dependent
• Multidimensional
• Single or multiple sets
• May have similarity or distance metric
• May have intuitive graphical representation (e.g. temperature with color)
• Has semantics which may be crucial in graphical consideration

Numbers in Computers
• Quantitative: Numeric vs. Non-numeric data
– Categorical Data: Finite set
– Text
• Number Types
– Binary
• Bytes, Integers, Floating point
• Fixed precision
• Not readable by humans
• Storage and processing efficient
– ASCII
• Text, Characters
• Variable precision
• Human readable
• Storage and processing inefficient

Evaluating Number Types
• Storage and processing efficiency
• Data range required
• Numeric precision required
• Calculation issues
• Portability

Bytes
• 8 bits represents 28 (256) distinct values
• Unsigned and Signed
– Twos-complement
• Representation
– Hexadecimal, Octal, Decimal, Binary

Integers
• Short and long integers
• Signed and unsigned
• Fixed point numbers
– Scale and Offset
number = scale x (value + offset)

Floating Point Numbers
• Single precision (4-byte)
– 9.10956 x 10-28
– sign exponent mantissa
– 0 -28 910956 decimal
– 0 -1011010 1001000001011000110001
binary
– IEEE Standard 754
• Double precision (8-byte)
• Zero, NaN, INF, Complex numbers, Extended

ASCII Text Numbers
• ASCII Characters
• Numbers
– Exponential notation
– Delimiters - space, comma, tab
– Line separators
– Position formats
• Unicode (16-bit characters)

Storage and Processing
Efficiency
• Bytes - efficient use of disk space and CPU
cycles
• Integers - efficient use of disk space and CPU
cycles, especially if no FPU
• Floating point - less disk efficient, needs FPU to
be processing efficient
• ASCII Text - disk and processing hog, no direct
access unless position formatted, processing
requires translation

Data Range
• Bytes - 0 to 255 unsigned or -128 to 127 signed,
easy to exceed range
• Integers - depends on size, -32768 to 32767 for
2-byte integers, easy to exceed range
• Floating point - very large, but user needs to
know when to use double precision
• ASCII Text - limited only by capabilities of
reading software (most software is limited to
integer or floating point ranges)

Numeric Precision
• Bytes - always one
• Integers - precision is always one for integers, 1/scale
for fixed point
• Floating point - can vary depending upon a variety of
numerical factors, but the maximum is about 7 and 15
decimal digits for single and double precision numbers
• ASCII Text - limited only by capabilities of reading
software (most software is limited to integer or floating
point precision)

Calculation Issues
• Bytes - dangerous because of likely overflow
• Integers - dangerous because of likely overflow
• Floating point - Usually easy, but be aware of problems:
roundoff, differencing similar numbers, comparisons
• ASCII Text - must first convert to integers or floating
point and then subject to same limitations as those
types

Portability
• Bytes - Most computers store bytes the same way
• Integers - byte ordering problems: little vs. big endian,
fixed point problematic because of scale and offset
• Floating point - IEEE standard is most common, but
heritage data may be in other forms
• ASCII Text - extremely portable and human readable on
most platforms, with minor problems associated with
delimiters, line end characters, and file transfer

Scientific Data Storage
• Text vs. Binary, Public vs. Private
• Issues:Numerical Precision
– Numerical Range
– Portability
– Efficiency
– Self-Documenting
– Power & Extensibility
• How do the Various Formats Rate?

How do the Various Formats Rate?
Binary Binary
Text Integer Float
Precision Variable Fair Good
Range Infinite Poor Excellent
Portability Excellent Fair Poor
Efficiency Poor Excellent Excellent
Private Private Standard
Binary Text Binary
Portability Poor Excellent Excellent
Efficiency Excellent Poor Excellent
Self-Document Poor Good Excellent
Power & Good Good Varies
Extensibility
• What the World Needs is a Powerful, Extensible,
Self-Documenting, Standard Binary Floating
Point File Format for Technical Data

Kinds of Science Data
• Science Data Types Tables &
Relational Tables
• Metadata
• Images 2D Arrays
• Atomic Types
– Integers Example text. This is example of
text block. It may include
parsealbe language. Or a variety
of other textual information. It
may include formatted text as
long as the formatting is
nD Arrays
– Floating Point
Example text. This is example of
text block. It may include
parsealbe language. Or a variety
of other textual information. It
Metadata
may include formatted text as
long as the formatting is
Example text. This is example of
text block. It may include parl
information. It may include ariety
– ASCII Text
mation. It may include formatted
text as long as the formatting is
Array of
Records

How are my Numbers Organized?
• Dimensionality, Data Locations, and Data Values
• Column Data: List of Data Locations and Values
X Y Velocity
0.5 0.5 0.0350
0.5 1.0 0.0714
0.5 1.5 0.3853
1.0 0.5 0.4911
1.0 1.0 0.2422
1.0 1.5 0.9207
1.5 0.5 0.5744
1.5 1.0 0.3305
1.5 1.5 0.8485
• 2D Matrix Data: Locations Implicit in Matrix
X
0.5 1.0 1.5
0.5 0.0350 0.4911 0.5744
Y 1.0 0.0714 0.2422 0.3305
1.5 0.3853 0.9207 0.8485
• 3D Matrix Data: Same as 2D
Z=6.0 0.5 1.0 1.5
Z=3.0 0.5 1.0 1.5 2.4064
Z=0.0 0.5 1.0 1.5 1.7672 2.5157
0.5 0.0350 0.4911 0.5744 1.7253 2.7190
1.0 0.0714 0.2422 0.3305 1.7757
1.5 0.3853 0.9207 0.8485
• Polygonal Data: List of Objects
Polygon Polygon Position Vertices Temp. Stress
Name
A (0.5,0.5,0.0) (vertex info) 72.2 0.034
B (0.5,1.0,0.5) • 74.8 0.056
C (1.0,0.5,0.5) • 71.3 0.089
• • • • •

Column Data
• Column, Record, Flat File or Table Data
– Records, fields
Column1 Column2 Column3
4727 1097 0
4470 1064 1
Time Free_Response Controlled_Response 4470 1047 1
0.00 0.000 0.0000 4501 1014 1
0.02 -0.0001 -0.0001 4501 964 1
0.04 -0.0012 -0.0012 4449 931 1
0.06 -0.0039 -0.0039 4464 948 1
0.08 -0.0081 -0.0081 4438 1031 1
0.1 -0.0137 -0.0137 4433 948 1
0.12 -0.0211 -0.0211 4407 956 1
0.14 -0.0305 -0.0305 4396 1014 1
0.16 -0.042 -0.042 4381 1196 1
0.18 -0.0554 -0.0553 4349 1717 1
0.2 -0.0702 -0.07 # 4580 at 694 stations. -99 means data not avail.
Data recorded on 01/02/91 1741 1
0.22 -0.0863 -0.0856 # X and Y is Lat-Long mapped onto1411
4664 1
polar sterographic projection
0.24 -0.1038 -0.1022 X-coord Y-coord 4706Temp(F)1312 1
Dewpt(F) Press(Mb) U(m/s) V(m/s)
0.26 -0.1232 -0.12 4158095.30 2769728.304690 15.00 12455.00 1020.60
1 -0.31 2.55
0.28 -0.1447 -0.1387 4206175.00 2711076.004727 17.00 10975.00 1022.30
1 -0.52 4.09
0.3 -0.1675 -0.1577 4157729.80 2479427.002376 24.00 11887.00 1025.70
0 -1.21 4.47
0.32 -0.1909 -0.1758 3925337.00 2395113.802182 27.00 1237 1
11.00 1026.70 -1.06 5.04
0.34 -0.2139 -0.1924 3975751.30 2386686.801883 22.00 15107.00 1025.40
1 -0.89 4.02
0.36 -0.2356 -0.2067 4151424.80 2401246.501893 25.00 1551 1
10.00 1026.70 0.68 2.48
0.38 -0.255 -0.2182 4089275.00 2322289.501899 27.00 15514.00 1027.10
1 2.24 2.13
0.4 -0.2719 -0.227 4105378.00 2298648.301925 30.00 1568 1
14.00 1027.80 3.60 0.22
4102765.80 2221281.001920 25.00 16424.00 1026.40
1 4.26 1.82
4157063.30 2257515.301946 32.00 16928.00 1029.10
1 3.03 4.16
4135176.50 2259278.801951 30.00 1733 1
10.00 1028.40 4.71 2.07
2009 1783 1
1988 1923 1
1993 1932 1

Temperature
Column Data
Variation
53.379
56.132
58.803
• Univariate Univariate 74.001
100.896
105.082
– One Parameter 74.425
61.110
90.954
• Bivariate 74.488
50.073
52.929
• Trivariate 73.256
68.178
82.253
• Hypervariate 102.230
69.395
82.663
– Multi-parameter 103.075
70.152
101.360
66.188
Hypervariate
Alpha Temperature Temperature Temperature
Channel Data Age z-fact Bias Drift Aging
0.9950 0.1987 0.1297 107.289 57.677 53.379
0.9801 0.3894 0.1739 106.358 69.561 56.132
0.9553 0.5646 0.2167 104.817 80.477 58.803
0.9211 0.7174 0.4607 102.682 89.991 74.001
0.8776 0.8415 0.8924 99.973 97.724 100.896
0.8253 0.9320 0.9596 96.718 103.366 105.082
0.7648 0.9854 0.4675 92.950 106.694 74.425
0.6967 0.9996 0.2538 88.705 107.573 61.110
0.6216 0.9738 0.7328 84.026 105.971 90.954
0.5403 0.9093 0.4685 78.961 101.949 74.488

Categorical Data
Test Site Source z-fact
• Non-numeric
alpha red 0.1297
gamma blue 0.1739
epsilon blue 0.2167
– Data broken into groups delta
nu
blue
blue
0.4607
0.8924
omicron blue 0.9596
– Examples: color, lambda
kappa
blue
blue
0.4675
0.2538
educational level, race, delta
nu
red
red
0.7328
0.4685
station, age group omicron
pi
red
red
0.0766
0.1224
• Numeric data can be sigma
tau
red
green
0.4487
0.3672
divided into categories:
gamma red 0.5931
kappa red 0.9138
delta blue 0.3868
– Low nu
omicron
blue
green
0.5997
0.9274
– Medium
– High
• Often tabular column data
with relations to numeric
paramters

Arrays of Data
• Scales and Grids
• Numeric Types
5.444 7.323 9.629 11.170 12.655 13.687 14.453
5.119 7.170 8.985 10.786 12.467 13.742 14.693
4.458 6.180 8.355 10.584 12.381 13.777 14.852
4.144 5.485 7.459 9.768 11.900 13.605 14.910
3.706 4.821 6.489 8.466 10.817 13.017 14.750
3.457 4.145 5.637 7.342 9.451 11.882 14.166
3.044 3.397 4.697 6.352 8.182 10.364 12.903
2.763 2.790 3.698 5.181 6.960 8.863 11.123
3.049 2.520 2.905 3.994 5.607 7.395 9.330

Grids
0.5 1 1.5
• Uniform grids and no grids 0.5 0.0350 0.4911 0.5744
1.0 0.0714 0.7477 0.3305
1.5 0.3853 0.9207 0.8485
0.5 0.7 1.8
• Non-uniform grids 0.5 0.0350 0.4911 0.5744
1.0 0.0714 0.7477 0.3305
1.1 0.3853 0.9207 0.8485
0.0350 0.4911 0.5744
(0.5, 0.5) (1.0, 0.7) (1.5, 0.5)
0.0714 0.7477 0.3305
• Warped grids (0.7, 1.0) (1.0, 1.0) (1.3, 1.0)
0.3853 0.9207 0.8485
(0.5, 1.5) (1.0, 1.2) (1.5, 1.5)
0.5 1 1.5
0.5 NaN 0.4911 0.5744
• Sparse grids 1.0 0.0714 0.7477 NaN
1.5 0.3853 NaN 0.8485

Grids
0.5 1 1.5
• Uniform grids and no grids 0.5 0.0350 0.4911 0.5744
1.0 0.0714 0.7477 0.3305
1.5 0.3853 0.9207 0.8485
2.00
1.75 X Y
0.5 0.5 0.0350
1.50 0.5 1 0.4911
0.5 1.5 0.5744
1.25 1 0.5 0.0714
1.00 1 1 0.7477
1 1.5 0.3305
0.75 1.5 0.5 0.3853
1.5 1 0.9207
0.50 1.5 1.5 0.8485
0.25
0.00
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00

Grids
0.5 0.7 1.8
• Non-uniform grids 0.5 0.0350 0.4911 0.5744
1.0 0.0714 0.7477 0.3305
1.1 0.3853 0.9207 0.8485
2.00
1.75
X Y
1.50 0.5 0.5 0.0350
0.5 1 0.4911
1.25 0.5 1.1 0.5744
0.7 0.5 0.0714
1.00
0.7 1 0.7477
0.7 1.1 0.3305
0.75
1.8 0.5 0.3853
0.50 1.8 1 0.9207
1.8 1.1 0.8485
0.25
0.00
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00

Grids
0.0350 0.4911 0.5744
• Warped grids (0.5, 0.5) (1.0, 0.7) (1.5, 0.5)
0.0714 0.7477 0.3305
(0.7, 1.0) (1.0, 1.0) (1.3, 1.0)
0.3853 0.9207 0.8485
(0.5, 1.5) (1.0, 1.2) (1.5, 1.5)
2.00
1.75
X Y
1.50 0.5 0.5 0.0350
1 0.7 0.4911
1.25
1.5 0.5 0.5744
1.00 0.7 1 0.0714
1 1 0.7477
0.75 1.3 1 0.3305
0.5 0.15 0.3853
0.50 1 1.2 0.9207
1.5 1.5 0.8485
0.25
0.00
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00

Grids
0.5 1 1.5
0.5 NaN 0.4911 0.5744
• Sparse grids 1.0 0.0714 0.7477 NaN
1.5 0.3853 NaN 0.8485
2.00
1.75
X Y
1.50 0.5 0.5 NaN
0.5 1 0.4911
1.25
0.5 1.5 0.5744
1.00 1 0.5 0.0714
1 1 0.7477
0.75 1 1.5 NaN
1.5 0.5 0.3853
0.50 1.5 1 NaN
1.5 1.5 0.8485
0.25
0.00
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00

Grids
• Hex grids

Grids & Scales
• Scales Support Gridding
1.1
1 1.2
2 1.3
3 4
1. 4 5
1. 5 6
1. 6 7
1. 7
2.3
1.3
1 5.444 7.323 9.629 11.170 12.655 13.687 14.453
22.7
.3 5.119 7.170 8.985 10.786 12.467 13.742 14.693
Indices
33.3 4.458 6.180 8.355 10.584 12.381 13.777 14.852
Uniform Gridding 44
3 .3
.4 4.144 5.485 7.459 9.768 11.900 13.605 14.910
Etc... 55.1
.3 3.706 4.821 6.489 8.466 10.817 13.017 14.750
66.3 3.457 4.145 5.637 7.342 9.451 11.882 14.166
77.8
.3 3.044 3.397 4.697 6.352 8.182 10.364 12.903
88.3
11.3 2.763 2.790 3.698 5.181 6.960 8.863 11.123
99.3
15.3 3.049 2.520 2.905 3.994 5.607 7.395 9.330

Volumes of Data
• Large I/O and processing
requirements
• Specialized Visualization
And Scales
4.46 6.18 8.36 10.58 12.38
2.76 2.79 3.70 5.18 6.96
4.14 5.49
4.40 7.46
3.16 9.77
2.26 11.90
1.82 1.94
3.05 2.52
4.46 2.91
6.18 3.99 10.58
8.36 5.61 12.38
3.71 4.82
4.94 6.49
3.86 8.47
2.87 10.82
2.10 1.78
2.76 2.79 3.70 5.18 6.96
3.02 2.38
4.14 2.38
5.49 3.05
7.46 4.31 11.90
9.77
3.46 4.15 4.40
5.64 3.16
7.34 2.26
9.45 1.82 1.94 2.3
5.17 4.64
3.05 3.86
2.52 2.94
2.91 2.22
3.99 5.61
3.47 2.52
3.71 2.14
4.82 2.41
6.49 3.29 10.82
8.47
3.04 3.40 4.94
4.70 3.86
6.35 2.87
8.18 2.10 1.78 2 .7
15 .3 5.95 5.61
3.02 5.04
2.38 4.17
2.38 3.24
3.05 4.31
3.71 2.71
3.46 2.08
4.15 2.00
5.64 2.50
7.34 9.45
7 .8 5.17 4.64 3.86 2.94 2.22 3 .3
5.81 6.16
3.47 6.03
2.52 5.44
2.14 4.58
2.41 3.29
11 .3 3.04 3.40
5.95 4.70
5.61 6.35
5.04 8.18
4.17 3.24 3 .4
3.71 2.71 2.08 2.00 2.50
15 .3
5.81 6.16 6.03 5.44 4.58 5 .1
6 .3
1.1 1.2 1.3 1. 4 1. 5

Visualization Polygonal Data
• Positioning of polygons in 3-space
P8 P5
C Polygon Nodes Nearest Temperature Stress
Name Neighbors
P2 P3 A P1,P2,P3,P4 B,C,D,E 34.7 .023
B B P4,P3,P5,P6 A,C,E,F 23.1 .028
P6
C P2,P3,P5,P8 A,B,D,F 24.5 .024
A
D P1,P2,P7,P8 A,C,E,F 29.4 .033
E P1,P4,P6,P7 A,B,D,F 28.6 .023
P1 P4 F P5,P6,P7,P8 B,C,D,E 31.9 .031
• Polygons may be colored according to data value
• Requires significant computational resources
• Animation

Data with Relations

Files of Data
• Proprietary Formats
• Images Files
• Science Data Formats
• Multi-object files
• Linking Metadata to Data

Collections of Data
• Databases
• Files and Directory Structures
• Tapes
• Hybrid Systems
• Boxes in Corners

Metadata
• Metadata is Data
• One person’s metadata is another
person’s data
• Types of Metadata
– Structural
– Attribute - Search and Labeling
– Descriptive
• XML

Metadata Example
Human Readable Computer Readable
…I mage o ob t GL 388 taken on Aprl 18 1990 w t
f jec i , ih DATE = ' 6-04-90 /
2 '
the IRTF telescope, us t Pro
ing he toCA M . The image is O RIGIN = 'UH IFA /INSTITUTION W RITING THE DATA
'
centered on 10 degrees 16 m tes 53 seconds r t
, inu , igh TELESC OP= 'NASA IRTF' / DATA ACQ UISIT ION TELESC OPE
ascens , 20 degrees 7 mi tes 21 seconds
ion , nu , INSTRU M E= 'P toCA M' / DATA AC Q UISIT
ro ION INSTR U M E NT
dec ina ion us the 1950 epoch …
l t , ing O BSE RVER= 'BBL W' / OBSERVER NA M E/ IDENTIFICATION
DATE_OBS= ' /04 '/ DATE OF AC Q UISIT
18 /90 ION( /m m/yy )
'dd '
TIME_OBS= ' 20:33 .77 / T
:26 ' IME OF ACQ UISIT ION(hh:m m:ss )
.ss
ITIME = 20 /INTEG R ATION TIM E IN SEC O N DS
.00
FILTER = 0 / 0=BRO A DBA N D 1=CVF
W A V E_LEN= 2 /W A V ELEN G TH IN MICRO N S
.20
PLATE_SC= 0 / PLATESCALE
.25
RA ='10:16:53 '/ R
.92 IGHT ASCENSIO N in degree
DE C = '20:07 .8 / DECLINATION i degree
:21 ' n
EP O C H = 1950.0 / EP O C H
AIR MASS = 1 .003 / AIRMASS
O BJECT = 'GL 388 '
C O M M E NT = ' .6 l=4 '
k=4 .6
V GATE = -2 / SBR C ARRAY G ATE VOLTA GE
.30
XML

Case Studies:
ASCI and EOSDIS

ASCI
• Accelerated Strategic Computing
Initiative
• Comprehensive Test Ban Treaty, 1992
• ASCI's vision:
– “to shift promptly from nuclear test-based
methods to computational-based methods
of ensuring the safety, reliability, and
performance of our nuclear weapons
stockpile.”

ASCI Data Requirements
• Computational mechanics: meshes & fields
• Sound data model w. robust data
abstractions
• Common format allows
– common tools
– sharing
• Common appl’n programming interface (API)
– shield apps from model complexities
– standardize data organization and semantics

ASCI Datatypes

EOSDIS: Understanding
Global Climate Change

EOSDIS Processing Levels

EOSDIS Example: Library Analogy

Example Categories for Granule- and
Collection-Level Metadata
Granule Collection
Platform, Instrument, Sensor Platform, Instrument, Sensor
Spatial and Temporal Delivered Algorithm Package
Orbit Parameters Guide
Browse Bibliographic Reference
QA Data Statistics Papers/Documents
Production History Keyword

Biases
• Disciplines
– Geospatial, simulation
• Data structures
– large multidimensional structures, multi-
layered structures, meshes, some indexed
structures
• Geometry
– space and time
• Operations
– visualization, partial access, filtering,
integration

What is Scientific Data?

What is scientific data?
• A variety of data types and structures
• Large data structures
• Many objects
• Metadata: parameters, variables, legacy
in a variety of forms

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Exploring Data: Accessing, Understanding
and Visualizing Data To Gain Insight.
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