Exploring Data Visualization

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Exploring Data Visualization Course Sampler

Exploring Data Visualization Course Sampler

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  • 1. Professional Development Short Course On: Exploring Data: Accessing, Understanding and Visualizing Data To Gain Insight. Instructor: Ted Meyer Dr. Brand Fortner ATI Course Schedule: http://www.ATIcourses.com/schedule.htm ATI's Exploring Data: Visualization course http://www.aticourses.com/data_presentation_and_visualization.htm 349 Berkshire Drive • Riva, Maryland 21140 888-501-2100 • 410-956-8805 Website: www.ATIcourses.com • Email: ATI@ATIcourses.com
  • 2. 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
  • 3. www.ATIcourses.com Boost Your Skills 349 Berkshire Drive Riva, Maryland 21140 with On-Site Courses Telephone 1-888-501-2100 / (410) 965-8805 Tailored to Your Needs Fax (410) 956-5785 Email: ATI@ATIcourses.com The Applied Technology Institute specializes in training programs for technical professionals. Our courses keep you current in the state-of-the-art technology that is essential to keep your company on the cutting edge in today’s highly competitive marketplace. Since 1984, ATI has earned the trust of training departments nationwide, and has presented on-site training at the major Navy, Air Force and NASA centers, and for a large number of contractors. Our training increases effectiveness and productivity. Learn from the proven best. For a Free On-Site Quote Visit Us At: http://www.ATIcourses.com/free_onsite_quote.asp For Our Current Public Course Schedule Go To: http://www.ATIcourses.com/schedule.htm
  • 4. NASA’s Data Pyramid
  • 5. Universe of Data • Data Elements • Data Structures (Objects) • Data Collections • Data Systems • Metadata
  • 6. 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?
  • 7. 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
  • 8. 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
  • 9. Evaluating Number Types • Storage and processing efficiency • Data range required • Numeric precision required • Calculation issues • Portability
  • 10. Bytes • 8 bits represents 28 (256) distinct values • Unsigned and Signed – Twos-complement • Representation – Hexadecimal, Octal, Decimal, Binary
  • 11. Integers • Short and long integers • Signed and unsigned • Fixed point numbers – Scale and Offset number = scale x (value + offset)
  • 12. 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
  • 13. ASCII Text Numbers • ASCII Characters • Numbers – Exponential notation – Delimiters - space, comma, tab – Line separators – Position formats • Unicode (16-bit characters)
  • 14. 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
  • 15. 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)
  • 16. 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)
  • 17. 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
  • 18. 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
  • 19. 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?
  • 20. 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
  • 21. 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
  • 22. 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 • • • • •
  • 23. 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
  • 24. 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
  • 25. 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
  • 26. 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
  • 27. 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
  • 28. 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
  • 29. 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
  • 30. 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
  • 31. 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
  • 32. Grids • Hex grids
  • 33. 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
  • 34. 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
  • 35. 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
  • 36. Data with Relations
  • 37. Files of Data • Proprietary Formats • Images Files • Science Data Formats • Multi-object files • Linking Metadata to Data
  • 38. Collections of Data • Databases • Files and Directory Structures • Tapes • Hybrid Systems • Boxes in Corners
  • 39. Metadata • Metadata is Data • One person’s metadata is another person’s data • Types of Metadata – Structural – Attribute - Search and Labeling – Descriptive • XML
  • 40. 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
  • 41. Case Studies: ASCI and EOSDIS
  • 42. 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.”
  • 43. 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
  • 44. ASCI Datatypes
  • 45. EOSDIS: Understanding Global Climate Change
  • 46. EOSDIS Processing Levels
  • 47. EOSDIS Example: Library Analogy
  • 48. 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
  • 49. 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
  • 50. What is Scientific Data?
  • 51. 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
  • 52. You have enjoyed an ATI's preview of Exploring Data: Accessing, Understanding and Visualizing Data To Gain Insight. Please post your comments and questions to our blog: http://www.aticourses.com/wordpress-2.7/weblog1/ Sign-up for ATI's monthly Course Schedule Updates : http://www.aticourses.com/email_signup_page.html