Table of contents
  1. Story
  2. Big Data for International Scientific Programmes: Challenges and Opportunities
    1. Recommendations to the Sponsors of International Scientific Programmes
      1. 1. Respond to the importance of Big Data for international scientific programmes
        1. 2. Exploit the benefits of Big Data for society
        2. 3. Improve understanding of Big Data through international collaboration
        3. 4. Promote universal access to Big Data through global research infrastructures
        4. 5. Explore and Address the Challenges of Big Data Stewardship
        5. 6. Encourage capacity building and skills development
        6. 7. Foster development of policies to maximize exploitation of Big Data
    2. Proposed Actions for a CODATA Working Group
      1. A. Establish a CODATA Working Group on Big Data for Scientific Programmes
        1. a. Produce case studies in Big Data for international scientific programmes
        2. b. Promote sharing of Big Data solutions across scientific disciplines
        3. c. Research policy, ethical and legal issues for Big Data
        4. d. Research stewardship and sustainability challenges for Big Data
    3. Background
  3. Slides
    1. Slide 1 Big Earth Sciences Data - From Descriptive to Prescriptive Analytics
    2. Slide 2 Overview
    3. Slide 3 Overview (continued)
    4. Slide 4 Data Science Central: 9 "must read" articles
    5. Slide 5 My Selection (Vincent Granville): List
    6. Slide 6 Big Data-From Descriptive to Prescriptive
    7. Slide 7 My Selection (Vincent Granville): Footnote and Comment
    8. Slide 8 My Selection (Vincent Granville): Other Internal Links
    9. Slide 9 Six Categories of Data Scientists
    10. Slide 10 Update About Our Data Science Apprenticeship-March 10, 2014
    11. Slide 11 Update About Our Data Science Apprenticeship-March 29, 2014
    12. Slide 12 Developing Analytic Talent
    13. Slide 13 Data Science Certification
    14. Slide 14 Write a data science research paper and win fame and award
    15. Slide 15 Dr. Vincent Granville: A Visionary Data Scientist
    16. Slide 16 Big Data-From Descriptive to Prescriptive Examples
    17. Slide 17 How was the data collected?
    18. Slide 18 Where is the data stored?
    19. Slide 19 What were the results?
    20. Slide 20 What is the data story?
    21. Slide 21 Developing Analytic Talent
    22. Slide 22 Chapter 1: What is Data Science?
    23. Slide 23 Chapter 2: Big Data is Different
    24. Slide 24 Chapter 3: Becoming a Data Scientist
    25. Slide 25 Chapter 4: Data Science Craftsmanship-Part I
    26. Slide 26 Chapter 5: Data Science Craftsmanship-Part II
    27. Slide 27 Chapter 6: Data Science Applications - Case Studies
    28. Slide 28 Chapter 7: Launching Your New Data Science Career
    29. Slide 29 Chapter 8: Data Science Resources
    30. Slide 30 Addendum
    31. Slide 31 Big Data – From Descriptive to Prescriptive Example: Forecasting Meteorite Hits, pages 248-252
    32. Slide 32 Forecasting Meteorite Hits
    33. Slide 33 Where is the data stored? And What are the results?
    34. Slide 34 Why Did It Happen (correlation analytics) and What Will Happen Next (predictive analytics)?
    35. Slide 35 What Should I Do About it (prescriptive analytics) 1
    36. Slide 36 What Should I Do About it (prescriptive analytics) 2
  4. Spotfire Dashboard
  5. Research Notes
    1. Editor
    2. General Interest
    3. Workshops on Extremely Large Databases
    4. Databases
    5. Ontology
  6. CODATA Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
  7. Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
    1. Summary
    2. Context and Rationale
    3. Indicative Workshop Programme​ 
    4. Further Information
  8. CODATA International Training Workshop in Big Data for Science
    1. Workshop Objectives
    2. Course Contents
    3. Training methods and activities
    4. Participants
    5. Program Language
    6. Examination and Certificate
    7. Date and Duration
    8. Venue
    9. Sponsors
    10. Organizers
    11. Training Expenses
    12. Participants Qualifications and Requirements For Admissions
    13. Application Procedure and Deadline
  9. Application Form: CODATA International Training Workshop in Big Data for Science
  10. ​​CODATA Blog
    1. March 2014
      1. CODATA International Training Workshop in Big Data for Science, for Researchers from Emerging and Developing Countries
      2. CODATA Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
      3. SciDataCon 2014: Call for Abstracts and Registration Open
      4. Great Success of CODATA-China’s First Scientific Data Conference
        1. Big Research Data and Data Science
    2. February 2014
      1. DAR-TG Goes to India (Twice)
      2. CODATA Task Group on Anthropometric Data and Engineering: recent activities
      3. CODATA Mourns Vice-President Fedor Kuznetsov
      4. Open Data Policies Are Much More Economically Generative Than Closed Ones
    3. January 2014
      1. Call for SciDataCon 2014 International Scientific Programme Committee Candidates
      2. Implementation of Data Sharing Principles: GEO Side Event
      3. DANS Symposium on the Future of Scholarly Communications – and the benefits of time to think
      4. Building Support for ‘Principle Guidelines’ for Data at Risk
      5. CODATA and the GEO Data Sharing Working Group
      6. Announcing SciDataCon 2014, New Delhi, India, 2-5 November 2014
    4. November 2013
      1. Data Citation Synthesis Group: Draft Declaration of Data Citation Principles
      2. Call for Proposals: CODATA Task Groups 2014-16
      3. CODATA Roads Task Group at the Global Geospatial Conference
    5. October 2013
      1. Talking about CODATA…
    6. September 2013
      1. Hello world! And welcome!
  11. CODATA Data Science Journal
  12. CODATA (Committee on Data for Science and Technology-Databases)
    1. CODATA Key Values for Thermodynamics
    2. International Register of Materials Database Managers
    3. CODATA/ICSTI Prototype Portal on Permanent Access to Scientific Data and Information
    4. Scientific Access to Data and Information
  13. Integrated Research on Disaster Risk
    1. IRDR Peril Classification and Hazard Glossary
      1. IRDR
      2. About IRDR
      3. Members of the IRDR DATA Project Working Group
      4. 1. Introduction
      5. 2. Background
        1. 2.1 The Integrated Research on Disaster Risk Programme
        2. 2.2 Why Loss Database Standards are Important
      6. 3. Peril Classification
        1. 3.1 Classification Structure
          1. Figure 1: Peril classification at the Family level
          2. Figure 2: Peril classification at the Family and Main Events levels
          3. Figure 3: Peril classification at the Family, Main Event and Peril levels
          4. Figure 4: Peril classification at the Family, Main Event and Peril levels
        2. 3.2 Glossary
          1. Table 1: Definitions of perils, main events, and families
      7. 4. Conclusion
      8. References
      9. Appendices
        1. Databases at a Glance
        2. Loss Indicators
        3. Peril Coverage
  14. SciDataCon 2014, the International Conference on Data Sharing and Integration for Global Sustainability
    1. Conference Forum
      1. Citizens' participation in science: how can citizen contribute to open science and open data?
      2. Introduction to Text and Data Mining—Technical and Legal Considerations
      3. Machine Learning in remote Sensing data
      4. Metadata Interoperability through an International Network of (Polar?) Data Portals
      5. Topic: Going beyond saving the data
      6. Topics for SciDataCon 2014
      7. Knowledge Discovery with "Unstructured Data"
      8. Big Data, Little Sharing: Challenges and Opportunities for Data-Intensive scientists
      9. Open Science Data Frameworks
      10. RS & GIS
      11. Open Data in Agricultural Sciences
      12. The Research Data Alliance: building bridges to enable open sharing of data
  15. ICSU World Data System
    1. Trusted Data Services for Global Science
  16. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
    1. Resources
      1. Scientific Database
      2. Coverage State of Landsat-7 Imageries In Part of China
      3. Flood in Heilongjiang River (Autumn, 2013  Landsat-8 Imagery)
      4. Landscape of Ruoqiang in Xinjiang Uygur Autonomous Region (Jul, 2007  Landsat-5 Imagery)
  17. NEXT

Big Data Science for CODATA

Last modified
Table of contents
  1. Story
  2. Big Data for International Scientific Programmes: Challenges and Opportunities
    1. Recommendations to the Sponsors of International Scientific Programmes
      1. 1. Respond to the importance of Big Data for international scientific programmes
        1. 2. Exploit the benefits of Big Data for society
        2. 3. Improve understanding of Big Data through international collaboration
        3. 4. Promote universal access to Big Data through global research infrastructures
        4. 5. Explore and Address the Challenges of Big Data Stewardship
        5. 6. Encourage capacity building and skills development
        6. 7. Foster development of policies to maximize exploitation of Big Data
    2. Proposed Actions for a CODATA Working Group
      1. A. Establish a CODATA Working Group on Big Data for Scientific Programmes
        1. a. Produce case studies in Big Data for international scientific programmes
        2. b. Promote sharing of Big Data solutions across scientific disciplines
        3. c. Research policy, ethical and legal issues for Big Data
        4. d. Research stewardship and sustainability challenges for Big Data
    3. Background
  3. Slides
    1. Slide 1 Big Earth Sciences Data - From Descriptive to Prescriptive Analytics
    2. Slide 2 Overview
    3. Slide 3 Overview (continued)
    4. Slide 4 Data Science Central: 9 "must read" articles
    5. Slide 5 My Selection (Vincent Granville): List
    6. Slide 6 Big Data-From Descriptive to Prescriptive
    7. Slide 7 My Selection (Vincent Granville): Footnote and Comment
    8. Slide 8 My Selection (Vincent Granville): Other Internal Links
    9. Slide 9 Six Categories of Data Scientists
    10. Slide 10 Update About Our Data Science Apprenticeship-March 10, 2014
    11. Slide 11 Update About Our Data Science Apprenticeship-March 29, 2014
    12. Slide 12 Developing Analytic Talent
    13. Slide 13 Data Science Certification
    14. Slide 14 Write a data science research paper and win fame and award
    15. Slide 15 Dr. Vincent Granville: A Visionary Data Scientist
    16. Slide 16 Big Data-From Descriptive to Prescriptive Examples
    17. Slide 17 How was the data collected?
    18. Slide 18 Where is the data stored?
    19. Slide 19 What were the results?
    20. Slide 20 What is the data story?
    21. Slide 21 Developing Analytic Talent
    22. Slide 22 Chapter 1: What is Data Science?
    23. Slide 23 Chapter 2: Big Data is Different
    24. Slide 24 Chapter 3: Becoming a Data Scientist
    25. Slide 25 Chapter 4: Data Science Craftsmanship-Part I
    26. Slide 26 Chapter 5: Data Science Craftsmanship-Part II
    27. Slide 27 Chapter 6: Data Science Applications - Case Studies
    28. Slide 28 Chapter 7: Launching Your New Data Science Career
    29. Slide 29 Chapter 8: Data Science Resources
    30. Slide 30 Addendum
    31. Slide 31 Big Data – From Descriptive to Prescriptive Example: Forecasting Meteorite Hits, pages 248-252
    32. Slide 32 Forecasting Meteorite Hits
    33. Slide 33 Where is the data stored? And What are the results?
    34. Slide 34 Why Did It Happen (correlation analytics) and What Will Happen Next (predictive analytics)?
    35. Slide 35 What Should I Do About it (prescriptive analytics) 1
    36. Slide 36 What Should I Do About it (prescriptive analytics) 2
  4. Spotfire Dashboard
  5. Research Notes
    1. Editor
    2. General Interest
    3. Workshops on Extremely Large Databases
    4. Databases
    5. Ontology
  6. CODATA Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
  7. Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
    1. Summary
    2. Context and Rationale
    3. Indicative Workshop Programme​ 
    4. Further Information
  8. CODATA International Training Workshop in Big Data for Science
    1. Workshop Objectives
    2. Course Contents
    3. Training methods and activities
    4. Participants
    5. Program Language
    6. Examination and Certificate
    7. Date and Duration
    8. Venue
    9. Sponsors
    10. Organizers
    11. Training Expenses
    12. Participants Qualifications and Requirements For Admissions
    13. Application Procedure and Deadline
  9. Application Form: CODATA International Training Workshop in Big Data for Science
  10. ​​CODATA Blog
    1. March 2014
      1. CODATA International Training Workshop in Big Data for Science, for Researchers from Emerging and Developing Countries
      2. CODATA Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
      3. SciDataCon 2014: Call for Abstracts and Registration Open
      4. Great Success of CODATA-China’s First Scientific Data Conference
        1. Big Research Data and Data Science
    2. February 2014
      1. DAR-TG Goes to India (Twice)
      2. CODATA Task Group on Anthropometric Data and Engineering: recent activities
      3. CODATA Mourns Vice-President Fedor Kuznetsov
      4. Open Data Policies Are Much More Economically Generative Than Closed Ones
    3. January 2014
      1. Call for SciDataCon 2014 International Scientific Programme Committee Candidates
      2. Implementation of Data Sharing Principles: GEO Side Event
      3. DANS Symposium on the Future of Scholarly Communications – and the benefits of time to think
      4. Building Support for ‘Principle Guidelines’ for Data at Risk
      5. CODATA and the GEO Data Sharing Working Group
      6. Announcing SciDataCon 2014, New Delhi, India, 2-5 November 2014
    4. November 2013
      1. Data Citation Synthesis Group: Draft Declaration of Data Citation Principles
      2. Call for Proposals: CODATA Task Groups 2014-16
      3. CODATA Roads Task Group at the Global Geospatial Conference
    5. October 2013
      1. Talking about CODATA…
    6. September 2013
      1. Hello world! And welcome!
  11. CODATA Data Science Journal
  12. CODATA (Committee on Data for Science and Technology-Databases)
    1. CODATA Key Values for Thermodynamics
    2. International Register of Materials Database Managers
    3. CODATA/ICSTI Prototype Portal on Permanent Access to Scientific Data and Information
    4. Scientific Access to Data and Information
  13. Integrated Research on Disaster Risk
    1. IRDR Peril Classification and Hazard Glossary
      1. IRDR
      2. About IRDR
      3. Members of the IRDR DATA Project Working Group
      4. 1. Introduction
      5. 2. Background
        1. 2.1 The Integrated Research on Disaster Risk Programme
        2. 2.2 Why Loss Database Standards are Important
      6. 3. Peril Classification
        1. 3.1 Classification Structure
          1. Figure 1: Peril classification at the Family level
          2. Figure 2: Peril classification at the Family and Main Events levels
          3. Figure 3: Peril classification at the Family, Main Event and Peril levels
          4. Figure 4: Peril classification at the Family, Main Event and Peril levels
        2. 3.2 Glossary
          1. Table 1: Definitions of perils, main events, and families
      7. 4. Conclusion
      8. References
      9. Appendices
        1. Databases at a Glance
        2. Loss Indicators
        3. Peril Coverage
  14. SciDataCon 2014, the International Conference on Data Sharing and Integration for Global Sustainability
    1. Conference Forum
      1. Citizens' participation in science: how can citizen contribute to open science and open data?
      2. Introduction to Text and Data Mining—Technical and Legal Considerations
      3. Machine Learning in remote Sensing data
      4. Metadata Interoperability through an International Network of (Polar?) Data Portals
      5. Topic: Going beyond saving the data
      6. Topics for SciDataCon 2014
      7. Knowledge Discovery with "Unstructured Data"
      8. Big Data, Little Sharing: Challenges and Opportunities for Data-Intensive scientists
      9. Open Science Data Frameworks
      10. RS & GIS
      11. Open Data in Agricultural Sciences
      12. The Research Data Alliance: building bridges to enable open sharing of data
  15. ICSU World Data System
    1. Trusted Data Services for Global Science
  16. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
    1. Resources
      1. Scientific Database
      2. Coverage State of Landsat-7 Imageries In Part of China
      3. Flood in Heilongjiang River (Autumn, 2013  Landsat-8 Imagery)
      4. Landscape of Ruoqiang in Xinjiang Uygur Autonomous Region (Jul, 2007  Landsat-5 Imagery)
  17. NEXT

  1. Story
  2. Big Data for International Scientific Programmes: Challenges and Opportunities
    1. Recommendations to the Sponsors of International Scientific Programmes
      1. 1. Respond to the importance of Big Data for international scientific programmes
        1. 2. Exploit the benefits of Big Data for society
        2. 3. Improve understanding of Big Data through international collaboration
        3. 4. Promote universal access to Big Data through global research infrastructures
        4. 5. Explore and Address the Challenges of Big Data Stewardship
        5. 6. Encourage capacity building and skills development
        6. 7. Foster development of policies to maximize exploitation of Big Data
    2. Proposed Actions for a CODATA Working Group
      1. A. Establish a CODATA Working Group on Big Data for Scientific Programmes
        1. a. Produce case studies in Big Data for international scientific programmes
        2. b. Promote sharing of Big Data solutions across scientific disciplines
        3. c. Research policy, ethical and legal issues for Big Data
        4. d. Research stewardship and sustainability challenges for Big Data
    3. Background
  3. Slides
    1. Slide 1 Big Earth Sciences Data - From Descriptive to Prescriptive Analytics
    2. Slide 2 Overview
    3. Slide 3 Overview (continued)
    4. Slide 4 Data Science Central: 9 "must read" articles
    5. Slide 5 My Selection (Vincent Granville): List
    6. Slide 6 Big Data-From Descriptive to Prescriptive
    7. Slide 7 My Selection (Vincent Granville): Footnote and Comment
    8. Slide 8 My Selection (Vincent Granville): Other Internal Links
    9. Slide 9 Six Categories of Data Scientists
    10. Slide 10 Update About Our Data Science Apprenticeship-March 10, 2014
    11. Slide 11 Update About Our Data Science Apprenticeship-March 29, 2014
    12. Slide 12 Developing Analytic Talent
    13. Slide 13 Data Science Certification
    14. Slide 14 Write a data science research paper and win fame and award
    15. Slide 15 Dr. Vincent Granville: A Visionary Data Scientist
    16. Slide 16 Big Data-From Descriptive to Prescriptive Examples
    17. Slide 17 How was the data collected?
    18. Slide 18 Where is the data stored?
    19. Slide 19 What were the results?
    20. Slide 20 What is the data story?
    21. Slide 21 Developing Analytic Talent
    22. Slide 22 Chapter 1: What is Data Science?
    23. Slide 23 Chapter 2: Big Data is Different
    24. Slide 24 Chapter 3: Becoming a Data Scientist
    25. Slide 25 Chapter 4: Data Science Craftsmanship-Part I
    26. Slide 26 Chapter 5: Data Science Craftsmanship-Part II
    27. Slide 27 Chapter 6: Data Science Applications - Case Studies
    28. Slide 28 Chapter 7: Launching Your New Data Science Career
    29. Slide 29 Chapter 8: Data Science Resources
    30. Slide 30 Addendum
    31. Slide 31 Big Data – From Descriptive to Prescriptive Example: Forecasting Meteorite Hits, pages 248-252
    32. Slide 32 Forecasting Meteorite Hits
    33. Slide 33 Where is the data stored? And What are the results?
    34. Slide 34 Why Did It Happen (correlation analytics) and What Will Happen Next (predictive analytics)?
    35. Slide 35 What Should I Do About it (prescriptive analytics) 1
    36. Slide 36 What Should I Do About it (prescriptive analytics) 2
  4. Spotfire Dashboard
  5. Research Notes
    1. Editor
    2. General Interest
    3. Workshops on Extremely Large Databases
    4. Databases
    5. Ontology
  6. CODATA Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
  7. Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
    1. Summary
    2. Context and Rationale
    3. Indicative Workshop Programme​ 
    4. Further Information
  8. CODATA International Training Workshop in Big Data for Science
    1. Workshop Objectives
    2. Course Contents
    3. Training methods and activities
    4. Participants
    5. Program Language
    6. Examination and Certificate
    7. Date and Duration
    8. Venue
    9. Sponsors
    10. Organizers
    11. Training Expenses
    12. Participants Qualifications and Requirements For Admissions
    13. Application Procedure and Deadline
  9. Application Form: CODATA International Training Workshop in Big Data for Science
  10. ​​CODATA Blog
    1. March 2014
      1. CODATA International Training Workshop in Big Data for Science, for Researchers from Emerging and Developing Countries
      2. CODATA Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities
      3. SciDataCon 2014: Call for Abstracts and Registration Open
      4. Great Success of CODATA-China’s First Scientific Data Conference
        1. Big Research Data and Data Science
    2. February 2014
      1. DAR-TG Goes to India (Twice)
      2. CODATA Task Group on Anthropometric Data and Engineering: recent activities
      3. CODATA Mourns Vice-President Fedor Kuznetsov
      4. Open Data Policies Are Much More Economically Generative Than Closed Ones
    3. January 2014
      1. Call for SciDataCon 2014 International Scientific Programme Committee Candidates
      2. Implementation of Data Sharing Principles: GEO Side Event
      3. DANS Symposium on the Future of Scholarly Communications – and the benefits of time to think
      4. Building Support for ‘Principle Guidelines’ for Data at Risk
      5. CODATA and the GEO Data Sharing Working Group
      6. Announcing SciDataCon 2014, New Delhi, India, 2-5 November 2014
    4. November 2013
      1. Data Citation Synthesis Group: Draft Declaration of Data Citation Principles
      2. Call for Proposals: CODATA Task Groups 2014-16
      3. CODATA Roads Task Group at the Global Geospatial Conference
    5. October 2013
      1. Talking about CODATA…
    6. September 2013
      1. Hello world! And welcome!
  11. CODATA Data Science Journal
  12. CODATA (Committee on Data for Science and Technology-Databases)
    1. CODATA Key Values for Thermodynamics
    2. International Register of Materials Database Managers
    3. CODATA/ICSTI Prototype Portal on Permanent Access to Scientific Data and Information
    4. Scientific Access to Data and Information
  13. Integrated Research on Disaster Risk
    1. IRDR Peril Classification and Hazard Glossary
      1. IRDR
      2. About IRDR
      3. Members of the IRDR DATA Project Working Group
      4. 1. Introduction
      5. 2. Background
        1. 2.1 The Integrated Research on Disaster Risk Programme
        2. 2.2 Why Loss Database Standards are Important
      6. 3. Peril Classification
        1. 3.1 Classification Structure
          1. Figure 1: Peril classification at the Family level
          2. Figure 2: Peril classification at the Family and Main Events levels
          3. Figure 3: Peril classification at the Family, Main Event and Peril levels
          4. Figure 4: Peril classification at the Family, Main Event and Peril levels
        2. 3.2 Glossary
          1. Table 1: Definitions of perils, main events, and families
      7. 4. Conclusion
      8. References
      9. Appendices
        1. Databases at a Glance
        2. Loss Indicators
        3. Peril Coverage
  14. SciDataCon 2014, the International Conference on Data Sharing and Integration for Global Sustainability
    1. Conference Forum
      1. Citizens' participation in science: how can citizen contribute to open science and open data?
      2. Introduction to Text and Data Mining—Technical and Legal Considerations
      3. Machine Learning in remote Sensing data
      4. Metadata Interoperability through an International Network of (Polar?) Data Portals
      5. Topic: Going beyond saving the data
      6. Topics for SciDataCon 2014
      7. Knowledge Discovery with "Unstructured Data"
      8. Big Data, Little Sharing: Challenges and Opportunities for Data-Intensive scientists
      9. Open Science Data Frameworks
      10. RS & GIS
      11. Open Data in Agricultural Sciences
      12. The Research Data Alliance: building bridges to enable open sharing of data
  15. ICSU World Data System
    1. Trusted Data Services for Global Science
  16. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
    1. Resources
      1. Scientific Database
      2. Coverage State of Landsat-7 Imageries In Part of China
      3. Flood in Heilongjiang River (Autumn, 2013  Landsat-8 Imagery)
      4. Landscape of Ruoqiang in Xinjiang Uygur Autonomous Region (Jul, 2007  Landsat-5 Imagery)
  17. NEXT

Story

CODATA International Workshop on Big Data for International Scientific Programmes

The CODATA invitation read: On behalf of CODATA President, Professor Huadong Guo, I would like to invite you to speak at the CODATA International Workshop on Big Data for International Scientific Programmes.  A letter of invitation is attached as are further details of the workshop, also available on the CODATA Blog. We would greatly appreciate your contribution to what we think will be an important and valuable workshop.  Do please let me know whether you are interested in attending. With very best wishes, Simon Hodson, CODATA Executive Director

My response wasThank you for the kind invitation and I am very honored and interested in attending. What are the expenses for attending and how are the arrangements to be made?

The CODATA response was: We hope that most attendees will be able to provide the cost of their travel.  CODATA has a small fund to help where this is not the case, but we are obliged to use this judiciously. RADI will provide invitation letters to smooth the process of getting a Chinese visa.

My response was: Thank you for your informative reply.

My situation is as follows: I am a retired US government data scientist doing very limited paid consulting work while donating considerable free effort at the request of senior US government officials to lead the new Federal Big Data Working Group Meetup. Foreign travel is expensive and time consuming for me. For a recent invitation to speak at an upcoming UN Conference in Abu Dhabi latter this month, I had to request that all my expenses be paid for.

I would gladly prepare a presentation and even make a video recording of it to submit as I have done for similar foreign travel requests where the organizers could not pay my expenses or I had a schedule conflict.

My current focus for the US Federal Government Big Data Initiative is on creating Scientific Data Papers for use in Data (Web) Browsers which is the subject of our April 15th Meetup. END FOR NOW

Reviewing the invitation and description, it would seem my presentation should be a scientific data paper in a data browser for the following sources of information "to put the data first in big data science for CODATA":

Elizabeth Griffin, chair of the CODATA Data at Risk Task Group explains how to convince skeptics of the scientific value of spending resources on heritage data, what results might be expected by incorporating them into modern research, and what major problems could be foreseen and how to tackle them and the answer is examples, examples, examples – cite projects that returned new scientific knowledge which could not have been gained by any other means.​

This CODATA Blog Post also caught my eye: CODATA, in collaboration with CODATA-China is delighted to invite applications from young researchers, research leaders and managers of research institutes in countries with emerging and developing economies to participate in the CODATA International Training Workshop in Big Data for Science, Beijing, 4-20 June 2014.  Participation for successful applicants will be financially supported, thanks to a grant from the Chinese Academy of Sciences (CAS).  The deadline for applications is 16 April.  Further details, application instructions (PDF) and the application form (Word) are available here.

The requirements, which ruled me out because you have to be under 50 years old and have an academic position which I am waiting for to teach my proposed Practical Data Science for Data Scientists at George Mason University, are:

  • What expertise would you bring as a participant?
    • 30+ years as a US Federal Government agency data architect and data scientist, now a working data scientist/data journalist, and co-organizer of the US Federal Big Data Working Group Meetup
  • What specific subjects would you like to cover and how will this help you?
  • How would you hope to apply the experience gained from the course in your own country?
  • Prepare a review paper on the development of the science and technology in your respective country and your work.
    • A White Paper “Making Big Data Small" using Data Science and Semantics is in process for the US NIH, NIST, and NSF/NITRD

So the idea is to start the data mining process by first making the 3 PDF files (Invitation, Workshop, and Training attached below) real data that can all be readily searched in this MindTouch Wiki page. Then I need to mine the 5 sources above to create a knowledge base of indices and data sources that can be visualized so the reader starts with the data results, then can see where the data is stored, and finally learn how the data was collected. This is the reverse of the process followed in the Federal Big Data Working Group Meetup to prepare presentations, so the actual presentation can start with the results then show the data and metadata behind the results.You might notice that my MindTouch Wiki pages usually go directly to the Spotfire Dashboards to hopefully capture the readers attention and then they can scroll through the rest of the supporting information if sufficiently interested.

So I mined the 5 data sources above in MindTouch (an advanced Wiki) and summarized those results in a spreadsheet. Mining the CODATA Data Science Journal required the most effort since it was 9 columns by 509 rows built by hand, but was well worth that effort because it helped identify what the author thought were key publications in the following areas since the articles are not classified by topics:

The articles thought most important to starting with the results were:

So the next task is to make these data publications in MindTouch, Excel, and Spotfire

Interestingly, I found that the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences has open scientific databases for the grids below.

Coverage State of Landsat-7 Imageries In Part of China

banner1.PNG

I needed to find some Shape files for China for visualization in Spotfire and found two sources:

I also found this comprehensive source, but it was not free:

  • The Online China Database Subscription: The China Data Online is the primary data source for China studies. It includes (1) China Statistical Databases; (2) China Census Databases; and (3) China Spatial Data Service (China Geo-Explorer).

The results are shown in a companion data science story and product: Can the Scientific Data Be Reused?, which shows that scientific data can be reused in the following ways:

  • When data tables in papers are real data (not PDF or HTML formats)
  • When papers contain persistent links to the complete/raw data in common formats
  • When data publications contain the data ecosystem that can be viewed in data browsers.
  • When science journals and organizations offer data publications

Please join me in making more data publications!

Big Data for International Scientific Programmes: Challenges and Opportunities

Sources: http://codata.org/blog/2014/06/16/bi...s-and-actions/

http://codata.org/blog/wp-content/up...-v07-FINAL.pdf (PDF)

A Statement of Recommendations and Actions Beijing 9 June 2014

Big Data approaches offer the opportunity to extract information from large and complex data sources from diverse disciplines, and present compelling reasons to embrace new scientific research methods. The international scientific community has a responsibility to examine these opportunities to use Big Data for knowledge discovery that will benefit society and the sustainability of the planet.

Big Data have huge potential and are already benefitting scientific research and discovery. They present particularly significant challenges and notable opportunities for transdisciplinary, international research programmes. Such initiatives aim to produce research results and data in ways that improve decision-­‐making on critical issues for humankind and the environment. To accomplish this requires the integration of increasingly diverse and complex datasets, the design of new forms of data collection, and the extraction and interpretation of knowledge utilizing sophisticated statistical techniques, complex simulation models, and other computationally intensive approaches. The challenges and opportunities of Big Data have significant implications for scientific data services and infrastructure providers.

In recognition of this, the co-­‐sponsors and participants of the Workshop on Big Data for International Scientific Programmes join with CODATA in presenting the following statement of recommendations and actions as a call for international collaborations to address the challenges and take advantage of the opportunities of the Big Data age.

Recommendations to the Sponsors of International Scientific Programmes

1. Respond to the importance of Big Data for international scientific programmes

Addressing the combined challenges posed by data volume, complexity and heterogeneity will bring significant benefits to international scientific programmes. Big Data offer notable opportunities for knowledge discovery, particularly in relation to interdisciplinary research questions: this importance should be recognized and acted upon.

2. Exploit the benefits of Big Data for society

Sponsors of international scientific programmes need to encourage activities that exploit Big Data to promote applied research for the benefit of society and to make provision to promote and support such activities. Exploiting Big Data will require coordination and collaboration: research funders, national academies, universities, data services and other research performing institutions should formulate coordinated strategies to encourage the development and application of Big Data to meet high priority science needs.

3. Improve understanding of Big Data through international collaboration

Research into the methodologies, theories, technologies and – above all – the practical applications of Big Data for international scientific programmes should be strengthened. This must involve a broad range of experts and research disciplines and sustained international collaboration will also be essential.

4. Promote universal access to Big Data through global research infrastructures

The knowledge generated by the exploitation of Big Data in international scientific programmes stands to benefit all humanity. Achieving this benefit depends, however, on the widest possible access to and participation in the creation of that knowledge. Where economically feasible, the coordinated development of universally accessible and global research infrastructures capable of handling Big Data should be supported by collaboration between all countries and international organizations. The sustainability of such research infrastructure needs to be addressed by demonstrating the contribution to science and return on investment where appropriate.

5. Explore and Address the Challenges of Big Data Stewardship

The principle that data sources should be available for verification and re-­‐use holds true for Big Data, providing an important incentive for selecting, preserving, documenting and further disseminating curated data products. Making the most effective use of Big Data will require curation, quality assessment and the quantification of uncertainty for relevant data sources. However, the challenges and considerable costs involved in the stewardship and long-­‐term preservation of Big Data need further examination and will have implications for the way in which this principle can in practice be realised. The ICSU World Data System (ICSU-­‐WDS), and others, can provide expertise and a coordinating function which will be essential for addressing the specific challenges of Big Data stewardship.

6. Encourage capacity building and skills development

The commercial potential of Big Data and data analytics has been much publicized, as has the pressing need for skills development in Big Data science. This undoubtedly holds true in a research context also. We call on the partners to this initiative to collaborate with appropriate national and international organizations to advance an agenda for capacity building and skills development in Big Data science. This involves prioritization of Big Data science in educational regimes and developing the career paths of early career researchers.

7. Foster development of policies to maximize exploitation of Big Data

Big Data raise new and more complex management, access and reuse problems. Policies, guidelines, international agreements and protocols should be developed to maximize the collection, sharing and potential exploitation of Big Data for scientific research. Such activities need to be advanced on an international and multi-­‐disciplinary basis. CODATA, together with the stakeholders involved, can play a significant role in support of such policy development to address the specific challenges of Big Data.

Proposed Actions for a CODATA Working Group

A. Establish a CODATA Working Group on Big Data for Scientific Programmes

CODATA should convene and coordinate a broad-­‐based and expert, international Working Group to examine and promote Big Data issues and opportunities, such as those initially identified below, for international scientific programmes; co-­‐sponsors to the Workshop and other partners are strongly invited to collaborate.

a. Produce case studies in Big Data for international scientific programmes

Greater clarity is needed about the precise nature of the challenges and opportunities of Big Data. Produced in partnership with relevant international research programmes, a series of case studies with reproducible examples, focusing on use-­‐oriented data for solutions-­‐oriented science, would be an effective means of improving understanding and sharing knowledge.

b. Promote sharing of Big Data solutions across scientific disciplines

The Workshop identified several possible approaches for technical, infrastructure, and analytic solutions, including activities that draw on and apply reference models, or transpose solutions developed in one research context and discipline to others. The Working Group should collaborate with other partners and with international scientific programmes to facilitate a mechanism for discussing, publicising, sharing and possibly adopting such solutions. A roadmap for exploiting Big Data in interdisciplinary and societally-­‐relevant research could be a possible outcome of this work.

c. Research policy, ethical and legal issues for Big Data

In pursuing a policy agenda for Big Data, specific issues must be addressed. Where Big Data draws on observation or sensor networks, social media and other means of data gathering involving or relating to human subjects, ethical and legal issues need to be addressed. Big Data are not just quantitatively different from smaller data sets; they have characteristics that are also qualitatively different and that raise new science and data policy issues that need to be identified and resolved. For example, licensing and access issues persist, but potentially on a different scale if the potential of vast data integration is to be achieved. The policy agenda should aim to remove inappropriate barriers, perceived or otherwise, to the use and reuse of Big Data sources.

d. Research stewardship and sustainability challenges for Big Data

In collaboration with ICSU-­‐ WDS and other partners, the proposed Working Group should help coordinate a detailed consideration of the dual challenges of long-­‐term preservation and the sustainability of data infrastructures in relation to the stewardship of Big Data products for international scientific programmes.

Background

This Statement of Recommendations and Actions was made at the Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities, convened by CODATA, the ICSU Committee on Data for Science and Technology, with a number of international partners, in Beijing, China, on 8-­‐9 June 2014.

The workshop was designed to provide a better understanding of the opportunities and challenges of ‘Big Data’ for international collaborative scientific programmes, including

  • ICSU-­‐sponsored programmes such as Future Earth and Integrated Research on Disaster Risk (IRDR),
  • International initiatives such as the Group on Earth Observations (GEO), the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), the International Society for Digital Earth (ISDE), and
  • Initiatives sponsored by the Belmont Forum.

The workshop was convened by CODATA, the ICSU Committee on Data for Science and Technology, and co-­‐ sponsored by

  • :The ICSU World Data System (ICSU-­‐WDS),
  • Future Earth,
  • Integrated Research on Disaster Risk,
  • The Group on Earth Observations,
  • The Research Data Alliance (RDA),
  • The International Society for Digital Earth (ISDE), and
  • The Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS).

The objective of the workshop was to help set an agenda for collaborative and coordinated activities on to help exploit Big Data for international science, in which CODATA should play a leading role.

Slides

Slides

Slide 2 Overview

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Slide 3 Overview (continued)

BrandNiemann04112014Slide3.PNG

Slide 4 Data Science Central: 9 "must read" articles

http://www.datasciencecentral.com/pr...-read-articles 

BrandNiemann04112014Slide4.PNG

Slide 5 My Selection (Vincent Granville): List

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Slide 6 Big Data-From Descriptive to Prescriptive

http://scn.sap.com/community/utiliti...ional-insights

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Slide 7 My Selection (Vincent Granville): Footnote and Comment

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Slide 8 My Selection (Vincent Granville): Other Internal Links

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Slide 9 Six Categories of Data Scientists

http://www.datasciencecentral.com/pr...ata-scientists

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Slide 10 Update About Our Data Science Apprenticeship-March 10, 2014

http://www.datasciencecentral.com/gr...apprenticeship

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Slide 11 Update About Our Data Science Apprenticeship-March 29, 2014

http://www.datasciencecentral.com/gr...-march-29-2014

http://www.datasciencecentral.com/pr...d-and-practice

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Slide 12 Developing Analytic Talent

http://semanticommunity.info/Data_Science/Data_Science_Central#Developing_Analytic_Talent

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Slide 13 Data Science Certification

http://www.datasciencecentral.com/gr...-certification

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Slide 15 Dr. Vincent Granville: A Visionary Data Scientist

http://www.datasciencecentral.com/pr...ncentGranville

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Slide 16 Big Data-From Descriptive to Prescriptive Examples

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Slide 17 How was the data collected?

http://semanticommunity.info/Data_Science/Data_Science_Central#Registered_meteorites_that_has_impacted_on_Earth_visualized

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Slide 18 Where is the data stored?

http://semanticommunity.info/@api/deki/files/27220/meteors.xlsx

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Slide 19 What were the results?

Web Player

BrandNiemann04112014Slide19.PNG

Slide 20 What is the data story?

http://semanticommunity.info/Data_Science/Data_Science_Central#From_Data_Science_Central_to_Data_Science_Results

BrandNiemann04112014Slide20.PNG

Slide 21 Developing Analytic Talent

http://semanticommunity.info/Data_Science/Data_Science_Central#Developing_Analytic_Talent

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Slide 22 Chapter 1: What is Data Science?

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Slide 23 Chapter 2: Big Data is Different

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Slide 24 Chapter 3: Becoming a Data Scientist

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Slide 25 Chapter 4: Data Science Craftsmanship-Part I

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Slide 26 Chapter 5: Data Science Craftsmanship-Part II

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Slide 27 Chapter 6: Data Science Applications - Case Studies

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Slide 28 Chapter 7: Launching Your New Data Science Career

http://bit.ly/1cGlFA5
http://bit.ly/197Jsfa
http://bit.ly/11WhOcu
http://bit.ly/1dmCouo

 

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Slide 30 Addendum

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Slide 31 Big Data – From Descriptive to Prescriptive Example: Forecasting Meteorite Hits, pages 248-252

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Slide 33 Where is the data stored? And What are the results?

http://bit.ly/1gaiIMm

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Slide 34 Why Did It Happen (correlation analytics) and What Will Happen Next (predictive analytics)?

Web Player

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Slide 35 What Should I Do About it (prescriptive analytics) 1

http://www.lsst.org/

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Spotfire Dashboard

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPad App

Error: Embedded data could not be displayed. Use Google Chrome

Research Notes

February Workshop: http://www.codata.cn/en/enabout.asp My Note: Cannot find agenda

International Journal of Digital Earth: http://www.tandfonline.com/toc/tjde2...t#.U0bIblWzGTM My Note: In process

Data Science Journal: https://www.jstage.jst.go.jp/browse/dsj/1/0/_contentsMy Note: Inventoried 509 for mining below

Mining results:

Editor

2008: Editor's Note: SCIENTIFIC "AGENDA" OF DATA SCIENCE: https://www.jstage.jst.go.jp/article...ditorial1/_pdf

General Interest

2013: CODATA-ICSTI Task Group on Data Citation Standards and Practices: https://www.jstage.jst.go.jp/article...SOM13-043/_pdf

2009: Building a Community of Data Scientists: An Explorative Analysis: https://www.jstage.jst.go.jp/article...8_008-004/_pdf

2013: Is Data Publication the Right Metaphor?: https://www.jstage.jst.go.jp/article...2_WDS-042/_pdf

2013: A Science Cloud for Data Intensive Sciences: https://www.jstage.jst.go.jp/article...2_WDS-024/_pdf

2013: ICSU and the Challenges of Data and Information Management for International Science: https://www.jstage.jst.go.jp/article...2_WDS-001/_pdf

2004: Integrated science for environmental decision-making: https://www.jstage.jst.go.jp/article.../0/3_0_38/_pdf

2005: A tool for public analysis of scientific data: https://www.jstage.jst.go.jp/article.../0/4_0_39/_pdf

2005: eScience and archiving for space science: https://www.jstage.jst.go.jp/article.../0/4_0_67/_pdf

2007: Big Opportunities in Access to "Small Science" Data: https://www.jstage.jst.go.jp/article.../6_0_OD58/_pdf

Workshops on Extremely Large Databases

2008: Report from the SciDB Workshop: https://www.jstage.jst.go.jp/article.../0/7_7-88/_pdf

2008: Report from the first Workshop on Extremely Large Databases: https://www.jstage.jst.go.jp/article...becla0223/_pdf

2008: Report from the 2nd Workshop on Extremely Large Databases:
https://www.jstage.jst.go.jp/article...0/7_7-196/_pdf

2009: Report from the 3rd Workshop on Extremely Large Databases: https://www.jstage.jst.go.jp/article.../8_xldb09/_pdf

2010: Report from the 4th Workshop on Extremely Large Databases: https://www.jstage.jst.go.jp/article.../9_xldb10/_pdf

2012: Report from the 5th Workshop on Extremely Large Databases: https://www.jstage.jst.go.jp/article...1_012-010/_pdf

Databases

2002: China's Natural Resources Database (CNRD): https://www.jstage.jst.go.jp/article...0/1_0_238/_pdf

2004: The PIR integrated protein databases and data retrieval system: https://www.jstage.jst.go.jp/article...0/3_0_163/_pdf

2013: Data Mining Approaches for Habitats and Stopovers Discovery of Migratory Birds: https://www.jstage.jst.go.jp/article...2_WDS-027/_pdf

2010: Data Management Activities of Canada's National Science Library - 2010 Update and Prospective: https://www.jstage.jst.go.jp/article...9_009-026/_pdf

Ontology

2010: Materials Ontology: An Infrastructure for Exchanging Materials Information and Knowledge: https://www.jstage.jst.go.jp/article...9_008-041/_pdf

2007: Geo-Information (Lake Data) Service Based on Ontology: https://www.jstage.jst.go.jp/article.../6_0_S884/_pdf

2013: Constructing Ontology for Knowledge Sharing of Materials Failure Analysis: https://www.jstage.jst.go.jp/article...12_12-047/_pdf

2010: A System for Ontology-Based Sharing of Expert Knowledge in Sustainability Science: https://www.jstage.jst.go.jp/article...9_Kraines/_pdf

CODATA Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities

Source: PDF

Dear Dr Niemann,

As President of the International Council for Science (ICSU) Committee on Data for Science and Technology (CODATA), and on behalf of the CODATA Executive Committee, I would like to invite you to attend and participate in the Workshop on Big Data for International Scientific Programmes that will take place in Beijing on 8-­‐9 June, running in parallel to the IRDR Conference and just after meetings of the Future Earth Scientific and Engagement committees as well as that of the Science and Technology Alliance for Global Sustainability.

The workshop is designed to help ICSU sponsored programmes such as Future Earth and IRDR, as well as other international research programmes and collaborations, take advantage of the Big Data age. Further details are contained in the attached document and a fuller programme will be available soon.

Specifically, we would like to invite you to give a presentation on both the imperatives and the challenges involved in extracting information from the data involved in your research in order to tackle the great environmental, developmental and societal challenges of our times. We would also be grateful if you could express your views on the opportunities, success and lessons for taking advantage of data intensive approaches and 'Big Data' in your research field and in related interdisciplinary research. I would also be greatly honoured also if you were able to attend the workshop reception on the evening of Sunday 8 June.

I sincerely hope that you will accept this invitation and I look forward to hearing from you.

Yours sincerely,
Huadong Guo, President, CODATA

Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities

Source: PDF

Beijing, 8-­‐9 June 2014

Summary

CODATA, the ICSU Committee on Data for Science and Technology, will convene a Workshop on Big Data for International
Scientific Programmes: Challenges and Opportunities to be held in Beijing, China, on 8-­‐9 June 2014.

The workshop is designed to provide a better understanding of the opportunities and challenges of ‘Big Data’ for international collaborative science programmes, including ICSU-­‐sponsored programmes such as Future Earth and Integrated Research on Disaster Risk (IRDR), as well as international initiatives such as the Group on Earth Observations (GEO), the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), the International Society for Digital Earth (ISDE), and initiatives sponsored by the Belmont Forum. The results of the workshop will also help set an agenda for CODATA activities on Big Data for international science.

The workshop is co-­‐sponsored by the ICSU World Data System (ICSU-­‐WDS), the Research Data Alliance (RDA), the International Society for Digital Earth (ISDEand the Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences (RADI).

The event will follow the Future Earth Scientific Committee and Engagement Committee Meetings being held in Beijing and take place in parallel with the 2nd IRDR International Conference to facilitate participation by attendees of those communities.

Context and Rationale

Rapid advances in technology are radically changing the way in which data are being collected, used, and stored. Digital data are gathered, replicated, moved, and processed more quickly and in greater volumes than ever before. As new information technologies, sensors, and communication networks develop, the range and complexity of scientific data continue to grow. With data volumes expanding beyond the petabyte and exabyte levels across many scientific disciplines, the capacity for storage and preservation and for long-­‐term use may be exceeded in many fields. Above all, the opportunities to extract information from complex data sources from diverse disciplines offers compelling reasons to embrace the new scientific methods and approaches of ‘Big Data’ and data-­‐driven research.

In a world dealing with growing populations, pressing economic and social needs, natural and technological hazards, and climate change, there is a clear need for more, robust and high-­‐quality data—along with new analytics and models and faster delivery and visualization of information—to support evidence-­‐based decision making and risk management by a wide range of stakeholders. There are lessons to be learned, both positive and negative, from big data efforts in genomics, business, astronomy, and other fields that can be applied in developing Future Earth, IRDR, and other programmes and in ensuring that these initiatives have greater and more lasting impacts than they might otherwise have.

‘Big Data’ presents particularly significant challenges and notable opportunities for transdisciplinary, international research programmes such as Future Earth and IRDR, as well as for international initiatives such as GEO, IPBES, and the Belmont Forum. These efforts aim to guide research and produce research results and data in ways that improve decision-­‐making on critical issues for humankind and the environment. To accomplish this requires the integration of increasingly diverse and complex datasets and the extraction and interpretation of knowledge utilizing sophisticated statistical techniques, complex simulation models, and other computationally intensive approaches.

‘Big Data’ has become an overarching and critical issue for both basic and applied scientific research, and one to which CODATA brings expertise and leadership. As an interdisciplinary body of ICSU, CODATA has a convening role and a mission to explore data issues in the context of international research, whether specific disciplines (as represented by International Scientific Unions) or the interdisciplinary research as promoted in international, collaborative programmes.

Indicative Workshop Programme​ 

The purpose of the Workshop on Big Data for International Scientific Programmes is to bring together Big Data leaders from discipline-­‐specific data efforts, major interdisciplinary programmes, and national and international organizations and activities to help identify opportunities and approaches for improving cooperation and collaboration in applications of Big Data in international scientific programmes and initiatives.

‘Using Big Data to Advance International Scientific Programmes’, 8 June: Day One of the two-­‐day workshop will focus on exploring and reviewing current Big Data-­‐related programmes, with special emphasis on how communities cooperate and approaches used to overcome issues.

Session 1.2 Data Challenges and Big Data in Future Earth, IRDR, GEO and ISDE

Session 1.1 Big Data Today: lessons from international science initiatives and the commercial world

Sessions 1.3 and 2.1 Success and Lessons Learnt in Data Intensive Science

Reception and Banquet sponsored by RADI (Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences).

‘Building International Cooperation and Collaboration on Big Data for International Science’, 9 June: Day Two will start by identifying opportunities for new levels of cooperation and coordination, especially for emerging large-­‐scale S&T programmes. The workshop will conclude with discussions on the role of CODATA in facilitating Big Data progress in S&T through working groups and other means.

Session 2.2 How to make progress: Opportunities for Collaboration on Big Data for International Science 

Session 2.3 Panel to discuss CODATA WG on Big Data for International Science 

The programme will feature keynote and other invited speakers from leaders in Big Data. There also are discussion sessions designed to identify key opportunities for new levels of cooperation and coordination among international scientific programmes, as well as contribute to defining the scope of a CODATA activities in Big Data.

Further Information

The workshop is envisaged as a relatively small and focused event, convening 40-­‐50 world experts. It is intended that proceedings and a report of the workshop will be published in the CODATA Data Science Journal, while a position piece will be submitted to a high impact journal. Above all, the workshop will bring together Big Data experts and researchers involved in international research programmes and identify opportunities for future collaboration to facilitate specific research objectives. These themes will feed into SciDataCon 2014, the International Conference on Data Sharing and Integration for Global Sustainability which ICSU CODATA is organizing with its sister organization, the ICSU World Data System. Finally, it is intended that the workshop should help set the agenda for ongoing CODATA activities in support of ‘Big Data for International Science’. 

The workshop will be supported by the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), and by other partner organizations. The full programme and other details will appear shortly.

CODATA International Training Workshop in Big Data for Science

Source: PDF

CODATA International Training Workshop in Big Data for Science, for Researchers from Emerging and Developing Countries Beijing, China; 4-­‐20 June 2014

Applications are invited from young researchers, research leaders and managers of research institutes in emerging and developing countries to participate in the CODATA International Training Workshop in Big Data for Science, Beijing, 4-­‐20 June 2014. Participation for successful applicants will be financially supported, thanks to a grant from the Chinese Academy of Sciences (CAS). The deadline for applications is 16 April.

Workshop Objectives

New types of ‘mega-­‐science’ facilities and sensors are generating streams of digital data from telescopes, video cameras, traffic monitors, magnetic resonance imaging machines, and biological and chemical sensors monitoring the environment. This is a Big Data age, and it presents many exciting opportunities to make scientific research more productive, to accelerate discovery and innovation, and thereby to address key environmental, developmental and societal challenges.

The mission of CODATA (Committee on Data for Science and Technology) is to strengthen international science for the benefit of society by promoting improved scientific and technical data management and use. Organizing, for international participants, technical training in data science, in data management and on the implications of ‘Big Data’ for science is one of the most important means by which this mission can be realized.

The training program offered aims to engage participants with a number of facets of data science and data management in the Big Data age. Topics include, but are not limited to, interdisciplinary applications of data intensive research, data management policies, cloud computing, visualization and data infrastructure development in the Big Data Age.
Through an intensive program of lectures and workshop activities, the course will promote interaction and exchange of knowledge between experts and participants. It is intended that participants should benefit greatly from participation in a group from widely varying academic and national backgrounds.

A number of activities will be organized involving elite Chinese scientists, in order to promote knowledge sharing and to develop opportunities for future exchanges and collaboration. Participants will benefit also from visits to a number of leading research institutes of the Chinese Academy of Sciences (CAS). In these visits, participants will have the opportunity to learn from the scientific approach, management expertise, knowledge development and practical application which characterize activities at CAS institutes working at the frontiers of research.

On 8-­‐9 June, CODATA is convening a major Workshop on Big Data for International Scientific Programmes, involving  any international experts. Participants in the training workshop will benefit from participation also in this important event.

The workshop will offer training in Big Data management, analytics, technology and scientific practice for research community members from emerging and developing countries. Through a mix of theoretical study, workshop and practical sessions as well as field visits, the organizers aim to help participants develop a deeper expertise in the latest Big Data technologies for scientific discovery.

Course Contents

The Training Workshop curriculum will include:

  • Welcome and introduction to CODATA
  • Overview of CODATA Strategic Initiatives and Activities
  • The Big Data Landscape: Definitions and Concepts
  • Big Data applications
  • Big Data management policies
  • Big Data analysis and processing technology
  • Cloud computing and data discovery, visualization
  • Data description and documentation
  • Data submission and acquisition
  • Data support services
  • Scientific data infrastructure development

More details about the training program will be provided shortly.

Training methods and activities

The program will take a comprehensive approach, including intensive theoretical lectures, advanced seminars and workshops covering workshop themes. Lectures and seminars will together account for 50% of the course time. For the remainder of the course, trainees will benefit from visits to famous CAS research institutes, high-­tech corporations, involving meetings with elite Chinese scientists.

Participants

Intended participants include young researchers as well as more senior research leaders and managers of research institutes in countries with emerging and developing economies. 1

1 The working definition used here is those countries other than the advance economies as defined by the IMF in its World Economic Outlook 20112 http://www.imf.org/external/pubs/ft/...1/pdf/text.pdf, Table B.

The total number of participants will not exceed twenty.​

Program Language

English. Participants should have good command of English.

Examination and Certificate

When the program is completed, trainees will be awarded the Certificate of Program Completion. Participants will be required, before attendance, to prepare a presentation on their own country’s scientific data initiatives and challenges. During the course, participants will be asked to develop this into a paper considering how Big Data may apply to scientific questions of importance within theircountry and internationally.

Date and Duration

The training program will take place from 4-­‐20 June 2014.

Venue

Computer Network Information Center, Chinese Academy of Sciences, No.4, Nan 4th, Zhongguancun, Haidian District, Bejing, CHINA.

Sponsors

Committee on Data for Science and Technology (CODATA) Chinese Academy of Sciences (CAS)

Organizers

CODATA Secretariat Chinese National Committee for CODATA Computer Network Information Center, Chinese Academy of Sciences

Training Expenses

1. The expenditure of training, international travel costs, board and lodging, local transportation for the purpose of training during the training period in China for the participants is to be funded by Chinese Academy of Sciences.

2. All the other possible expenses, such as insurance, cost of medical care, and telephone fees during the training period will be borne by the participants

3. The program organizer will buy Personal Accident Insurance for all participants during their training.

Participants Qualifications and Requirements For Admissions

1. To be nominated by his/her National Committee of CODATA or CODATA Taskgroups or related government;

2. To be under 50 years old and meet the requirements set in “trainees identity”;

3. Have a good health, no infectious disease and physically capability to fulfill all course activities

4. To be proficient in English reading, listening, speaking and writing

5. Not to bring any family members to attend the program

6. Pledge to observe all the laws and regularities of the P.R. China and respect the local customs during the training period in China

7. Prepare a review paper on the development of the science and technology in your respective country and your work.

Application Procedure and Deadline

Applications should be completed on the proper form, providing requested information and an academic CV. Completed applications should be submitted to the addresses below by the deadline of 00.00 UTC on 16 April 2014:

Dr. Simon Hodson, Executive Director, CODATA 

E-­‐mail: execdir@codata.org

Ms. LI Chang, CODATA-­‐China Secretariat

E-­‐mail: lichang@cnic.cn

Application Form: CODATA International Training Workshop in Big Data for Science

Source: Word

 

Application Form:

CODATA International Training Workshop in Big Data for Science,

for Researchers from Emerging and Developing Countries

Computer Network Information Center, Chinese Academy of Sciences,

Beijing, China; 4-20 June 2014

 

Please complete the form below and send it, with an academic CV, to the addresses below by the deadline of 00.00 UTC on 16 April 2014:

Dr. Simon Hodson, Executive Director, CODATA

E-mail: execdir@codata.org

Ms. LI Chang, CODATA-China Secretariat

E-mail: lichang@cnic.cn

 

Family Name:

 

Given Name:

 

Date of Birth:

 

Sex:

 

Nationality:

 

Academic Title/Position:

 

Area of Research:

 

Organization:

 

Address:

 

Tel:

 

Fax:

 

E-mail:

 

Education Degree:

 

Proficiency in English

Excellent:

 

Good:

 

Fair:

 

Poor:

 

                         

Please provide a short statement (no more than a page in total) providing information on the following:

  1. What expertise would you bring as a participant?
  2. What specific subjects would you like to cover and how will this help you?
  3. How would you hope to apply the experience gained from the course in your own country? 

​​CODATA Blog

Source: http://codata.org/blog/

ARCHIVES

March 2014

Great Success of CODATA-China’s First Scientific Data Conference

The First Scientific Data Conference, organised by the Chinese National Committee for CODATA (CODATA-China), was recently held in Beijing, on 24-25 February 2014.  Entitled Big Research Data and Data Science, the conference aimed to improve understanding of the central issues in the era of Big Data, to promote multidisciplinary communication, to help the development of young data scientists, to encourage revitalisation of traditional research approaches and to contribute to and support the Chinese national strategy to promote innovation.

Big Research Data and Data Science

My Note: Find Agenda

CODATA Data Science Journal

The CODATA Data Science Journal Volume 12 (2013) is now available.

ISSN 1683-1470

The Data Science Journal is a peer-reviewed, open access, electronic journal publishing papers on the management of data and databases in Science and Technology. The scope of the Journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agricultural and the Medical Sciences.

The Journal publishes data or data compilations, if the quality of data is excellent or if significant efforts are required in compilation.

The Journal publishes online simulation, database, and other experiments overcoming the inherent limitations of traditional, static print journals, thereby adding an entirely new dimension to the communication and exchange of data research results and educational materials.

We would like to acknowledge, with thanks, the financial contribution of UNESCO to the funding of the journal.

Current Issue 2013: https://www.jstage.jst.go.jp/browse/...12/0/_contents

View All Abstracts

My Note: Inventory All and then select one to be a Data Paper

CODATA (Committee on Data for Science and Technology-Databases)

Databases and Database Issues

CODATA Key Values for Thermodynamics

My Note: Starting to mine this page

All Values (ascii): http://physics.nist.gov/cuu/Constant...e/allascii.txt

Fundamental Physical Constants --- Complete Listing
From:  http://physics.nist.gov/constants

My Note: See spreadsheet

International Register of Materials Database Managers

My Note: See spreadsheet

 

1 Mr. Fujio Abe 
National Research Institute for Metals (NRIM) 
1-2-1 Sengen, Tsukuba 305 
JAPAN 
Telephone: +81 298 531074 
Fax: +81 298 531088 
Email: abe@nrim.go.jp

NRIM CDS
NRIM Creep Data Sheets contain long-term creep and rupture data, including 10[5] h-creep rupture strength data for many kinds of heat resisting steels and alloys.

2 Mr. Tom Amoscato 
Texas Research Institute Austin/NTIAC 
415 Crystal Creek Drive, Austin TX 78746-4725 
USA 
Telephone: +1 512 2632106 
Fax: +1 512 2633530 
Email: ntiac@access.texas.gov

Non-destructive Testing Information Analysis Center (NTIAC)
Technical reports on all facets of nondestructive testing, evaluation and inspection. All materials from 1996 onwards.  

3 Mr. D. Arthur 
Institute of Materials 
1 Carlton House Terrace 
London SW14 5DB 
UK 
Telephone: +44 171 8394071 
Fax: +44 171 8395513 
Email: instmat@cityscape.co.uk

MIS
Collection of contacts and expertise in the field of engineering materials. Ferrous metals, plastics, ceramics, non-ferrous metals, composites  

4

Dr. Yuji Asada 
National Research Institute for Metals (NRIM) 
1-2-1 Sengen, Tsukuba 305 
JAPAN 
Telephone: +81 298 531082 
Fax: +81 298 531090 
Email: asada@nrim.go.jp

SUPERCON
Superconductivity, Hall effect, thermopower, specific heat, thermal conductivity.

5

Prof. Michael F. Ashby 
Cambridge University Department of Engineering 
Trumpington Street Cambridge CB2 1PZ 
UK 
Telephone: +44 1223 332635 
Fax: +44 1223 332662 
Telex: 81239

CMS
Software for materials and process selection, selection of section shapes.

6

Dr. Günther Breitkopf 
Motoren und Turbinen Union München GmbH 
Postfach 50 06 40, Dachauer Strasse 665 
D80995 München 
GERMANY 
Telephone: +49 89 14893323 
Fax: +49 89 14896101 
Telex: 52950015 mt d

MATADOR 
Ferrous and non-ferrous metals, numerical data for physical and mechanical properties.

7

Mr. Joel Calhoun 
Rare Earth Information Center (RIC) 
112 Wilhelm, Iowa State University 
Ames, IA 
USA 
Telephone: +1 515 2942272 
Fax: +1 515 2943709 
Email: RIC@ameslab.gov

RICIRS 
More than 80 000 documents on the science and technology, properties, research and toxicity of rare earth metals, alloys and compounds.

NNCIRS 
Trade leads for Rare Earth Metals, Alloys and Compounds. Buyers and sellers of Rare Earths.  

8 Mr. J.-P. Caliste 
SQUALPI 
22, rue Monge, 75005 Paris 
FRANCE 
Telephone: +33 1 43195073 
Fax: +33 1 43195044 
Email: jean-pierre.caliste@centrale.industrie.fr

REFDATA-COMAR

 

SPAO

9 Mr. Chen, Yunyuan 
Shanghai Research Institute of Materials 
99 Han Dan Road, Shanghai 
CHINA 
Telephone: +86 21 65420775 
Fax: +86 21 65420554

DMPME, Database of Material Properties for Mechanical Engineering 
Includes: mechanical properties (including fatigue), physical, corrosion, wear and resistance properties of irons, steels, non-ferrous metals and engineering plastics. 

10 Dr. Bertrand Cheynet 
THERMODATA 
B P 66, 38402 Saint Martin d'Heres Cedex F 
FRANCE 
Telephone: +33 4 76427690 
Fax: +33 4 76631537 
Email: bcheynet@grenet.fr

THERMALLOY 
Thermodynamic properties of alloys and inorganic mixtures.

THERMODATA 
Bibliographical database on thermochemical properties of inorganic substances and alloys.

THERMCOMP 
Thermochemical properties of inorganic substances. 

 

11 Ms. Fran Cverna 
ASM International 
Route 87, Materials Park, OH 44073-0002 
USA 
Telephone: +1 216 3385151 
Fax: +1 216 3384634 
Telex: 98 0619 ASMINT 
Email: FCverna@po.ASM-Intl.org

Property Data from ASM (EMPS, MAPP, Mat.DB, Rover) 
Mechanical and physical properties (tabular, graphs, text, references) available with MAPP, MVISION, Rover Electronic Databook and STN software; primarily metals and alloys.

12

Dr. G. Dathe 
BFI Betriebstechnik GmbH 
Postfach 105145, D-40042 Düsseldorf 
GERMANY 
Telephone: +49 211 6707250 
Fax: +49 211 6707310 
Telex: 8582512

STAHLDATEN 
Nominal values of registered steels (EN, DIN, works standard specifications) on flexible discs.

13

Dr. E. T. Denisov 
Institute of Physical Chemistry 
Vorobevskoye Shosse 2 B 
117334 Moscow 
RUSSIA 
Telephone: +7 095 5171914 
Fax: +7 095 5153588 
Email: denisov@icph226.sherna.msk.su

DBBDEOM: Database of Bond Dissociation Energies of Organic Molecules 
Contains values of bond dissociation energies of organic compounds evaluated from experimental data. 

 

14 Mrs. Catherine Droniou 
Centre d'Etudes de Chimie Metallurgique (CNRS/CECM) 
15, rue Georges Urbain 
94407 Vitry-sur-Seine Cedex 
FRANCE 
Telephone: +33 1 46873593 
Fax: +33 1 46750433 
Email: droniou@glvt_cnrs.fr

HYDROGENE DATA 
Ferrous and non-ferrous metals, ceramics: interaction of hydrogen with materials. 

 

15 Dr. G. Effenberg 
Materials Science Int. Services, GmbH 
Postfach 800749, D-70507 Stuttgart 
GERMANY 
Telephone: +49 711 6771240 
Fax: +49 711 6771248 
Email: effenberg@gold.tz.rus.uni_stuttgart.de

MSI-Phase Diagram Centre 
All metal systems: phase diagrams, structure data and literature, from 1935 onwards; abstracts of current literature from 1990 onwards.

16 Mrs. M. A. Fleming 
ASM International 
Route 87, Materials Park, OH 44073-0002 
USA 
Telephone: +1 216 3385151 
Fax: +1 216 3384334 
Email: MAFlemin@po.ASM-Intl.org

Alloy Phase Diagram data

17 Ms. S. K. Foss 
Deere & Company 
Technical Center, 3300 River Drive, Moline, IL 61265 
USA 
Telephone: +1 309 7653820 
Fax: +1 309 7653807 
Email: fosss@de.deere.com

Fatdb 
Ferrous material monotonic, low cycle fatigue, chemical and metallurgy data developed and housed at the Technical Center. 

 

18 Mr. Daniel G. Friend 
National Institute of Standards and Technology (NIST) 
Fluid Mixtures Data Center 
Thermophysics Division, 838.08 
325 Broadway, Boulder CO 80303 
USA 
Telephone: +1 303 4975424 
Fax: +1 303 4975224 
Email: dfriend@boulder.nist.gov

NIST 14 
Calculates thermophysical properties of selected fluids and their mixtures. 

 

19 Dr. Bruce S. Hemingway 
U S Geological Survey 
National Center for Thermodynamic Data of Minerals 
955 National Center, Reston, UA 22092 
USA 
Telephone: +1 703 6486740 
Fax: +1 703 6486789 
Email: bhemingw@usgs.gov

THERM PROP 
Thermodynamic properties of minerals and related substances.

20

Sen. Eng. Hu, Xing-jun 
Research Institute of Synthetic Materials Ageing 
Tangxia, Tianhe, Guangzhou (510665) 
CHINA 
Telephone: +86 2305059

DPWA/Datbase of Polymer Weathering Aging 
Weathering Aging of Polymers (plastics, rubber, painting), data of mechanical properties in six areas of China 

 

21 Dr. Takao Inukai 
Toshiba Corporation 
Heavy Apparatus Engineering Laboratory 
1-9 Suehirocho, Tsurumiku, Yokohama 230 
JAPAN 
Telephone: +81 45 5096681 
Fax: +81 45 5096792 
Telex: 3822-727

Fatigue Data 
Retrieval system for metallic materials.

22

Dr. Said Jahanmir 
National Institute of Standards and Technology (NIST) 
A329 Materials Building, Gaithersburg, MD 20899 
USA 
Telephone: +1 301 9753671 
Fax: +1 301 9908729

TRIBOMATERIALS DATABASE 
Ceramics machining database

23

Mr. G. Jaroma-Weiland 
Institute for Nuclear Energy and Energy Systems 
University of Stuttgart, FRG, Pfaffenwaldring 31, D-70550 Stuttgart 
GERMANY 
Telephone: +49 711 6852125 
Fax: +49 711 6852010 
Email: weiland@ike.uni_stuttgart.de

THERSYST 
Factual database for thermophysical properties of solids.

24

Dr. A. I. Johns 
National Engineering Laboratory (NEL) 
Fluids and Process Technologies Division 
Reynolds Avenue, Scottish Enterprise Technology Park
East Kilbride, Scotland G75 0QU 
UK 
Telephone: +44 1355 272152 
Fax: +44 1355 272265 
Email: ajohns@nel.uk

PPDS 
Thermophysical properties of fluids (liquids and gases).

25

Prof. Dr. Masahiro Jono 
The Society of Materials Science, Japan 
Yoshida Izumidono-cho 1-101, Sakyo-ku, Kyoto 606 
JAPAN 
Telephone: +81 75 7615321 
Fax: +81 75 7615325

Fatigue strength of metallic materials.

Resistance to crack propagation of metallic materials.

Ceramics strength.

26

Mr. J. G. Kaufman 
Aluminum Association 
3662 Pevensey Drive, Columbus, OH 43220 
USA 
Telephone: +1 614 4593949 
Fax: +1 614 4593949 
Email: gkaufman@aluminum.org

AAASD 
Properties and specifications for aluminum alloys and products.

27

Dr. V. Kearley 
TRADA Technology Ltd 
Stocking Lane, Hughenden Valley 
High Wycombe, Bucks HP14 4ND 
UK 
Telephone: +44 1494 563091 
Fax: +44 1494 565487 
Email: vckearley@ttlchiltern.co.uk

PPD 
Information on the sources, supply and uses of more than 1600 brands of wood-based panels traded in the UK.

28

Mr. Turqui Kharchi 
nCode International Limited 
230 Woodburn Road, Sheffield S9 3LQ 
UK 
Telephone: +44 114 2755292 
Fax: +44 114 2758272 
Email: info@ncode.demon.co.uk

nSOFT FATIMAS MDM 
Fatigue related data for crack initiation and propagation.

29

Prof. Dr. Koji Kokubo 
Technical Research Institute 
Japan Society for the Promotion of Machine Industry 
1-1-12 Hachimancho, Higashi-Kurume, Tokyo 203 
JAPAN 
Telephone: +81 424 751177 
Fax: +81 424 741980

Machinability Database

30

Prof. Kenjiro Komai 
The Society of Materials Science, Japan 
Yoshida Izumidono-cho 1-101, Sakyo-ku, Kyoto 606 
JAPAN 
Telephone: +81 75 7615321 
Fax: +81 75 7615325

Fatigue strength of metallic materials.

Resistance to crack propagation of metallic materials.

Ceramics strength.

31

Dr. Jelal Kouznetsov 
VNITS SMV, Gosstandart Russia 
21, Dolgorukovskaya Str., Moscow, 103006 
RUSSIA 
Telephone: +7 095 5788850 
Fax: +7 095 9732549 
Email: str@stinfo.msk.su

Materials Safety Data Sheets 
numerical data and other information pertaining to the safety of substances and materials.

32

Prof. Alexander D. Kozlov 
Russian Research Center for Material Problems 
21, Dolgorukovskaya Str., Moscow, 103006 
RUSSIA 
Telephone: +7 095 9780190 
Fax: +7 095 9786877 
Email: str@stinfo.msk.su

GOSMATERIALBANK

Data and information on materials manufactured in Russia, including all industrial fluids.

33

Ms. Sonja Krause 
Rensselaer Polytechnic Institute 
Department of Chemistry 
Troy, NY 12180-3590 
USA 
Telephone: +1 518 2768445 
Fax: +1 518 2768554 
Email: userap3q@rpitsmts

Polymer-Polymer miscibility 
Experimental data on inter-miscibility of polymers and assessment thereof. 

 

34 Mr. Claude Le Breton 
CETIM 
52, ave Felix Louat, B.P. 67, 60304 Senlis Cedex 
FRANCE 
Telephone: +33 3 44583326 
Fax: +33 3 44583158 
Telex: 140006 
Email: Claude.Lebreton@CETIM.fr

CETIM-BDM 
Physical and mechanical characteristics of engineering materials: steels, cast irons, non-ferrous, polymers, composites, adhesives. Physical, mechanical, fatigue properties. Multi-criteria selection. 

 

35 Mr. Richard Lewis 
Health and Safety Executive 
Broad Lane, Sheffield S3 7HQ 
UK 
Telephone: +44 114 2892342 
Fax: +44 114 2892333

HSELINE 
Bibliographic database (1977), referring to British and worldwide hazardous substances.

36

Prof. Lu, Yunwen 
Tsinghua University 
321, Main Building, Tsinghua University, Beijing 100084 
CHINA 
Telephone: +86 10 2595767 
Fax: +86 10 2562768 
Telex: 22617 QHTSC CN 
Email: dmszrf@tsinghua.edu.cn

ACDB/Advanced Ceramics Data Base 
Composition, physical and mechanical properties of silicon nitrides and zirconia.

37

Mr. Stanford Lyon 
Los Alamos National Laboratory 
Group T-1, MS-B221, LANL, Los Alamos, NM 87545 
USA 
Telephone: +1 505 6677024 
Fax: +1 505 6655757 
Email: lyon@lanl.gov

SESAME 
Equation of State data over broad temperature-density range, ferrous and non-ferrous metals, plastics, composites, and ceramics.

38

Dr. Jurij V. Mamonov 
VNITS SMV 
21, Dolgorukovskaya, Moscow 103006 
RUSSIA 
Telephone: +7 095 9780386 
Fax: +7 095 9786877

Database and System on Thermophysical Properties of Fluids

 

(ASSISTENT) 
Data on thermophysical properties of fluids and mixtures for research and development.

39

Mr. Douglas E. Marinaro 
The MacNeal-Schwendler Corporation 
2975 Redhill Avenue, Costa Mesa, CA 92626 
USA 
Telephone: +1 714 4445072 
Fax: +1 714 9792990 
Email: douglas.marinaro@macsch.com

MSC/MVISION 
Information for predictive engineering, ensuring consistent data for engineers evaluating new designs and reducing cycle time by integrating materials data directly into CAD/CAE. 

40

Mr. Yves Mégoz 
Centre Technique de l'Industrie du Décolletage 
750, Avenue de Colomby, BP 65, 74301 Cluses Cedex 
FRANCE 
Telephone: +33 4 50983898 
Fax: +33 4 50983898 
Telex: 385 213 
Email: ctdec@cur-archamps.fr

VULCAIN BdM PLUS [Access Télétel: 01 36 29 00 68] 
Materials data bank, normative reference equivalents, chemical composition, mechanical and physical characteristics, manufacturing conditions, suppliers, choice guide. Ferrous and non-ferrous metals, plastics. 

41

Mrs. Gill Money 
Materials Information Service 
The Institute of Materials 
1 Carlton House Terrace, London SW1Y 5DB 
UK 
Telephone: +44 171 8394071 
Fax: +44 171 8395513 
Email: instmat@cityscape.co.uk

MIS
Expert information on all aspects of engineering materials' use and processing.

42

Mr. Patrick Morand 
Centre Scientifique et Technique du Bâtiment (CSTB) 
290, route des Lucioles, BP 141, 06561 Valbonne Cedex 
FRANCE 
Telephone: +33 4 93956735 
Fax: +33 4 93652937 
Telex: 970 194 F

CSTB 
Certified materials.

43

Mr. Takeshi Morikawa 
New Glass Forum 
Japan Glass Industries Center Building, 3-1-9 Shimbashi, Minatoku 
Tokyo 105 
JAPAN 
Telephone: +81 3 5952775 
Fax: +81 3 5950255

INTERGLAD Glass database on compositions and properties.  

44

Dr. Masatoshi Nihei 
National Research Institute for Metals (NRIM) 
1-2-1 Sengen, Tsukuba 305 
JAPAN 
Telephone: +81 298 531215 
Fax: +81 298 531091 
Email: nihei@nrim.go.jp

DIMS Materials strength and life prediction system for engineering.

45

Dr. Satoshi Nishijima 
National Research Institute for Metals (NRIM) 
Center for Advanced Physical Fields 
1-2-1 Sengen, Tsukuba 305 
JAPAN 
Telephone: +81 298 531071 
Fax: +81 298 531019 
Email: nishijima@nrim.go.jp

NRIM-JICST Materials Strength Database Strength data for engineering steels and alloys.

46

Dr. H. H. Over
Joint Research Centre of the European Commission
Institute Energy
PO Box 2, Petten, NL-1755 ZG
<country-region w:st="on"><place w:st="on">NETHERLANDS</place></country-region>
Telephone: +31 224 565256
Fax: +31 224 56
565632
Email:hans-helmut.over@jrc.nl
  

MAT-DB Materials database for mechanical and physical properties data of engineering alloys including high temperature corrosion for exposed alloys, ceramics and coatings. 

47

Mr. Richard B. Pettit 
Sandia National Laboratories 
Division 7243, P O Box 5800 
Albuquerque, NM 87185 
USA 
Telephone: +1 505 8446242 
Fax: +1 505 8444372 
Email: rbpetti@.sandia.govt

Optical Materials Properties 
Properties of optical materials 300-2500nm. Plastics, coatings/glass.

48

Mrs. M. J. Rami 
Institut des Matériaux Composites 
Site Montesquieu, 33650 Martillac 
FRANCE 
Telephone: +33 5 56649090 
Fax: +33 5 56649030

Composite Data 
Literature compilation on composite materials. manufacturer's data on composite materials, compilation of slides and addresses.

49

Mr. Keith Reynard 
Wilkinson Consultancy 
Stable Cottage, Broad Lane 
Newdigate, Dorking, Surrey RH5 5AT 
UK 
Telephone: +44 1306 631247 
Fax: +44 1306 631896 
Email: keith1@wilkicon.demon.co.uk

UK Materials Information Sources 
Directory of sources of materials information available to UK users.

50

Dr. John R. Rodgers 
TOTH Information Systems Inc. 
Montreal Road, Bldg. M-55 
Ottawa, Ontario, K1A 0S2 
CANADA 
Telephone: +1 613 9989076 
Fax: +1 613 9528246 
Email: rodgers@snd.cisti.nrc.ca

CRYSTMET 
Crystallographic, chemical, physical and related bibliographical data for metallics and intermetallics.

51

Ms. Anneliese Sachs-Brandt 
Papiertechnische Stiftung PTS 
Hess-Strasse 134, D-80797 München 
GERMANY 
Telephone: +49 89 121460 
Fax: +49 89 1236592 
Email: 100607.2045@compuserve.com

Papiertechnik-Datenbank 
German database for the papermaking and paper converting industry.

52

Prof. Tatsuo Sakai 
Ritsumeikan University 
Faculty of Science and Engineering 
1916 Nojicho Kusatsu, Shiga 525 
JAPAN 
Telephone: +81 775 612745 
Fax: +81 775 612665 
Email: sakai@bkc.ritsumei.ac.jp

FASMET: database on Fatigue Strength of Metallic Materials 
Compilation of experimental data on fatigue strength of metallic materials and related information.

CRAMET: database on Fatigue Crack Growth Rates of Metallic Materials Compilation of experimental data on fatigue crack growth rates of metallic materials and related information.

MSDRD: Material Strength Database for Reliability Design Compilation of experimental data on statistical distribution of mechanical properties for metallic, composite and ceramic materials.

53

Mr. Akira Sakamoto 
R&D Institute of Metals and Composites for Future Industries 
Bridgestone Toranomon Bldg. 
25-2, Toranomon 3-chome, Minato-ku, Tokyo 105 
JAPAN 
Telephone: +81 3 34596900 
Fax: +81 3 34596911 
Email: rimcof@bnn-net.or.jp

PRODACOM 
The research project on advanced composites materials performed in Japan from 1981 to 1988, the fact data of tested materials' properties have been stored in PRODACOM

54 Mr. Masao Sakamoto 
National Research Institute for Metals (NRIM) 
5th Research Group 
1-2-1 Sengen, Tsukuba 305 
JAPAN 
Telephone: +81 298 531215 
Fax: +81 298 531091 
Email: sakamoto@nrim.go.jp

JSSR Database 
Data for springs.

55

Ms. Joan Sauerwein 
National Institute of Standards and Technology (NIST) 
Standard Reference Data Program 
Room 113, Bldg. 820, Gaithersburg, MD 20899 
USA 
Telephone: +1 301 9752208 
Fax: +1 301 9260416 
Email: SRDATA@nist.gov

NIST Crystal Data 
Chemical, physical and crystallographic information useful in characterizing more than 210 403 inorganic and organic crystalline materials.

NIST/Sandia/ICDD Electron Diffraction Database 
Contains chemical, physical and crystallographic information on a wide variety of materials (over 81 534), including minerals, metals, intermetallics and general inorganic compounds.

NIST High Temperature Superconductors Database 
Materials properties for high temperature superconductors, providing evaluated property data for oxide superconductors. Covers compounds from Y-Ba-Cu-O, Bi-Sr-Ca-Cu-O, Tl-Sr-Ca-Cu-O and La-Cu-O chemical families,and variants of cuprate and bismate materials.

NIST Structural Ceramics Database: Version 2.0 
Thermal, mechanical and corrosion properties of silicon carbides and silicon nitrides in a self-contained database system.

56

Mr. J. Sears 
Institute of Electrical Engineers 
Michael Faraday House 
Six Hills Way, Stevenage, Herts SG1 2AY 
UK 
Telephone: +44 1438 313311 
Fax: +44 1438 360079 
Telex: 825578 IEESTV G 
Email: jsears@iee.org.uk

Electronic Materials Information Service (EMIS) 
Semiconductors, etc., properties, growth and processing of electronic materials.

57

P. Eng C. Seni 
AECL-CANDU 
2251 Speakman Drive, Mississauga, Ontario, L5K 1A2 
CANADA 
Telephone: +1 905 8239040/3103 and +1 905 8272784 
Fax: +1 905 8239754 
Email: senic@aecl.ca

International Database on Aging 
Management and Life Extension on Nuclear Materials properties and performance database, reinforced and prestressed concrete.
58

Mr. Masami Shindo 
Japan Atomic Energy Research Institute 
Department of Materials Science and Engineering 
JAERI, Tokai-mura, Naga-gun, Ibaraki-ken, 319-11 
JAPAN 
Telephone: +81 029 2825381 
Fax: +81 029 2825922 
Email: shindo@jmpdsun.tokai.jaeri.go.jp

JAERI Material Performance Database (JMPD) 
Ferrous and non-ferrous metallic materials data for nuclear applications.

59

Dr. Isao Soya 
Nippon Steel Corporation 
Steel Research Laboratories 
20-1 Shintomi, Futtsu 293 
JAPAN 
Telephone: +81 439 802214 
Fax: +81 439 802744

FADAPS 
Fatigue data processing system.

60

Prof. Roland Streiff 
CDC Université de Provence 
3, place Victor Hugo, 13331 Marseille Cedex 3 
FRANCE 
Telephone: +33 4 91106449 
Fax: +33 4 91854110 
Telex: 402014 
Email: rstreiff@newsup.univ-mrs.fr 

C and HTC-DATA 
Coatings and high temperature corrosion: includes five databases, four of them active.  

61

Dr. Jun Takahashi 
National Institute of Materials and Chemical Research (NIMC) 
AIST, MITI 
1-1 Higashi, Tsukuba 305 
JAPAN 
Telephone: +81 298 546296 
Fax: +81 298 546232 
Email: jun@nimc.go.jp

M-LCADB for ACM 
Being developed: database to evaluate whether advanced composite materials are environmentally friendly or not.

62

Mr. John A. Tarvardian 
Institute of Advanced Manufacturing Sciences 
1111 Edison Drive, Cincinnati, Ohio 45216 
USA 
Telephone: +1 513 9482075 
Fax: +1 513 9482109 
Email: tarvardian@iams.org

CUTDATA 
A comprehensive system for PC or UNIX containing machining data for over 3750 work materials, over 40 machining operations delivering more than 90 000 recommendations. Calculates cutting time and horsepower requirements/machine selection. Interpolates within database.

63

Dr. Isao Tomizuka 
National Research Institute for Metals (NRIM) 
Materials Design Division 
1-2-1 Sengen, Tsukuba 305 
JAPAN 
Telephone: +81 298 531157 
Fax: +81 298 531022

Database for superalloy development project.

64

Mr. Edwin A. R. Trout 
British Cement Association 
Century House, Telford Avenue, Crowthorne RG45 6YS
UK 
Telephone: +44 1344 725703 
Fax: +44 1344 727202

Concrete Information Disc 
84 000 bibliographic references to published information on cement and concrete.

LIONS 
Bibliographic database of information on cement and concrete from sources worldwide in the English language.

Concrete Contacts 
Company addresses and product descriptions of firms associated with the British Concrete Industry.

65

Dr. David C. Wright 
RAPRA Technology Ltd. 
Shawbury, Shrewsbury, Shropshire SY4 4NR 
UK 
Telephone: +44 1939 250383 
Fax: +44 1939 251118 
Telex: 35134

PLASCAMS 
A knowledge/database system for plastics selection. 

RUBACAMS 
A knowledge-based system for the selection of rubbers.  

66

Mr. Satoru Yusa 
Ishikawajima-Harima Heavy Industries Co. Ltd 
1-15, Toyosu 3-Chome, Kotoku, Tokyo 135 
JAPAN 
Telephone: +81 3 35343394 
Fax: +81 3 35343388 
Email: yusa@rimat.ty.ihi.co.jp

DASMAT/Data Sheet system of Material 
Text-type database for retrieval and image bank of tables and graphs of material properties that are related to text-type databases. 

 

 

Scientific Access to Data and Information

​​My Note: 22 February, 2002, so probably out of date!

This Web site provides information on the activities of the Joint ICSU/CODATA ad hoc Group on Data and Information.
The International Council for Science (ICSU) was created in 1931 to promote international scientific activities in all areas of natural science and their applications for the benefit of humanity. Since its creation, a major objective of ICSU has been to assure that scientists in all nations can obtain access to data and other types of technical information that are essential to their work. ICSU recommends as a general policy the fundamental principle of full and open exchange of data and information for scientific and educational purposes. 

The Committee on Data for Science and Technology (CODATA) is an interdisciplinary scientific committee of ICSU that seeks to improve the quality, reliability, management, and accessibility of data of importance to all fields of science and technology.

Comments and queries are welcome. Please send messages to the chair of the Group:

Ferris@udel.edu

Ferris Webster
College of Marine Studies
University of Delaware, USA

Integrated Research on Disaster Risk

Integrated Research on Disaster Risk (IRDR) is a decade-long research programme co-sponsored by the International Council for Science (ICSU), the International Social Science Council (ISSC), and the United Nations International Strategy for Disaster Reduction (UNISDR). It is a global, multi-disciplinary approach to dealing with the challenges brought by natural disasters, mitigating their impacts, and improving related policy-making mechanisms.

Understanding and documenting impacts from natural hazards is the foundation for decision-making and policy-setting in disaster risk reduction. The impacts range from human effects such as displacement, homelessness and fatalities, to environmental (wetland loss, desertification) and economic losses (damage to property and crops). Documenting impacts in a standardised and comprehensive way is challenging largely due to the lack of common terminologies for perils, measurement methodologies, and human loss indicators. The inability to compare losses across hazards, space, and time hampers the assessment of the burden of disasters at global to local levels.

To overcome these challenges, the Integrated Research on Disaster Risk (IRDR) programme established a project on disaster loss data (DATA) to “study issues related to the collection, storage, and dissemination of disaster loss data” (IRDR 2013, 10). A recent product of the DATA Project Working Group is a standard hazard terminology as well as peril classification for operational use in loss databases, which was agreed upon by all members of the Working Group. The peril glossary offered in this document provides guidelines on event classification and a unified terminology for operating loss databases only. It is not intended as a comprehensive list of perils or as a conclusive definitional standard of hazards. This technical paper details the classification scheme and hazard definitions used in loss databases, which will be implemented over time in global databases such EM-DAT, NatCatService, and Sigma as well as in national databases such as DesInventar and SHELDUS (see Annex).

Publication date: March 2014
Number of pages: 25 pp.

Download [PDF 6.38 MB] (PDF)

IRDR

IRDR was established by the International Council for Science (ICSU) in 2010 in cooperation with the International Social Science Council (ISSC) and the United Nations International Strategy for Disaster Reduction (UNISDR). IRDR’s main legacy will be an enhanced capacity around the world to address hazards and make informed decisions on actions to reduce their impacts. This will include a shift in focus from response–recovery towards prevention–mitigation strategies, and the building of resilience and reduction of risk through learning from experience and the avoidance of past mistakes. Suggested citation: Integrated Research on Disaster Risk. (2014). Peril Classification and Hazard Glossary (IRDR DATA Publication No. 1). Beijing: Integrated Research on Disaster Risk.

About IRDR

The impacts of natural hazards continue to increase around the world; the frequency of recorded disasters affecting communities has risen significantly over the past century. Although earthquakes and tsunamis can have horrific impacts, most disaster losses stem from climate-related hazards such as hurricanes, cyclones, other major storms, floods, landslides, wildfires, heat waves, and droughts.

The Integrated Research on Disaster Risk (IRDR) programme is a decade-long integrated research initiative co-sponsored by the International Council for Science (ICSU), the International Social Science Council (ISSC), and the United Nations International Strategy for Disaster Reduction (UNISDR) – the Co-Sponsors. It is a global, trans-disciplinary research programme created to address the major challenges of natural and human-induced environmental hazards. The complexity of the task is such that it requires the full integration of research expertise from the natural, socio-economic, health and engineering sciences as well as policy-making, coupled with an understanding of the role of communications, and public and political responses to reduce the risk.

Unfortunately, there is a great shortfall in current research on how science is used to shape social and political decision-making in the context of hazards and disasters. Addressing this problem requires an approach that integrates research and policy-making across all hazards, disciplines, and geographic regions. The IRDR programme endeavors to bring together the natural, socioeconomic, health, and engineering sciences in a coordinated effort to reduce the risks associated with natural hazards.

The programme is guided by three research objectives:

1. Characterisation of hazards, vulnerability and risk.
2. Understanding decision-making in complex and changing risk contexts.
3. Reducing risk and curbing losses through knowledge-based actions.

Three cross-cutting themes support these objectives:

  • Capacity building, including mapping capacity for disaster reduction and building self-sustaining capacity at various levels for different hazards.
  • Development and compilation of case studies and demonstration projects.
  • Assessment, data management, and monitoring of hazards, risks, and disasters

Attainment of these objectives through successful projects will lead to a better understanding of hazards, vulnerability and risk; an enhanced capacity to model and project risk into the future; better understanding of decision-making choices that lead to risk plus how they may be influenced; and how this knowledge can better guide disaster risk reduction.

Members of the IRDR DATA Project Working Group

  • Susan L. CUTTER (Co-Chair), University of South Carolina
  • Daniele EHRLICH (Co-Chair), EU Joint Research Center
  • Sisi ZLATANOVA (Co-Chair), Delft University
  • Robert S. CHEN, Columbia University
  • Regina BELOW, Centre for Research on the Epidemiology of Disasters (CRED), Université Catholique de Louvain
  • Lucia BEVERE, Swiss Re
  • Jan EICHNER, Munich Re
  • Julio SERJE, United Nations International Strategy for Disaster Reduction (UNISDR)
  • Carlos VILLACIS, United Nations Development Programme (UNDP)
  • Adam SMITH, U.S. National Climatic Data Center/NOAA
  • Wei-Sen LI, Taiwan National Science and Technology Center for Disaster Reduction (NCDR)
  • Maria PATEK, Austrian Government
  • Frederic ZANETTA, International Federation of Red Cross and Red Crescent Societies (IFRC)
  • Ricardo ZAPATA MARTI, United Nations Economic Commission for Latin America (UNECLAC)
  • Francis GHESQUIERE, The World Bank
  • Melanie GALL (Ex-Officio), University of South Carolina

1. Introduction

Understanding and documenting impacts from natural hazards is the foundation for decision-making and policy-setting in disaster risk reduction. The impacts range from human effects such as displacement, homelessness and fatalities, to environmental (wetland loss, desertification) and economic losses (damage to property and crops). Documenting impacts in a standardised and comprehensive way is challenging largely due to the lack of common terminologies for perils, measurement methodologies, and human loss indicators. The inability to compare losses across hazards, space, and time hampers the assessment of the burden of disasters at global to local levels.

To overcome these challenges, the Integrated Research on Disaster Risk (IRDR) programme established a project on disaster loss data (DATA) to “study issues related to the collection, storage, and dissemination of disaster loss data” (IRDR 2013, 10). A recent product of the DATA Project Working Group is a standard hazard terminology as well as peril classification for operational use in loss databases, which was agreed upon by all members of the Working Group. The peril glossary offered in this document provides guidelines on event classification and a unified terminology for operating loss databases only. It is not intended as a comprehensive list of perils or as a conclusive definitional standard of hazards. This technical paper details the classification scheme and hazard definitions used in loss databases, which will be implemented over time in global databases such EM-DAT, NatCatService and Sigma, as well as in national databases such as DesInventar and SHELDUS (see Annex).

2. Background

2.1 The Integrated Research on Disaster Risk Programme

The IRDR initiative is a decade-long research programme to better understand the challenges associated with environmental hazards originating from both natural and human-induced processes and actions. IRDR was established in 2008, and is jointly sponsored by the International Council for Science (ICSU), the International Social Science Council (ISSC) and the United Nations International Strategy for Disaster Reduction (UNISDR). The overarching objective of IRDR is to work across disciplinary boundaries and to integrate “research expertise from the natural, socioeconomic, health and engineering sciences, as well as policy-making, coupled with an understanding of the role of communications, and public and political responses to reduce the risk” from disasters (IRDR 2013, 3; ICSU 2008, 18).

The goals of IRDR are to (IRDR 2013:6ff):

  • Promote integrated research, advocacy and awareness-raising by developing and promoting integration and collaboration within the disaster risk reduction community to avoid unnecessary duplication and to maximise research outcomes.
  • Characterise hazards, vulnerability, and risk by identifying hazards and vulnerability leading to risks, and forecasting, assessing, and dynamic modeling of risk.
  • Understand decision-making in complex and changing risk contexts by identifying decision-making systems, their contexts, and their interactions, and improving the quality of decision-making practices.
  • Reduce risk and curb losses through knowledge-based actions such as vulnerability assessments, and the analysis of effective approaches to risk reduction.
2.2 Why Loss Database Standards are Important

In recent years the international community has made significant advances in improving the documentation of losses from natural hazards. These advancements are first and foremost visible in the significantly increased number of countries that now operate disaster loss databases, either through governmental, non-governmental, academic and/or private organisations. At present there are three global loss databases (CRED’s EM-DAT, MunichRe’s NatCatSERVICE, and SwissRe’s Sigma) of which the latter two have limited public accessibility (see Annex). At the national level there are currently more than 55 loss databases although they vary in data quality, temporal coverage, loss indicators, and update frequency (see Annex). About 35 national databases that offer loss data through 2010 could only do so through financial and/or technical support provided by UNISDR for GAR 2011 and GAR 2013 (UNDP/BCPR 2013). Thus, database sustainability and long-term maintenance are critical needs for many database operators (Wirtz et al. 2014). For many databases data gaps are common. There are gaps regarding: a) temporal coverage with missing years and/or months; b) spatial coverage with missing reports from some regions, communities, etc.; c) loss estimation with no losses reported for some events, particularly low impact/high frequency events; and d) loss indicators with inconsistent completeness across events.

Most databases record some form of human and economic losses (e.g., property damage, number of people killed) but there is neither a common set of loss indicators across all databases nor are these indicators defined based on a common understanding or standard. Agreement on common definitions and measures of human and economic loss is therefore a key objective of the IRDR DATA Project, and will be addressed in the future.

To improve the comparability of existing loss databases, event classifications must be standardised. If event and peril categories diverge from each other any subsequent efforts to standardise human loss indicators will be futile. A consistent peril classification will allow data users to compare losses from, for example, landslides in database A with losses from landslides in database B, thereby illustrating that differences are due to estimations of loss, not different definitions of landslides or how they were categorised.

Some members of the IRDR DATA Project Working Group have promoted the idea and concept of peril classifications for operational use in loss databases for many years. A preliminary classification scheme proposed by CRED and Munich Re (Below et al. 2009; Wirtz et al. 2014) was implemented in select databases to test its operational feasibility. The Working Group concluded that the preliminary framework was difficult to implement, particularly for national databases (e.g., DesInventar, SHELDUS) that operate specifically at the peril level. As a result, further revisions of the framework were necessary to reduce inconsistencies and better adhere to scientific hazard classifications and terminologies.

This report summarises the agreement on peril classifications and hazard definitions by the IRDR DATA Project Working Group. This new and revised framework is implementable by loss databases with either a high level of hazard aggregation (only includes categories such as meteorological or geophysical) that do not distinguish specific perils, as well as databases that use perils (hurricane, tsunami, earthquake) rather than the more general categories. In this way, the classification serves the needs of multiple types of loss databases often managed for very different purposes.

3. Peril Classification

The classification schema is designed to serve multiple types of databases—global, national and sub-national—in order to make loss information more comparable. The list of perils is not comprehensive and includes only the most common events. For perils not included in this list, loss database operators will decide on peril naming and classification on a case-by-case basis. Furthermore, only perils that cause measurable damage (e.g., fatalities, crop loss, etc.) are considered here.

It is important to note that the association between perils and main events is not a one-to-one relationship. A peril can be linked to one or more main event categories. It is highly recommended that decisions about classification and aggregations from perils to main events be made on a case-by-case basis. For instance, a snow avalanche may be triggered by an earthquake, which would be considered a mass movement/geophysical event, or a snow avalanche may be caused by the weight and/or instability of the snow pack, which would define it as a landslide/hydrological event.

The revised classification distinguishes three classification levels moving from the most generalised (family) to the most specific (peril), or from the most specific (peril) to the most generalised (family). Although an attempt was made to follow scientific classifications and terminology as much as possible, in some cases a more pragmatic approach was chosen to align with the needs of the loss database operators. For example, mass movements are frequently subsumed under geophysical/geological hazards. The peril classification proposed here distinguishes between geophysical and hydrological mass movements. Landslides following earthquakes or volcanic eruptions fall into the geophysical main events category, whereas perils such as debris or mud flows fall under hydrological hazards.

The peril classification system is not intended as a hierarchical top-down approach. Many databases, especially at the national and subnational levels, only document perils. For analytical purposes these perils are often aggregated into broader categories, a feature captured in our classification system. On the other hand, some databases operate with a top-down approach, starting with the most general category and then becoming more specific. The approach taken by loss databases varies depending on the original purpose or mission, user audience, and structure of the database. The peril classification system proposed here attempts to accommodate both approaches.

3.1 Classification Structure

This revised classification system distinguishes three levels: family, main events and perils. There are six broad hazard categories within the family group (Figure 1), the most generalised level:

  • Geophysical: a hazard originating from solid earth. This term is used interchangeably with the term geological hazard.
  • Hydrological: a hazard caused by the occurrence, movement, and distribution of surface and subsurface freshwater and saltwater.
  • Meteorological: a hazard caused by short-lived, micro- to meso-scale extreme weather and atmospheric conditions that last from minutes to days.
  • Climatological: a hazard caused by long-lived, meso- to macro-scale atmospheric processes ranging from intra-seasonal to multi-decadal climate variability.
  • Biological: a hazard caused by the exposure to living organisms and/or their toxic substances (e.g. venom, mold) or vector-borne diseases that they may carry. Examples are venomous wildlife and insects, poisonous plants, algae blooms, and mosquitoes carrying disease-causing agents such as parasites, bacteria, or viruses (e.g., malaria).
  • Extraterrestrial: a hazard caused by asteroids, meteoroids, and comets as they pass near-earth, enter the Earth’s atmosphere, and/or strike the Earth, or changes in inter planetary conditions that effect the Earth’s magnetosphere, ionosphere, and thermosphere.
Figure 1: Peril classification at the Family level

IRDRReport2014Figure1.png

Each hazard family can be further classified by a set of generic hazards – or what we term, main events (Figure 2). For example, the geophysical hazard category can be further subdivided into earthquake, mass movement and volcanic activity. The hydrological hazard category is further differentiated into flood, landslide, and wave action. The meteorological hazard category includes convective storms, extratropical storms, tropical storms, extreme temperature, and fog. The climatological main event is further refined to include drought, glacial lake outburst and wildfire. The biological hazard family details animal incidents, diseases, and insect infestation. And lastly the extraterrestrial hazard family encompasses impacts, airburst and space weather.

Figure 2: Peril classification at the Family and Main Events levels

IRDRReport2014Figure2.png

Whenever more detailed or specific hazard information is available, loss data can also be reported at the peril level (Figures 3 and 4). A peril is the specific cause of the loss, such as lightning or a tornado. It is important to note that perils are associated with one or more hazards in the main event category. For example, lightning could be associated with convective storms in the main event but could also be associated with tropical cyclones. In other words, there is not an exclusive one-to-one relationship between perils and main events (a departure from the preliminary peril classification proposed by CRED and Munich Re). Finally, some peril terms are artificial (e.g., fire following earthquake (EQ), landslide following earthquake (EQ) but were included in this system due to the operational needs of database operators.

Figure 3: Peril classification at the Family, Main Event and Peril levels

without a pre-determined association of perils with a main event.

IRDRReport2014Figure3.png

Figure 4: Peril classification at the Family, Main Event and Peril levels

The association of perils with main events is solely a suggestion. Some perils may change their main event association based on the actual event and loss trigger.

IRDRReport2014Figure4.png

3.2 Glossary

To facilitate the adoption and implementation of the peril classification scheme, a standardised set of definitions was developed. These definitions (Table 1) are based on descriptions developed by the World Meteorological Organization (WMO) 1; the U.S. Centers for Disease Control and Prevention (CDC) 2; the U.S. National Weather Service 3; the U.S. Geological Survey (USGS) 4; the National Aeronautics and Space Administration (NASA) 5; Keller and DeVecchio (2012); as well as definitions published by Below et al. (2009) in their preliminary peril classification document.

1 http://www.wmo.int/pages/themes/hazards/index_en.html

2 http://www.cdc.gov/

3 http://w1.weather.gov/glossary/

4 http://vulcan.wr.usgs.gov/Glossary/v...rminology.html

5 http://www.nasa.gov/mission_pages/su...l#.UpYJ5OKQMrg

Table 1: Definitions of perils, main events, and families

My Note: See spreadsheet

Term Definition
Airburst An explosion of a comet or meteoroid within the Earth’s atmosphere without striking the ground.
Animal Incident Human encounters with dangerous or exotic animals in both urban and rural environments.
Ash Fall Fine (less than 4 mm in diameter) unconsolidated volcanic debris blown into the atmosphere during an eruption; can remain airborne for long periods of time and travel considerable distance from the source.
Avalanche A large mass of loosened earth material, snow, or ice that slides, flows or falls rapidly down a mountainside under the force of gravity. • Snow Avalanche: Rapid downslope movement of a mix of snow and ice. • Debris Avalanche: The sudden and very rapid downslope movement of unsorted mass of rock and soil. There are two general types of debris avalanches - a cold debris avalanche usually results from an unstable slope suddenly collapsing whereas a hot debris avalanche results from volcanic activity leading to slope instability and collapse.
Bacterial Disease An unusual increase in the number of incidents caused by the exposure to bacteria either through skin contact, ingestion or inhalation. Examples include salmonella, MSRA, and cholera, among others.
Biological Hazard A hazard caused by the exposure to living organisms and their toxic substances (e.g. venom, mold) or vector-borne diseases that they may carry. Examples are venomous wildlife and insects, poisonous plants, and mosquitoes carrying disease-causing agents such as parasites, bacteria, or viruses (e.g. malaria).
Climatological Hazard A hazard caused by long-lived, meso- to macro-scale atmospheric processes ranging from intra-seasonal to multi-decadal climate variability.
Coastal Erosion The temporary or permanent loss of sediments or landmass in coastal margins due to the action of waves, winds, tides, or anthropogenic activities.
Coastal Flood Higher-than-normal water levels along the coast caused by tidal changes or thunderstorms that result in flooding, which can last from days to weeks.
Cold Wave A period of abnormally cold weather. Typically a cold wave lasts two or more days and may be aggravated by high winds. The exact temperature criteria for what constitutes a cold wave vary by location.
Convective Storm A type of meteorological hazard generated by the heating of air and the availability of moist and unstable air masses. Convective storms range from localised thunderstorms (with heavy rain and/or hail, lightning, high winds, tornadoes) to meso-scale, multi-day events.
Debris Flow, Mud Flow, Rock Fall Types of landslides that occur when heavy rain or rapid snow/ice melt send large amounts of vegetation, mud, or rock downslope by gravitational forces.
Derecho Widespread and usually fast-moving windstorms associated with convection/convective storm. Derechos include downburst and straight-line winds. The damage from derechos is often confused with the damage from tornadoes.
Disease Either an unusual, often sudden, increase in the number of incidents of an infectious disease that already existed in the region (e.g., flu, E. coli) or the appearance of an infectious disease previously absent from the region (e.g., plague, polio).
Drought An extended period of unusually low precipitation that produces a shortage of water for people, animals and plants. Drought is different from most other hazards in that it develops slowly, sometimes even over years, and its onset is generally difficult to detect. Drought is not solely a physical phenomenon because its impacts can be exacerbated by human activities and water supply demands. Drought is therefore often defined both conceptually and operationally. Operational definitions of drought, meaning the degree of precipitation reduction that constitutes a drought, vary by locality, climate and environmental sector.
Earthquake Sudden movement of a block of the Earth’s crust along a geological fault and associated ground shaking.
Energetic Particles Emissions from solar radiation storms consisting of pieces of matter (e.g., protons and other charged particles) moving at very high speed. The magnetosphere and atmosphere block (solar) energetic particles (SEP) from reaching humans on Earth but they are damaging to the electronics of space-borne technology (such as satellites) and pose a radiation hazard to life in space and aircrafts travelling at high altitudes.
Expansive Soil Earthen material, particularly clays that, upon wetting, freezing, or drying will alternately expand or contract causing damage to foundations of buildings and other structures. Shrinkage is generally referred to as desiccation.
Extraterrestrial Hazard A hazard caused by asteroids, meteoroids, and comets as they pass near-earth, enter the Earth’s atmosphere, and/or strike the Earth, and by changes in interplanetary conditions that effect the Earth’s magnetosphere, ionosphere, and thermosphere.
Extratropical Storm A type of low-pressure cyclonic system in the middle and high latitudes (also called mid-latitude cyclone) that primarily gets its energy from the horizontal temperature contrasts (fronts) in the atmosphere. When associated with cold fronts, extratropical cyclones may be particularly damaging (e.g., European winter/windstorm, Nor’easter).
Extreme Temperature A general term for temperature variations above (extreme heat) or below (extreme cold) normal conditions.
Fire following Earthquake Urban fires triggered by earthquakes. Particularly susceptible areas include densely spaced, wooden buildings that dominate local architecture, and where the earthquake has damaged or ruptured water and gas pipelines. Small local fires have the potential to merge into conflagrations destroying many city blocks.
Flash Flood Heavy or excessive rainfall in a short period of time that produce immediate runoff, creating flooding conditions within minutes or a few hours during or after the rainfall.
Flood A general term for the overflow of water from a stream channel onto normally dry land in the floodplain (riverine flooding), higher-than-normal levels along the coast and in lakes or reservoirs (coastal flooding) as well as ponding of water at or near the point where the rain fell (flash floods).
Fog Water droplets that are suspended in the air near the Earth’s surface. Fog is simply a cloud that is in contact with the ground.
Forest Fire A type of wildfire in a wooded area.
Frost, Freeze Frost is the consequence of radiative cooling resulting in the formation of thin ice crystals on the ground or other surfaces in the form of needles, feathers, scales, or fans. Frost occurs when the temperature of surfaces is below freezing and water vapor from humid air forms solid deposits on the cold surface. Freeze occurs when the air temperature is at (32˚F/0˚C) or below over a widespread area for a climatologically significant period of time. Use of the term is usually restricted to advective situations or to occasions when wind or other conditions prevent frost. Frost and freeze are particularly damaging during the crop growing season.
Fungal Disease Exposure to fungi either through skin contact, ingestion or inhalation of spores resulting in an unusual increase in the number of incidents. Examples are fungal pneumonia, fungal meningitis, etc.
Geomagnetic Storm A type of extraterrestrial hazard caused by solar wind shockwaves that temporarily disturb the Earth’s magnetosphere. Geomagnetic storms can disrupt power grids, spacecraft operations, and satellite communications.
Glacial Lake Outburst A flood that occurs when water dammed by a glacier or moraine is suddenly released. Glacial lakes can be at the front of the glacier (marginal lake) or below the ice sheet (sub-glacial lake).
Ground Movement Surface displacement of earthen materials due to ground shaking triggered by earthquakes or volcanic eruptions.
Hail Solid precipitation in the form of irregular pellets or balls of ice more than 5 mm in diameter.
Heat Wave A period of abnormally hot and/or unusually humid weather. Typically a heat wave lasts two or more days. The exact temperature criteria for what constitutes a heat wave vary by location.
Hydrological Hazard A hazard caused by the occurrence, movement, and distribution of surface and subsurface freshwater and saltwater.
Ice Jam Flood The accumulation of floating ice restricting or blocking a river’s flow and drainage. Ice jams tend to develop near river bends and obstructions (e.g., bridges).
Impact A type of extraterrestrial hazard caused by the collision of the Earth with a meteoroid, asteroid or comet.
Insect Infestation The pervasive influx, swarming and/or hatching of insects affecting humans, animals, crops, and perishable goods. Examples are locusts and African Bees.
Lahar Hot or cold mixture of earthen material flowing on the slope of a volcano either during or between volcanic eruptions.
Landslide following Earthquake Independent of the presence of water, mass movement may also be triggered by earthquakes.
Lava Flow The ejected magma that moves as a liquid mass downslope from a volcano during an eruption.
Lightning A high-voltage, visible electrical discharge produced by a thunderstorm and followed by the sound of thunder.
Liquefaction The transformation of (partially) water-saturated soil from a solid state to a liquid state caused by an earthquake. Liquefaction reduces the strength and stiffness of soil causing buildings to topple over.
Mass Movement Any type of downslope movement of earth materials.
Meteorological Hazard A hazard caused by short-lived, micro- to meso-scale extreme weather and atmospheric conditions that last from minutes to days.
Parasitic Disease Exposure to a parasite–an organism living on or in a host–causes an unusual increase in the number of incidents. Exposure to parasites occurs mostly through contaminated water, food or contact with insects, animals (zoonotic), pets, etc. Examples are malaria, chagas disease, giardiasis and trichinellosis.
Prion Disease A type of biological hazard caused by prion proteins. Prion diseases or transmissible spongiform encephalopathies (TSEs) are a family of rare progressive neurodegenerative disorders that affect both humans and animals characterised by long incubation periods and neural loss. Examples are Bovine Spongiform Encephalophathy (BSE), Creutzfeldt-Jakob-Disease (CJD), Kuru, etc.
Pyroclastic Flow Extremely hot gases, ash, and other materials of more than 1,000 degrees Celsius that rapidly flow down the flank of a volcano (more than 700 km/h) during an eruption.
Radio Disturbance Triggered by x-ray emissions from the Sun hitting the Earth’s atmosphere and causing disturbances in the ionosphere such as jamming of high and/or low frequency radio signals. This affects satellite radio communication and Global Positioning Systems (GPS).
Rain Water vapour condenses in the atmosphere to form water droplets that fall to the Earth.
Riverine Flood A type of flooding resulting from the overflow of water from a stream or river channel onto normally dry land in the floodplain adjacent to the channel.
Rogue Wave An unusual single crest of an ocean wave far out at sea that is much higher and/or steeper than other waves in the prevailing swell system.
Sandstorm, Dust Storm Strong winds carry particles of sand aloft, but generally confined to less than 50 feet (15 m), especially common in arid and semi-arid environments. A dust storm is also characterised by strong winds but carries smaller particles of dust rather than sand over an extensive area.
Seiche A standing wave of water in a large semi- or fully-enclosed body of water (lakes or bays) created by strong winds and/or a large barometric pressure gradient,
Shockwave A shockwave carries energy from a disturbance through a medium (solid, liquid, gas) similar to a wave though it travels at much higher speed. It can be a type of extraterrestrial hazard caused by the explosion (airburst) or impact of meteorites that generate energy shockwaves capable of shattering glass, collapsing walls, etc.
Sinkhole Collapse of the land surface due to the dissolving of the subsurface rocks such as limestone or carbonate rock by water.
Snow, Ice Precipitation in the form of ice crystals/snowflakes or ice pellets (sleet) formed directly from freezing water vapour in the air. Ice accumulates when rain hits the cold surface and freezes.
Space Weather A general term for extraterrestrial weather conditions driven by solar eruptions such as geomagnetic storms, radio disturbances, and solar energetic particles.
Storm Surge An abnormal rise in sea level generated by a tropical cyclone or other intense storms.
Subsidence Subsidence refers to the sinking of the ground due to groundwater removal, mining, dissolution of limestone (e.g., karst, sinkholes), extraction of natural gas, and earthquakes.
Tornado A violently rotating column of air that reaches the ground or open water (waterspout).
Tropical Cyclone A tropical cyclone originates over tropical or subtropical waters. It is characterised by a warm-core, non-frontal synoptic-scale cyclone with a low pressure centre, spiral rain bands and strong winds. Depending on their location, tropical cyclones are referred to as hurricanes (Atlantic, Northeast Pacific), typhoons (Northwest Pacific), or cyclones (South Pacific and Indian Ocean).
Tsunami A series of waves (with long wavelengths when traveling across the deep ocean) that are generated by a displacement of massive amounts of water through underwater earthquakes, volcanic eruptions or landslides. Tsunami waves travel at very high speed across the ocean but as they begin to reach shallow water they slow down and the wave grows steeper.
Volcanic Activity A type of volcanic event near an opening/vent in the Earth’s surface including volcanic eruptions of lava, ash, hot vapour, gas, and pyroclastic material.
Wave Action Wind-generated surface waves that can occur on the surface of any open body of water such as oceans, rivers and lakes, etc. The size of the wave depends on the strength of the wind and the traveled distance (fetch).
Wildfire Any uncontrolled and non-prescribed combustion or burning of plants in a natural setting such as a forest, grassland, brush land or tundra, which consumes the natural fuels and spreads based on environmental conditions (e.g., wind, topography). Wildfires can be triggered by lightning or human actions.
Wind Differences in air pressure resulting in the horizontal motion of air. The greater the difference in pressure, the stronger the wind. Wind moves from high pressure toward low pressure.
Winter Storm, Blizzard A low pressure system in winter months with significant accumulations of snow, freezing rain, sleet or ice. A blizzard is a severe snow storm with winds exceeding 35 mph (56 km/h) for three or more hours, producing reduced visibility (less than .25 mile (400 m).

4. Conclusion

Over the course of the next months and years, the peril classications and denitions proposed by the IRDR DATA Project will be implemented in member databases (see Annex). This harmonised peril classication system and denitions serve the international disaster risk reduction community and contributes to the Hyogo Framework for Action in regard to improving information on key hazards and their impacts.

References

Below, Regina, Angelika Wirtz, and Debarati Guha-Sapir. 2009. “Disaster Category Classification
and Peril Terminology for Operational Purposes.” Louvain-la Neuve: Centre for Research on the
Epidemiology of Disasters and Munich Reinsurance Company.

http://www.cred.be/publication/disas...ional-purposes

ICSU. 2008. A Science Plan for Integrated Research on Disaster Risk: Addressing the Challenge of
Natural and Human-Induced Environmental Hazard. Paris, France: International Council for
Science.

http://www.icsu.org/publications/rep...ecutivesummary

IRDR. 2013. “Integrated Research on Disaster Risk Strategic Plan 2013-2017.” Beijing, China.

http://www.irdrinternational.org/wp-...-2013-2017.pdf

Keller, Edward A, Duane E. DeVecchio, and Robert H. Blodgett. 2012. Natural Hazards: Earth’s
Processes as Hazards, Disasters and Catastrophes. 3rd ed., Uppder Saddle River, New Jersey:
Prentice Hall.

UNDP/BCPR. 2013. “A Comparative Review of Country-Level and Regional Disaster Loss and Damage Databases.” New York, NY.

http://www.undp.org/content/undp/en/...mage-database/

Wirtz, Angelika, Wolfgang Kron, Petra Löw, and Markus Steuer. 2014. “The Need for Data: Natural
Disasters and the Challenges of Database Management.” Natural Hazards 70 (1): 135–157.
doi:10.1007/s11069-012-0312-4.

Appendices

My Note: See spreadsheet

Databases at a Glance
Database EM-DAT NatCatSERVICE Sigma GLIDE DesInventar SHELDUS
Spatial Coverage Global Global Global Global National National
Spatial Resolution Country Country Country Country County, municipality U.S. county
Temporal Coverage 1900 – present 79 AD – present   1930 – present Varies by country, more than 30 countries operate DesInventar databasess 1960 – present
Number of Records >20,000 >33,000   >5,000 Varies by country >800,000
Recording Thresholds ≥10 fatalities, ≥100 affected, declaration of state of emergency, or call for international assistance     ≥10 fatalities, ≥100 affected, declaration of state of emergency, or call for international assistance ≥1 human loss or ≥$1 in economic loss ≥1 human loss or ≥$1 in economic loss
Data Sources U.N agencies, IFRC, World Bank, reinsurers, press, news agencies, etc. Property claims service, insurance clients, U.N agencies, World Bank,press,   U.N agencies, IFRC, World Bank, reinsurers, press, news agencies, etc. U.N agencies, weather services, geological services, press, etc. U.S. National Climatic Data Center, National Geophysical Data Center, U.S. Geological Survey, etc.
Audience Humanitarian community, academia General public, insurance industry General public, insurance industry Loss database operators Emergency management, hazard mitigation planning, academia Emergency management, hazard mitigation planning, academia
Download URL Web Link     Web Link Web Link Web Link
Owner Centre for Research on the Epidemiology of Disasters (CRED), Université Catholique de Louvain, Belgium Munich Re, Germany Swiss Re, Switzerland Asian Disaster Reduction Center (ADRC), Japan Varies by country Hazards and Vulnerability Research Institute (HVRI), University of South Carolina, USA
Loss Indicators
Indicator EM-DAT NatCatSERVICE Sigma GLIDE DesInventar SHELDUS
Killed x   x   x x
Injured x x x   x x
Missing     x   x  
Homeless x   x   x  
Affected x x x      
Evacuated   x     x  
Relocated         x  
Displaced   x        
Property Loss x         x
Crop Loss x         x
Environmental Loss x          
Insured Loss   x x      
Relocated            
Displaced   x        
Property Loss x          
Crop Loss x          
Environmental Loss x          
Insured Loss   x x      
Aggregate Economic Loss x x x   x  
Infrastructure Damage x x     x  
Economic Sector Damage x x     x  
Peril Coverage
Indicator EM-DAT NatCatSERVICE Sigma GLIDE DesInventar SHELDUS
Geophysical x x x​ x x x
Hydrological​ x x​ x x x x
Meteorological x x x x x x​
Climatological​ x x​ x​ x x x​
Biological x     x​ x  
Extraterrestrial            
Technological x​   x​ x​ x​  
Terrorism​     x      

SciDataCon 2014, the International Conference on Data Sharing and Integration for Global Sustainability

Research data are essential to all scientific endeavours. The emerging cultures of data sharing and publication, open access to, and reuse of data are the positive signs of an evolving research environment. Nevertheless, several cultural and technological challenges are still preventing the research community from realizing the full benefits of these tendencies.
 
The Committee on Data for Science and Technology (CODATA) and the World Data System (WDS), interdisciplinary committees of the International Council for Science (ICSU) are supporting and encouraging these positive changes by actively promoting effective data policies and good data management practices in the research community, to produce better science, which ultimately benefits society. Likewise, the challenges and opportunities of ‘Big Data’ may have ramifications for the conduct of science and for the whole of society: the value and importance of data are being recognized more than ever before.
 
As a major contribution to this effort, the two organizations are co-sponsoring and organizing a high profile international biennial conference on scientific research data. SciDataCon will provide a unique platform bringing together international experts and practitioners in data sciences, technologies and management; researchers from the natural, social, health, and computer sciences; research funders and sponsors; and policy makers and advisors.
 
The sustainability challenges facing society today cannot be solved without multidisciplinary and transdisciplinary research on global sustainability, which requires the use, sharing and integration of data across scientific disciplines and domains and from international sources. The effectiveness and credibility of this research will rely on the availability to the research community of quality-assessed and interoperable datasets.  In order to facilitate the work of international research undertakings—including the Future Earth international research programme on global sustainability launched by ICSU and its partners in 2014 — and amplify the message of like-minded global data initiatives promoting data sharing and interoperability—including the Group on Earth Observations (GEO) and the recently established Research Data Alliance (RDA) — SciDataCon 2014 will highlight the theme of Data Sharing and Integration for Global Sustainability. 
 
With high-level keynotes, a mix of plenary and parallel sessions, and a stimulating poster session, SciDataCon is conceived as a focussed—yet inclusive—conference to address major issues in global data management. An International Scientific Programme Committee will play key roles in implementing the scientific programme with innovative online consultation and input from research and data communities worldwide. We are pleased to invite you to contribute to this exciting conference which will take place on 2–5 November 2014 in New Delhi, India, hosted by the Indian National Science Academy.

Conference Forum

 
My Note: Could include complete descriptions
 
Proposed Topics

Citizens' participation in science: how can citizen contribute to open science and open data?

(1)By Weigang Yan Mar 17

Introduction to Text and Data Mining—Technical and Legal Considerations

(2) By P Kishor Mar 16

Machine Learning in remote Sensing data

(1) By Siddharth Hariharan Mar 4

Metadata Interoperability through an International Network of (Polar?) Data Portals

(2) By CCIN Manager Feb 26

Topic: Going beyond saving the data

(2) By Tom Stein Feb 26

Topics for SciDataCon 2014

(2) By Abdul Rahaman Feb 26

Knowledge Discovery with "Unstructured Data"

(2) By Paul Berkman Feb 19

Big Data, Little Sharing: Challenges and Opportunities for Data-Intensive scientists

(2) By Sulayman Sowe Feb 18

Open Science Data Frameworks

(1) By anup kumar das Feb 12

RS & GIS

(2) By SANDHYA FARSWAN Feb 12

Open Data in Agricultural Sciences

(3) By Sridhar Gutam Feb 12

The Research Data Alliance: building bridges to enable open sharing of data

(1) By Herman Stehouwer Jan 24

ICSU World Data System

Trusted Data Services for Global Science

Source: http://www.icsu-wds.org/services/data-portal

My Note: See first paragraph below: Started in 2007 as a portal frameworkand and will be relaunched in 2014.

The prototype was started in 2007 with initial candidates willing to participate in setting up the portal framework. It is currently being updated to include more participating WDS Members and will be relaunched in 2014.

Interested WDS Members need to implement current standards in the field of Spatial Data Infrastructures (SDI) and can generally use the resulting interoperability to network also with other communities and portals such as the planned Ocean Data Portal (IODE), the Global Change Master Directory (GCMD) or the Global Earth Observations System of Systems (GEOSS).

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences

Resources

Source: http://english.radi.cas.cn/Resources/VI/

Scientific Database

Source: http://ids.ceode.ac.cn/en/

My Note: See spreadsheet

Mission Headline

Since March 16th, 2011, RADI has launched the Open Spatial Data Sharing Project for customers in China in order to change the customer service mode with an innovative way. Users in China can access and download the image data with standard size scene from various satellites involving LANDSAT-5/7, IRS-P6, ERS-1/2, ENVISAT-1. Currently, LANDSAT-8 data are available in the project. The latest LANDSAT-8 data which are received every day can be accessed and downloaded.

Data Summary

Total Number of Standard Scenes: 44217
Total Volumes: 12277
Site Members: 16042
Visits: 501517
Total Number of Data Downloads: 247085
Total Volumes of Data Downloads: 54119 GB​

 

Data Sharing Situation

 Satellite  Number  The Volumes (GB)  Product types  File format  Date Range
 LANDSAT5 9264 2676  Level 2 Level 4  FASTB,GEOTIFF 1988.01-2011.05
 LANDSAT7 7855 4455  Level 2 Level 4  FASTL7A,GEOTIFF 1999.08-2003.05
 LANDSAT8 10564 16997  Level 2 Level 4  FASTB,GEOTIFF 2013.06-
 IRS-P6 8822 4128  Level 2  FASTc 2005.01-2009.12
ENVISAT1 322 103  MER ENVISAT 2003.12-2006.01
ERS2 7115 914  PRI ENVISAT 2006.01-2009.12

Coverage State of Landsat-7 Imageries In Part of China

Flood_Temporal.jpg

Flood in Heilongjiang River (Autumn, 2013  Landsat-8 Imagery)

Flood_Temporal.jpg

Landscape of Ruoqiang in Xinjiang Uygur Autonomous Region (Jul, 2007  Landsat-5 Imagery)

bj1.jpg

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