�ݺ�ߣshows by User: JianQin1

�ݺ�ߣshows by User: JianQin1 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: JianQin1 / Wed, 30 Oct 2013 15:21:04 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: JianQin1 Infrastructure, Standards, and Policies for Research Data Management /JianQin1/coinfo2013-qin coinfo2013qin-131030152104-phpapp01
This presentation discusses the needs and importance of research data management and introduces the concept of research data management as an infrastructure service. Although many resources have been made available for research data management, most of them are developed as “islands” and lack linking mechanisms. The lack of integrated and interconnected resources has contributed to high cost and duplicated efforts in data management operations. The vision of research data management as an infrastructure service is not only to improve the efficiency of research data management but also the productivity of the research enterprise. Each of the three dimensions—infrastructure, standards, and policies—addresses a critical aspect of research data management to make the data infrastructure services work. ]]>
This presentation discusses the needs and importance of research data management and introduces the concept of research data management as an infrastructure service. Although many resources have been made available for research data management, most of them are developed as “islands” and lack linking mechanisms. The lack of integrated and interconnected resources has contributed to high cost and duplicated efforts in data management operations. The vision of research data management as an infrastructure service is not only to improve the efficiency of research data management but also the productivity of the research enterprise. Each of the three dimensions—infrastructure, standards, and policies—addresses a critical aspect of research data management to make the data infrastructure services work. ]]> Wed, 30 Oct 2013 15:21:04 GMT /JianQin1/coinfo2013-qin JianQin1@slideshare.net(JianQin1) Infrastructure, Standards, and Policies for Research Data Management JianQin1 This presentation discusses the needs and importance of research data management and introduces the concept of research data management as an infrastructure service. Although many resources have been made available for research data management, most of them are developed as “islands” and lack linking mechanisms. The lack of integrated and interconnected resources has contributed to high cost and duplicated efforts in data management operations. The vision of research data management as an infrastructure service is not only to improve the efficiency of research data management but also the productivity of the research enterprise. Each of the three dimensions—infrastructure, standards, and policies—addresses a critical aspect of research data management to make the data infrastructure services work. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/coinfo2013qin-131030152104-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This presentation discusses the needs and importance of research data management and introduces the concept of research data management as an infrastructure service. Although many resources have been made available for research data management, most of them are developed as “islands” and lack linking mechanisms. The lack of integrated and interconnected resources has contributed to high cost and duplicated efforts in data management operations. The vision of research data management as an infrastructure service is not only to improve the efficiency of research data management but also the productivity of the research enterprise. Each of the three dimensions—infrastructure, standards, and policies—addresses a critical aspect of research data management to make the data infrastructure services work.

Infrastructure, Standards, and Policies for Research Data Management from Jian Qin

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0 How Portable Are the Metadata Standards for Scientific Data? /slideshow/dc2013-portability-09-01/25848850 dc2013portability09-01-130903092056-
The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.]]>
The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.]]> Tue, 03 Sep 2013 09:20:56 GMT /slideshow/dc2013-portability-09-01/25848850 JianQin1@slideshare.net(JianQin1) How Portable Are the Metadata Standards for Scientific Data? JianQin1 The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dc2013portability09-01-130903092056--thumbnail.jpg?width=120&height=120&fit=bounds" /><br> The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.

How Portable Are the Metadata Standards for Scientific Data? from Jian Qin

]]> 786 2 https://cdn.slidesharecdn.com/ss_thumbnails/dc2013portability09-01-130903092056--thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 Data Science: An Emerging Field for Future Jobs /slideshow/su-graduate-school-presentation2013-222/16702395 sugraduateschoolpresentation2013-2-22-130222125927-phpapp01
Data deluge has become a reality in today's scientific research. What does it mean to future science workforce? How can you prepare yourself to embrace the data challenges and opportunities? This presentation will provide you with an overview of data science and what it means to you as future researchers and career scientists. ]]>
Data deluge has become a reality in today's scientific research. What does it mean to future science workforce? How can you prepare yourself to embrace the data challenges and opportunities? This presentation will provide you with an overview of data science and what it means to you as future researchers and career scientists. ]]> Fri, 22 Feb 2013 12:59:26 GMT /slideshow/su-graduate-school-presentation2013-222/16702395 JianQin1@slideshare.net(JianQin1) Data Science: An Emerging Field for Future Jobs JianQin1 Data deluge has become a reality in today's scientific research. What does it mean to future science workforce? How can you prepare yourself to embrace the data challenges and opportunities? This presentation will provide you with an overview of data science and what it means to you as future researchers and career scientists. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sugraduateschoolpresentation2013-2-22-130222125927-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Data deluge has become a reality in today's scientific research. What does it mean to future science workforce? How can you prepare yourself to embrace the data challenges and opportunities? This presentation will provide you with an overview of data science and what it means to you as future researchers and career scientists.

Data Science: An Emerging Field for Future Jobs from Jian Qin

]]> 932 6 https://cdn.slidesharecdn.com/ss_thumbnails/sugraduateschoolpresentation2013-2-22-130222125927-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Educating a New Breed of Data Scientists for Scientific Data Management /JianQin1/educating-a-new-breed-of-data-scientists-for-scientific-data-management msrescienceworkshop2012qin-121009154105-phpapp01
This presentation reports the data science curriculum development and implementation at Syracuse iSchool, which has shaped by the fast changing data-intensive environment not only for science but also for business and research at large. ]]>
This presentation reports the data science curriculum development and implementation at Syracuse iSchool, which has shaped by the fast changing data-intensive environment not only for science but also for business and research at large. ]]> Tue, 09 Oct 2012 15:41:03 GMT /JianQin1/educating-a-new-breed-of-data-scientists-for-scientific-data-management JianQin1@slideshare.net(JianQin1) Educating a New Breed of Data Scientists for Scientific Data Management JianQin1 This presentation reports the data science curriculum development and implementation at Syracuse iSchool, which has shaped by the fast changing data-intensive environment not only for science but also for business and research at large. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/msrescienceworkshop2012qin-121009154105-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This presentation reports the data science curriculum development and implementation at Syracuse iSchool, which has shaped by the fast changing data-intensive environment not only for science but also for business and research at large.

Educating a New Breed of Data Scientists for Scientific Data Management from Jian Qin

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0 https://cdn.slidesharecdn.com/profile-photo-JianQin1-48x48.jpg?cb=1398582865 Syracuse University facutly https://cdn.slidesharecdn.com/ss_thumbnails/coinfo2013qin-131030152104-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds JianQin1/coinfo2013-qin Infrastructure, Standa... https://cdn.slidesharecdn.com/ss_thumbnails/dc2013portability09-01-130903092056--thumbnail.jpg?width=320&height=320&fit=bounds slideshow/dc2013-portability-09-01/25848850 How Portable Are the M... https://cdn.slidesharecdn.com/ss_thumbnails/sugraduateschoolpresentation2013-2-22-130222125927-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/su-graduate-school-presentation2013-222/16702395 Data Science: An Emerg...