<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40"><head><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=us-ascii"><meta name=Generator content="Microsoft Word 14 (filtered medium)"><title>7 AGU Sessions addressing: Science Data Analytics; Data Science</title><style><!--
/* Font Definitions */
@font-face
{font-family:Calibri;
panose-1:2 15 5 2 2 2 4 3 2 4;}
@font-face
{font-family:Tahoma;
panose-1:2 11 6 4 3 5 4 4 2 4;}
@font-face
{font-family:"Arial Bold";
panose-1:0 0 0 0 0 0 0 0 0 0;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin:0cm;
margin-bottom:.0001pt;
font-size:12.0pt;
font-family:"Times New Roman","serif";}
a:link, span.MsoHyperlink
{mso-style-priority:99;
color:blue;
text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
{mso-style-priority:99;
color:purple;
text-decoration:underline;}
span.EmailStyle17
{mso-style-type:personal-reply;
font-family:"Calibri","sans-serif";
color:#1F497D;}
.MsoChpDefault
{mso-style-type:export-only;
font-size:10.0pt;}
@page WordSection1
{size:612.0pt 792.0pt;
margin:72.0pt 72.0pt 72.0pt 72.0pt;}
div.WordSection1
{page:WordSection1;}
--></style><!--[if gte mso 9]><xml>
<o:shapedefaults v:ext="edit" spidmax="1026" />
</xml><![endif]--><!--[if gte mso 9]><xml>
<o:shapelayout v:ext="edit">
<o:idmap v:ext="edit" data="1" />
</o:shapelayout></xml><![endif]--></head><body lang=EN-GB link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:black'>If any of you are planning to attend AGU , this might be of interest.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:black'>Suchith<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:black'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:black'> </span><span style='font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D'><o:p></o:p></span></p><div><div style='border:none;border-top:solid #B5C4DF 1.0pt;padding:3.0pt 0cm 0cm 0cm'><p class=MsoNormal><b><span lang=EN-US style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'>From:</span></b><span lang=EN-US style='font-size:10.0pt;font-family:"Tahoma","sans-serif"'> Steven.J.Kempler=nasa.gov@rda-groups.org [mailto:Steven.J.Kempler=nasa.gov@rda-groups.org] <b>On Behalf Of </b>Steve Kempler<br><b>Sent:</b> 07 July 2014 02:26<br><b>To:</b> rda-bigdata-ig@rda-groups.org<br><b>Subject:</b> [rda-bigdata-ig] 7 AGU Sessions addressing: Science Data Analytics; Data Science<o:p></o:p></span></p></div></div><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal style='margin-bottom:12.0pt'><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>Hi,<br>In case you have not heard, AGU is coming again this year... in December.<br><br>Please allow me to bring your attention to 7 sessions that specifically address Science Data Analytics and/or Data Science (listed below for your convenience). Note one (the first one) targets the education-minded community. The others are associated with Earth and Space Science Informatics, but conveners are very interested in insights and experiences from the physical science communities, as well as from the informatics community. See you in SF.<br><br>Steve <br></span><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'><br></span></b><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'><br></span><span style='font-size:14.0pt;font-family:"Arial Bold","serif"'>EDUCATION<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'><br>Teaching Science Data Analytics Skills Needed to Facilitate Heterogeneous Data/Information Research: The Future Is Here<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session ID#: </span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>1879<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session Description:<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br>Scientists are increasingly exploring heterogeneous data analysis methodologies to tease out information and knowledge. Science data analytics techniques need to be well understood and advanced in order to maximize cross dataset integration and usability. Data Scientist required skills in performing data analytics, to better understand unobvious relationships across various datasets, are becoming more and more appreciated and significant, given the increasing amount of heterogeneous data available. This session seeks papers that: Describe university and non-university science domain oriented data scientist (and data analytics) training being provided to students, and; Desirable science research oriented analytics skills and expertise that are needed to be taught, so that students can move into high demand, science domain data scientist (data analytics) positions. Topics covered include curriculums and science data research projects, that teach/utilize machine learning, statistics, data mining, decision support modeling, or other analytics techniques.<br><br>P</span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>rimary Convener: <br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>S</span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>teven J Kempler<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>C</span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>o-conveners: <br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>E</span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>mily Law,</span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'> S</span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>ara J Graves,</span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'> C</span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>hung-Lin Shie<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br><b>---------------<br></b></span><span style='font-size:10.0pt;font-family:"Calibri","sans-serif"'><br></span><span style='font-size:14.0pt;font-family:"Arial Bold","serif"'>EARTH AND SPACE SCIENCE INORMATICS<br></span><span style='font-size:11.0pt;font-family:"Calibri","sans-serif"'><br></span><span style='font-size:11.0pt;font-family:"Arial Bold","serif"'>Identifying and Better Understanding Data Science Activities, Experiences, Challenges, and Gaps Areas<br></span><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:#323232'><br></span><span style='font-size:11.0pt;font-family:"Arial Bold","serif";color:#323232'>Session ID#: </span><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:#323232'>1809<br></span><span style='font-size:11.0pt;font-family:"Arial Bold","serif";color:#323232'>Session Description:<br></span><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:#323232'><br>Today, industries are calling Data Science “the sexiest job of the 2</span><span style='font-size:8.0pt;font-family:"Arial","sans-serif";color:#323232'>1s</span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>t century”. But how do Data Scientists contribute to scientific research? What experiences, challenges, and solutions have Data Scientists had that future Data Scientists can learn from? What do Data Scientists need to know, to support Earth and Spaces science research? This session seeks papers that describe Data Science activities, experiences, and challenges, as well as the expertise and skills Data Scientists need. Areas that may be covered include data lifecycle phases: data modelling, acquisition, cleaning, integration, analysis, and interpretation, each of which introduces challenges, problems, and solutions. We invite papers that address:<br> Type of work a Data Scientist performs<br> Data Science experiences and lessons<br> Data Science challenges<br> Data Science top problems (and solutions). For example:<br> Ensuring data and meta-data consistency<br> Maintaining analytics expertise per science domain<br> Supporting quality and uncertainty<br> Advancing data analytics techniques<br><br><b>Primary Convene</b></span><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>r: <br>Emily Law<br></span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>Co-conveners: <br></span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>John S Hughes</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'> and Steven J Kemple</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>r<br></span></b><b><span style='font-size:10.0pt;font-family:"Calibri","sans-serif"'><br>------------------<br></span></b><span style='font-size:10.0pt;font-family:"Calibri","sans-serif"'><br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'>Advancing Analytics using Big Data Climate Information System<br><br><span style='color:#323232'>Session ID#: </span></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>3022<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session Description:<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br>Earth system science has seen massive increase in both observational data and modeling outputs. This constitutes a Big-Data challenge that demands Big-Data technologies to address. However, it is difficult for individual investigators or research groups to implement petabyte-scale platforms required to tackle the data analyses needed. It is also increasingly obvious that we must share and leverage our infrastructure investments in order to scale our research and development efforts and to increase the scientific productivity or throughput. Thus, in this session we seek presentations for innovative techniques, systems, or infrastructures that address the petascale data analysis and collaborative research and development challenges.<br>The focus of this session is on data analytics, rather than search, access, or curation. Subtopics of interest include:<br>Science applications focusing on integrating climate modeling and satellite observations.<br>Techniques, systems, infrastructures that enable seamless collaborations.<br>Innovative approaches of interactive visualization enhancing analytics of large data sets.<br><br><b>Prima</b></span><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>ry Convener: <br>Kwo-</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>S</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>en Kuo<br></span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>Co-co</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>nveners: <br>Tsen</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>g</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>dar J Lee</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>, Micha</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>el S Seablom,</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'> Ramak</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>rishna R Nemani<br></span></b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'><br>-----------------<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'><br>Big Data in the Geosciences: New Analytics Methods and Parallel Algorithm<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session ID#: </span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>3292<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session Description:<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br>Earth and space science data are increasingly large and complex--often representing high spatial/temporal/spectral resolution and dimensions from remote sensing or model results--making such data difficult to analyze, visualize, interpret, and understand by traditional methods. This session focuses on application and development of new geoscientific data analytics approaches (statistical, data mining, assimilation, machine learning, etc.) and parallel algorithms and software employing high performance computing resources for scalable analysis and novel applications of traditional methods on large geoscience data sets. Analysis methods that operate in-situ with parallel simulations to reduce output data volumes are also of interest. Abstracts focused on analysis, synthesis and knowledge extraction from large and complex Earth science data from all disciplines are invited<br><br><b>Primary</b></span><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'> Convener: <br>Jitend</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>r</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>a Kumar<br></span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>Co-conv</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>eners: <br>Robert</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'> </span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>L Jacob</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>, Forrest</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'> M Hoffman, </span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>Miguel </span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>D Mahecha<br></span></b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'><br>-----------------<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'><br>Leveraging Enabling Technologies and Architectures to enable Data Intensive Science<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session ID#: </span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>3041<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session Description:<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br>The objective of this session is to share innovative concepts, emerging solutions, and applications for Big Earth and Space Data to enable Data-Intensive Science. Data-Intensive Science defines three high-level activities: capture, curation, and analysis of data. Being able to handle massive amount of data impacts our architectural decisions and approaches. Topics include demonstration, studies, methods, and/or architectural discussion on<br>Common enabling technologies<br>Automated techniques for data analysis<br>Science analysis and visualization<br>Real time decision support<br>Implication of Data Intensive science to education<br>Data management lifecycle functions from data capture through analysis<br>Architecture that spans multiple data systems and organizations<br><br><b>Primary Conven</b></span><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>er: <br>Thomas Huang<br></span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>Co-conveners: </span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'><br>Rahul Ramacha</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>n</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>dran,</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'> Daniel J Crich</span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>ton, </span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>Morris Riedel<br></span></b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'><br>-----------------<br></span><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'><br></span></b><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'>Open source solutions for analyzing big earth observation data<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'><br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'>Session ID#: </span><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'>3080<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'>S<span style='color:#323232'>ession Description:<br></span></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br>Most current earth observation data has become freely available, but has also become too large to download and analyse on local machines. Several solutions exist to analyse "near the data", e.g. array data bases, solutions build on hadoop, solutions that use R or python to organize a cluster, or google earth engine. Not all of these are open source and hence suitable for transparent reproducible scientific research purposes. This session will attract papers that present solutions to and experiences with analysing big earth observation data near the data, using open source software. It will also accept contributions with non open-source solutions that are willing to discuss transparency and reproducibility.<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'><br><b>Primary Convener: </b></span><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br></span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Edzer J Pebesm</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>a<br></span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Co-conveners: </span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br></span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>James Fre</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>w, </span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Robert J Hijman</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>s, </span></b><b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Jonathan A Greenber</span></b><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>g<br><br></span></b><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>-----------------<br></span><b><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'><br></span></b><span style='font-size:10.0pt;font-family:"Arial Bold","serif"'>Technology Trends for Big Science Data Management<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif"'><br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session ID#: </span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>2525<br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Session Description:<br></span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'><br>The technology trend toward the use of ontologies, models and information representation [1] is predicted to favorably impact system architectures for big science data management. Data scientists in the space and earth sciences are developing system architectural components that incorporate these technologies into the data lifecycle - from ground systems through to the archives – and that help drive science analysis by producing interoperable systems and correlatable data. Technologies include ontologies for model driven development, science and engineering discipline ontologies, metadata and provenance standards vocabularies, and the use of semantic infrastructure for integration, publication, and analysis of science data to promote cross-disciplinary studies. This session invites papers on these and related technologies that are intended to improve the discovery and correct use of data and help meet the expectations of modern scientists in the Big Data era. [1] National Research Council (U.S.). Frontiers in Massive Data Analysis. 2013.<br><br></span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Primary Convener: <br>John S Hughes<br>Co-conveners: <br>Daniel J Crichton</span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>, </span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Yolanda Gil</span><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:#323232'>, </span><span style='font-size:10.0pt;font-family:"Arial Bold","serif";color:#323232'>Bernd Ritschel</span><o:p></o:p></p></div>
<br><p>This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please send it back to me, and immediately delete it. Please do not use, copy or disclose the information contained in this message or in any attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham.</p><p>This message has been checked for viruses but the contents of an attachment may still contain software viruses which could damage your computer system, you are advised to perform your own checks. Email communications with the University of Nottingham may be monitored as permitted by UK legislation.</p>
<br></body></html>