Summary of the 1 st Phase Linked Open Data for Global Disaster Risk Research Task Group a hand-in-hand collaboration mechanism between CODATA and IRDR

1. Introduction Process 2012 LODGD task group was set up and approved by CODATA st Phase finished nd Phase approved Objective Linked Open Data for Global Disaster Risk Research (LODGD) aims to identify gaps and to study the mechanism to connect dispersed disaster related scientific data to enable easier and faster discovery, search, and access to data, reducing the barriers faced by researchers today due to limited interconnection of existing disaster-related data. The objectives of LODGD Task Group+: To promote disaster data using model from open accessing to linked analyzing To identify,address and disseminate the ideas that cross-disciplinary data should be linked together for disaster study To collect best practices of multi-disciplinary data linking To expand resources of task group, especially the experts network

2.Composition Members: Carol Song*(Purdue University), Chuang Liu(CAS), Guoqing Li* (CAS), Jan Eichner*(MunichRe), Jan-Ming Ho(NSC Taipei), Jiahua Pan(CASS), Michael Rast*(ESA), Pakorn Apaphant(GISTDA), Sisi Zlatanova*(TUDelft), Susan L. Cutter(USC), Shuichi Iwata(U of Tokyo) Brenda Jones(USGS) Masaru YARIME (Japan) K T Murata(Japan) Youth scientist members: Jinglong Fan, Guoqing Li, Mingrui Huang, Shifeng Huang, Xinlu Xie, Xiuling Qing, Xiaotao Li, Jingjuan Liao,Ching-Teng Hsiao, Thunayawan Suvarnasara * person server as co-chair Figure 1. Composition and Stakeholders of LODGD

1 ） White Paper on Linking Open Data for Disaster Risk Study (will be released at 2015) A writing team leaded by Li Guoqing and Carol Song is working on this report. Review progress will be taken at end of April. 2 ） Published articles Zhang hongyue, Qing xiuling, Huang mingrui, Li guoqing, CORRELATION ANALYSIS MODEL ON MULTIDISCIPLINARY DATA FOR DISASTER RESEARCH, CODATA Data Science Journal 3 ） Internal & International seminars a)Kick-off meeting of IRDR-CN Data project, Beijing, April 2012 b)LODGD kick-off and IRDR-CN project progress meeting, Beijing, April of 2013 c)LODGD TG whitepaper team meeting and LODGD TG & IRDR-CN project joint meeting, Sanya, Nov 2013 d)Gap analysis seminar for social data used for disaster mitigation, Beijing, Feb of 2014 e)Achievement report of LODGD TG & IRDR-CN project joint meeting, Beijing, 17th April 2014 f)IRDR-CN Project Knot meeting, Beijing, 12th Dec of 2014 g) Application report of LODGD TG, 2012 CODATA conference, Taipei, 2012 h)Joint meeting of LODGD and IRDR-Data TG and IRDR-SC, during IRDR-CN conference, Chengdu, Nov 2012 i)LODGD TG co-chair meeting (Guoqing and Rast), Frascati, May of 2013 j)LODGD report to 2013 IRDR international conference, Sanya, Nov 2013 k)LODGD Lecture to 1st International Training Workshop on Space Technology for Disaster Mitigation, Sanya, November, 2013 l)LODGD Report to 2014 Scientific Data Conference, Huairou, Feb of 2014 m)MAIRS Open Science Conference 2014(“Climate Change and Natural Disasters" session),Beijing,April 2014 n)CODATA Breakout session in 2014 IRDR international conference, Beijing, 7~8th June of 2014 o)LODGD Lecture to 2nd International Training Workshop on Space Technology for Disaster Mitigation by TWAS CoE, Beijing, 20th June, 2013 p)LODGD Report to annual China-US CODATA National Committee Roundtable meeting, Washington DC, 24th ~26th June of 2014 q)LODGD task group meeting in Annual China-US CODATA National Committee Roundtable meeting, Washington DC, 27th June of 2014 r)LODGD Poster to SciDataCon, India, Nov of Main Achievement

4. Study case The Upper level is technical approach employed The Lower level is knowledge about disaster events and the multidisciplinary data. Figure 2. Knowledge Discovery Model of Literature-based multidisciplinary data for disaster research Knowledge raised from case study Researchers need multidisciplinary data for earthquake study, and the data dependency of different earthquake events varies greatly. In the early earthquake study (Tangshan,1976),It shows identical data usage trend from AI view and RI view, while the data usage differs a lot from the AI view and RI view in the latest research( Wenchuan and Haidi earthquake events) With the temporal evolution, usage of Geophysical data continuous declining both from the AI view and RI view, however, the usage of Clinical data continues to rise up. The statistical data displays opposite trend in AI view and RI view, it declines in the AI view but goes up in the RI view with temporal evolution.

Note: ISI Web of Science (WOS) was adopted as the data source of case study. Author Index (AI) refers to the statistics of multidisciplinary data usage from the whole-set papers, Where is the Paper numbers using the specific data in whole-set papers and is the whole-set paper numbers. Reader Index(RI) refers to the statistics of multidisciplinary data usage from the High-cited papers, Where is the Paper numbers using the specific data in High-cited papers and is the High-cited paper numbers. Figure 3. Comparison of multidisciplinary data used in the three earthquake events by literature analysis (AI view and RI view)

5. Activity photos