News & Articles

Disaster Risk Reduction and Open Data Newsletter: December 2019/January 2020 Edition

UNDRR SRSG Mami Mizutori speaks in Auckland
The Special Representative of the United Nations Secretary-General (SRSG) for Disaster Risk Reduction and the head of the United Nations Office for Disaster Risk Reduction, Mami Mizutori, recently spoke on resiliency and sustainable development at Tonkin + Taylor’s Auckland office. Click above for a link to the livestream video.

Fiji: Cyclone Early Warning System for Pacific goes live
An operational system has been developed and implemented for the Fiji Islands to produce and disseminate new early warning information on coastal flooding, which will help save lives and protect property in low-lying, populated coastal areas. For more on the CIFDP click the link above, and to listen to an interview with Bapon Fakhruddin on the development of the system, click here.

Countdown starts for Sendai 2020 deadline
Target (e) of the Sendai Framework's seven targets, sets a 2020 deadline for developing national and local strategies for disaster risk reduction. The same deadline applies to UN member states finalising National Adaptation Plans under the Paris Agreement on climate.

Data For Now Inception Workshop in Rwanda
SDSN TReNDS recently joined the World Bank, the UN Statistics Division, The Global Partnership for Sustainable Development Data and representatives from eight very diverse countries (Bangladesh, Columbia, Ghana, Mongolia, Nepal, Paraguay, Rwanda, and Senegal) in Kigali, Rwanda to discuss priority data needs as part of the new Data For Now initiative.

Bangladeshi farmers reap the benefits of new weather forecasts
Customised weather forecasts delivered to smartphones and rural meeting halls are helping farmers in Bangladesh better manage crops in the field as rain becomes more erratic.

Asia and the Pacific set priorities for accelerated disaster risk reduction
In the face of growing disaster losses and risk in the Asia-Pacific region, government disaster risk management agencies, international organizations, and civil society groups met in Brisbane to agree on priorities for accelerating action for reducing the risk of disasters.

NASA Space Data Can Cut Disaster Response Times, Costs
According to a new study, emergency responders could cut costs and save time by using near-real-time satellite data along with other decision-making tools after a flooding disaster.


November 2019: Publications in the Data Science Journal

  Title: Reviving an Old and Valuable Collection of Microscope Slides Through the Use of Citizen Science
Author
: John Pring, Lesley Wyborn, Neal Evans
URL: http://doi.org/10.5334/dsj-2019-057
  Title: Efficient Stratified Sampling Graphing Method for Mass Data
Author: Jianjun Wang, Yingang Zhao, Jun Chen, Suqing Zhang, Xudong Zhao, Yufei He
URL: http://doi.org/10.5334/dsj-2019-056
 

Title: A Comprehensive Video Dataset for Multi-Modal Recognition Systems 
Author: Anand Handa, Rashi Agarwal, Narendra Kohli 
URL: http://doi.org/10.5334/dsj-2019-055

  Title: Proper Attribution for Curation and Maintenance of Research Collections: Metadata Recommendations of the RDA/TDWG Working Group
Author
: Anne E. Thessen , Matt Woodburn, Dimitrios Koureas, Deborah Paul, Michael Conlon, David P. Shorthouse, Sarah Ramdeen
URL: 
http://doi.org/10.5334/dsj-2019-054
  Title: Intelligent Electronic Management of Library by Radio Frequency Identification Technology
Author
: Qinglan Huang, Hongyi Huang
URL: 
http://doi.org/10.5334/dsj-2019-053
  Title: The History and Future of Data Citation in Practice
Author
: Mark A. Parsons, Ruth E. Duerr, Matthew B. Jones
URL: 
http://doi.org/10.5334/dsj-2019-052
 


Summary Report: "Interoperability of Metadata Standards in Cross-Domain Science, Health, and Social Science Applications II

Schloss Dagstuhl Event 19413

Workshop participants standing in front of the chapel at Schloss Dagstuhl in Wadern, Germany.Description automatically generated

A week-long workshop was held on the subject of standards in cross-domain data use for science, health, and social science at Schloss Dagstuhl - the Leibniz Center for Informatics in Wadern, Germany, 6-11 October 2019. The meeting was sponsored by CODATA, the data-focused arm of the International Science Council (ISC), and the DDI Alliance, an international member-driven consortium which provides technical standards for research data in the social, behavioral, economic, and health sciences. CODATA is currently working towards a launch of a decadal programme on cross-disciplinary data as part of the ISC’s Science Action Plan. The DDI Alliance is now developing an information model for integrating data across domain boundaries. The workshop was subsidized by Schloss Dagstuhl - the Leibniz Center for Informatics.

Workshop series

This workshop was the second addressing this important topic, the earlier one also taking place at Dagstuhl in October 2018. Both focused on specific real-world use cases: the first was an exploration of specific issues encountered in the use of data across domain boundaries; the second aimed at producing practical guidance for addressing them. 

Among other outcomes, the first workshop contributed to the work on the next-generation DDI model, making it a more suitable tool for dealing with cross-domain data integration independent of the social sciences. Other standards and models were also examined (e.g. spatio-temporal aspects of DCAT). This focus on practical guidance for cross-domain data use is expected to continue into the ISC’s decadal programme.

Workshop participants included representatives of use cases and technology and standards experts across several different domains, including the social, behavioral, and economic sciences, geophysical and environmental science, health research, disaster risk reduction, urban planning and policy, and the UN's Sustainable Development Goals (SDGs). Standards concerned with statistical and research data and metadata were an important consideration (e.g., DDI, SDMX) and specialized application schemas (e.g. OBO vocabularies from the life sciences, ISO 19115 from geospatial data community). Technical experts ranged from those involved in developing standards to systems implementers.  Almost half of the participants overlapped with the first workshop.

Read this report as a pdf: https://doi.org/10.5281/zenodo.3552296
 


Watch: CODATA/RDA Summer School 2018-2019 Interviews

Please watch - CODATA/RDA Summer School 2018-2019 Interviews:

Read more about CODATA-RDA School of Research Data Science

 


The Beijing Declaration on Research Data

Grand challenges related to the environment, human health, and sustainability confront science and society. Understanding and mitigating these challenges in a rapidly changing environment require data[1] to be FAIR (Findable, Accessible, Interoperable, and Reusable) and as open as possible on a global basis. Scientific discovery must not be impeded unnecessarily by fragmented and closed systems, and the stewardship of research data should avoid defaulting to the traditional, proprietary approach of scholarly publishing. Therefore, the adoption of new policies and principles, coordinated and implemented globally, is necessary for research data and the associated infrastructures, tools, services, and practices. The time to act on the basis of solid policies for research data is now.
 
The Beijing Declaration is intended as a timely statement of core principles to encourage global cooperation, especially for public research data. It builds on and acknowledges the many national and international efforts that have been undertaken in the policy and technical spheres on a worldwide basis.  These major contributions are listed in the Appendix. 
 
 Several emergent global trends justify and precipitate this declaration of principles:
 
  • Massive global challenges require multilateral and cross-disciplinary cooperation and the broad reuse of data to improve coherence concerning recent UN landmark agreements, such as the Paris Climate Agreement, the Sendai Framework for Disaster Risk Reduction, the Sustainable Development Goals (SDGs), the Convention on Biological Diversity, the Plant Treaty, the World Humanitarian Summit, and others. The comprehensive agendas for action provided by these agreements requires access to and reuse of all kinds of data.
  • Research and problem-solving, especially addressing the SDG challenges, are increasingly complex and driven by ‘big data’, resulting in the need to combine and reuse very diverse data resources across multiple fields. This poses an enormous challenge in the interoperability of data and responsible stewardship, with full respect for privacy.
  • Rapid advances in the technologies that generate and analyze data pose major challenges concerning data volume, harmonization, management, sharing, and reuse. At the same time, emerging technologies (including machine learning) offer new opportunities that require access to reusable data available in distributed, yet interoperable, international data resources.
  • Changing norms and ethics encourage high-quality research through greater transparency, promote the reuse of data, and improve trustworthiness through the production of verifiable and reproducible research results. Increasing the openness of research data is efficient, improving the public return on investment, and generating positive externalities.
  • Open Science initiatives are emerging globally, including in less economically developed countries. There consequently are opportunities for these countries to take advantage of technological developments to develop a greater share in scientific production. Without determined action, there is also a risk that the divide in scientific production will widen.
 
In September 2019, CODATA and its Data Policy Committee convened in Beijing to discuss current data policy issues and developed a set of data policies adapted to the new Open Science paradigm. The Declaration proposed below is the result of that meeting and is now put forward for public review.
 
The Beijing Declaration on Research Data: https://doi.org/10.5281/zenodo.3552330
 
[1] In the attached document we deliberately use the word data very broadly, to comprise data (stricto sensu) and the ecosystem of digital things that relate to data, including metadata, software and algorithms, as well as physical samples and analogue artefacts (and the digital representations and metadata relating to these things).


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