Scientists from The Australian National University (ANU) have used new space technology to predict droughts and increased bushfire risk up to five months in advance. ANU researcher Siyuan Tian said the team knew they needed to move into space to get closer to understanding the complex nature of drought. They used data from multiple satellites to measure water below the Earth's surface with unprecedented precision, and were able to relate this to drought impacts on the vegetation several months later.
As a participant to groups and meetings from almost the beginning of RDA, INRA (French National Institute for Agricultural Research) became a natural adopter of recommendations from agricultural related working groups and several others. In fact, RDA has contributed to many aspects of INRA’s Open Science policy either directly or indirectly. Being part of the Interest Group on Agricultural Data has been particularly fruitful in terms of strategic and technical advances to increase scientific data discoverability and interoperability at community level on Wheat Data for instance, or regarding semantic-based solutions for data handling and analyzing. Other RDA outputs are of importance for INRA, naming only those by the Libraries for Research Data IG which help defining the role of librarians from INRA in data management and sharing. RDA has also been the place to develop INRA’s research networks and federate to address major issues induced by data sharing.
Environmental Modelling & Software
Data Mining (DM) is a fundamental component of the Data Science process. Over recent years a huge library of DM algorithms has been developed to tackle a variety of problems in fields such as medical imaging and traffic analysis. Many DM techniques are far more flexible than more classical numerial simulation or statistical modelling approaches. These could be usefully applied to data-rich environmental problems. Certain techniques such as artificial neural networks, clustering, case-based reasoning or Bayesian networks have been applied in environmental modelling, while other methods, like support vector machines among others, have yet to be taken up on a wide scale. There is greater scope for many lesser known techniques to be applied in environmental research, with the potential to contribute to addressing some of the current open environmental challenges. However, selecting the best DM technique for a given environmental problem is not a simple decision, and there is a lack of guidelines and criteria that helps the data scientist and environmental scientists to ensure effective knowledge extraction from data. This paper provides a broad introduction to the use of DM in Data Science processes for environmental researchers. Data Science contains three main steps (pre-processing, data mining and post-processing). This paper provides a conceptualization of Environmental Systems and a conceptualization of DM methods, which are in the core step of the Data Science process. These two elements define a conceptual framework that is on the basis of a new methodology proposed for relating the characteristics of a given environmental problem with a family of Data Mining methods. The paper provides a general overview and guidelines of DM techniques to a non-expert user, who can decide with this support which is the more suitable technique to solve their problem at hand. The decision is related to the bidimensional relationship between the type of environmental system and the type of DM method. An illustrative two way table containing references for each pair Environmental System-Data Mining method is presented and discussed. Some examples of how the proposed methodology is used to support DM method selection are also presented, and challenges and future trends are identified.
[科技报告] The Future of Science and Science of the Future: Vision and Strategy for the African Open Science Platform (v02) 进入全文
[学术文献] Geospatial sensor web: A cyber-physical infrastructure for geoscience research and application 进入全文
In the last half-century, geoscience research has advanced due to multidisciplinary technologies, among which Information and Communication Technology (ICT) has played a vital role. However, scientifically organizing these ICTs toward improving geoscience measurements, data processing, and information services has encountered tremendous challenges. This paper reviews a profound revolution in geoscience that has resulted from the Geospatial Sensor Web (GSW), serving as a new cyber-physical spatio-temporal information infrastructure for geoscience on the World Wide Web (WWW). In contrast to previous experiment-based and sensor-based paradigms, the GSW-based paradigm is able to accomplish the following: (1) achieve integrated and sharable management of diverse sensing resources, (2) obtain real-time or near real-time and spatiotemporal continuous data, (3) conduct interoperable and online geoscience data processing and analysis, and (4) provide focusing services with web-based geoscience information and knowledge. As a benefit of the GSW, increasingly more geoscience disciplines are enjoying the value of real-time data, multi-source monitoring, online processing, and intelligence services. This paper reviews the evolution of geoscience research paradigm to demonstrate the scientific background of GSW. Then, we elaborates on four key methods provided by GSW, namely, integrated management, collaborative observation, scalable processing and fusion, and focusing service web capacity. Furthermore, current GSW prototypes and applications for environmental, hydrological, and natural disaster analysis are also reviewed. Moreover, four challenges to the future GSW in geoscience research are identified and analyzed, including integration with the Model Web initiative for sophisticated geo-processing, integration with humans for pervasive sensing, integration with Internet of Things (IoT) to achieve high-quality performance and data mining, and integration with Artificial Intelligence (AI) to provide smart geoservices. We have concluded that GSW has become an indispensable cyber-physical infrastructure, and will play a greater role in geoscience research and application.
[前沿资讯] An improved method for estimating the probability of extreme events was developed at VTT 进入全文
Researchers at VTT Technical Research Centre of Finland have developed a new and more accurate method for estimating the probability of extreme events, such as storms, floods and earthquakes. The new method will be used in updating building codes and land-use regulations, and is applicable also in developing artificial intelligence, as well as in economics and medical data analysis. Extreme events, such as storms, floods and earthquakes have always been disastrous to civilizations. Communities prepare for them by rigid constructions, flood banks, drainage channels and avoiding building at hazardous locations. For all such preparations, being able to estimate the probability of hazardous extremes is crucial. The estimation is based on the statistics of previously observed extremes, studied by so-called extreme value analysis. Many extreme value analysis methods exist and it has not been clear which of them should be preferred.