Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Are critically endangered fish back on the menu? Analysis of U.K. fisheries data suggest post-ban landings of prohibited skates in European waters

Skates (Rajidae) have been commercially exploited in Europe for hundreds of years with some species’ abundances declining dramatically during the twentieth century. In 2009 it became “prohibited for EU vessels to target, retain, tranship or land” certain species in some ICES areas, including the critically endangered common skate and the endangered white skate. To examine compliance with skate bans the official UK landings data for 2011–2014 were analysed. Surprisingly, it was found that after the ban prohibited species were still reported landed in UK ports, including 9.6 t of common skate during 2011–2014. The majority of reported landings of common and white skate were from northern UK waters and landed into northern UK ports. Although past landings could not be validated as being actual prohibited species, the landings’ patterns found reflect known abundance distributions that suggest actual landings were made, rather than sporadic occurrence across ports that would be evident if landings were solely due to systematic misidentification or data entry errors. Nevertheless, misreporting and data entry errors could not be discounted as factors contributing to the recorded landings of prohibited species. These findings raise questions about the efficacy of current systems to police skate landings to ensure prohibited species remain protected. By identifying UK ports with the highest apparent landings of prohibited species and those still landing species grouped as'skates and rays’, these results may aid authorities in allocating limited resources more effectively to reduce landings, misreporting and data errors of prohibited species, and increase species-specific landing compliance.

Are critically endangered fish back on the menu? Analysis of U.K. fisheries data suggest post-ban landings of prohibited skates in European waters

Skates (Rajidae) have been commercially exploited in Europe for hundreds of years with some species’ abundances declining dramatically during the twentieth century. In 2009 it became “prohibited for EU vessels to target, retain, tranship or land” certain species in some ICES areas, including the critically endangered common skate and the endangered white skate. To examine compliance with skate bans the official UK landings data for 2011–2014 were analysed. Surprisingly, it was found that after the ban prohibited species were still reported landed in UK ports, including 9.6 t of common skate during 2011–2014. The majority of reported landings of common and white skate were from northern UK waters and landed into northern UK ports. Although past landings could not be validated as being actual prohibited species, the landings’ patterns found reflect known abundance distributions that suggest actual landings were made, rather than sporadic occurrence across ports that would be evident if landings were solely due to systematic misidentification or data entry errors. Nevertheless, misreporting and data entry errors could not be discounted as factors contributing to the recorded landings of prohibited species. These findings raise questions about the efficacy of current systems to police skate landings to ensure prohibited species remain protected. By identifying UK ports with the highest apparent landings of prohibited species and those still landing species grouped as'skates and rays’, these results may aid authorities in allocating limited resources more effectively to reduce landings, misreporting and data errors of prohibited species, and increase species-specific landing compliance.

Decisions, decisions everywhere (in the open data era).

Abstract: Decision support systems have been widely used for years in companies to gain insights from internal data, thus making successful decisions. Lately, thanks to the increasing availability of open data, these systems are also integrating open data to enrich decision making process with external data. On the other hand, within an open-data scenario, decision support systems can be also useful to decide which data should be opened, not only by considering technical or legal constraints, but other requirements, such as "reusing potential" of data. In this talk, we focus on both issues: (i) open data for decision making, and (ii) decision making for opening data. We will first briefly comment some research problems regarding using open data for decision making. Then, we will give an outline of a novel decision-making approach (based on how open data is being actually used in open-source projects hosted in Github) for supporting open data publication. Bio of the speaker: Jose-Norberto Mazón holds a PhD from the University of Alicante (Spain). He is head of the "Cátedra Telefónica" on Big Data and coordinator of the Computing degree at the University of Alicante. He is also member of the WaKe research group at the University of Alicante. His research work focuses on open data management, data integration and business intelligence within "big data" scenarios, and their application to the tourism domain (smart tourism destinations). He has published his research in international journals, such as Decision Support Systems, Information Sciences, Data & Knowledge Engineering or ACM Transaction on the Web. Finally, he is involved in the open data project in the University of Alicante, including its open data portal at http://datos.ua.es

Publication Bias

According to the Declaration of Helsinki, as well as the Statement on Public Disclosure of Clinical Trial Results of the World Health Organization, every researcher has the ethical obligation to publish research results on all trials with human participants in a complete and accurate way within 12 months after the end of the trial.1,2 Nevertheless, for several reasons, not all research results are published in an accurate way in case they are released at all. This phenomenon of publication bias may not only create a false impression on the reliability of clinical research business, but it may also affect the evidence of clinical conclusions about the best treatments, which are mostly based on published data and results.

Exploring the feasibility of applying data mining for library reference service improvement : a case study of Turku Main Library

Data mining, as a heatedly discussed term, has been studied in various fields. Its possibilities in refining the decision-making process, realizing potential patterns and creating valuable knowledge have won attention of scholars and practitioners. However, there are less studies intending to combine data mining and libraries where data generation occurs all the time. Therefore, this thesis plans to fill such a gap. Meanwhile, potential opportunities created by data mining are explored to enhance one of the most important elements of libraries: reference service. In order to thoroughly demonstrate the feasibility and applicability of data mining, literature is reviewed to establish a critical understanding of data mining in libraries and attain the current status of library reference service. The result of the literature review indicates that free online data resources other than data generated on social media are rarely considered to be applied in current library data mining mandates. Therefore, the result of the literature review motivates the presented study to utilize online free resources. Furthermore, the natural match between data mining and libraries is established. The natural match is explained by emphasizing the data richness reality and considering data mining as one kind of knowledge, an easy choice for libraries, and a wise method to overcome reference service challenges. The natural match, especially the aspect that data mining could be helpful for library reference service, lays the main theoretical foundation for the empirical work in this study. Turku Main Library was selected as the case to answer the research question: whether data mining is feasible and applicable for reference service improvement. In this case, the daily visit from 2009 to 2015 in Turku Main Library is considered as the resource for data mining. In addition, corresponding weather conditions are collected from Weather Underground, which is totally free online. Before officially being analyzed, the collected dataset is cleansed and preprocessed in order to ensure the quality of data mining. Multiple regression analysis is employed to mine the final dataset. Hourly visits are the independent variable and weather conditions, Discomfort Index and seven days in a week are dependent variables. In the end, four models in different seasons are established to predict visiting situations in each season. Patterns are realized in different seasons and implications are created based on the discovered patterns. In addition, library-climate points are generated by a clustering method, which simplifies the process for librarians using weather data to forecast library visiting situation. Then the data mining result is interpreted from the perspective of improving reference service. After this data mining work, the result of the case study is presented to librarians so as to collect professional opinions regarding the possibility of employing data mining to improve reference services. In the end, positive opinions are collected, which implies that it is feasible to utilizing data mining as a tool to enhance library reference service.

KE & Science Europe briefing paper: Funding Research Data Management and related infrastructures

Responsible Research Data Management (RDM) is a pillar of quality research. In practice good RDM requires the support of a well-functioning Research Data Infrastructure (RDI). One of the challenges the research community is facing is how to fund the management of research data and the required infrastructure. Knowledge Exchange and Science Europe have both defined activities to explore how RDM/RDI are, or can be, funded. Independently they each planned to survey users and providers of data services and on becoming aware of the similar objectives and approaches, the Science Europe Working Group on Research Data and the Knowledge Exchange Research Data expert group joined forces and devised a joint activity to to inform the discussion on the funding of RDM/RDI in Europe.

Heat flow from the Southeast Indian Ridge flanks between 80°E and 140°E: Data review and analysis - art. no. B01101

We analyze available heat flow data from the flanks of the Southeast Indian Ridge adjacent to or within the Australian-Antarctic Discordance (AAD), an area with patchy sediment cover and highly fractured seafloor as dissected by ridge- and fracture-parallel faults. The data set includes 23 new data points collected along a 14-Ma old isochron and 19 existing measurements from the 20- to 24-Ma old crust. Most sites of measurements exhibit low heat flux (from 2 to 50 mW m(-2)) with near-linear temperature-depth profiles except at a few sites, where recent bottom water temperature change may have caused nonlinearity toward the sediment surface. Because the igneous basement is expected to outcrop a short distance away from any measurement site, we hypothesize that horizontally channelized water circulation within the uppermost crust is the primary process for the widespread low heat flow values. The process may be further influenced by vertical fluid flow along numerous fault zones that crisscross the AAD seafloor. Systematic measurements along and across the fault zones of interest as well as seismic profiling for sediment distribution are required to confirm this possible, suspected effect.