scholarly journals Crossing the digital divide: an interoperable solution for sharing time series and coverages in Earth sciences

2012 ◽  
Vol 12 (10) ◽  
pp. 3013-3029 ◽  
Author(s):  
F. R. Salas ◽  
E. Boldrini ◽  
D. R. Maidment ◽  
S. Nativi ◽  
B. Domenico

Abstract. In a world driven by the Internet and the readily accessible information it provides, there exists a high demand to easily discover and collect vast amounts of data available over several scientific domains and numerous data types. To add to the complexity, data is not only available through a plethora of data sources within disparate systems but also represents differing scales of space and time. One clear divide that exists in the world of information science and technology is the disjoint relationship between hydrologic and atmospheric science information. These worlds have long been split between observed time series at discrete geographical features in hydrologic science and modeled or remotely sensed coverages or grids over continuous space and time domains in atmospheric science. As more information becomes widely available through the Web, data are being served and published as Web services using standardized implementations and encodings. This paper illustrates a framework that utilizes Sensor Observation Services, Web Feature Services, Web Coverage Services, Catalog Services for the Web and GI-cat Services to index and discover data offered through different classes of information. This services infrastructure supports multiple servers of time series and gridded information, which can be searched through multiple portals, using a common set of time, space and concept query filters.

2010 ◽  
Vol 23 (10) ◽  
pp. 2782-2800 ◽  
Author(s):  
Martin P. Tingley ◽  
Peter Huybers

Abstract Part I presented a Bayesian algorithm for reconstructing climate anomalies in space and time (BARCAST). This method involves specifying simple parametric forms for the spatial covariance and temporal evolution of the climate field as well as “observation equations” describing the relationships between the data types and the corresponding true values of the climate field. As this Bayesian approach to reconstructing climate fields is new and different, it is worthwhile to compare it in detail to the more established regularized expectation–maximization (RegEM) algorithm, which is based on an empirical estimate of the joint data covariance matrix and a multivariate regression of the instrumental time series onto the proxy time series. The differing assumptions made by BARCAST and RegEM are detailed, and the impacts of these differences on the analysis are discussed. Key distinctions between BARCAST and RegEM include their treatment of spatial and temporal covariance, the prior information that enters into each analysis, the quantities they seek to impute, the end product of each analysis, the temporal variance of the reconstructed field, and the treatment of uncertainty in both the imputed values and functions of these imputations. Differences between BARCAST and RegEM are illustrated by applying the two approaches to various surrogate datasets. If the assumptions inherent to BARCAST are not strongly violated, then in scenarios comparable to practical applications BARCAST results in reconstructions of both the field and the spatial mean that are more skillful than those produced by RegEM, as measured by the coefficient of efficiency. In addition, the uncertainty intervals produced by BARCAST are narrower than those estimated using RegEM and contain the true values with higher probability.


2019 ◽  
Author(s):  
FRANCISCO CARLOS PALETTA

This work aims to presents partial results on the research project conducted at the Observatory of the Labor Market in Information and Documentation, School of Communications and Arts of the University of São Paulo on Information Science and Digital Humanities. Discusses Digital Humanities and informational literacy. Highlights the evolution of the Web, the digital library and its connections with Digital Humanities. Reflects on the challenges of the Digital Humanities transdisciplinarity and its connections with the Information Science. This is an exploratory study, mainly due to the current and emergence of the theme and the incipient bibliography existing both in Brazil and abroad.Keywords: Digital Humanities; Information Science; Transcisciplinrity; Information Literacy; Web of Data; Digital Age.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.


2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.


2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.


2016 ◽  
Vol 9 (4) ◽  
pp. 225 ◽  
Author(s):  
Rosemary M. Shafack

The world faces immense challenges which range from people living in poverty and denied dignity, rising inequalities, unemployment, global health threats, natural disasters, spiraling conflicts, violent extremism, terrorism and related humanitarian crises leading to force displacement of people, the depletion of natural resources and environmental degradation and the resultant climate change problem, just to name these. Fortunately, there are recognized human rights in the context of the United Nations (UN) Universal Declaration of Human Right in Article 19 and the Africa Chatter. These problems have thus challenged the world’s organizations to think and reflect on the way forward and some of these ways are developmental plans which include the UN Post 2015 Sustainable Development Agenda, the African Union (AU) 2063 Development Agenda with 17 aspirations, the International Federation of Library Associations and Institutions (IFLA) Strategy Plan and key initiative and the Cameroon Development Vision 2035 emergence programme. These agendas require a number of stakeholders to intervene if these challenges must be reduced for the benefit of humanity. One of such stakeholders is the Library and Information Science (LIS) Profession. The question that comes up with respect to the Cameroon context is, “Is the LIS profession in Cameroon able to meet its information role?” In line with this, three research questions were coined to guide data collection for this paper. The survey method was adopted with document analysis and interview schedule constituting the main data collecting instruments. The simple descriptive statistical method was used for data analysis. The information profession is critical in the development agenda, because it provides the platform for access to various information that enhance the progress of all human activities. The LIS profession drives the knowledge economy. Thus it is well placed to roll the information literacy programmes of any nation to help people have access to quality information, enhance community education, social, health and economic needs thereby improving lives and development. There is equally the shift from a print to a digital information environment as supported by the advent of new Internet technology such as mobile or broadband. This is changing the means and mechanisms of information delivery in libraries which have the potential to lead, improve and provide more relevant services and programmes for users. This profession has proven to be the most suitable with skills and mandate to pull together, organize and make available and accessible information in all forms and formats to all irrespective of their social, educational and physical status. From the study it is clear that the LIS profession in Cameroon is not able to play its role of collector and steward of human heritage, is not able to play its fundamental role in enhancing education through the different libraries and information services, is not able to enhance and ensure inclusive, equitable, quality education and promote lifelong learning and is unable to increase access to information and knowledge assisted by ICTs to support sustainable development to help Cameroon in its development agenda. The recommendation is that it will be unfortunate for a nation like Cameroon not to afford to accord an appropriate attention to the LIS profession which is a suitable developmental tool. The government needs therefore to provide the needed status for this sector and put it on its agenda and this will usher in a new spirit of information professionalism in Cameroon that will go a long way to enhance literacy that is needed if Cameroon must develop.


Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. O39-O47 ◽  
Author(s):  
Ryan Smith ◽  
Tapan Mukerji ◽  
Tony Lupo

Predicting well production in unconventional oil and gas settings is challenging due to the combined influence of engineering, geologic, and geophysical inputs on well productivity. We have developed a machine-learning workflow that incorporates geophysical and geologic data, as well as engineering completion parameters, into a model for predicting well production. The study area is in southwest Texas in the lower Eagle Ford Group. We make use of a time-series method known as functional principal component analysis to summarize the well-production time series. Next, we use random forests, a machine-learning regression technique, in combination with our summarized well data to predict the full time series of well production. The inputs to this model are geologic, geophysical, and engineering data. We are then able to predict the well-production time series, with 65%–76% accuracy. This method incorporates disparate data types into a robust, predictive model that predicts well production in unconventional resources.


2021 ◽  
Vol 7 ◽  
Author(s):  
Anthonia Carter ◽  
Marianthi Papalexandri-Alexandri ◽  
Guy Hoffman

We report on a series of workshops with musicians and robotics engineers aimed to study how human and machine improvisation can be explored through interdisciplinary design research. In the first workshop, we posed two leading questions to participants. First, what can AI and robotics learn by how improvisers think about time, space, actions, and decisions? Second, how can improvisation and musical instruments be enhanced by AI and robotics? The workshop included sessions led by the musicians, which provided an overview of the theory and practice of musical improvisation. In other sessions, AI and robotics researchers introduced AI principles to the musicians. Two smaller follow-up workshops comprised of only engineering and information science students provided an opportunity to elaborate on the principles covered in the first workshop. The workshops revealed parallels and discrepancies in the conceptualization of improvisation between musicians and engineers. These thematic differences could inform considerations for future designers of improvising robots.


2002 ◽  
Vol 752 ◽  
Author(s):  
Peter Lingenfelter ◽  
Tomasz Sokalski ◽  
Andrzej Lewenstam

ABSTRACTA numerical model is presented for analyzing the propagation of ionic concentrations and electrical potential in space and time in the solution ion-exchanging membrane system. Diffusion and migration according to the Nernst-Planck (NP) flux equation govern the transport of ions, and the electrical interaction of the species is described by the Poisson (P) equation. These two equations and the continuity equation form a system of partial non-linear differential equations that is solved numerically. As a result of the physicochemical properties of the system, both the contact/boundary potential and the diffusion potential contribute to the overall membrane potential. It is shown that interpreting the electrical potential of ion-exchanging membranes exclusively in terms of boundary potential at steady-state is incorrect. The Nernst-Planck-Poisson (NPP) model is general and applies to ions of any charge in space and time domains.


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