Technology and research in a global Networked University Digital Library (NUDL)

Digital Libraries (DLs) are extremely complex information systems that support the creation, management, distribution, and preservation of complex information resources, while allowing effective and efficient interaction among the several societies that benefit from DL content and services. In this paper, we focus on our experience facing challenges of building, maintaining, and developing the Networked University Digital Library (www.nudl.org), an extension of the Networked Digital Library of Theses and Dissertations (www.ndltd.org). NUDL is a worldwide initiative that addresses making the intellectual property produced in universities more accessible, stimulating international collaboration across all disciplines. We detail technological aspects of our solutions and research activities carried out to provide powerful and enriched services for the communities served by this initiative.

A model of multilingual digital library

This paper addresses the problem of multilingual digital libraries. The motivation for a such a digital library comes from the diversity of languages of the Internet users as well as the diversity of content authors, from e-book authors to writers of courseware. The basic definitions of such a system, the specifications of its functionality and the identification of the items it holds are discussed. The impact of multilinguism in each of the former aspects is presented. A case study of a multilingual digital library - in the Maxwell System in PUC-Rio - is described in the last sections. Its main characteristics are described and the current status of its digital library is shown.

Working in a digital world: new leadership practices for the development of a digital library workforce

Digital library developments are part of a global move in many sectors of society toward virtual work and electronic services made possible by the advances in information technology. This environment requires new attitudes and skills in the workforce and therefore leaders who understand the global changes underlying the new information economy and how to lead and develop such a workforce. This article explores ways to develop human resources and stimulate creativity to capitalize on the immense potential of digital libraries to educate and empower social change. There is a shortage of technically skilled workers and even more so of innovators. Retention and recruitment is one of the greatest obstacles to developing digital library services and information products.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

The Brazilian electronic theses and dissertations digital library: providing open access for scholarly information

This paper describes a project led by the Instituto Brasileiro de Informações em Ciência e Tecnologia (Ibict), a government institution, to build a national digital library for electronic theses and dissertations - Bibliteca Digital de Teses e Dissertações (BDTD). The project has been a collaborative effort among Ibict, universities and other research centers in Brazil. The developers adopted a system architecture based on the Open Archives Initiative (OAI) in which universities and research centers act as data providers and Ibict as a service provider. A Brazilian metadata standard for electronic theses and dissertations was developed for the digital library. A toolkit including open source package was also developed by Ibict to be distributed to potential data providers. BDTD has been integrated with the international initiative: the Networked Digital Library of Thesis and Dissertation (NDLTD). Discussions in the paper address various issues related to project design, development and management as well as the role played by Ibict. Conclusions highlight some important lessons learned to date and challenges for the future in expanding the BDTD project.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Iowa Library Services/State Library Update, February 2014, Vol. 12, no. 1

Quarterly update for Iowa Library Services/State Library patrons.

Iowa Public Library Statistics Pocket Digest, 2011

The information in this digest comes from the FY11 Iowa Annual Public Library Survey. It reflects the activities of 525 of the 543 public libraries in Iowa.

Iowa Public Library Statistics, July 1, 2011-June 30, 2012

The 2010-2011 (FY11) edition of Iowa Public Library Statistics includes information on income, expenditures, collections, circulation, and other measures, including staff. Each section is arranged by size code, then alphabetically by city. The totals and percentiles for each size code grouping are given immediately following the alphabetical listings. Totals and medians for all reporting libraries are given at the end of each section. There are 543 libraries included in this publication; 525 submitted a report. The table of size codes (page 5) lists the libraries alphabetically. The following table lists the size code designations, the population range in each size code, the number of libraries reporting in each size code, and the total population of the reporting libraries in each size code. The total population served by the 543 libraries is 2,339,070. Population data is used to determine per capita figures throughout the publication.

Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.