Gestão de dados científicos: produção e impacto a partir de dados da base Dimensions

The study aims to analyze the scientific production of research data management indexed in Dimensions. Using the term “research data management”, 677 articles were retrieved and analyzed using output and citation bibliometric indicators. The multidisciplinary in research data management was demonstrated by publications occurring in different research areas, such as computer science, information systems, library and information science, medicine and health sciences, and history and archeology. The countries with the highest publication rates were the United States, Germany, and the United Kingdom. About 60% of the publications had at least one citation, with 3,598 citations found, featuring a growing academic impact since the volume of production and citations have grown over time. When it comes to the Big Data era, data management is a topic under development that ensures its sharing and reuse and, consequently, the advancement of science. This bibliometric study made it possible to monitor the literature performance on research data management.A pesquisa objetivou analisar a produção científica sobre gestão de dados científicos indexada na Dimensions. A partir da busca pelo termo “research data management” foram recuperados 677 artigos, analisados por meio de indicadores bibliométricos de produção e citação. A multidisciplinaridade em gestão de dados de pesquisa foi demonstrada pelas publicações ocorrerem em diferentes áreas de pesquisa, como Information and Computing Sciences, Information Systems, Library and Information Studies, Medical and Health Sciences e History and Archaeology. Os países com maiores índices de publicações foram Estados Unidos, Alemanha e Reino Unido. Cerca de 60% das publicações tiveram pelo menos uma citação, com um total de 3.598 citações encontradas, caracterizando-se um impacto acadêmico crescente uma vez que o volume de produção e de citações têm crescido ao longo do tempo. Ao pensar na era do Big Data, a gestão de dados é um tema em desenvolvimento para garantir o compartilhamento e reuso destes, e consequentemente, o avanço da ciência. Desta forma, este estudo bibliométrico permitiu acompanhar o desempenho da literatura sobre gestão de dados científicos

Deep Well Injection of Liquid Radioactive Waste at Krasnoyarsk-26: Analysis of Hypothetical Scenarios. Volume II

The Mining and Chemical Combine (MCC), located approximately 60 km north of the city of Krasnoyarsk,is one of two major sites in the Russian Federation where liquid radioactive wastes (LRW) are disposed of by deep well injection. Disposal of LRW at the MCC through the use of deep well injectio started in 1967. The Severny ("Northern") site, approximately 15 km north of the MCC, was launched after the completion of special geological surveys and explorations performed by istitutions of the Ministry of Geology and Russian Academy of Sciences. The site was designed by Mintom institutions. As of 1995, 5 million cubic meters (m3) of LRW had been injected into two deep aquifers at the site. The waste includes both radioactive fission products and nonradioactive chemicas used in reprocessing of spent fuel. The total activity, decay corrected to 1995, is approximately 250 million Curies (Ci). Detailed information about radioactive waste disposal at the Severny site is presented in Volume I of this report (Compton et al., 2000), which includes an evaluation of the safety of the site under normal post-operational conditions. For further information on the background data contained in Chapter 2 of that report, see Appendix I. The subject of the current report is the likelihood and consequence of hypothetical accidents and extreme natural events after site decommissioning, including a brief overview of the factors involved in the development of decommissioning plans at the site

Creating Knowledge, volume 8, 2015

Dear reader, I am delighted to introduce this eighth volume of Creating Knowledge: The LAS Journal of Undergraduate Scholarship. This volume features 19 essays and 14 art works, representing advanced coursework produced in twenty different departments and programs during the 2014-2015 academic year. Several of the essays have been honored with department awards and several draw on research supported by undergraduate research grants. Many were originally written in senior capstone seminars, research-intensive seminars, and independent studies, and many were presented in some form at one of the numerous conferences and showcases sponsored by departments and programs throughout the year. All have been selected by department-based faculty committees as the best of the year’s student research writing and all have been revised for submission under the supervision of faculty. (The first footnote to each essay provides information about the class in which it was written and the processes of selection and revision.) Together they represent the rich variety of research questions, methods and materials used in the arts, humanities, social sciences and interdisciplinary studies. The readers of this volume are also many and various. They include the faculty who taught the classes in which this work was produced and encouraged their students to submit it for publication, the faculty who reviewed and selected the work and those who assisted with the editing, the proud parents, siblings, and classmates, and, of course, the featured students themselves. The volume’s readers also include alumni and supporters of the college and, perhaps most important of all, future student scholars—prospective students and recently admitted students who are curious about what advanced work in this or that field looks like: What does a sociology, Latino and Latin American studies, or philosophy major do? What are the key research questions and ways of thinking or writing or knowing in history of art and architecture or Italian or women’s and gender studies? For these students, this volume provides a vivid and inspiring illustration of what they have to look forward to as they embark upon their chosen courses of study. Many thanks and hearty congratulations are due to the student scholars for their contributions to this volume and also to the more than 60 faculty who supported, reviewed, selected, and helped to edit these students’ work. Thanks are also due to the three Department of Art, Media and Design faculty who served as jurors of the art work and the three masters in writing and publication students who proofread the volume. Most of all, thanks are due to Warren Schultz, associate dean of undergraduate studies in the College of Liberal Arts and Social Sciences, who serves as editor of the volume, putting out the call for submissions, supporting the faculty work of reviewing, selecting, and editing the student essays, and coordinating the production of the print and digital editions. To all, congratulations! And to you, dear reader, enjoy. Lucy Rinehart, PhD Interim Deanhttps://via.library.depaul.edu/ckgallery/1007/thumbnail.jp

Access NASA Satellite Global Precipitation Data Visualization on YouTube

Since the satellite era began, NASA has collected a large volume of Earth science observations for research and applications around the world. The collected and archived satellite data at 12 NASA data centers can also be used for STEM education and activities such as disaster events, climate change, etc. However, accessing satellite data can be a daunting task for non-professional users such as teachers and students because of unfamiliarity of terminology, disciplines, data formats, data structures, computing resources, processing software, programming languages, etc. Over the years, many efforts including tools, training classes, and tutorials have been developed to improve satellite data access for users, but barriers still exist for non-professionals. In this presentation, we will present our latest activity that uses a very popular online video sharing Web site, YouTube (https://www.youtube.com/), for accessing visualizations of our global precipitation datasets at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). With YouTube, users can access and visualize a large volume of satellite data without the necessity to learn new software or download data. The dataset in this activity is a one-month animation for the GPM (Global Precipitation Measurement) Integrated Multi-satellite Retrievals for GPM (IMERG). IMERG provides precipitation on a near-global (60 deg. N-S) coverage at half-hourly time interval, providing more details on precipitation processes and development compared to the 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multisatellite Precipitation Analysis (TMPA, 3B42) product. When the retro-processing of IMERG during the TRMM era is finished in 2018, the entire video will contain more than 330,000 files and will last ~3.6 hours. Future plans include development of flyover videos for orbital data for an entire satellite mission or project. All videos, including the one-month animation, will be uploaded and available at the GES DISC site on YouTube (https://www.youtube.com/user/NASAGESDISC)

Digital reference services : a snapshot of the current practices in scottish libraries

Discusses the current practices followed by some major libraries in Scotland for providing digital reference services(DRS). Refers to the DRSs provided by three academic libraries, namely Glasgow University Library, the University of Strathclyde Library, and Glasgow Caledonian University Library, and two other premier libraries in Scotland, the Mitchell Library in Glasgow and the National Library of Scotland in Edinburgh. Concludes that digital reference services are effective forms of service delivery in Scotland's academic, national and public libraries, but that their full potential has not yet been exploited. E-mail is the major technology used in providing digital reference, although plans are under way to use more sophisticated Internet technologies. Notes that the majority of enquiries handled by the libraries are relatively low-level rather than concerning specific knowledge domains, and training the users to extract information from the best digital resources still remains a challenge

European HYdropedological Data Inventory (EU-HYDI)

There is a common need for reliable hydropedological information in Europe. In the last decades research institutes, universities and government agencies have developed local, regional and national datasets containing soil physical, chemical, hydrological and taxonomic information often combined with land use and landform data. A hydrological database for western European soils was also created in the mid-1990s. However, a comprehensive European hydropedological database, with possible additional information on chemical parameters and land use is still missing. A comprehensive joint European hydropedological inventory can serve multiple purposes, including scientific research, modelling and application of models on different geographical scales. The objective of the joint effort of the participants is to establish the European Hydropedological Data Inventory (EU-HYDI). This database holds data from European soils focusing on soil physical, chemical and hydrological properties. It also contains information on geographical location, soil classification and land use/cover at the time of sampling. It was assembled with the aim of encompassing the soil variability in Europe. It contains data from 18 countries with contributions from 29 institutions. This report presents an overview of the database, details the individual contributed datasets and explains the quality assurance and harmonization process that lead to the final database

Benchmark of machine learning methods for classification of a Sentinel-2 image

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performanc

Environmental urbanization assessment using gis and multicriteria decision analysis: a case study for Denizli (Turkey) municipal area

In recent years, life quality of the urban areas is a growing interest of civil engineering. Environmental quality is essential to display the position of sustainable development and asserts the corresponding countermeasures to the protection of environment. Urban environmental quality involves multidisciplinary parameters and difficulties to be analyzed. The problem is not only complex but also involves many uncertainties, and decision-making on these issues is a challenging problem which contains many parameters and alternatives inherently. Multicriteria decision analysis (MCDA) is a very prepotent technique to solve that sort of problems, and it guides the users confidence by synthesizing that information. Environmental concerns frequently contain spatial information. Spatial multicriteria decision analysis (SMCDA) that includes Geographic Information System (GIS) is efficient to tackle that type of problems. This study has employed some geographic and urbanization parameters to assess the environmental urbanization quality used by those methods. The study area has been described in five categories: very favorable, favorable, moderate, unfavorable, and very unfavorable. The results are momentous to see the current situation, and they could help to mitigate the related concerns. The study proves that the SMCDA descriptions match the environmental quality perception in the city. © 2018 Erdal Akyol et al