Iowa Department of Transportation Office of Research & Analytics Guidance on Data Management Plan (DMP) Requirements for Federally Funded Research Projects

The Iowa Department of Transportation Office of Research & Analytics has created this Guide to help researchers and contractors of the Iowa DOT attain compliance with Federal and Iowa DOT Public Access Policies for transportation-related research publications and datasets. This guide provides direction for filling out the data management plan template (also attached to this record) that will help satisfy Iowa DOT and U.S. DOT requirements.

Educational data mining and learning analytics : Clasificación de las matriculaciones de A.D.E. en la UOC

Trabajo de investigación que realiza un estudio clasificatorio de las asignaturas matriculadas en la carrera de Administración y Dirección de Empresas de la UOC en relación a su resultado. Se proponen diferentes métodos y modelos de comprensión del entorno en el que se realiza el estudio.

Topographic visual analytics of multibeam dynamic SONAR data

This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic.

Using knowledge management to give context to analytics and big data and reduce strategic risk

At the moment, the phrases “big data” and “analytics” are often being used as if they were magic incantations that will solve all an organization’s problems at a stroke. The reality is that data on its own, even with the application of analytics, will not solve any problems. The resources that analytics and big data can consume represent a significant strategic risk if applied ineffectively. Any analysis of data needs to be guided, and to lead to action. So while analytics may lead to knowledge and intelligence (in the military sense of that term), it also needs the input of knowledge and intelligence (in the human sense of that term). And somebody then has to do something new or different as a result of the new insights, or it won’t have been done to any purpose. Using an analytics example concerning accounts payable in the public sector in Canada, this paper reviews thinking from the domains of analytics, risk management and knowledge management, to show some of the pitfalls, and to present a holistic picture of how knowledge management might help tackle the challenges of big data and analytics.

Visual analytics of medical and biological data

Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik, Habilitationsschrift, 2016

Exploraty Multivariate Statistical Methods Applied to Pharmaceutical Industry CRM Data

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Building clusters for CRM strategies by mining airlines customer data

Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Detection of road accident accumulation zones with a visual analytics approach

Nowadays, road accidents are a major public health problem, which increase is forecasted if road safety is not treated properly, dying about 1.2 million people every year around the globe. In 2012, Portugal recorded 573 fatalities in road accidents, on site, revealing the largest decreasing of the European Union for 2011, along with Denmark. Beyond the impact caused by fatalities, it was calculated that the economic and social costs of road accidents weighted about 1.17% of the Portuguese gross domestic product in 2010. Visual Analytics allows the combination of data analysis techniques with interactive visualizations, which facilitates the process of knowledge discovery in sets of large and complex data, while the Geovisual Analytics facilitates the exploration of space-time data through maps with different variables and parameters that are under analysis. In Portugal, the identification of road accident accumulation zones, in this work named black spots, has been restricted to annual fixed windows. In this work, it is presented a dynamic approach based on Visual Analytics techniques that is able to identify the displacement of black spots on sliding windows of 12 months. Moreover, with the use of different parameterizations in the formula usually used to detect black spots, it is possible to identify zones that are almost becoming black spots. Through the proposed visualizations, the study and identification of countermeasures to this social and economic problem can gain new grounds and thus the decision- making process is supported and improved.

Tècniques de Webmetria : anàlisi de logs i Google Analytics per a la obtenció de variables mètriques del web de la Biblioteca Virtual de la Universitat Oberta de Catalunya (UOC)

This presentation aims to make understandable the use and application context of two Webometrics techniques, the logs analysis and Google Analytics, which currently coexist in the Virtual Library of the UOC. In this sense, first of all it is provided a comprehensive introduction to webometrics and then it is analysed the case of the UOC's Virtual Library focusing on the assimilation of these techniques and the considerations underlying their use, and covering in a holistic way the process of gathering, processing and data exploitation. Finally there are also provided guidelines for the interpretation of the metric variables obtained.

Learning analytics in practice: setting up a laboratory for action research at the Universitat Oberta de Catalunya

Since its inception in 1994 as a purely online university, the Universitat Oberta de Catalunya(UOC) has been able to position itself among the main universities of the Catalan and Spanish university systems. Most of the students at the UOC (currently more than 60,000) are adults who have a profile that could hardly fit into the traditional university system, thus finding in the UOC an opportunity to start or continue their higher education grades, in a very innovative environment. The intensive use of ICT for both theteaching/learning processes and management allowsresearchers and practitioners to obtain data aboutwhat takes place in the UOC Virtual Campus, which is continuously being improved according to suchfindings.

Els entorns virtuals d'aprenentatge com escenari Big Data: el cas de la UOC

En aquest treball es presenta el laboratori que l'eLearn Center posa a disposició del professorat i investigadors en e-learning de la UOC per al disseny sistemàtic d'experiments sota una perspectiva de Learning Analytics, però tambémecanismes per fer seguiment i documentar tot el procés que envolta el disseny d'experiències docents de forma que sigui més senzill transferir-les a altres àmbits.

Research Data Management in the Cloud

Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.

Using Google Analytics to evaluate an email information literacy program for medical and dental students

Objectives: An email information literacy program has been effective for over a decade at Université de Montréal’s Health Library. Students periodically receive messages highlighting the content of guides on the library’s website. We wish to evaluate, using Google Analytics, the effects of the program on specific webpage statistics. Using the data collected, we may pinpoint popular guides as well as others that need improvement. Methods: In the program, first and second-year medical (MD) or dental (DMD) students receive eight bi-monthly email messages. The DMD mailing list also includes graduate students and professors. Enrollment to the program is optional for MDs, but mandatory for DMDs. Google Analytics (GA) profiles have been configured for the libraries websites to collect visitor statistics since June 2009. The GA Links Builder was used to design unique links specifically associated with the originating emails. This approach allowed us to gather information on guide usage, such as the visitor’s program of study, duration of page viewing, number of pages viewed per visit, as well as browsing data. We also followed the evolution of clicks on GA unique links over time, as we believed that users may keep the library's emails and refer to them to access specific information. Results: The proportion of students who actually clicked the email links was, on average, less than 5%. MD and DMD students behaved differently regarding guide views, number of pages visited and length of time on the site. The CINAHL guide was the most visited for DMD students whereas MD students consulted the Pharmaceutical information guide most often. We noted that some students visited referred guides several weeks after receiving messages, thus keeping them for future reference; browsing to additional pages on the library website was also frequent. Conclusion: The mitigated success of the program prompted us to directly survey students on the format, frequency and usefulness of messages. The information gathered from GA links as well as from the survey will allow us to redesign our web content and modify our email information literacy program so that messages are more attractive, timely and useful for students.

WAIS seminar: Provenance Analytics and Crowdsourcing

Wednesday 26th March 2014 Speaker(s): Dr Trung Dong Huynh Organiser: Dr Tim Chown Time: 26/03/2014 11:00-11:50 Location: B32/3077 File size: 349Mb Abstract Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. In this talk, I will present an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. I will also talk about CollabMap (www.collabmap.org), an online crowdsourcing mapping application, and show how we applied the approach above to the trust classification of data generated by the crowd, achieving an accuracy over 95%.