The Problem of Data

Jahnke and Asher explore workflows and methodologies at a variety of academic data curation sites, and Keralis delves into the academic milieu of library and information schools that offer instruction in data curation. Their conclusions point to the urgent need for a reliable and increasingly sophisticated professional cohort to support data-intensive research in our colleges, universities, and research centers.

Methodological issues in quantifying the magnitude of the tuberculosis problem in a prison population

SETTING: Correctional settings and remand prisons. OBJECTIVE: To critically discuss calculations for epidemiological indicators of the tuberculosis (TB) burden in prisons and to provide recommendations to improve study comparability. METHODS: A hypothetical data set illustrates issues in determining incidence and prevalence. The appropriate calculation of the incidence rate is presented and problems arising from cross-sectional surveys are clarifi ed. RESULTS: Cases recognized during the fi rst 3 months should be classifi ed as prevalent at entry and excluded from any incidence rate calculation. The numerator for the incidence rate includes persons detected as having developed TB during a specifi ed period of time subsequent to the initial 3 months. The denominator is persontime at risk from 3 months onward to the end point (TB or end of the observation period). Preferably, entry time, exit time and event time are known for each inmate to determine person-time at risk. Failing that, an approximation consists of the sum of monthly head counts, excluding prevalent cases and those persons no longer at risk from both the numerator and the denominator. CONCLUSIONS: The varying durations of inmate incarceration in prisons pose challenges for quantifying the magnitude of the TB problem in the inmate population. Recommendations are made to measure incidence and prevalence.

Preprocessing and analyzing genetic data with complex networks: An application to Obstructive Nephropathy

Many diseases have a genetic origin, and a great effort is being made to detect the genes that are responsible for their insurgence. One of the most promising techniques is the analysis of genetic information through the use of complex networks theory. Yet, a practical problem of this approach is its computational cost, which scales as the square of the number of features included in the initial dataset. In this paper, we propose the use of an iterative feature selection strategy to identify reduced subsets of relevant features, and show an application to the analysis of congenital Obstructive Nephropathy. Results demonstrate that, besides achieving a drastic reduction of the computational cost, the topologies of the obtained networks still hold all the relevant information, and are thus able to fully characterize the severity of the disease.

Replication Data Management,needs and solutions: an initial evaluation of conceptual approaches for integrating heterogeneous replication study data

Replication Data Management (RDM) aims at enabling the use of data collections from several iterations of an experiment. However, there are several major challenges to RDM from integrating data models and data from empirical study infrastructures that were not designed to cooperate, e.g., data model variation of local data sources. [Objective] In this paper we analyze RDM needs and evaluate conceptual RDM approaches to support replication researchers. [Method] We adapted the ATAM evaluation process to (a) analyze RDM use cases and needs of empirical replication study research groups and (b) compare three conceptual approaches to address these RDM needs: central data repositories with a fixed data model, heterogeneous local repositories, and an empirical ecosystem. [Results] While the central and local approaches have major issues that are hard to resolve in practice, the empirical ecosystem allows bridging current gaps in RDM from heterogeneous data sources. [Conclusions] The empirical ecosystem approach should be explored in diverse empirical environments.

A Semantic Approach to Problem-Based Learning with Conceptual Models

El aprendizaje basado en problemas se lleva aplicando con éxito durante las últimas tres décadas en un amplio rango de entornos de aprendizaje. Este enfoque educacional consiste en proponer problemas a los estudiantes de forma que puedan aprender sobre un dominio particular mediante el desarrollo de soluciones a dichos problemas. Si esto se aplica al modelado de conocimiento, y en particular al basado en Razonamiento Cualitativo, las soluciones a los problemas pasan a ser modelos que representan el compotamiento del sistema dinámico propuesto. Por lo tanto, la tarea del estudiante en este caso es acercar su modelo inicial (su primer intento de representar el sistema) a los modelos objetivo que proporcionan soluciones al problema, a la vez que adquieren conocimiento sobre el dominio durante el proceso. En esta tesis proponemos KaiSem, un método que usa tecnologías y recursos semánticos para guiar a los estudiantes durante el proceso de modelado, ayudándoles a adquirir tanto conocimiento como sea posible sin la directa supervisión de un profesor. Dado que tanto estudiantes como profesores crean sus modelos de forma independiente, estos tendrán diferentes terminologías y estructuras, dando lugar a un conjunto de modelos altamente heterogéneo. Para lidiar con tal heterogeneidad, proporcionamos una técnica de anclaje semántico para determinar, de forma automática, enlaces entre la terminología libre usada por los estudiantes y algunos vocabularios disponibles en la Web de Datos, facilitando con ello la interoperabilidad y posterior alineación de modelos. Por último, proporcionamos una técnica de feedback semántico para comparar los modelos ya alineados y generar feedback basado en las posibles discrepancias entre ellos. Este feedback es comunicado en forma de sugerencias individualizadas que el estudiante puede utilizar para acercar su modelo a los modelos objetivos en cuanto a su terminología y estructura se refiere. ABSTRACT Problem-based learning has been successfully applied over the last three decades to a diverse range of learning environments. This educational approach consists of posing problems to learners, so they can learn about a particular domain by developing solutions to them. When applied to conceptual modeling, and particularly to Qualitative Reasoning, the solutions to problems are models that represent the behavior of a dynamic system. Therefore, the learner's task is to move from their initial model, as their first attempt to represent the system, to the target models that provide solutions to that problem while acquiring domain knowledge in the process. In this thesis we propose KaiSem, a method for using semantic technologies and resources to scaffold the modeling process, helping the learners to acquire as much domain knowledge as possible without direct supervision from the teacher. Since learners and experts create their models independently, these will have different terminologies and structure, giving rise to a pool of models highly heterogeneous. To deal with such heterogeneity, we provide a semantic grounding technique to automatically determine links between the unrestricted terminology used by learners and some online vocabularies of the Web of Data, thus facilitating the interoperability and later alignment of the models. Lastly, we provide a semantic-based feedback technique to compare the aligned models and generate feedback based on the possible discrepancies. This feedback is communicated in the form of individualized suggestions, which can be used by the learner to bring their model closer in terminology and structure to the target models.

DUI program as OEF/OIF Veterans Outreach: Descriptive Data from Two Community-Based Outpatient Clinics

Although initially conceived as providing simply the preventive portion of an extended continuum of care for veterans, the Driving Under the Influence (DUI) program has turned out to be an important outreach service for active duty or recently discharged OEF/OIF (Operation Enduring Freedom/Operation Iraqi Freedom) veterans. Veterans receive empirically-based, state-mandated education and therapy under the only Department of Veterans Affairs (VA) - sponsored DUI program in the State of Colorado, with the advantage of having providers who are sensitive to symptoms of Post-Traumatic Stress Disorder (PTSD) and other relevant diagnoses specific to this population, including Traumatic Brain Injury (TBI). In this paper, the rapid growth of this program is described, as well as summary data regarding the completion, discontinuation, and augmentation of services from the original referral concern. Key results indicated that for nearly one third (31.9%) of the OEF/OIF veterans who were enrolled in the DUI program, this was their initial contact with the VA health care system. Furthermore, following their enrollment in the DUI program, more than one fourth (27.6%) were later referred to and attended other VA programs including PTSD rehabilitation and group therapy, anger management, and intensive inpatient or outpatient dual diagnosis programs. These and other findings from this study suggest that the DUI program may be an effective additional pathway for providing treatment that is particularly salient to the distinctive OEF/OIF population; one that may also result in earlier intervention for problem drinking and other problems related to combat. Relevant conclusions discussed herein primarily aim to improve providers' understanding of effective outreach, and to enhance the appropriate linkages between OEF/OIF veterans and existing VA services.

Boston, Massachusetts Region LIDAR First Return Elevation Data, 2002

LIDAR (LIght Detection And Ranging) first return elevation data of the Boston, Massachusetts region from MassGIS at 1-meter resolution. This LIDAR data was captured in Spring 2002. LIDAR first return data (which shows the highest ground features, e.g. tree canopy, buildings etc.) can be used to produce a digital terrain model of the Earth's surface. This dataset consists of 74 First Return DEM tiles. The tiles are 4km by 4km areas corresponding with the MassGIS orthoimage index. This data set was collected using 3Di's Digital Airborne Topographic Imaging System II (DATIS II). The area of coverage corresponds to the following MassGIS orthophoto quads covering the Boston region (MassGIS orthophoto quad ID: 229890, 229894, 229898, 229902, 233886, 233890, 233894, 233898, 233902, 233906, 233910, 237890, 237894, 237898, 237902, 237906, 237910, 241890, 241894, 241898, 241902, 245898, 245902). The geographic extent of this dataset is the same as that of the MassGIS dataset: Boston, Massachusetts Region 1:5,000 Color Ortho Imagery (1/2-meter Resolution), 2001 and was used to produce the MassGIS dataset: Boston, Massachusetts, 2-Dimensional Building Footprints with Roof Height Data (from LIDAR data), 2002 [see cross references].

Boston, Massachusetts, 2-Dimensional Building Footprints with Height Data (from LIDAR data), 2002

This dataset consists of 2D footprints of the buildings in the metropolitan Boston area, based on tiles in the orthoimage index (orthophoto quad ID: 229890, 229894, 229898, 229902, 233886, 233890, 233894, 233898, 233902, 237890, 237894, 237898, 237902, 241890, 241894, 241898, 241902, 245898, 245902). This data set was collected using 3Di's Digital Airborne Topographic Imaging System II (DATIS II). Roof height and footprint elevation attributes (derived from 1-meter resolution LIDAR (LIght Detection And Ranging) data) are included as part of each building feature. This data can be combined with other datasets to create 3D representations of buildings and the surrounding environment.

Migration and Asylum Data for Policy-making in the European Union – The problem with numbers. CEPS Liberty and Security in Europe No. 89/March 2016

Among many other problems, the migration, humanitarian and policy crises in the European Union in 2015 and early 2016 have highlighted a pressing need for reliable, timely and comparable statistical data on migration, asylum and arrivals at national borders. In this fast-moving policy field, data production and the timeliness of dissemination have seen some improvements but the sources of data remain largely unchanged at national level. In this paper the author examines the reasons for some of the problems with the data for policy and for public discussion, and makes a set of recommendations that call for a complete and updated inventory of data sources and for an evaluation of the quality of data used for policy-making.

Handling the Problem of Unbalanced Data Sets in the Classification of Technical Equipment States

Questions of handling unbalanced data considered in this article. As models for classification, PNN and MLP are used. Problem of estimation of model performance in case of unbalanced training set is solved. Several methods (clustering approach and boosting approach) considered as useful to deal with the problem of input data.

Selected data on initial operations under the disability freeze provision of the Social Security Amendments of 1954.

Mode of access: Internet.

An analysis of mopeds as a potential safety problem in the United States. Volume II: data analysis and usage and accident projections. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

General revenue sharing: initial interstate data & allocations.

Mode of access: Internet.