scholarly journals Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

Informatics ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 29 ◽  
Author(s):  
Florian Windhager ◽  
Saminu Salisu ◽  
Eva Mayr
Keyword(s):  
Data Quality ◽  
Interface Design ◽  
Standard Condition ◽  
Data Uncertainty ◽  
Uncertainty Visualization ◽  
Uncertainty Representation ◽  
User Groups ◽  
Representation Techniques ◽  
Research Domains ◽  
Data Quality Indicators

Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, or plainly missing data. Visualization approaches and interfaces to cultural collections have started to represent data quality and uncertainty metrics, yet all existing work is limited to representations for isolated metadata dimensions only. With this article, we advocate for a more systematic, synoptic and self-conscious approach to uncertainty visualization for cultural collections. We introduce omnipresent types of data uncertainty and discuss reasons for their frequent omission by interfaces for galleries, libraries, archives and museums. On this basis we argue for a coordinated counter strategy for uncertainty visualization in this field, which will also raise the efforts going into complex interface design and conceptualization. Building on the PolyCube framework for collection visualization, we showcase how multiple uncertainty representation techniques can be assessed and coordinated in a multi-perspective environment. As for an outlook, we reflect on both the strengths and limitations of making the actual wealth of data quality questions transparent with regard to different target and user groups.

Workshop on Toxicological Data Quality Indicators

1993 ◽  
Vol 9 (4) ◽  
pp. 577-604 ◽  
Author(s):  
Barry L. Johnson ◽  
T. Damstra ◽  
Chris Derosa ◽  
C. Elmer ◽  
M. Gilbert
Keyword(s):  
Data Quality ◽  
Quality Indicators ◽  
Toxicological Data ◽  
Data Quality Indicators

scholarly journals Facilitating Harmonized Data Quality Assessments. A Data Quality Framework for Observational Health Research Data Collections With Software Implementations in R

2020 ◽  
Author(s):  
Carsten Schmidt ◽  
Stephan Struckmann ◽  
Cornelia Enzenbach ◽  
Achim Reineke ◽  
Jürgen Stausberg ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Indicators ◽  
Health Research ◽  
Research Data ◽  
Formal Concept ◽  
Software Implementations ◽  
Quality Framework ◽  
Data Collections ◽  
Quality Assessments ◽  
Data Quality Indicators

Abstract Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).Results The data quality framework comprises 34 data quality indicators. These target three aspects of data quality: compliance with pre-specified structural and technical requirements (Integrity), presence of data values (completeness), and error in the data values (correctness). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses.

scholarly journals Health monitoring modular panel: Interface design and evaluation

2018 ◽  
Vol 22 (Suppl. 4) ◽  
pp. 1259-1270
Author(s):  
Milica Vujovic ◽  
Milan Ristanovic ◽  
Marko Milos ◽  
Francisco Perales-López
Keyword(s):  
Interface Design ◽  
The Elderly ◽  
Control Group ◽  
Processing Unit ◽  
Residential Unit ◽  
Lack Of Information ◽  
User Groups ◽  
Future Work ◽  
Functional Areas ◽  
Elderly Users

In this paper we present a conceptual solution of modular panel for measuring health parameters of the elderly. The conceptual solution was followed by a study that analyzed the design and evaluated interface of the system. Modular panel contains sensors, processing unit, and interface enabling data acquisition and communication between the user and the medical staff. Positioning of the panel within the residential unit was determined by the categories of actions which it should provide and functional areas of typical housing unit. Interface design is based on a specific type of users and is on the basis of the type of data that should be collected and displayed. Evaluation of interface is conducted by using two user groups, where the first is made up of people older than 60 years and represents the interest group of the study, while the second group consisted of people younger than 60 years as the control group. The collected data were analyzed and the results indicate that the simplicity of the interface suits good to the users. Elderly users need more time to conduct certain commands, but most of them understood interface completely. The limitations of the system, such as lack of information provided for the users, will be considered in the future work.

Usability Evaluation of E-Learning Systems

2011 ◽  
pp. 3897-3903 ◽  
Author(s):  
Shirish C. Srivastava ◽  
Shalini Chandra ◽  
Hwee Ming Lam
Keyword(s):  
Interface Design ◽  
Usability Evaluation ◽  
Learning System ◽  
Heuristic Evaluation ◽  
Usability Problems ◽  
E Learning ◽  
Small Set ◽  
User Groups ◽  
Definition Of ◽  
Usability Evaluation Methods

Usability evaluation which refers to a series of activities that are designed to measure the effectiveness of a system as a whole, is an important step for determining the acceptance of system by the users. Usability evaluation is becoming important since both user groups, as well as tasks, are increasing in size and diversity. Users are increasingly becoming more informed and, consequently, have higher expectations from the systems. Moreover “system interface” has become a commodity and, hence, user acceptance plays a major role in the success of the system. Currently, there are various usability evaluation methods in vogue, like cognitive walkthrough, think aloud, claims analysis, heuristic evaluation, and so forth. However, for this study we have chosen heuristic evaluation because it is relatively inexpensive, logistically uncomplicated, and is often used as a discount usability-engineering tool (Nielsen, 1994). Heuristic evaluation is a method for finding usability problems in a user interface design by having a small set of evaluators examine an interface and judge its compliance with recognized usability principles. The rest of the chapter is organized as follows: we first look at the definition of e-learning, followed by concepts of usability, LCD, and heuristics. Subsequently, we introduce a methodology for heuristic usability evaluation (Reeves, Benson, Elliot, Grant, Holschuh, Kim, Kim, Lauber, & Loh, 2002), and then use these heuristics for evaluating an existing e-learning system, GETn2. We offer our recommendations for the system and end with a discussion on the contributions of our chapter.

Going beyond FAIR to increase data reliability

2020 ◽  
Author(s):  
Uta Koedel ◽  
Peter Dietrich
Keyword(s):  
Data Quality ◽  
Information Needs ◽  
Transfer Functions ◽  
Measurement Data ◽  
Secondary Data ◽  
Data Uncertainty ◽  
Calibration Data ◽  
Data Reliability ◽  
Secondary Users ◽  
Site Field

<p>The FAIR principle is on its way to becoming a conventional standard for all kinds of data. However, it is often forgotten that this principle does not consider data quality or data reliability issues. If the data quality isis not sufficiently described, a wrong interpretation and use of these data in a common interpretation can lead to false scientific conclusions. Hence, the statement about data reliability is an essential component for secondary data processing and joint interpretation efforts. Information on data reliability, uncertainty, quality as well as information on the used devices are essential and needs to be introduced or even implemented in the workflow from the sensor to a database if data is to be considered in a broader context.</p><p>In the past, many publications have shown that the same devices at the same location do not necessarily provide the same measurement data. Likewise, statistical quantities and confidence intervals are rarely given in publications in order to assess the reliability of the data. Many secondary users of measurement data assume that calibration data and the measurement of other auxiliary variables are sufficient to estimate the data reliability. However, even if some devices require on-site field calibration, that does not mean that the data are comparable. Heat, cold, internal processes on electronic components can lead to differences in measurement data recorded with devices of the same type at the same location, especially with the increasingly complex devices themselves.</p><p>The data reliability can be increased by implementing data uncertainty issues within the FAIR principle. The poster presentation will show the importance of comparative measurements, the information needs for the application of proxy-transfer functions, and suitable uncertainty analysis for databases.</p>

scholarly journals Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Carsten Oliver Schmidt ◽  
Stephan Struckmann ◽  
Cornelia Enzenbach ◽  
Achim Reineke ◽  
Jürgen Stausberg ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Indicators ◽  
Health Research ◽  
Research Data ◽  
Formal Concept ◽  
Software Implementations ◽  
Quality Framework ◽  
Data Collections ◽  
Quality Assessments ◽  
Data Quality Indicators

Abstract Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP). Results The data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.

scholarly journals Resource-Efficient Sensor Data Management for Autonomous Systems Using Deep Reinforcement Learning

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4410 ◽  
Author(s):  
Seunghwan Jeong ◽  
Gwangpyo Yoo ◽  
Minjong Yoo ◽  
Ikjun Yeom ◽  
Honguk Woo
Keyword(s):  
Reinforcement Learning ◽  
Data Quality ◽  
Data Management ◽  
Autonomous Systems ◽  
Sensor Data ◽  
Data Uncertainty ◽  
Seamless Integration ◽  
Digital Twin ◽  
Physical Attributes ◽  
Sensor Data Management

Hyperconnectivity via modern Internet of Things (IoT) technologies has recently driven us to envision “digital twin”, in which physical attributes are all embedded, and their latest updates are synchronized on digital spaces in a timely fashion. From the point of view of cyberphysical system (CPS) architectures, the goals of digital twin include providing common programming abstraction on the same level of databases, thereby facilitating seamless integration of real-world physical objects and digital assets at several different system layers. However, the inherent limitations of sampling and observing physical attributes often pose issues related to data uncertainty in practice. In this paper, we propose a learning-based data management scheme where the implementation is layered between sensors attached to physical attributes and domain-specific applications, thereby mitigating the data uncertainty between them. To do so, we present a sensor data management framework, namely D2WIN, which adopts reinforcement learning (RL) techniques to manage the data quality for CPS applications and autonomous systems. To deal with the scale issue incurred by many physical attributes and sensor streams when adopting RL, we propose an action embedding strategy that exploits their distance-based similarity in the physical space coordination. We introduce two embedding methods, i.e., a user-defined function and a generative model, for different conditions. Through experiments, we demonstrate that the D2WIN framework with the action embedding outperforms several known heuristics in terms of achievable data quality under certain resource restrictions. We also test the framework with an autonomous driving simulator, clearly showing its benefit. For example, with only 30% of updates selectively applied by the learned policy, the driving agent maintains its performance about 96.2%, as compared to the ideal condition with full updates.

Sign in / Sign up

Export Citation Format

Share Document