Anastasija Nikiforova

University of Tartu · Institute of Computer Science

To raise awareness of the importance of "data quality", despite the age of the topic, highlighting its continuous importance both in Information Systems (incl., incorporating the "data quality" concept in their design with the reference to the "data quality by design" putting it into the context of SE), data analytics (incl. BI), and, even more importantly, in the open data domain, i.e. open data quality. The topic of data quality was developed over the years and resulted in a series of articles, as well as a doctoral thesis (actually, my master thesis defended with a distinction and recognized to be the best Master’s Thesis in Computer Science of the year (by ZIBIT), was also devoted to it), in which the user-centered data object-driven approach for data quality assessment was developed and applied to open data served as a domain of application. The proposed approach served as an input for several projects, including DQMBT – data quality model-based testing approach for information systems developed and applied for real-life projects where the e-scooter system was a central object thereby demonstrating the proposed approach in the context of IoT or IoV (Internet of Vehicles). This example, however, has served as an input to the current study on car-sharing services in Latvia, more specifically on optimization processes read more… Later, data quality analysis became an integral part of my activities, including my experience with Latvian Biomedical Research and Study Centre (BBMRI-ERIC Latvian National Node), where I have inspected the current data ecosystem of both Latvian Biomedical Research and Study Centre and related data artifacts, including Latvian Genome database. This resulted in a set of guidelines towards efficient data management for heterogeneous data holders and exchangers developed as a deliverable of the HORIZON2020 INTEGROMED project (Deliverable 2.1 “Guidelines for the maintenance of efficient biobank, health register and research associated data“) and presented during the European Biobank Week 2021 (read more about it here…). It, however, serves as an input for the DECIDE – Development of a dynamic informed consent system for biobank and citizen science data management, quality control and integration. In addition, as part of the digitization of processes running within the LBMC, I have developed and introduced the set of e-surveys to substitute paper-based surveys and questionnaires, which were typically completed by hand by either patients, donors or done doctorates and then inserted in the database by manual rewriting of answers from paper-based surveys. Therefore, data tended to be incomplete, non-compliant with the database design, inaccurate errors are made, when the person reenter the data to the database from the paper-based survey or interpret the answer to made it compliant with it. E-surveys allow to ensure data integrity and in-built data quality checks, and compliance with the actual database and reduce the number of errors made within the process of data collection and insertion. September 2021 I have joined European Open Science Cloud (EOSC) Association Task Force FAIR Metrics and Data Quality, which oversees the implementation of FAIR metrics for the EOSC, testing them with research communities to ensure they are fit for purpose.

This is a collection of IT-related research for sharing with interested academic friends.

Today's society is in a so-called digital transformation. The increasing degree of digitization has become a much-discussed phenomenon in society, politics, the media, and business. The economy is facing the so-called digital business transformation. This describes the transition phase from the third to the fourth industrial age, Industry 4.0, and is referred to as the digital age. The intelligent networking of machines, systems, and people plays a key role in this. Today, the digital twin is increasingly used in Germany as part of the IT infrastructure for Industry 4.0. There, it is used to describe and virtually simulate existing systems (e.g., production plants) based on the data generated. Supplemented by a simulation model, the digital twin can then be used to analyze the underlying system and, if necessary, to make predictions about its behavior. This, in turn, supports business-relevant decisions, for example in relation to the status and productivity of the plant. The increasing use of digital twins in the context of Industry 4.0 is mainly due to improvements in the underlying technologies (such as the Internet of Things, Big Data, machine learning, data security, etc.). The concept of the digital twin is currently very prominent in various debates and is interpreted very differently in terms of its relevance for smart cities. However, it offers great potential to create added value on the basis of classic, established approaches such as geoportals, data platforms, etc., which are already widespread in today's smart cities. The application scenarios in the city are very diverse. The basis is the combination of data from different sources. If these are available with a uniform interface, this already has its own advantage. Digital twins enable simulations and planning before they are implemented, in particular to identify problems that may arise before they become reality. Current studies by various authors on digital twins in the smart city have mainly only dealt with technical, organizational, and social issues, but it is noticeable that there is still no uniform understanding of the use and potential of the term digital twin in the science and practice context of a smart city. The digital twin is often misleadingly used for various forms of intelligent solutions without formally taking into account the criteria of a real digital twin. In order to sensibly classify the currently realized potential and still open challenges around digital twins for urban initiatives, we would like to invite your contribution to our Special Issue, using practical examples to show which development possibilities the integration of a digital twin in smart cities entails and which areas of application are currently are implemented particularly frequently. As simulation has been used for planning and gaining knowledge about systems for several years, and simulation models are therefore already available in many companies, the question arises as to how these simulation models can also be used as part of the digital twin in the context of smart cities. For more information here is the link: https://www.mdpi.com/journal/smartcities/special_issues/digital_twin_cities