Data Management Concepts Research Articles

Towards FAIR Data Management in Heritage Science Research: Updates and Progress on the INFRA-ART Spectral Library

The heritage science sector is facing a critical need for accessible and comprehensive data resources to facilitate research, preservation efforts, and interdisciplinary collaboration. The concept of FAIR data management involves embracing principles and practices that ensure that data are Findable, Accessible, Interoperable, and Reusable. This work presents an overview of the latest updates on the INFRA-ART Spectral Library, an open access spectral database of cultural-heritage-related materials that was designed as a digital support tool for heritage research specialists that work with (portable) non- or minimally invasive spectroscopic techniques such as X-ray fluorescence (XRF), attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, or Raman spectroscopy, among others. The database is an ongoing compilation of high-quality curated data that currently incorporates primary ATR-FTIR and XRF spectra and a preliminary dataset of Raman and short-wave infrared (SWIR) reflectance spectra on over 900 different materials typically found in painted works of art. For increased and sustainable accessibility, the database follows the European Commission’s recommendations on access to scientific information, as well as the FAIR guiding principles on research data that result from publicly funded research. The INFRA-ART Spectral Library is registered as a resource within the Open Science Cloud (EOSC) Portal and is among the services offered by the Romanian hub within E-RIHS (European Research Infrastructure for Heritage Science) DIGILAB.

Back in the Driver’s Seat: How New EU Greenhouse-Gas Reporting Schemes Challenge Corporate Accounting

Greenhouse-gas (GHG) reporting schemes for companies are increasingly part of climate-mitigation policies worldwide. Notably, the European Green Deal (2019) boosts new public regulations that oblige companies to compile GHG emission inventories, i.e., account for their emissions in a given system boundary. Along with this boost, the workload for companies increases; at the same time, the quality of reporting is questioned. Given the overarching goal to improve companies’ climate-mitigation performance, the quality of reporting is inseparably connected to the quality of the respective accounting. However, the literature discusses carbon accounting as a universal umbrella term focusing on managerial issues, thus disregarding the crucial role of accounting methodologies in the sense of calculation approaches. In this publication, we apply an analytical approach introducing a clear differentiation between the task of quantitatively accounting for GHG inventories and the task of reporting results from calculated inventories in response to stakeholder or policy expectations. We use this approach to investigate European GHG reporting schemes and related GHG accounting methodologies in detail. Our findings indicate that the current phase of the European Green Deal depicts a quantitative growth in reporting schemes and a significant qualitative change by shifting from formerly voluntary to mandatory reporting schemes, along with the application of accounting methodologies originally not intended for politically compulsory purposes. We analyze the consequences of this shift, which poses new challenges for companies and policymakers, i.e., data-management concepts and refined methodological frameworks.

ANALISIS KESULITAN SISWA PADA PEMBAHASAN MATERI PENGOLAAN DATA KELAS VI SDN PONDOK JAGUNG TIMUR

This study seeks to investigate the challenges that students encounter while discussing data management concepts in their sixth-grade curriculum. Data management is a substantial component of their mathematics lessons, where they delve into collecting, organizing, and assessing data. However, within this learning journey, certain students may encounter difficulties in comprehending and applying data management concepts effectively. Our study is geared towards understanding the specific hurdles faced by Class VI students when engaging with data management topics. The research methodology entails data collection through classroom observations, student interviews, and the analysis of student practice questions. The findings of the study highlight that several students encounter challenges across various aspects of comprehending data management material.

Data Management in IoT: A Detailed Survey

This paper surveys the current state of data management in the Internet of Things (IoT). It begins by outlining the challenges and opportunities that data management in IoT presents. Firstly, data management deals with the technical challenges and solutions related to data management in IoT, including data acquisition, storage, and integration. The paper concludes with a set of recommendations for the development of effective data management strategies in the context of IoT. Secondly, the requirement of IoT for data management extends offline storage, query processing, and transaction management activities into online-offline communication and storage dual operations, and the idea of data management is broadened. This is accomplished by IPv6, as well as IoT-specific capabilities and protocols including CoAP, HTTP, and WebSocket. Users may track, monitor, and manage devices with Internet of Things (IoT) device management, ensuring that they operate effectively and securely after deployment. Finally, the paper discusses the various applications of IoT based on the concept of data management in IoT. Numerous more objects, including wearables, medical equipment, houses, cities, farms, industries, and workplaces, are being interacted with by billions of sensors. The IoT platforms assist in establishing and maintaining criteria to enhance and preserve data appropriately. The paper concludes with a set of recommendations for the development of effective data management strategies in the context of IoT. Smart gadgets automate processes so we may save time by controlling the environment. The most valuable data is protected by edge devices for data management, which also lowers bandwidth costs. These also offer excellent performance, data ownership, and cheap maintenance costs.

Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions.

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.

Master Data Management Disruptive Modern Architecture

Master Data Management concept came into the mainstream during the mid 2000s, to generate a single Golden profile of the customer. As the Enterprise IT architecture started adopting and integrating various Commercial Products for CRM, ERP, Finance, HR, Supply Change Management etc. along with other homegrown custom apps, the information around the Customer started drifting across the various applications which resulted in various inefficiencies in day-to-day business operation. Master Data Management tools and technologies provide a way to perform identity resolution and survive latest and greatest information. Traditionally, the MDM tools always adopt a lean approach that uses minimal attributions to identify the smallest amount of (master) data with the biggest influence on business outcomes like Name, Phone, SSN, Email and Address. These constitute only less than 1% of the enterprise data, In the age of generative AI, there is a greater need to understand the context and the relationship of ALL data. This paper explores an alternate approach to mitigate the shortcomings of the currently available MDM tools

Review of Managing Data for Patron Privacy: Comprehensive Strategies for Libraries by Kristin Briney and Becky Yoose

This review critiques Managing Data for Patron Privacy: Comprehensive Strategies for Libraries by Kristin Briney and Becky Yoose. Briney and Yoose's book structures considerations for patron data privacy around the data lifecycle, utilizing data management concepts. It addresses a selection of areas where librarians can introduce protocols for minimizing data breach risks through improving data management skills. In the broader context of library data stakeholders, it situates library policy and training through practical guidance and scenario-based examples.

The Concept of Big Data Management with Various Transportation Systems Sources as a Key Role in Smart Cities Development

An increasing number of devices and their communication with each other generates huge amounts of data. The efficiency of processing such large and heterogeneous data is crucial for extracting the reliable and consistent information that is needed for the effective management of smart cities within the field of transport. Data heterogeneity and volume as well as its integration and analytics are big challenges for decision-makers. The development of urban agglomerations is largely dependent on the proper management of such data. Therefore, this paper explores the role of these data repositories, their acquisition from different sources, and the ways to combine them. The main goal of this paper is to propose a concept of Smart City management based on Big Data Analytics and technology related to UAVs (Unmanned Aerial Vehicle) which may reduce costs and resource consumption. The presented concept includes successive data generation and collection, data type identification, problem and requirement identification, filtering, classification, pre-processing, and data optimization, as well as decision support analysis. A key part of this analysis utilizes computer algorithms, such as Speeded Up Robust Features (SURF) and Thresholding and Blob detection, to develop a multi-camera image recognition system for freight transport management and logistics in smart cities. The objective is to design a system that optimizes the route planning and time of vehicle passage on selected road sections, ultimately leading to the reduction of emissions. During the study, data obtained from multiple sources were compared, and the analysis uncovered different results for the same assumptions. We discuss the reasons for these variances. Overall, the results obtained in the analysis indicated that it is necessary to correct the predictions of the multi-camera image recognition system with additional methods and algorithms.

DATA MANAGEMENT APPROACHES IN SMART CITIES

Bu çalışmada, ilk olarak T.C. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı’nın hazırladığı “2020-2023 Ulusal Akıllı Şehir Stratejileri ve Eylem Planı” belgesinin içerik analizi yöntemiyle incelenmesi sonucunda veri yönetimi konusunun eksikliği tespit edilmiş ve bundan hareketle ‘kentsel veriler nasıl yönetilebilir’ sorusuna kamu yönetimi bağlamında yanıt aranmıştır. Amaç, kamu yönetimi literatüründe ve bahsi geçen belgede veri yönetimine dönük eksiklikleri ortaya koymak ve buna uygun çözüm önerileri getirmektir. Bu amaçla, akıllı şehir, veri, veri yönetimi kavramlarına ilişkin literatür taranmıştır. Kavramsal çerçeve belirlendikten sonra, akıllı şehir tasarımını belirleyecek belgeler ve akıllı şehirlerde veri yönetimine dönük çalışmalar içerik analiziyle incelenmiştir. Bu incelemeyle, literatürün temel eksikliğinin, sosyo-teknik incelemelerin göz ardı edilmesi ve veri yönetimi yaklaşımlarının kamu yönetimi perspektifinden değerlendirilmemesi olduğu anlaşılmıştır. Bu bulgular ışığında, akıllı şehir tasarım, strateji ve politikalarına veri yönetimi bağlamında çözüm önerileri getirilmiştir.

Exploring the relevance of ‘smart city’ approaches to low-income communities in Medellín, Colombia

Smart city approaches have tended to be top-down, techno-centric/corporate and expensive approaches which are promoted by large, global companies. Smart city narratives focus on their expected capacity to improve citizens’ quality of life. However, can technology-driven and municipally-led smart city initiatives address the issues faced by poor and vulnerable communities in urban and peri-urban areas? This paper explores key aspects of the implementation of smart city approaches in the Global South taking as a case study the Colombian city of Medellín, which has been recognized internationally for its social innovation and is considered a smart city by the Inter-American Development Bank. The paper draws on a local, national and international literature review; interviews with key local stakeholders involved in smart city initiatives; and an international workshop on smart cities and community data management organized by the authors in Medellín and bringing together three perspectives: academia, government and community. The paper provides a reflection on the concept of smart city, data management and citizen participation in smart city initiatives in Medellín, contrasting each of these with the literature. Our research found gaps between broader conceptualisations and community-oriented intentions in smart city initiatives in Medellín, as well as very low uptake and reported impact of such initiatives. We conclude that a redefinition of smart city approaches that may respond to the needs of the population while improving liveability is still urgently needed, and we set out a series of questions for an international research agenda on low-income community-oriented smart city research.

Data Management in Healthcare Research as a Guarantee of its Quality

Today, in times of outbreaks of epidemics such as the Zika virus and COVID-19, health systems around the globe face an urgent need to respond quickly to overcome their spread and prevent the re-infection of humanity. Among the solutions was the World Health Organization’s call for immediate, rapid, and, most importantly, open dissemination of medical research data. Although data sharing benefits science and society, there is a range of ethical, legal, cultural, financial, and technical barriers to the dissemination and reuse of medical research data today. Therefore, the quality of data, namely the possibility of their easy search, availability, compatibility, and reusability, is considered relevant in developing data infrastructure in healthcare. Consequently, there is an urgent need to develop an appropriate research data management system in the healthcare system. The aim is to study the efficiency of data management in the healthcare system. This goal is proposed to be achieved in the following sequence: to consider the essence of the concept of research data management; to conduct a bibliometric study of the concept of data management in the medical fields of knowledge, to analyze the frequency of publications on the issue of data management of medical research, indexed by the Scopus database. The object of research is the healthcare sector. The subject is the determinants of the impact of proper management of medical research data on the healthcare sector. The terminological maps of term relationships were constructed using the VOSviewer visualization tool. The study found that 8% of all data management research was conducted in the medical fields of knowledge. The study found an upward trend in the number of health data management publications with the highest growth rate in 2019 and 2020. Analysis of terminology clusters revealed that the keywords «Big Data», «Machine Learning», «Data Collection», «Data Quality», «Data Sharing», «Data Reuse», «COVID-19», and «Blockchain» have the highest number of mentions in publications and strong connection with other publications. Thus, we justified the importance of developing a coherent program and strategic plans for managing research data in the health care system. The results of the study can be used to support decision-making on future opportunities to effectively influence the development of access to medical research data, as well as to ensure the improvement of the quality and confidentiality of research data in the health system.

‘Sharing is caring’: A socio-technical analysis of the sharing and governing of hydrometeorological hazard, impact, vulnerability, and exposure data in Aotearoa New Zealand

There has been a growing recognition of the need to collect disaster and risk data over the last two decades. Accordingly, better collection and management of disaster data was identified as a priority of the Sendai Framework for Disaster Risk Reduction. The introduction and implementation of Impact Forecasts and Warnings (IFWs) have further highlighted this need to collect and access hazard, impact, vulnerability, and exposure (HIVE) data. However, challenges have been met with reporting and using disaster data, which have resulted in an identified need to establish principles for data collection, recording, reporting, exchange/sharing, and comparability. This introduces the concept of data governance and management for disaster data, particularly with regards to data custodianship, stewardship, and sharing.Using Grounded Theory, a series of interviews were conducted with users and creators of HIVE data to develop further understanding around managing and accessing it for severe weather hazards in New Zealand. A socio-technical lens guided the analysis to identify the organisational and technical intervening conditions and action/interaction strategies for accessing and sharing HIVE data in NZ.Findings indicated that there is a need to establish data governance principles for HIVE data in New Zealand. An additional need was identified for nurturing partnerships to continue building trust between stakeholders for sharing data. Furthermore, integration challenges continue to interfere with the use of various sources of HIVE data for effective risk and impact assessments for IFWs and beyond. Systematic and standardised data collection approaches using GIS-based tools can support integration.

Controlling my genome with my smartphone: first clinical experiences of the PROMISE system

BackgroundThe development of Precision Medicine strategies requires high-dimensional phenotypic and genomic data, both of which are highly privacy-sensitive data types. Conventional data management systems lack the capabilities to sufficiently handle the expected large quantities of such sensitive data in a secure manner. PROMISE is a genetic data management concept that implements a highly secure platform for data exchange while preserving patient interests, privacy, and autonomy.MethodsThe concept of PROMISE to democratize genetic data was developed by an interdisciplinary team. It integrates a sophisticated cryptographic concept that allows only the patient to grant selective access to defined parts of his genetic information with single DNA base-pair resolution cryptography. The PROMISE system was developed for research purposes to evaluate the concept in a pilot study with nineteen cardiomyopathy patients undergoing genotyping, questionnaires, and longitudinal follow-up.ResultsThe safety of genetic data was very important to 79%, and patients generally regarded the data as highly sensitive. More than half the patients reported that their attitude towards the handling of genetic data has changed after using the PROMISE app for 4 months (median). The patients reported higher confidence in data security and willingness to share their data with commercial third parties, including pharmaceutical companies (increase from 5 to 32%).ConclusionPROMISE democratizes genomic data by a transparent, secure, and patient-centric approach. This clinical pilot study evaluating a genetic data infrastructure is unique and shows that patient’s acceptance of data sharing can be increased by patient-centric decision-making.Graphic abstract

On-Board Data Management Layer: Connected Vehicle as Data Platform

For connected vehicles, as well as generally for the transportation sector, data are now seen as a precious resource. They can be used to make right decisions, improve road safety, reduce CO2 emissions, or optimize processes. However, analyzing these data is not so much a question of which technologies to use, but rather about where these data are analyzed. Thereby, the emerging vehicle architecture has to become a data-oriented architecture based on embedded computing platforms and take into account new applications, artificial intelligence elements, advanced analytics, and operating systems. Accordingly, in this paper, we introduce the concept of data management to the vehicle by proposing an on-board data management layer, so that the vehicle can play the role of data platform capable of storing, processing, and diffusing data. Our proposed layer supports analytics and data science to deliver additional value from the connected vehicle data and stimulate the development of new services. In addition, our data platform can also form or contribute to shaping the backbone of data-driven transport. An on-board platform was built where the dataset size was reduced 80% and a rate of 99% accuracy was achieved in a 5 min traffic flow prediction using artificial neural networks (ANNs).

Data Management for Quality Improvement: How to Collect and Manage Data.

Data Management for Quality Improvement: How to Collect and Manage Data.

Management and maintenance of research data by researchers in Zimbabwe

PurposeThe concept of research data management (RDM) is new in Zimbabwe and other developing countries. Research institutions are developing research data repositories and promoting the archiving of research data. As a way of creating awareness to researchers on RDM, the purpose of this paper is to determine how researchers are managing their research data and whether they are aware of the developments that are taking place in RDM.Design/methodology/approachA survey using a mixed method approach was done and an online questionnaire was administered to 100 researchers in thirty research institutions in Zimbabwe. Purposive sampling was done by choosing participants from the authors of articles published in journals indexed by Google Scholar, Scopus and Web of Science. Interviews were done with five top researchers. The data was analysed using NVIVO. The results were presented thematically. The questionnaire was distributed using the research offices of the selected 30 research institutions. There was a 75% response rate.FindingsThe findings indicated that all the researchers are aware of the traditional way of managing research data. A total of 70% of the respondents are not aware of the current trends in RDM services, as they are keeping their data on machines and external hard drives, while 97.3% perceive RDM services as useful, as it is now a requirement when applying for research grants. Librarians have a bigger role to play in creating awareness on RDM among researchers and hosting the data repositories for archiving research data.Practical implicationsResearch institutions can invest in research data services and develop data repositories. Librarians will participate in educating researchers to come up with data management plans before they embark on a research project. This study also helps to showcase the strategies that can be used in awareness creation campaigns. The findings can also be used in teaching RDM in library schools and influence public policy both at institutional and national level.Social implicationsThis study will assist in building capacity among stakeholders about RDM. Based on the findings, research institutions should prioritise research data services to develop skills and knowledge among librarians and researchers.Originality/valueFew researches on RDM practices in Zimbabwe were done previously. Most of the papers that were published document the perception of librarians towards RDM, but this study focused mainly on researchers’ awareness and perception. The subject is still new and people are beginning to research on it and create awareness amongst the stakeholders in Zimbabwe.

The Finnish National Seismic Network: Toward Fully Automated Analysis of Low-Magnitude Seismic Events

Abstract We present an overview of the seismic networks, products, and services in Finland, northern Europe, and the challenges and opportunities associated with the unique combination of prevailing crystalline bedrock, low natural intraplate seismic background activity, and a high level of anthropogenic seismicity. We introduce national and local seismic networks, explain the databases, analysis tools, and data management concepts, outline the Finnish macroseismic service, and showcase data from the 2017 M 3.3 Liminka earthquake in Ostrobothnia, Finland.

A Case Study in Managing the Analytics “Iceberg”: Data Cleaning and Management using Alteryx

ABSTRACTThis case introduces accounting students to data management using a “no code” approach. Many tools can be used for extract, transform, load (ETL) processes. Herein, we introduce ETL using Alteryx Designer, a market-leading tool for managing and manipulating data. In the case, students learn basic data cleaning and joining skills, explore key data management concepts including workflows and metadata, and learn how to create and document workflows in Alteryx. Undergraduate or graduate accounting students who have basic database knowledge (e.g., in an introduction to accounting systems class) and data processing skills will benefit from the case. Evidence from use suggests that the case is relevant, challenging, and useful for the goal of teaching introductory and intermediate-level ETL skills, using Alteryx. The case is easy to implement and an important resource for teaching data management and Alteryx Designer.

Integrative data processing for cyber-physical off-site and on-site construction promoting co-design

The term co-design describes a collaborative design process, usually across the boundaries of individual areas. A major aspect, and often an obstacle in the implementation of the co-design approach, is the establishment of a centralized data management system. In the past, the data management concept has been approached from different perspectives. Current approaches usually are oriented along the process chain and thus reflect a unidirectional flow of information. Neither the information feedback nor the reaction to adjustments in upstream model steps is supported, which makes continuous collaboration more difficult and at best reduces the co-design approach for continuous data usage. This paper addresses these weaknesses while outlining an integrative data processing concept enabling co-design between the domains of construction and industrial prefabrication.Geometrical information generated during architectural design and construction planning is reused for the production of components, manufacturing-related design adjustments are transferred back into the domain of construction. Changes throughout the entire process are centrally recorded. To ensure independence from the software tools used and to consider their great diversity, data exchange is largely based on standards, namely industry foundation classes and the standard for the exchange of product model data. In order to counteract the sequential processing according to a waterfall model and to support the co-design approach, the industrial prefabrication toolchain, consisting of computer-aided manufacturing software and downstream postprocessors, is largely automated, so that reprocessing of a workpiece for minor geometric adjustments no longer requires any interaction of the production planner.

Towards a scalable implementation of digital twins - A generic method to acquire shopfloor data

New strategies in factory management focus on shopfloor data as a mean to achieve more flexible mass productions. In reality, the value adding usage of smart manufacturing approaches depends on the effort it takes to provide sensor and actuator data for further analysis. The concept of digital twins, that are based on a physical, a virtual and a communication component, promises a scalable solution to read and standardize shopfloor data. So far, there exists no generic method describing the implementation of the data acquisition as part of the communication component of digital twins. This contribution therefore focusses on necessary steps to consider when realizing a scalable data acquisition and examines how existing standards and open source solutions can support the implementation. The first part of the paper derives the necessary steps for acquiring shopfloor data. It starts by defining the architecture and aim of digital twins. Afterwards, a method to implement the generic data acquisition of digital twins is introduced. The approach is inspired by the Plug-and-Produce paradigm of the control engineering field and is adapted to the concepts of data acquisition and management. The second part examines the technical implementation of the proposed method. Identified approaches from a literature review are structured within the generic method. This describes the realization of the individual steps but also systematically differentiates existing approaches to build Digital Twins. With the aim of creating scalable architectures, a special focus is set on available open source solutions and standards when presenting the implementation part of the generic method.