Data Management Practices Research Articles

Influence of Data Intelligence on the Structuring of Content in Web Publications

The article provides a comprehensive examination of the influence of data intelligence on the structuring of content in web publications. Beginning with an exploration of the different strategies and technologies underpinning data intelligence, the article highlights the transformative impact of data intelligence on the presentation and usability of information in web publications. The article goes on to explore the opportunities and challenges associated with implementing data intelligence in web publications, including the potential for enhanced user engagement, privacy concerns, and the need for continuous investments in technology. As the field of data intelligence continues to evolve, the article discusses the anticipated impact of emerging technologies. Furthermore, the article delves into the need for structuring data in web publications and the process of transforming unstructured and semi-structured data into structured data. It underscores the significance of structured data in enhancing user experience, facilitating data intelligence operations, and improving data management practices. The article concludes by underlining the need for further exploration of strategies and technologies for data structuring to ensure the application of data intelligence in web publications.

One Data Indonesia Policy Adoption for Telkom University Data Warehouse Framework

The Indonesian government has implemented a data warehouse named One Data (Satu Data) Indonesia (ODI) to support its operations since 2019. However, the implementation of this concept in universities has been limited, with only a few universities adopting it. Telkom University is one of the few universities in Indonesia that has already taken steps to implement ODI at the university level. The adoption of ODI at Telkom University is known as the One Data Telkom University (ODTU) project. This project aims to create a platform for universities to share data and collaborate more effectively. This paper thoroughly examines the implementation of the ODI policy and data warehouse framework at Telkom University, focusing on the ODTU data warehouse design and architecture. This paper discusses the implementation of ODTU into several applications, including the One Data Portal, One Data Dashboard, and One Data Market. Moreover, it identifies the challenges encountered during the implementation process, such as data integration, data privacy and security, standardized data models, and the promotion of a shared vision among stakeholders with varying levels of data literacy. Our analysis results demonstrate the effectiveness of the ODTU framework in improving data management practices at Telkom University. The customer satisfaction index (CSI) shows that across key reliability, assurance, and responsiveness measures, Telkom University experienced average score improvements of 3-6% after implementing ODTU. This study contributes to the existing literature on ODI policy adoption in the context of higher education institutions, providing insights for institutions seeking to improve their data management practices.

Analysis of Farm Data License Agreements: Do Data Agreements Adequately Reflect on Farm Data Practices and Farmers’ Data Rights?

Farm data license agreements are legal documents that play an important role in informing farmers about farm data processing practices such as collection, use, safeguarding, and sharing. These legal documents govern the exchange, access, and dissemination of farm data and are expected to provide legal protection against misuse of data. Despite their significant influence on farm data processing and governance, there is limited understanding of the content of farm data license agreements and standards for drafting them. Although online privacy policy content has been extensively studied, farm data agreements’ evaluation and analysis have been overlooked. This study aims to investigate the structure, content, and transparency of farm data licenses. We collected 141 agricultural terms of use agreements and used natural language processing methods such as keyword and keyphrase analysis to perform text feature analysis, Flesch Readability Ease Score and Flesch Grade Level readability analysis, transparency analysis, and content analysis to gain insight into common data practices adopted by the agriculture technology providers. We also manually reviewed these agreements to validate the results and strengthen the observations. The findings show that data agreements are long, complex, and difficult to read and comprehend. The results suggest that 95% of the agreements fall under the difficult-to-read category and close to 75% of the policies require university-level education to understand the content. Furthermore, it is noted that some of the data management practices are not given adequate attention and are not as frequently mentioned in the agreements as expected. Finally, our analysis enabled us to provide recommendations on the content of farm data license agreements and strategies to improve them.

The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study.

In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status. Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data. In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies. The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.

Realization of Disability Equity Through Ethical Data Management Practices

People with disabilities (PWDs) experience worse health outcomes than people who do not have disabilities. Making meaningful progress on disability equity requires new ways of thinking about disability, new tools and processes, and new ways of working within the existing health care system. Immediate actions to increase disability equity include expanded data collection including patient-reported outcomes measures, more transparent, person-centered data governance and management, integration of public health and clinic-based health data, and renewed efforts to communicate with and treat PWDs with respect. Prevention of threats to realization of disability equity including a resurgence in the practice of eugenics, misuse of emerging technologies such as CRISPR, and surveillance-promoting technologies also are critical.

Securing the Cloud: A Critical Appraisal of Data Security Strategies

Cloud computing, a paradigm shift in the evolution of the internet, has garnered significant attention. However, security remains a primary concern, hindering its widespread adoption. Cloud computing essentially transfers user data and applications to remote data centers, where users relinquish control, and data management practices may not always adhere to the highest security standards. This unique characteristic of cloud computing raises a multitude of security concerns that warrant careful consideration and understanding. One of the most crucial and prevalent security concerns is the potential exposure of user data and applications stored on service provider premises. In this article, an attempt is made to review the literature in this area of research.

Citizen-Centric Personal Data Storage in The Public Sector: An Exploration of Opportunities, Challenges, and Preconditions

Public sector organizations are driven to re-imagine personal data management practices. Notably, the Flemish government aims to set up a Solid-enabled PDS-based data platform, which is to serve as the foundation of (personal) data sharing ecosystems that respect EU fundamental rights ‘by design.’ Of course, a critical success factor for this novel Flemish data platform is the broad adoption of citizen-centric data storage by public organizations in the region. This article constitutes an exploration of opportunities, challenges, and preconditions for the Flemish public sector through 13 expert interviews. Our findings show that citizen-centric personal data storage is expected to optimize intra-public sector data use, enable proactive public service provision, and foster trust. Challenges relate to a lack of human and financial resources, and data literacy/ inclusivity issues. For continued public sector engagement in PDS-based data ecosystems, preconditions in terms of moderation of ecosystem openness and data sharing reciprocity, as well as clustering of sub-ecosystems by use-cases, are to be considered.

Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors.

Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), and analysis of new organic devices. To curate organic device databases that contain reproducible and findable, accessible, interoperable, and reusable (FAIR) experimental data records, data ontologies that fully describe sample provenance and process history are required. However, standards for generating such process ontologies are not widely adopted for experimental materials domains. In this work, we design and implement an object-relational database for storing experimental records of OFETs. A data structure is generated by drawing on an international standard for batch process control (ISA-88) to facilitate the design. We then mobilize these representative data records, curated from the literature and laboratory experiments, to enable data-driven learning of process-structure-property relationships. The work presented herein opens the door for the broader adoption of data management practices and design standards for both the organic electronics and the wider materials community.

Journeying towards best practice data management in biodiversity genomics.

Advances in sequencing technologies and declining costs are increasing the accessibility of large-scale biodiversity genomic datasets. To maximize the impact of these data, a careful, considered approach to data management is essential. However, challenges associated with the management of such datasets remain, exacerbated by uncertainty among the research community as to what constitutes best practices. As an interdisciplinary team with diverse data management experience, we recognize the growing need for guidance on comprehensive data management practices that minimize the risks of data loss, maximize efficiency for stand-alone projects, enhance opportunities for data reuse, facilitate Indigenous data sovereignty and uphold the FAIR and CARE Guiding Principles. Here, we describe four fictional personas reflecting differing user experiences with data management to identify data management challenges across the biodiversity genomics research ecosystem. We then use these personas to demonstrate realistic considerations, compromises and actions for biodiversity genomic data management. We also launch the Biodiversity Genomics Data Management Hub (https://genomicsaotearoa.github.io/data-management-resources/), containing tips, tricks and resources to support biodiversity genomics researchers, especially those new to data management, in their journey towards best practice. The Hub also provides an opportunity for those biodiversity researchers whose expertise lies beyond genomics and are keen to advance their data management journey. We aim to support the biodiversity genomics community in embedding data management throughout the research lifecycle to maximize research impact and outcomes.

DataOps for OSDU - Transforming Oil and Gas Data Management

The oil and gas industry is undergoing a digital transformation, fueled by the need for increased efficiency, cost reduction, and improved decision-making. The Open Subsurface Data Universe (OSDU) initiative has emerged as a catalyst for this transformation by providing a standardized platform for data access and sharing. However, the successful implementation of OSDU relies heavily on effective data management practices. This white paper explores the concept of DataOps within the context of OSDU, outlining how it can revolutionize data management, accelerate integration, and streamline operations. We delve into the principles, benefits, and best practices of DataOps for OSDU, offering insights and strategies to help organizations navigate the evolving data landscape in the oil and gas sector.

Research Data Management Competencies for Academic Libraries: Perspectives from Two Universities in South Africa

The rapid pace of technological change has significantly transformed the librarianship profession, impacting the required competencies for professionals working in library and information services (LIS). As new roles emerge within academic libraries, librarians need to assess their current knowledge and skills as well as identify additional knowledge and skills needed to adapt to evolving roles and responsibilities. This paper reports findings of a study conducted at two university libraries in South Africa. The objective of the study was to identify the competencies necessary for academic librarians in providing research data management services (RDM). While previous studies have explored the knowledge and skills required for academic librarians in Southern Africa and globally, there is a research gap concerning the specific competencies necessary for RDM. This study employed semi-structured interviews with librarians from two selected university libraries, using purposive sampling. The research also included document reviews and content analysis of relevant academic library job advertisements. The study’s findings highlight key competencies such as information and data management practices, repository management, data curation, data collection management, understanding funders’ policies, research Research Data Management Competencies for Academic Libraries: Perspectives from Two Universities in South Africa methods and processes, as well as ICT and digitization skills. The findings also highlight the importance of robust training and selfdevelopment through online courses, webinars, and freely available materials as strategies to support RDM.

Performance based assessment of LIS professionals toward digital data sets management in university libraries: A TAM model approach

In recent times, computer systems have acquired the capacity to gather, process and analyze massive quantities of data, as well as techniques for distributing, transmitting, and computing estimates. Therefore, the study is conducted with an objective to ascertain the performance-based assessment of LIS professionals toward digital data sets management DDSM in university libraries. A six-factor measurement model was tested based on the 28-valid items obtained from the literature. The hypothesized research model was validated through the application of confirmatory factor analysis (CFA), and structural equation modeling (SEM) technique. LIS professionals in research-oriented organizations possess the necessary data analytical skills and technical expertise to extract insights from the data. The findings indicated that analytical skills have a significant positive impact on PEOU (β =246, SE = 0.54, p < 0.000). Perceived usefulness (PU) of digital data management practices is significantly influenced by analytical skills (AS) (β =0.443, SE = 0.077, p ⩽ 0.000), PEOU significantly influence the attitude of LIS professionals toward digital data management practices (β =0.215, SE = 0.214, p ⩽ 0.000), and attitude of LIS professionals has a positive impact on their performance in managing digital data sets in university libraries (β =0.584, SE = 0.178, p ⩽ 0.005). The study reports primary data gathered through survey on constructs given in technology acceptance (TAM) model. The study provides a valuable insight for organizational leaders, stakeholders, and planners to enhance and refine DDSM practices and methodologies in academic libraries. This will enable them to effectively utilize library resources and services through the implementation of DDSM techniques.

Reference Data Management: A Cornerstone of Financial Data Integrity

Reference data management plays a crucial role in ensuring the integrity and accuracy of financial data in the banking and finance sector. This paper examines the significance of reference data management as a cornerstone of maintaining data integrity within financial institutions. It discusses the challenges faced by organizations in managing reference data effectively and explores strategies for implementing robust reference data management frameworks. By addressing these challenges and implementing best practices in reference data management, financial institutions can enhance data accuracy, improve regulatory compliance, and mitigate operational risks.

Assessment of immunization data management practices in Cameroon: unveiling potential barriers to immunization data quality

BackgroundOne crucial obstacle to attaining universal immunization coverage in Sub-Saharan Africa is the paucity of timely and high-quality data. This challenge, in part, stems from the fact that many frontline immunization staff in this part of the world are commonly overburdened with multiple data-related responsibilities that often compete with their clinical tasks, which in turn could affect their data collection practices. This study assessed the data management practices of immunization staff and unveiled potential barriers impacting immunization data quality in Cameroon.MethodsA descriptive cross-sectional study was conducted, involving health districts and health facilities in all 10 regions in Cameroon selected by a multi-stage sampling scheme. Structured questionnaires and observation checklists were used to collect data from Expanded Program of Immunization (EPI) staff, and data were analyzed using STATA VERSION 13.0 (StataCorp LP. 2015. College Station, TX).ResultsA total of 265 facilities in 68 health districts were assessed. There was limited availability of some data recording tools like vaccination cards (43%), maintenance registers (8%), and stock cards (57%) in most health facilities. Core data collection tools were incompletely filled in a significant proportion of facilities (37% for registers and 81% for tally sheets). Almost every health facility (89%) did not adhere to the recommendation of filling tally sheets during vaccination; the filling was instead done either before (51% of facilities) or after (25% of facilities) vaccinating several children. Moreso, about 8% of facilities did not collect data on vaccine administration. About a third of facilities did not collect data on stock levels (35%), vaccine storage temperatures (21%), and vaccine wastage (39%).ConclusionOur findings unveil important gaps in data collection practices at the facility level that could adversely affect Cameroon’s immunization data quality. It highlights the urgent need for systematic capacity building of frontline immunization staff on data management capacity, standardizing data management processes, and building systems that ensure constant availability of data recording tools at the facility level.

Ethics-Based Leadership in Managing Information Security and Data Privacy

In the advancing digital age, technological transformation has created endless opportunities for efficiency, innovation and connectivity. However, behind these advancements lie significant challenges related to information security and data privacy. Adverse data breaches and cyber-attacks have reminded us of the vulnerabilities inherent in today's digital environment. Therefore, protecting information security and data privacy has become a top priority for organisations around the world. This research aims to explore how ethical leaders can influence decision-making and practices related to information security and data privacy. This research is a literature review that adopts a qualitative method approach, which means it will analyse and interpret data by relying on information and text from various sources. The study results arrive at a statement that ethical leaders ensure that technical aspects and ethical values go hand in hand in making decisions related to information security and data privacy. Through ethics-based leadership, organisations are able to build a culture that cares about data privacy, create a secure and transparent environment for customers and users, and comply with increasingly stringent regulations. The trust of customers and business partners built through ethical practices in information and data management will be a competitive advantage for organisations in the ever-changing digital age.

Enhancing Reproducibility in Research Through FAIR Digital Objects

The FAIR principles were introduced to enhance data reuse by providing guidelines for effective data management practices. In the broader context of research, assets encompass not only data but also artifacts such as code, software, and publications. FAIRifying these artifacts is as essential as FAIRifying data, given the increasing complexity of current AI approaches that make reproducibility extremely challenging. Therefore, the reuse of these artifacts is growing in importance. The concept of FAIR Digital Objects (FDOs) presents a solution to FAIRify these artifacts, treating them as FDOs. NFDI4DataScience is embracing FDOs and proposing an architecture to efficiently manage them.

Benefits and Security Challenges of Big Data Analytics

Big data analytics is a powerful technique used by companies to secure their data. However, data security remains a significant challenge for cultural entities such as libraries and museums, as hackers can target database systems and gain access to sensitive information. This study examines the benefits and security issues associated with big data analytics and proposes a manual approach for analyzing data with application to digital presentation and preservation of cultural heritage. By providing insights into the strengths and weaknesses of big data analytics and exploring strategies for securing data, the goal of the research is to help organizations make informed decisions about their data management practices.

The significance of general data protection regulation in the compliant data contribution to the European Society of Thoracic Surgeons database.

The General Data Protection Regulation (GDPR), enacted in the European Union in 2018, has significantly transformed the landscape of personal data management and protection. This article provides an overview of GDPR's impact, focusing on its applicability, fundamental principles and influence on data management practices, particularly within the European Society of Thoracic Surgeons (ESTS) database. GDPR's reach extends to all entities collecting and processing personal data of European Union residents, regardless of their location. It encompasses various data types, emphasizing meticulous handling and protection of identifiable information. Special categories of data, such as health and sensitive attributes, require even more stringent protection. The regulation sets legal, fair and transparent data processing principles, emphasizing accuracy, purpose limitation and data minimization. It also stresses accountability, leading to the appointment of Data Protection Officers and significant penalties for non-compliance. The ESTS database, designed to enhance thoracic surgical research and care, collects data on European procedures. It follows GDPR principles by pseudonymizing data, ensuring secure data transmission and providing clear instructions for data submission. The database contributes to research, policymaking and practice improvement in thoracic surgery by offering a comprehensive dataset for analysis. Here, we aim to shed light on the complexities of GDPR implementation and emphasize the need for comprehensive data management strategies to ensure compliance and enhance privacy protection with the contribution to the ESTS database. GDPR compliance comes with challenges, including potential human dignity and privacy rights violations. Data breaches can result in unauthorized disclosures, and non-compliance can lead to substantial fines and reputational damage. The implementation of GDPR encourages organizations to prioritize ethical data practices, security measures and transparent data handling. In conclusion, GDPR has revolutionized personal data protection by emphasizing accountability, transparency and individual rights. It has impacted organizations globally, promoting responsible data management practices. Adhering to GDPR ensures privacy protection, trust-building and overall enhancement of data management in today's data-driven environment.

Metadata stewardship in nanosafety research: learning from the past, preparing for an “on-the-fly” FAIR future

Introduction:Significant progress has been made in terms of best practice in research data management for nanosafety. Some of the underlying approaches to date are, however, overly focussed on the needs of specific research projects or aligned to a single data repository, and this “silo” approach is hampering their general adoption by the broader research community and individual labs.Methods:State-of-the-art data/knowledge collection, curation management FAIrification, and sharing solutions applied in the nanosafety field are reviewed focusing on unique features, which should be generalised and integrated into a functional FAIRification ecosystem that addresses the needs of both data generators and data (re)users.Results:The development of data capture templates has focussed on standardised single-endpoint Test Guidelines, which does not reflect the complexity of real laboratory processes, where multiple assays are interlinked into an overall study, and where non-standardised assays are developed to address novel research questions and probe mechanistic processes to generate the basis for read-across from one nanomaterial to another. By focussing on the needs of data providers and data users, we identify how existing tools and approaches can be re-framed to enable “on-the-fly” (meta) data definition, data capture, curation and FAIRification, that are sufficiently flexible to address the complexity in nanosafety research, yet harmonised enough to facilitate integration of datasets from different sources generated for different research purposes. By mapping the available tools for nanomaterials safety research (including nanomaterials characterisation, nonstandard (mechanistic-focussed) methods, measurement principles and experimental setup, environmental fate and requirements from new research foci such as safe and sustainable by design), a strategy for integration and bridging between silos is presented. The NanoCommons KnowledgeBase has shown how data from different sources can be integrated into a one-stop shop for searching, browsing and accessing data (without copying), and thus how to break the boundaries between data silos.Discussion:The next steps are to generalise the approach by defining a process to build consensus (meta)data standards, develop solutions to make (meta)data more machine actionable (on the fly ontology development) and establish a distributed FAIR data ecosystem maintained by the community beyond specific projects. Since other multidisciplinary domains might also struggle with data silofication, the learnings presented here may be transferrable to facilitate data sharing within other communities and support harmonization of approaches across disciplines to prepare the ground for cross-domain interoperability.

COPO - Managing sample metadata for biodiversity: considerations from the Darwin Tree of Life project.

Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.