Data Management Activities Research Articles

Health Data Management Practice and Associated Factors Among Health Professionals Working at Public Health Facilities in Resource Limited Settings.

BackgroundDespite the vast amount of resources invested in the development of health information systems, health professionals in developing countries are still suffering from lack of adequate skill to perform health data management activities. There is a lack of sound evidence to overcome health data management challenges in this setting. This study aimed to assess health data management practice and its associated factors among health professionals working at public health facilities in North Wollo Zone, Northeast Ethiopia.MethodsA quantitative cross-sectional study was conducted at public health facilities in North Wollo Zone, Northeast Ethiopia from March 2 to April 15, 2020. A total of 715 health professionalswere selected using a stratified random sampling technique. EpiData version 4.6 and STATA version 15 were used for data entry and analysis, respectively. Descriptive statistics were computed. Multi-variable logistic regression analyses techniques were carried out to show the association between explanatory and outcome variables. Odd ratio at 95% confidence level was used to describe the strength of association.ResultsA total of 643 health professionals participated in this study. The response rate was 90%. Among them, 56.1% (95% CI: 52.3%–59.9%) demonstrated good data management practice. Working in health center [AOR=1.31 (95% CI: 1.853, 2.003)], having knowledge on data management [AOR=3.74 (95% CI: 2.454, 5.713)], favorable attitude toward data management [AOR=2.64 (95% CI: 1.746, 3.976)], high competency level on data management tasks [AOR=3.12 (95% CI: 1.873, 5.197)], friendliness of data management format [AOR=2.26 (95% CI: 1.478, 3.454)], supervision [AOR=1.78 (95% CI: 1.153, 2.745)] and training [AOR=1.84 (95% CI: 1.115, 3.022)] were significantly associated with good practice of health data management.ConclusionHealth data management practices of health professionals’ were found to be inadequate. Capacity building to enhance health professionals’ data management knowledge, attitude and their competency level, providing continuous supportive supervision, designing friendly data management format, providing comprehensive data management training are necessary measures to improve data management practice in this study setting.

Researchers' Perceptions of Research Data Management Activities at an Academic Library in a Developing Country

This study investigated researchers perceptions of RDM at the MGGL to recommend measures to enhance managing, sharing, and reusing data. The study was underpinned by the DCC Curation lifecycle model and the CCMF which enabled the investigator to employ a descriptive research design to purposively capture data from a broad cross-section of 330 researchers. The data was analysed using SPSS to generate descriptive and inferential statistics that enabled the Investigator to answer the problem.Study findings demonstrate no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in the knowledge and skills capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited managing, sharing, and reusing data.The study recommends establishing a CMU within the MGGL to oversee the implementation of RDM activities by assembling all the needed capabilities to support data curation activities and enable efficient managing, sharing and reusing research data

Researchers’ Perceptions of Research Data Management Activities

This study investigated researchers perceptions of RDM at the MGGL to recommend measures to enhance managing, sharing, and reusing data. The study was underpinned by the DCC Curation lifecycle model and the CCMF which enabled the investigator to employ a descriptive research design to purposively capture data from a broad cross-section of 330 researchers. The data was analysed using SPSS to generate descriptive and inferential statistics that enabled the Investigator to answer the problem.Study findings demonstrate no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in the knowledge and skills capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited managing, sharing, and reusing data.The study recommends establishing a CMU within the MGGL to oversee the implementation of RDM activities by assembling all the needed capabilities to support data curation activities and enable efficient managing, sharing and reusing research data

Database Management System in Accounting: Assessing the Role of Internet Service Communication of Accounting System Information

Data management is part of information resource management which includes all activities that ensure that company data resources are accurate, up-to-date, and safe from tampering, and are also available to users and the company. This study aims to find out about the role of concept database management in managing large volumes of company data. The result showed that data management activities include data collection, integrity, and testing, storage, maintenance, security, organization, retrieval. Meanwhile, the database structure includes hierarchical database structure, network database structure, and relational database structure.

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.

Hybrid optimization for virtual machine migration of utilizing healthcare text in the cloud

Cloud computing as the modern technology that generates, processing, storing, and sharing of medical data has evolved significantly. The health industry has made a lot of progress in transforming its data management activities, from regular storage to the digitalization of health care data. Cloud computing impacting based on lowering of costs, availability of resources, and power. moreover patient has the right or ownership of data in-the-cloud virtualization technology. Keeping the data of the patient in the cloud also facilitates interoperability between the various sectors of the health-care sector-pharmacy, insurance, and payments. Cloud offers virtual hardware, runtime settings, and facilities for those with a credit card. Cloud infrastructure has become a common term for reference to various devices, resources, and concepts. The proposed framework provides a simulated migration approach that is complex and energy-intensive. By activating idle physical machinery mode, this mechanism reduces the power to conserve electricity. This study suggests a modern cloud-based Health care services paradigm for optimizing VM migration utilizing Parallel Particle Swarm Optimization (PPSO). To measure the performance of our VMs model, a new model for health care service is also provided. The findings reveal that, in the overall deployment period, the new model approaches 60% of the state-of-the-art implementations. Furthermore, device performance is increased by 6.2% for demanded data in real-time. Furthermore, the accuracy of the smart hybrid model of resource utilization is 96.8%. In all associated activities, the suggested model is 67% better than other referred versions.

A Cognitive Research Tendency in Data Management of Sensor Network

In today’s World sensor networks offer various opportunities for data management applications because of their low cost, reliability, scalability, high-speed data processing, and other versatile advantageous purposes. It is a great challenge to organize data effectively and to retrieve the appropriate data from the large volume of various data sets in ad-hoc network databases, mobile databases, etc. The sensor network is necessary for routing of data, performance analysis of data management activities, and data incorporation for the right application. Data management involves intranet and extranet query handling, data access mechanism, modeling of data, different data movement algorithm, data warehousing, and data mining of network database. Additionally, connectivity, design, and lifetime are important issues for sensor networks to perform all data management activities smoothly. In this paper, we are trying to give a cognitive research tendency of Sensor network data management in the last two decades considering all the challenges and issues of both sensor network database and data management functions using Scopus and Web of Science database. To analyze data, different assessments are done considering various parameters like the author, time, publication and citation number, place, source, document separately for Web of Science and Scopus database in global perspective. It is noticed that there is a significant growth of research in data management for sensor networks because of the popularity of this topic.

Scientific data management in the federal government: A case study of NOAA and responsibility for preserving digital data

AbstractIn this paper, we examine the ways in which the evolution of federal and agency‐specific data management policies has affected and continues to affect the long‐term preservation of digital scientific data produced by the United States government. After reviewing the existing literature on the role of archival theory and practice in the preservation of scientific data, we present the case of the National Oceanic and Atmospheric Administration (NOAA) to analyze how data management activities at this agency are shaped by legislative mandates as well as both government‐wide and agency‐specific information‐management policies. Through the connected network of law, federal policy, agency policy, and the records schedules which govern recordkeeping practice in the federal government, we propose a number of further questions on how government agencies can effectively provide for the management of scientific data as federal records.

Pengembangan Perangkat Lunak Pengolahan Data Farmasi Pada Klinik Kesehatan

Disruptive innovation makes it possible for various data management and delivery of information using more information technology. The development of information technology, which is fast and advanced, has a very influential role in various services. For the sake of creating complete services, all health service providers need a system in serving patients. One of the services that currently requires accuracy is the pharmacy department.Pharmacy is a vital part of the health sector. The problem that often arises in data management activities in the pharmaceutical sector is the need for time in fast drug transaction services. The obstacle when implementing it using conventional methods is that it takes a long time and requires more accuracy so that services are hampered.By applying information technology in managing pharmaceutical data, it is able to provide convenience in managing drug data and producing the required reports. The purpose of this research is to build a pharmaceutical data management software that is able to provide convenience in computerized pharmaceutical data management, so that data and information can be stored and managed effectively and efficiently. This research is expected to provide solutions to health services, especially in the field of pharmacy, to provide good service so as to improve the quality of service to patients.

Role of Content Analysis in Improving the Curation of Experimental Data


 As researchers are increasingly seeking tools and specialized support to perform research data management activities, the collaboration with data curators can be fruitful. Yet, establishing a timely collaboration between researchers and data curators, grounded in sound communication, is often demanding. In this paper we propose manual content analysis as an approach to streamline the data curator workflow. With content analysis curators can obtain domain-specific concepts used to describe experimental configurations in scientific publications, to make it easier for researchers to understand the notion of metadata and for the development of metadata tools. We present three case studies from experimental domains, one related to sustainable chemistry, one to photovoltaic generation and another to nanoparticle synthesis. The curator started by performing content analysis in research publications, proceeded to create a metadata template based on the extracted concepts, and then interacted with researchers. The approach was validated by the researchers with a high rate of accepted concepts, 84 per cent. Researchers also provide feedback on how to improve some proposed descriptors. Content analysis has the potential to be a practical, proactive task, which can be extended to multiple experimental domains and bridge the communication gap between curators and researchers.
 [This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.]

Hitting set enumeration with partial information for unique column combination discovery

Unique column combinations (UCCs) are a fundamental concept in relational databases. They identify entities in the data and support various data management activities. Still, UCCs are usually not explicitly defined and need to be discovered. State-of-the-art data profiling algorithms are able to efficiently discover UCCs in moderately sized datasets, but they tend to fail on large and, in particular, on wide datasets due to run time and memory limitations. In this paper, we introduce HPIValid , a novel UCC discovery algorithm that implements a faster and more resource-saving search strategy. HPIValid models the metadata discovery as a hitting set enumeration problem in hypergraphs. In this way, it combines efficient discovery techniques from data profiling research with the most recent theoretical insights into enumeration algorithms. Our evaluation shows that HPIValid is not only orders of magnitude faster than related work, it also has a much smaller memory footprint.

Research data management at an African medical university: Implications for academic librarianship

The study investigates and provides results on the current status of research data management at a medical school in a developing African country. The study focusses on four aspects of research data management: generation; preservation and backup; competencies; and challenges. Quantitative data collected using a questionnaire from 84 health researchers and 16 librarians was complemented by qualitative data gathered through conducting structured interviews with the Director of Research of the university. The study results provide the research community with a better understanding of research data management perspectives amongst health researchers in a developing country. This could help research data management stakeholders to adopt better strategies for championing research data management activities in this particular country and in other developing countries. The study notes that the current research data management status creates an opportunity for an academic library to consolidate and fortify its widely viewed natural role in research data management. In view of the findings, the study proposes that the adoption of research data management policies is essential in inspiring the conceptualisation, popularisation, adoption, and operationalisation of various research data management activities within the African university environment.

Coordination of long-term data management in the Gulf of Mexico: Lessons learned and recommendations from two years of cross-agency collaboration

This paper presents a summary of collaborative work, lessons learned, and suggestions for next steps in coordinating long-term data management in the Gulf of Mexico in the years following the Deepwater Horizon oil spill (DWH). A decade of increased research and monitoring following the DWH has yielded a vast amount of diverse data collected from response and assessment efforts as well as ongoing restoration efforts. To maximize the benefits of this data through proper management and coordination, a cross-agency and organization Long-Term Data Management (LTDM) working group was established in 2017 with sponsorship from NOAA’s Office of Response and Restoration (OR&R) and NOAA’s National Marine Fisheries Service Restoration Center (NMFS RC) and facilitated by the University of New Hampshire’s Coastal Response Research Center. This paper will describe the LTDM working group’s efforts to foster collaboration, data sharing, and best data management practices among the many state, federal, academic and non-governmental entities working to restore and improve the coastal environment in the Gulf following the DWH. Through collaborative workshops and working groups, participants have helped to characterize region-specific challenges, identify areas for growth, leverage existing connections, and develop recommended actions for stakeholders at all organizational levels who share an interest in data coordination and management activities.

Research data management in health and biomedical citizen science: practices and prospects.

BackgroundPublic engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?ObjectiveThe aim of this article is to analyze research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.MethodsA scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers.ResultsHealth and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not prevalent in the review.ConclusionsThere is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle.

SISTEM INFORMASI PENGELOLAAN DATA LAUNDRY PADA RUMAH LAUNDRY BEKASI

Laundry house is a business engaged in services in washing and drying, especially clothing. To serve the public in the field of laundry services, the laundry house in managing the data is still done manually and has not been computerized so there are still errors in the transaction process. Problems that occur in a laundry house such as incorrectly recording the type of package, the calculation is still using a calculator tool, in making the report must see the notes that have been collected previously, must see previous records recorded in the ledger, and be vulnerable to losing previous note notes so that it takes a long time and the results obtained are less accurate. The purpose of this study is to build a laundry data management program using the waterfall method which consists of 5 stages, namely, software requirements analysis, design, programming code, testing and support or maintenance. While the data collection is through observation, interviews and searching for literature related to this research so that it can solve problems related to laundry data management activities, especially in the process of receiving laundry services transactions to be more effective and efficient and can improve service to customers. The results of the study show that this laundry data management program can help and simplify the process of receiving laundry services at the laundry house.

Changing platforms without stopping the train: experiences of data management and data management systems when adapting platform protocols by adding and closing comparisons

BackgroundThere is limited research and literature on the data management challenges encountered in multi-arm, multi-stage platform and umbrella protocols. These trial designs allow both (1) seamless addition of new research comparisons and (2) early stopping of accrual to individual comparisons that do not show sufficient activity. FOCUS4 (colorectal cancer) and STAMPEDE (prostate cancer), run from the Medical Research Council Clinical Trials Unit (CTU) at UCL, are two leading UK examples of clinical trials implementing adaptive platform protocol designs. To date, STAMPEDE has added five new research comparisons, closed two research comparisons following pre-planned interim analysis (lack of benefit), adapted the control arm following results from STAMPEDE and other relevant trials, and completed recruitment to six research comparisons. FOCUS4 has closed one research comparison following pre-planned interim analysis (lack of benefit) and added one new research comparison, with a number of further comparisons in the pipeline. We share our experiences from the operational aspects of running these adaptive trials, focusing on data management.MethodsWe held discussion groups with STAMPEDE and FOCUS4 CTU data management staff to identify data management challenges specific to adaptive platform protocols. We collated data on a number of case report form (CRF) changes, database amendments and database growth since each trial began.DiscussionWe found similar adaptive protocol-specific challenges in both trials. Adding comparisons to and removing them from open trials provides extra layers of complexity to CRF and database development. At the start of an adaptive trial, CRFs and databases must be designed to be flexible and scalable in order to cope with the continuous changes, ensuring future data requirements are considered where possible. When adding or stopping a comparison, the challenge is to incorporate new data requirements while ensuring data collection within ongoing comparisons is unaffected. Some changes may apply to all comparisons; others may be comparison-specific or applicable only to patients recruited during a specific time period. We discuss the advantages and disadvantages of the different approaches to CRF and database design we implemented in these trials, particularly in relation to use and maintenance of generic versus comparison-specific CRFs and databases. The work required to add or remove a comparison, including the development and testing of changes, updating of documentation, and training of sites, must be undertaken alongside data management of ongoing comparisons. Adequate resource is required for these competing data management tasks, especially in trials with long follow-up. A plan is needed for regular and pre-analysis data cleaning for multiple comparisons that could recruit at different rates and periods of time. Data-cleaning activities may need to be split and prioritised, especially if analyses for different comparisons overlap in time.ConclusionsAdaptive trials offer an efficient model to run randomised controlled trials, but setting up and conducting the data management activities in these trials can be operationally challenging. Trialists and funders must plan for scalability in data collection and the resource required to cope with additional competing data management tasks.

Evaluasi Kualitas Layanan Jaringan Komputer pada Jaringan Komputer STIKOM Dinamika Bangsa Jambi

STIKOM Dinamika Bangsa Jambi is an institution engaged in education. It's utilize computer networks to carry out institutional and other data management activities. Network service quality / Quality of Service (QOS) is used to measure the level of performance of internet network connections which aims to improve the quality of internet services for institutions. The method used is the action reseach where in this method before evaluating the quality of the existing network, first step in the diagnosis, planning and retrieving the output of the Axence NetTools5 application. The Quality of Service (QOS) parameters that will be seen through this application include delay, packet loss, bandwidth (throughput). Evaluation is done by comparing the catch with Tiphon's standard where for the delay output obtained between 0-1 ms so that based on the tiphon standard includes a very good category. For packet loss the output obtained is 0% and includes a very good category. As for throughput, the output obtained is at least 75.5287%, so it can be concluded that the quality of output on the network is good.

PO 8522 ESTABLISHING AN EQUITABLE GOVERNANCE FRAMEWORK FOR AN EBOLA DATA-SHARING PLATFORM

BackgroundDespite the potential public health gains of enabling access to patient-level data on emerging infections, the launch of a centralised, international platform to deliver on this has not been achieved to date. Barriers include: concerns over retention of national data ownership; patient privacy; appropriate consent; loss of academic recognition; criticism or exploitation of the data generators; perceived data misuse; and the challenges of sharing benefits with communities where data is generated.MethodsTo determine the best approach to these issues in the context of Ebola, we have assembled a collaboration of partners including governments of Ebola-affected countries, non-government organisations, academic institutions, funders and public health authorities to form the Steering Committee of the Ebola Data Platform. Following stakeholder consultation, research and debate, the committee has developed a governance framework to enable access to emerging infections data, beginning with the data from the West African Ebola outbreak.ResultsPromoting fair and equitable sharing of benefits that arise from the use of data is a key element of the framework. Strategies to secure this principle include integration of platform data management activities with the national health institutions in Ebola-affected countries and collaboration with research communities in these countries to determine research priorities and plan analyses. Public health benefit in affected countries is further supported via training and strengthening of research capacity and infrastructure.ConclusionDeveloping a governance framework centered on the principle of equity has focused activities of the Ebola Data Platform on the affected health and research communities where they can have the most impact on patient outcomes, public health preparedness and future outbreak response.

Research data management compliance: is there a bigger role for university libraries?

ABSTRACTThis article explores how 13 Australian universities are assisting their researchers to manage the growing expectation to make research data more accessible. It identifies which university groups are supporting staff with research data management (RDM) activities and queries whether university libraries might have a bigger role to play in this space. We found that there was not a consistent approach to RDM at the 13 universities and that while there was generally strong encouragement to store research data securely during and after the project, there was overall a lack of practical support in how to undertake this activity. From our findings, a question of whether library staff have the appropriate experience, training and professional development to enable academic libraries in Australia to expand their RDM role arises and warrants further research.

Gamifying Data Management

While 95% of C-level executives state data plays a crucial role in developing their business strategy, 63% of data challenges are resource related. This challenge makes sense when you consider that often data management resources especially data stewards are not recognized for their efforts, resulting in a lack of adoption & training. Furthermore, since these data management resources are either unknown or work in silos, they do not leverage each other’s work often duplicating efforts across the data life cycle value chain. This paper will demonstrate how the use of game dynamics & using social media can resolve these issues by develop a collaborative firm wide data community and increasing engagement and speeding up adoption of data management initiatives. It provides transparency reducing duplication of data management activities & allows management to track tool adoption & usage, provide training when needed & quickly deploy the right resources on data focused business initiatives.