Implementation Of Data Management Research Articles

Data Management Sharing Plan: Fostering Effective Trans-Disciplinary Communication in Collaborative Research

This paper scrutinizes the integral role of Trans-Disciplinary Communication (TDC) in conceptualizing and implementing Data Management and Sharing (DMS) plans in alignment with federal guidelines such as those from NIH and NSF. We begin by dissecting existing federal mandates for research data management, elucidating their pivotal role in maintaining data integrity and enabling accessibility. Using a case study as our methodological approach, we exemplify how TDC can effectively enhance the dialogue and practices surrounding DMS plans, fulfilling the requirements imposed by funding organizations. Furthermore, this research offers a novel contribution to the ongoing discourse by highlighting how DMS plans are foundational in promoting effective TDC and, by extension, advancing discussions in areas like Diversity Equity and Inclusion (DEI), STEM Education, and Collaboration.

An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing.

Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.

RDMkit: The Research Data Management Toolkit for Life Sciences

Effective data management extends over the entire life cycle of data, from the point of creation through to dissemination and archiving, and usually continues long after a research project is concluded. Tailored guidance for management of research data is increasing its importance among data-driven scientific investigations. There is now increasing demand for Research Data Management (RDM), by funders and host institutions, for researchers to develop and implement data management plans for projects. The RDMkit, by ELIXIR Europe, is an open community-led resource designed to share knowledge on how to carry out RDM in life sciences research. It fills the training and learning gap by providing knowledge on domain-focused RDM considerations, task-focused solutions and resources, and tool assemblies that showcase real-life examples addressing how combinations of tools can be used to go through the data life cycle. The technical infrastructure of the RDMkit is reproducible, making it possible for other organisations to use the structure as a guide and inspiration to set up RDM source for their users. In this talk, RDMkit will be introduced with example best practice guidelines, and integrations and knowledge sources.

Data management in life cycle assessment: a case study of wastewater treatment.

To assess the environmental impact of wastewater treatment, life cycle assessment (LCA) is a frequently applied instrument. However, these studies often require large amounts of data. The complexity and heterogeneity of these data result in the need for a systematic data management approach. Especially the generation of the life cycle inventory (LCI) holds the potential to be facilitated by automation. A case study in the wastewater sector was used to demonstrate the implementation of data management. A database structure was developed to store the raw data of the wastewater plants (WWTPs) and make it accessible through code. The code interacted with the database, implemented calculations, and automatically created the inventory based on the processed data. The database provides a consistent structure for the raw data and can also be used for backup purposes. Because it is machine-readable it can be accessed through the code that enables the automated generation of the LCI. As a proof of concept, a sequence of the code is provided with a user interface and can be tested online. We found that for most use cases, basic programming tools were sufficient for systematic data management, and, therefore, the approach is considered accessible for LCA practitioners.

Enabling Efficient Personally Identifiable Information Detection with Automatic Consent Discovery

Personal data leakage prevention has now become a critical issue for implementing data management and sharing in many industries. Several data privacy regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPPA), California Consumer Privacy Act (CCPA), and Thailand's Personal Data Protection Act (PDPA) have been issued to enforce organizations to collect, process, and transfer personally identifiable information (PII) securely. In this paper, we propose a design and development of PII RapidDiscover, an efficient Thai and English PII discovery system featured with automatic consent discovery. At the core of our proposed system, we introduce the PII scanning algorithm based on the Presidio library and a natural language processing (NLP) technique to improve the scan result of PII written in Thai and English. Finally, we conducted the experiments to demonstrate the efficiency of our proposed system.

Understanding data governance requirements in IoT adoption for smart ports – a gap analysis

ABSTRACT Maritime shipping is considered the backbone of the global supply chain. It is expected that future fully automated smart ports will be data-driven, harvesting power from advanced analytics and artificial intelligence. Under the hood, Internet of Things (IoT) technologies are considered as an enabling foundation of new data sources for innovations and high-level automation. Thus, enterprise data management becomes critical when implementing IoT projects. However, the implementation of data management needs to be governed. Although the IoT adoption in smart ports receives significant interest from the port authority, the effectiveness of the IoT adoption lacks proper evaluation and support of comprehensive data management policy and guidelines. By using the systematic literature review and a case study to analyze the current gaps in IoT data governance research, this research shows that IoT data governance still needs to catch up in smart ports. In particular, compared to other industries, data lifecycle management and data quality management are two key data governance areas that should receive increasing attention from the port authorities. It is believed that with adequate data governance, smart ports can truly harvest the power of IoT to improve operational efficiency and sustainability with an increasing level of automation.

Application of the Methodology of Six Sigma in Public Health Institution

New world trends in product quality assurance and processes in companies around the world are based on quality control in all processes (basic and logistics). Quality control is in all processes: receiving requests from clients, implementation of required services, issuing final results, maintenance of instruments, procurement, providing quality and trained staff, and commercial service. Health care institutions in Republic of North Macedonia has got old models of management. By accepting the new world philosophies, the healthcare institutions are oriented towards a different quality system, transformation of the organizational structure, patient involvement, monitoring the key processes, implementing data management tools and team work. The TQM (Total Quality Management) system in a healthcare institution is based on the main principles of this philosophy: sanitary standardization, full commitment of patients, involvement of all employees in the organization and teamwork, continuous improvement and implement of six sigma methodology. The aim of the research is to implement and apply the six-sigma methodology as a tool for improving business processes in the Public Health Institution. With proactive application of the methodology six sigma in the department of clinical microbiology were determinedbusiness processes, potential operational errors, statistical processing of the obtained data and finding a solution for the identified defects in business processes. The research conducted in the Department of Clinical Microbiology showed that with the practical application of the 6-sigma methodology, excellent improvements were obtained in all business processes by: involving more employees, correction the time for delivery of materials to admission department, and complete training for employees.

Tinjauan Pengelolaan Data Berbasis Digital Bagian Registrasi Pendaftaran Di RSUP Haji Adam Malik Medan

Data management is a data place that will become information which will later become health information. Where patient registration if done manually will be slow automatic management of data will have an impact on the quality of hospital services. Therefore digitization is required. The purpose of this study was to see how the implementation of digital-based data management at RSUP H. Adam Malik Medan. Subjects in this study were registration officers who entered 7 people, with the object being not 5M. This research was conducted with a descriptive method with a qualitative approach. Instrumentation of interview data and observation checklist. With the TOTAL 3 informants. The data analysis used in this research is descriptive qualitative to describe the implementation of data management. Based on the results of the research that there is no one who is not in accordance with the competence, the money is not yet available and adequate, the method is appropriate but has not worked well, the machine is not as expected while the material is in accordance with the competence. Suggestions from researchers are that officers should distribute leaflets (paper leaflets) to the public so that the public can register via cellphones (cellular) and that officers should change the internet speed to MBPS so that the registration process does not occur sluggishly.

Implementing Data Management Workflows in Research Groups Through Integrated Library Consultancy

Comprehensive research data management is fundamental to ensuring reproducible, open scientific research. However, sufficient research data assistance is often not offered at universities, and when offered, typically only provides basic services that are viewed as optional. Integrating information specialists into research groups provides a potentially promising means of improving data management by providing personalized data management workflows. Workflows are comprehensive, executable guides that require planning, implementation, feedback, and adaptation. Comprehensive data management workflows should include a file organization scheme, the creation of data management roles for members, a data storage/sharing guide, and training and evaluation. Librarians, who regularly interact with faculty and students and are familiar with data management tools, are uniquely situated to assist with the creation and assessment of these workflows.

Research Data Management Implementation at Peking University Library: Foster and Promote Open Science and Open Data

Research Data Management (RDM) has become increasingly important for more and more academic institutions. Using the Peking University Open Research Data Repository (PKU-ORDR) project as an example, this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff, needs assessment, partnerships establishment, software investigation and selection, software customization, as well as data curation services and training. Through the review, some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data, demands from data providers and users, data policies and requirements from home institution, requirements from funding agencies and publishers, the collaboration between administrative units and libraries, and concerns from data providers and users. The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions. The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015, the Peking University Library (PKUL) has helped numerous researchers to support the entire research life cycle and enhanced Open Science (OS) practices on campus, as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL.

Menoci: lightweight extensible web portal enhancing data management for biomedical research projects

BackgroundBiomedical research projects deal with data management requirements from multiple sources like funding agencies’ guidelines, publisher policies, discipline best practices, and their own users’ needs. We describe functional and quality requirements based on many years of experience implementing data management for the CRC 1002 and CRC 1190. A fully equipped data management software should improve documentation of experiments and materials, enable data storage and sharing according to the FAIR Guiding Principles while maximizing usability, information security, as well as software sustainability and reusability.ResultsWe introduce the modular web portal software menoci for data collection, experiment documentation, data publication, sharing, and preservation in biomedical research projects. Menoci modules are based on the Drupal content management system which enables lightweight deployment and setup, and creates the possibility to combine research data management with a customisable project home page or collaboration platform.ConclusionsManagement of research data and digital research artefacts is transforming from individual researcher or groups best practices towards project- or organisation-wide service infrastructures. To enable and support this structural transformation process, a vital ecosystem of open source software tools is needed. Menoci is a contribution to this ecosystem of research data management tools that is specifically designed to support biomedical research projects.

Data: A collaborative ?

This study examines the interdependence of relational strategies and data management policies of SMEs during product innovation. The type of data management developed by a small firm to support its innovation efforts requires it to engage in competitive, vertical cooperative or coopetitive relationships. An empirical study of 109 leaders of French high-tech SMEs provides a descriptive and explanatory analysis of this question. This empirical study combines three theoretical dimensions: the characteristics of a Big Data policy, of an innovation product and of a relational strategy. We enrich the existing knowledge concerning the exploitation of data by SMEs by presenting a typology of their data strategies. We also find that Big data and Smart data policies are deployed by SMEs to support product innovation. Finally, we show that SMEs implement data management individually to support radical product innovation but will collaborate to support incremental product innovation. The nature of the data innovation guides the relational context of the SME. This study deepens the interdependence of data management and relational strategies among SMEs.

Development and implementation of data management and analysis system for new power energy based on MVC

Development and implementation of data management and analysis system for new power energy based on MVC

Advancements in data management services for distributed e-infrastructures: the eXtreme-DataCloud project

The development of data management services capable to cope with very large data resources is a key challenge to allow the future einfrastructures to address the needs of the next generation extreme scale scientific experiments. To face this challenge, in November 2017 the H2020 eXtreme DataCloud - XDC project has been launched. Lasting for 27 months and combining the expertise of eight large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), and the Worldwide LHC Computing Grid (WLCG). The project is use-case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: High Energy Physics, Astronomy, Photon and Life Science, Medical research. XDC is aimed at implementing data management scalable services, combining already established data management and orchestration tools, to address the following high level topics: policy driven data management based on Quality-of-Service, Data Life-cycle management, smart placement of data with caching mechanisms to reduce access latency, meta-data with no predefined schema handling, execution of pre-processing applications during ingestion, data management and protection of sensitive data in distributed e-infrastructures, intelligent data placement based on access patterns. This contribution introduces the project, presents the foreseen overall architecture and the developments that are being carried on to implement the requested functionalities.

DESIGN OF AUTOMATED INFORMATION SUPPORT SYSTEMFOR MARINE AGENT IN SERVICE ERGATIC SYSTEMS

Within the framework of this article, the problematic of the process of information support of data management pro-cesses by the marine agent in the ergatic service system is indicated and a project of an automated information system that takes into account the specifics of the agency company is proposed. The specificity of the marine agent’s activities as an element of the ergatic service system is indicated, and the need to take into account its social and psychological aspects of labouris substantiated. The relevance of the automated information systems usage to support the recording of production activities by a marine agent based on the use of modern information technologies and software has been substantiated. The specificity of the introduction port community modern information systems is described, main functional capabilities and advantages of such systems are given. The criteria for the effective implementation of software solutions and systems for automating the process of recording information in service ergatic systems for structuring and ensuring the availability of technical information are considered. Methods of system analysis, functional modeling of business processes, methods of object modeling of software development, methods of relational description of conceptu-al schemes of the subject area, as well as the method of profiling database performance are used as a scientific basis of thepaper. The results of the marine agent key business processes in the activities formalization are presented, a structural-organizational dia-gram of the agent company’s departments and a conceptual contextual scheme of his work in IDEF0 notation are developed. The structure of the logical database model using the MySQL system has been developed and described. The profiling of the createdda-tabase was carried out to assess the effectiveness of its use when performing standard CRUID operations using phpMyAdmin. The processes of sending data and updating the status of a record are numerically evaluated, and features of the balance of the server's hardware resources consumed by the database management system used while managing the database created are revealed.Object model for the information system functioning has been created in the form of a list of its use options, class diagrams and components, which makes it possible to reflect the main connections between key object entities. The capabilities of the created class for perform-ing CRUID operations, using the Entity class for implementing data management methods, authorization and registration classesfor delegation to the data validation class for checking the results of user input in the system interface for their correctness are de-scribed. The composition of the operating forms package is describes, which contains classes of named user interface forms used by the spacing and positioning functional components to support the process of viewing electronic documents, filtering data, sending emails, sending data to print, generating reports and exporting data, searching data with the DataSearcher class. The purposeand functional composition of the registration and authorization components, the formation of the information system interface, data management, data backup and archiving, logging of completed actions, generation of reporting documents and processing of elec-tronic forms are described.

Reconstruction Model of Ocean Observing Complex Virtual Instrument

With the newly proposed Global Ocean Observing Integration, ocean observing scope has been expanded from the region to the global, therefore the need of large-scale ocean observing system integration has become more and more urgent. Currently, ocean observing systems enabled ocean sensor networks are commonly developed by different organizations using specific technologies and platforms, which brings several challenges in ocean observing instrument (OOI) access and ocean observing system seamless integration. Furthermore, the development of ocean observing systems often suffers from low efficiency due to the complex programming and debugging process. To solve these problems, a novel model, Complex Virtual Instrument (CVI) Model, is proposed. The model provides formal definitions on observing instrument description file, CVI description file, model calculation method, development model and interaction standard. In addition, this model establishes mathematical expressions of two model calculation operations, meanwhile builds the mapping relationship between observing instrument description file and CVI description file. The CVI based on the new model can achieve automatic access to different OOIs, seamless integration and communication for heterogeneous environments, and further implement standardized data access and management for the global unified ocean observing network. Throughout the development, integration and application of such CVI, the rationality and feasibility of the model have been evaluated. The results confirm that the proposed model can effectively implement heterogeneous system integration, improve development efficiency, make full usage of reusable components, reduce development cost, and enhance overall software system quality. We believe that our new model has great significance to promote the large-scale ocean observing system integration.

The Development of Data Science Education in China from the LIS Perspective

The aim of this paper is to introduce the development of data science in higher education in China, including the policy and educational programs at various levels. We investigated the data science education of five LIS (Library and Information Studies) schools in China, using Fudan University’s Data Management and Application Master’s Program as an example for more specific information about the curriculum structure, course focus and teaching methods in data science education. The paper further describes the action of promoting data science and data science education in the field of LIS by the China Academic Library Research Data Management Implementation Group.

Collaboration in RDM activities – Practices and development at Aalto University

Watch the VIDEO here. Presenter – Maria SöderholmResearch data management (RDM) is a complex and dynamic topic, and demands diverse expertise, skills and knowledge. The RDM expertise includes subtopics like collection/provision of data; storage and processing of data; long-term preservation requirements of the data; and funders’ demands and solutions to share, re-find and re-use research data. Usually the expertise related to these RDM subtopics is spread to several university units, both academic and administrative. Therefore, many tasks related to RDM, for example, day-to-day practices, the supply of services and the development work are best carried out in a network-based cooperation.In the presentation, we will introduce our RDM related partnership and networking of Aalto University. As a starting point, we introduce the internal service development working principles that our RDM network work is based on. However, the focus will be on describing our RDM working group and development activities.Aalto’s Research Data Management Programme forms the backbone for RDM work. It establishes seven separate action points for RDM activities: 1) open access publishing; 2) implementation of data management planning tool; 3) metadata catalogue for open data; 4) data publishing; 5) comprehensive repository service for storage, back-up and collaboration; 6) combining openness and innovation; and 7) RDM awareness building activities. The actors in the RDM network consist of Research and Innovation Services (leading the group), IT Services, and Learning Centre (previously Library).In the presentation, we will introduce the core actors in more detail, paying attention to the complementarity of the roles; and the activities and the aims, which steer the work. We also discuss the meaning and consequences of the network-based cooperation for the working group.First, the RDM group acts as a joint platform for comprehensive RDM information. Important means of data gathering are surveys and informal discussion with researcher. In the future, formal group discussions on RDM needs are hosted. The previous discussions with researchers have underlined the importance of arranging services for both disciplinary and data specific needs in addition to common university level service needs.Second, the group is a messenger of the RDM topics. Our task is to rise internal RDM awareness and disseminate national and international information and development trends in the university.The third and the most challenging aim is the RDM service planning and delivery. Our task is to identify the existing in-house services, to map the suitable services provided by national and international agents as well as to recognize the needs for new services. Our service portfolio covers both consultation/informational services and technical, hands-on services. However, many of our research data services are still in the planning or piloting stage, thus cooperation with researchers is essential.In our experience, the network-based collaboration model that foster individuals’ interconnectedness is crucial for surviving with the built-in dynamism of RDM. This model provides a non-hierarchical and flexible environment for actions to meet the increasing expectations for research data services we face from the funders, governments, and researchers.

Implementation of data management and effect on chronic disease coding in a primary care organisation: A parallel cohort observational study.

Consistent and standardized coding for chronic conditions is associated with better care; however, coding may currently be limited in electronic medical records (EMRs) used in Canadian primary care.Objectives To implement data management activities in a community-based primary care organisation and to evaluate the effects on coding for chronic conditions. Fifty-nine family physicians in Toronto, Ontario, belonging to a single primary care organisation, participated in the study. The organisation implemented a central analytical data repository containing their EMR data extracted, cleaned, standardized and returned by the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a large validated primary care EMR-based database. They used reporting software provided by CPCSSN to identify selected chronic conditions and standardized codes were then added back to the EMR. We studied four chronic conditions (diabetes, hypertension, chronic obstructive pulmonary disease and dementia). We compared changes in coding over six months for physicians in the organisation with changes for 315 primary care physicians participating in CPCSSN across Canada. Chronic disease coding within the organisation increased significantly more than in other primary care sites. The adjusted difference in the increase of coding was 7.7% (95% confidence interval 7.1%-8.2%, p < 0.01). The use of standard codes, consisting of the most common diagnostic codes for each condition in the CPCSSN database, increased by 8.9% more (95% CI 8.3%-9.5%, p < 0.01). Data management activities were associated with an increase in standardized coding for chronic conditions. Exploring requirements to scale and spread this approach in Canadian primary care organisations may be worthwhile.

MSARI: A Database for Large Volume Storage and Utilisation of Maritime Data

The volume of maritime vessel data, such as available from the Automatic Identification System (AIS), places considerable burden on systems designed and developed to manage data pertaining to maritime traffic. A properly designed and implemented data management infrastructure can provide benefits to the maritime domain awareness research community by supporting data volumes, diverse user needs, and product management. Such an infrastructure has been constructed on a modest budget by utilising open-source technologies. This paper describes the Maritime Situational Awareness Research Infrastructure (MSARI), and the design of the underlying database to meet data volume and user analysis needs. The resulting infrastructure currently handles input rates of approximately two billion vessel reports per month. This work is of potential benefit to those in the navigational community interested in the long-term storage and usage of global vessel data such as that available from AIS.