데이터 큐레이터의 핵심 직무 요건 고찰에 관한 연구

Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries.

PurposeThe purpose of this study is to assess whether educated non-researcher audiences understand how to reuse research data stored in a data repository.Design/methodology/approachA total of 44 participants in two user studies were asked to study a data set accessed from re3data.org. The participants were non-researcher audiences of the disciplines of the selected data sets. They were asked to figure out whether they understood how to reuse a data set after reading all the metadata or contextual information about the data set.FindingsMost participants reported that they figured out how to reuse the data, although their self-reports can be an overestimated assessment. However, the participants understand how to reuse a data set either numerically or statistically significantly worse than what the data set is, how it was collected or created and its purpose. Data set type tends to play a role in understanding how to reuse data sets and the purpose of data sets. Participants reported that unless a data set is self-explanatory, instructions on data set reuse and the purpose of data set were necessary for understanding how to reuse data set. However, because data reuse requires domain knowledge and data processing skills, some non-researcher audiences who lack domain knowledge and data processing skills may not understand how to reuse the data set in any way.Research limitations/implicationsThis study’s findings enrich the theoretical framework of data sharing and reuse by expanding the necessary information to be included in data documentation to support non-researchers’ data reuse. The findings of the study complement previous literature.Practical implicationsThis study extended previous literature by suggesting detailed data reuse instructions be included in data documentation if data producers and data curators wish to support educated non-researchers’ data reuse. This study’s findings enable policymakers of research data management (RDM) to formulate guidelines for supporting non-researchers’ data reuse. If data curators need to work with data producers to prepare the instructions on data reuse for non-researcher audiences, they probably need computing and data processing skills. This has implications for Library and Information Science schools to educate data librarians.Originality/valueThe research question is original because non-researcher audiences in the context of RDM have not been studied before. This study extended previous literature by suggesting detailed data reuse instructions be included in data documentation if data curators and data producers and data curators wish to support educated non-researchers’ data reuse. This study’s findings enable policymakers of RDM to formulate guidelines for supporting non-researchers’ data reuse.

University of Tennessee Graduate School of Medicine, Knoxville, Tennessee. dbustama@utmck.eduCore Clinical Competencies in Anesthesiology: A Case-based Approach. Edited by Christopher J. Gallagher, M.D., Michael C. Lewis, M.D., Deborah A. Schwengel, M.D. New York, Cambridge University Press, 2010. Pages: 486. Price: $85.00.How do the Accreditation Council for Graduate Medical Education (ACGME) core competencies apply to the daily practice of anesthesiology? What are these competencies anyway? What are they good for? Can you list them? Can you define each of the six core competencies? These are questions that regularly face anesthesiology residents, anesthesiology residency programs, and anesthesiology faculty members who endeavor to teach in a “competency-based” manner. Core Clinical Competencies in Anesthesiology: A Case-based Approach , edited by three anesthesiology residency program directors, was written to address these questions specifically.This softbound book begins by introducing the six ACGME core competencies: patient care, medical knowledge, practice-based learning and improvement, interpersonal and communication skills, professionalism, and systems-based practice. Next, the competencies are expounded upon and then related specifically to the learning and practice of anesthesiology. With the competency framework defined, the reader is launched directly into the 77 case reports, divided into 6 sections, which comprise the remainder of the book. Three of the six sections consist of cases contributed by the editors' own anesthesiology programs, Stony Brook University, University of Miami-Miller School of Medicine, and Johns Hopkins Medical Institutions. Contributions from the University of Medicine and Dentistry of New Jersey, the University of Texas M. D. Anderson Cancer Center, and the Medical College of Wisconsin form the remaining three sections. Although the cases are contributed by multiple authors from different institutions, they are presented using a fairly consistent format. Each case is presented concisely with a discussion directed at exploring the core competencies. Although some cases focus only on the competencies that are most applicable to the particular clinical situation, most examine each of the six core competencies.The tone is humorous, often irreverent. The writing style is conversational, having more in common with a collegial discussion than with a formal case report. The cases are self-contained such that each chapter can be read either independently or in any order without lessening the educational experience. Anesthesiologists and anesthesiology residents will find much to identify with in many of the 77 clinical situations contained here. Although the style is casual and humor is often present, the focus always remains sharply on learning and applying the core competencies. Each case report is carefully dissected, and elements of the anesthetic management are placed within the structure of the six competencies. This consistent examination of the core competencies leads to some repetition but adds emphasis to their application to anesthesiology education.A degree of ambiguity certainly exists in the structure of the core competencies. Some elements of clinical practice fall neatly into one of the competency categories. As a result, some elements are repeated several times throughout the book to illustrate a specific competency. Avoiding discussion of patients where it is possible to be overheard, for example, is frequently cited as an element of professionalism. Similarly, timely administration of prophylactic antibiotics fits well with the patient care competency and the competency language “provide health-care services aimed at preventing health problems or maintaining health.” In contrast, other behaviors are shown to fall under multiple categories. Hand washing is variously used as an example for the competencies of patient care, practice-based learning and improvement, interpersonal and communication skills, and professionalism. The challenging nature of the included cases and the humor in the presentations serve to keep the reader engaged during the process of conforming clinical situations in anesthesiology to the structure of the ACGME core competencies. Because the book is focused so intensely on the competencies, one is sometimes left wanting for further medical information, details of the anesthetic management, or clinical outcome related to a specific case. After a thorough reading, a reader is unlikely to have difficulty remembering the competencies or applying them to clinical situations.Many of the cases presented are unusual, interesting, educational, and challenging. One particularly memorable case is presented in chapter 10. The author recounts his experience with a patient who suffered total body burns, which unfortunately resulted in death. The role the author took in caring for this patient during the last hours before death illustrates, much more clearly than competency-based language, what professionalism means.Core Clinical Competencies in Anesthesiology: A Case-based Approach was written to assist those of us who have difficulty grasping the competencies, specifically those who have difficulty defining and applying the ACGME core competencies to the learning, teaching, and practice of anesthesiology. The book is targeted primarily at those involved in anesthesiology education, whether as a resident or in the role of teacher. There is no question that editors and contributing authors have hit their target. The reader is left with a strong understanding of the six core competencies and their application to the learning, teaching, and practicing of anesthesiology. Now about that hand washing, which competency does it fall under?University of Tennessee Graduate School of Medicine, Knoxville, Tennessee. dbustama@utmck.edu

Task Force 8: Pediatric Cardiology Fellowship Training in Research and Scholarly Activity. SPCTPD/ACC/AAP/AHA.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

INTRODUCTION As funders and journals increasingly create policies that require effective data management and data sharing, many institutions have developed research data management (RDM) programs to help researchers meet these mandates. While there is not a standard set of services for these RDM programs, some institutions, particularly those with repositories that accept data deposits, provide data curation services as a way to add value to research data and help make data more accessible and reusable. Stakeholder communities within the field, such as the Data Curation Network (DCN), are also developing guidelines, procedures, and best practices to support and expand data curation practices. DESCRIPTION OF PROJECT This paper examines the data curation activities defined by the DCN, and describes an activity undertaken by library staff at Duke University and the University of North Carolina at Chapel Hill to create a structured model of these tasks to more easily conceptualize and communicate data curation within these two institutional settings. The purpose of this paper is to describe how this model provided a basis for the implementation and expansion of data curation services at each institution and concludes with overall lessons learned. NEXT STEPS As we develop our services, libraries have an opportunity to make the often-invisible work of curation more transparent. This paper aims to provide a point of reference for other libraries as they consider how to scale up their data curation programs as well as contribute to discussions around prioritization of services, program assessment, and communication with stakeholders.

Health research using routinely collected National Health Service (NHS) data derived from electronic health records (EHRs) and health service information systems has been growing in both importance and quantity. Wide population coverage and detailed patient-level information allow this data to be applied to a variety of research questions. However, the sensitivity, complexity and scale of such data also hamper researchers from fully exploiting this potential. Here, we establish the current challenges preventing researchers from making optimal use of the data sets at their disposal, on both the legislative and practical levels, and give recommendations as to how these challenges can be overcome. A number of projects has recently been launched in the UK to address poor research data-management practices. Rapid Organisation of Healthcare Research Data (ROHRD) at Imperial College, London produced a useful prototype that provides local researchers with a one-stop index of available data sets together with relevant metadata. Increased transparency of data sets' availability and their provenance leads to better utilisation and facilitates compliance with regulatory requirements. Research data resulting from NHS data is often not utilised fully, or is handled in a haphazard manner that prevents full auditability of the research. Furthermore, lack of informatics and data management skills in research teams act as a barrier to implementing more advanced practices, such as provenance capture and detailed, regularly updated, data management strategies. Only by a concerted effort at the levels of research organisations, funding bodies and publishers, can we achieve full transparency and reproducibility of the research.

Identification of Hub genes associated with infection of three lung cell lines by SARS-CoV-2 with integrated bioinformatics analysis.

While Peer-to-Peer (P2P) model gains significant attention in distributed computing, it is also expected to be a powerful model for information sharing. P2P systems are expected to provide exhaustive reliable computational resources and scalable accessibility. The data management and distribution in such systems requires storage, replication, data modeling, indexing, querying, retrieval, streaming, and topology management. While a lot of data management strategies have been proposed through the last years, these strategies have not been investigated with respect to a common model for P2P systems. However, since the services provided by the P2P systems are so diverse, it is very challenging to come up with a common layer-based model for all P2P systems. In this paper, we firstly propose a conceptual model for P2P systems, and then provide a classification and summary of data management and distribution strategies by referring to this model. The horizontal layers of the model correspond to modules of a P2P system whereas the columns are related to the services provided. The modules include base P2P service, storage, indexing, logical, service, and application modules. The services include security, querying, publish, join/leave, collaboration, and streaming. The paper concludes by providing a comprehensive list of data management and distribution strategies used in the existing P2P systems.

In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), “Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment”. However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-to-day research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices. This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft.

This study aims to design, build, and implement a decision support system for selecting Elementary School recipients of face-to-face learning facilities and infrastructure in the New Normal Era with the Weighted Product (WP) method in Jiwan District, Madiun Regency, East Java. The data used is secondary data in the form of public data obtained from the site http://school.data.kemdikbud.go.id/. and the number of elementary school data obtained with the boundaries of Jiwan District, Madiun Regency, amounted to 23 schools. At the stage of designing the system for this research, it is divided into three parts, namely the UML System, User Interface System and Database with ERD. The research findings show that the decision support system for selecting primary schools that receive face-to-face learning facilities and infrastructure in the New Normal Era with the WP method has several user interfaces, namely (1) User interface Login is used for security features of the Elementary School election system. (2) The Home Page user interface contains facilities for criteria data management, criteria weight data management, alternative data management, SPK value data management, user data management, and logout. (3) Criteria Data User Interface can perform criteria data management by adding and saving, changing, and deleting criteria data. (4) The Criteria Weighting Data User Interface can fill in the value of the weight of each criterion. (5) Alternative Data User Interface can add and save, change, and delete alternative data. (6) The Value Data User Interface can view and save by adding values to each alternative according to the criteria. (7) The DSS Management User Interface can make decisions by calculating the weight correction, calculating the S vector value, and calculating the V vector value. (8) User Interface Viewing Results can see the results of decision making after calculating the SPK. (9) User Management Interface Users can add, edit, and delete data from users containing usernames and passwords. Then by using the 57 item test scenarios of the blackbox method, the decision support system that was built had 100% success according to the expectations of the test scenario.

The success of research in the field of maternal–infant health, or in any scientific field, relies on the adoption of best practices for data and knowledge management. Prior work by our group and others has identified evidence-based solutions to many of the data management challenges that exist, including cost–effective practices for ensuring high-quality data entry and proper construction and maintenance of data standards and ontologies. Quality assurance practices for data entry and processing are necessary to ensure that data are not denigrated during processing, but the use of these practices has not been widely adopted in the fields of psychology and biology. Furthermore, collaborative research is becoming more common. Collaborative research often involves multiple laboratories, different scientific disciplines, numerous data sources, large data sets, and data sets from public and commercial sources. These factors present new challenges for data and knowledge management. Data security and privacy concerns are increased as data may be accessed by investigators affiliated with different institutions. Collaborative groups must address the challenges associated with federating data access between the data-collecting sites and a centralized data management site. The merging of ontologies between different data sets can become formidable, especially in fields with evolving ontologies. The increased use of automated data acquisition can yield more data, but it can also increase the risk of introducing error or systematic biases into data. In addition, the integration of data collected from different assay types often requires the development of new tools to analyze the data. All of these challenges act to increase the costs and time spent on data management for a given project, and they increase the likelihood of decreasing the quality of the data. In this paper, we review these issues and discuss theoretical and practical approaches for addressing these issues.

Research data management includes more than the question how researchers handle their data. In the sense of the FAIR principles, it is also about the sustainable safeguarding and organized reusability of research data. For social science, data-intensive research, research data centers and their data curating staff are therefore becoming increasingly important: data curators usually take on curation-specific tasks such as data preparation, securing research data in suitable archival environments, ensuring data accessibility, and the related control of the conditions of data re-use by third parties. Hence, they are specialized in the entire data curation process and, in particular, take on tasks of archiving and providing research data for reuse. Although the standards of comprehensive research data management are becoming more and more specific, this trend has not yet arrived in the corresponding training and further education measures. As a result, there is a gap between the growing demands on data curators and the development of competencies in the field of research data management with a focus on data curation. The competency framework presented in this article is intended to help close this gap: based on a Data Curation Lifecycle Model, a competency framework has been developed to support the development of targeted training and continuing education programs in the field of data curation, the formulation of learning objectives, and the evaluation of the corresponding trainings. The article points out the necessity to advance the development of competencies for this field, illustrates the schematic substructure of the data curation lifecycle, describes the development as well as the central core elements of the presented competency framework and discusses its perspectives. Overall, this competence framework is aimed in particular at (future) data curators, or as a schematic basis for the training of the relevant personnel. The focus is primarily on the data-intensive discipline of social sciences, although large parts can certainly be adapted for other disciplines and the corresponding data curation. The competency framework and this companion article are thereby intended to assist in advancing the sustainable professionalization of the previously understudied competency field of data curation.

In this work, we present a multi-resolution data management scheme for efficiently storing and diffusing information, which is originating from sensors and augmenting mobile applications. The proposed mechanism exploits the discrete wavelet transform properties for providing a location-dependent data management scheme, which alleviates the constraints on the storage resources needed for running context-aware mobile applications. The data management scheme has been implemented and evaluated by means of numerical simulations.

Ambient Intelligence (AmI) is a vision of future Information Society, where people are surrounded by an electronic environment which is sensitive to their needs, personalized to their requirements, anticipatory of their behavior, and responsive to their presence. It emphasizes on greater user-friendliness, user-empowerment, and more effective service support, with an aim to make peoplersquos daily activities more convenient, thus improving the quality of human life. To make AmI real, effective data management support is indispensable. High-quality information must be available to any user, anytime, anywhere, and on any lightweight device. Beyond that, AmI also raises many new challenges related to context-awareness and natural user interaction, entailing us to re-think current database techniques. The aim of this paper is to address the impact of AmI, particularly its user-centric context-awareness requirement on data management strategies and solutions. We first provide a multidimensional view of database access context. Taking diverse contextual information into account, we then present five context-aware data management strategies, using the most fundamental database operation-context-aware query request as a case in point. We execute the proposed strategies via a two-layered infrastructure, consisting of public data manager(s) and a private data manager. Detailed steps of processing a context-aware query are also described in the paper.