检索结果-内蒙古大学图书馆

Enhancing Findability and Searchability of Research data: Metadata Conversion and Registration in Institutional Repositories

引用

data Science Journal 2024年 23卷

作者： Nosé, Masahito Shinbori, Atsuki Miyoshi, Yoshizumi Hori, Tomoaki Ohira, Tsukasa Hashiba, Junko Naoe, Chizuko Gakiya, Rui Okamoto, Maiko Sagara, Takeshi Aoki, Takaaki Matsubara, Shigeki Takahashi, Ichiro Hayashi, Hidekazu Yamada, Kazunari Minamiyama, Yasuyuki Tanaka, Yoshimasa Abe, Shuji Ueno, Satoru Imajo, Shun Saito, Yasuo Ashikita, Takuya Hori, Yuko Shimizu, Toshiyuki Okamura, Nanako Hirano, Kaoru Bargatze, Lee School of Data Science Nagoya City University Nagoya Japan Institute for Space-Earth Environmental Research Nagoya University Nagoya Japan Nagoya University Library Nagoya Japan Info Proto Co. Ltd Tokyo Japan Information Strategy Office Information and Communications Nagoya University Nagoya Japan Information Technology Center Information and Communications Nagoya University Nagoya Japan Research Center for Open Science and Data Platform National Institute of Informatics Tokyo Japan Polar Environment Data Science Center Research Organization of Information and Systems Tachikawa Japan National Institute of Polar Research Tachikawa Japan Graduate Institute for Advanced Studies SOKENDAI Tachikawa Japan International Research Center for Space and Planetary Environmental Science Kyushu University Fukuoka Japan Astronomical Observatory Graduate School of Science Kyoto University Kyoto Japan Data Analysis Center for Geomagnetism and Space Magnetism Graduate School of Science Kyoto University Kyoto Japan Kyushu University Library Fukuoka Japan Earth Planetary and Space Sciences University of California Los Angeles United States

This paper outlines our practice to enhance the findability and searchability of research data through metadata conversion from the Space Physics Archive Search and Extract (SPASE) schema to a more generic schema, the Japan Consortium for Open Access Repository (JPCOAR) schema, and registration of converted metadata in institutional repositories. Traditionally, earth and space science research data have been organized using the SPASE schema. Although the SPASE schema is comprehensive, its usage has been restricted to highly professional databases, limiting broader accessibility and impeding cross-disciplinary research. We discuss a case study at Nagoya University where 284 metadata records were converted from SPASE to JPCOAR, and illustrate the process and benefits of this conversion. This approach significantly improved the visibility and usability of metadata across various platforms like the Institutional Repositories dataBase (IRDB), the data Catalog Cross-Search System, and Google’s dataset Search, extending access to a wider range of users beyond the highly professional scientific community. This approach also aligns with national policies in Japan on research data management and simplifies metadata handling for researchers. Future direction includes expanding this conversion and registration model to other universities and institutions by leveraging the ubiquity of the SPASE schema in the earth and space science fields. Our practice may be useful in other research fields. This initiative aims to improve the overall findability of research data, to foster cross-disciplinary collaboration, and to enhance the value of research data itself and of creators and managers of the data. © 2024 The Author(s).

关键词： data consistency

来源：评论

学校读者我要写书评

暂无评论

Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations

arXiv

引用

arXiv 2024年

作者： Fleurence, Rachael L. Bian, Jiang Wang, Xiaoyan Xu, Hua Dawoud, Dalia Higashi, Mitch Chhatwal, Jagpreet National Institute of Biomedical Imaging and Bioengineering National Institutes of Health BethesdaMD United States Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida GainesvilleFL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt ISPOR - The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States

Objective: To provide an introduction to the uses of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), in the field of health technology assessment (HTA). Methods: We reviewed applications of generative AI in three areas: systematic literature reviews, real world evidence (RWE) and health economic modeling. Results: (1) Literature reviews: generative AI has the potential to assist in automating aspects of systematic literature reviews by proposing search terms, screening abstracts, extracting data and generating code for meta-analyses;(2) Real World Evidence (RWE): generative AI can facilitate automating processes and analyze large collections of real-world data (RWD) including unstructured clinical notes and imaging;(3) Health economic modeling: generative AI can aid in the development of health economic models, from conceptualization to validation. Limitations in the use of foundation models and LLMs include challenges surrounding their scientific rigor and reliability, the potential for bias, implications for equity, as well as nontrivial concerns regarding adherence to regulatory and ethical standards, particularly in terms of data privacy and security. Additionally, we survey the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools. Conclusions: While generative AI technology holds promise with respect to HTA applications, it is still undergoing rapid developments and improvements. Continued careful evaluation of their applications to HTA is required. Both developers and users of research incorporating these tools, should familiarize themselves with their current capabilities and limitations. © 2024, CC BY.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications – an ISPOR Working Group Report

arXiv

引用

arXiv 2024年

作者： Fleurence, Rachael L. Wang, Xiaoyan Bian, Jiang Higashi, Mitchell K. Ayer, Turgay Xu, Hua Dawoud, Dalia Chhatwal, Jagpreet Office of the Director National Institutes of Health National Institute of Biomedical Imaging and Bioengineering BethesdaMD United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States ISPOR The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Center for Health & Humanitarian Systems Georgia Institute of Technology AtlantaGA United States Value Analytics Labs BostonMA United States Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States

Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outlines methods to enhance the accuracy and reliability of AI-generated outputs. Methods: The review defines foundational generative AI concepts and highlights current HEOR applications, including systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Approaches such as prompt engineering (zero-shot, few-shot, chain-of-thought, persona pattern prompting), retrieval-augmented generation, model fine-tuning, and the use of domain-specific models are introduced to improve AI accuracy and reliability. Results: Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration. The article discusses strategies to improve the accuracy of these AI tools. Conclusion: Generative AI could transform HEOR by increasing efficiency and accuracy across various applications. However, its full potential can only be realized by building HEOR expertise and addressing the limitations of current AI technologies. As AI evolves, ongoing research and innovation will shape its future role in the field. © 2024, CC BY.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Agent-based modeling of the COVID-19 pandemic in Florida

arXiv

引用

arXiv 2023年

作者： Pillai, Alexander N. Toh, Kok Ben Perdomo, Dianela Bhargava, Sanjana Stoltzfus, Arlin Longini, Ira M. Pearson, Carl A.B. Hladish, Thomas J. Department of Biology University of Florida GainesvilleFL United States Institute of Global Health Department of Preventive Medicine Northwestern University ChicagoIL United States Office of Data and Informatics National Institute of Standards and Technology GaithersburgMD United States Department of Biostatistics University of Florida GainesvilleFL United States Emerging Pathogens Institute University of Florida GainesvilleFL United States Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London United Kingdom Stellenbosch University Stellenbosch South Africa

The onset of the COVID-19 pandemic drove a widespread, often uncoordinatedeffort by research groups to develop mathematical models of SARS-CoV-2 to studyits spread and inform control efforts. The urgent demand for insight at theoutset of the pandemic meant early models were typically either simple orrepurposed from existing research agendas. Our group predominantly usesagent-based models (ABMs) to study fine-scale intervention scenarios. Thesehigh-resolution models are large, complex, require extensive empirical data,and are often more detailed than strictly necessary for answering qualitativequestions like "Should we lockdown?" During the early stages of anextraordinary infectious disease crisis, particularly before clear empiricalevidence is available, simpler models are more appropriate. As more detailedempirical evidence becomes available, however, and policy decisions become morenuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as wemodeled the pandemic. The role of modelers often included nearly real-timeanalysis, and the massive undertaking of adapting our tools quickly. We wereoften playing catch up with a firehose of evidence, while simultaneouslystruggling to do both academic research and real-time decision support, underconditions conducive to neither. By reflecting on our experiences of respondingto the pandemic and what we learned from these challenges, we can betterprepare for future demands. © 2023, CC BY-NC-ND.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research

引用

BMC Infectious Diseases 2023年第1期23卷 733-733页

作者： Pokutnaya, Darya Van Panhuis, Willem G Childers, Bruce Hawkins, Marquis S Arcury-Quandt, Alice E Matlack, Meghan Carpio, Kharlya Hochheiser, Harry Department of Epidemiology University of Pittsburgh Pittsburgh PA United States Office of Data Science and Emerging Technologies National Institute of Allergy and Infectious Diseases Rockville MD United States Department of Computer Science University of Pittsburgh Pittsburgh PA United States Department of Environmental and Occupational Health University of Pittsburgh Pittsburgh PA United States Department of Biomedical Informatics Intelligent Systems Program and Clinical and Translational Science Institute University of Pittsburgh Pittsburgh PA United States

Background: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. Methods: Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss’ kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. Results: Questions related to the computational environment (mean κ = 0.90, range = 0.90–0.90), analytical software (mean κ = 0.74, range = 0.68–0.82), model description (mean κ = 0.71, range = 0.58–0.84), model implementation (mean κ = 0.68, range = 0.39–0.86), and experimental protocol (mean κ = 0.63, range = 0.58–0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23–0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. Conclusions: The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater re

关键词： Coronavirus Disease 2019 COVID-19 Epidemiology Infectious Disease Modeling Reproducibility

来源：评论

学校读者我要写书评

暂无评论

Performance Boost of Attribute-aware Semantic Segmentation via data Augmentation for Driver Assistance

Performance Boost of Attribute-aware Semantic Segmentation v...

引用

International Conference of Information and Communication Technology (ICoICT)

作者： Mahmud Dwi Sulistiyo Yasutomo Kawanishi Daisuke Deguchi Ichiro Ide Takatsugu Hirayama Hiroshi Murase Graduate School of Informatics Nagoya University Japan Graduate School of Informatics Information Strategy Office Nagoya University Japan Mathematical and Data Science Center Graduate School of Informatics Nagoya University Japan Institutes of Innovation for Future Society Nagoya University Japan

The following topics are dealt with: learning (artificial intelligence); feature extraction; pattern classification; Internet of Things; Internet; neural nets; natural language processing; social networking (online); ... 详细信息

ISBN: (数字)9781728161426

ISBN: (纸本)9781728161433

关键词： Semantics Task analysis Image segmentation Training Vehicles informatics Training data

来源：评论

学校读者我要写书评

暂无评论

Implementing a registry federation for materials science data discovery

引用

data Science Journal 2021年第1期20卷 105334/dsj-2021页

作者： Plante, Raymond L. Becker, Chandler A. Medina-Smith, Andrea Brady, Kevin Dima, Alden Long, Benjamin Bartolo, Laura M. Warren, James A. Hanisch, Robert J. National Institute of Standards and Technology Material Measurement Laboratory Office of Data and Informatics GaithersburgMD United States National Institute of Standards and Technology Information Services Office GaithersburgMD United States National Institute of Standards and Technology Information Technology Laboratory GaithersburgMD United States Northwestern University Center for Hierarchical Materials Design EvanstonIL United States National Institute of Standards and Technology Material Measurement Laboratory GaithersburgMD United States

As a result of a number of national initiatives, we are seeing rapid growth in the data important to materials science that are available over the web. Consequently, it is becoming increasingly difficult for researchers to learn what data are available and how to access them. To address this problem, the Research data Alliance (RDA) Working Group for International Materials Science Registries (IMRR) was established to bring together materials science and information technology experts to develop an international federation of registries that can be used for global discovery of data resources for materials science. A resource registry collects high-level metadata descriptions of resources such as data repositories, archives, websites, and services that are useful for data-driven research. By making the collection searchable, it aids scientists in industry, universities, and government laboratories to discover data relevant to their research and work interests. We present the results of our successful piloting of a registry federation for materials science data discovery. In particular, we out a blueprint for creating such a federation that is capable of amassing a global view of all available materials science data, and we enumerate the requirements for the standards that make the registries interoperable within the federation. These standards include a protocol for exchanging resource descriptions and a standard metadata schema for encoding those descriptions. We summarize how we leveraged an existing standard (OAI-PMH) for metadata exchange. Finally, we review the registry software developed to realize the federation and describe the user experience. © 2021 The Author(s).

关键词： Metadata

来源：评论

学校读者我要写书评

暂无评论

A controlled vocabulary and metadata schema for materials science data discovery

引用

data Science Journal 2021年第1期20卷

作者： Medina-Smith, Andrea Becker, Chandler A. Plante, Raymond L. Bartolo, Laura M. Dima, Alden Warren, James A. Hanisch, Robert J. National Institute of Standards and Technology Information Services Office GaithersburgMD United States National Institute of Standards and Technology Material Measurement Laboratory Office of Data and Informatics GaithersburgMD United States Northwestern University Center for Hierarchical Materials Design EvanstonIL United States National Institute of Standards and Technology Information Technology Laboratory GaithersburgMD United States National Institute of Standards and Technology Material Measurement Laboratory GaithersburgMD United States

The International Materials Resource Registries (IMRR) working group of the Research data Alliance (RDA) was created to spur initial development of a federated registry system to allow for easier discovery and access to materials data. As part of this effort, a controlled vocabulary and metadata schema were developed with contributions from members of the working group and other experts. Here we describe the process, the resulting vocabulary and XML schema, and lessons learned in the development and use of the schema. © 2021 The Author(s).

关键词： Thesauri

来源：评论

学校读者我要写书评

暂无评论

Scientific Evidence Based Knowledge Graph in Rare Diseases

Scientific Evidence Based Knowledge Graph in Rare Diseases

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qian Zhu Ruizheng Liu Gunjan Vatas Andrew Clough Yanji Xu Ðac-Trung Nguyễn Ewy Mathé Eric Sid Division of Pre-Clinical Innovation National Center for Advancing Translational Sciences Rockville USA Axle Informatics Inc Rockville USA GMG ArcData LLC Washington USA Data Decode LLC Washington USA Office of Rare Disease Research National Center for Advancing Translational Sciences Besthasta USA

ISBN: (纸本)9781665429825

Rare diseases are naturally associated with low prevalence rate, which raises a big challenge due to less data available for supporting preclinical and clinical studies. Therefore, it is critical to fully utilize the accumulated scientific publications in rare diseases over years, in order to access full spectrum of scientific research and enable relevant scientific evidence extraction and generation. In this study, we obtained rare disease related PubMed articles, extracted multiple types of biomedical information, and semantically presented the data in a knowledge graph, which is hosted in Neo4j based on a predefined data model to support further rare disease research.

关键词： Conferences data models data mining Bioinformatics Diseases

来源：评论

学校读者我要写书评

暂无评论

On the robustness of EEG tensor completion methods

引用

Science China(Technological Sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：