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Li Q, Liu C, Hou J, Wang P. Affective memories and perceived value: motivators and inhibitors of the data search-access process. JOURNAL OF DOCUMENTATION 2023. [DOI: 10.1108/jd-06-2022-0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
PurposeAs an emerging tool for data discovery, data retrieval systems fail to effectively support users' cognitive processes during data search and access. To uncover the relationship between data search and access and the cognitive mechanisms underlying this relationship, this paper examines the associations between affective memories, perceived value, search effort and the intention to access data during users' interactions with data retrieval systems.Design/methodology/approachThis study conducted a user experiment for which 48 doctoral students from different disciplines were recruited. The authors collected search logs, screen recordings, questionnaires and eye movement data during the interactive data search. Multiple linear regression was used to test the hypotheses.FindingsThe results indicate that positive affective memories positively affect perceived value, while the effects of negative affective memories on perceived value are nonsignificant. Utility value positively affects search effort, while attainment value negatively affects search effort. Moreover, search effort partially positively affects the intention to access data, and it serves a full mediating role in the effects of utility value and attainment value on the intention to access data.Originality/valueThrough the comparison between the findings of this study and relevant findings in information search studies, this paper reveals the specificity of behaviour and cognitive processes during data search and access and the special characteristics of data discovery tasks. It sheds light on the inhibiting effect of attainment value and the motivating effect of utility value on data search and the intention to access data. Moreover, this paper provides new insights into the role of memory bias in the relationships between affective memories and data searchers' perceived value.
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Johnson TR, Bernstam EV. Why is biomedical informatics hard? A fundamental framework. J Biomed Inform 2023; 140:104327. [PMID: 36893995 DOI: 10.1016/j.jbi.2023.104327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/01/2023] [Accepted: 03/06/2023] [Indexed: 03/09/2023]
Abstract
Building on previous work to define the scientific discipline of biomedical informatics, we present a framework that categorizes fundamental challenges into groups based on data, information, and knowledge, along with the transitions between these levels. We define each level and argue that the framework provides a basis for separating informatics problems from non-informatics problems, identifying fundamental challenges in biomedical informatics, and provides guidance regarding the search for general, reusable solutions to informatics problems. We distinguish between processing data (symbols) and processing meaning. Computational systems, that are the basis for modern information technology (IT), process data. In contrast, many important challenges in biomedicine, such as providing clinical decision support, require processing meaning, not data. Biomedical informatics is hard because of the fundamental mismatch between many biomedical problems and the capabilities of current technology.
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Affiliation(s)
- Todd R Johnson
- UTHealth Houston School of Biomedical Informatics, Houston, TX 77030, United States of America.
| | - Elmer V Bernstam
- UTHealth Houston School of Biomedical Informatics, Houston, TX 77030, United States of America; UTHealth Houston McGovern Medical School, Division of General Internal Medicine, Houston, TX 77030, United States of America.
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Tsueng G, Cano MAA, Bento J, Czech C, Kang M, Pache L, Rasmussen LV, Savidge TC, Starren J, Wu Q, Xin J, Yeaman MR, Zhou X, Su AI, Wu C, Brown L, Shabman RS, Hughes LD. Developing a standardized but extendable framework to increase the findability of infectious disease datasets. Sci Data 2023; 10:99. [PMID: 36823157 PMCID: PMC9950378 DOI: 10.1038/s41597-023-01968-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/13/2023] [Indexed: 02/25/2023] Open
Abstract
Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.
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Affiliation(s)
- Ginger Tsueng
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
| | - Marco A Alvarado Cano
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - José Bento
- Department of Computer Science, Boston College, 245 Beacon St, Chestnut Hill, MA, 02467, USA
| | - Candice Czech
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Mengjia Kang
- Division of Pulmonary and Critical Care, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Lars Pache
- Infectious and Inflammatory Disease Center, Immunity and Pathogenesis Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Tor C Savidge
- Texas Children's Microbiome Center & Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Justin Starren
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Qinglong Wu
- Texas Children's Microbiome Center & Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jiwen Xin
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Michael R Yeaman
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Divisions of Molecular Medicine and Infectious Diseases, Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Lundquist Institute for Infection & Immunity at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Xinghua Zhou
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Andrew I Su
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
- Scripps Research Translational Institute, La Jolla, CA, 92037, USA
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Chunlei Wu
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
- Scripps Research Translational Institute, La Jolla, CA, 92037, USA
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Liliana Brown
- Office of Genomics and Advanced Technologies, National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, USA
| | - Reed S Shabman
- Office of Genomics and Advanced Technologies, National Institute of Allergy and Infectious Diseases, Rockville, MD, 20852, USA
| | - Laura D Hughes
- Department of Integrative, Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
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Cho S, Ensari I, Elhadad N, Weng C, Radin JM, Bent B, Desai P, Natarajan K. An interactive fitness-for-use data completeness tool to assess activity tracker data. J Am Med Inform Assoc 2022; 29:2032-2040. [PMID: 36173371 PMCID: PMC9667174 DOI: 10.1093/jamia/ocac166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/29/2022] [Accepted: 09/16/2022] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE To design and evaluate an interactive data quality (DQ) characterization tool focused on fitness-for-use completeness measures to support researchers' assessment of a dataset. MATERIALS AND METHODS Design requirements were identified through a conceptual framework on DQ, literature review, and interviews. The prototype of the tool was developed based on the requirements gathered and was further refined by domain experts. The Fitness-for-Use Tool was evaluated through a within-subjects controlled experiment comparing it with a baseline tool that provides information on missing data based on intrinsic DQ measures. The tools were evaluated on task performance and perceived usability. RESULTS The Fitness-for-Use Tool allows users to define data completeness by customizing the measures and its thresholds to fit their research task and provides a data summary based on the customized definition. Using the Fitness-for-Use Tool, study participants were able to accurately complete fitness-for-use assessment in less time than when using the Intrinsic DQ Tool. The study participants perceived that the Fitness-for-Use Tool was more useful in determining the fitness-for-use of a dataset than the Intrinsic DQ Tool. DISCUSSION Incorporating fitness-for-use measures in a DQ characterization tool could provide data summary that meets researchers needs. The design features identified in this study has potential to be applied to other biomedical data types. CONCLUSION A tool that summarizes a dataset in terms of fitness-for-use dimensions and measures specific to a research question supports dataset assessment better than a tool that only presents information on intrinsic DQ measures.
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Affiliation(s)
- Sylvia Cho
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Ipek Ensari
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine, New York, New York, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Noémie Elhadad
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- Data Science Institute, Columbia University, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- Data Science Institute, Columbia University, New York, New York, USA
| | - Jennifer M Radin
- Scripps Research Translational Institute, La Jolla, California, USA
| | - Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Pooja Desai
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- Data Science Institute, Columbia University, New York, New York, USA
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Brennan PF, Chiang MF, Ohno-Machado L. Biomedical informatics and data science: evolving fields with significant overlap. J Am Med Inform Assoc 2019; 25:2-3. [PMID: 29267964 DOI: 10.1093/jamia/ocx146] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Patricia Flatley Brennan
- 9500 Gilman Dr, MC 0728, La Jolla, CA 92093, USA. Phone: 858-822-4931; Fax: 858-822-7685; E-mail:
| | - Michael F Chiang
- 9500 Gilman Dr, MC 0728, La Jolla, CA 92093, USA. Phone: 858-822-4931; Fax: 858-822-7685; E-mail:
| | - Lucila Ohno-Machado
- 9500 Gilman Dr, MC 0728, La Jolla, CA 92093, USA. Phone: 858-822-4931; Fax: 858-822-7685; E-mail:
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Chen X, Gururaj AE, Ozyurt B, Liu R, Soysal E, Cohen T, Tiryaki F, Li Y, Zong N, Jiang M, Rogith D, Salimi M, Kim HE, Rocca-Serra P, Gonzalez-Beltran A, Farcas C, Johnson T, Margolis R, Alter G, Sansone SA, Fore IM, Ohno-Machado L, Grethe JS, Xu H. DataMed - an open source discovery index for finding biomedical datasets. J Am Med Inform Assoc 2018; 25:300-308. [PMID: 29346583 PMCID: PMC7378878 DOI: 10.1093/jamia/ocx121] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/20/2017] [Accepted: 09/28/2017] [Indexed: 12/17/2022] Open
Abstract
Objective Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. Materials and Methods DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health–funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. Results and Conclusion Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.
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Affiliation(s)
- Xiaoling Chen
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Anupama E Gururaj
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Ruiling Liu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ergin Soysal
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Trevor Cohen
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Firat Tiryaki
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yueling Li
- Center for Research in Biological Systems
| | - Nansu Zong
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
| | - Min Jiang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Deevakar Rogith
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Mandana Salimi
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hyeon-Eui Kim
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
| | | | | | - Claudiu Farcas
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
| | - Todd Johnson
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ron Margolis
- National Institutes of Health, Bethesda, MD, USA
| | | | | | - Ian M Fore
- National Institutes of Health, Bethesda, MD, USA
| | - Lucila Ohno-Machado
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
| | | | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
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