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Journal of Biomedical Semantics Nov 2023Open Science Graphs (OSGs) are scientific knowledge graphs representing different entities of the research lifecycle (e.g. projects, people, research outcomes,...
BACKGROUND
Open Science Graphs (OSGs) are scientific knowledge graphs representing different entities of the research lifecycle (e.g. projects, people, research outcomes, institutions) and the relationships among them. They present a contextualized view of current research that supports discovery, re-use, reproducibility, monitoring, transparency and omni-comprehensive assessment. A Data Management Plan (DMP) contains information concerning both the research processes and the data collected, generated and/or re-used during a project's lifetime. Automated solutions and workflows that connect DMPs with the actual data and other contextual information (e.g., publications, fundings) are missing from the landscape. DMPs being submitted as deliverables also limit their findability. In an open and FAIR-enabling research ecosystem information linking between research processes and research outputs is essential. ARGOS tool for FAIR data management contributes to the OpenAIRE Research Graph (RG) and utilises its underlying services and trusted sources to progressively automate validation and automations of Research Data Management (RDM) practices.
RESULTS
A comparative analysis was conducted between the data models of ARGOS and OpenAIRE Research Graph against the DMP Common Standard. Following this, we extended ARGOS with export format converters and semantic tagging, and the OpenAIRE RG with a DMP entity and semantics between existing entities and relationships. This enabled the integration of ARGOS machine actionable DMPs (ma-DMPs) to the OpenAIRE OSG, enriching and exposing DMPs as FAIR outputs.
CONCLUSIONS
This paper, to our knowledge, is the first to introduce exposing ma-DMPs in OSGs and making the link between OSGs and DMPs, introducing the latter as entities in the research lifecycle. Further, it provides insight to ARGOS DMP service interoperability practices and integrations to populate the OpenAIRE Research Graph with DMP entities and relationships and strengthen both FAIRness of outputs as well as information exchange in a standard way.
Topics: Humans; Data Management; Reproducibility of Results
PubMed: 37919767
DOI: 10.1186/s13326-023-00297-5 -
Frontiers in Plant Science 2023The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large,... (Review)
Review
The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.
PubMed: 38098789
DOI: 10.3389/fpls.2023.1279694 -
BMC Health Services Research Nov 2023This study aims to develop a scale that measures individuals' perceptions of privacy, security, use, sharing, benefit and satisfaction in the digital health environment.
PURPOSE
This study aims to develop a scale that measures individuals' perceptions of privacy, security, use, sharing, benefit and satisfaction in the digital health environment.
METHOD
Within the scope of the study, in the scale development process; The stages of literature review, creation of items, getting expert opinion, conducting a pilot study, ensuring construct and criterion validity, and reliability analyses were carried out. The literature was searched for the formation of the question items. To evaluate the created question items, expert opinion was taken, and the question items were arranged according to the feedback from the experts. In line with the study's purpose and objectives, the focus group consisted of individuals aged 18 and above within the community. The convenience sampling method was employed for sample selection. Data were collected using an online survey conducted through Google Forms. Before commencing the survey, participants were briefed on the research's content. A pilot study was conducted with 30 participants, and as a result of the feedback from the participants, eliminations were made in the question items and the scale was made ready for application. The research was conducted by reference to 812 participants in the community. Expert evaluations of the question items were obtained, and a pilot study was conducted. A sociodemographic information form, a scale developed by the researcher, Norman and Skinner's e-Health Literacy Scale, and the Mobile Health and Personal Health Record Management Scale were used as data collection tools.
RESULTS
The content validity of the research was carried out by taking expert opinions and conducting a pilot study. Exploratory factor analysis and confirmatory factor analysis were performed to ensure construct validity. The total variance explained by the scale was 60.43%. The results of confirmatory factor analysis indicated that the 20-Item 5-factor structure exhibited good fit values. According to the analysis of criterion validity, there are significant positive correlations among the Data Management in the Digital Health Environment Scale, Norman and Skinner's e-Health Literacy Scale and the Mobile Health and Personal Health Record Management Scale (p < 0.01; r = .669, .378). The Cronbach's alpha value of the scale is .856, and the test-retest reliability coefficient is .909.
CONCLUSION
The Data Management in the Digital Health Environment Scale is a valid and reliable measurement tool that measures individuals' perceptions of privacy, security, use, sharing, benefit and satisfaction in the digital health environment.
Topics: Humans; Data Management; Reproducibility of Results; Pilot Projects; Surveys and Questionnaires; Personal Satisfaction; Psychometrics
PubMed: 37964225
DOI: 10.1186/s12913-023-10205-3 -
Online sleep diaries: considerations for system development and recommendations for data management.Sleep Oct 2023To present development considerations for online sleep diary systems that result in robust, interpretable, and reliable data; furthermore, to describe data management...
STUDY OBJECTIVES
To present development considerations for online sleep diary systems that result in robust, interpretable, and reliable data; furthermore, to describe data management procedures to address common data entry errors that occur despite those considerations.
METHODS
The online sleep diary capture component of the Sleep Healthy Using the Internet (SHUTi) intervention has been designed to promote data integrity. Features include diary entry restrictions to limit retrospective bias, reminder prompts and data visualizations to support user engagement, and data validation checks to reduce data entry errors. Despite these features, data entry errors still occur. Data management procedures relying largely on programming syntax to minimize researcher effort and maximize reliability and replicability. Presumed data entry errors are identified where users are believed to have incorrectly selected a date or AM versus PM on the 12-hour clock. Following these corrections, diaries are identified that have unresolvable errors, like negative total sleep time.
RESULTS
Using the example of one of our fully-powered, U.S. national SHUTi randomized controlled trials, we demonstrate the application of these procedures: of 45,598 total submitted diaries, 487 diaries (0.01%) required modification due to date and/or AM/PM errors and 27 diaries (<0.001%) were eliminated due to unresolvable errors.
CONCLUSION
To secure the most complete and valid data from online sleep diary systems, it is critical to consider the design of the data collection system and to develop replicable processes to manage data.
CLINICAL TRIAL REGISTRATION
Sleep Healthy Using The Internet for Older Adult Sufferers of Insomnia and Sleeplessness (SHUTiOASIS); https://clinicaltrials.gov/ct2/show/NCT03213132; ClinicalTrials.gov ID: NCT03213132.
Topics: Humans; Aged; Data Management; Retrospective Studies; Reproducibility of Results; Sleep; Sleep Initiation and Maintenance Disorders
PubMed: 37480840
DOI: 10.1093/sleep/zsad199 -
Proteins Dec 2023Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In...
Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.
Topics: Protein Conformation; Proteins; Models, Molecular; Computational Biology; Ligands
PubMed: 37306011
DOI: 10.1002/prot.26515 -
Emotion (Washington, D.C.) Sep 2023Depressed individuals show a wide range of difficulties in executive functioning (including working memory), which can be a significant burden on everyday mental...
Depressed individuals show a wide range of difficulties in executive functioning (including working memory), which can be a significant burden on everyday mental processes. Theoretical models of depression have proposed these difficulties to be especially pronounced in affective contexts. However, evidence investigating affective working memory (WM) capacity in depressed individuals has shown mixed results. The preregistered study used a complex span task, which has been shown to be sensitive to difficulties with WM capacity in affective relative to neutral contexts in other clinical groups, to explore affective WM capacity in clinical depression. Affective WM capacity was compared between individuals with current depression ( = 24), individuals in remission from depression ( = 25), and healthy controls (n = 30). The results showed that, overall, WM capacity was more impaired in the context of negative distractor images, relative to neutral images. Furthermore, those with a lifetime history of depression (individuals with current depression and individuals remitted from depression), performed worse on the task, compared to healthy controls. However, there was no support for the greater disruption of WM capacity in affective compared to neutral contexts in those with a lifetime history of depression. These findings' implications for current models of depression are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Topics: Humans; Memory, Short-Term; Depression; Executive Function; Depressive Disorder, Major; Data Management
PubMed: 36441997
DOI: 10.1037/emo0001130 -
JAMIA Open Jul 2023This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research.
OBJECTIVE
This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research.
MATERIALS AND METHODS
TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment.
RESULTS
TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data.
CONCLUSIONS
The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.
PubMed: 37193038
DOI: 10.1093/jamiaopen/ooad035 -
Scientific Reports Apr 2024With the acceleration of China's economic integration process, enterprises have gained greater advantages in the fierce market competition, and gradually formed the...
With the acceleration of China's economic integration process, enterprises have gained greater advantages in the fierce market competition, and gradually formed the trend of grouping and large-scale. However, as the scale of the company increases, the establishment of a branch also causes many problems. For example, in order to obtain more benefits, the business performance of the company can generate false growth, resulting in financial and operational risks. This paper analyzed the current situation and needs of enterprise financial control from two aspects of theory and practice, combined with specific engineering projects, taking ZH Group as an example, according to the actual situation of the enterprise. The article first introduces the basic situation of the enterprise; Then, the financial control strategy was designed, and different modules were designed to achieve financial control; Afterwards, use a reverse neural network to evaluate the effectiveness of financial management and risk warning; Relying on particle swarm optimization algorithm to seek the optimal solution and applying it to financial management and risk warning, in order to improve the level of introspection and risk management in decision-making. Finally, the value of computer intelligence algorithms in financial big data management is evaluated by constructing a financial risk indicator system. Through the analysis of enterprise financial management, the total asset turnover rate of ZH Group decreased by 0.39 times in 5 years. After 5 years of adjustment of the company's business, the company's overall operational capabilities still needed to be improved, and the company's comprehensive business capabilities also still needed to be improved. Therefore, the application of intelligent algorithms for financial control is very necessary.
PubMed: 38658586
DOI: 10.1038/s41598-024-59244-8 -
Histochemistry and Cell Biology Sep 2023
Topics: Data Management; Microscopy; Data Analysis
PubMed: 37646975
DOI: 10.1007/s00418-023-02226-0 -
Trends in Cardiovascular Medicine Dec 2023Despite treatment advancements, HF mortality remains high, prompting interest in reducing HF-related hospitalizations through remote monitoring. These advances are... (Review)
Review
Despite treatment advancements, HF mortality remains high, prompting interest in reducing HF-related hospitalizations through remote monitoring. These advances are necessary considering the rapidly rising prevalence and incidence of HF worldwide, presenting a burden on hospital resources. While traditional approaches have failed in predicting impending HF-related hospitalizations, remote hemodynamic monitoring can detect changes in intracardiac filling pressure weeks prior to HF-related hospitalizations which makes timely pharmacological interventions possible. To ensure successful implementation, structural integration, optimal patient selection, and efficient data management are essential. This review aims to provide an overview of the rationale, the available devices, current evidence, and the implementation of remote hemodynamic monitoring.
PubMed: 38109949
DOI: 10.1016/j.tcm.2023.12.003