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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 -
Indian Journal of Occupational and... 2023Reproducibility is a preferred aim in any scientific research, including occupational health research. Datamanagement is an important and essential step in marching...
Reproducibility is a preferred aim in any scientific research, including occupational health research. Datamanagement is an important and essential step in marching towards reproducibility. A good datamanagement helps us stay organized, improve transparency, quality and fosters collaboration. Here we discuss how to organize and prepare for data management, how data management facilitates interoperability and accessibility, followed by storing and dissemination of data. We wrap up by providing pointers on what needs to be included in the data management plans.
PubMed: 38390491
DOI: 10.4103/ijoem.ijoem_342_22 -
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 -
Experimental Neurology Aug 2024Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and...
Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and knowledge to address this challenge. This article provides an introductory guide into research data management (RDM), and the importance of FAIR (Findable, Accessible, Interoperable, and Reusable) data-sharing principles for laboratory researchers produced by practicing scientists. We explore the advantages of implementing organized data management strategies and introduce key concepts such as data standards, data documentation, and the distinction between machine and human-readable data formats. Furthermore, we offer practical guidance for creating a data management plan and establishing efficient data workflows within the laboratory setting, suitable for labs of all sizes. This includes an examination of requirements analysis, the development of a data dictionary for routine data elements, the implementation of unique subject identifiers, and the formulation of standard operating procedures (SOPs) for seamless data flow. To aid researchers in implementing these practices, we present a simple organizational system as an illustrative example, which can be tailored to suit individual needs and research requirements. By presenting a user-friendly approach, this guide serves as an introduction to the field of RDM and offers practical tips to help researchers effortlessly meet the common data management and sharing mandates rapidly becoming prevalent in biomedical research.
Topics: Humans; Biomedical Research; Data Management; Information Dissemination; Research Personnel
PubMed: 38762093
DOI: 10.1016/j.expneurol.2024.114815 -
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 -
EJIFCC Oct 2023With the recent COVID-19 pandemic, point-of-care testing has gained tremendous attention, particularly in acute care settings. The point-of-care testing landscape is... (Review)
Review
With the recent COVID-19 pandemic, point-of-care testing has gained tremendous attention, particularly in acute care settings. The point-of-care testing landscape is rapidly expanding and being contemplated for any crucial test with a central laboratory turnaround time >25% of the clinical decision time. A typical point-of-care testing program within a large hospital system encompasses a multitude of operators utilizing a wide range of devices across multiple testing sites. Thus, managing a large point-of-care testing network remains a daunting task with challenges related to staffing, standardization, quality management, training and competency assessment, and data management. This review will focus on understanding the general organization as well as the roles and responsibilities of various point-of-care testing stakeholders in addressing these challenges. More importantly, it will discuss the strategies and best practices for effective point-of-care testing management based on consensus recommendations from professional societies as well as our experience at Texas Childrens Hospital.
PubMed: 37868087
DOI: No ID Found -
Open Research Europe 2023This document outlines the types of data collected for the Digital Ludeme Project, an ERC-funded research project that aims to improve our understanding of the...
This document outlines the types of data collected for the Digital Ludeme Project, an ERC-funded research project that aims to improve our understanding of the development of games throughout human history through computational analysis of the available (partial) historical data of games. This document outlines how this data is collected, formatted and stored, and how it can be accessed. It is the aim of the Digital Ludeme Project to provide a data resource of unprecedented depth and scope for the benefit of historical games researchers worldwide. Special attention is paid to the FAIR Guiding Principles for scientific data management and stewardship.
PubMed: 38550771
DOI: 10.12688/openreseurope.16524.1 -
Cells Dec 2023The European Bank for induced pluripotent Stem Cells (EBiSC) was established in 2014 as a non-profit project for the banking, quality control, and distribution of human...
The Management of Data for the Banking, Qualification, and Distribution of Induced Pluripotent Stem Cells: Lessons Learned from the European Bank for Induced Pluripotent Stem Cells.
The European Bank for induced pluripotent Stem Cells (EBiSC) was established in 2014 as a non-profit project for the banking, quality control, and distribution of human iPSC lines for research around the world. EBiSC iPSCs are deposited from diverse laboratories internationally and, hence, a key activity for EBiSC is standardising not only the iPSC lines themselves but also the data associated with them. This includes enabling unique nomenclature for the cells, as well as applying uniformity to the data provided by the cell line generator versus quality control data generated by EBiSC, and providing mechanisms to share personal data in a secure and GDPR-compliant manner. A joint approach implemented by EBiSC and the human pluripotent stem cell registry (hPSCreg) has provided a solution that enabled hPSCreg to improve its registration platform for iPSCs and EBiSC to have a pipeline for the import, standardisation, storage, and management of data associated with EBiSC iPSCs. In this work, we describe the experience of cell line data management for iPSC banking throughout the course of EBiSC's development as a central European banking infrastructure and present a model for how this could be implemented by other iPSC repositories to increase the FAIRness of iPSC research globally.
Topics: Humans; Induced Pluripotent Stem Cells; Pluripotent Stem Cells; Cell Line; Registries; Reference Standards
PubMed: 38067184
DOI: 10.3390/cells12232756 -
Histochemistry and Cell Biology Sep 2023
Topics: Data Management; Microscopy; Data Analysis
PubMed: 37646975
DOI: 10.1007/s00418-023-02226-0 -
Scientific Reports Oct 2023Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the...
Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster. However, the amount of data is increasing at a rapid pace, which leads to challenges in storage, analysis, and various processing tasks. In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo-distributed data centers. The goal of the proposed framework is to enable efficient use of the distributed data blocks for various data analysis tasks. The architecture of the proposed framework is composed of a grid of geo-distributed data centers connected to a data controller (DCtrl). The DCtrl is responsible for organizing and managing the block replicas across the geo-distributed data centers. We use the BDMS system as the installed system on the distributed data centers. BDMS stores the big data file as a set of random sample data blocks, each being a random sample of the whole data file. Then, DCtrl distributes these data blocks into multiple data centers with replication. In analyzing a big data file distributed based on the proposed framework, we randomly select a sample of data blocks replicated from other data centers on any data center. We use simulation results to demonstrate the performance of the proposed framework in big data analysis across geo-distributed data centers.
PubMed: 37853092
DOI: 10.1038/s41598-023-44789-x