ReUseData: an R/Bioconductor tool for reusable and reproducible genomic data management

BACKGROUND

The increasing volume and complexity of genomic data pose significant challenges for effective data management and reuse. Public genomic data often undergo similar preprocessing across projects, leading to redundant or inconsistent datasets and inefficient use of computing resources. This is especially pertinent for bioinformaticians engaged in multiple projects. Tools have been created to address challenges in managing and accessing curated genomic datasets, however, the practical utility of such tools becomes especially beneficial for users who seek to work with specific types of data or are technically inclined toward a particular programming language. Currently, there exists a gap in the availability of an R-specific solution for efficient data management and versatile data reuse.

RESULTS

Here we present ReUseData, an R software tool that overcomes some of the limitations of existing solutions and provides a versatile and reproducible approach to effective data management within R. ReUseData facilitates the transformation of ad hoc scripts for data preprocessing into Common Workflow Language (CWL)-based data recipes, allowing for the reproducible generation of curated data files in their generic formats. The data recipes are standardized and self-contained, enabling them to be easily portable and reproducible across various computing platforms. ReUseData also streamlines the reuse of curated data files and their integration into downstream analysis tools and workflows with different frameworks.

CONCLUSIONS

ReUseData provides a reliable and reproducible approach for genomic data management within the R environment to enhance the accessibility and reusability of genomic data. The package is available at Bioconductor ( https://bioconductor.org/packages/ReUseData/ ) with additional information on the project website ( https://rcwl.org/dataRecipes/ ).

SciDataFlow: a tool for improving the flow of data through science

MOTIVATION

Managing data and code in open scientific research is complicated by two key problems: large datasets often cannot be stored alongside code in repository platforms like GitHub, and iterative analysis can lead to unnoticed changes to data, increasing the risk that analyses are based on older versions of data.

RESULTS

SciDataFlow is a fast, concurrent command-line tool paired with a simple Data Manifest specification that streamlines tracking data changes, uploading data to remote repositories, and pulling in all data necessary to reproduce a computational analysis.

AVAILABILITY AND IMPLEMENTATION

SciDataFlow is available at https://github.com/vsbuffalo/scidataflow.

Retrieving Rare Cases: A Protocol for Searching Complex Medical Cases

This study sought to provide a protocol for searching complex medical cases of grand rounds. A clinical informationist was embedded in gastroenterology grand rounds to use comprehensive search strategies and summarize patients' information through concept mapping. Our proposed protocol classifies into three categories: (1) The general search strategy, (2) The protocol for searching for evidence about rare diseases, and (3) Identifying other resources more than routine medical databases. This approach represents a novel method beyond previous studies which were focused on usual ward rounds to facilitate evidence-based decision-making by providing and simplifying a comprehensive summary view of complex medical cases.

A knowledge graph-based data harmonization framework for secondary data reuse

BACKGROUND AND OBJECTIVE

The adoption of new technologies in clinical care systems has propitiated the availability of a great amount of valuable data. However, this data is usually heterogeneous, requiring its harmonization to be integrated and analysed. We propose a semantic-driven harmonization framework that (1) enables the meaningful sharing and integration of healthcare data across institutions and (2) facilitates the analysis and exploitation of the shared data.

METHODS

The framework includes an ontology-based common data model (i.e. SCDM), a data transformation pipeline and a semantic query system. Heterogeneous datasets, mapped to different terminologies, are integrated by using an ontology-based infrastructure rooted in a top-level ontology. A graph database is generated by using these mappings, and web-based semantic query system facilitates data exploration.

RESULTS

Several datasets from different European institutions have been integrated by using the framework in the context of the European H2020 Precise4Q project. Through the query system, data scientists were able to explore data and use it for building machine learning models.

CONCLUSIONS

The flexible data representation using RDF, together with the formal semantic underpinning provided by the SCDM, have enabled the semantic integration, query and advanced exploitation of heterogeneous data in the context of the Precise4Q project.

IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model

PURPOSE

Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM).

MATERIALS AND METHODS

We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across databases.

RESULTS

IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the pandemic.

CONCLUSION

For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.

Applying the American Association of Endodontists and American Academy of Oral and Maxillofacial Radiology guidelines for cone-beam computed tomography prescription: Impact on endodontic clinical decisions

BACKGROUND

The American Association of Endodontists (AAE) and the American Academy of Oral and Maxillofacial Radiology (AAOMR) developed guidelines for the prescription of cone-beam computed tomographic (CBCT) imaging. The impact of appropriately prescribed CBCT imaging on endodontic diagnosis and treatment (Tx) decisions was examined.

METHODS

The clinical databases at the School of Dentistry at the University of California, Los Angeles, Los Angeles, California, were queried to identify patients referred for CBCT imaging from the postgraduate endodontic clinic over a consecutive 36-month period. Primary and secondary indications for CBCT imaging were recorded. Pre-CBCT uncertainty in diagnosis, Tx of the teeth in question, and post-CBCT changes to the diagnosis and Tx plan were recorded.

RESULTS

CBCT imaging was prescribed for 12% of patients. A total of 442 scans were prescribed to evaluate 526 teeth. Molars accounted for 51% of teeth examined. Overall, CBCT effected a change in periapical diagnosis (21%) and in the Tx plan (69%). The 5 most frequent primary indications for CBCT imaging were, in order, AAE-AAOMR recommendations 7, 9, 2, 12, and 6. The impact of these recommendations on Tx decisions varied from 48% through 93%.

CONCLUSIONS

This study validates the use of the AAE-AAOMR guidelines for prescribing CBCT imaging for endodontic evaluations. CBCT imaging contributed predominantly to Tx decisions rather than diagnostic determinations.

PRACTICAL IMPLICATIONS

This study validates AAE-AAOMR case selection guidelines for CBCT imaging and shows a positive impact of prescription imaging on endodontic decision making.

Flapjack: Data Management and Analysis for Genetic Circuit Characterization

Flapjack presents a valuable solution for addressing challenges in the Design, Build, Test, Learn (DBTL) cycle of engineering synthetic genetic circuits. This platform provides a comprehensive suite of features for managing, analyzing, and visualizing kinetic gene expression data and associated metadata. By utilizing the Flapjack platform, researchers can effectively integrate the test phase with the build and learn phases, facilitating the characterization and optimization of genetic circuits. With its user-friendly interface and compatibility with external software, the Flapjack platform offers a practical tool for advancing synthetic biology research.This chapter provides an overview of the data model employed in Flapjack and its hierarchical structure, which aligns with the typical steps involved in conducting experiments and facilitating intuitive data management for users. Additionally, this chapter offers a detailed description of the user interface, guiding readers through accessing Flapjack, navigating its sections, performing essential tasks such as uploading data and creating plots, and accessing the platform through the pyFlapjack Python package.

The PACIFIC ontology for heterogeneous data management in cardiology

With the emergence of health data warehouses and major initiatives to collect and analyze multi-modal and multisource data, data organization becomes central. In the PACIFIC-PRESERVED (PhenomApping, ClassIFication, and Innovation for Cardiac Dysfunction - Heart Failure with PRESERVED LVEF Study, NCT04189029) study, a data driven research project aiming at redefining and profiling the Heart Failure with preserved Ejection Fraction (HFpEF), an ontology was developed by different data experts in cardiology to enable better data management in a complex study context (multisource, multiformat, multimodality, multipartners). The PACIFIC ontology provides a cardiac data management framework for the phenomapping of patients. It was built upon the BMS-LM (Biomedical Study -Lifecycle Management) core ontology and framework, proposed in a previous work to ensure data organization and provenance throughout the study lifecycle (specification, acquisition, analysis, publication). The BMS-LM design pattern was applied to the PACIFIC multisource variables. In addition, data was structured using a subset of MeSH headings for diseases, technical procedures, or biological processes, and using the Uberon ontology anatomical entities. A total of 1372 variables were organized and enriched with annotations and description from existing ontologies and taxonomies such as LOINC to enable later semantic interoperability. Both, data structuring using the BMS-LM framework, and its mapping with published standards, foster interoperability of multimodal cardiac phenomapping datasets.

Crime Stereotypicality and Severity Database (CriSSD): Subjective norms for 63 crimes

The existence of crime-related racial stereotypes has been well documented. People tend to associate certain groups with specific crimes, which, in turn, impacts criminal-sentencing decisions through the perceptions of crime severity. This evidence calls for regular updating of rating norms combining these variables. With this objective, and given that most of the normative studies provide norms for a small number of crimes and/or with an insufficient number of participants, a new norming study was conducted. Furthermore, norms from European countries are absent, and the existing ones (mostly with USA-based populations) do not simultaneously examine crime stereotypicality and crime severity. The Crime Stereotypicality and Severity Database (CriSSD) presents normative ratings for a set of 63 crimes on three dimensions: White stereotypicality, Black stereotypicality, and crime severity. The crimes were selected according to a comprehensive procedure. A total of 340 Portuguese participants (72.6% female; Mage = 26.86, SD = 7.65) answered an online survey. Each crime was evaluated by a range of 46-60 participants. Data allowed us to identify a crime typology with three clusters. We present descriptive data (means, standard deviations, and 95% confidence intervals) for each crime. Crime evaluations were associated with sociodemographic characteristics. Additionally, this study gives input regarding the understudied link between crime stereotypes and crime severity, showing that crime severity is predicted by ratings of both Black and White stereotypicality. The CriSSD (available at osf.io/gkbrm ) provides a valuable resource for researchers in the field of social psychology to conduct studies with controlled materials on potential disparities in criminal-sentencing decisions.

Efficient and Reliable Data Management for Biomedical Applications

This chapter discusses the challenges and requirements of modern Research Data Management (RDM), particularly for biomedical applications in the context of high-performance computing (HPC). The FAIR data principles (Findable, Accessible, Interoperable, Reusable) are of special importance. Data formats, publication platforms, annotation schemata, automated data management and staging, the data infrastructure in HPC centers, file transfer and staging methods in HPC, and the EUDAT components are discussed. Tools and approaches for automated data movement and replication in cross-center workflows are explained, as well as the development of ontologies for structuring and quality-checking of metadata in computational biomedicine. The CompBioMed project is used as a real-world example of implementing these principles and tools in practice. The LEXIS project has built a workflow-execution and data management platform that follows the paradigm of HPC-Cloud convergence for demanding Big Data applications. It is used for orchestrating workflows with YORC, utilizing the data documentation initiative (DDI) and distributed computing resources (DCI). The platform is accessed by a user-friendly LEXIS portal for workflow and data management, making HPC and Cloud Computing significantly more accessible. Checkpointing, duplicate runs, and spare images of the data are used to create resilient workflows. The CompBioMed project is completing the implementation of such a workflow, using data replication and brokering, which will enable urgent computing on exascale platforms.

The relationship between electronic health records user interface features and data quality of patient clinical information: an integrative review

OBJECTIVES

Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ).

MATERIALS AND METHODS

We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998).

RESULTS

Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce.

DISCUSSION

The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.

A benefit to biodiversity assessment: An Indian survey demonstrates that Cheiriphotis geniculata is a misidentification of the valid species Photis geniculata Barnard, 1935 (Crustacea: Amphipoda)

The amphipod crustaceans are an essential taxonomic group in the marine biodiversity assessment and response to environmental pollution or climate change. They play an important role in benthic food webs due to their high biomass, abundance and highly variable modes of feeding. However, our knowledge of the amphipod fauna is somehow incomplete and literature shows shortcomings regarding misidentification or lack of identification to species-level. A case of misidentification is herein reported and solved. The present paper aims at allocating Cheiriphotis geniculata K.H. Barnard, 1916 to the correct taxon Photis. The observations herein presented demonstrate that Cheiriphotis geniculata does fit the genus Photis and its nomenclature should be revised in future checklists and updated in the World Amphipoda Database. Photis geniculata is characterized by a gnathopod 2 with three processes on the palm of propodus and a geniculated dactylus. The present paper recommends Cheiriphotis geniculata is considered a nomen nudum and changed to the valid name Photis geniculata Barnard, 1935.

American local government elections database

The study of urban and local politics in the United States has long been hindered by a lack of centralized sources of election data. We introduce a new database of about 78,000 candidates in 57,000 electoral contests that encompasses races for seven distinct local political offices in most medium and large cities and counties in the U.S. over the last three decades. This is the most comprehensive publicly-available source of information on local elections across the country. We provide partisan and demographic information about candidates in these races as well as electoral outcomes. This new database will facilitate a myriad of new research on representation and elections in local governments.

A real-world data analysis-based study of Chinese medicine treatment patterns after breast cancer surgery

Based on the real clinical data of Hospital Information System to explore the common clinical syndromes of traditional Chinese medicine after breast cancer surgery, analysis of traditional Chinese medicine in the treatment of breast cancer after the compatibility law. The real medical records of breast cancer patients after surgery in a tertiary hospital in Sichuan Province were collected and screened to build a medical record database. Python language was used for data preprocessing to remove outliers and fill in missing values. Using International Business Machines Corporation (IBM) Statistical Product and Service Solutions (SPSS) Modeler software, Apriori association rules algorithm for data analysis, mining Chinese medicine treatment of breast cancer after common syndromes and the corresponding medication rules. A total of 472 cases of clinical real medical record data were included. Data analysis showed that there were 42 TCM syndromes after breast cancer surgery, of which the highest frequency was liver depression and spleen deficiency, qi deficiency and blood stasis, qi stagnation and blood stasis, qi and blood deficiency, qi and yin deficiency, phlegm and blood stasis. A total of 416 kinds of traditional Chinese medicine were involved. High-frequency drugs included angelica sinensis, coix seed, bupleurum, ginger magnolia bark, keel, oyster, astragalus, platycodon grandiflorum, antler frost, vinegar tortoise shell, poria cocos, lily, Jianqu, Ophiopogon japonicus (Maidong), Shancigu, etc. A total of 18 pairs of commonly used drug combinations were excavated, such as Fushen-Gancao-Chaihu-Angelica, Huangqi-Baishao-Jianghoupu, Chaihu-Huanhua-Maidong-Lily, Baizhu-Huangqi-Maidong, Fuling-Baishao, etc. The clinical syndrome type of traditional Chinese medicine after breast cancer surgery is mainly liver depression and spleen deficiency syndrome. The clinical treatment is mainly soothing liver and relieving depression, and harmonizing liver and spleen. Analyze the syndrome type and the corresponding drug compatibility law, and provide decision support for the clinical dialectical prescription of traditional Chinese medicine after breast cancer surgery.

The role of playgrounds in the development of children's fundamental movement skills: A scoping review

Fundamental movement skills (FMS) are the basic skills children should develop but are low in children from high-income countries. Literature indicates that playgrounds can play an important role challenging children's balance, agility, and coordination. However, knowledge on the influence of playgrounds on children's FMS development is fragmented. The aim of the present scoping review was to create an overview of all research that is relevant when studying the influence of unstructured playground play on children's FMS. Four electronic databases (Scopus, Web of Science, SportDiscus, and PsycInfo) were searched systematically in May 2022 and October 2023 following the PRISMA guidelines, leading to a final set of 14 publications meeting the inclusion criteria. The results of these publications indicate that it is important to design playgrounds with various features targeting balance, climbing, throwing, and catching to provide opportunities for children to enhance each FMS (i.e., stability, locomotor skills, and object control skills). Also, spreading features over a large area of the playground seems to ensure ample space per child, stimulate children to use locomotor skills by moving to and from features, and to play active games without equipment. Possibly, also natural play settings develop children's FMS. These findings, however, should be read with caution. More experimental studies using objective and standardized FMS tests are needed in this research field for a more robust conclusion.

From pilot to a multi-site trial: refining the Early Detection of Deterioration in Elderly Residents (EDDIE +) intervention

BACKGROUND

Early Detection of Deterioration in Elderly Residents (EDDIE +) is a multi-modal intervention focused on empowering nursing and personal care workers to identify and proactively manage deterioration of residents living in residential aged care (RAC) homes. Building on successful pilot trials conducted between 2014 and 2017, the intervention was refined for implementation in a stepped-wedge cluster randomised trial in 12 RAC homes from March 2021 to May 2022. We report the process used to transition from a small-scale pilot intervention to a multi-site intervention, detailing the intervention to enable future replication.

METHODS

The EDDIE + intervention used the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to guide the intervention development and refinement process. We conducted an environmental scan; multi-level context assessments; convened an intervention working group (IWG) to develop the program logic, conducted a sustainability assessment and deconstructed the intervention components into fixed and adaptable elements; and subsequently refined the intervention for trial.

RESULTS

The original EDDIE pilot intervention included four components: nurse and personal care worker education; decision support tools; diagnostic equipment; and facilitation and clinical support. Deconstructing the intervention into core components and what could be flexibly tailored to context was essential for refining the intervention and informing future implementation across multiple sites. Intervention elements considered unsustainable were updated and refined to enable their scalability. Refinements included: an enhanced educational component with a greater focus on personal care workers and interactive learning; decision support tools that were based on updated evidence; equipment that aligned with recipient needs and available organisational support; and updated facilitation model with local and external facilitation.

CONCLUSION

By using the i-PARIHS framework in the scale-up process, the EDDIE + intervention was tailored to fit the needs of intended recipients and contexts, enabling flexibility for local adaptation. The process of transitioning from a pilot to larger scale implementation in practice is vastly underreported yet vital for better development and implementation of multi-component interventions across multiple sites. We provide an example using an implementation framework and show it can be advantageous to researchers and health practitioners from pilot stage to refinement, through to larger scale implementation.

TRIAL REGISTRATION

The trial was prospectively registered with the Australia New Zealand Clinical Trial Registry (ACTRN12620000507987, registered 23/04/2020).

Enhancing biodiversity conservation and monitoring in protected areas through efficient data management

A scientifically informed approach to decision-making is key to ensuring the sustainable management of ecosystems, especially in the light of increasing human pressure on habitats and species. Protected areas, with their long-term institutional mandate for biodiversity conservation, play an important role as data providers, for example, through the long-term monitoring of natural resources. However, poor data management often limits the use and reuse of this wealth of information. In this paper, we share lessons learned in managing long-term data from the Italian Alpine national parks. Our analysis and examples focus on specific issues faced by managers of protected areas, which partially differ from those faced by academic researchers, predominantly owing to different mission, governance, and temporal perspectives. Rigorous data quality control, the use of appropriate data management tools, and acquisition of the necessary skills remain the main obstacles. Common protocols for data collection offer great opportunities for the future, and complete recovery and documentation of time series is an urgent priority. Notably, before data can be shared, protected areas should improve their data management systems, a task that can be achieved only with adequate resources and a long-term vision. We suggest strategies that protected areas, funding agencies, and the scientific community can embrace to address these problems. The added value of our work lies in promoting engagement with managers of protected areas and in reporting and analysing their concrete requirements and problems, thereby contributing to the ongoing discussion on data management and sharing through a bottom-up approach.

Effective data quality management for electronic medical record data using SMART DATA

OBJECTIVES

In the medical field, we face many challenges, including the high cost of data collection and processing, difficult standards issues, and complex preprocessing techniques. It is necessary to establish an objective and systematic data quality management system that ensures data reliability, mitigates risks caused by incorrect data, reduces data management costs, and increases data utilization. We introduce the concept of SMART data in a data quality management system and conducted a case study using real-world data on colorectal cancer.

METHODS

We defined the data quality management system from three aspects (Construction - Operation - Utilization) based on the life cycle of medical data. Based on this, we proposed the "SMART DATA" concept and tested it on colorectal cancer data, which is actual real-world data.

RESULTS

We define "SMART DATA" as systematized, high-quality data collected based on the life cycle of data construction, operation, and utilization through quality control activities for medical data. In this study, we selected a scenario using data on colorectal cancer patients from a single medical institution provided by the Clinical Oncology Network (CONNECT). As SMART DATA, we curated 1,724 learning data and 27 Clinically Critical Set (CCS) data for colorectal cancer prediction. These datasets contributed to the development and fine-tuning of the colorectal cancer prediction model, and it was determined that CCS cases had unique characteristics and patterns that warranted additional clinical review and consideration in the context of colorectal cancer prediction.

CONCLUSIONS

In this study, we conducted primary research to develop a medical data quality management system. This will standardize medical data extraction and quality control methods and increase the utilization of medical data. Ultimately, we aim to provide an opportunity to develop a medical data quality management methodology and contribute to the establishment of a medical data quality management system.

Multimodality and skewness in emotion time series

The ability to measure emotional states in daily life using mobile devices has led to a surge of exciting new research on the temporal evolution of emotions. However, much of the potential of these data still remains untapped. In this paper, we reanalyze emotion measurements from seven openly available experience sampling methodology studies with a total of 835 individuals to systematically investigate the modality (unimodal, bimodal, and more than two modes) and skewness of within-person emotion measurements. We show that both multimodality and skewness are highly prevalent. In addition, we quantify the heterogeneity across items, individuals, and measurement designs. Our analysis reveals that multimodality is more likely in studies using an analog slider scale than in studies using a Likert scale; negatively valenced items are consistently more skewed than positive valenced items; and longer time series show a higher degree of modality in positive and a higher skew in negative items. We end by discussing the implications of our results for theorizing, measurement, and time series modeling. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Gender Hypothesis of sex disparities in adverse drug events

Pharmacovigilance databases contain larger numbers of adverse drug events (ADEs) that occurred in women compared to men. The cause of this disparity is frequently attributed to sex-linked biological factors. We offer an alternative Gender Hypothesis, positing that gendered social factors are central to the production of aggregate sex disparities in ADE reports. We describe four pathways through which gender may influence observed sex disparities in pharmacovigilance databases: healthcare utilization; bias and discrimination in the clinic; experience of a drug event as adverse; and pre-existing social and structural determinants of health. We then use data from the U.S. FDA Adverse Event Reporting System (FAERS) to explore how the Gender Hypothesis might generate novel predictions and explanations of sex disparities in ADEs in existing widely referenced datasets. Analyzing more than 3 million records of ADEs between 2014 and 2022, we find that patient-reported ADEs show a larger female skew than healthcare provider-reported ADEs and that the sex disparity is markedly smaller for outcomes involving death or hospitalization. We also find that the sex disparity varies greatly across types of ADEs, for example, cosmetically salient ADEs are skewed heavily female and sexual dysfunction ADEs are skewed male. Together, we interpret these findings as providing evidence of the promise of the Gender Hypothesis for identifying intervenable mechanisms and pathways contributing to sex disparities in ADEs. Rigorous application of the Gender Hypothesis to additional datasets and in future research studies could yield new insights into the causes of sex disparities in ADEs.

Building Data Infrastructure for Disease-Focused Health Economics Research

BACKGROUND

Data infrastructure for cancer research is centered on registries that are often augmented with payer or hospital discharge databases, but these linkages are limited. A recent alternative in some states is to augment registry data with All-Payer Claims Databases (APCDs). These linkages capture patient-centered economic outcomes, including those driven by insurance and influence health equity, and can serve as a prototype for health economics research.

OBJECTIVES

To describe and assess the utility of a linkage between the Colorado APCD and Colorado Central Cancer Registry (CCCR) data for 2012-2017.

RESEARCH DESIGN, PARTICIPANTS, AND MEASURES

This cohort study of 91,883 insured patients evaluated the Colorado APCD-CCCR linkage on its suitability to assess demographics, area-level data, insurance, and out-of-pocket expenses 3 and 6 months after cancer diagnosis.

RESULTS

The linkage had high validity, with over 90% of patients in the CCCR linked to the APCD, but gaps in APCD health plans limited available claims at diagnosis. We highlight the advantages of the CCCR-APCD, such as granular race and ethnicity classification, area-level data, the ability to capture supplemental plans, medical and pharmacy out-of-pocket expenses, and transitions in insurance plans.

CONCLUSIONS

Linked data between registries and APCDs can be a cornerstone of a robust data infrastructure and spur innovations in health economics research on cost, quality, and outcomes. A larger infrastructure could comprise a network of state APCDs that maintain linkages for research and surveillance.

Tropical Data: Approach and Methodology as Applied to Trachoma Prevalence Surveys

PURPOSE

Population-based prevalence surveys are essential for decision-making on interventions to achieve trachoma elimination as a public health problem. This paper outlines the methodologies of Tropical Data, which supports work to undertake those surveys.

METHODS

Tropical Data is a consortium of partners that supports health ministries worldwide to conduct globally standardised prevalence surveys that conform to World Health Organization recommendations. Founding principles are health ministry ownership, partnership and collaboration, and quality assurance and quality control at every step of the survey process. Support covers survey planning, survey design, training, electronic data collection and fieldwork, and data management, analysis and dissemination. Methods are adapted to meet local context and needs. Customisations, operational research and integration of other diseases into routine trachoma surveys have also been supported.

RESULTS

Between 29th February 2016 and 24th April 2023, 3373 trachoma surveys across 50 countries have been supported, resulting in 10,818,502 people being examined for trachoma.

CONCLUSION

This health ministry-led, standardised approach, with support from the start to the end of the survey process, has helped all trachoma elimination stakeholders to know where interventions are needed, where interventions can be stopped, and when elimination as a public health problem has been achieved. Flexibility to meet specific country contexts, adaptation to changes in global guidance and adjustments in response to user feedback have facilitated innovation in evidence-based methodologies, and supported health ministries to strive for global disease control targets.

LncPCD: a manually curated database of experimentally supported associations between lncRNA-mediated programmed cell death and diseases

Programmed cell death (PCD) refers to controlled cell death that is conducted to keep the internal environment stable. Long noncoding RNAs (lncRNAs) participate in the progression of PCD in a variety of diseases. However, no specialized online repository is available to collect and store the associations between lncRNA-mediated PCD and diseases. Here, we developed LncPCD, a comprehensive database that provides information on experimentally supported associations of lncRNA-mediated PCD with diseases. The current version of LncPCD documents 6666 associations between five common types of PCD (apoptosis, autophagy, ferroptosis, necroptosis and pyroptosis) and 1222 lncRNAs in 331 diseases. We also manually curated a wealth of information: (1) 7 important lncRNA regulatory mechanisms, (2) 310 PCD-associated cell types in three species, (3) detailed information on lncRNA subcellular locations and (4) clinical applications for lncRNA-mediated PCD in diseases. Additionally, 10 single-cell sequencing datasets were integrated into LncPCD to characterize the dynamics of lncRNAs in diseases. Overall, LncPCD is an extremely useful resource for understanding the functions and mechanisms of lncRNA-mediated PCD in diseases. Database URL:  http://spare4.hospital.studio:9000/lncPCD/Home.jsp.

What drives researcher preferences for chemical compounds? Evidence from conjoint analysis

We investigated the attributes and attribute levels that affect researcher preferences for chemical compounds. We conducted a conjoint analysis on survey data of Korean researchers using chemical compounds from the Korean Chemical Bank (KCB). The analysis estimated the part-worth utility for each attribute's level, calculated relative importance of attributes, and classified user segmentation with different patterns. The results show that the structure database offers the highest part-worth utility to researchers, followed by high new functionality, price, screening service, and drug action data provided only by the KCB. Notably, researchers view the offer of a structured database and high new functionality as more important than other attributes in decision-making about research and development of chemical compounds. Furthermore, the results of segmentation analysis demonstrated that researchers have distinct usage patterns of chemical compounds: researchers consider structure database and high new functionality in cluster 1; and high new functionality and price in cluster 2, to be the most appealing. We discussed some policy and strategic implications based on the findings of this study and proposed some limitations.

Modelling ready-made spectacle coverage for children and adults using a large global database

BACKGROUND/AIMS

To model the suitability of conventional ready-made spectacles (RMS) and interchangeable-lens ready-made spectacles (IRMS) with reference to prescribing guidelines among children and adults using a large, global database and to introduce a web-based application for exploring the database with user-defined eligibility criteria.

METHODS

Using refractive power and interpupillary distance data for near and distance spectacles prescribed to children and adults during OneSight clinics in 27 countries, from 2 January 2016 to 19 November 2019, we modelled the expected suitability of RMS and IRMS spectacle designs, compared with custom-made spectacles, according to published prescribing guidelines.

RESULTS

Records of 18 782 presbyopic adult prescriptions, 70 619 distance adult prescriptions and 40 862 paediatric prescriptions were included. Globally, 58.7%-63.9% of adults could be corrected at distance with RMS, depending on the prescribing cut-off. For presbyopic adult prescriptions, coverage was 44.1%-60.9%. Among children, 51.8% were eligible for conventional RMS. Coverage for all groups was similar to the above for IRMS. The most common reason for ineligibility for RMS in all service groups was astigmatism, responsible for 27.2% of all ineligible adult distance prescriptions using the strictest cut-off, 31.4% of children's prescriptions and 28.0% of all adults near prescriptions globally.

CONCLUSION

Despite their advantages in cost and convenience, coverage delivered by RMS is limited under current prescribing guidelines, particularly for children and presbyopic adults. Interchangeable designs do little to remediate this, despite extending coverage for anisometropia. Our free application allows users to estimate RMS coverage in specific target populations.

Non-fungible tokens in healthcare: a scoping review

Introduction

Non-Fungible Tokens (NFTs) are digital assets that are verified using blockchain technology to ensure authenticity and ownership. NFTs have the potential to revolutionize healthcare by addressing various issues in the industry.

Method

The goal of this study was to identify the applications of NFTs in healthcare. Our scoping review was conducted in 2023. We searched the Scopus, IEEE, PubMed, Web of Science, Science Direct, and Cochrane scientific databases using related keywords. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

Results

After applying inclusion and exclusion criteria, a total of 13 articles were chosen. Then extracted data was summarized and reported. The most common application of NFTs in healthcare was found to be in health data management with 46% frequency, followed by supply chain management with 31% frequency. Furthermore, Ethereum is the main blockchain platform that is applied in NFTs in healthcare with 70%.

Discussion

The findings from this review indicate that the NFTs that are currently used in healthcare could transform it. Also, it appears that researchers have not yet investigated the numerous potentials uses of NFTs in the healthcare field, which could be utilized in the future.

RUBic: rapid unsupervised biclustering

Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took [Formula: see text] s to extract 494,872 biclusters. In the human PPI database of size [Formula: see text], our method generates 1840 biclusters in [Formula: see text] s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes   101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only.

Data sharing and ontology use among agricultural genetics, genomics, and breeding databases and resources of the Agbiodata Consortium

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL  https://www.agbiodata.org/databases.

Data management in the digital health environment scale development study

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.

The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study

BACKGROUND

In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status.

OBJECTIVE

Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data.

METHODS

In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist.

RESULTS

Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies.

CONCLUSIONS

The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality.

Data management plans as linked open data: exploiting ARGOS FAIR and machine actionable outputs in the OpenAIRE research graph

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.

Detection of atypical response trajectories in biomedical longitudinal databases

Many health care professionals and institutions manage longitudinal databases, involving follow-ups for different patients over time. Longitudinal data frequently manifest additional complexities such as high variability, correlated measurements and missing data. Mixed effects models have been widely used to overcome these difficulties. This work proposes the use of linear mixed effects models as a tool that allows to search conceptually different types of anomalies in the data simultaneously.

An Explainable Framework for Predicting Drug-Side Effect Associations via Meta-Path-Based Feature Learning in Heterogeneous Information Network

Side effects of drugs have gained increasing attention in the biomedical field, and accurate identification of drug side effects is essential for drug development and drug safety surveillance. Although the traditional pharmacological experiments can accurately detect the side effects of drugs, the identifying process is time-consuming, costly, and may lead to incomplete identification of side effects. With the expanding of various biomedical databases, many computational methods have been developed for the task of drug-side effect associations (DSAs) prediction. However, existing methods have the following three drawbacks: 1). multiple drug-related databases are not fully used; 2). the complex semantics among drugs and side effects are not effectively captured; 3). the explainability of the predicted DSAs is missed for most existing methods. Therefore, there is an urgent need to find a more effective method for predicting DSAs. To address these issues, we propose a novel meta-path-based graph neural network model for drug-side effect associations prediction (MPGNN-DSA). In MPGNN-DSA, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a meta-path-based feature learning module is utilized for learning high-quality representations of drugs and side effects by capturing the semantics contained in meta-paths of the constructed HIN. With the learned features, the prediction module is conducted to derive the predicted side effects for drugs. In addition, the explainability of the predicted DSAs can be provided as well with the semantics contained in meta-paths. We conduct comprehensive experiments, and the results demonstrate the effectiveness of MPGNN-DSA, suggesting that the proposed method will be a feasible solution to the task of DSAs prediction.

Implementation of California COVIDNet - a multi-sector collaboration for statewide SARS-CoV-2 genomic surveillance

Introduction

The SARS-CoV-2 pandemic represented a formidable scientific and technological challenge to public health due to its rapid spread and evolution. To meet these challenges and to characterize the virus over time, the State of California established the California SARS-CoV-2 Whole Genome Sequencing (WGS) Initiative, or "California COVIDNet". This initiative constituted an unprecedented multi-sector collaborative effort to achieve large-scale genomic surveillance of SARS-CoV-2 across California to monitor the spread of variants within the state, to detect new and emerging variants, and to characterize outbreaks in congregate, workplace, and other settings.

Methods

California COVIDNet consists of 50 laboratory partners that include public health laboratories, private clinical diagnostic laboratories, and academic sequencing facilities as well as expert advisors, scientists, consultants, and contractors. Data management, sample sourcing and processing, and computational infrastructure were major challenges that had to be resolved in the midst of the pandemic chaos in order to conduct SARS-CoV-2 genomic surveillance. Data management, storage, and analytics needs were addressed with both conventional database applications and newer cloud-based data solutions, which also fulfilled computational requirements.

Results

Representative and randomly selected samples were sourced from state-sponsored community testing sites. Since March of 2021, California COVIDNet partners have contributed more than 450,000 SARS-CoV-2 genomes sequenced from remnant samples from both molecular and antigen tests. Combined with genomes from CDC-contracted WGS labs, there are currently nearly 800,000 genomes from all 61 local health jurisdictions (LHJs) in California in the COVIDNet sequence database. More than 5% of all reported positive tests in the state have been sequenced, with similar rates of sequencing across 5 major geographic regions in the state.

Discussion

Implementation of California COVIDNet revealed challenges and limitations in the public health system. These were overcome by engaging in novel partnerships that established a successful genomic surveillance program which provided valuable data to inform the COVID-19 public health response in California. Significantly, California COVIDNet has provided a foundational data framework and computational infrastructure needed to respond to future public health crises.

A web-based platform for the annotation and analysis of NAR-published databases

Biological databases are essential resources for life science research, but finding and selecting the most relevant and up-to-date databases can be challenging due to the large number and diversity of available databases. The Nucleic Acids Research (NAR) journal publishes annual database issues that provide a comprehensive list of databases in the molecular biology domain. However, the information provided by NAR is limited and sometimes does not reflect the current status and quality of the databases. In this article, we present a web-based platform for the annotation and analysis of NAR-published databases. The platform allows users to manually curate and enrich the NAR entries with additional information such as availability, downloadability, source code links, cross-references, and duplicates. Statistics and visualizations on various aspects of the database landscape, such as recency, status, category, and curation history are also provided. Currently, it contains a total of 2,246 database entries of which 2,025 are unique with the majority updated within the last five years. Around 75% of all databases are still available and more than half provide a download option. Cross references to Database Commons are available for 1,889 entries. The platform is freely available online at https://nardbstatus.kalis-amts.de and aims to help researchers in database selection and decision-making. It also provides insights into the current state and challenges of a subset of all databases in the life sciences.

Fisheries data management systems in the NW Mediterranean: from data collection to web visualization

The European Union Data Collection Framework (DCF) states that scientific data-driven assessments are essential to achieve sustainable fisheries. To respond to the DCF call, this study introduces the information systems developed and used by Institut Català de Recerca per a la Governança del Mar (ICATMAR), the Catalan Institute of Research for the Governance of the Seas. The information systems include data from a biological monitoring, curation, processing, analysis, publication and web visualization for bottom trawl fisheries. Over the 4 years of collected data (2019-2022), the sampling program developed a dataset of over 1.1 million sampled individuals accounting for 24.6 tons of catch. The sampling data are ingested into a database through a data input website ensuring data management control and quality. The standardized metrics are automatically calculated and the data are published in the web visualizer, combined with fishing landings and Vessel Monitoring System (VMS) records. As the combination of remote sensing data with fisheries monitoring offers new approaches for ecosystem assessment, the collected fisheries data are also visualized in combination with georeferenced seabed habitats from the European Marine Observation and Data Network (EMODnet), climate and sea conditions from Copernicus Monitoring Environment Marine Service (CMEMS) on the web browser. Three public web-based products have been developed in the visualizer: geolocated bottom trawl samplings, biomass distribution per port or season and length-frequency charts per species. These information systems aim to fulfil the gaps in the scientific community, administration and civil society to access high-quality data for fisheries management, following the Findable, Accessible, Interoperable, Reusable (FAIR) principles, enabling scientific knowledge transfer. Database URL  https://icatmar.github.io/VISAP/(www.icatmar.cat).

International Clinical Research Data Ecosystem: From Data Standardization to Federated Analysis

Within the HORIZON 2020 project ORCHESTRA, patient data from numerous clinical studies in Europe related to COVID-19 were harmonized to create new knowledge on the disease. In this article, we describe the ecosystem that was established for the management of data collected and contributed by project partners. Study protocols elements were mapped to interoperability standards to establish a common terminology. That served as the basis of identifying common concepts used across several studies. Harmonized data were used to perform analysis directly on a central database and also through federated analysis when data was not permitted to leave the local server(s). This ecosystem facilitates the answering of research questions and generation of new knowledge available for the scientific community.

PainRE-Life: A FHIR Based Telemonitoring Ecosystem for the Management of Patients with Chronic Pain

Chronic pain is a condition in which the use of digital health technologies, ecological momentary assessments, and digital communication tools may boost patient's engagement and coping. Here we present the results of the PainRE-Life a project, financed by the Lombardy Region (Italy), aimed to develop a dynamic and integrated technology ecosystem based on big data management and analysis to allow care continuity in patients with pain, and able to act as a decision aid for patients and caregivers.

Checklist of Omophoita Chevrolat, 1836 (Coleoptera: Chrysomelidae: Galerucinae: Alticini) and diagnoses for some species from southern Brazil: notes on the taxonomic history, redescriptions and new records

Morphological descriptions, taxonomic history and distribution data of Omophoita species recorded for southern Brazil (states of Paran, Santa Catarina and Rio Grande do Sul) are presented. Through the analysis of the existing literature, databases, loaned material, and specimen collecting, we studied seven species of Omophoita recorded for southern Brazil: O. communis (Bechyn 1959), O. equestris (Fabricius 1787), O. magniguttis (Bechyn 1955), O. octoguttata (Fabricius 1775), O. personata (Illiger 1807), O. sesquilunata (Klug 1829), and O. sexnotata (Harold 1876). We report O. sesquilunata for this region for the first time. Updated morphological descriptions, including novel information for male and female genitalia are presented for those taxa with dissected specimens.

Under the Hood: The Scientific Leadership, Clinical Operations, Statistical and Data Management, and Laboratory Centers of the Antibacterial Resistance Leadership Group

Developing and implementing the scientific agenda of the Antibacterial Resistance Leadership Group (ARLG) by soliciting input and proposals, transforming concepts into clinical trials, conducting those trials, and translating trial data analyses into actionable information for infectious disease clinical practice is the collective role of the Scientific Leadership Center, Clinical Operations Center, Statistical and Data Management Center, and Laboratory Center of the ARLG. These activities include shepherding concept proposal applications through peer review; identifying, qualifying, training, and overseeing clinical trials sites; recommending, developing, performing, and evaluating laboratory assays in support of clinical trials; and designing and performing data collection and statistical analyses. This article describes key components involved in realizing the ARLG scientific agenda through the activities of the ARLG centers.

Online sleep diaries: considerations for system development and recommendations for 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.

Data management]

Data management "behind the scenes" refers to collection, cleaning, imputation, and demarcation; and despite of being indispensable processes, they are usually neglected and thus, generate erroneous information. During the collection are errors: omission of covariates, deviation from the objective, and insufficient quality. The omission of covariates distorts the result attributed to the main manoeuvre. Deviation from the primary objective commonly occurs when the outcome is rare, delayed, or subjective and promotes substitution by non-equivalent surrogate variables. Moreover, insufficient quality occurs due to inadequate instruments, omission of the measurement procedure, or measurements out of context, such as attribution at the wrong time or equivalent. Furthermore, cleaning implies identifying erroneous, extreme, and missing values, which may or may not be imputed, depending on the percentage. The values of the manoeuvre or the outcome are never imputed, nor are patients eliminated due to a lack of values. Finally, the demarcation of each variable seeks to give it a clinical meaning about the outcome, for which a hierarchical sequence of criteria is followed: 1) previous clinical study, 2) expert agreement, 3) clinical judgment of the investigator/investigators, and 4) statistics. Acting without quality controls in data management frequently causes involuntary lies and confuses instead of clarifying.

The Pacific Innovations, Knowledge, and Opportunities (PIKO) Program: A Data Lifecycle Research Experience

Pacific evidence-based clinical and translational research is greatly needed. However, there are research challenges that stem from the creation, accessibility, availability, usability, and compliance of data in the Pacific. As a result, there is a growing demand for a complementary approach to the traditional Western research process in clinical and translational research. The data lifecycle is one such approach with a history of use in various other disciplines. It was designed as a data management tool with a set of activities that guide researchers and organizations on the creation, management, usage, and distribution of data. This manuscript describes the data lifecycle and its use by the Biostatistics, Epidemiology, and Research Design core data science team in support of the Center for Pacific Innovations, Knowledge, and Opportunities program.

The Importance of Quality Assurance in Radiation Oncology Clinical Trials

Clinical trials have been the center of progress in modern medicine. In oncology, we are fortunate to have a structure in place through the National Clinical Trials Network (NCTN). The NCTN provides the infrastructure and a forum for scientific discussion to develop clinical concepts for trial design. The NCTN also provides a network group structure to administer trials for successful trial management and outcome analyses. There are many important aspects to trial design and conduct. Modern trials need to ensure appropriate trial conduct and secure data management processes. Of equal importance is the quality assurance of a clinical trial. If progress is to be made in oncology clinical medicine, investigators and patient care providers of service need to feel secure that trial data is complete, accurate, and well-controlled in order to be confident in trial analysis and move trial outcome results into daily practice. As our technology has matured, so has our need to apply technology in a uniform manner for appropriate interpretation of trial outcomes. In this article, we review the importance of quality assurance in clinical trials involving radiation therapy. We will include important aspects of institution and investigator credentialing for participation as well as ongoing processes to ensure that each trial is being managed in a compliant manner. We will provide examples of the importance of complete datasets to ensure study interpretation. We will describe how successful strategies for quality assurance in the past will support new initiatives moving forward.

Blockchain, Information Security, Control, and Integrity: Who Is in Charge?

SUMMARY

Blockchain technology has attracted substantial interest in recent years, most notably for its effect on global economics through the advent of cryptocurrency. Within the health care domain, blockchain technology has been actively explored as a tool for improving personal health data management, medical device security, and clinical trial management. Despite a strong demand for innovation and cutting-edge technology in plastic surgery, integration of blockchain technologies within plastic surgery is in its infancy. Recent advances and mainstream adoption of blockchain are gaining momentum and have shown significant promise for improving patient care and information management. In this article, the authors explain what defines a blockchain and discuss its history and potential applications in plastic surgery. Existing evidence suggests that blockchain can enable patient-centered data management, improve privacy, and provide additional safeguards against human error. Integration of blockchain technology into clinical practice requires further research and development to demonstrate its safety and efficacy for patients and providers.

Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs

Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We further introduce a web-based tool ( https://lancaster.ac.uk/psychology/smdistance ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Supplementary materials are available at https://osf.io/d42q6/ .