The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary

The sharing and documentation of cardiovascular research data are essential for efficient use and reuse of data, thereby aiding scientific transparency, accelerating the progress of cardiovascular research and healthcare, and contributing to the reproducibility of research results. However, challenges remain. This position paper, written on behalf of and approved by the German Cardiac Society and German Centre for Cardiovascular Research, summarizes our current understanding of the challenges in cardiovascular research data management (RDM). These challenges include lack of time, awareness, incentives, and funding for implementing effective RDM; lack of standardization in RDM processes; a need to better identify meaningful and actionable data among the increasing volume and complexity of data being acquired; and a lack of understanding of the legal aspects of data sharing. While several tools exist to increase the degree to which data are findable, accessible, interoperable, and reusable (FAIR), more work is needed to lower the threshold for effective RDM not just in cardiovascular research but in all biomedical research, with data sharing and reuse being factored in at every stage of the scientific process. A culture of open science with FAIR research data should be fostered through education and training of early-career and established research professionals. Ultimately, FAIR RDM requires permanent, long-term effort at all levels. If outcomes can be shown to be superior and to promote better (and better value) science, modern RDM will make a positive difference to cardiovascular science and practice. The full position paper is available in the supplementary materials.

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application

BACKGROUND

The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively.

MATERIALS AND METHODS

The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers.

RESULTS

WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness.

CONCLUSIONS

Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Development and Use of a Tech-Based Data Management System for a Cognitive Rehabilitation Randomized Controlled Trial for People With Type 2 Diabetes

Successful technology-based interventions to improve patients' self-management are providing an incentive for researchers to develop and implement their own technology-based interventions. However, the literature lacks guidance on how to do this. In this article, we describe the electronic process with which we designed and implemented a technology-based data management system to implement a randomized controlled trial of a comprehensive cognitive rehabilitation intervention to improve cognitive function and diabetes self-management in people with type 2 diabetes. System development included feasibility assessment, interdisciplinary collaboration, design mapping, and use of institutionally and commercially available software. The resulting framework offers a template to support the development of technology-based interventions. Initial development may be time-consuming, but the benefits of the technology-based format surpass any drawbacks.

A multinational study on artificial intelligence adoption: Clinical implementers' perspectives

BACKGROUND

Despite substantial progress in AI research for healthcare, translating research achievements to AI systems in clinical settings is challenging and, in many cases, unsatisfactory. As a result, many AI investments have stalled at the prototype level, never reaching clinical settings.

OBJECTIVE

To improve the chances of future AI implementation projects succeeding, we analyzed the experiences of clinical AI system implementers to better understand the challenges and success factors in their implementations.

METHODS

Thirty-seven implementers of clinical AI from European and North and South American countries were interviewed. Semi-structured interviews were transcribed and analyzed qualitatively with the framework method, identifying the success factors and the reasons for challenges as well as documenting proposals from implementers to improve AI adoption in clinical settings.

RESULTS

We gathered the implementers' requirements for facilitating AI adoption in the clinical setting. The main findings include 1) the lesser importance of AI explainability in favor of proper clinical validation studies, 2) the need to actively involve clinical practitioners, and not only clinical researchers, in the inception of AI research projects, 3) the need for better information structures and processes to manage data access and the ethical approval of AI projects, 4) the need for better support for regulatory compliance and avoidance of duplications in data management approval bodies, 5) the need to increase both clinicians' and citizens' literacy as respects the benefits and limitations of AI, and 6) the need for better funding schemes to support the implementation, embedding, and validation of AI in the clinical workflow, beyond pilots.

CONCLUSION

Participants in the interviews are positive about the future of AI in clinical settings. At the same time, they proposenumerous measures to transfer research advancesinto implementations that will benefit healthcare personnel. Transferring AI research into benefits for healthcare workers and patients requires adjustments in regulations, data access procedures, education, funding schemes, and validation of AI systems.

The French Experience with a Population-Based Esophageal Atresia Registry (RENATO)

This paper presented a national register for esophageal atresia (EA) started in January 2008. We report our experience about the conception of this database and its coordination. Data management and data quality are also detailed. In 2023, more than 2,500 patients with EA are included. Prevalence of EA in France was calculated at 1.8/10,000 live birth. Main clinical results are listed with scientific publications issued directly from the register.

Artificial Intelligence in Hand Surgery - How Generative AI is Transforming the Hand Surgery Landscape

Artificial intelligence (AI) has witnessed significant advancements, reshaping various industries, including healthcare. The introduction of ChatGPT by OpenAI in November 2022 marked a pivotal moment, showcasing the potential of generative AI in revolutionising patient care, diagnosis and treatment. Generative AI, unlike traditional AI systems, possesses the ability to generate new content by understanding patterns within datasets. This article explores the evolution of AI in healthcare, tracing its roots to the term coined by John McCarthy in 1955 and the contributions of pioneers like John Von Neumann and Alan Turing. Currently, generative AI, particularly Large Language Models, holds promise across three broad categories in healthcare: patient care, education and research. In patient care, it offers solutions in clinical document management, diagnostic support and operative planning. Notable advancements include Microsoft's collaboration with Epic for integrating AI into electronic medical records (EMRs), enhancing clinical data management and patient care. Furthermore, generative AI aids in surgical decision-making, as demonstrated in plastic, orthopaedic and hepatobiliary surgeries. However, challenges such as bias, hallucination and integration with EMR systems necessitate caution and ongoing evaluation. The article also presents insights from the implementation of NUHS Russell-GPT, a generative AI chatbot, in a hand surgery department, showcasing its utility in administrative tasks but highlighting challenges in surgical planning and EMR integration. The survey showed unanimous support for incorporating AI into clinical settings, with all respondents being open to its use. In conclusion, generative AI is poised to enhance patient care and ease physician workloads, starting with automating administrative tasks and evolving to inform diagnoses, tailored treatment plans, as well as aid in surgical planning. As healthcare systems navigate the complexities of integrating AI, the potential benefits for both physicians and patients remain significant, offering a glimpse into a future where AI transforms healthcare delivery. Level of Evidence: Level V (Diagnostic).

Quality control data management with unity real-time in molecular virology

INTRODUCTION

Quality control (QC) is one component of an overarching quality management system (QMS) that aims at assuring laboratory quality and patient safety. QC data must be acceptable prior to reporting patients' results. Traditionally, QC statistics, records, and corrective actions were tracked at the Johns Hopkins Molecular Virology Laboratory using Microsoft Excel. Unity Real-Time (UnityRT), a QMS software (Bio-Rad Laboratories), which captures and analyzes QC data by instrument and control lot per assay, was implemented and its impact on the workflow was evaluated. The clinical utility of real-time QC monitoring using UnityRT is highlighted with a case of subtle QC trending of HIV-1 quantitative control results.

METHODS

A comprehensive workflow analysis was performed, with a focus on Epstein Barr Virus (EBV) and BKV quantitative viral load testing (Roche cobas 6800). The number of QC steps and time to complete each step were assessed before and after implementing UnityRT.

RESULTS

Our assessment of monthly QC data review revealed a total of 10 steps over 57 min when using Microsoft Excel, versus 6 steps over 11 min when using UnityRT. HIV-1 QC monitoring revealed subtle trending of the low positive control above the mean from November to December 2022, correlating with a change in the reagent kit lot. This associated with a shift in patients' results from positives below the lower limit of quantification to positives between 20 and 100 copies/mL.

CONCLUSIONS

UnityRT consolidated QC analyses, monitoring, and tracking corrective actions. UnityRT was associated with significant time savings, which along with the interfaced feature of the QC capture and data analysis, have improved the workflow and reduced the risk of laboratory errors. The HIV-1 case revealed the value of the real-time monitoring of QC.

Understanding the subtypes of non-suicidal self-injury: A new conceptual framework based on a systematic review

Non-suicidal self-injury (NSSI) is a significant public health problem, but there is no consistent evidence of its risk factors. One possibility is that there are subtypes NSSI that have different risk factors and clinical symptoms. In this review we evaluated the evidence of subtypes to determine if there were consistent subtypes of NSSI that emerged across studies. Four databases (Medline; Embase; PsycINFO; Web of Science) were searched to identify studies that used data-driven approaches and were published before November 9, 2022. There were 21 studies with 23 unique samples for review. Most of the included studies used NSSI symptoms or personal characteristics as the subtyping indicators, revealing 2-5 subtypes of NSSI. Variations in subtyping indicators, sample characteristics, and statistical methods may have contributed to the inconsistent number and characteristics of subtypes across studies. A new conceptual framework is proposed to integrate these diverse findings, highlighting the important roles of NSSI function and psychological pain in differentiating NSSI subtypes. This framework sheds light on the differences among self-injurers and offers insights for future endeavors to address the complexities of NSSI.

Empathic concern motivates willingness to help in the absence of interdependence

Previous research suggests that empathic concern selectively promotes motivation to help those with whom we typically have interdependent relationships, such as friends or siblings, rather than strangers or acquaintances. In a sample of U.S. participants (collected between 2018 and 2020), our studies not only confirmed the finding that empathic concern is directed somewhat more strongly toward interdependent relationship partners, but also showed cross-sectionally (Studies 1a-1b), and when manipulating target distress experimentally (Study 2), that empathic concern predicts higher willingness to help only when people perceive low interdependence in their relationship with the target. In Study 3, we manipulated perceived interdependence with an acquaintance via shared fate, and found that empathic concern only predicted helping motivation when we reduced shared fate, but not when we increased shared fate. These results suggest that when people perceive high interdependence in their relationships, shared fate is the driving force behind their desire to help, whereas when people perceive low interdependence with someone in need, empathic concern motivates them to help. A relationship-building perspective on empathic concern provides avenues for testing additional moderators, including those related to target-specific characteristics and culture and ecology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Best practices for multimodal clinical data management and integration: An atopic dermatitis research case

BACKGROUND

In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science.

METHODS

We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis.

RESULTS

MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA.

CONCLUSIONS

The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.

[Functional requirements of patient data management systems in intensive care medicine]

BACKGROUND

As part of the German government's digitization initiative, the paper-based documentation that is still present in many intensive care units is to be replaced by digital patient data management systems (PDMS). In order to simplify the implementation of such systems, standards for basic functionalities that should be part of basic configurations of PDMS would be of great value.

PURPOSE

This paper describes functional requirements for PDMS in several categories.

METHODS

Criteria for standardized data documentation were defined by the authors and derived functional requirements were classified into two priority categories.

RESULTS

Overall, general technical requirements, functionalities for intensive care patient care, and additional functionalities for PDMS were defined and prioritized.

DISCUSSION

Using this paper as a starting point for a discussion about basic functionalities of PDMS, it is planned to develop and obtain consensus on definitive standards with representatives from medical societies, medical informatics and PDMS manufacture.

Harnessing Big Data in Critical Care: Exploring a new European Dataset

Freely available datasets have become an invaluable tool to propel data-driven research, especially in the field of critical care medicine. However, the number of datasets available is limited. This leads to the repeated reuse of datasets, inherently increasing the risk of selection bias. Additionally, the need arose to validate insights derived from one dataset with another. In 2023, the Salzburg Intensive Care database (SICdb) was introduced. SICdb offers insights in currently 27,386 intensive care admissions from 21,583 patients. It contains cases of general and surgical intensive care from all disciplines. Amongst others SICdb contains information about: diagnosis, therapies (including data on preceding surgeries), scoring, laboratory values, respiratory and vital signals, and configuration data. Data for SICdb (1.0.6) was collected at one single tertiary care institution of the Department of Anesthesiology and Intensive Care Medicine at the Salzburger Landesklinik (SALK) and Paracelsus Medical University (PMU) between 2013 and 2021. This article aims to elucidate on the characteristics of the dataset, the technical implementation, and provides analysis of its strengths and limitations.

SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

GPDminer: a tool for extracting named entities and analyzing relations in biological literature

PURPOSE

The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers.

METHODS

GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction.

RESULTS

The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images.

CONCLUSION

GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management.

Key interoperability Factors for patient portals and Electronic health Records: A scoping review

AIM

To identify the key requirements and challenges to interoperability between patient portals and electronic health records (EHRs).

INTRODUCTION

Patient portals provide patients with access to their health information directly from EHRs within hospitals, primary care centres and general practices (GPs). Patient portals offer many benefits to patients including improved communication with healthcare providers and care coordination. However, many challenges exist with the integration and automatic and secure sharing of information between EHRs and patient portals. It is critical that countries learn from international experiences to successfully develop interoperable national patient portals.

METHODS

A scoping review methodology was undertaken. A search strategy using index terms and keywords was applied across four key databases, an additional grey literature search was also run. The identified studies were screened by two reviewers to determine eligibility against defined inclusion criteria. Data were abstracted from the eligible studies and reviewed to identify the key requirements and challenges to interoperability of patient portals with EHRs.

RESULTS

After screening 3,462 studies, 34 were included across 11 countries. Of the 29 unique patient portals studied, few offered patients access to their entire healthcare record across multiple sites and a number of different functionalities were available. Key interoperability requirements and challenges identified were: Data Sharing Incentives & Supports; Heterogenous Organisations & Information Systems; Data Storage & Management; Available Information & Functionalities; Data Formats & Standards; Identification of Individuals; User Access, Control & Consent; and Security & Privacy.

CONCLUSION

Seamless exchange of health information across patient portals and EHRs required organisational and individual factors, as well as technical considerations. Interorganisational collaboration and engagement of key stakeholders to determine standards and guidelines for consent and sharing of information, as well as technical standards and security measures were recommended.

REDCapDM: An R package with a set of data management tools for a REDCap project

BACKGROUND

Research Electronic Data CAPture (REDCap) is a web application for creating and managing online surveys and databases. Clinical data management is an essential process before performing any statistical analysis to ensure the quality and reliability of study information. Processing REDCap data in R can be complex and often benefits from automation. While there are several R packages available for specific tasks, none offer an expansive approach to data management.

RESULTS

The REDCapDM is an R package for accessing and managing REDCap data. It imports data from REDCap to R using either an API connection or the files in R format exported directly from REDCap. It has several functions for data processing and transformation, and it helps to generate and manage queries to clarify or resolve discrepancies found in the data.

CONCLUSION

The REDCapDM package is a valuable tool for data scientists and clinical data managers who use REDCap and R. It assists in tasks such as importing, processing, and quality-checking data from their research studies.

Antidepressants use in Italy: an ecological study of national and regional trends and associated factors

The present study aimed to (1) provide an update on trends in AD consumption both at the national and regional unit of analysis for the period 2000-2020 in Italy and (2) analyze sociodemographic and healthcare system-related factors associated with AD prescribing at the regional-population level between 2000 and 2019. Data were extracted from reports of the Italian Medicines Agency and databases of the Italian National Institute of Statistics. Linear regression and mixed models were applied to analyze trends in AD use (DDD/1000/day) and ecological factors associated with AD prescribing. Between 2000 and 2010 AD prescription rates constantly increased. Thereafter they stabilized until 2017 when a positive trend began again. There was a positive ecological association between AD prescribing and rates of hospital discharge due to affective disorders, antibiotics prescribing, public non-drug healthcare spending per capita, and Northern regions compared to Southern regions. AD consumption increased massively during the 2000s, flattened during the 2010s but thereafter increased again until 2020. The ecological correlation between healthcare provision/spending and AD consumption suggests that health-economic factors may play an important role.

Orthography-phonology consistency in English: Theory- and data-driven measures and their impact on auditory vs. visual word recognition

Research on orthographic consistency in English words has selectively identified different sub-syllabic units in isolation (grapheme, onset, vowel, coda, rime), yet there is no comprehensive assessment of how these measures affect word identification when taken together. To study which aspects of consistency are more psychologically relevant, we investigated their independent and composite effects on human reading behavior using large-scale databases. Study 1 found effects on adults' naming responses of both feedforward consistency (orthography to phonology) and feedback consistency (phonology to orthography). Study 2 found feedback but no feedforward consistency effects on visual and auditory lexical decision tasks, with the best predictor being a composite measure of consistency across grapheme, rime, OVC, and word-initial letter-phoneme. In Study 3, we explicitly modeled the reading process with forward and backward flow in a bidirectionally connected neural network. The model captured latent dimensions of quasi-regular mapping that explain additional variance in human reading and spelling behavior, compared to the established measures. Together, the results suggest interactive activation between phonological and orthographic word representations. They also validate the role of computational analyses of language to better understand how print maps to sound, and what properties of natural language affect reading complexity.

Landscape of toxin-neutralizing therapeutics for snakebite envenoming (2015-2022): Setting the stage for an R&D agenda

BACKGROUND

Progress in snakebite envenoming (SBE) therapeutics has suffered from a critical lack of data on the research and development (R&D) landscape. A database characterising this information would be a powerful tool for coordinating and accelerating SBE R&D. To address this need, we aimed to identify and categorise all active investigational candidates in development for SBE and all available or marketed products.

METHODOLOGY/PRINCIPAL FINDINGS

In this landscape study, publicly available data and literature were reviewed to canvas the state of the SBE therapeutics market and research pipeline by identifying, characterising, and validating all investigational drug and biologic candidates with direct action on snake venom toxins, and all products available or marketed from 2015 to 2022. We identified 127 marketed products and 196 candidates in the pipeline, describing a very homogenous market of similar but geographically bespoke products and a diverse but immature pipeline, as most investigational candidates are at an early stage of development, with only eight candidates in clinical development.

CONCLUSIONS/SIGNIFICANCE

Further investment and research is needed to address the shortfalls in products already on the market and to accelerate R&D for new therapeutics. This should be accompanied by efforts to converge on shared priorities and reshape the current SBE R&D ecosystem to ensure translation of innovation and access.

Three facets of emotion regulation in old and very old age: Strategy use, effectiveness, and variability

The ability to regulate emotions in stressful situations is an important building block for high well-being across the lifespan. Yet, very little is known about how old and very old adults regulate their emotions. In this study, 123 young old adults (Mage = 67.18, SD = 0.94) and 47 very old adults (Mage = 86.70, SD = 1.46) were prompted 6 times a day for 7 consecutive days to report both their stressors and 10 emotion regulation strategies. Overall, there was little indication of age differences in the use of emotion regulation strategies during exposure to stressors, but very old, as compared with young old, individuals used three of the 10 strategies considered here more intensively. The 10 emotion regulation strategies were similarly effective across age groups based on their association with perceived overall emotion regulation success. We also did not find age group differences in within-strategy variability, defined as the variation in using a given strategy across stressor situations. By contrast, between-strategy variability, defined as the selective use of fewer rather than many strategies across stressor situations, was lower for very old participants. Only between-strategy, and not within-strategy, variability contributed to overall emotion regulation success. There was no age group difference in this regard. Taken together, the evidence suggests small age differences in emotion regulation if at all. This is noteworthy given the advanced age of the very old subsample in this study and the deficits in multiple domains of functioning reported in the literature for this advanced age. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dental divisions: exploring racial inequities of dental caries amongst children

DATA SOURCES

The search strategy involved three sequential stages. Initially, MEDLINE/PubMed was explored for relevant articles, identifying pertinent terms for formal searching. Using the terms ethnic, race, minoritised and dental caries, a strategy was formed and nine databases searched. Finally, hand-searching of reference lists of included articles and sourcing grey literature from relevant government reports, national oral health surveys, and registries which had comparative data for dental caries between racial groups, completed the search.

STUDY SELECTION

Studies included were original primary research which reported dental caries and compared racially minoritised children, aged 5-11 years, to similarly aged from national, majority, or privileged populations. Dental caries had to be recorded from a clinical examination which assessed decayed, missing, and filled teeth (dmft) in primary dentitions. Studies were excluded if they used immigration status as a basis of racial status, or they were a case report, case series, in vitro study, or literature review.

DATA EXTRACTION AND SYNTHESIS

After removing duplicates, two independent researchers screened abstracts, prior to extracting critical data following full-text reviews of included articles. Information collected included study and participant characteristics, definitions of race, and dental caries measurement. The authors of studies which had missing data were contacted, whilst those not written in the English language were translated. Methodological quality of each study was independently assessed by two reviewers using a modified version of the Newcastle-Ottawa scale. All studies were included in the review regardless of quality. A narrative overview of all included studies was conducted. Meta-analyses were completed using studies that reported the mean and standard deviation of the caries outcomes in both groups. Caries outcomes included severity (defined as mean dmft) or prevalence (percentage of teeth with untreated dental caries > 0%). Due to anticipated heterogeneity, statistical analyses approaches such as I2 statistics were used to estimate between-study variability. Additional sub-group analyses were conducted based on country of study and world income index. Contour-enhanced funnel plots and trim-and-fill analysis were completed to explore potential publication bias. Sensitivity analyses were performed to ensure robustness of the findings.

RESULTS

Seventy-five studies were included from a variety of countries. A higher mean dmft score of 2.30 (0.45, 4.15) and prevalence of decayed teeth (d > 0) was 23% (95% CI: 16, 31) was noted amongst racially minoritised children compared to privileged children's populations. Notable disparities were reported in high-income countries, with minoritised children burdening the greatest distribution of caries incidence. The study faced challenges in consistent racial classification and encountered high heterogeneity in its findings, leading to varied GRADE assessment scores.

CONCLUSIONS

The study calls for global, social, and political changes to tackle the substantial disparities in dental caries among minoritised children to achieve oral health equity.

The national distribution of lymphatic filariasis cases in Malawi using patient mapping and geostatistical modelling

BACKGROUND

In 2020 the World Health Organization (WHO) declared that Malawi had successfully eliminated lymphatic filariasis (LF) as a public health problem. Understanding clinical case distributions at a national and sub-national level is important, so essential care packages can be provided to individuals living with LF symptoms. This study aimed to develop a national database and map of LF clinical cases across Malawi using geostatistical modelling approaches, programme-identified clinical cases, antigenaemia prevalence and climate information.

METHODOLOGY

LF clinical cases identified through programme house-to-house surveys across 90 sub-district administrative boundaries (Traditional Authority (TA)) and antigenaemia prevalence from 57 sampled villages in Malawi were used in a two-step geostatistical modelling process to predict LF clinical cases across all TAs of the country. First, we modelled antigenaemia prevalence in relation to climate covariates to predict nationwide antigenaemia prevalence. Second, we modelled clinical cases for unmapped TAs based on our antigenaemia prevalence spatial estimates.

PRINCIPLE FINDINGS

The models estimated 20,938 (95% CrI 18,091 to 24,071) clinical cases in unmapped TAs (70.3%) in addition to the 8,856 (29.7%), programme-identified cases in mapped TAs. In total, the overall national number of LF clinical cases was estimated to be 29,794 (95% CrI 26,957 to 32,927). The antigenaemia prevalence and clinical case mapping and modelling found the highest burden of disease in Chikwawa and Nsanje districts in the Southern Region and Karonga district in the Northern Region of the country.

CONCLUSIONS

The models presented in this study have facilitated the development of the first national LF clinical case database and map in Malawi, the first endemic country in sub-Saharan Africa. It highlights the value of using existing LF antigenaemia prevalence and clinical case data together with modelling approaches to produce estimates that may be used for the WHO dossier requirements, to help target limited resources and implement long-term health strategies.

High rate of species misidentification reduces the taxonomic certainty of European biodiversity databases of ivies (Hedera L.)

The digitization of natural history specimens and the popularization of citizen science are creating an unprecedented availability of large amounts of biodiversity data. These biodiversity inventories can be severely affected by species misidentification, a source of taxonomic uncertainty that is rarely acknowledged in biodiversity data management. For these reasons, taxonomists debate the use of online repositories to address biological questions at the species level. Hedera L. (ivies) provides an excellent case study as it is well represented in both herbaria and online repositories with thousands of records likely to be affected by high taxonomic uncertainty. We analyze the sources and extent of taxonomic errors in the identification of the European ivy species by reviewing herbarium specimens and find a high misidentification rate (18% on average), which varies between species (maximized in H. hibernica: 55%; H. azorica: 48%; H. iberica: 36%) and regions (maximized in the UK: 38% and Spain: 27%). We find a systematic misidentification of all European ivies with H. helix behind the high misidentification rates in herbaria and warn of even higher rates in online records. We compile a spatial database to overcome the large discrepancies we observed in species distributions between online and morphologically reviewed records.

Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM

Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers.Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site.Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting.Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data.Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings.

Co-design for stroke intervention development: Results of a scoping review

BACKGROUND

Co-design methodology seeks to actively engage end-users in developing interventions. It is increasingly used to design stroke interventions; however, limited guidance exists, particularly with/for individuals with stroke who have diverse cognitive, physical and functional abilities. Thus, we describe 1) the extent of existing research that has used co-design for stroke intervention development and 2) how co-design has been used to develop stroke interventions among studies that explicitly used co-design, including the rationale, types of co-designed stroke interventions, participants involved, research methodologies/approaches, methods of incorporating end-users in the research, co-design limitations, challenges and potential strategies reported by researchers.

MATERIALS AND METHODS

A scoping review informed by Joanna Briggs Institute and Arksey & O'Malley methodology was conducted by searching nine databases on December 21, 2022, to locate English-language literature that used co-design to develop a stroke intervention. Additional data sources were identified through a hand search. Data sources were de-duplicated, and two research team members reviewed their titles, abstracts and full text to ensure they met the inclusion criteria. Data relating to the research objectives were extracted, analyzed, and reported numerically and descriptively.

RESULTS

Data sources used co-design for stroke intervention development with (n = 89) and without (n = 139) explicitly using the term 'co-design.' Among studies explicitly using co-design, it was commonly used to understand end-user needs and generate new ideas. Many co-designed interventions were technology-based (65%), and 48% were for physical rehabilitation or activity-based. Co-design was commonly conducted with multiple participants (82%; e.g., individuals with stroke, family members/caregivers and clinicians) and used various methods to engage end-users, including focus groups and workshops. Limitations, challenges and potential strategies for recruitment, participant-engagement, contextual and logistical and ethics of co-designed interventions were described.

CONCLUSIONS

Given the increasing popularity of co-design as a methodology for developing stroke interventions internationally, these findings can inform future co-designed studies.

RiceProteomeDB (RPDB): a user-friendly database for proteomics data storage, retrieval, and analysis

Rice, feeding a significant portion of the world, poses unique proteomic challenges critical to agricultural research and global food security. The complexity of the rice proteome, influenced by various genetic and environmental factors, demands specialized analytical approaches for effective study. The central challenges in rice proteomics lie in developing custom methods suited to the unique aspects of rice biology. These include data preprocessing, method selection, and result validation, all of which are essential for advancing rice research. Our aim is to decode these proteomic intricacies to facilitate breakthroughs in strain improvement, disease resistance, and yield optimization, all vital for combating global food insecurity. To achieve this, we have created the RiceProteomeDB (RPDB), a React + Django database, offering a streamlined and comprehensive platform for the analysis of rice proteomics data. RiceProteomeDB (RPDB) simplifies proteomics data management and analysis. It offers features for data organization, preprocessing, method selection, result validation, and data sharing. Researchers can access processed rice proteomics data, conduct analyses, and explore experimental conditions. The user-friendly web interface enhances navigation and interaction. RPDB fosters collaboration by enabling data sharing and proper acknowledgment of sources, contributing to proteomics research and knowledge dissemination. Availability and implementation: Web application: http://riceproteome.plantprofile.net/ . The web application's source code, user's manual, and sample data: https://github.com/dongu7610/Riceproteome .

A systematic review on the direct approach to elicit the demand-side cost-effectiveness threshold: Implications for low- and middle-income countries

Several literature review studies have been conducted on cost-effectiveness threshold values. However, only a few are systematic literature reviews, and most did not investigate the different methods, especially in-depth reviews of directly eliciting WTP per QALY. Our study aimed to 1) describe the different direct approach methods to elicit WTP/QALY; 2) investigate factors that contribute the most to the level of WTP/QALY value; and 3) investigate the relation between the value of WTP/QALY and GDP per capita and give some recommendations on feasible methods for eliciting WTP/QALY in low- and middle-income countries (LMICs). A systematic review concerning select studies estimating WTP/QALY from a direct approach was carried out in seven databases, with a cut off date of 03/2022. The conversion of monetary values into 2021 international dollars (i$) was performed via CPI and PPP indexes. The influential factors were evaluated with Bayesian model averaging. Criteria for recommendation for feasible methods in LMICs are made based on empirical evidence from the systematic review and given the resource limitation in LMICs. A total of 12,196 records were identified; 64 articles were included for full-text review. The WTP/QALY method and values varied widely across countries with a median WTP/QALY value of i$16,647.6 and WTP/QALY per GDP per capita of 0.53. A total of 11 factors were most influential, in which the discrete-choice experiment method had a posterior probability of 100%. Methods for deriving WTP/QALY vary largely across studies. Eleven influential factors contribute most to the level of values of WTP/QALY, in which the discrete-choice experiment method was the greatest affected. We also found that in most countries, values for WTP/QALY were below 1 x GDP per capita. Some important principles are addressed related to what LMICs may be concerned with when conducting studies to estimate WTP/QALY.

Applying sequence analysis to uncover 'real-world' clinical pathways from routinely collected data: a systematic review

OBJECTIVES

This systematic review aims to elucidate the methodological practices and reporting standards associated with sequence analysis (SA) for the identification of clinical pathways in real-world scenarios, using routinely collected data.

STUDY DESIGN AND SETTING

We conducted a methodological systematic review, searching five medical and health databases: MEDLINE, PsycINFO, CINAHL, EMBASE and Web of Science. The search encompassed articles from the inception of these databases up to February 28, 2023. The search strategy comprised two distinctive sets of search terms, specifically focused on sequence analysis and clinical pathways.

RESULTS

19 studies met the eligibility criteria for this systematic review. Nearly 60% of the included studies were published in or after 2021, with a significant proportion originating from Canada (n = 7) and France (n = 5). 90% of the studies adhered to the fundamental SA steps. The optimal matching (OM) method emerged as the most frequently employed dissimilarity measure (63%), while agglomerative hierarchical clustering using Ward's linkage was the preferred clustering algorithm (53%). However, it is imperative to underline that a majority of the studies inadequately reported key methodological decisions pertaining to SA.

CONCLUSION

This review underscores the necessity for enhanced transparency in reporting both data management procedures and key methodological choices within SA processes. The development of reporting guidelines and a robust appraisal tool tailored to assess the quality of SA would be invaluable for researchers in this field.

Emotion regulation efficacy beliefs: The outsized impact of base rates

To regulate others' emotions effectively we must learn about the efficacy of our regulation attempts. Deciding whether we made someone else feel better involves a causal judgment about the effect of our intervention on their emotional state. The current study examined whether, like other causal judgments, beliefs about emotion regulation efficacy are disproportionately affected by base rates. In two experiments, we showed that participants' perceived efficacy at helping a target regulate their emotions was more influenced by the target's average emotion levels than the relative effect of regulating versus not regulating the target's emotion. This led participants to conclude that they were helpful both when they were not (Experiment 1) and even when they made the target feel worse (Experiment 2). These findings suggest that our beliefs about the effectiveness of other-directed emotion regulation are notably biased by the average level of emotion expressed by the regulation target. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Golden tears: A cross-country study of crying in the Olympics

This article studies tears of joy by exploring data on the behavior of gold medalists of all 450 individual events at the 2012 and 2016 Summer Olympic Games at the end of the medalists' respective competitions and during the medal ceremonies. We find that women cry more than men, older athletes cry more than younger athletes, athletes from the host country cry more at the end of the competition, and athletes cry more when they receive information on their victory immediately after completing their task. When looking at the socioeconomic characteristics of athletes' countries, we find that men from countries with larger female labor force participation rates cry more than men from countries with lower female labor force participation, and athletes from countries with higher religious fractionalization cry less than those from countries with lower fractionalization. Finally, we find no relationship between the wealth of a country and the propensity of its athletes of any gender to cry. We discuss possible mechanisms that drive our results and suggest future directions for observational studies on emotions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Burn data management and usage across Canada

INTRODUCTION

While some countries collect burn clinical data as part of nonspecific trauma datasets, others have developed burn registries allowing for benchmarking of outcome and quality-of-care data. The objectives of this project are to characterize the current state of burn clinical data collection and analysis in Canada, and to explore the interest of Canadian burn centers in contributing to a nation-wide burn registry.

METHODS

A 23-item mixed methods survey was created and delivered via REDCap® to burn directors of 22 burn centers across Canada. Quantitative items were analyzed by means of descriptive statistics, and thematic analysis was used to explore qualitative data.

RESULTS

Sixteen (72 %) complete survey responses were received. All respondent units collect burn clinical data. Data are largely collected for quality improvement (69 %) and clinical research (50 %) purposes. Half of the institutions did not analyze their data, and a majority (67 %) did not benchmark their data against other datasets. The majority of respondents (93 %) demonstrated interest in contributing to a Canada-wide burn registry.

CONCLUSION

Although all respondent units are currently collecting burn clinical data, there is an opportunity to improve data analysis, benchmarking, and knowledge translation. Most centers demonstrated interest in contributing to a novel Canadian burn registry.

Architecture of the Mass Spectrometry Data Management Pipeline in the SMART-CARE Project

In the SMART-CARE project- a systems medicine approach to stratification of cancer recurrence in Heidelberg, Germany - a streamlined mass-spectrometry (MS) workflow for identification of cancer relapse was developed. This project has multiple partners from clinics, laboratories and computational teams. For optimal collaboration, consistent documentation and centralized storage, the linked data repository was designed. Clinical, laboratory and computational group members interact with this platform and store meta- and raw-data. The specific architectural choices, such as pseudonymization service, uploading process and other technical specifications as well as lessons learned are presented in this work. Altogether, relevant information in order to provide other research groups with a head-start for tackling MS data management in the context of systems medicine research projects is described.

Case-Reported Data Management Methodology Using an RDF Data Model for Building a Multicenter Clinical Registry

In multicenter clinical research, case-reported clinical data are managed for each research project. Participating institutions manage the mapping between standardized codes and in-house codes. To use the data extracted from electronic medical records in case report forms, it is necessary to pay attention to the gap in the semantic hierarchy. Managing mapping information between in-house and standardized codes is centralized in Resource Description Framework (RDF) stores. The relationship between standardized and in-house codes is described in RDF and stored in RDF stores. RESTful APIs for accessing RDF stores in SPARQL was developed and verified. The relationship between standardized codes and in-house codes of pharmaceuticals was expressed in RDF triples. As a +result of the operational verification of the implemented APIs, it was confirmed that data management with knowledge bases expressed in RDF graphs is possible. The ability to dynamically modify mapping definitions enables flexible data management and ease of operational restrictions.

Development of Integrated Data Quality Management System for Observational Medical Outcomes Partnership Common Data Model

The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.

An Overview of Data Management in Human Subjects Research

Research studies generate data in various forms. Data can be quantitative or qualitative. Research involving human subjects requires protection of data to ensure privacy. Various regulations and local policies need to be followed to ensure data security. Data management plans are critical for effective data stewardship and include details plan on data collection, management, storage, and formatting. This paper will review data collection tools, data security strategies, file management, data storage, government regulations, prepping data for analysis, reference management, and file management.

neo4jsbml: import systems biology markup language data into the graph database Neo4j

Systems Biology Markup Language (SBML) has emerged as a standard for representing biological models, facilitating model sharing and interoperability. It stores many types of data and complex relationships, complicating data management and analysis. Traditional database management systems struggle to effectively capture these complex networks of interactions within biological systems. Graph-oriented databases perform well in managing interactions between different entities. We present neo4jsbml, a new solution that bridges the gap between the Systems Biology Markup Language data and the Neo4j database, for storing, querying and analyzing data. The Systems Biology Markup Language organizes biological entities in a hierarchical structure, reflecting their interdependencies. The inherent graphical structure represents these hierarchical relationships, offering a natural and efficient means of navigating and exploring the model's components. Neo4j is an excellent solution for handling this type of data. By representing entities as nodes and their relationships as edges, Cypher, Neo4j's query language, efficiently traverses this type of graph representing complex biological networks. We have developed neo4jsbml, a Python library for importing Systems Biology Markup Language data into a Neo4j database using a user-defined schema. By leveraging Neo4j's graphical database technology, exploration of complex biological networks becomes intuitive and information retrieval efficient. Neo4jsbml is a tool designed to import Systems Biology Markup Language data into a Neo4j database. Only the desired data is loaded into the Neo4j database. neo4jsbml is user-friendly and can become a useful new companion for visualizing and analyzing metabolic models through the Neo4j graphical database. neo4jsbml is open source software and available at https://github.com/brsynth/neo4jsbml.

Promoting TEFCA with Blockchain Technology: A Decentralized Approach to Patient-centered Healthcare Data Management

The Trusted Exchange Framework and Common Agreement (TEFCA) is a framework consisting of seven principles designed to create a secure and seamless health information exchange system across various healthcare settings. The ultimate goal of TEFCA is to facilitate public health surveillance, increase interoperability, promote data sharing, and ensure patient-centered healthcare data management. While the implementation of these principles is challenging, blockchain technology, with its unique features such as transparency, auditability, immutability, and anonymity, can provide a promising solution to the development of TEFCA. This article delves into the potential of blockchain technology to promote TEFCA design. By providing an immutable and transparent ledger, blockchain ensures data integrity, openness, and patient privacy. Overall, the use of blockchain technology can help address the challenges of implementing TEFCA principles and promote patient empowerment and control over their health data, improve data interoperability, and enhance healthcare quality.

Methods and measures to evaluate the impact of participatory model building on public policymakers: a scoping review protocol

INTRODUCTION

Public policymakers are increasingly engaged in participatory model building processes, such as group model building. Understanding the impacts of policymaker participation in these processes on policymakers is important given that their decisions often have significant influence on the dynamics of complex systems that affect health. Little is known about the extent to which the impacts of participatory model building on public policymakers have been evaluated or the methods and measures used to evaluate these impacts.

METHODS AND ANALYSIS

A scoping review protocol was developed with the objectives of: (1) scoping studies that have evaluated the impacts of facilitated participatory model building processes on public policymakers who participated in these processes; and (2) describing methods and measures used to evaluate impacts and the main findings of these evaluations. The Joanna Briggs Institute's Population, Concept, Context framework was used to formulate the article identification process. Seven electronic databases-MEDLINE (Ovid), ProQuest Health and Medical, Scopus, Web of Science, Embase (Ovid), CINAHL Complete and PsycInfo-will be searched. Identified articles will be screened according to inclusion and exclusion criteria and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist for scoping reviews will be used and reported. A data extraction tool will collect information across three domains: study characteristics, methods and measures, and findings. The review will be conducted using Covidence, a systematic review data management platform.

ETHICS AND DISSEMINATION

The scoping review produced will generate an overview of how public policymaker engagement in participatory model building processes has been evaluated. Findings will be disseminated through peer-reviewed publications and to communities of practice that convene policymakers in participatory model building processes. This review will not require ethics approval because it is not human subject research.

PRMD: an integrated database for plant RNA modifications

The scope and function of RNA modifications in model plant systems have been extensively studied, resulting in the identification of an increasing number of novel RNA modifications in recent years. Researchers have gradually revealed that RNA modifications, especially N6-methyladenosine (m6A), which is one of the most abundant and commonly studied RNA modifications in plants, have important roles in physiological and pathological processes. These modifications alter the structure of RNA, which affects its molecular complementarity and binding to specific proteins, thereby resulting in various of physiological effects. The increasing interest in plant RNA modifications has necessitated research into RNA modifications and associated datasets. However, there is a lack of a convenient and integrated database with comprehensive annotations and intuitive visualization of plant RNA modifications. Here, we developed the Plant RNA Modification Database (PRMD; http://bioinformatics.sc.cn/PRMD and http://rnainformatics.org.cn/PRMD) to facilitate RNA modification research. This database contains information regarding 20 plant species and provides an intuitive interface for displaying information. Moreover, PRMD offers multiple tools, including RMlevelDiff, RMplantVar, RNAmodNet and Blast (for functional analyses), and mRNAbrowse, RNAlollipop, JBrowse and Integrative Genomics Viewer (for displaying data). Furthermore, PRMD is freely available, making it useful for the rapid development and promotion of research on plant RNA modifications.

EVLncRNAs 3.0: an updated comprehensive database for manually curated functional long non-coding RNAs validated by low-throughput experiments

Long noncoding RNAs (lncRNAs) have emerged as crucial regulators across diverse biological processes and diseases. While high-throughput sequencing has enabled lncRNA discovery, functional characterization remains limited. The EVLncRNAs database is the first and exclusive repository for all experimentally validated functional lncRNAs from various species. After previous releases in 2018 and 2021, this update marks a major expansion through exhaustive manual curation of nearly 25 000 publications from 15 May 2020, to 15 May 2023. It incorporates substantial growth across all categories: a 154% increase in functional lncRNAs, 160% in associated diseases, 186% in lncRNA-disease associations, 235% in interactions, 138% in structures, 234% in circular RNAs, 235% in resistant lncRNAs and 4724% in exosomal lncRNAs. More importantly, it incorporated additional information include functional classifications, detailed interaction pathways, homologous lncRNAs, lncRNA locations, COVID-19, phase-separation and organoid-related lncRNAs. The web interface was substantially improved for browsing, visualization, and searching. ChatGPT was tested for information extraction and functional overview with its limitation noted. EVLncRNAs 3.0 represents the most extensive curated resource of experimentally validated functional lncRNAs and will serve as an indispensable platform for unravelling emerging lncRNA functions. The updated database is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs3/.

Selecting an Ecological Momentary Assessment Platform: Tutorial for Researchers

BACKGROUND

Although ecological momentary assessment (EMA) has been applied in psychological research for decades, delivery methods have evolved with the proliferation of digital technology. Technological advances have engendered opportunities for enhanced accessibility, convenience, measurement precision, and integration with wearable sensors. Notwithstanding, researchers must navigate novel complexities in EMA research design and implementation.

OBJECTIVE

In this paper, we aimed to provide guidance on platform selection for clinical scientists launching EMA studies.

METHODS

Our team includes diverse specialties in child and adolescent behavioral and mental health with varying expertise on EMA platforms (eg, users and developers). We (2 research sites) evaluated EMA platforms with the goal of identifying the platform or platforms with the best fit for our research. We created a list of extant EMA platforms; conducted a web-based review; considered institutional security, privacy, and data management requirements; met with developers; and evaluated each of the candidate EMA platforms for 1 week.

RESULTS

We selected 2 different EMA platforms, rather than a single platform, for use at our 2 research sites. Our results underscore the importance of platform selection driven by individualized and prioritized laboratory needs; there is no single, ideal platform for EMA researchers. In addition, our project generated 11 considerations for researchers in selecting an EMA platform: (1) location; (2) developer involvement; (3) sample characteristics; (4) onboarding; (5) survey design features; (6) sampling scheme and scheduling; (7) viewing results; (8) dashboards; (9) security, privacy, and data management; (10) pricing and cost structure; and (11) future directions. Furthermore, our project yielded a suggested timeline for the EMA platform selection process.

CONCLUSIONS

This study will guide scientists initiating studies using EMA, an in vivo, real-time research tool with tremendous promise for facilitating advances in psychological assessment and intervention.

SWECO25: a cross-thematic raster database for ecological research in Switzerland

Standard and easily accessible cross-thematic spatial databases are key resources in ecological research. In Switzerland, as in many other countries, available data are scattered across computer servers of research institutions and are rarely provided in standard formats (e.g., different extents or projections systems, inconsistent naming conventions). Consequently, their joint use can require heavy data management and geomatic operations. Here, we introduce SWECO25, a Swiss-wide raster database at 25-meter resolution gathering 5,265 layers. The 10 environmental categories included in SWECO25 are: geologic, topographic, bioclimatic, hydrologic, edaphic, land use and cover, population, transportation, vegetation, and remote sensing. SWECO25 layers were standardized to a common grid sharing the same resolution, extent, and geographic coordinate system. SWECO25 includes the standardized source data and newly calculated layers, such as those obtained by computing focal or distance statistics. SWECO25 layers were validated by a data integrity check, and we verified that the standardization procedure had a negligible effect on the output values. SWECO25 is available on Zenodo and is intended to be updated and extended regularly.

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.