Data Quality Checks Research Articles

Abstract 3568: Managing large-scale cancer research data programs

Abstract Purpose: The National Cancer Institute’s (NCI’s) Cancer Research Data Commons (CRDC) is a data ecosystem for the cancer research community that provides cloud-based, secure storage and analytic tools for many cancer data types, including genomic, proteomic, imaging, and clinical data. CRDC requires a carefully designed, federated data governance framework to enable CRDC’s goals to sustain a collaborative data environment where researchers can access timely, accurate, and relevant cancer data. Methods: In 2023, CRDC chartered a Data Governance Framework of Committees and Working Groups across all CRDC components to: Enable diverse data type sharing; provide secure data access; optimize common infrastructure components and functions; and adhere to FAIR data principles (Findable, Accessible, Interoperable, and Reusable). We considered many designs for the CRDC Data Governance Framework, including fully centralizing or decentralizing decision-making bodies. Implementing a fully centralized data governance framework for a diverse data ecosystem like CRDC would risk top-down decisions that do not meet the unique needs of the CRDC and cancer research community. A fully decentralized data governance framework risks inconsistencies and compliance challenges. Additional governance design considerations included a federated approach that emphasizes clear roles and responsibilities; prioritizing cross-component policies to ensure consistency and promote operational efficiency; defining measurable performance standards; and keeping data owners, stewards, and users informed, trained, and supported. Results: We have determined that long term sustainment of CRDC requires a federated governance approach with broad stakeholder participation through Committees and Working Groups. Currently established CRDC governance committees include the Enterprise Architecture Review Team (EART), Submission Review Team, Data Standards Working Group, and Data Advisory Board consisting of cancer research domain experts, researchers, and skilled cloud engineers and developers. In 2023, the CRDC EART defined data lifecycle stages and identified technical optimization opportunities for submission coordination and indexing. The established governance groups are working to identify optimization opportunities for data quality checks, submission review workflows, study acceptance criteria, data submission reviews, and cross-CRDC emerging data standards requirements. Conclusions: Sustaining large, complex, cancer research data requires a federated governance approach with participation from all relevant stakeholder groups and cannot simply be top-down imposition of policies, standards, and procedures. The newly established CRDC federated governance framework will contribute to improving the long-term sustainability of the CRDC infrastructure and ensuring CRDC data drive meaningful research outcomes. Citation Format: Juergen Klenk, Dina Mikdadi, Chelsea Owens, Angela Maggio, Bhavani Singh, Eric Barner, Tanja Davidsen, Erika Kim. Managing large-scale cancer research data programs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3568.

De novo transcriptome analysis of Argemone mexicana plant (SRR287649) and annotation for secondary metabolite producing genes

Argemone mexicana (family: Papaveraceae) is an important medicinal plant that produces a wide range of secondary metabolites. In this study, the RNA-seq data of Argemone mexicana plant stem was retrieved from the NCBI SRA database (SRR287649). The quality checking and trimming of the raw data were performed by using FastQC, Cutadapt and Trimmomatic 0.39 software. Further, de-novo sequence assembly by Trinity software package generated a total 29591 number of contig sequences. As subsequent work, BLASTX and annotation of all the contig by using Blast2GO and WEGO2 tool produced 19780 annotated genes with 44440 gene ontology (GO) terms. Further, the genes involved in secondary metabolite production of the Argemone mexicana plant were predicted by functional assignment of the contig sequences.

District health management and stillbirth recording and reporting: a qualitative study in the Ashanti Region of Ghana

BackgroundDespite global efforts to reduce maternal and neonatal mortality, stillbirths remain a significant public health challenge in many low- and middle-income countries. District health systems, largely seen as the backbone of health systems, are pivotal in addressing the data gaps reported for stillbirths. Available, accurate and complete data is essential for District Health Management Teams (DHMTs) to understand the burden of stillbirths, evaluate interventions and tailor health facility support to address the complex challenges that contribute to stillbirths. This study aims to understand stillbirth recording and reporting in the Ashanti Region of Ghana from the perspective of DHMTs.MethodsThe study was conducted in the Ashanti Region of Ghana. 15 members of the regional and district health directorates (RHD/DHD) participated in semi-structured interviews. Sampling was purposive, focusing on RHD/DHD members who interact with maternity services or stillbirth data. Thematic analyses were informed by an a priori framework, including theme 1) experiences, perceptions and attitudes; theme 2) stillbirth data use; and theme 3) leadership and support mechanisms, for stillbirth recording and reporting.ResultsUnder theme 1, stillbirth definitions varied among respondents, with 20 and 28 weeks commonly used. Fresh and macerated skin appearance was used to classify timing with limited knowledge of antepartum and intrapartum stillbirths. For theme 2, data quality checks, audits, and the district health information management system (DHIMS-2) data entry and review are functions played by the DHD. Midwives were blamed for data quality issues on omissions and misclassifications. Manual entry of data, data transfer from the facility to the DHD, limited knowledge of stillbirth terminology and periodic closure of the DHIMS-2 were seen to proliferate gaps in stillbirth recording and reporting. Under theme 3, perinatal audits were acknowledged as an enabler for stillbirth recording and reporting by the DHD, though audits are mandated for only late-gestational stillbirths (> 28 weeks). Engagement of other sectors, e.g., civil/vital registration and private health facilities, was seen as key in understanding the true population-level burden of stillbirths.ConclusionEffective district health management ensures that every stillbirth is accurately recorded, reported, and acted upon to drive improvements. A large need exists for capacity building on stillbirth definitions and data use. Recommendations are made, for example, terminology standardization and private sector engagement, aimed at reducing stillbirth rates in high-mortality settings such as Ghana.

Health systems' capacity in availability of human resource for health towards implementation of Universal Health Coverage in Kenya.

Kenya faces significant challenges related to health worker shortages, low retention rates, and the equitable distribution of Human Resource for Health (HRH). The Ministry of Health (MOH) in Kenya has established HRH norms and standards that define the minimum requirements for healthcare providers and infrastructure at various levels of the health system. The study assessed on the progress of Universal Health Coverage (UHC) piloting on Human Resource for Health in the country. The study utilized a Convergent-Parallel-Mixed-Methods design, incorporating both quantitative and qualitative approaches. The study sampled diverse population groups and randomly selected health facilities. Four UHC pilot counties are paired with two non-UHC pilot counties, one neighboring county and the second county with a geographically distant and does not share a border with any UHC pilot counties. Stratification based on ownership and level was performed, and the required number of facilities per stratum was determined using the square root allocation method. Data on the availability of human resources for health was collected using a customized Kenya Service Availability and Readiness Assessment Mapping (SARAM) tool facilitated by KoBo ToolKitTM open-source software. Data quality checks and validation were conducted, and the HRH general service availability index was measured on availability of Nurses, Clinician, Nutritionist, Laboratory technologist and Pharmacist which is a minimum requirement across all levels of health facilities. Statistical analyses were performed using IBM SPSS version 27 and comparisons between UHC pilot counties and non-UHC counties where significance threshold was established at p < 0.05. Qualitative data collected using focus group discussions and in-depth interview guides. Ethical approval and research permits were obtained, and written informed consent was obtained from all participants. The study assessed 746 health facilities with a response rate of 94.3%. Public health facilities accounted for 75% of the sample. The overall healthcare professional availability index score was 17.2%. There was no significant difference in health workers' availability between UHC pilot counties and non-UHC pilot counties at P = 0.834. Public health facilities had a lower index score of 14.7% compared to non-public facilities at 27.0%. Rural areas had the highest staffing shortages, with only 11.1% meeting staffing norms, compared to 31.8% in urban areas and 30.4% in peri-urban areas. Availability of health workers increased with the advancement of The Kenya Essential Package for Health (KEPH Level), with all Level 2 facilities across counties failing to meet MOH staffing norms (0.0%) except Taita Taveta at 8.3%. Among specific cadres, nursing had the highest availability index at 93.2%, followed by clinical officers at 52.3% and laboratory professionals at 55.2%. The least available professions were nutritionists at 21.6% and pharmacist personnel at 33.0%. This result is corroborated by qualitative verbatim. The study findings highlight crucial challenges in healthcare professional availability and distribution in Kenya. The UHC pilot program has not effectively enhanced healthcare facilities to meet the standards for staffing, calling for additional interventions. Rural areas face a pronounced shortage of healthcare workers, necessitating efforts to attract and retain professionals in these regions. Public facilities have lower availability compared to private facilities, raising concerns about accessibility and quality of care provided. Primary healthcare facilities have lower availability than secondary facilities, emphasizing the need to address shortages at the community level. Disparities in the availability of different healthcare cadres must be addressed to meet diverse healthcare needs. Overall, comprehensive interventions are urgently needed to improve access to quality healthcare services and address workforce challenges.

Changes in substance use, recovery, and quality of life during the initial phase of the COVID-19 pandemic.

The COVID-19 pandemic disrupted lives on a massive scale. While the pandemic appeared to worsen mental health outcomes broadly, its effects on alcohol/substance use and recovery are unclear. Many studies convolved the sociopolitical unrest beginning in May 2020 with the pandemic. We assessed pandemic-related changes in substance use, recovery involvement, and quality of life among US adults at two specified time periods that isolated pandemic effects from potentially confounding sociopolitical factors. We tested the following hypotheses: the pandemic and consequent policies (1) increased use of alcohol and illicit substances in active users; (2) increased use of alcohol/substances among people in early recovery; (3) reduced participation in recovery activities among those in early recovery, and that (4) use amount and use events correlated with impulsivity in both groups and that (5) substance use and abstinence correlated with resilience. We recruited 1,685 participants through Amazon's Mechanical Turk (MTurk). We assessed demographics, quality of life, alcohol/substance use, recovery activities, and measures of impulsivity and resilience at two time points, pre-pandemic and (early) during-pandemic. Only n = 45 (Active Users; males n = 32) and n = 34 (Recovery; males n = 20) passed data quality checks and were included in the primary analyses. Among Active Users, weekly alcohol consumption and days spent using alcohol and illicit substances decreased during the pandemic. Resilience negatively correlated with pandemic-related substance use in early recovering participants. Significant reduction in the quality of life was coincident with a trend of lower recovery activity participation (31% decline) during the pandemic. The reduced alcohol/substance use and participation in recovery activities might be expected from conditions that promote social isolation. The high prevalence of low-quality data from MTurk cautions for careful use of online data sourcing.

Unsupervised Machine Learning Clustering of Seismic and Infrasound Data Quality Metrics

Abstract Developing techniques for improving quality control (QC) schemes to catch seismic and infrasound data defects continues to be an area of active research. Selecting universal thresholds for the automation of data quality (DQ) checks is an efficient way to find QC issues, but these thresholds may not apply well to multiple stations with varying DQ characteristics. In addition, these thresholds may not catch subtle changes in DQ parameters that still indicate problems. Machine learning can be an alternative way of diagnosing QC issues. K-means clustering, an unsupervised machine learning clustering algorithm, has been effectively used in the past for geophysical pattern exploration. This study furthers k-means applications to DQ analysis through clustering on DQ metrics derived from day-long segments of nuclear explosion monitoring data. Our k-means implementation on broadband seismometer DQ metrics separately clustered mass recenters, calibrations lasting at least one hour, and days without either. Applying this technique to infrasound DQ metrics revealed clusters related to physical issues at the stations, such as missing back volume screws and the flooding of ported pipe inlets. These are both examples of QC issues that are difficult to diagnose or detect through the thresholding of metrics or by inspecting waveforms and spectra. Our results show that k-means clustering can be a useful QC tool in exploring DQ patterns to assist analyst review of station operation and maintenance. The learned knowledge from this exploration can then inform a thresholding workflow on how to tailor to individual stations, or the k-means model could classify data directly.

SUITABILITY AND CHALLENGES OF OPEN MAPS FOR EUROPE DATA FOR CREATING A GENERAL PURPOSE SMALL-SCALE MAP OF CROATIA

Abstract. The adoption of the Open Data Directive in 2019 compelled the launch of the Open Maps for Europe project, aiming to provide accessible and harmonized geospatial data from over 40 European countries. This research investigates the suitability of Open Maps for Europe data for creating a general purpose small-scale map of Croatia. The study applies digital cartographic procedures to produce the map, while assessing data quality and fitness for this task. The methodology for map creation involved seven key steps: (1) extracting data from the 1:250,000 scale EuroRegionalMap dataset, (2) performing geometric, topological, and attribute conversions, (3) applying cartographic generalization to adapt the map content to a 1:500,000 scale, (4) conducting data quality checks, (5) addressing exceptions, (6) symbolizing and placing names, and (7) designing a printable map. The resulting general-purpose map of Croatia at a 1:500 000 scale demonstrates that Open Maps for Europe data is generally suitable for map creation at this scale. However, challenges such as data heterogeneity among countries, data gaps in certain areas, inappropriate levels of generalization, and disordered geometry may arise. The map created should be considered an intermediate, rather than a reliable and trustworthy final product since rigorous quality control measures should be undertaken to produce the final map.

IMPROVING DATA QUALITY AND MANAGEMENT FOR REMOTE SENSING ANALYSIS: USE-CASES AND EMERGING RESEARCH QUESTIONS

Abstract. During the last decades satellite remote sensing has become an emerging technology producing big data for various application fields every day. However, data quality checking as well as the long-time management of data and models are still issues to be improved. They are indispensable to guarantee smooth data integration and the reproducibility of data analysis such as carried out by machine learning models. In this paper we clarify the emerging need of improving data quality and the management of data and models in a geospatial database management system before and during data analysis. In different use cases various processes of data preparation and quality checking, integration of data across different scales and references systems, efficient data and model management, and advanced data analysis are presented in detail. Motivated by these use cases we then discuss emerging research questions concerning data preparation and data quality checking, data management, model management and data integration. Finally conclusions drawn from the paper are presented and an outlook on future research work is given.

Electronic health record impact on data quality: An integrated review

Few studies have assessed the impact of Electronic Health Record (EHR) on data quality and how it has impacted timeliness, accuracy, availability, integrity, consistency and uniqueness of health data. Assessing the current gaps between electronic health record utilisation in relation to enhancing data quality can help to identify and recommend key measures to improve healthcare delivery. This review examines the impact of electronic health record on data quality, and proposes recommendations desirable to improving healthcare delivery. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Integrative Review Frameworks (IRF) were adapted as a sign post for the study. These frameworks guided literature search, data analysis and presentation of findings. Boolean AND/OR operators were combined with search terms to increase relevant search results. Six (6) electronic databases that were used for literature search are Scopus, Web of Science, Science Direct, PubMed, ProQuest and Google Scholar. Searched articles were between 2012 to 2022, with the exception of two articles on the Technology Acceptance Model (TAM) published in 1986 and 1975. EHR implementation impact key health outcomes and contribute to timely and complete data submission. Implementation of Electronic Health Systems (EHS) embellish consistency, completeness, accuracy and integrity of clinical data significantly through embedded interoperability solutions. By reducing the scope of manual reviews, integrating a technology that automates data integrity and quality checks into EHR may potentially lessen the strain of maintaining data quality. Our findings depict that interoperability of clinical data sources will ensure consistent, timely and available health data when using EHR. The automated data integrity and consistency checks in the EHR improve data quality. The study contributes to advancing the theory of Technology Acceptance Model (TAM) in electronic health records and its impact on data quality in healthcare delivery.

Management of fraudulent participants in online research: Practical recommendations from a randomized controlled feasibility trial.

Fraudulent participation is an escalating concern for online clinical trials and research studies and can have a significant negative impact on findings. We aim to shed light on the risk and to provide practical recommendations for detecting and managing such instances. The FREED-Mobile (FREED-M) study is an online, randomized controlled feasibility trial to assess a digital early intervention for young people (aged 16-25) in England or Wales with eating problems. The trial involved baseline (week 0), post-intervention (week 4), and follow-up (week 12) surveys, alongside weekly modules provided over 4 weeks on the study website. Study completers were compensated with £20 shopping vouchers. Despite the complexity of the trial design, two instances of fraudulent sign-ups occurred in January and March 2023. To counter this, we developed a "fraudulent participants protocol" following internal investigations and discussions with collaborators. The implementation of prevention measures such as reCAPTCHA updates, IP address review, and changes in reimbursement effectively halted further fraudulent sign-ups. Our protocol facilitated the systematic identification and withdrawal of suspected or clear fraudsters and was demonstrably robust at distinguishing between fraudsters and genuine responders. All remote, online trials or studies are at risk of fraudulent participation. Drawing from our experience and existing literature, we offer practical recommendations for researchers considering online recruitment and data collection. Vigilance and the integration of deterrents, and data quality checks into the study design from the outset are advised to safeguard research integrity. Fraudulent participation in digital research can have asignificant impact on research findings, potentially leading to biased resultsand misinformed decisions. We developed an effective protocol for theprevention, identification, and management of fraudulent participants. Bysharing our insights and recommendations, we hope to raise awareness of thisissue and provide other researchers with the knowledge and strategies necessaryto safeguard research integrity moving forward.

Screening respondents to increase data quality in consumer tests

Product tests are often conducted to understand consumer opinions and perceptions. A screener is often used to determine which respondents are consumers. Our goal was to determine how filtering consumers who gave low-quality screener responses affects test results. Respondents in Finland (n = 343) and Turkey (n = 342) completed an online questionnaire. The questionnaire began with a screener. Respondents who consumed category products regularly were classified as consumers. Points were allocated to respondents who gave dubious answers to screener questions. Respondents with too many points “failed”; others “passed”. Regardless, all respondents completed an online test of three product concepts and a short version of the portrait values questionnaire. Screener-passing consumers better discriminated the product concepts and had larger directional differences between the concepts than a conventional panel comprised of all consumers. Fixed-sized panels composed first of screener-passing consumers, then of all consumers, were simulated using the bootstrap procedure. Screener-passing simulated panels were more discriminating of the concepts and had larger between-concept differences than conventional simulated panels. These empirical results indicate the size of the benefit realized by incorporating data quality checks into screening. If consumers who give dubious screener responses are dropped, then other consumers can take their places or their places can be left unfilled, yielding either better differentiation or cost savings. Further study is needed to develop best practices in screening that can be used in different countries and cultures.

Enhancing Business Strategies with Advanced Location Intelligence Techniques: A Multifaceted Approach Using GIS and Python

This paper delves into the transformative impact of advanced location intelligence (LI) techniques on business strategy formulation across several critical areas, such as optimizing service coverage, determining strategic retail locations, planning network infrastructure, and analyzing market penetration. By harnessing the capabilities of geographic information systems (GIS) and Python for advanced analytics, we underscore the significance of LI in informing strategic decisions, elevating customer experiences, and achieving a competitive market edge. Our approach employs a holistic methodology that includes rigorous data quality checks, in-depth geospatial analysis, and detailed demographic assessments, enriched by practical case studies. This synthesis offers valuable, actionable insights for businesses aiming to leverage LI in strategic planning, thereby driving growth and operational efficiency.

MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.

Evaluation of precipitation measurement methods using data from a precision lysimeter network

Abstract. Accurate precipitation data are essential for assessing the water balance of ecosystems. Methods for point precipitation determination are influenced by wind, precipitation type and intensity and/or technical issues. High-precision weighable lysimeters provide precipitation measurements at ground level that are less affected by wind disturbances and are assumed to be relatively close to actual precipitation. The problem in previous studies was that the biases in precipitation data introduced by different precipitation measurement methods were not comprehensively compared with and quantified on the basis of those obtained by lysimeters in different regions in Germany. The aim was to quantify measurement errors in standard precipitation gauges as compared to the lysimeter reference and to analyze the effect of precipitation correction algorithms on the gauge data quality. Both correction methods rely on empirical constants to account for known external influences on the measurements, following a generic and a site-specific approach. Reference precipitation data were obtained from high-precision weighable lysimeters of the TERrestrial ENvironmental Observatories (TERENO)-SOILCan lysimeter network. Gauge types included tipping bucket gauges (TBs), weighable gauges (WGs), acoustic sensors (ASs) and optical laser disdrometers (LDs). From 2015-2018, data were collected at three locations in Germany, and 1 h aggregated values for precipitation above a threshold of 0.1 mm h−1 were compared. The results show that all investigated measurement methods underestimated the precipitation amounts relative to the lysimeter references for long-term precipitation totals with catch ratios (CRs) of between 33 %–92 %. Data from ASs had overall biases of −0.25 to −0.07 mm h−1, while data from WGs and LDs showed the lowest measurement bias (−0.14 to −0.06 mm h−1 and −0.01 to −0.02 mm h−1). Two TBs showed systematic deviations with biases of −0.69 to −0.61 mm h−1, while other TBs were in the previously reported range with biases of −0.2 mm h−1. The site-specific and generic correction schemes reduced the hourly measurement bias by 0.13 and 0.08 mm h−1 for the TBs and by 0.09 and 0.07 mm h−1 for the WGs and increased long-term CRs by 14 % and 9 % and by 10 % and 11 %, respectively. It could be shown that the lysimeter reference operated with minor uncertainties in long-term measurements under different site and weather conditions. The results indicate that considerable precipitation measurement errors can occur even at well-maintained and professionally operated stations equipped with standard precipitation gauges. This generally leads to an underestimation of the actual precipitation amounts. The results suggest that the application of relatively simple correction schemes, manual or automated data quality checks, instrument calibrations, and/or an adequate choice of observation period can help improve the data quality of gauge-based measurements for water balance calculations, ecosystem modeling, water management, assessment of agricultural irrigation needs, or radar-based precipitation analyses.

Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research.

The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population. We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022. After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died. We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.

Understanding professional development challenges of Chinese public health professionals: association and prediction analyses with data validity screening.

Little is known about the public health professionals engaged in educating and training new or future researchers in public health. Research in this direction identifies their issues, concerns, challenges, and needs. This study focused on the professional development challenges of Chinese public health professionals. Snowball sampling was utilized. A total of 265 public health professionals participated. An instrument of 6 dimensions (burnout, sleep issue, mood issue, friends' support, exercise, and challenges) was developed, revised, and administered online. Two different approaches, the conventional and data screening approaches, were applied. The former started with item quality analyses, whereas the latter began with data quality checks. The chi-square tests of associations and logistic regressions were performed on both approaches. 19.25% of the participants were detected and deleted as careless respondents. Using both approaches, six professional development challenges except one ("Multidisciplinary learning") were significantly associated with various demographic features. The two approaches produced different models though they converged sometimes. The latent variables of exercise predicted professional development challenges more frequently than other latent variables. Regarding correct classification rates, results from the data screening approach were comparable to those from the conventional approach. The latent variables of exercise, such as "Exercise effects," "Expectations of exercise," and "Belief in exercise," might be understudied. More research is necessary for professional development challenges using exercise as a multidimensional construct. Based on the current study, screening and deleting careless responses in survey research is necessary.

Modelling Research Expeditions in Wikidata: Best Practice for Standardisation and Contextualisation

Expeditions and other collecting events are a major source of objects in natural history museums (e.g., Mesibov 2021). Historically, these trips were often transdisciplinary: biological and Earth science specimens were collected at the same time as ethnological or anthropological objects. As a result, specimens and other material gathered during the same expedition, as well as the related data and metadata, are often distributed across multiple institutions. Many expeditions were driven by colonial agendas, aiming to discover new resources to exploit, and their findings were seldom shared with the source countries and local people. Understanding these expeditions illuminates the colonial origins of museum collections, and contributes to recognizing and addressing their impacts (e.g., Das and Lowe 2018, Ashby and Machin 2021). Research expeditions continue to contribute to natural history collections. There is a need to link historical or contemporary research expeditions to other entities, requiring the unambiguous labelling (and persistent identifiers) of such events. Stable identifiers for expeditions plus the sharing of metadata and descriptions in a wide range of languages will facilitate access to scattered information about the event, the institutions housing specimens and objects, the participants and the locations visited, and assist with the linking of distributed material and related research data. However, structured data for scientific expeditions are currently lacking. While identifier systems have been created for many entities over the last few decades, there is no dedicated identifier for research expeditions and similar events. Several studies have shown the importance of people identifiers for linking collection data (e.g., Groom et al. 2022), and we argue the same is true for expeditions. Wikidata is a multilingual community-curated knowledge base containing data structured in a human- and machine-readable format. It allows easy creation, updating and enriching of items on expeditions, and provides stable identifiers for them that can be used in collection management systems. Expeditions can be linked to participants and other agents, regions, localities, objects, archival material, maps, publications, field notebooks, documentary footage and art works resulting from the expeditions, thus making historical information more easily accessible and assisting with the acknowledgment of any imperial or colonial impact that may have resulted from the expedition. Expeditions in Wikidata can be hierarchical, e.g., linking a series of related events or under an umbrella project together providing a machine-readable way to harvest all project data. Wikidata also can provide links between present day countries and historical names for locations (e.g., former colonial names). Expeditions published as Linked Open Data make datasets more FAIR (Findable, Accessible, Interoperable, Reusable), and are also useful in data transcription and validation processes. Visualisation of itinerary data and travel routes also facilitate data quality checks. An informal working group of people interested in the topic was formed to discuss standards and share best practices and recommendations regarding terminology, data modelling and contextualisation. Building upon previous work (e.g., Bauer et al. 2022, von Mering et al. 2022, Leachman 2023), we aim to work towards the enrichment, linking and standardisation of data about research expeditions. If the Wikidata identifiers of these expeditions and participants are added to the records of the corresponding entities in the collection management system, institutions can link from their own collection metadata to the relations made in Wikidata, including to collections in other institutions. The participants of the expedition can be further linked to specimens gathered during the expedition with the use of tools, such as Bionomia, which can facilitate data round-tripping between these collections and specimen records, the Global Biodiversity Information Facility (GBIF) and Wikidata (Shorthouse 2020). Other initiatives such as the Distributed System of Scientific Collections (DiSSCo) are also interested in incorporating these identifiers as links and annotations.

Ceftriaxone-resistant Salmonella Typhi isolated from paediatric patients in north India: Insights into genetic profiles and antibiotic resistance mechanisms

PurposeTo investigate the antibiotic resistance and genetic profile of ceftriaxone-resistant Salmonella Typhi isolated from the blood culture of two paediatric cases of typhoid fever and one from the stool culture of their household contact, in North India. MethodsIn this study, whole-genome sequencing was carried out with paired-end 2 ​× ​150 bp reads on Illumina MiSeq (Illumina, USA) employing v2 and v3 chemistry. To check data quality, adapters and low-quality sequences were removed through Trimmomatic-v0.36. High quality reads were then assembled de novo using A5-miseq pipeline. For further refinement, reference-guided contig ordering and orienting were performed on the scaffold assemblies using ABACAS (http://abacas.sourceforge.net/). The assembled genome was annotated using Prokka v1.12 to identify and annotate the gene content. Plasmid replicons in bacterial isolates were identified by PlasmidFinder, whereas mobile genetic elements were predicted using Mobile Element Finder. Referenced-based SNP tree with maximum likelihood method was built with CSI phylogeny v1.4. ResultsAll three isolates exhibited resistance to ceftriaxone, cefixime, ciprofloxacin, ampicillin, and co-trimoxazole, while demonstrating sensitivity to azithromycin and chloramphenicol. The whole-genome sequencing of these strains revealed the presence of blaCTX-M-15 gene for cephalosporin resistance in addition to gyrA, qnr and IncY plasmid replicon. A 5 ​kb IS91 Sbo1 gene cassette (IncY plasmid) was identified which carried extended spectrum β-lactamase blaCTX-M-15, blaTEM-1D (resistance to ampicillin and cephalosporin), sul2, dfrA14 (resistant to trimethoprim-sulfamethoxazole) and qnrS (resistant to ciprofloxacin). These isolates belong to H58 lineage and grouped as sequence type 1 (ST1) on multilocus sequence typing (MLST) analysis. ConclusionIn the present study we report the isolation of blaCTX-M-15 positive S. Typhi from two paediatric patients presenting with fever and one from stool culture of their contact from North India and highlight the need for further investigations to understand the different factors contributing to ceftriaxone resistance in Salmonella Typhi.

Delusion-like beliefs and data quality: Are classic cognitive biases artifacts of carelessness?

There is widespread agreement that delusions in clinical populations and delusion-like beliefs in the general population are, in part, caused by cognitive biases. Much of the evidence comes from two influential tasks: the Beads Task and the Bias Against Disconfirmatory Evidence Task. However, research using these tasks has been hampered by conceptual and empirical inconsistencies. In an online study, we examined relationships between delusion-like beliefs in the general population and cognitive biases associated with these tasks. Our study had four key strengths: A new animated Beads Task designed to reduce task miscomprehension, several data-quality checks to identify careless responders, a large sample (n = 1,002), and a preregistered analysis plan. When analyzing the full sample, our results replicated classic relationships between cognitive biases and delusion-like beliefs. However, when we removed 82 careless participants from the analyses (8.2% of the sample) we found that many of these relationships were severely diminished and, in some cases, eliminated outright. These results suggest that some (but not all) seemingly well-established relationships between cognitive biases and delusion-like beliefs might be artifacts of careless responding. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Genetic differentiation among Aedes aegypti populations from different eco-geographical zones of India.

The present study explicitly evaluated the genetic structure of Aedes aegypti Linn, the vector of dengue, chikungunya, and Zika viruses, across different geo-climatic zones of India and also elucidated the impact of ecological and topographic factors. After data quality checks and removal of samples with excess null alleles, the final analysis was performed on 589 individual samples using 10 microsatellite markers. Overall findings of this study suggested that, Ae. aegypti populations are highly diverse with moderate genetic differentiation between them. Around half of the populations (13 out of 22) formed two genetic clusters roughly associated with geographical regions. The remaining nine populations shared genetic ancestries with either one or both of the clusters. A significant relationship between genetic and geographic distance was observed, indicating isolation by distance. However, spatial autocorrelation analysis predicted the signs of long-distance admixture. Post-hoc environmental association analysis showed that 52.7% of genetic variations were explained by a combination of climatic and topographic factors, with latitude and temperature being the best predictors. This study indicated that though overall genetic differentiation among Ae. aegypti populations across India is moderate (Fst = 0.099), the differences between the populations are developing due to the factors associated with geographic locations. This study improves the understanding of the Ae. aegypti population structure in India that may assist in predicting mosquito movements across the geo-climatic zones, enabling effective control strategies and assessing the risk of disease transmission.