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Digital transformation and data projects are not new and yet, for many, they are a challenge. Driving Data Projects is a compelling guide that empowers data teams and professionals to navigate the complexities of data projects, fostering a more data-informed culture within their organizations. With practical insights and step-by-step methodologies, this guide provides a clear path how to drive data projects effectively in any organization, regardless of its sector or maturity level whilst also demonstrating how to overcome the overwhelming feelings of where to start and how to not lose momentum. This book offers the keys to identifying opportunities for driving data projects and how to overcome challenges to drive successful data initiatives. Driving Data Projects is highly practical and provides reflections, worksheets, checklists, activities, and tools making it accessible to students new to driving data projects and culture change. This book is also a must-have guide for data teams and professionals committed to unleashing the transformative power of data in their organizations.

The Farmworker Family Health Program (FWFHP) annually supports 600 farmworkers in connectivity-challenged rural areas. Traditional paper-based data collection poses validity concerns, prompting a pilot of direct data entry using tablets and satellite internet to enhance efficiency. The purpose of this article is to describe, using the TIDier checklist, a real-time, live data-entry EMR intervention made possible by satellite internet. Utilizing a customized REDCap database, direct data entry occurred through tablets and satellite internet. Patients received a unique medical record number (MRN) at the mobile health clinic, with an interprofessional team providing care. Medication data, captured in REDCap before the mobile pharmacy visit, exhibited minimal defects at 6.9% of 319 prescriptions. To enhance data collection efficiency, strategies such as limiting free text variables and pre-selecting options were employed. Adequate infrastructure, including tablets with keyboards and barcode scanners, ensured seamless data capture. Wi-Fi extenders improved connectivity in open areas, while backup paper forms were crucial during connectivity disruptions. These practices contributed to enhanced data accuracy. Real-time data entry in connectivity-limited settings is viable. Replacing paper-based methods streamlines healthcare provision, allowing timely collection of occupational and environmental health metrics. The initiative stands as a scalable model for healthcare accessibility, addressing unique challenges in vulnerable communities.

[This corrects the article DOI: 10.1371/journal.pone.0275898.].

Citizen Science projects aim to make data entry as easy as possible and often provide online data recording or data recording with an App. However, many participants cannot or do not want to use these possibilities and record their data the “old-fashioned” way with pen on paper. We ask whether the quality of data recorded in the “old-fashioned” way (transect walkers record their data with pen on paper) is of the same, better or worse quality than data recorded “online” (transect walkers enter their data via an online tool). We use the project “Butterfly Monitoring Germany” as an example, where we identify three different types of volunteers: those who enter their data online, those who send their data to the project coordination via email in different formats and those who send their data to the project coordination via ordinary mail. We observed minor quantitative differences for transect walkers not entering their data online but significant qualitative differences. Transect walkers who send their data via email record significantly more data for some rare or difficult to determine species. This is essential to properly calculate these species’ trends. In addition, the results of a questionnaire showed that “old fashioned” transect walkers did not use the online data entry because (i) data entry takes too long, (ii) is too cumbersome, (iii) they have bad or no internet connection or (iv) lack of technical capabilities. Accounting for different preferences of Citizen Scientists, different ways of data-submission should be made available (e.g. online, via app, or the old-fashioned way on paper). For the future, projects that collect large amounts of Citizen Science data should further develop low-threshold input data pipelines.Implications for insect conservationOur results show that data recorded in the old-fashioned way contributes significantly to increasing data quality. It is therefore very important to continue to enable different forms of data recording in the future. Furthermore, it is crucial to keep in mind that Citizen Science projects are only partly for the sake of science, but also volunteers should benefit by being part of a community and having access to information about (butterfly) biodiversity.

To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.

Clinical decision support systems are software tools that help clinicians to make medical decisions. However, their acceptance by clinicians is usually rather low. A known problem is that they often require clinicians to manually enter a lot of patient data, which is long and tedious. Existing solutions, such as the automatic data extraction from electronic health record, are not fully satisfying, because of low data quality and availability. In practice, many systems still include long questionnaire for data entry. In this paper, we propose an original solution to simplify patient data entry, using an adaptive questionnaire, i.e. a questionnaire that evolves during user interaction, showing or hiding questions dynamically. Considering a rule-based decision support systems, we designed methods for determining the relationships between rules and translating the system’s clinical rules into display rules that determine the items to show in the questionnaire, and methods for determining the optimal order of priority among the items in the questionnaire. We applied this approach to a decision support system implementing STOPP/START v2, a guideline for managing polypharmacy. We show that it permits reducing by about two thirds the number of clinical conditions displayed in the questionnaire, both on clinical cases and real patient data. Presented to clinicians during focus group sessions, the adaptive questionnaire was found “pretty easy to use”. In the future, this approach could be applied to other guidelines, and adapted for data entry by patients.

Objective Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1370/12,530). Overall 64% (1499/2352) of all discrepancies were due to data omissions, 76.6% (1148/1499) of missing entries were among categorical data. Omissions in PBDC (n = 1002) were twice as frequent as in EDC (n = 497, p < 0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.

There is a lack of comprehensive and uniform data on head and neck paragangliomas (HNPGLs), and research is challenging due to its rarity and the involvement of multiple medical specialties. To improve current research data collection, we initiated the Head and Neck Paraganglioma Registry (HNPGL Registry). The aim of the HNPGL Registry is to a) collect extensive data on all HNPGL patients through a predefined protocol, b) give insight in the long term outcomes using patient reported outcome measures (PROMs), c) create uniformity in the diagnostic and clinical management of these conditions, and thereby d) help provide content for future (randomized) research. The HNPGL Registry is designed as a prospective longitudinal observational registry for data collection on HNPGL patients and carriers of (likely) pathogenic variants causative of HNPGLs. All patients, regardless of the received treatment modality, can be included in the registry after informed consent is obtained. All relevant data regarding the initial presentation, diagnostics, treatment, and follow-up will be collected prospectively in an electronic case report form. In addition a survey containing the EuroQol 5D-5L (EQ-5D-5L), European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), Modified Fatigue Impact Scale (MFIS), Short QUestionnaire to Assess Health-enhancing physical activity (SQUASH), Cancer Worry Scale (CWS) and Hospital Anxiety and Depression Scale (HADS) will be sent periodically. The registry protocol was approved by the Medical Ethical Review Board of the University Medical Center Utrecht. The HNPGL Registry data will be used to further establish the optimal management for HNPGL patients and lay the foundation for guideline recommendations and the outline of future research.

Manual data entry in dialysis centers is time-consuming, error-prone, and increases the administrative burden on healthcare professionals. Traditional optical character recognition (OCR) systems partially automate this process but lack the ability to handle complex data anomalies and ensure reliable clinical documentation. This study presents the design and evaluation of an AI-enhanced OCR system that integrates advanced image processing, rule-based validation, and large language model-driven anomaly detection to improve data accuracy, workflow efficiency, and user experience. A total of 65 laboratory reports, each containing approximately 35 fields, were processed and compared under two configurations: a basic OCR system and the AI-enhanced OCR system. System performance was evaluated using three key metrics: error detection accuracy across three error categories (Missing Values, Out-of-Range, and Typo/Free-text), workflow efficiency measured by average processing time per record and total completion time, and user acceptance measured using the System Usability Scale (SUS). The AI-enhanced OCR system outperformed the basic OCR system in all metrics, particularly in detecting and correcting Out-of-Range errors, such as decimal placement issues, achieving near-perfect precision and recall. It reduced the average processing time per record by almost 50% (85.2 to 42.1 s) and improved usability, scoring 81.0 (Excellent) compared to 75.0 (Good). These results demonstrate the potential of AI-driven OCR to reduce clerical workload, improve healthcare data quality, and streamline clinical workflows, while maintaining a human-in-the-loop verification process to ensure patient safety and data integrity.