Lessons learned from the MOMENT study on how to recruit and retain a target population online, across borders, and with automated remote data collection.

Increasingly, studies use social media to recruit, enroll, and collect data from participants. This introduces a threat to data integrity: efforts to produce fraudulent data to receive participant compensation, e.g., gift cards. MOMENT is an online symptom-monitoring and self-care study that implemented safeguards to protect data integrity. Facebook, Twitter, and patient organizations were used to recruit participants with chronic health conditions in four countries (USA, Italy, The Netherlands, Sweden). Links to the REDCap baseline survey were posted to social media accounts. The initial study launch, where participants completed the baseline survey and were automatically re-directed to the LifeData ecological momentary assessment app, was overwhelmed with fraudulent responses. In response, safeguards (e.g., reCAPTCHA, attention checks) were implemented and baseline data was manually inspected prior to LifeData enrollment. The initial launch resulted in 411 responses in 48 hours, 265 of which (64.5%) successfully registered for the LifeData app and were considered enrolled. Ninety-nine percent of these were determined to be fraudulent. Following implementation of safeguards, the re-launch yielded 147 completed baselines in 3.5 months. Eighteen cases (12.2%) were found fraudulent and not invited to enroll. Most fraudulent cases in the re-launch (15 of 18) were identified by a single attention check question. In total, 96.1% of fraudulent responses were to the USA-based survey. Data integrity safeguards are necessary for research studies that recruit online and should be reported in manuscripts. Three safeguard strategies were effective in preventing and removing most of the fraudulent data in the MOMENT study. Additional strategies were also used and may be necessary in other contexts.

Priorities for data collection through a prospective cohort study on gender-affirming hormone therapy in Aotearoa New Zealand: community and clinical perspectives.

Introduction Gender affirming hormone therapy (GAHT) is an important aspect of health care for many transgender and non-binary (TNB) people, but little is known about the long-term outcomes for TNB people in Aotearoa New Zealand (NZ). Pathways to access GAHT are shifting from secondary care towards primary care, so this is an opportune time to commence local research on long-term health and wellbeing outcomes for people initiating GAHT. Aim This paper aims to report on the key findings from four meetings held to inform the design of a prospective cohort study to follow the journey of people initiating GAHT in primary and secondary care settings in NZ. Methods We worked with a community advisory group of six TNB young people and sought input from 14 health care providers involved in the care of TNB people initiating GAHT (GPs, secondary care doctors, and mental health providers). Semi-structured interview schedules were used to guide discussions. Template analysis was used to initially code data based on themes identified from the interview schedule and new themes from discussions were added. Results Participants shared ideas about recruitment and data collection priorities for baseline and follow-up surveys. These included understanding the journey to starting hormone therapy (information-seeking, decision-making), access to services for GAHT initiation, appropriateness of information provision, receipt of the first prescription, goals for and experience of GAHT, and the unique needs of non-binary people. Discussion Input from a TNB advisory group and health care professionals has informed the development of a survey that will be used to understand the experience of, and outcomes for, people starting GAHT in NZ. Findings from this planned prospective cohort study have the potential to improve access to GAHT for TNB people who wish to pursue this option.

Establishing Priorities for Improving Data Collection and Measurement of Mental Health and Well-Being of Adolescents With Special Educational Needs Within Nonmainstream Schools: Protocol for a Delphi Study.

BACKGROUND: There are more than 1.5 million children and young people in England with special educational needs (SEN), with over 160,000 young people in the United Kingdom attending a special school or alternative provision (AP) setting. Young people with SEN have been found to be at risk for poorer mental health and well-being than non-SEN peers. However, there is a range of both school-related and research challenges associated with identifying difficulties in a timely manner. OBJECTIVE: This Delphi study aims to determine a list of stakeholder priorities for improving school-based measurement of mental health and well-being among young people with SEN, at an aggregated level, within secondary special school or AP settings. A secondary objective is to inform the implementation of school-based well-being surveys, improve engagement in special schools or AP settings, and improve survey response rates among children and young people with SEN. METHODS: A mixed methods Delphi study will be conducted, including a scoping review and preliminary focus groups with school staff members and researchers to establish key issues. This will be followed by a 2-round Delphi survey to determine a list of stakeholder priorities for improving the measurement of mental health and well-being at an aggregate level within special schools and AP settings. A final stakeholder workshop will be held to discuss the findings. A list of recommendations will be drafted as a report for special schools and AP settings. RESULTS: The study has received ethical approval from the University College London Research Ethics Committee. The stage 1 scoping review has commenced. Recruitment for focus groups will begin in Autumn 2024. The first round of the Delphi survey will commence in early 2025, and the second round of the Delphi survey in the spring of 2025. The final workshop will commence in mid-2025 with final results expected in late 2025. CONCLUSIONS: There is a need for clear recommendations for special schools and AP settings on priorities for improving the measurement of mental health and well-being problems among young people with SEN. There is also a need for recommendations to researchers implementing school-based well-being surveys, including the #BeeWell program, to enable them to improve their engagement in special schools and AP settings and ensure surveys are accessible. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/58610.

Data collection methods for patient-reported outcome measures in cancer randomised controlled trials: a protocol for a rapid scoping review.

BACKGROUND: There are different modes and ways to assess patient-reported outcomes (PROs) in clinical trials. However, there is little systematic information on how often different modes of assessment (MOA) are used in cancer clinical trials and how exactly assessments are conducted. The goal of this scoping review is to gain an understanding of the MOA and data management of PROs in cancer randomised controlled trials (RCTs) and the reporting quality thereof. METHODS AND ANALYSIS: This scoping review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Relevant trials will be identified via their indexed publications. We will search PubMed for RCTs conducted in cancer populations that evaluate a biomedical treatment with a PRO endpoint. Trials with publications published between January 2019 and November 2023 will be included. Two independent reviewers will review the references for both the abstract and full-text screening. We will extract data from the publications from a trial and the trial protocol if a protocol can be traced. Data will be summarised at the trial level. We will focus on a descriptive analysis of the MOA of PROs and on the relative frequencies of the different MOA. We will also evaluate the quality of reporting for the relevant SPIRIT and CONSORT guidelines that refer to the assessment of PROs in trials. Due to the scoping nature of our review, we will not perform a dedicated quality assessment of all trials. ETHICS AND DISSEMINATION: The proposed review is based on secondary, published data. Hence, no ethics review is necessary. The review is part of an ongoing project on the use of electronic data capture methods in cancer clinical trials. The findings from the review will support the project and contribute to synthesising guidance to ultimately improve the (electronic) measurement of patient-reported outcome measures in clinical trials.

Researchers' experience in data collection with families of femicide victims.

OBJECTIVES: to describe researchers' experience in collecting data from families of femicide victims. METHODS: this descriptive, qualitative study took the form of an experience report and was conducted in Manaus, Amazonas, Brazil. It involved documentary consultation, training researchers, scheduling and conducting interviews, and using a field diary to record the researchers' perceptions and experiences. RESULTS: the descriptions and photographs of the crime scene were both distressing and impactful for the researchers. The mementos of the victims (including clothing, objects, and childhood photos) shown by their families were deeply moving. Identifying with these experiences facilitated listening to the stories told by the relatives. It was essential to maintain a non-judgmental attitude, acknowledge the loss, provide support for the suffering, and demonstrate a willingness to help. FINAL CONSIDERATIONS: the experience encompassed both theoretical and methodological aspects that were planned and executed in data collection, fostering the development of skills and sensitivity towards the cases. Beyond knowledge and preparation, researchers are expected to exhibit ethical conduct and empathetic capacity.

Demonstrating the data integrity of routinely collected healthcare systems data for clinical trials (DEDICaTe): A proof-of-concept study.

Introduction/aims: Healthcare systems data (also known as real-world or routinely collected health data) could transform the conduct of clinical trials. Demonstrating integrity and provenance of these data is critical for clinical trials, to enable their use where appropriate and avoid duplication using scarce trial resources. Building on previous work, this proof-of-concept study used a data intelligence tool, the "Central Metastore," to provide metadata and lineage information of nationally held data. Methods: The feasibility of NHS England's Central Metastore to capture detailed records of the origins, processes, and methods that produce four datasets was assessed. These were England's Hospital Episode Statistics (Admitted Patient Care, Outpatients, Critical Care) and the Civil Registration of Deaths (England and Wales). The process comprised: information gathering; information ingestion using the tool; and auto-generation of lineage diagrams/content to show data integrity. A guidance document to standardise this process was developed. Results/Discussion: The tool can ingest, store and display data provenance in sufficient detail to support trust and transparency in using these datasets for trials. The slowest step was information gathering from multiple sources, so consistency in record-keeping is essential.

[Cooperative Intelligence Platform for Primary Health Care (Picaps): techno-scientific solutions in digital health to confront COVID-19 and other crises]. / Plataforma de Inteligência Cooperativa com a Atenção Primária à Saúde (Picaps): soluções tecnocientíficas em saúde digital no enfrentamento da COVID-19 e outras crises.

The arrival of COVID-19 in Brazil had a marked impact on the health network as well as on the professionals involved, due to the significant number of cases and constantly updated information. In this context, the Cooperative Intelligence Platform for Primary Health Care - Picaps - is set up as a technological platform that assists by systematizing the communication and negotiation processes among actors, with a focus on science and technology. This article sets out to describe the processes of Picaps, which consist of a collaborative system conceived of as a public solution capable of generating innovations in the areas of Science, Technology and Health, in order to tackle COVID-19 in territories with populations in socially vulnerable situations in the Federal District (DF). It integrates data collection and processing, as well as the dissemination of information for its use, both at the governmental and societal levels. Picaps can be seen as a powerful government instrument to help societies across the country, especially those in vulnerable situations, capable of assisting in the use of cooperative intelligence in primary care and health surveillance actions to build innovative solutions such as rapid responses to face health crises in modern times.

A chegada da COVID-19 ao Brasil gerou grande impacto no sistema de saúde, bem como em seus profissionais, com o elevado número de casos e informações constantemente publicadas. Nesse contexto, criou-se a Plataforma de Inteligência Cooperativa para a Atenção Primária à Saúde (Picaps), uma plataforma tecnológica que ajuda a sistematizar os processos de comunicação e negociação de atores, tendo como foco a ciência e a tecnologia. O presente trabalho tem como objetivo descrever os processos da Picaps, sendo esta constituída por um sistema colaborativo concebido como solução pública capaz de gerar inovações nas áreas de ciência, tecnologia e saúde para o enfrentamento da COVID-19 no Distrito Federal (DF). Nela integram-se processos de coleta e tratamento de dados, além da disseminação de informações visando o seu uso tanto em nível governamental quanto em nível societal. A Picaps pode ser vista como um poderoso instrumento governamental para auxiliar sociedades espalhadas pelo país, sobretudo as que se encontram em situação de vulnerabilidade, com capacidade de nortear o uso da inteligência cooperativa em ações de atenção primária e vigilância em saúde para construção de soluções inovadoras como respostas rápidas para o enfrentamento de crises sanitárias em tempos contemporâneos.

Data Integrity Issues With Web-Based Studies: An Institutional Example of a Widespread Challenge.

This paper reports on the growing issues experienced when conducting web-based-based research. Nongenuine participants, repeat responders, and misrepresentation are common issues in health research posing significant challenges to data integrity. A summary of existing data on the topic and the different impacts on studies is presented. Seven case studies experienced by different teams within our institutions are then reported, primarily focused on mental health research. Finally, strategies to combat these challenges are presented, including protocol development, transparent recruitment practices, and continuous data monitoring. These strategies and challenges impact the entire research cycle and need to be considered prior to, during, and post data collection. With a lack of current clear guidelines on this topic, this report attempts to highlight considerations to be taken to minimize the impact of such challenges on researchers, studies, and wider research. Researchers conducting web-based research must put mitigating strategies in place, and reporting on mitigation efforts should be mandatory in grant applications and publications to uphold the credibility of web-based research.

Experiences and prospects for national survey of terrestrial wildlife resources in China.

Wildlife resources are strategic resources of a country, and the investigation of which is a key task for effective management in protection and utilization. Since the 1990s, two national surveys of terrestrial wildlife resources have been carried out in China, and the situation of wildlife resources has been known to a certain extent. Due to the complexity and difficulty of national wildlife survey, we are still not able to grasp the background and dynamics of wildlife resources as a whole promptly and effectively. The results and effectiveness of wildlife resources investigation will directly affect the decision-making related in wildlife protection. According to Law of the People's Republic of China on the Protection of Wildlife and Regulations of the People's Republic of China for the Implementation of the Protection of Terrestrial Wildlife, it is imperative to carry out the third national survey of terrestrial wildlife resources, and to be integrated with the national strategy of ecological civilization construction. The aims of this review were to summarize the earlier experiences in time, to further improve the investigation scheme and technical methods, to serve the third national survey of terrestrial wildlife resources, in addition to obtain more comprehensive and reliable data of wildlife resources, grasp the development trend of domestic wildlife resources, and provide more effective supports for the wildlife conservation in China.

SATURN: assessing the feasibility of utilising existing registries for real-world evidence data collection to meet patients, regulatory, health technology assessment and payer requirements.

BACKGROUND: SATURN (Systematic Accumulation of Treatment practices and Utilisation, Real world evidence, and Natural history data) for the rare condition osteogenesis imperfecta (OI) has the objective to create a common core dataset by utilising existing, well-established data sources to meet the needs of the various stakeholders (physicians, registry/dataset owners, patients and patient associations, OI community leaders, European [EU] policymakers, regulators, health technology assessments [HTA]s, and healthcare systems including payers). This paper describes the steps taken to assess the feasibility of one existing OI registry (i.e., the Registry of OI [ROI]) as a candidate for SATURN. The same methodology will be applied to other existing OI registries in the future and this same concept could be utilised for other rare disease registries. METHODS: The approach to assessing the feasibility of the ROI registry consisted of three steps: (1) an assessment of the registry characteristics using the Registry Evaluation and Quality Standards Tool (REQueST); (2) a gap analysis comparing SATURN required Core Variables to those being captured in the registry's Case Report Form (CRF); and (3) a compliance check on the data exchange process following the Title 21 of Code of Federal Regulations (CFR) Part 11/EudraLex Annex 11 Compliance Checklist. The first registry that SATURN has assessed is the ROI database at the Istituto Ortopedico Rizzoli (IOR) in Italy. RESULTS: The results from the ROI REQueST have demonstrated satisfactory complete responses in terms of methodology, essential standards, interpretability, and interoperability-readiness for data linkage, data sources, and ethics to meet the needs of data customers. However, the ROI data is from a tertiary referral centre which may limit the ability to understand the full patient journey. The gap analysis has revealed that an exact or logical match between SATURN requested variables and the ROI current variables exists for the following items: patient characteristics, treatment of OI (medical and surgical) and treatment of pain (with the exception of frequency of treatment and reasons for discontinuation), fracture history and bone density. However, data on safety was missing. The compliance check has implied that the ROI implemented appropriate controls for the web-based platform (i.e., Genotype-phenotype Data Integration Platform [GeDI]) that is involved in processing the electronic patient data, and GeDI is a validated/compliant application that follows relevant 21 CFR Part 11/EudraLex Annex 11 regulations. CONCLUSIONS: This robust feasibility process highlights potential limitations and opportunities to develop and to refine the collaboration with the ROI as the SATURN programme moves forward. It also ensures that the existing datasets in the rare condition OI are being maximised to respond to the needs of patients, data customers and decision-makers. This feasibility process has allowed SATURN to build a compliant methodology that aligns with the requirements from the European Medicines Agency (EMA) and HTAs. More data variables will continue to be developed and refined along the way with more registries participating in SATURN. As a result, SATURN will become a meaningful and truly collaborative core dataset, which will also contribute to advancing understanding of OI diagnosis, treatment, and care.

Development of an IoT-Based Device for Data Collection on Sheep and Goat Herding in Silvopastoral Systems.

To evaluate the ecosystem services of silvopastoral systems through grazing activities, an advanced Internet of Things (IoT) framework is introduced for capturing extensive data on the spatial dynamics of sheep and goat grazing. The methodology employed an innovative IoT system, integrating a Global Navigation Satellite System (GNSS) tracker and environmental sensors mounted on the animals to accurately monitor the extent, intensity, and frequency of grazing. The experimental results demonstrated the high performance and robustness of the IoT system, with minimal data loss and significant battery efficiency, validating its suitability for long-term field evaluations. Long Range (LoRa) technology ensured consistent communication over long distances, covering the entire grazing zone and a range of 6 km in open areas. The superior battery performance, enhanced by a solar panel, allowed uninterrupted operation for up to 37 days with 5-min interval acquisitions. The GNSS module provided high-resolution data on movement patterns, with an accuracy of up to 10 m after firmware adjustments. The two-part division of the device ensured it did not rotate on the animals' necks. The system demonstrated adaptability and resilience in various terrains and animal conditions, confirming the viability of IoT-based systems for pasture monitoring and highlighting their potential to improve silvopastoral management, promoting sustainable practices and conservation strategies. This work uniquely focuses on documenting the shepherd's role in the ecosystem, providing a low-cost solution that distinguishes itself from commercial alternatives aimed primarily at real-time flock tracking.

Remote data collection of infant activity and sleep patterns via wearable sensors in the HEALthy Brain and Child Development Study (HBCD).

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Wearable and remote sensing technologies have advanced data collection outside of laboratory settings to enable exploring, in more detail, the associations of early experiences with brain development and social and health outcomes. In the HBCD Study, the Novel Technology/Wearable Sensors Working Group (WG-NTW) identified two primary data types to be collected: infant activity (by measuring leg movements) and sleep (by measuring heart rate and leg movements). These wearable technologies allow for remote collection in the natural environment. This paper illustrates the collection of such data via wearable technologies and describes the decision-making framework, which led to the currently deployed study design, data collection protocol, and derivatives, which will be made publicly available. Moreover, considerations regarding actual and potential challenges to adoption and use, data management, privacy, and participant burden were examined. Lastly, the present limitations in the field of wearable sensor data collection and analysis will be discussed in terms of extant validation studies, the difficulties in comparing performance across different devices, and the impact of evolving hardware/software/firmware.

Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection.

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists' mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists' sampling effort is focused more in Barcelona's lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city's upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists' sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Advancing high quality longitudinal data collection: Implications for the HEALthy Brain and Child Development (HBCD) Study design and recruitment.

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The HBCD Study aims to reflect the sociodemographic diversity of pregnant individuals in the U.S. The study will also oversample individuals who use substances during pregnancy and enroll similar individuals who do not use to allow for generalizable inferences of the impact of prenatal substance use on trajectories of child development. Without probability sampling or a randomization-based design, the study requires innovation during enrollment, close monitoring of group differences, and rigorous evaluation of external and internal validity across the enrollment period. In this article, we discuss the HBCD Study recruitment and enrollment data collection processes and potential analytic strategies to account for sources of heterogeneity and potential bias. First, we introduce the adaptive design and enrollment monitoring indices to assess and enhance external and internal validity. Second, we describe the visit schedule for in-person and remote data collection where dyads are randomly assigned to visit windows based on a jittered design to optimize longitudinal trajectory estimation. Lastly, we provide an overview of analytic procedures planned for estimating trajectories.

Landscape analysis of environmental data sources for linkage with SEER cancer patients database.

One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients' potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients' records collected by the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).

BACKGROUND: In recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked. METHODS: We systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it. RESULTS: A standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm's suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance. CONCLUSIONS: The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.

Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

PURPOSE: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct. METHODS: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe. RESULTS: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype. CONCLUSIONS: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

Exploring Qualitative Research.

Qualitative research is a relatively new approach for conducting studies in health disciplines. The value of this research approach is to explore peoples' experiences and gain a deeper understanding of the meaning of their experiences. Qualitative inquiries answer research questions about what, why and how by implementing various research designs such as qualitative descriptive, qualitative case study, ethnological, phenomenology, or grounded theory designs. Purposive and snowball sampling methods are commonly used to recruit participants followed by personal interviews or focus group discussions to collect data. Data analysis requires several coding procedures performed by the researcher or an alternative is using a coding software program. Preparing a manuscript for dissemination of the results can be challenging, although achievable.