Dark Data in Real-World Evidence: Challenges, Implications, and the Imperative of Data Literacy in Medical Research

Randomized controlled trials (RCTs) and real-world evidence (RWE) studies are crucial and complementary in generating clinical evidence. RCTs provide controlled settings to validate the clinical effect of specific drugs or medical devices, while RWE integrates extrinsic factors, encompassing external influences affecting real-world scenarios, thus challenging RCT results in practical applications. In this study, we explore the impact of extrinsic factors on RWE outcomes, focusing on "dark data," which refers to data collected but not used or excluded from the analyses. Dark data can arise in many ways during research process, from selecting study samples to data collection and analysis. However, even unused or unanalyzed dark data hold potential insights, providing a comprehensive view of clinical contexts. Extrinsic factors lead to divergent RWE outcomes that could differ from RCTs beyond statistical correction's scope. Two main types of dark data exist: "known-unknown" and "unknown-unknown." The distinction between these dark data types highlights RWE's complexity. The transformation of unknown into known depends on data literacy-powerful utilization capabilities that can be interpreted based on medical expertise. Shifting the focus to excluded subjects or unused data in real-world contexts reveals unexplored potential. Understanding the significance of dark data is vital in reflecting the complexity of clinical settings. Connecting RCTs and RWEs requires medical data literacy, enabling clinicians to decipher meaningful insights. In the big data and artificial intelligence era, medical staff must navigate data complexities while promoting the core role of medicine. Prepared clinicians will lead this transformative journey, ensuring data value shapes the medical landscape.

[Establishment of a German ICCR dataset : Translation and integration of SNOMED CT using the example of TUR-B]

BACKGROUND

The structured recording of data from histopathological findings and their interoperability is critical for quality assurance in pathology.

MATERIALS AND METHODS

To harmonize the content of the reports, the International Collaboration on Cancer Reporting (ICCR) has defined standardized datasets. These datasets are not yet available in German nationwide. This gap is addressed here using the transurethral bladder resection (TUR-B) dataset as a use case.

RESULTS

We describe the process of establishing the datasets by carrying out translation, mapping on SNOMED CT codes, and using SNOMED CTs hierarchy to fill dropdown menus. Furthermore, we identified rules for checking for self-consistency of reports by using the example of the TUR bladder.

DISCUSSION

With this article, we have created an example of a German version of the ICCR TUR‑B dataset including mapping to the SNOMED CT terminology. Further activities should include the definition of overarching cancer disease models to further exploit the potential of SNOMED CT.

NeuroLab 2.0: An Alternative Storyline Design Approach for Translating a Research-Based Summer Experience into an Advanced STEM+M Curriculum Unit that Supports Three-Dimensional Teaching and Learning in the Classroom

In this case study, we describe an alternative storyline design approach that we adopted to translate an informal, out-of-school summer science experience with a strong emphasis on developmental neuroscience and data literacy into a more inclusive, replicable, and scalable experience for formal high school science instruction. Combining elements of problem- and project-based learning, a storyline is a curriculum model that engages students in the application of investigative science and engineering practices to incrementally build conceptual models that explain an observable (anchoring) phenomenon. Published reports on the storyline design process describe procedures and tools that are well suited to the creation of novel instructional units. However, these design methods are difficult to apply to projects aimed at translating pre-existing science experiences and resources into classroom storyline units. In this descriptive case study, we discuss a series of alternative design procedures that we utilized to achieve this adaptation. Our overarching project goal was to create the resources necessary to engage high school students in the construction of a multidimensional explanatory model for an unusual movement disorder that assimilates converging lines of behavioral, neuroanatomical, neurophysiological, molecular genetic, developmental, and cellular data. The methods described in this case study establish a design template for other biomedical scientists who are interested in adopting a storyline approach to bring aspects of their work or educational projects into science classrooms and into closer alignment with a new vision for science teaching and learning articulated in the National Research Council's A Framework for K-12 Science Education and the Next Generation Science Standards.

Investigating the potential of the semantic web for education: Exploring Wikidata as a learning platform

Wikidata is a free, multilingual, open knowledge-base that stores structured, linked data. It has grown rapidly and as of December 2022 contains over 100 million items and millions of statements, making it the largest semantic knowledge-base in existence. Changing the interaction between people and knowledge, Wikidata offers various learning opportunities, leading to new applications in sciences, technology and cultures. These learning opportunities stem in part from the ability to query this data and ask questions that were difficult to answer in the past. They also stem from the ability to visualize query results, for example on a timeline or a map, which, in turn, helps users make sense of the data and draw additional insights from it. Research on the semantic web as learning platform and on Wikidata in the context of education is almost non-existent, and we are just beginning to understand how to utilize it for educational purposes. This research investigates the Semantic Web as a learning platform, focusing on Wikidata as a prime example. To that end, a methodology of multiple case studies was adopted, demonstrating Wikidata uses by early adopters. Seven semi-structured, in-depth interviews were conducted, out of which 10 distinct projects were extracted. A thematic analysis approach was deployed, revealing eight main uses, as well as benefits and challenges to engaging with the platform. The results shed light on Wikidata's potential as a lifelong learning process, enabling opportunities for improved Data Literacy and a worldwide social impact.