Generic Longitudinal Business Process Model

Barkow, Ingo; Block, William; Greenfield, Jay; Gregory, Arofan; Hebing, Marcel; Hoyle, Larry; Zenk-Moltgen, Wolfgang

doi:10.3886/ddilongitudinal05

Cited by 3 publications

(2 citation statements)

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“…Every step of computer based assessments produces data and metadata. For that reason, we would like to use the Generic Longitudinal Business Process Model (GLBPM) (Barkow et. al, 2013) as means for the analysis.…”

Section: Enhancement Of the Processesmentioning

confidence: 99%

Enhancing the Research Data Management of Computer-Based Educational Assessments in Switzerland

Wasner

Barkow

Odoni

2018

Data Science Journal

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Since 2006 the education authorities in Switzerland have been obliged by the Constitution to harmonize important benchmarks in the educational system throughout Switzerland. With the development of national educational objectives in four disciplines an important basis for the implementation of this constitutional mandate was created. In 2013 the Swiss National Core Skills Assessment Program (in German: ÜGK-Überprüfung der Grundkompetenzen) was initiated to investigate the skills of students, starting with three of four domains: mathematics, language of teaching and first foreign language in grades 2, 6 and 9. ÜGK uses a computer-based test and a sample size of 25.000 students per year. A huge challenge for computer-based educational assessment is the research data management process. Data from several different systems and tools existing in different formats has to be merged to obtain data products researchers can utilize. The long term preservation has to be adapted as well. In this paper, we describe our current processes and data sources as well as our ideas for enhancing the data management.

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Section: Enhancement Of the Processesmentioning

confidence: 99%

Enhancing the Research Data Management of Computer-Based Educational Assessments in Switzerland

Wasner

Barkow

Odoni

2018

Data Science Journal

Self Cite

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“…This work also influenced other domains like the social sciences. Inspired by the work of the GSBPM a modified version called the Generic Longitudinal Business Process Model (GLBPM) was developed by a group of scientist from different agencies at the DDI Alliance Dagstuhl Workshop in September 2011 (Barkow, Block, Greenfield, Gregory, Hebing, Hoyle and Zenk-Möltgen, 2012). Though the name stands first the author of this dissertation was part of this group, but not the main driver.…”

Section: The Generic Longitudinal Business Process Model (Glbpm)mentioning

confidence: 99%

The challenges of metadata management in computer-based surveys and assessments

Barkow¹

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Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study (Preprint)

Amadi,

Kiwuwa-Muyingo,

Bhattacharjee

et al. 2024

Preprint

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BACKGROUND Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. OBJECTIVE To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. METHODS The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. RESULTS The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. CONCLUSIONS The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide.

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Generic Longitudinal Business Process Model

Cited by 3 publications

References 1 publication

Enhancing the Research Data Management of Computer-Based Educational Assessments in Switzerland

Enhancing the Research Data Management of Computer-Based Educational Assessments in Switzerland

The challenges of metadata management in computer-based surveys and assessments

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study (Preprint)

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