The IeDEA Data Exchange Standard: a common data model for global HIV cohort collaboration

Objective To describe content domains and applications of the IeDEA Data Exchange Standard, its development history, governance structure, and relationships to other established data models, as well as to share open source, reusable, scalable, and adaptable implementation tools with the informatics community. Methods In 2012, the International Epidemiology Databases to Evaluate AIDS (IeDEA) collaboration began development of a data exchange standard, the IeDEA DES, to support collaborative global HIV epidemiology research. With the HIV Cohorts Data Exchange Protocol as a template, a global group of data managers, statisticians, clinicians, informaticians, and epidemiologists reviewed existing data schemas and clinic data procedures to develop the HIV data exchange model. The model received a substantial update in 2017, with annual updates thereafter. Findings The resulting IeDEA DES is a patient-centric common data model designed for HIV research that has been informed by established data models from US-based electronic health records, broad experience in data collection in resource-limited settings, and informatics best practices. The IeDEA DES is inherently flexible and continues to grow based on the ongoing stewardship of the IeDEA Data Harmonization Working Group with input from external collaborators. Use of the IeDEA DES has improved multiregional collaboration within and beyond IeDEA, expediting over 95 multiregional research projects using data from more than 400 HIV care and treatment sites across seven global regions. A detailed data model specification and REDCap data entry templates that implement the IeDEA DES are publicly available on GitHub. Conclusions The IeDEA common data model and related resources are powerful tools to foster collaboration and accelerate science across research networks. While currently directed towards observational HIV research and data from resource-limited settings, this model is flexible and extendable to other areas of health research.

ORD-GAP: a hybrid-based labeling schemes to support XML dynamic updates

<span>eXtensible Markup Language (XML) has been widely used as the standard for data exchange standard over the Internet. With the fast growing rate of data, especially with high updates, it is crucial to ensure that the XML is able to cope with frequent changes with very least effect on the existing structure. Therefore, in this paper, we investigate on the existing labeling schemes and mapping approaches to gauge a better understanding in terms of the robustness of the labeling schemes and the importance of the mapping schemes. Next, we propose ORD-GAP labeling schemes to identify the structural relationship among XML nodes and yet, it is persistent to re-labeling when new nodes are inserted. Subsequently, a mapping scheme is proposed to transform XML into Relational Database (RDB). Preliminary experimental evaluation demonstrated that the proposed approach achieve 66% better as compared to ORDPATH, and 56% better as compared to ME labeling in terms of data loading time. </span>

A Proposed Metadata Standard for Recording and Sharing Attribution Information in Biodiversity

Research collections are an important tool for understanding the Earth, its systems, and human interaction. Despite the importance of collections, many are not maintained or curated as thoroughly as we would like. Part of the reason for this is the lack of professional reward for collection, curation, or maintenance. To address this gap in attribution metadata, the Research Data Alliance (RDA) and the Biodiversity Information Standards (TDWG) organization co-endorsed a Working Group to create recommendations for the representation of attribution metadata. After 18 months, this Working Group recommended a very simple data exchange standard to link people, the curatorial actions they perform, and the digital or physical objects they are curating. These recommendations are discussed in the context of community-developed use cases. Future work includes: exploration of a Darwin Core extension, best practices on how to best adopt these recommendations, and possible solutions to help accelerate the process of connecting people and their activities in legacy and future data. exploration of a Darwin Core extension, best practices on how to best adopt these recommendations, and possible solutions to help accelerate the process of connecting people and their activities in legacy and future data. To explore options for making collections work more visible and citable, we tested the use of digital annotation tools and person identifiers in curation workflows. The results from this pilot project in collaboration with ORCID and Data Futures to display specimens on an ORCID profile will be presented.

IFC File Analyzer Software

The IFC File Analyzer software generates a spreadsheet or a set of CSV (comma-separated value) files from an IFC file. IFC (Industry Foundation Classes) is the data exchange standard used to facilitate interoperability in the building and construction industry. IFC is developed by buildingSMART and is an ISO standard—ISO 16739. Typical IFC viewers show avisualization of the building represented by the IFC file. The user can drill down to the individual attributes for a single building object. However, there is no way to view all of the entities and their attributes at once. The IFC File Analyzer provides this capability by creating a spreadsheet or CSV files from the IFC file. In the spreadsheet, a worksheet is created for each type of IFC entity in the file. Every row in the worksheet contains the attributes for an instance of an IFC entity. Multiple IFC files can be analyzed at once to compare entity usage. There are options to select which types of IFC entities are processed and to report some of the IFC entity Inverse relationships.