Development of a Command Line Interface for the Analysis of Result Sets from Automated Queries to Literature Databases.

The first step of a systematic review is the identification of publications related to a research question in different literature databases. The quality of the final review is mainly influenced by finding the best search query resulting in high precision and recall. Usually, this process is iterative and requires refining the initial query and comparing the different result sets. Furthermore, result sets of different literature databases must be compared as well. Objective of this work is to develop a command line interface, which supports the automated comparison of result sets of publications from literature databases. The tool should incorporate existing application programming interfaces of literature database and should be integrable into more complex analysis scripts. We present a command line interface written in Python and available as open-source application at https://imigitlab.uni-muenster.de/published/literature-cli under MIT license. The tool calculates the intersection and differences of the result sets of multiple queries on a single literature database or of the same query on different databases. These results and their configurable metadata can be exported as CSV-files or in Research Information System format for post-processing or as starting point for a systematic review. Due to the support of inline parameters, the tool can be integrated into existing analysis scripts. Currently, the literature databases PubMed and DBLP are supported, but the tool can easily be extended to support any literature database providing a web-based application programming interface.

MainzelHandler: A Library for a Simple Integration and Usage of the Mainzelliste.

Pseudonymization plays a vital role in medical research. In Germany, the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) has developed guidelines on how to create pseudonyms and how to handle personally identifiable information (PII) during this process. An open-source implementation of a pseudonymization service following these guidelines and therefore recommended by the TMF is the so-called "Mainzelliste". This web application supports a REST-API for (de-) pseudonymization. For security reasons, a complex session and tokening mechanism for each (de-) pseudonymization is required and a careful interaction between front- and backend to ensure a correct handling of PII. The objective of this work is the development of a library to simplify the integration and usage of the Mainzelliste's API in a TMF conform way. The frontend library uses JavaScript while the backend component is based on Java with an optional Spring Boot extension. The library is available under MIT open-source license from https://github.com/DanielPreciado-Marquez/MainzelHandler.

ODM Clinical Data Generator: Syntactically Correct Clinical Data Based on Metadata Definition.

The Operational Data Model (ODM) is a data standard for interchanging clinical trial data. ODM contains the metadata definition of a study, i.e., case report forms, as well as the clinical data, i.e., the answers of the participants. The portal of medical data models is an infrastructure for creation, exchange, and analysis of medical metadata models. There, over 23000 metadata definitions can be downloaded in ODM format. Due to data protection law and privacy issues, clinical data is not contained in these files. Access to exemplary clinical test data in the desired metadata definition is necessary in order to evaluate systems claiming to support ODM or to evaluate if a planned statistical analysis can be performed with the defined data types. In this work, we present a web application, which generates syntactically correct clinical data in ODM format based on an uploaded ODM metadata definition. Data types and range constraints are taken into account. Data for up to one million participants can be generated in a reasonable amount of time. Thus, in combination with the portal of medical data models, a large number of ODM files including metadata definition and clinical data can be provided for testing of any ODM supporting system. The current version of the application can be tested at https://cdgen.uni-muenster.de and source code is available, under MIT license, at https://imigitlab.uni-muenster.de/published/odm-clinical-data-generator.