Abstract Background FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data. Objectives This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by “GO FAIR” which addresses the identified gaps that such process has when dealing with health datasets. Methods A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing. Results A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow. Discussion Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets. Conclusion Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture.

TriControl is a controller working position (CWP) prototype developed by German Aerospace Center (DLR) to enable more natural, efficient, and faster command inputs. The prototype integrates three input modalities: speech recognition, eye tracking, and multi-touch sensing. Air traffic controllers may use all three modalities simultaneously to build commands that will be forwarded to the pilot and to the air traffic management (ATM) system. This paper evaluates possible speed improvements of TriControl compared to conventional systems involving voice transmission and manual data entry. 26 air traffic controllers participated in one of two air traffic control simulation sub-studies, one with each input system. Results show potential of a 15% speed gain for multimodal controller command input in contrast to conventional inputs. Thus, the use and combination of modern human machine interface (HMI) technologies at the CWP can increase controller productivity.

No abstract

No abstract