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The integration of methodologies from cognitive psychology and neuroscience into the study of IT systems has seen a notable uptick in recent years. This surge is spurred by the escalating complexity of IT systems, which increasingly tax both novice and seasoned users alike Ma et al. (2023); Leger et al. (2014).Consequently, insights into constructing IT systems with a reduced cognitive burden are gaining paramount importance.A survey of the current literature reveals several discernible trends. Researchers are delving into the cognitive processes involved in programming Peitek et al. (2021) 2022), which establish a direct connection between the brain and input/output devices.With the proliferation of digital devices such as smartwatches and wristbands, as well as advanced facial recognition services, neuroscience is increasingly finding its way into everyday working and private life.Many use cases have been identified, including using emotion-sensing devices to significantly reduce stress and supporting flow in people's work processes Whelan et al. (2019). Research has also begun to investigate the design of so-called neuro-adaptive processes, which capture body data during process execution and adapt the process flow and human-computer interaction accordingly in real time vom Brocke (2022).Against this backdrop, this topic aims to foster a neuroscientific perspective on the subject matter. A total of 76 authors were actively solicited to contribute, with 29 ultimately participating. Out of the 22 targeted submissions, 17 papers were received. Following a rigorous review process, 12 papers were regrettably declined, while 5 were accepted, yielding an acceptance rate of 29%. Moreover, the topic has amassed 12,200 visits to date, underscoring the continued relevance of its content.Spanning a duration of 11 months in 2022, this topic enjoyed the collaborative support of both computer scientists and neuroscientists on the editorial board. In the subsequent chapters, we provide a comprehensive overview of the 5 accepted papers, culminating in a summary and future outlook in this editorial.In this topic, five papers were accepted. These papers bear the following titles:• On the accuracy of code complexity metrics: A neuroscience-based guideline for improvement Hao et al. (2023)

BackgroundThe volume and complexity of patient data – especially in personalised medicine – is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare.MethodsSemantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don’t provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository.ResultsA web-based tool called ODMedit (https://odmeditor.uni-muenster.de/) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal.ConclusionUniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository.

As more and more data-centric services are emerging on the Web, the diversity of APIs in this field increases rapidly. In addition to the traditional relational model, the scope of Database-as-a-Service (DaaS) products is broadened by new approaches, such as the more lenient key-value stores. Many DaaS APIs address the same concepts, making them incompatible with each other, yet not fundamentally different. In this paper, we address this issue by drafting a concept for a "universal" API for DaaS systems based on the REST architectural style. The new API is universal in the sense that it is aimed to be suitable for DaaS systems ranging from those that allow storage of schema-less data to systems with the capabilities of a traditional relational database system. After the presentation of the concept, we critically review it, revealing general problems of such a universal API, and point out a more promising path for future work.

Standardisation in e-learning is characterised by the development of numerous specifications. Main efforts address the annotation of metadata, content aggregation, educational modelling languages, or the definition of interfaces for platforms and runtime systems to execute learning objects. Each of these fields comprises several competing activities that are generally not compatible to each other. In this paper, we discuss typical problems of common e-learning standards and contrast this with a service-oriented approach to deliver major functionalities of e-learning platforms and content. The approach is based on web services, which can be plugged together to build platforms that solve many of the compatibility problems inherent in present-day standards. It turns out that the consistent implementation of e-learning functionalities based on web services can make several e-learning standards obsolete.