Linked Data - wo bleiben die Anwendungen?

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Linked Data ist als Stichwort inzwischen seit einigen Jahren auch im Bibliotheksumfeld virulent. Trotzdem gibt es noch keine große bekannte Linked Data Anwendung. Woran liegt es und wann kommt endlich die Killer-App

Evaluation of the socially evaluated cold-pressor group test (SECPT-G) in the general population

Background In stress research, economic instruments for introducing acute stress responses are needed. In this study, we investigated whether the socially evaluated cold-pressor group test (SECPT-G) induces salivary alpha-amylase (sAA) and/or cortisol responses in the general population and whether this is associated with anthropometric, experimental, and lifestyle factors. Methods A sample of 91 participants from the general population was recruited. Salivary cortisol and sAA levels were assessed prior to (t0), immediately after (t1), and 10 min after the SECPT-G (t2). Results A robust cortisol increase was found immediately after the SECPT-G, which further increased between t1 and t2. This was independent of most of the control variables. However, men showed a trend toward higher cortisol increases than women (p = 0.005). No sAA responses were found at all. However, sAA levels were dependent on measurement time point with highest levels between 9 pm and 9:30 pm. Participants who immersed their hands into the ice water for the maximally allowed time of 3 min showed higher sAA levels at all time points than participants who removed their hands from the water earlier. Conclusions We conclude that the SECPT-G is a good means of an acute stress test when cortisol—but not necessarily sAA—responses are intended

Leveraging Reinforcement Learning for Task Resource Allocation in Scientific Workflows

Scientific workflows are designed as directed acyclic graphs (DAGs) and consist of multiple dependent task definitions. They are executed over a large amount of data, often resulting in thousands of tasks with heterogeneous compute requirements and long runtimes, even on cluster infrastructures. In order to optimize the workflow performance, enough resources, e.g., CPU and memory, need to be provisioned for the respective tasks. Typically, workflow systems rely on user resource estimates which are known to be highly error-prone and can result in over- or underprovisioning. While resource overprovisioning leads to high resource wastage, underprovisioning can result in long runtimes or even failed tasks. In this paper, we propose two different reinforcement learning approaches based on gradient bandits and Q-learning, respectively, in order to minimize resource wastage by selecting suitable CPU and memory allocations. We provide a prototypical implementation in the well-known scientific workflow management system Nextflow, evaluate our approaches with five workflows, and compare them against the default resource configurations and a state-of-the-art feedback loop baseline. The evaluation yields that our reinforcement learning approaches significantly reduce resource wastage compared to the default configuration. Further, our approaches also reduce the allocated CPU hours compared to the state-of-the-art feedback loop by 6.79% and 24.53%.Comment: Paper accepted in 2022 IEEE International Conference on Big Data Workshop BPOD 202

Development and Validation of the Open Matrices Item Bank

Figural matrices tasks are one of the most prominent item formats used in intelligence tests, and their relevance for the assessment of cognitive abilities is unquestionable. However, despite endeavors of the open science movement to make scientific research accessible on all levels, there is a lack of royalty-free figural matrices tests. The Open Matrices Item Bank (OMIB) closes this gap by providing free and unlimited access (GPLv3 license) to a large set of empirically validated figural matrices items. We developed a set of 220 figural matrices based on well-established construction principles commonly used in matrices tests and administered them to a sample of N = 2572 applicants to medical schools. The results of item response models and reliability analyses demonstrate the excellent psychometric properties of the items. In the discussion, we elucidate how researchers can already use the OMIB to gain access to high-quality matrices tests for their studies. Furthermore, we provide perspectives for features that could additionally improve the utility of the OMIB

How did the beginnings of the global COVID-19 pandemic affect mental well-being?

The present study aims to investigate longitudinal changes in mental well-being as well as the role of individual differences in personality traits (Big Five) and the level of Personality Organisation during the first lockdown of the COVID-19 pandemic in Germany. Overall, 272 adults (Mage= 36.94, SDage= 16.46; 68.62% female, 23.45% male, 0.69% non-binary) took part in our study with four weekly surveys during the lockdown as well as a follow-up one month after restrictions were lifted. To analyse the development of mental well-being during and shortly after the first lockdown in Germany latent growth curve models (LGCM) were calculated. The considered facets of well-being differ by their trajectory. Additionally, results suggest that the lockdown did not affect all facets to the same extent. While Life Satisfaction decreases in the short term as a reaction to the lockdown, Stress and Psychological Strain were reduced after the second week of contact restrictions. When adding personality characteristics, our results showed that Neuroticism and Conscientiousness were the two dimensions associated most strongly with SWB during the first month of the pandemic. Thus, our research suggests that personality traits should be considered when analysing mental well-being

Is the Correlation between Storage Capacity and Matrix Reasoning Driven by the Storage of Partial Solutions? A Pilot Study of an Experimental Approach

Working memory capacity (WMC) and reasoning abilities—as assessed by figural matrices tests—are substantially correlated. It is controversially discussed whether this correlation is only caused by controlled attention or also by storage capacity. This study aims at investigating storage of partial solutions as a possible mechanism by which storage capacity may contribute to solving figural matrices tests. For this purpose, we analyzed how an experimental manipulation of storage demands changes the pattern of correlations between WMC and performance in a matrix task. We manipulated the storage demands by applying two test formats: one providing the externalization of partial solutions and one without the possibility of externalization. Storage capacity was assessed by different types of change detection tasks. We found substantial correlations between storage capacity and matrices test performance, but they were of comparable size for both test formats. We take this as evidence that the necessity to store partial solutions is not the limiting factor which causes the association between storage capacity and matrices test. It is discussed how this approach can be used to investigate alternative mechanisms by that storage may influence performance in matrices tests

Sometimes More Is Better, and Sometimes Less Is Better: Task Complexity Moderates the Response Time Accuracy Correlation

This study addresses the relationship between item response time and item accuracy (i.e., the response time accuracy correlation, RTAC) in figural matrices tests. The dual processing account of response time effects predicts negative RTACs in tasks that allow for relatively automatic processing and positive RTACs in tasks that require controlled processing. Contrary to these predictions, several studies found negative RTACs for reasoning tests. Nevertheless, it was demonstrated that the RTAC is moderated by task complexity (i.e., the interaction between person ability and item difficulty) and that under conditions of high complexity (i.e., low ability and high difficulty) the RTAC was even slightly positive. The goal of this study was to demonstrate that with respect to task complexity the direction of the RTAC (positive vs. negative) can change substantially even within a single task paradigm (i.e., figural matrices). These predictions were tested using a figural matrices test that employs a constructed response format and has a broad range of item difficulties in a sample with a broad range of ability. Confirming predictions, strongly negative RTACs were observed when task complexity was low (i.e., fast responses tended to be correct). With increasing task complexity, the RTAC flipped to be strongly positive (i.e., slow responses tended to be correct). This flip occurred earlier for people with lower ability, and later for people with higher ability. Cognitive load of the items is suggested as an explanation for this phenomenon