Max Planck Innovation

Abstract The temporal dimension of citation accumulation poses fundamental challenges for quantitative research evaluations, particularly in assessing disruptive and consolidating research through the disruption index ( D ). While prior studies emphasize minimum citation windows (e.g., 5 years) for reliable citation impact measurements, the time‐sensitive nature of D —which quantifies a paper's capacity to eclipse prior knowledge—remains underexplored. This study addresses two critical gaps: (1) determining the temporal thresholds required for publications to meet citation/reference prerequisites, and (2) identifying meaningful citation windows that balance early predictability and longitudinal stability. By analyzing millions of publications across four fields with varying citation dynamics, we employ several metrics to track D stabilization patterns. Key findings reveal that a 10‐year window achieves >80% agreement with findings without using any fixed citation window (here, D values are calculated based on all bibliographic records in the dataset), while shorter windows exhibit instability. Publications with ≥30 references stabilize 1–3 years faster, and extreme cases (top/bottom 5% D values) become identifiable within 5 years—enabling early detection of 60–80% of highly disruptive and consolidating works. The findings offer significant implications for scholarly evaluation and science policy, emphasizing the need for careful consideration of citation window length in research assessment (based on D ).

SummaryUncovering the mental representations underlying symbolic domains is a long-standing goal of cognitive science. Music, a relatively unexplored aspect of cognition, presents a unique testbed for understanding the format of symbolic representations. Theoretical accounts have emphasized the geometric structure underlying musical scales, yet empirical work directly engaging with these structures remains limited. Based on data collected from 961 participants across four experiments, we demonstrate that certain geometric structures strongly modulate perceptual sensitivity to out-of-scale notes in melodies. Furthermore, we show that geometric regularity predicts sensitivity to note deviations across diverse musical structures. Crucially, these effects persist even in an entirely unfamiliar tuning system, suggesting the role of specific geometric properties independent of learned cultural associations. Our findings situate scale geometry as a critical, time-invariant representation in melodic processing and reveal organizational principles that may underlie mental representations across domains.

Abstract The study objective was to evaluate the efficacy of ChatGPT in identifying “similar” institutions for benchmarking the research performance of a university. Benchmarking is deemed a promising approach to compare “similar with similar” as a better alternative to rankings (comparing “different” universities). Current approaches either focus on a limited number of “quantitative” dimensions or are too complex for most users. We conducted large‐scale testing by tasking ChatGPT with identifying the most similar European universities in terms of research performance, utilizing the European Tertiary Education Register data. We tested whether the peers suggested by ChatGPT were similar to the focal university on size, research intensity, and subject composition. Additionally, we evaluated whether providing more specific instructions improved the results. The findings offer a nuanced perspective on the potential and risks of using ChatGPT to identify peer institutions for benchmarking. On one hand, solely using ChatGPT would replicate the visibility biases associated with university rankings, thereby undermining the rationale for benchmarking. On the other hand, relying on semantic associations might capture dimensions of university similarity that are relevant and difficult to capture through quantitative methods. We finally reflected on the broader implications for scholars in higher education and science studies research.

The Disruption index has attracted significant media attention after a study revealed a trend of decreasing disruptiveness in papers and patents. Critiques of the index question these results and highlight issues of inconsistency, time-sensitive biases, and data-induced biases.

We investigated the historical origins of developmental care for newborn infants using Reference Publication Year Spectroscopy (RPYS), an innovative method of bibliometric analysis. A Web of Science search query that combined infant and intervention-related synonyms was performed on 2 February 2022. RPYS analysis was performed on this dataset to identify the most referenced historical publications for developmental care in newborn infants. Median deviation analysis identified the peak publication years, including the most cited historical references. Landmark publications were defined as those belonging to the top 10% of the most frequently referenced publications for at least 20 years. The RPYS peaks showed an early phase (1936-1986), during which infant development was studied and analysed, leading to a conceptualisation of developmental care for newborn infants. The following years (1987-2020), showed an explosion of interest in developmental care and highlighted two main programmes. The Neonatal Individualized Developmental Care Assessment Program (NIDCAP) and the Infant Health and Development Program (IHDP) inspired numerous publications during those years, which strove to demonstrate evidence of their clinical benefits. Developmental care has become increasingly important, thanks to the implementation of NIDCAP and IHDP.

Abstract Silicon nitride-based compositions containing zirconia and ceria additives were prepared by hot pressing in argon atmosphere. The compositions studied can be divided into two groups: (a) with a constant total amount of ZrO2 + CeO2 additive (32 mol.%) within which the CeO2 content was varied from 0 to 6.2 mol.%, and (b) with various contents (10 to 42 mol.%) of ZrO2 + CeO2 additive, the ratio of ZrO2 to CeO2 staying constant at a level of 7.35:1. Phase compositions were determined by X-ray powder diffraction (XRD), while energy-dispersive X-ray microanalysis (EDX) was used for compositional analysis of the ZrO2 grains. The results showed that after sintering the Ce4+ ions were reduced to Ce3+. At low initial ceria concentrations, Si2N2O was formed beside β-Si3N4 and ZrO2 (the majority of which was monoclinic). At higher ceria concentrations, however, Si2N2O disappeared by reaction with ceria and silica to form Ce – N apatite. Nitrogen-containing zirconia phases were not found in the studied compositions, up to 1750 °C.

Abstract Open Science is an umbrella term that encompasses many recommendations for possible changes in research practices, management, and publishing with the objective to increase transparency and accessibility. This has become an important science policy issue that all disciplines should consider. Many Open Science recommendations may be valuable for the further development of research and publishing, but not all are relevant to all fields. This opinion paper considers the aspects of Open Science that are most relevant for scientometricians, discussing how they can be usefully applied.

The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation. Our work is the first that targets this task in a real-world setting requiring dense interpretation in both spatial and temporal domains. As the ground-truth for this task is difficult and expensive to obtain, existing datasets are either constructed synthetically or only sparsely annotated within short video clips. To overcome this, we introduce a new benchmark encompassing two datasets, KITTI-STEP, and MOTChallenge-STEP. The datasets contain long video sequences, providing challenging examples and a test-bed for studying long-term pixel-precise segmentation and tracking under real-world conditions. We further propose a novel evaluation metric Segmentation and Tracking Quality (STQ) that fairly balances semantic and tracking aspects of this task and is more appropriate for evaluating sequences of arbitrary length. Finally, we provide several baselines to evaluate the status of existing methods on this new challenging dataset. We have made our datasets, metric, benchmark servers, and baselines publicly available, and hope this will inspire future research.

Research on heat waves (periods of excessively hot weather, which may be accompanied by high humidity) is a newly emerging research topic within the field of climate change research with high relevance for the whole of society. In this study, we analyzed the rapidly growing scientific literature dealing with heat waves. No summarizing overview has been published on this literature hitherto. We developed a suitable search query to retrieve the relevant literature covered by the Web of Science (WoS) as complete as possible and to exclude irrelevant literature (n = 8,011 papers). The time evolution of the publications shows that research dealing with heat waves is a highly dynamic research topic, doubling within about 5 years. An analysis of the thematic content reveals the most severe heat wave events within the recent decades (1995 and 2003), the cities and countries/regions affected (USA, Europe, and Australia), and the ecological and medical impacts (drought, urban heat islands, excess hospital admissions, and mortality). An alarming finding is that the limit for survivability may be reached at the end of the twenty-first century in many regions of the world due to the fatal combination of rising temperatures and humidity levels measured as “wet-bulb temperature” (WBT). Risk estimation and future strategies for adaptation to hot weather are major political issues. We identified 104 citation classics, which include fundamental early works of research on heat waves and more recent works (which are characterized by a relatively strong connection to climate change).