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Abstract Creativity is increasingly recognized as a core competency for the 21st century, making its development a priority in education, research, and industry. To effectively cultivate creativity, researchers and educators need reliable and accessible assessment tools. Recent software developments have significantly enhanced the administration and scoring of creativity measures; however, existing software often requires expertise in experiment design and computer programming, limiting its accessibility to many educators and researchers. In the current work, we introduce CAP—the Creativity Assessment Platform—a free web application for building creativity assessments, collecting data, and automatically scoring responses (cap.ist.psu.edu). CAP allows users to create custom creativity assessments in ten languages using a simple, point-and-click interface, selecting from tasks such as the Short Story Task, Drawing Task, and Scientific Creative Thinking Test. Users can automatically score task responses using machine learning models trained to match human creativity ratings—with multilingual capabilities, including the new Cross-Lingual Alternate Uses Scoring (CLAUS), a large language model achieving strong prediction of human creativity ratings in ten languages. CAP also provides a centralized dashboard to monitor data collection, score assessments, and automatically generate text for a Methods section based on the study’s tasks, metrics, and instructions—with a single click—promoting transparency and reproducibility in creativity assessment. Designed for ease of use, CAP aims to democratize creativity measurement for researchers, educators, and everyone in between. 

Abstract The visual modality is central to both reception and expression of human creativity. Creativity assessment paradigms, such as structured drawing tasks Barbot (2018), seek to characterize this key modality of creative ideation. However, visual creativity assessment paradigms often rely on cohorts of expert or naïve raters to gauge the level of creativity of the outputs. This comes at the cost of substantial human investment in both time and labor. To address these issues, recent work has leveraged the power of machine learning techniques to automatically extract creativity scores in the verbal domain (e.g., SemDis; Beaty & Johnson 2021). Yet, a comparably well-vetted solution for the assessment of visual creativity is missing. Here, we introduce AuDrA – an Automated Drawing Assessment platform to extract visual creativity scores from simple drawing productions. Using a collection of line drawings and human creativity ratings, we trained AuDrA and tested its generalizability to untrained drawing sets, raters, and tasks. Across four datasets, nearly 60 raters, and over 13,000 drawings, we found AuDrA scores to be highly correlated with human creativity ratings for new drawings on the same drawing task (r= .65 to .81; mean = .76). Importantly, correlations between AuDrA scores and human raters surpassed those between drawings’ elaboration (i.e., ink on the page) and human creativity raters, suggesting that AuDrA is sensitive to features of drawings beyond simple degree of complexity. We discuss future directions, limitations, and link the trained AuDrA model and a tutorial (https://osf.io/kqn9v/) to enable researchers to efficiently assess new drawings. 

Metaphor is crucial in human cognition and creativity, facilitating abstract thinking, analogical reasoning, and idea generation. Typically, human raters manually score the originality of responses to creative thinking tasks – a laborious and error-prone process. Previous research sought to remedy these risks by scoring creativity tasks automatically using semantic distance and large language models (LLMs). Here, we extend research on automatic creativity scoring to metaphor generation – the ability to creatively describe episodes and concepts using nonliteral language. Metaphor is arguably more abstract and naturalistic than prior targets of automated creativity assessment. We collected 4,589 responses from 1,546 participants to various metaphor prompts and corresponding human creativity ratings. We fine-tuned two open-source LLMs (RoBERTa and GPT-2) – effectively “teaching” them to score metaphors like humans – before testing their ability to accurately assess the creativity of new metaphors. Results showed both models reliably predicted new human creativity ratings (RoBERTa r = .72, GPT-2 r = .70), significantly more strongly than semantic distance (r = .42). Importantly, the fine-tuned models generalized accurately to metaphor prompts they had not been trained on (RoBERTa r = .68, GPT-2 r = .63). We provide open access to the fine-tuned models, allowing researchers to assess metaphor creativity in a reproducible and timely manner. 

Abstract. Carbonyl sulfide (COS) is the most abundant sulfur gas in the atmosphere with links to terrestrial and oceanic productivity. We measured COS in ice core air from an intermediate-depth ice core from the South Pole using both dry and wet extraction methods, recovering a 52 500-year record. We find evidence for COS production in the firn, altering the atmospheric signal preserved in the ice core. Mean sea salt aerosol concentrations from the same depth are a good proxy for the COS production, which disproportionately impacts the measurements from glacial period ice with high sea salt aerosol concentrations. The COS measurements are corrected using sea salt sodium (ssNa) as a proxy for the excess COS resulting from the production. The ssNa-corrected COS record displays substantially less COS in the glacial period atmosphere than the Holocene and a 2 to 4-fold COS rise during the deglaciation synchronous with the associated climate signal. The deglacial COS rise was primarily source driven. Oceanic emissions in the form of COS, carbon disulfide (CS2), and dimethylsulfide (DMS) are collectively the largest natural source of atmospheric COS. A large increase in ocean COS emissions during the deglaciation suggests enhancements in emissions of ocean sulfur gases via processes that involve ocean productivity, although we cannot quantify individual contributions from each gas. 

The atmospheric history of molecular hydrogen (H 2 ) from 1852 to 2003 was reconstructed from measurements of firn air collected at Megadunes, Antarctica. The reconstruction shows that H 2 levels in the southern hemisphere were roughly constant near 330 parts per billion (ppb; nmol H 2 mol −1 air) during the mid to late 1800s. Over the twentieth century, H 2 levels rose by about 70% to 550 ppb. The reconstruction shows good agreement with the H 2 atmospheric history based on firn air measurements from the South Pole. The broad trends in atmospheric H 2 over the twentieth century can be explained by increased methane oxidation and anthropogenic emissions. The H 2 rise shows no evidence of deceleration during the last quarter of the twentieth century despite an expected reduction in automotive emissions following more stringent regulations. During the late twentieth century, atmospheric CO levels decreased due to a reduction in automotive emissions. It is surprising that atmospheric H 2 did not respond similarly as automotive exhaust is thought to be the dominant source of anthropogenic H 2. The monotonic late twentieth century rise in H 2 levels is consistent with late twentieth-century flask air measurements from high southern latitudes. An additional unknown source of H 2 is needed to explain twentieth-century trends in atmospheric H 2 and to resolve the discrepancy between bottom-up and top-down estimates of the anthropogenic source term. The firn air–based atmospheric history of H 2 provides a baseline from which to assess human impact on the H 2 cycle over the last 150 y and validate models that will be used to project future trends in atmospheric composition as H 2 becomes a more common energy source.