Scientific Data最新文献

This paper describes a dataset containing field measurement data from classrooms in Belgium. The dataset comprises of survey responses, indicating occupants' satisfaction with the indoor environmental quality (IEQ) (i.e., thermal, indoor air quality, visual, acoustic and overall IEQ conditions), measurements of the indoor environmental conditions in the classroom during heating and intermediate season, and contextual variables like occupant location in classroom. Data were collected in three educational case studies: three classrooms in a secondary school (Case 1), seven primary school classrooms (Case 2), and a university lecture room (Case 3). The full dataset consists of 6834 satisfaction assessments collected from 321 unique occupants in classrooms, merged with corresponding measurements of the classroom IEQ. The dataset benefits the research community since a longitudinal dataset of this size on occupants' satisfaction in classrooms is unavailable at this moment. The dataset can serve as a training or validation dataset for novel comfort models, a basis for statistical analyses to establish a causal model or to examine inter- and intra-individual differences in occupant satisfaction.

Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a specific sensor and corresponding reference values for developing novel methods. To this end, we present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset, which contains field data from various sensors collected over a growing season, covering six crop species. All data was georeferenced for alignment across sensors and dates. To collect our dataset, we deployed aerial and ground robotic platforms equipped with RGB cameras, LiDARs, and multispectral cameras, aiming to capture a wide variety of modalities and observations from different viewpoints. In addition to sensor data, we also provide manually collected leaf area index and biomass reference measurements. Our dataset enables the development of novel automatic phenotypic trait estimation methods, allows comparisons across different sensors, and generalizability across crop species.

Tibial plateau fractures account for approximately 1% of skeletal fractures, with treatment strategies varying based on fracture type, displacement, and articular involvement. Diagnosis is labor-intensive, time-consuming, repetitive, and subject to considerable inter-observer variability. Automated and precise approaches could improve accuracy and efficiency in fracture severity classification. With advances in artificial intelligence (AI), especially deep learning, such techniques are increasingly applied in medicine, yet their performance depends on high-quality training data. Here, we present a first-of-its-kind open-access dataset for AI-based analysis of tibial plateau fractures. The dataset comprises 421 heterogeneous anterior-posterior radiographs from 186 patients (mean age 45.88 ± 17.54 years; 37 females, 149 males), including normal and fractured knees. Fractures were classified by expert orthopedic surgeons and radiologists using the Schatzker system: type I (14.51%), II (18.27%), III (6.45%), IV (5.91%), V (6.45%), VI (17.20%), and normal (31.18%). All images were segmented to generate tibial bone masks, supporting morphological analysis, AI training, and automated fracture assessment. This dataset facilitates AI-driven fracture detection, classification, preoperative planning, and orthopedic assistant education.

Research on metaphor has steadily increased over the last decades, as this phenomenon opens a window into a range of linguistic and cognitive processes. At the same time, the demand for rigorously constructed and extensively normed experimental materials increased as well. Here, we present the Figurative Archive, an open database of 996 metaphors in Italian enriched with ratings and corpus-based measures (from familiarity to semantic distance and preferred interpretations), derived by collecting stimuli used across 11 studies. It includes both everyday and literary metaphors, varying in structure and semantic domains, and is validated based on correlations between familiarity and other measures. The Archive has several aspects of novelty: it is increased in size compared to previous resources; it offers a measure of metaphor inclusiveness, to comply with recommendations for non-discriminatory language use; it is displayed in a web-based interface, with features for a customized consultation. We provide guidelines for using the Archive to source materials for studies investigating metaphor processing and the relationships between metaphor features in humans and computational models.

Small and medium-sized islands (SMI) combine high ecological value with limited resources and vulnerability to climatic and environmental risks. Nature-based solutions (NbS) can contribute to addressing some of these challenges, but studies on the uptake and effectiveness of NbS in SMI remain scattered, with few systematic syntheses. Here, we introduce the SMI-NbS compendium, a comprehensive and open-access dataset compiling 280 NbS case studies implemented across SMI worldwide, developed through a systematic review of published and grey literature. Each SMI-NbS case study includes information on the location, NbS category, ecosystem types, societal challenges addressed, associated co-benefits, and links to the United Nations' Sustainable Development Goals (SDGs). The SMI-NbS compendium provides practical information on NbS implementation and identifies current research trends and gaps, such as the dominance of ecological and climate-focused NbS, with limited integration of other socio-economic challenges, thereby supporting further research and enabling knowledge exchange across the science-policy-practice interface to inform sustainable development pathways in SMI.

Clarifying the spatiotemporal patterns of Holocene (~11.7 - 0 ka BP) precipitation variability over the Northern Hemisphere (NH) is essential for contextualizing modern hydroclimate variability. However, the scarcity of spatiotemporally-completed, temporally well-resolved and highly-reliable reconstructions has limited a full-field understanding of Holocene hydroclimate. Here, we present a reconstruction of NH annual precipitation spanning 12-0 ka BP at 3.75° spatial and 100-year temporal resolution. This dataset was generated using paleoclimate data assimilation (PDA) approach, integrating 2,421 Holocene precipitation records with two transient simulations via an Ensemble Optimal Interpolation algorithm. Validation suggested the PDA-based reconstructions have significantly improved reliability compared to model-only simulations, particularly at mid-high latitudes. As a core product of the Holocene Climate Reanalysis supported by Chinese Academy of Sciences (CAS-HCR), this dataset represents a substantial progress in applying the PDA approach to reconstruct Holocene precipitation fields at hemispherical scale, and provides a key resource for analyzing the spatiotemporal dynamics of Holocene hydroclimate, contextualizing modern precipitation changes and evaluating the performance of climate models in simulating long-term precipitation variability.

Multiple global change drivers including land-use and climate change, and pollution threaten alpine biodiversity and ecosystem functions. Experimental approaches can be used to disentangle the single and interactive effects of these drivers. Across three sites along an elevational gradient (469-1290 m a.s.l.) in south-western Norway, we exposed plant communities to warmer climate, nitrogen fertilization, and grazing, as well as simulated grazing by clipping, in a split-plot design. After three years of treatment, we recorded data on vegetation, ecosystem functioning, and microclimate in 160 experimental and control plots. This database consists of records of the following datasets: aboveground standing biomass (3,417 records), aboveground plant productivity (2,071), reflectance (1,769), vascular plant community composition (8,954 records covering 95 taxa), belowground productivity and traits (796), soil characteristics (193), soil nutrient (1,132), ecosystem CO₂ fluxes (2447), soil ecosystem CO₂ respiration (64), and microclimate (30,751,264). The data can be combined with long-term climate and plant functional traits collected within the study region.

A five-year hygrothermal dataset (23 September 2020 to 8 August 2025) was compiled from Rifugio Boé, a 1905 stone mountain refuge situated at 2870 m a.s.l. in the Dolomites. The dataset includes hourly recordings of outdoor climate parameters (temperature, relative humidity, global tilted irradiance, and driving rain), indoor conditions across six functionally distinct rooms, and temperature-humidity profiles at three depths within the retrofitted south-east wall. The monitoring system was engineered for robust, long-term operation under harsh alpine conditions. Data are provided in CSV format, accompanied by comprehensive sensor metadata. The dataset supports: (i) validation of hygrothermal simulation models under extreme alpine conditions; (ii) evaluation of the long-term performance and durability of internal insulation within historic masonry; and (iii) benchmarking of moisture-related risk in high-altitude heritage structures. Moreover, the dataset offers a rare opportunity to examine hygrothermal responses in high-elevation-built heritage, a research domain where long-term, high-frequency data remain scarce.

We present a unique dataset of historical tropical tree phenology observations at two sites from different bioclimatic regions across the Congo Basin. We cover both the Atlantic Mayombe forest and the tropical forest in the central Congo Basin. To our knowledge this is the complete extant historical (1937-1957) phenology data across the Congo basin. The data contains ~10 million observations of 876 species, across 6339 individuals, and phenology metrics including leaf, flowering, and fruiting phenology. The data were recovered through expert transcription and validated community science based crowdsourcing. These data may provide a reference baseline and key information on how tree species are responding to a changing climate.

The lateral transport of dissolved organic carbon (DOC) from terrestrial uplands to aquatic ecosystems, particularly headwater streams, plays a critical role in linking terrestrial and aquatic carbon cycles, but its spatial distribution and magnitude at the global scale remain highly uncertain. Here, we compiled 20,403 field-based DOC concentration measurements and modeled them against catchment properties to generate monthly predictions across over 90,000 forested catchments (30°S-70°N). These models explain between 64% and 90% of the total variability in DOC concentrations (R² values from predictions versus withheld observations), revealing previously unrecognized hotspots of terrestrial-aquatic carbon transfer. By coupling these predictions with a physically-based hydrological routing scheme (HydroBASINS database), we provide the first catchment-resolved estimates of DOC yields. This dataset represents a valuable resource for the carbon-cycle community, supporting the validation of terrestrial biosphere models and cross-checking of datasets.