Scientific Data最新文献

Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanisms of facial expression have been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural dynamic facial expression videos still needs to be investigated. To our knowledge, this type of data specifically on large-scale natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), an fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips are annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.

The mud carp (Cirrhinus molitorella) is an important economic farmed fish, mainly distributed in South China and Southeast Asia due to its strong adaptability and high yield. Despite its economic importance, the paucity of genomic information has constrained detailed genetic research and breeding efforts. In this study, we utilized PacBio HiFi long-read sequencing and Hi-C technologies to generate a meticulously assembled chromosome-level genome of the mud carp. This assembly spans 1,033.41 Mb, with an impressive 99.82% distributed across 25 chromosomes. The contig N50 and scaffold N50 are 33.29 Mb and 39.86 Mb, respectively. The completeness of the mud carp genome assembly is highlighted by a BUSCO score of 98.05%. We predict 25,865 protein-coding genes, with a BUSCO score of 96.54%, and functional annotations for 91.83% of these genes. Approximately 52.21% of the genome consists of repeat elements. This high-fidelity genome assembly is a vital resource for advancing molecular breeding, comparative genomics, and evolutionary studies of the mud carp and related species.

Encarsia sophia, a heteronomous hyperparasitoid wasp, is a well-known biological control agent, but its genomic information is limited, hindering molecular investigations and understanding of multitrophic interactions. In this study, we present a chromosome-level genome assembly for E. sophia using Illumina, PacBio HiFi, and Hi-C technologies. The assembled genome size is 398.3 Mb, with a contig N50 of 1.0 Mb and a scaffold N50 of 74.0 Mb. The BUSCO completeness score is 97.1%, and genome coverage reaches 99.1%. Utilizing Hi-C assisted assembly, the genome was organized into five chromosomes, with a mounting rate of 95.1%. Repetitive sequences make up 54.6% of the genome, and 14,914 protein-coding genes were predicted, with 95.5% functionally annotated. The high-quality genome assembly of E. sophia is a significant achievement, marking the first complete genome for a heteronomous hyperparasitoid wasp. This milestone offers valuable insights into the evolution and host interactions of heteronomous hyperparasitoids, laying the foundation for extensive research in biological control.

In the Antarctic, Arctic, and Tibetan Plateau-recognized as the Earth's three poles characterized by extremely harsh environments-lichens prevail in the ecosystem and play crucial roles as pioneer species. Despite their importance, studies investigating the spatial distribution patterns of lichen attributes are scarce due to a lack of appropriate datasets. To bridge this gap and enhance our understanding of the growth preferences of lichens in these areas, here we present a geospatial dataset encompassing key attributes of lichens, such as color type and growth form, for over 2800 lichen species and 170,000 in-situ lichen records. The dataset facilitates the creation of the first spatial distribution map illustrating the variation of lichen attributes across different latitudes and longitudes. This can serve as a foundational resource for studies on the relationship between lichen types and their growing environment, which is a vital scientific question in the ecology domain. Additionally, it can contribute to the development of specialized remote sensing technique tailored for lichen monitoring, which is currently lacking.

We present detailed annual land cover maps for the Baltic Sea region, spanning more than two decades (2000-2022). The maps provide information on eighteen land cover (LC) classes, including eight general LC types, eight major crop types and grassland, and two peat bog-related classes. Our maps represent the first homogenized annual dataset for the region and address gaps in current land use and land cover products, such as a lack of detail on crop sequences and peat bog exploitation. To create the maps, we used annual multi-temporal remote sensing data combined with a data encoding structure and deep learning classification. We obtained the training data from publicly available open datasets. The maps were validated using independent field survey data from the Land Use/Cover Area Frame Survey (LUCAS) and expert annotations from high-resolution imagery. The quantitative and qualitative results of the maps provide a reliable data source for monitoring agricultural transformations, peat bog exploitation, and restoration activities in the Baltic Sea region and its surrounding countries.

Water quality degradation due to heavy metal contamination poses serious threats to both human health and aquatic ecosystems. The rise in the concentration of heavy metals in aquatic environments is largely attributable to anthropogenic activities. These metals accumulate over time in water bodies, necessitating rigorous monitoring to accurately assess pollution levels. The present study is concerned with the assessment of heavy metal pollution in the Styr River (Ukraine) before and after the discharge of water from a nuclear power plant. The assessment is based on three indices: the Heavy Metal Pollution Index, the Heavy Metal Evaluation Index, and the Degree of Contamination. Therefore, heavy metals, including zinc (Zn), cadmium (Cd), lead (Pb), copper (Cu), nickel (Ni), manganese (Mn), arsenic (As) and chromium (Cr), were analyzed in this study. Water samples were collected at two locations on a monthly basis over the course of five years (2018-2022) and subsequently analysed using inductively coupled plasma optical emission spectroscopy. The results indicates a low contamination level at both sampling sites, indicating stable and uniform concentrations of metals across the study area. Moreover, statistical analysis highlights significant associations between certain metals and pollution indices, supporting the indices' utility in tracking pollution trends and assessing environmental impacts. This dataset underscores the importance of ongoing monitoring for effective water quality management.

This study develops a 73-year dataset of water balance components from 1950 to 2022 for the Laurentian Great Lakes Basins. This is carried out using the Large Lakes Statistical Water Balance Model (L2SWBM), which provides a Bayesian statistical framework that assimilates binational input datasets sourced from the United States and Canada. The L2SWBM infers feasible water balance component estimates through this Bayesian framework by constraining the output with a standard water balance equation. The result is value-added time series, including expressions of uncertainty, that ultimately close the water balance across the interconnected Great Lakes system. Therefore, the L2SWBM facilitates the understanding of discrepancies in datasets and hydroclimate parameters. This enhanced reliability stemming from coordinated data, with an understanding and quantification of uncertainty, could significantly boost confidence in decision support tools for water resources practitioners and policymakers. This joint effort advances scientific understanding and strengthens strategies and policies designed to bolster resilience in Great Lakes communities and its ecosystem in the face of a shifting climate.

In this study, a chromosome-level genome of the tropical sea cucumber Stichopus monotuberculatus was generated by a combination of Nanopore long-read, Illumina short-read, and Hi-C sequencing technologies. The final assembly was 810.54 Mb in length, with contig N50 and scaffold N50 values of 10.15 Mb and 35.36 Mb, respectively. This assembly comprised 23 pseudo-chromosomes, covering 99.82% of the genome. Completeness analysis using BUSCO indicated that 97.8% of the metazoan conserved genes were presented in their entirety. A total of 29,596 protein-coding genes were predicted, with functional annotations available for 94.43% of these genes. The high-quality genome assembly produced in this study may provide an essential foundation for future researches on resource conservation and genetic breeding of S. monotuberculatus.

Here we describe the data obtained by a successful proof-of-concept initiative to launch the first ocean color imager on board a CubeSat satellite and collect research-grade imagery at severalfold higher spatial resolution than any other ocean color satellite mission. The 3U CubeSat, named SeaHawk, flew at a nominal altitude of 585 km. Its ocean color sensor, HawkEye, collected 7,471 research-grade push-broom images of 230 × 780 km² at best-in-class 130 × 130 m² per pixel. The sensor is built with comparatively low-cost commercial off-the-shelf optoelectronics and was designed to match NASA SeaWiFS ocean color specifications, including wavelengths, bandwidths, and signal-to-noise ratios. HawkEye's design for ocean color remote sensing combined with its high spatial resolution make the imagery especially well-suited for coastal, estuarine, and limnological applications. Ultimately, the successful mission provided open access to a rich global dataset of calibrated and quality-controlled imagery for use in aquatic ecology and environmental change studies.

Here we provide a database comprising electronic band structures of 9,004 layered intercalation compounds, where atoms are intercalated into a host layered compound with different intercalant atoms, along with 468 structures related to the layered host compounds. Additionally, we provide properties derived from the electronic states such as band gap as well as stability-related properties like formation energies. Direct comparison of the band structures before and after intercalation is generally challenging due to changes in their space group and k-path. However, in this study, we developed new k-paths consistent with the host materials, allowing for the direct comparison of band structures before and after intercalation. This enables direct and quantitative discussion of the band structure changes induced by the intercalations and provides a valuable database for intercalant-driven band engineering. Layered intercalation compounds are widely used in many fields, including superconductivity and energy applications, and understanding of electronic structures is necessary. The feature of our database holds promises for the development of layered compounds with enhanced functionalities through database utilization.