Scientific Data最新文献

Videos of magic tricks offer lots of opportunities to study the human mind. They violate the expectations of the viewer, causing prediction errors, misdirect attention, and elicit epistemic emotions. Herein we describe and share the Magic, Memory, and Curiosity (MMC) Dataset where 50 participants watched 36 magic tricks filmed and edited specifically for functional magnetic imaging (fMRI) experiments. The MMC Dataset includes a contextual incentive manipulation, curiosity ratings for the magic tricks, and incidental memory performance tested a week later. We additionally measured individual differences in working memory and constructs relevant to motivated learning. fMRI data were acquired before, during, and after learning. We show that both behavioural and fMRI data are of high quality, as indicated by basic validation analysis, i.e., variance decomposition as well as intersubject correlation and seed-based functional connectivity, respectively. The richness and complexity of the MMC Dataset will allow researchers to explore dynamic cognitive and motivational processes from various angles during task and rest.

A Rural Observatory System (ROS) was established in Madagascar to address the lack of socioeconomic data on rural areas. It collected, analyzed, and disseminated data to help formulate and evaluate development policies. From 1995 to 2015, the ROS surveyed a total of 26 areas. The ROS methodology involved annual household panel surveys using consistent questionnaires supplemented by modules covering new themes. Qualitative community surveys were used to understand local features and dynamics. The site selection combined quantitative and qualitative insights to reflect the diversity of Madagascar's rural challenges. Quality control was comprehensive, with measures such as limiting the number of daily surveyor interviews and daily field supervision. By making this data available for 21 consecutive years, along with documentation, metadata, and code with analysis examples, we aim to facilitate their discovery, assessment, and understanding by researchers, policymakers, and social organizations. To our knowledge, this is the only available data for an in-depth analysis of the situation and trends in the rural areas of Madagascar.

The soybean hawkmoth Clanis bilineata tsingtauica Mell (Lepidoptera, Sphingidae; CBT), as one of the main leaf-chewing pests of soybeans, has gained popularity as an edible insect in China recently due to its high nutritional value. However, high-quality genome of CBT remains unclear, which greatly limits further research. In the present study, we assembled a high-quality chromosome-level genome of CBT using PacBio HiFi reads and Hi-C technologies for the first time. The size of the assembled genome is 477.45 Mb with a contig N50 length of 17.43 Mb. After Hi-C scaffolding, the contigs were anchored to 29 chromosomes with a mapping rate of 99.61%. Benchmarking Universal Single-Copy Orthologues (BUSCO) completeness value is 99.49%. The genome contains 252.16 Mb of repeat elements and 14,214 protein-coding genes. In addition, chromosomal synteny analysis showed that the genome of CBT has a strong synteny with that of Manduca sexta. In conclusion, this high-quality genome provides an important resource for future studies of CBT and contributes to the development of integrated pest management strategies.

The bay scallop, Argopecten irradians, is a species of major commercial, cultural, and ecological importance. It is endemic to the eastern coast of the United States, but has also been introduced to China, where it supports a significant aquaculture industry. Here, we provide an annotated chromosome-level reference genome assembly for the bay scallop, assembled using PacBio and Hi-C data. The total genome size is 845.9 Mb, distributed over 1,503 scaffolds with a scaffold N50 of 44.3 Mb. The majority (92.9%) of the assembled genome is contained within the 16 largest scaffolds, corresponding to the 16 chromosomes confirmed by Hi-C analysis. The assembly also includes the complete mitochondrial genome. Approximately 36.2% of the genome consists of repetitive elements. The BUSCO analysis showed a completeness of 96.2%. We identified 33,772 protein-coding genes. This genome assembly will be a valuable resource for future research on evolutionary dynamics, adaptive mechanisms, and will support genome-assisted breeding, contributing to the conservation and management of this iconic species in the face of environmental and pathogenic challenges.

The rovibrational spectrum of the water molecule is the crown jewel of high-resolution molecular spectroscopy. While its significance in numerous scientific and engineering applications and the challenges behind its interpretation have been well known, the extensive experimental analysis performed for this molecule, from the microwave to the ultraviolet, is admirable. To determine empirical energy levels for $H_2^{16}O$ , this study utilizes an improved version of the MARVEL (Measured Active Rotational-Vibrational Energy Levels) scheme, which now takes into account multiplet constraints and first-principles energy-level splittings. This analysis delivers 19027 empirical energy values, with individual uncertainties and confidence intervals, utilizing 309 290 transition wavenumbers collected from 189 (mostly experimental) data sources. Relying on these empirical, as well as some computed, energies and first-principles intensities, an extensive composite line list, named CW2024, has been assembled. The CW2024 dataset is compared to lines in the canonical HITRAN 2020 spectroscopic database, providing guidance for future experimental investigations.

In nature, diploids and tetraploids are two common types of polyploid evolution. Misgurnus anguillicaudatus (mud loach) is a remarkable fish species that exhibits both diploid and tetraploid forms. However, reconstructing the four haplotypes of its autotetraploid genome remains unresolved. Here, we generated the first haplotype-resolved, chromosome-level genome of autotetraploid M. anguillicaudatus with a size of 4.76 Gb, contig N50 of 6.78 Mb, and scaffold N50 of 44.11 Mb. We identified approximately 2.9 Gb (61.03% of genome) of repetitive sequences and predicted 91,485 protein-coding genes. Moreover, allelic gene expression levels indicated the absence of significant dominant haplotypes within the autotetraploid loach genome. This genome will provide a valuable biological model for unraveling the mechanisms of polyploid formation and evolution, adaptation to environmental changes, and benefit for aquaculture applications and biodiversity conservation.

The advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications. However, AI models often prioritize frequently encountered material examples in the scientific literature, limiting the selection of suitable candidates based on inherent physical and chemical attributes. To address this imbalance, we generated a dataset consisting of 1,453,493 natural language-material narratives from OQMD, Materials Project, JARVIS, and AFLOW2 databases based on ab initio calculation results that are more evenly distributed across the periodic table. The generated text narratives were then scored by both human experts and GPT-4, based on three rubrics: technical accuracy, language and structure, and relevance and depth of content, showing similar scores but with human-scored depth of content being the most lagging. The integration of multimodal data sources and large language models holds immense potential for AI frameworks to aid the exploration and discovery of solid-state materials for specific applications of interest.

The integration and reuse of digital research products can be only ensured through the adoption of machine-actionable (meta)data standards enriched with semantic artefacts. This study compiles 540 semantic artefacts in environmental sciences to: i. examine their coverage in scientific domains and topics; ii. assess key aspects of their FAIRness; and iii. evaluate management and governance concerns. The analyses showed that the majority of semantic artefacts concern the terrestrial biosphere domain, and that a small portion of the total failed to meet the FAIR principles. For example, 5.5% of semantic artefacts were not available in semantic catalogues, 8% were not built with standard model languages and formats, 24.6% were published without usage licences and 22.4% without version information or with divergent versions across catalogues in which they were available. This investigation discusses common semantic practices, outlines existing gaps and suggests potential solutions to address semantic interoperability challenges in some of the resources originally designed to guarantee it.

Satellite-based Quantitative Precipitation Estimates (QPE) are indirect estimates of precipitation rates and as such are often prone to errors, warranting a need for characterizing the associated uncertainties before being used in application-specific studies. Moreover, multiple satellite-based QPE products are offered through different agencies, each with their own specifications, formats and requirements, posing a challenge to understanding the products uncertainties. This manuscript presents a standardized validation system named NPreciSe - NOAA Satellite-based Precipitation Validation System, which assesses the performance of satellite-based precipitation products in near real-time over the continental United States. NPreciSe is coupled with a user-interactive web platform and built using an open-source software, Python. It is structured to help (1) the end-users determine the best satellite QPE for their specific application, and (2) the algorithm developers identify systematic biases in QPE retrievals. This manuscript presents the capabilities of the NPreciSe, discusses the methodology adopted in developing the standardized validation system, and introduces the web portal.

To provide the information needed for a detailed monitoring of crop types across the European Union (EU), we present an advanced 10-metre resolution map for the EU and Ukraine with 19 crop types for 2022, updating the 2018 version. Using Earth Observation (EO) and in-situ data from Eurostat's Land Use and Coverage Area Frame Survey (LUCAS) 2022, the methodology included 134,684 LUCAS Copernicus polygons, Sentinel-1 and Sentinel-2 satellite imagery, land surface temperature and a digital elevation model. Based on this data, two classification layers were developed using a Random Forest machine learning approach: a primary map and a gap-filling map to address cloud-covered gaps. The combined maps, covering 27 EU countries, show an overall accuracy of 79.3% for seven major land cover classes and 70.6% for all 19 crop types. The trained model was used to derive the 2022 map for Ukraine, demonstrating its robustness even in regions without labelled samples for model training.