Scientific Data最新文献

Data-driven match analysis in soccer is a growing discipline in both research and practice. However, public data is scarce, which raises the barrier for entering this field and decreases reproducibility of methods and results. To bridge this gap, this paper presents a dataset of official match information, event, and position data from seven matches of the German Bundesliga's first and second division. The match information contains meta data about the matches and their participants. The event data contain timestamps along with descriptions of discrete events, like passes, shots, or fouls. The position data contain the x/y-coordinates of every player and the ball. By integrating multiple data modalities - i.e., event logs with timestamps, and x-y coordinates of player and ball positions - the dataset offers a multidimensional view of match dynamics. This dataset supports the validation of existing analytical techniques and facilitates the development of new methodologies in sports analytics. With availability under CC-BY 4.0, it promotes transparency, reproducibility, and the idea of open science in match analysis research.

The remarkable progress of deep learning in dermatological tasks has brought us closer to achieving diagnostic accuracies comparable to those of human experts. However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. Several factors can impact data quality, such as the presence of duplicates, data leakage across train-test partitions, mislabeled images, and the absence of a well-defined test partition. In this paper, we conduct meticulous analyses of three popular dermatological image datasets: DermaMNIST, its source HAM10000, and Fitzpatrick17k, uncovering these data quality issues, measure the effects of these problems on the benchmark results, and propose corrections to the datasets. Besides ensuring the reproducibility of our analysis, by making our analysis pipeline and the accompanying code publicly available, we aim to encourage similar explorations and to facilitate the identification and addressing of potential data quality issues in other large datasets.

Customizing the structure and format of scientific data facilitates the publication of diverse and heterogeneous data. Many data publishing platforms empower users to create self-designed schemas, leading to schema proliferation and more intricate creation processes. To address these challenges, we present a semi-automatic method and system for constructing heterogeneous material data schemas based on structure and context-aware recommendation. We propose a schema fragment tree structure to represent data schemas with hierarchical relationships, transforming the recommendation into subtree matching. Fragment index and semantic search techniques are introduced to identify candidate fragments, and a tree editing distance algorithm calculates similarity scores. Evaluated on the Data Schema Construction System, the algorithm outperforms baselines-TF-IDF and BM25 for schemas matching-in precision, recall, and F1-score. The baseline for reduced workload refers to the effort required to create schemas without recommendation. Our recommendation improves schema creation efficiency by 50.5% and reduces schema proliferation by 16.5%.

The mulberry looper (Phthonandria atrilineata), a geometrid moth, plays a pivotal role in the destruction of mulberry trees (Morus spp.). In China, P. atrilineata is the most significant insect pest to sericulture, as it feeds on mulberry leaves and spreads diseases. The outbreak trend of P. atrilineata has been expanding yearly, causing substantial economic losses. Despite its ecological and economic importance, knowledge about the genomic background of P. atrilineata remains limited. Here, we report a chromosome-level reference genome of P. atrilineata, with a total size of 336.55 Mb, containing 15,026 protein-coding genes and 39.72% repeat sequences. These findings have the potential to shed light on the genetic basis of the destructive nature and environmental adaptation of P. atrilineata, offering valuable genomic resources for understanding genome evolution and pest management within this Lepidopteran pest.

Today, MALDI-ToF MS is an established technique to characterize and identify pathogenic bacteria. The technique is increasingly applied by clinical microbiological laboratories that use commercially available complete solutions, including spectra databases covering clinically relevant bacteria. Such databases are validated for clinical, or research applications, but are often less comprehensive concerning highly pathogenic bacteria (HPB). To improve MALDI-ToF MS diagnostics of HPB we initiated a program to develop protocols for reliable and MALDI-compatible microbial inactivation and to acquire mass spectra thereof many years ago. As a result of this project, databases covering HPB, closely related bacteria, and bacteria of clinical relevance have been made publicly available on platforms such as ZENODO. This publication in detail describes the most recent version of this database. The dataset contains a total of 11,055 spectra from altogether 1,601 microbial strains and 264 species and is primarily intended to improve the diagnosis of HPB. We hope that our MALDI-ToF MS data may also be a valuable resource for developing machine learning-based bacterial identification and classification methods.

Visuomotor integration is a complex skill set encompassing many fundamental abilities, such as visual search, attention monitoring, and motor control. To explore the dynamic interplay between visual inputs and motor outputs, it is necessary to simultaneously record multiple brain activities with high temporal and spatial resolution, as well as to record implicit and explicit behaviors. However, there is a lack of public datasets that provide simultaneous multiple modalities during a visual-motor task. Functional near-infrared spectroscopy and electroencephalography to record brain activity simultaneously facilitate more precise capture of the complex visuomotor of brain mechanisms. Additionally, by employing a combined eye movement and manual response, it is possible to fully evaluate the effects of visuomotor outputs from implicit and explicit dimensions. We recorded whole-brain EEG (34 electrodes) and fNIRS (44 channels) covering the frontal and parietal cortex along with eye movements, behavior sampling, and operant behavior. The dataset underwent rigorous synchronization, quality control to highlight the effectiveness of our experiments and to demonstrate the high quality of our multimodal data framework.

Efforts on climate change have demonstrated tangible impacts through various actions and policies. However, a significant knowledge gap remains: comparing the stringency of climate change policies over time or across jurisdictions is challenging due to ambiguous definitions, the lack of a unified assessment framework, complex causal effects, and the difficulty in achieving effective measurement. Furthermore, China's climate governance is expected to address multiple objectives by integrating main effects and side effects, to achieve synergies that encompass environmental, economic, and social impacts. This paper employs an integrated framework comprising lexicon, text analysis, machine learning, and large-language model applied to multi-source data to quantify China's policy stringency on climate change (PSCC) from 1954 to 2022. To achieve effective, robust, and explainable measurement, Chain-of-Thought and SHAP analysis are integrated into the framework. By framing the PSCC on varied sub-dimensions covering mitigation, adaptation, implementation, and spatial difference, this dataset maps the government's varied stringency on climate change and can be used as a robust variable to support a series of downstream causal analysis.

We present a comprehensive gridded GDP per capita dataset downscaled to the admin 2 level (43,501 units) covering 1990-2022. It updates existing outdated datasets, which use reported subnational data only up to 2010. Our dataset, which is based on reported subnational GDP per capita data from 89 countries and 2,708 administrative units, employs various novel methods for extrapolation and downscaling. Downscaling with machine learning algorithms showed high performance (R² = 0.79 for cross-validation, R² = 0.80 for the test dataset) and accuracy against reported datasets (Pearson R = 0.88). The dataset includes reported and downscaled annual data (1990-2022) for three administrative levels: 0 (national; reported data for 237 administrative units), 1 (provincial; reported data for 2,708 administrative units for 89 countries), and 2 (municipality; downscaled data for 43,501 administrative units). The dataset has a higher spatial resolution and wider temporal range than the existing data do and will thus contribute to global or regional spatial analyses such as socioenvironmental modelling and economic resilience evaluation. The data are available at https://doi.org/10.5281/zenodo.10976733 .

Root-knot nematodes (RKN) of the genus Meloidogyne are obligatory plant endoparasites that cause substantial economic losses to agricultural production and impact the global food supply. These plant parasitic nematodes belong to the most widespread and devastating genus worldwide, yet few measures of control are available. The most efficient way to control RKN is deployment of resistance genes in plants. However, current resistance genes that control other Meloidogyne species are mostly inefficient on Meloidogyne enterolobii. Consequently, M. enterolobii was listed as a European Union quarantine pest requiring regulation. To gain insight into the molecular characteristics underlying its parasitic success, exploring the genome of M. enterolobii is essential. Here, we report a high-quality genome assembly of M. enterolobii using the high-fidelity long-read sequencing technology developed by Pacific Biosciences, combined with a gap-aware sequence transformer, DeepConsensus. The resulting triploid genome assembly spans 285.4 Mb with 556 contigs, a GC% of 30 ± 0.042 and an N50 value of 2.11 Mb, constituting a useful platform for comparative, population and functional genomics.