Data

Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae) is a subterranean termite species from Southeast Asia which has been unintentionally introduced to many parts of the world through commerce and modern transportation. Known for causing extensive damage to timber used in the built environment, the termite also has a habit of nesting in carton nests in wood and wooden structures in buildings. As so little is known of its breeding system, colony, and genetic structure, we initiated work to sequence its genome with an Illumina HiSeq™ 2000 sequencer. In this publication, we announce our paired-end sequencing data and report the isolation of 119,190 microsatellite markers from our DNA assembly. The microsatellite marker reported in this publication can be used to elucidate the mating system and genetic structure of this highly invasive termite species. Additionally, in this announcement the study authors make the Bio Project sequence accession number SRR13105492 accessible from the Sequence Read Archive database.

Li Yang Lim

Shawn Cheng

Abdul Hafiz Ab Majid

Isolation of Microsatellite Markers from De Novo Whole Genome Sequences of Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae)

从新测序的全基因组序列中分离出格斯特乳白蚁（Wasmann）（蜚蠊目：鼻白蚁科）的微卫星标记

The importance of evaluating the stress field of loaded structures lies in the need for identifying the forces which make them fail, redesigning their geometry to increase the mechanical resistance, or characterizing unstressed regions to remove material. In such work line, digital photoelasticity highlights with the possibility of revealing the stress information through isochromatic color fringes, and quantifying it through inverse problem strategies. However, the absence of public data with a high variety of spatial fringe distribution has limited developing new proposals which generalize the stress evaluation in a wider variety of industrial applications. This dataset shares a variated collection of stress maps and their respective representation in color fringe patterns. In this case, the data were generated following a computational strategy that emulates the circular polariscope in dark field, but assuming stress surfaces and patches derived from analytical stress models, 3D reconstructions, saliency maps, and superpositions of Gaussian surfaces. In total, two sets of ‘101430’ raw images were separately generated for stress maps and isochromatic color fringes, respectively. This dataset can be valuable for researchers interested in characterizing the mechanical response in loaded models, engineers in computer science interested in modeling inverse problems, and scientists who work in physical phenomena such as 3D reconstruction in visible light, bubble analysis, oil surfaces, and film thickness.

Juan-Carlos Briñez-De-Leon

Mateo Rico-Garcia

Alejandro Restrepo-Martínez

Isochromatic-Art: A Computational Dataset for Digital Photoelasticity Studies

同色艺术：数字光弹性研究的计算数据集

Early fault detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain-specific public datasets are scarce. Many comparisons of different approaches either use benchmarks composed of data from many different domains, inaccessible data, or one of the few publicly available datasets that lack detailed information about the faults. Moreover, many publications highlight a couple of case studies where fault detection was successful. With this paper, we publish a high quality dataset that contains data from 36 wind turbines across 3 different wind farms as well as the most detailed fault information of any public wind turbine dataset as far as we know. The new dataset contains 89 years worth of real-world operating data of wind turbines, distributed across 44 labeled time frames for anomalies that led up to faults, as well as 51 time series representing normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point. Furthermore, we propose a new scoring method, called CARE (Coverage, Accuracy, Reliability and Earliness), which takes advantage of the information depth that is present in the dataset to identify good early fault detection models for wind turbines. This score considers the anomaly detection performance, the ability to recognize normal behavior properly, and the capability to raise as few false alarms as possible while simultaneously detecting anomalies early.

Christian Gück

Cyriana M. A. Roelofs

Stefan Faulstich

CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data

CARE 比较：风力涡轮机数据早期故障检测的现实世界基准数据集

In the era of Big Data, entity resolution (ER), i.e., the process of identifying which records refer to the same entity in the real world, plays a critical role in data-integration tasks, especially in mission-critical applications where accuracy is mandatory, since we want to avoid integrating different entities or missing matches. However, existing approaches struggle with the challenges posed by rapidly changing data and the presence of dirtiness, which requires an iterative refinement during the time. We present Detective Gadget, a novel system for iterative ER that seamlessly integrates data-cleaning into the ER workflow. Detective Gadgetemploys an alias-based hashing mechanism for fast and scalable matching, check functions to detect and correct mismatches, and a human-in-the-loop framework to refine results through expert feedback. The system iteratively improves data quality and matching accuracy by leveraging evidence from both automated and manual decisions. Extensive experiments across diverse real-world scenarios demonstrate its effectiveness, achieving high accuracy and efficiency while adapting to evolving datasets.

Marcello Buoncristiano

Giansalvatore Mecca

Donatello Santoro

Enzo Veltri

Detective Gadget: Generic Iterative Entity Resolution over Dirty Data

侦探工具：在脏数据上进行通用迭代实体解析

In this work, we present two datasets for specific areas located on the Alpine arc that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of water supply over five defined test areas over the South Tyrol (Italy) and Tyrol (Austria) areas in alpine environments. The snow cover fraction (SCF) and Soil Moisture Content (SMC) datasets are derived from machine learning algorithms based on remote sensing data. Both SCF and SMC products are characterized by a spatial resolution of 20 m and are provided for the period from October 2020 to May 2023 (SCF) and from October 2019 to September 2022 (SMC), respectively, covering winter seasons for SCF and spring–summer seasons for SMC. For SCF maps, the validation with very high-resolution images shows high correlation coefficients of around 0.9. The SMC products were originally produced with an algorithm validated at a global scale, but here, to obtain more insights into the specific alpine mountain environment, the values estimated from the maps are compared with ground measurements of automatic stations located at different altitudes and characterized by different aspects in the Val Mazia catchment in South Tyrol (Italy). In this case, an MAE between 0.05 and 0.08 and an unbiased RMSE between 0.05 and 0.09 m3·m−3 were achieved. The datasets presented can be used as input for hydrological models and to hydrologically characterize the study alpine area starting from different sources of information.

Ludovica De Gregorio

Giovanni Cuozzo

Riccardo Barella

Francisco Corvalán

Felix Greifeneder

Peter Grosse

Abraham Mejia-Aguilar

Georg Niedrist

Valentina Premier

Paul Schattan

Alessandro Zandonai

Claudia Notarnicola

Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions

两个数据集用于理解和表征南蒂罗尔和蒂罗尔地区山区水资源动态

Alternaria brassicicola is a seed-borne pathogen that causes black spot disease in Brassica crops, yet the seed defense mechanisms against this fungus remain poorly understood. Building upon recent reports that highlighted the involvement of indole pathways in seeds infected by Alternaria, this study provides transcriptomic resources to further elucidate the role of these metabolic pathways during the interaction between seeds and fungal pathogens. Using RNA sequencing, we examined the gene expression of glucosinolate-deficient mutant lines (cyp79B2/cyp79B3 and qko) and a camalexin-deficient line (pad3), generating a dataset from 14 samples. These samples were inoculated with Alternaria or water, and collected at 3, 6, and 10 days after sowing to extract total RNA. Sequencing was performed using DNBseq™ technology, followed by bioinformatics analyses with tools such as FastQC (version 0.11.9), multiQC (version 1.13), Venny (version 2.0), Salmon software (version 0.14.1), and R packages DESeq2 (version 1.36.0), ClusterProfiler (version 4.12.6) and ggplot2 (version 3.4.0). By providing this valuable dataset, we aim to contribute to a deeper understanding of seed defense mechanisms against Alternaria, leveraging RNA-seq for various analyses, including differential gene expression and co-expression correlation. This work serves as a foundation for a more comprehensive grasp of the interactions during seed infection and highlights potential targets for enhancing crop protection and management.

Mailen Ortega-Cuadros

Laurine Chir

Sophie Aligon

Nubia Velasquez

Tatiana Arias

Jerome Verdier

Philippe Grappin

Dual Transcriptome of Post-Germinating Mutant Lines of Arabidopsis thaliana Infected by Alternaria brassicicola

拟南芥萌后突变体感染芸薹链格孢菌的双转录组分析

Green initiatives are popular mechanisms globally to enhance environmental and human wellbeing. However, multiple green initiatives, when overlapping geographically and targeting the same participants, may interact with each other, giving rise to what is termed “spillover effects”, where one initiative and its outcomes influence another. This study examines the spillover effects among four major concurrent initiatives in the United States (U.S.) and China using a comprehensive dataset. In the U.S., we analysed county-level data in 2018 for the Conservation Reserve Program (CRP) and the Environmental Quality Incentives Program (EQIP), both operational for over 25 years. In China, data from Fanjingshan and Tianma National Nature Reserves (2014–2015) were used to evaluate the Grain-to-Green Program (GTGP) and the Forest Ecological Benefit Compensation (FEBC) program. The dataset comprises 3106 records for the U.S. and 711 plots for China, including several socio-economic variables. The results of multivariate linear regression indicate that there exist significant spillover effects between CRP &amp; EQIP and GTGP &amp; FEBC, with one initiative potentially enhancing or offsetting another’s impacts by 22% to 100%. This dataset provides valuable insights for researchers and policymakers to optimize the effectiveness and resilience of concurrent green initiatives.

短名	Data. (Basel).
Journal Impact	2.06
国际分区	MULTIDISCIPLINARY SCIENCES(Q2)

ISSN	2306-5729
h-index	38

涉及主题	计算机科学生物工程类物理数学地理地质学人工智能操作系统医学程序设计语言经济生态学政治学量子力学法学哲学统计化学数据挖掘机器学习历史考古万维网心理学
出版信息	出版商: Multidisciplinary Digital Publishing Institute (MDPI)，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2016，原创研究文献占比: 98.33%，自引率:4.50%， Gold OA占比: 99.19%
期刊费用	$详情
平均审稿周期	网友分享经验：10 Weeks

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Isolation of Microsatellite Markers from De Novo Whole Genome Sequences of Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae)

Isochromatic-Art: A Computational Dataset for Digital Photoelasticity Studies

CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data

Detective Gadget: Generic Iterative Entity Resolution over Dirty Data

Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions

Detective Gadget: Generic Iterative Entity Resolution over Dirty Data

CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data

Dual Transcriptome of Post-Germinating Mutant Lines of Arabidopsis thaliana Infected by Alternaria brassicicola

Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions

Dataset to Quantify Spillover Effects Among Concurrent Green Initiatives