Historical Database Research Articles

2D Echocardiographic aortic root dimensions and longitudinal analysis with artificial intelligence using an open dataset from a UK-wide collaborative

Abstract Background Surveillance of aortic dimensions requires reproducible measurements, and knowledge of the rate of progression in those with dilatation. Purpose To develop an open machine-learning method for measuring the aortic root and proximal ascending aorta on echocardiograms, validate it through a multi-expert panel from the Unity UK Echocardiography AI Collaborative, and applying the AI to derive the rate of progression in historical databases. Methods The neural network was trained on 1478 parasternal long-axis images. Each image was labelled with key points for the aortic annulus, sinus and sinotubular junction, and proximal ascending aorta dimensions. Labels accommodated inner-to-inner and leading-to-leading conventions. For validation, the end-diastolic and mid-systolic images were identified (accommodating different guidelines recommendations) from 100 studies, and 10 expert echocardiographers made the aortic measurements. The consensus of experts defined the reference standard, and the variation between individual experts defined the acceptable variation for the AI. The AI was then applied to 724 echocardiograms of 102 patients under surveillance spanning 12 years (ranging from 4 to 17 scans per patient) without intervention. Training data labels and networks are made freely available on our project website. Results The median absolute deviations between the AI and the expert consensus ranged from 0.06cm to 0.17cm across the 16 measurements. For the individual expert opinions this was from 0.08cm to 0.19cm. For 7/16 measurements, there was no statistically significant difference between the AI deviation and the individual-expert deviation. The AI’s confidence level was a useful indicator of the reliability of the AI measure. For the top 9 deciles of AI confidence level, the AI measurements had significantly smaller deviations than the individual experts. The rates of progression averaged across the three aortic measurements was 2.0 mm/decade (95% CI 1.0 to 3.0; p =0.007). Conclusion Expert consensus can be used to both define the reference standard and also the range of acceptable deviation from consensus. Overall, the AI performs similarly to experts and more importantly, provides an automated estimate of confidence. The validated AI is consistent over time, and when applied retrospectively to patients with dilated aortic roots, found the rate of progression was more rapid than in previously reported healthy patients. This approach could be applied generally in developing medical AI to not only make reproducible measurements but to automate large scale longitudinal studies with little input, and ultimately serves as a useful research and clinical tool.Figure: AI(red) vs Expert (blue)Table: AIDeviations from Consensus

Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China

In recent decades, global warming has significantly altered both the spatial and temporal distribution of rainfall patterns. This change has heightened the risk of rainfall-induced landslides, which are the most prevalent natural disasters in the mountainous regions of southwestern China. These events pose unpredictable and severe threats to the region, making it essential to forecast future rainfall trends and assess how landslide susceptibility will respond to these changes. Understanding these dynamics is crucial for developing effective strategies to mitigate and adapt to the changing rainfall patterns that influence landslides. This study focuses on Sichuan Province, China, and uses annual cumulative rainfall (ACR) as a key dynamic variable to create landslide susceptibility maps (LSMs). The goal is to explore the evolving relationship between rainfall and landslide susceptibility and use future rainfall projections to predict these risks. To achieve this, a historical landslide geospatial database was compiled across five temporal categories: 2000, 2001–2005, 2006–2010, 2011–2015, and 2016–2020. The extreme learning machine (ELM) was applied to generate LSMs for the years 2000 to2020, while an elasticity framework was used to assess how sensitive landslide susceptibility is to rainfall variations. To project future scenarios, a long short-term memory (LSTM) model was employed to project the ACR for 2030, using monthly rainfall data from 2000 to 2020. This projected ACR was then used to estimate future landslide susceptibility. Results showed a marked increase in high landslide susceptibility areas: 5.6 % by 2005, 0.3 % by 2010, 0.2 % by 2015, and 12.9 % by 2020, all relative to the year 2000. The elasticity analysis revealed that from 2000 to 2020, a 1 % change in rainfall would cause an average 1.35 % change in landslide susceptibility. Looking forward to 2030, the projected rise in ACR is expected to lead to a 2.44 % increase in areas of high landslide susceptibility. Multiple validation techniques were applied to ensure reliability and robustness of these findings.

Nest Defense Behavior of Suburban and Rural Red-shouldered Hawks

ABSTRACT Urban and suburban raptors live in close proximity to humans, and some species defend their nests and young vigorously, even diving at or striking humans walking on the ground. Such raptors may be more defensive of their nests than rural birds. We investigated (1) whether Red-shouldered Hawk (Buteo lineatus) nest defense behavior differs between suburban and rural birds, (2) whether any environmental conditions are associated with aggressive nest defense, and (3) whether nest defense behavior is associated with reproductive rate. First, we used an experimental walk-up protocol to approach nests with incubating or brooding Red-shouldered Hawks at suburban and rural sites, and we scored the hawk’s response to our approach. We measured environmental variables (nest height, distance to the nearest road and nearest house, and habitat proportions within 500 m of the nest) and determined reproductive rate. Second, we used our historical database to retrospectively classify hawks as most aggressive, moderately aggressive, or not aggressive, and we measured the same environmental variables at their nest sites. We found that most birds at both suburban and rural study areas responded to our experimental approach with minimal nest defense, though suburban birds responded with higher levels of nest defense. Environmental variables were unrelated to nest defense intensity in the experimental study. For the retrospective study in the suburban area, nest height was the only environmental factor distinguishing aggressive hawks from non-aggressive ones; aggressive nest defensive behavior was associated with lower nests. Reproductive rate was unrelated to nest defense behavior in both studies. Knowing which environmental factors may contribute to more aggressive behavior may help researchers, managers, and residents better understand bird behavior and predict the circumstances under which problems might develop, particularly in urban/suburban environments.

Understanding patterns of engagement in the citizen humanities: The civil records of Suriname

This study aims to identify engagement profiles in the citizen humanities and assess whether these profiles match those found in prior studies on crowd-based projects in the natural sciences. To this purpose, we use the log data from the citizen humanities project ‘Historical Database Suriname and the Caribbean’, in which volunteers transcribe the civil records of Suriname, and analyze the differences between engagement in data entry and forum activity. We identify seven engagement profiles, six of which are similar to the profiles found in crowd projects in the natural sciences. However, their pattern of occurrence differs with a more equal distribution of effort. Additionally, we discuss implications for project design and recommend choices that match project goals and foster engagement diversity.

METHODS OF FORECASTING GRAIN CROP YIELD INDICATORS TAKING INTO ACCOUNT THE INFLUENCE OF METEOROLOGICAL CONDITIONS IN THE INFORMATION-ANALYTICAL SUBSYSTEM

Forecasting crop yields is one of the key challenges for the agricultural sector, especially in the context of a changing climate and unstable weather conditions. Kazakhstan, possessing significant territories suitable for growing grain crops, faces many challenges related to the effective management of agricultural activities. In this regard, yield forecasting becomes an integral part of planning and decision-making processes in agriculture. Information and analytical subsystems that integrate yield forecasting methods allow agribusinesses to estimate future production more accurately, minimise risks associated with climate change and optimise resource use. An important component of such systems is the consideration of weather conditions, as weather factors have a direct impact on crop growth and development. The purpose of this article is to develop and evaluate modern methods of forecasting grain yields taking into account the influence of weather conditions, as well as their integration into information-analytical subsystems to improve the accuracy of agricultural forecasting. To achieve this goal, the article addresses the following tasks: to analyse existing methods of yield forecasting and identify their advantages and disadvantages, to develop forecasting models, including machine learning methods such as gradient bousting and recurrent neural networks, to validate the developed models on the basis of historical data using cross-validation methods, to evaluate the effectiveness of the proposed methods and compare them with basic models such as linear regression and simple average, to evaluate the effectiveness of the proposed methods and to compare them with the basic models such as linear regression and simple average. This article reviews modern methods of forecasting grain crop yields in Kazakhstan, as well as technologies used in information-analytical subsystems. Particular attention is paid to the analysis of the influence of meteorological conditions on yields and the development of models that take this factor into account. The presented review and research results are aimed at improving the existing approaches to the management of agricultural processes under conditions of growing uncertainty caused by climate change. The article explores an important scientific task related to the development of methods for step-by-step forecasting of agrometeorological factors and grain yields, relying on the principle of analogy.

Future climate risks to world cultural heritage sites in Spain: A systematic analysis based on shared socioeconomic pathways

Rapidly advancing anthropogenic climate change is a severe threat to cultural heritage worldwide. World cultural heritage sites (WCHS) are recognized by UNESCO as having outstanding international importance. Identifying and quantifying the impacts of climate change on the WCHS is therefore crucial for the development of long-term protection policies. Spain, the subject of the present study, is one of the most important world heritage countries in terms of the number of sites and is highly vulnerable to climate change under global scenarios.This study identified the main climate risks faced by Spain's WCHS on the basis of future climate data from four shared socioeconomic pathway (SSP) scenarios under the most recent version of the Coupled Model Intercomparison Project (CMIP6), the most up-to-date global climate model available. Risks include sea level rise and coastal flooding, fire risk and drought risk, hydrodynamic scouring and corrosion, and biodegradation. Some WCHS are located in regions that are projected to experience surface warming exceeding the global average by 2100 under the SSP5--8.5 scenario, with a doubling of extreme climate risk (a gradient from scenarios SSP1--2.6, SSP2--4.5, SSP3--7.0 to SSP5--8.5). Under this shared socioeconomic pathway, three Spanish WCHS are at risk of being submerged by seawater by 2100. On the basis of historical data, we identified greater potential fire risk in regions where Spain's WCHS is located than in other regions in Europe, with eight WCHS experiencing weather-induced fire hazards exceeding 95 %. Considering the combination of LUC types within the Spanish WCHS, we identified two WCHS that exemplify this risk: 1) the archaeological site of Atapuerca, which is primarily occupied by broad-leaved forest, and 2) the Caliphate city of Medina Azahara, which is surrounded by natural grasslands, emphasizing the need for fire prevention in woodland and grassland areas. Furthermore, we quantified the hydrodynamic scouring risk, corrosion and biodegradation risk, and drought risk in the entire region where Spain's WCHS are located from 2081 to 2100 and clustered them on the basis of risk characteristics. We found that under the SSP1-2.6 scenario, the overall pattern of climate risk faced by Spain's WCHS did not change significantly from the present, but there were clear changes under the SSP5-8.5 scenario. We also identified that Spain's WCHS listings are at greater risk from different climate risks. This study predicts the potential impact of climate change on Spain's WCHS, emphasizing the crucial role of “carbon neutrality” in reducing various climate risks to Spain's WCHS and providing guidance for the development of future long-term protection policies.

Advancing predictive maintenance: a comprehensive case study through industry 4.0

The paper is focused on predictive maintenance in the automotive industry with a specialization in technologies related to Industry 4.0. The emergence of predictive maintenance with Industry 4.0 technologies is also called Maintenance 4.0. The article describes specific Industry 4.0 technologies, such as supervisory control and data acquisition (SCADA) and Predictive Maintenance (PdM), that are being implemented in manufacturing companies. The sample for the case study consisted of a paint shop from the automotive sector that operates in Turkey. In the data collection step, SCADA collected data from pressure sensors to create a historical database for PdM. The purpose of this article is to illustrate, through a case study, the importance of PdM using SCADA using Industry 4.0 instruments such as the Internet of Things and big data. 18 filters, which were replaced every 6 months during preventive maintenance, are now replaced at different intervals through PDM after this study. Based on SCADA data, a total of 2 filters experience blockage in both the primer and topcoat paint processes every 6 months. Over a 12-month period, an extra 9 filters are blocked across all processes. Furthermore, within a 24-month timeframe, a total of 7 more filters become blocked in the entirety of the operational processes. The results showed that the PdM combination increased the effectiveness of care by 45.83%.

How Well Can We Measure Chronic Pain Impact in Existing Longitudinal Cohort Studies? Lessons Learned

Multiple large longitudinal cohorts provide opportunities to address questions about predictors of pain and pain trajectories, even when not anticipated in the design of the historical databases. This focus article uses 2 empirical examples to illustrate the processes of assessing the measurement properties of data from large cohort studies to answer questions about pain. In both examples, data were screened to select candidate variables that captured the impact of chronic pain on self-care activities, productivity, and social activities. We describe a series of steps to select candidate items and evaluate their psychometric characteristics in relation to the measurement of pain impact proposed. In the UK Biobank, a general lack of internal consistency of variables selected prevented the identification of a satisfactory measurement model, with lessons for the measurement of chronic pain impact. In the English Longitudinal Study of Ageing, a measurement model for chronic pain impact was identified, albeit limited to capturing the impact of pain on self-care and productivity but lacking coverage related to social participation. In conjunction with its Supplementary Material, this focus article aims to encourage exploration of these valuable prospectively collected data to support researchers to make explicit the relationships between items in the databases and constructs of interest in pain research and to use empirical methods to estimate the possible biases in these variables. PerspectiveThis focus article outlines a theory-driven approach for fitting new measurement models to data from large cohort studies and evaluating their psychometric properties. This aims to help researchers develop an empirical understanding of the gains and limitations connected with the process of repurposing the data stored in these datasets.

Bridging the gap between molecular and genomic epidemiology in tuberculosis: inferring MIRU-VNTR patterns from genomic data.

The transition from molecular epidemiology in tuberculosis (TB), based on the analysis of repetitive regions (VNTR-based genotyping), to genomic epidemiology transforms in the precision with which we track transmission. However, short-read sequencing, the most common method for performing genomic analysis, is poor at analyzing repetitive regions. This means that we face a gap between the new genomic data and the large amount of information stored in historical databases, which is also an obstacle to cross-national surveillance involving settings where only molecular data are available. Long-read sequencing could help bridge this knowledge gap by allowing analysis of repetitive regions. Our study demonstrates that MIRU-VNTR patterns can be successfully inferred from long-read sequences, allowing the correct assignment of new cases as clustered/orphan by linking new data extracted from genomic analysis to historical MIRU-VNTR databases. Our data may provide a starting point for bridging the knowledge gap between the molecular and genomic eras in tuberculosis epidemiology.

Causes of German Inventiveness, 1815–1990. What We Can Learn from Patent Statistics

Abstract In the second half of the 19th century, Germany developed into one of the most innovative economies in the world and was able to defend this position in the 20th century. In order to investigate the causes of this inventiveness, it is necessary to quantify innovations and assign them to inventors, regions and industries. For this reason, various historical patent databases have been set up over the last two decades, currently covering the period from 1815 to 1990. We present these patent databases and give an overview of the main empirical studies based on these statistics.

Building a Historic Population Platform

ObjectivesWith the increasing digital availability of large population databases of historical census or vital event records, the tasks of storing, cleaning, processing, linking and analysing such data become more challenging. Suitable computing platforms and software systems are required to handle such databases, and facilitate the application of complex record linkage algorithms, for example to reconstruct populations that cover a full country over many decades. We present our efforts to achieve these goals on a database of over 20 million vital event records spanning over 120 years to create a “Historic Population Platform” (HiPP). ApproachWe created a graph database using the Neo4J software, where each birth, death and marriage certificate is represented as a node. We then generated actor nodes from these certificates which represent individuals (such as birth babies and their parents, or marriage brides and grooms). Data cleaning steps included the correction and imputation of invalid, corrupted, and missing age and date values using information from related certificates. ResultsOur initial graph database contains over 100 million nodes and nearly 200 million edges, while our data cleaning methods help to substantially increase the number of valid age and date values. ConclusionThe availability of large historical population databases provides exciting opportunities for social science and health research. However, existing methods have limitations in handling data quality and the sizes of such databases. We presented novel methods to deal with these challenges which we hope will be of use for other projects that aim to build a HiPP.

Design of real-time & historical database for photovoltaic power plant

Abstract The real-time & historical database of photovoltaic power plant monitoring system is used to store and manage the real-time and historical data, including power plant parameters, environmental data, and photovoltaic unit data and so on. Analyzing of this data is helpful for monitoring the status of the plant equipment, performing maintenance, and enhancing the operational efficiency of the power plant. The data models and database function of the database is designed. The data models include tags, data processing, and data storage models. Database function include the modules of the collection, storage, compression, and index query. The data collection module utilizes dynamic link libraries to access database data and configures parameters through a comprehensive data collection framework. The storage strategies involve in a combination of in-memory databases and disk-based historical databases, incorporating secondary caching technology and cloud server storage techniques. The data compression module is achieved through secondary compression methods. The index query module encompass time index files and the secondary index files to improve query speed and real-time system performance. The designed elements collectively establish an efficient monitoring system database.

A Study on Historical Big Data Analysis of Surface Ecological Damage in the Coal Mining Area of Lvliang City Based on Two Mining Modes

Coal mining inevitably causes damage to the surface ecological environment. In response to the characteristics of multiple factors, wide scope, and long time series of surface ecological environment damage in coal mining subsidence areas, how to integrate multiple data sources and monitoring methods to achieve monitoring and trend analysis of ecological damage throughout the entire mining cycle still needs to be studied. In addition, the 110 mining method provides an innovative method for underground coal mining to reduce its surface ecological damage, but the differences in surface damage between the two mining modes and the effectiveness of the 110 method in realizing surface ecological damage-reducing mining still need to be studied in depth. Therefore, this study takes the surface ecological damage in the mining area of Lvliang City, a typical resource city in Shanxi Province, China, as the object. It establishes a four-in-one “Satellite–UAV–Ground–Underground” information monitoring method, proposes a historical big data evolution analysis method, constructs three spatial scales of historical big databases, clarifies the current situation and development trend of damage in coal mining areas in Lvliang City and explores the differences in surface ecological environment damage characteristics in coal mining areas based on the 121 and 110 mining methods. The results show that: (1) The existing coal mining subsidence area in Lvliang City is as high as 92,191.47 hectares, and it is expected to continue to increase to 130,739.55 hectares in the future 2035, with a growth rate of 41.812%, which realizes the goals of mapping the current situation, retracing the history and predicting the future of the ecological damage of the surface in Lvliang City. (2) The surface NDVI of the 110 working face is restored to the average level of the mining area faster than that of the 121 working face. The surface crack width, step displacement, length, distribution density, and surface settlement height of the 110 working face are all smaller than those of the 121 working face. It has been verified that the unique top-cutting and swelling filling effect of the 110 methods can effectively reduce the ecological damage caused by coal mining subsidence. And its widespread application can effectively realize the ecological environmental protection of the coal mine area and contribute to the high-quality development of the coal industry in Lvliang City.

P02-30 Generation and evaluation of a historical control database for the creation of assay acceptance criteria for a comparative in vitro hepatic enzyme assay

P02-30 Generation and evaluation of a historical control database for the creation of assay acceptance criteria for a comparative in vitro hepatic enzyme assay

Electric vehicle supply equipment monitoring and early fault detection through autoencoders

This paper presents a novel approach to detecting anomalies in Electric Vehicle charging unit power profiles using a combination of Autoencoders with LSTM techniques. This study presents a robust methodology, combining the two Machine Learning techniques, for early fault estimation in a real-world case study. The proposed methodology offers significant advantages over existing methods by providing a more comprehensive analysis of anomalous trends. To validate the effectiveness of the proposed methodology, the authors tested it on real Electric Vehicles charging power curves provided by an Italian Distribution System Operator recorded on a historical database and compared the performances with the ones of a traditional anomaly detection technique. The results of the study, tested on Electric Vehicles Supply Equipment or charging stations, demonstrate that the proposed approach is highly effective in detecting anomalous trends in Electric Vehicles charging profiles.

One hundred years of inlet processes and barrier adjustments at North Inlet, SC

Debidue Beach and North Inlet are a coupled mixed-energy inlet-barrier system along the South Atlantic Bight in South Carolina, USA. Long-term chronic erosion along much of Debidue throughout the 20th century is the result of a shoreline adjustment triggered by an avulsion in the main channel of North Inlet occurring between 1926 and 1934. We use a historical database of shoreline positions compiled by the United States Geological Survey (USGS) and the Analysis of Moving Boundaries Using R (AMBUR) package to quantify changes in the Debidue Beach shoreline over nearly 150 years from 1872 to 2011. This analysis documents relatively large-scale shoreline changes (and equivalent volumetric changes above local depth of closure “DOC”) following the shift in channel position, and a logarithmic decrease in erosion rates over the following decades. Mixed-energy ebb-dominant inlets have considerable effects on adjacent beaches due to their ability to retain and shed large quantities of sand relatively quickly. This study demonstrates that even systems exhibiting long-term stability — like North Inlet — are indeed migrational landforms and should be considered as such when formulating shoreline management plans along adjacent beaches.

Mapping the past with historical geographic information systems: layered characteristics of the historic urban landscape of Nanjing, China, since the Ming Dynasty (1368–2024)

The layering process of the historic urban landscape (HUL) has spatial and temporal characteristics. With the help of digital technology, building a database to collect and manage spatial information on HUL is an effective research method. Taking Nanjing since the Ming Dynasty (1368–2024) as an example, our paper proposes a framework for constructing a historical geographic information system (HGIS) for HUL. The results show that (1) ancient Chinese historical archives have good potential for use in the construction of historical landscape databases. The HGIS can visualize the evolution of HUL and realize the storage, expression, querying, and analysis of information in different formats. (2) The Republic of China is a turning point in the evolution of Nanjing’s HUL. In terms of spatial evolution, the main distribution areas of urban form and characteristic places, as well as the NACH (normalized angular choice) and NAIN (normalized angular integration) cores of the road network, all moved from the south to the north of the city. In terms of typological evolution, the urban form changes from a predominantly residential area to one that emphasizes residential, administrative, educational and industrial areas. The main types of characteristic places shifted from house gardens, temples and ancestral halls to scenic buildings and public gardens. (3) Political factors, urban planning, commercial ports, and land prices influence the layering process of the HUL. The constructed database guides the preservation of HUL from the digital heritage perspective and the construction of other historical databases with spatial characteristics.

Cotton in the Indo-Pacific: Historical Origins, Linguistic Diffusion, and Socioeconomic Impacts

This study investigates the historical origins, linguistic diffusion, and socioeconomic impacts of cotton (Gossypium spp.) in the Indo-Pacific region. By reviewing secondary data from historical texts, linguistic databases, and archaeological reports, the research explores how Gossypium species, domesticated in both the Old and New Worlds, spread through complex trade networks and cultural exchanges. The study analyzes the dissemination patterns of cotton-related terminology across various language families and examines the socioeconomic effects of cotton cultivation and trade, including its influence on labor systems, economic structures, and cultural practices. While the findings highlight cotton’s significant role in shaping global markets and cultural inter-actions, the study’s limitations – such as its reliance on secondary sources, a two-month research duration, and a focus on a specific geographical area – warrant cautious interpretation. These constraints may limit the comprehensiveness and depth of the analysis, suggesting the need for future research to incorporate primary data collection, a broader geographical scope, and an extended study period. Despite these limitations, the study contributes to a nuanced understanding of cotton’s historical, economic, and cultural significance, emphasizing the need for sustainable agricultural practices in contemporary cotton production.

Wage–price spirals: what is the historical evidence?

AbstractHow common are wage–price spirals, and what has happened in their aftermath? We construct a new historical database of wage–price spirals—identified as episodes with consumer price inflation and average nominal wage growth rising jointly for at least a year—going back to the 1960s for a large sample of advanced economies. We find that only about a quarter of such episodes were followed by sustained accelerations in wages and prices. Instead, nominal wage growth and inflation tended to stabilize at a higher level on average, and then gradually revert, with real wage growth broadly unchanged. A decomposition of average wage dynamics during wage–price spiral episodes using a wage Phillips curve suggests that nominal wage growth normally stabilizes at levels consistent with observed inflation and labour market tightness. After historical episodes exhibiting rising inflation, falling real wages, and tightening labour markets—similar to what was observed in the early post‐COVID‐19 recovery in 2021—inflation tended to decline and nominal wage growth to rise, allowing real wages to gradually catch up. Our findings suggest that an acceleration of nominal wages against a backdrop of rising inflation does not necessarily signal that a persistent wage–price spiral dynamic is taking hold.

Extraction of Sunspots from Chinese Sunspot Drawings Based on Semisupervised Learning

China has six observing stations, providing over 52,000 handwritten sunspot drawings from 1947–2016. The observing stations are the Purple Mountain Astronomical Observatory (PMO), Yunnan Astronomical Observatory (YNAO), Qingdao Observatory Station (QDOS), Sheshan Observatory Station (SSOS), Beijing Planetarium (BJP), and Nanjing University (NJU). In this paper, we propose a new cotraining semisupervised learning method combining a semantic segmentation method named dynamic mutual training (DMT) boundary-guided semantic segmentation (BGSeg), i.e., DMT_BGSeg, which makes full use of the labeled data from PMO and the unlabeled data from the other five stations to detect and segment sunspot components in all sunspot drawings of the six Chinese stations. The sunspot is detected and segmented. Additionally, each sunspot is split into four types of components: pore, spot, umbra, and hole. The testing results show the mIoU values of PMO, YNAO, BJP, NJU, QDOS and SSOS are 85.29, 72.65, 73.82, 64.28, 62.26, and 60.07, respectively. The results of the comparison also show that DMT_BGSeg is effective in detecting and segmenting sunspots in Chinese sunspot drawings. The numbers and areas of sunspot components are measured separately. All of the detailed data are publicly shared on China-VO, which will advance the comprehensive augmentation of the global historical sunspot database and further the understanding of the long-term solar activity cycle and solar dynamo.