Stages Of Data Processing Research Articles

Best practices for germline variant and DNA methylation analysis of second- and third-generation sequencing data.

This comprehensive review provides insights and suggested strategies for the analysis of germline variants using second- and third-generation sequencing technologies (SGS and TGS). It addresses the critical stages of data processing, starting from alignment and preprocessing to quality control, variant calling, and the removal of artifacts. The document emphasized the importance of meticulous data handling, highlighting advanced methodologies for annotating variants and identifying structural variations and methylated DNA sites. Special attention is given to the inspection of problematic variants, a step that is crucial for ensuring the accuracy of the analysis, particularly in clinical settings where genetic diagnostics can inform patient care. Additionally, the document covers the use of various bioinformatics tools and software that enhance the precision and reliability of these analyses. It outlines best practices for the annotation of variants, including considerations for problematic genetic alterations such as those in the human leukocyte antigen region, runs of homozygosity, and mitochondrial DNA alterations. The document also explores the complexities associated with identifying structural variants and copy number variations, underscoring the challenges posed by these large-scale genomic alterations. The objective is to offer a comprehensive framework for researchers and clinicians, ensuring that genetic analyses conducted with SGS and TGS are both accurate and reproducible. By following these best practices, the document aims to increase the diagnostic accuracy for hereditary diseases, facilitating early diagnosis, prevention, and personalized treatment strategies. This review serves as a valuable resource for both novices and experts in the field, providing insights into the latest advancements and methodologies in genetic analysis. It also aims to encourage the adoption of these practices in diverse research and clinical contexts, promoting consistency and reliability across studies.

Data Organization Patterns in Data Mesh: Optimizing Data Management for the Modern Enterprise

This article explores the implementation of Data Mesh principles through a structured layered approach to data organization, addressing the challenges of managing large-scale, diverse data ecosystems in modern enterprises. As global data creation surges towards 181 zettabytes by 2025, traditional centralized data architectures are proving inadequate. We propose a four-layer methodology—Raw, Work, Gold, and Publish—that aligns with Data Mesh philosophy while providing clear delineation of data processing stages. Through case studies across various industries, we demonstrate how this approach leads to significant improvements in data quality, time-to-insight, and incident resolution. A large financial institution achieved a 40% reduction in time-to-insight, while a global manufacturer reported a 30% improvement in data quality. The article also discusses the challenges of implementation, including cultural shifts and skill gaps. Our findings suggest that combining Data Mesh principles with a layered organizational structure can transform enterprise data landscapes, enhancing agility, cross-functional collaboration, and alignment of data assets with business objectives. This article contributes to the evolving field of decentralized data architectures and provides practical insights for organizations seeking to optimize their data management strategies in the face of exponential data growth.

Early Detection of Congenital Heart Diseases among Infants Using Artificial Neural Network Algorithm

Congenital Heart Disease (CHD) detection has continued to witness a consistent increase in research attention. CHD diseases are vast and also span across diverse demographics, without sparing pregnant women, unborn babies, or newly born babies. The aim of this study is to develop a detection model capable of detecting heart disease among infants with high accuracy and to also suggest solutions to manage it. To achieve this, the CHD types were identified to develop a data model that considered infants. The data model was processed through imputation, feature selection, and transformation using the imputation method and Principal Component Analysis (PCA). After the data processing stage, Artificial Neural Network (ANN) algorithm is adopted and trained with the data to generate the model for the detection of the CHD. Comparative analysis was used to evaluate the performance of the models in comparison with other models adopted in other works, considering metrics that defined the success of the detection models. The results showed that the ANN has the best detection outcome with an accuracy of 97.44%. Although the use of Logistic Regression algorithm attained a high level of performance with an accuracy of 95.00%, but it still falls below the proposed ANN algorithm. Another highly performing algorithm is the use of Extreme Gradient Boosting (XGB) which achieved an accuracy of 92.00%.

Integrated Curriculum Model: Reformulation and Actualization in the Basics of Islamic Science Course

Reformulation and actualization of the integrated curriculum has become one of the characteristics of every State Islamic University. However, conceptually the terminology for integrating science and curriculum has not yet reached an agreement among experts. Even now, it is still a discourse that has not ended. This research tries to reveal two main problems: first, how the integrated curriculum is applied in the learning process of the basics of Islamic science in the chemistry education study program. Second, the extent to which the application of the integrative curriculum is appropriate in relation to learning the basics of Islamic science in the chemistry education study program. This research uses a qualitative descriptive-analysis approach with purposive sampling consisting of head of the study program, 2 lecturers, 5 students from unit 01 and 5 students from unit 02 in semester 2 (two) who took the basics of Islamic science course in the Chemistry Education Study Program. Data were collected using direct observation techniques, in-depth interviews, and document studies and questionnaires. The data analysis technique goes through the stages of data reduction, data presentation, data processing and interpretation and drawing conclusions (verification). The research results show that the implementation of the integrated curriculum in the basics of Islamic science courses has been realized at the planning and presentation stage of integrated content in line with CPL and the material taught in class is in sync with CPMK and RPS. Meanwhile, the model used is an integration model based on monotheism and fiqh. At the implementation stage in unit 01 the lecturer did not carry out apperception. Meanwhile, in unit 02 the lecturer begins learning by praying together, and after that the lecturer conducts an apperception and explores the students' learning experiences. The suitability of the curriculum implemented in the Chemistry Education Study Program with the integrated curriculum in the Basics of Islamic Science course has been realized adequately although it still needs improvement. It is hoped that the results of this research will become an initial reference for lecturers, observers and educational policy makers.

CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.

Advances in in vivo Ca2+ imaging using miniatured microscopes have enabled researchers to study single-neuron activity in freely moving animals. Tools such as MiniAN and CalmAn have been developed to convert Ca2+ visual signals to numerical information, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including Ca2+ imaging, neuronal footprints, Ca2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient Ca2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for Ca2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.

Cross‐equalization for time‐lapse sparker seismic data

AbstractTime‐lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time‐lapse seismic exploration has been conducted using a large‐scale exploration system. However, for efficient monitoring of shallow subsurface, time‐lapse monitoring based on the small‐scale exploration system is required. Small‐scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal‐to‐noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small‐scale time‐lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre‐stack, post‐stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre‐stack data processing stage. In the post‐stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high‐resolution time‐lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.

Examining Student Perception and Enthusiasm for The MBKM Program: A Comprehensive Study

Merdeka Belajar Kampus Merdeka (MBKM) is a new policy that has not been widely understood by students. It is necessary to assess students' perceptions and interests in this policy. This study was conducted to determine the extent of student perceptions of this policy and the level of student interest in the programs offered. The stages of the research method carried out include the preparation stage, data collection stage, data processing stage, and data analysis stage. The research method used in this study is a survey method using questionnaires as a data collection tool. The respondents of this study were 232 students of the elementary school teacher education study program starting from the Class of 2020 until 2022. Data analysis techniques use descriptive analysis, categorization and cross-tabulation as well as spherman rank correlation analysis. The results showed that students' interest in participating in the MBKM program was in the medium category. Student perceptions of MBKM policies are highest in the good category, and these perceptions are related to interest in participating in the MBKM program. Meanwhile, students' perceptions of MBKM programs are in the good category and these perceptions are related to their interest in participating in the MBKM program.

The role of epigenetic factors in childhood atopic dermatitis

Atopic dermatitis (AD) is a genetically determined chronic inflammatory skin disease characterized by itching, relapsing course, age-related features of the lesion morphology and localization. The etiological factors contributing to the development of AD are: allergic reactions, inflammatory reactions in the skin, leading to a barrier disfunction, the affect and interaction of genetic and environmental factors. Epigenetic mechanisms also play a key role in immune regulation. The aim of this work is to study the genome-wide methylation profile in the skin of children with AD in the acute stage, as well as to determine changes in the expression level of immune response genes associated with common signaling pathways during therapy. Whole-genome sequencing was conducted to examine methylation in the skin samples from AD patients and controls. The stages of bioinformatics data processing were carried out. Changes in the expression levels of the TLR2, TLR9, IL4, IL13, CAMP, and DEFB1 genes before and after treatment were carried out using RT-PCR-RT on samples of mononuclear blood cells. When comparing samples from patients with AD and healthy children, changes were shown in the methylation of 2364 regions of genomic DNA with their corresponding specific genes. Among the identified genes, those related to processes associated with the pathogenesis of AD were selected. At the next stage, changes in the expression of the described immunity factors (TLR2, TLR9, IL-4, IL-13, DEFB1, CAMP) were assessed over time according to the type of treatment. For this purpose, patients were divided into 2 groups depending on the prescription of systemic therapy. Three months after treatment, the level of expression for all studied immune factors decreased. However, a significant decrease in expression for almost all parameters (except for the DEFB1 gene) was recorded in the group in which systemic therapy (dupilumab) was used in addition to topical treatment. At the same time, the SCORAD index after treatment improved by an average of 63% and amounted to 17±6.0 points. Thus, systemic therapy aimed at suppressing signaling of IL-4 and IL-13 cytokines indirectly leads to normalization of the expression levels of innate immune factors. Studying the regulation of molecular genetic mechanisms of immunity involved in the processes of inflammation in AD helps to clarify the immunopathogenesis of this disease, determine diagnostic markers and targets for drug therapy.

Methodological Challenges when Using Routinely Collected Health Data for Research: A scoping review.

Routinely collected health data (RCD) including electronic health records, disease registries, health administrative data and wearables data are not specifically collected for research purposes. Analysis of these data poses unique methodological challenges that must be addressed when conducting research, particularly as availability and use increase. This scoping review aimed to identify methodological challenges in research using RCD from existing literature (registered protocol: https://doi.org/10.17605/OSF.IO/EBM4D). We searched 6 electronic databases, including medical, health economics, nursing and psychology research databases, between Jan 2015 and Jan 2023, combining multiple “RCD” and “research” search terms (e.g., epidemiologic, informatics, pharmaceutical research). After screening abstracts and full-texts, we doubly extracted methodological themes, categorizing them into different study stages. We screened more than 23,000 records and included 430 papers. Bias and confounding were the most common methodological issues identified, discussed in relation to both study design and data analysis. Data quality, including data accuracy, validation, completeness, timeliness and cleaning, also posed substantial challenges, particularly during data processing stage. Record linkage and conducting analyses using distributed health networks also pose unique methodological challenges. Heterogeneity, incorporating social determinants of health and statistical models that address methodological challenges are also described in the literature. External validity and reporting are important considerations for RCD research. Our review identified several methodological challenges facing researchers using RCD. These issues should be addressed to ensure methodologically sound research. These findings will inform the development of a standardized protocol template and accompanying educational platform aimed at enhancing methodological quality and transparency when conducting research using RCD.

Data-Driven Multi-Fault Detection in Pipelines Utilizing Frequency Response Function and Artificial Neural Networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an artificial neural network (ANN) algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088×10−3to0.3062×10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model's effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.

Sentiment Analysis of BMKG Weather Information Service Using K-Nearest Neighbor Method

The Meteorology, Climatology, and Geophysics Agency (BMKG) is a government institution that provides information related to air quality, climate, dry days, satellite imagery, wave forecasts, wind forecasts, and fire potential. This information is disseminated not only through BMKG's official website but also via the social media platform X, making it easier for the public to access up-to-date information. This study aims to classify user sentiment towards the weather information services provided by BMKG using the K-Nearest Neighbor (KNN) method. Data was collected through a web crawling technique, resulting in 1,031 data points analyzed in this research. The data processing stages included Pre-Processing and sentiment calculation using Vader's Sentiment and Random Forest. The classification results using the KNN algorithm showed an accuracy rate of 96%, with an average precision of 96%, an average recall of 96%, and an average f-measure of 96%. These findings indicate that the KNN model can effectively classify user sentiment towards BMKG's services.

Kualitas Pelayanan terhadap Loyalitas Pelanggan yang Dimediasi oleh Kepuasan Pelanggan Grab Bike di Kota Bandung

This research aims to determine the effect of Service Quality (Variable X) and Customer Satisfaction (Variable Z) on Customer Loyalty (Y). The method used in this research is quantitative methods with an associative descriptive approach. The type of data in this research is primary data collected using a questionnaire from respondents with predetermined characteristics. This research uses a validity test with the product moment formula and a reliability test with the Cronbach's alpha formula. After the data is collected, the data is processed through four stages of data processing, namely data coding, data entry, data editing, and data transformation. Was carried out using two multiple regression models and hypothesis testing to determine the relationship between the variables previously studied. Based on the results of the first multiple regression analysis, it can be seen that service quality has a positive effect on customer satisfaction, which can be seen from the significance value of 0.000 < 0.05. Then from the results of the second multiple regression analysis it can be seen that service quality and customer satisfaction have a positive effect on customer loyalty which can be seen from the respective significance values ​​of 0.003 and 0.000 <0.05. In conclusion, customer satisfaction mediates the influence of service quality on customer loyalty. Keywords: Service Quality, Customer Satisfaction, Customer Loyalty

Benchmarking computational methods for single-cell chromatin data analysis

BackgroundSingle-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices.ResultsWe benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection.ConclusionsOur analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.

Field-programmable gate array implementation of efficient deep neural network architecture

Deep neural network (DNN) comprises multiple stages of data processing sub-systems with one of the primary sub-systems is a fully connected neural network (FCNN) model. This fully connected neural network model has multiple layers of neurons that need to be implemented using arithmetic units with suitable number representation to optimize area, power, and speed. In this work, the network parameters are analyzed, and redundancy in weights is eliminated. A pipelined and parallel structure is designed for the fully connected network information. The proposed FCNN structure has 16 inputs, 3 hidden layers, and an output layer. Each hidden layer consists of 4 neurons and describes how the inputs are connected to hidden layer neurons to process the raw data. A hardware description language (HDL) model is developed for the proposed structure and the verified model is implemented on Xilinx field-programmable gate array (FPGA). The modified structure comprises registers, demultiplexers, weight registers, multipliers, adders, and read-only memory lookup table (ROM/LUT). The modified architecture implemented on FPGA is estimated to reduce area by 87.5% and improve timing by 3x compared with direct implementation methods.

The Relationship of the 4P Model to Purchasing Decisions: Evidence on Residential Property

This paper explores the application of the 4P model (product, price, place, promotion) in analyzing the factors that influence house purchasing decisions in the Anging Mammiri Residence housing complex, Makassar City. Through quantitative research with a descriptive approach and collecting primary data through questionnaires, validity and reliability tests were carried out on these variables to ensure the robustness of the variables and the formation of a linear relationship between the variables. The population in this research is the number of consumers who have bought a house at Anging Mammiri Residence, Makassar City from 2016 to 2022. In this research, the sample was taken using the Census Sampling technique where the researcher took the entire sample or part of that number. Questionnaires were distributed to all home buyers totaling 131 respondents. However, at the data processing stage, 61 respondents did not return the questionnaire. So, the number of samples used was 70 respondents. The research results found that product, price, location and promotion had a significant and positive influence on the decision to purchase a house at Anging Mammiri Residence, Makassar City. This research contributes to understanding the elements of the marketing mix and analyzes the 4P model in shaping consumer decisions and its implications for the property market. Apart from that, it contributes to the government as a source of literature in formulating appropriate policies and in building infrastructure and regional development such as roads and other public facilities.

The Effect of Social Support and Academic Resilience on Burnout in Students Working on a Thesis

The objective of this study is to analyze the effect of social support and academic resilience on burnout in students engaged in thesis work. The study involved 324 students engaged in thesis work on the island of Java. This research method employs a quantitative approach. The data processing stage utilizes the Jamovi 2.3 software, which employs multiple linear regression models. The data were collected via an online questionnaire. The results of hypothesis testing indicate that social support has no effect on burnout. Conversely, academic resilience has a positive effect on burnout. Furthermore, social support and academic resilience simultaneously affect burnout by 2.35%, with an F value of 4.89 and a significance value of 0.008 (p<0.05). The findings of this research are of value to the field of education with regard to the topics of social support, academic resilience, and burnout. In particular, this research is of interest to students engaged in thesis work. The update of this research is the population on the island of Java. The results of the study indicated that social support has no effect on burnout. Conversely, the results of the academic resilience research demonstrated a positive influence on burnout.

CMB polarization signal demodulation with a rotating half-wave plate

ABSTRACT Several forthcoming Cosmic Microwave Background polarization experiments will employ a Continuously Rotating Half-Wave Plate (CRHWP), the primary purpose of which is to mitigate instrumental systematic effects. The use of a CRHWP necessitates demodulating the time-ordered data during the early stages of data processing. The standard approach is to “lock in” on the polarization signal using the known polarization modulation frequency and use Fourier techniques to filter out the remaining unwanted components. However, an alternative, less well-studied option is to incorporate the demodulation directly into the map-making step. Using simulations, we compare the performance of these two approaches to determine which is most effective for B-mode signal recovery. Testing the two techniques in multiple experimental scenarios, we find that the lock-in technique performs best over the full multipole range explored. However, for the recovery of the largest angular scales (multipoles, $\ell \lt 100$) we find essentially no difference in the recovery of the signal between the lock-in and map-making approaches, suggesting that a parallel analysis based on the latter approach could represent a powerful consistency check for primordial B-mode experiments employing a CRHWP. We also investigate the impact of a detector-differencing step, implemented prior to demodulation, finding that, in most scenarios, it makes no difference whether differencing is used or not. However, analysing detectors individually allows the point at which information from multiple detectors is combined to be moved to later stages in the analysis pipeline. This presents alternative options for dealing with additional instrumental systematic effects that are not mitigated by the CRHWP.

A dataset of the 1 km-grid road network density in China (2019)

Road network density is a basic index of evaluating regional road development scale and road network development degree. The measurement and monitoring of road network density (as a comprehensive characterization index) plays an important role in the assessment of regional traffic and economic development, and provide reference for urban road management and control measures, which is of great significance to urban planning and municipal management. In this paper, based on the vector data of railway land, highway land, urban and village road land and rail transit in the Third National Land Survey, we extracted the road center line, using the km-grade network production software, to obtain a dataset of the 1km-grid road network density in China in 2019. Rigorous quality control and quality inspection measures throughout all stages of data processing have been implemented, including road surface vector extraction, road skeleton line extraction, and kilometer grid density calculation, to ensure the correctness, integrity and consistency of the data. This dataset can be used for the study on the preparation for the national territory development plan, the evaluation of plan implementation, the calculation of status factors for regional transportation development, and accessibility calculation.

Efektivitas Kegiatan Pendampingan Penderita Diabetes Melitus terhadap Peningkatan Kualitas Manajemen Diri

Diabetes mellitus (DM) is a non-communicable disease which is currently causing complex problems both globally and nationally, because apart from the high morbidity and mortality rates. The main problem for DM sufferers is non-compliance in implementing a healthy lifestyle for DM sufferers, which has an impact on their quality of life due to complications. Efforts that can be made are to improve self-management with health education. The aim of this research is to determine the effectiveness of mentoring activities in improving the quality of self-management of DM sufferers. This research uses quantitative methods, with a quasi-experimental design, one group pre-test and post-test approach. The target activity is 64 people suffering from diabetes mellitus in Gunung Talang District, Solok Regency for more than 3 months, namely 27 May - 30 September 2022. The stages of data processing are editing, coding, transfer and tabulation. Data were analyzed bivariately using paired t-test with a confidence level of 95% (α=0.05). Data is presented in the form of tables and narratives. This research found that the average pre-test self-management score was = 45.6 while the post-test score = 50.89. The average pre-test blood sugar value = 226.09mg/dl and post-test value = 220.90 mg/dl. The conclusion is that mentoring activities are effective in changing the quality of self-management, but not blood sugar levels. It is recommended that this assistance activity be continued by the community health center so that the results can be maintained in the long term so that they can improve the quality of life of DM sufferers and have an impact on reducing their blood sugar.

A dataset of 1 km-grid cultivated land fragmentation in China (2020)

Cultivated land fragmentation is a quantitative description of cultivated land fragmentation, which not only affects the structure and function of cultivated land ecosystem, but also is not conducive to improving the utilization efficiency of cultivated land resources and the scale effect of grain production. In this paper, based on the grid data of the 2020 annual change survey, the 1 km grid farmland fragmentation data set of China in 2020 was obtained by grid space calculation of three landscape indicators, such as patch density index, separation index and landscape farmland type separation index. Strict quality inspection and quality control are carried out in the data processing stages such as farmland map reading, kilometer grid fragmentation calculation and comparison verification, which ensures the correctness, integrity and comparability of the data. This data set can be used to study the protection of cultivated land resources and the sustainable development of agriculture.