Processing Standards Research Articles

Reliability of multi-parameter mapping (MPM) in the cervical cord: A multi-center multi-vendor quantitative MRI study

MRI based multicenter studies which target neurological pathologies affecting the spinal cord and brain – including spinal cord injury (SCI) – require standardized acquisition protocols and image processing methods. We have optimized and applied a multi-parameter mapping (MPM) protocol that simultaneously covers the brain and the cervical cord within a traveling heads study across six clinical centers (Leutritz et al., 2020). The MPM protocol includes quantitative maps (magnetization transfer saturation (MT), proton density (PD), longitudinal (R1), and effective transverse (R2*) relaxation rates) sensitive to myelination, water content, iron concentration, and morphometric measures, such as cross-sectional cord area. Previously, we assessed the repeatability and reproducibility of the brain MPM data acquired in the five healthy participants who underwent two scan-rescans (Leutritz et al., 2020). This study focuses on the cervical cord MPM data derived from the same acquisitions to determine its repeatability and reproducibility in the cervical cord. MPM matrices of the cervical cord were generated and processed using the hMRI and the spinal cord toolbox. To determine reliability of the cervical MPM data, the intra-site (i.e., scan-rescan) coefficient of variation (CoV), inter-site CoV, and bias within region of interests (C1, C2 and C3 levels) were determined. The range of the mean intra- and inter-site CoV of MT, R1 and PD was between 2.5% and 12%, and between 1.1% and 4.0% for the morphometric measures. In conclusion, the cervical MPM data showed a high repeatability and reproducibility for key imaging biomarkers and hence can be employed as a standardized tool in multi-center studies, including clinical trials.

Matrix-assisted laser desorption/ionization mass spectrometry imaging reveals “Spatial-Temporal-Content” changes of parishins in Gastrodiae Rhizoma during the steaming process

Gastrodiae Rhizoma is a commonly used plant material for both medicine and food in China for thousands of years. Steaming as the main pre-processing method of Gastrodiae Rhizoma is the key to its quality formation. In this study, we established a high-coverage matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) method to visualize the spatial distribution of phenols in Gastrodiae Rhizoma. By optimizing 1,5-diaminonaphthalene as MALDI matrix in negative mode, the “spatial-temporal-content” changes of 13 phenols including 11 parishins in Gastrodiae Rhizoma during the steaming process were imaged. We also characterized the activity of two hydrolases related to parishins degradation during the steaming. Moreover, the degradation pathways of parishins in Gastrodiae Rhizoma were summarized. The integration of spatially-resolved parishins and corresponding hydrolases information revealed the scientific connotation of steaming as the processing method of Gastrodiae Rhizoma is to inactivate hydrolases and protect parishins, which provided theoretical support for the quality improvement and the standardized processing of Gastrodiae Rhizoma.

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model.

In view of the limited number of extracted sound features, the lack of in-depth analysis of applicable sound features, and the lack of in-depth study of the selection basis and optimization process of classification models in the existing broiler sound classification or recognition research, the author proposes a recognition method for broiler sound signals based on multi-domain sound features and classification models. The implementation process is divided into the training stage and the testing stage. In the training stage, the experimental area is built, and multiple segments of broiler sound signals are collected and filtered. Through sub-frame processing and endpoint detection, the combinations of start frames and end frames of multiple sound types in broiler sound signals are obtained. A total of sixty sound features from four aspects of time domain, frequency domain, Mel-Frequency Cepstral Coefficients (MFCC), and sparse representation are extracted from each frame signal to form multiple feature vectors. These feature vectors are labeled manually to build the data set. The min-max standardization method is used to process the data set, and the random forest is used to calculate the importance of sound features. Then, thirty sound features that contribute more to the classification effect of the classification model are retained. On this basis, the classification models based on seven classification algorithms are trained, the best-performing classification model based on k-Nearest Neighbor (kNN) is obtained, and its inherent parameters are optimized. Then, the optimal classification model is obtained. The test results show that the average classification accuracy achieved by the decision-tree-based classifier (abbreviated as DT classifier) on the data set before and after min–max standardization processing is improved by 0.6%, the average classification accuracy achieved by the DT classifier on the data set before and after feature selection is improved by 3.1%, the average classification accuracy achieved by the kNN-based classification model before and after parameter optimization is improved by 1.2%, and the highest classification accuracy is 94.16%. In the testing stage, for a segment of the broiler sound signal collected in the broiler captivity area, the combinations of the start frames and end frames of multiple sound types in the broiler sound signal are obtained through signal filtering, sub-frame processing, endpoint detection, and other steps. Thirty sound features are extracted from each frame signal to form the data set to be predicted. The optimal classification model is used to predict the labels of each piece of data in the data set to be predicted. By performing majority voting processing on the predicted labels of the data combination corresponding to each sound type, the common labels are obtained; that is, the predicted types are obtained. On this basis, the definition of recognition accuracy for broiler sound signals is proposed. The test results show that the classification accuracy achieved by the optimal classification model on the data set to be predicted is 93.57%, and the recognition accuracy achieved on the multiple segments of the broiler sound signals is 99.12%.

Modeling and Analysis of Group User Portrait through WeChat Mini Program

Meeting users’ preferences and increasing business revenue is an ongoing challenge in the mobile service application. In this paper, we address these challenges by mining mobile user behavior patterns and propose an approach to construct a group user portrait by analyzing access data collected from the users of the WeChat Mini Program. We extract the attributes of mobile users considering their geographic information, online duration, and age group. UsingZ-score standardized processing andK-means clustering algorithm, we then model the user portraits through three dimensions including daily average duration, interaction intensity, and access frequency. Our analysis has two important features. Firstly, the significant log data used in our experiments was collected from the production environment ensuring that the results reflect the real attributes of WeChat Mini Program users’ behavior. Secondly, we provide data-driven decision-making to help marketers enhance the quality of the product and improve user experience. The experimental results indicate that by distilling and analyzing the key factors from the log data, the characters of typical users can be properly profiled to help product owners better optimize the exact set of the features which need to sustain and further grow.

Prediction of veneer moisture content based on near infrared spectroscopy

The physical properties of wood, particularly the dimensional stability, are affected by the water content. Most wood properties can be detected by near infrared spectroscopy (NIRS), which is used as a nondestructive testing method. At different wavelengths, different absorption peaks are presented with the moisture absorbed by wood. According to this feature, the absorption peaks can be collected, and the data can be processed by partial least squares method combined with NIRS. In this study, softwood oak and hardwood ash tree specimens were studied. In the infrared spectrum range, the wood moisture absorption curve was noticeable and the curve trend was similar, although the tree species were different. After centralization, standardization, and derivative processing of the spectral data, the correlation coefficients of oak and ash tree validations were high, reaching 0.9021 and 0.9661, respectively. The wood moisture content was predicted using NIRS and an algorithm. The experiments showed that this method is feasible.

From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin

In recent years, the digital twin has attracted widespread attention as an important means of digitalization and intelligence. However, the digital twin is becoming more and more complex due to the expansion of need on the simulation of multi-scale and multi-scenario in reality. The instance of digital twin in references mostly concentrates a particular application, while it is still a lack of a method for constructing the complex digital twin in the total elements, the variable scale of working environments, changeable process, not even the coupling effects. In this paper, a novel modeling method for such a complex digital twin is proposed based on the standardized processing on the model division and assembly. Firstly, the complex model of digital twin is divided into several simple models according to the composition, context, component, and code in 4C architecture. Composition and context make the digital twin focus on the effective elements in a specific scale and scenario. Component and code develop the digital twin in standard-based modularization. Secondly, assemble the simple models of digital twins into the complex model through information fusion, multi-scale association and multi-scenarios iterations. Ontology establishes the complete information library of the entities on different digital twins. Knowledge graph bridges the structure relationship between the different scales of digital twins. The scenario iterations realize the behavior interaction and the accuracy calculation results. It provides an implementable method to construct a complex model of digital twin, and the reuse of components and code also enables rapid development of digital twins.

Data Enhancement of Underwater High-Speed Vehicle Echo Signals Based on Improved Generative Adversarial Networks

Underwater target recognition is currently one of the hottest topics in computational intelligence research. However, underwater target recognition tasks based on deep learning techniques are difficult to conduct due to the shortage of acoustic echo signal samples, which results in poor training performance for existing deep learning models. Generative adversarial networks (GANs) have been widely used in data enhancement and image generation, providing a novel strategy for dealing with challenges in the research field mentioned above. To address the insufficiency of echo signal data for underwater high-speed vehicles, this paper proposes an underwater echo signal data enhancement method that uses an improved GAN based on convolution units for small sample sizes. First, we take pool test data as the training sample input and carry out data standardization, data interception, and copy-related processing work. Secondly, this paper proposes an improved generative adversarial network underwater (IGAN-UW) model to generate underwater echo signals. Finally, a CNN model combines the generated data with the original data to conduct classification training for underwater targets. Experimental results show that the IGAN-UW model is suitable for the generation of highly realistic original echo signals in cases with small sample sizes, providing a new approach to the active detection and recognition of underwater targets.

Chinese Food Image Database for Eating and Appetite Studies.

Modern people live in an environment with ubiquitous food cues, including food advertisements, videos, and smells. Do these food cues change people’s eating behavior? Since diet plays a crucial role in maintaining health, it has been researched for decades. As convenient alternatives for real food, food images are widely used in diet research. To date, researchers from Germany, Spain, and other countries have established food photo databases; however, these food pictures are not completely suitable for Chinese studies because of the ingredients and characteristics of Chinese food. The main goal of this research is to create a library of Chinese food images and to provide as complete a data reference as possible for future studies that use food images as experimental material. After standardized processing, we selected 508 common Chinese food pictures with high familiarity and recognizability and attached detailed classifications concerning taste, macronutrients, calories, and participants’ emotional responses to the pictures. Additionally, with food pictures as material, we conducted research on how people make dietary decisions in order to identify the variables that may affect a person’s food choices. The effects of individual perceived healthiness and palatability, gender, BMI, family income, and levels of emotional and restricted eating were examined using eating decisions based on healthiness and palatability as dependent variables. The results showed that people with low household incomes are more likely to be influenced by food taste in their dietary decision-making process, while individuals with high household incomes are more likely to consider the healthy aspects of food. Moreover, parental BMI affects what children consume, with children who have parents with higher BMIs being more prone to overlook the healthiness value of food.

RS-FetMRI: a MATLAB-SPM Based Tool for Pre-processing Fetal Resting-State fMRI Data.

Resting-state functional magnetic resonance imaging (rs-fMRI) most recently has proved to open a measureless window on functional neurodevelopment in utero. Fetal brain activation and connectivity maps can be heavily influenced by 1) fetal-specific motion effects on the time-series and 2) the accuracy of time-series spatial normalization to a standardized gestational-week (GW) specific fetal template space.Due to the absence of a standardized and generalizable image processing protocol, the objective of the present work was to implement a validated fetal rs-fMRI preprocessing pipeline (RS-FetMRI) divided into 6 inter-dependent preprocessing modules (i.e., M1 to M6) and designed to work entirely as an extension for Statistical Parametric Mapping (SPM).RS-FetMRI pipeline output analyses on rs-fMRI time-series sampled from a cohort of fetuses acquired on both 1.5T and 3T MRI scanning systems showed increased efficacy of estimation of the degree of movement coupled with an efficient motion censoring procedure, resulting in increased number of motion-uncorrupted volumes and temporal continuity in fetal rs-fMRI time-series data. Moreover, a "structural-free" SPM-based spatial normalization procedure granted a high degree of spatial overlap with high reproducibility and a significant improvement in whole-brain and parcellation-specific Temporal Signal-to-Noise Ratio (TSNR) mirrored by functional connectivity analysis.To our knowledge, the RS-FetMRI pipeline is the first semi-automatic and easy-to-use standardized fetal rs-fMRI preprocessing pipeline completely integrated in MATLAB-SPM able to remove entry barriers for new research groups into the field of fetal rs-fMRI, for both research or clinical purposes, and ultimately to make future fetal brain connectivity investigations more suitable for comparison and cross-validation.

DescePrep Significantly Increases Descemet Membrane Endothelial Keratoplasty Processing Efficiency and Success Rate in Diabetic Human Donor Corneas in Comparison With Manual Dissection.

The purpose of this study was to compare the safety, efficacy, and efficiency of a Descemet membrane endothelial keratoplasty (DMEK) graft processing device, DescePrep, with a manual dissection technique through the measurement of tissue yield, processing time, and graft viability in nondiabetic and diabetic donor corneas. Nondiabetic (n = 20) and diabetic (n = 20) donor corneas were processed using DescePrep, which standardizes the liquid bubble technique. Nondiabetic (n = 20) and diabetic (n = 24) donor corneas were also processed through manual dissection. Corneas were stained, processed, and then evaluated for processing success rate and time. Randomly selected corneas (n = 5, each) were evaluated for cell viability using live/dead staining. One hundred percent of nondiabetic and 95% of diabetic corneas were processed successfully with DescePrep in an average of 3.37 minutes. Ninety percent of nondiabetic and 50% of diabetic corneas were processed successfully with manual dissection in an average of 9.87 minutes. DescePrep had a significantly lower processing time ( P < 0.0001) and significantly higher success rate in comparison with manual dissection. DescePrep grafts had an average cell viability of 91.1% ± 3.3% in nondiabetic and 91.5% ± 2.4% in diabetic corneas. Grafts prepared with manual dissection had an average cell viability of 89.5% ± 5.8% in nondiabetic and 88.1% ± 4.3% in diabetic corneas. DescePrep provides a more effective and efficient method of cornea preparation in comparison with the current standard, particularly in diabetic corneas, while providing comparable cell viability. Thus, DescePrep offers standardized DMEK processing that produces high-quality grafts at high yields, with the potential to expand access and improve the quality of DMEK graft preparation in a larger pool of donors.

A Semi-Automatic Data Cleaning & Coding Tool for Chinese Clinical Data Standardization.

The clinical data often have limited usefulness because of the diversified expression. Chinese clinical data standardization can improve the usability of clinical data. The complexity of data cleaning and coding for Chinese clinical data prompted the turn of low-effective manual coding into the computer-aided tool. This study established the universal data cleaning and coding process and tool for Chinese clinical data standardization, which can greatly improve human efficiency. The process included the preprocessing, text similarity algorithm, and manual review. The standardization process proved effective for the diagnosis, drug, and examination data standardization task and can be used gradually in other clinical domains. The semi-automatic data cleaning and coding can reduce the half time for standardization, and it was used in hospitals in Beijing.

Images Are Data: Challenges and Opportunities in the Clinical Translation of Radiomics

Radiomics provides an opportunity to uncover image-based biomarkers through the conversion and analysis of standard-of-care medical images into high-dimensional mineable data. In the last decade, thousands of studies have been published on different clinical applications, novel analysis algorithms, and the stability and reproducibility of radiomics. Despite this, interstudy comparisons are challenging because there is not a generally accepted analytic and reporting standard. The ability to compare and combine results from multiple studies using interoperative platforms is an essential component on the path toward clinical application. The NCI supported study from van Griethuysen and colleagues published in Cancer Research in 2017 proposed PyRadiomics: an open-source radiomics quantification platform for standardized image processing. Since released, it has become a frequently utilized analytic tool in the radiomics literature and has accelerated the capability of combining data from different studies. The subsequent challenge will be the design of multicenter trials with a fixed and immutable version of software, which is currently open-source, readily modified and freely distributed. Generally, this is accomplished with a commercial partner to navigate the regulatory processes. See related article by van Griethuysen and colleagues, Cancer Res 2017;77:e104-7.

Distinguishing Abrupt and Gradual Forest Disturbances With MODIS-Based Phenological Anomaly Series.

Capturing forest disturbances over time is increasingly important to determine the ecosystem's capacity to recover as well as aiding a timely response of foresters. With changes due to climate change increasing the frequencies, a better understanding of forest disturbances and their role in historical development is needed to, on the one hand, develop forest management approaches promoting ecosystem resilience and, on the other hand, provide quick and spatially explicit information to foresters. A large, publicly available satellite imagery spanning more than two decades for large areas of the Earth's surface at varying spatial and temporal resolutions represents a vast, free data source for this. The challenge is 2-fold: (1) obtaining reliable information on forest condition and development from satellite data requires not only quantification of forest loss but rather a differentiated assessment of the extent and severity of forest degradation; (2) standardized and efficient processing routines both are needed to bridge the gap between remote-sensing signals and conventional forest condition parameters to enable forest managers for the operational use of the data. Here, we investigated abiotic and biotic disturbances based on a set of ground validated occurrences in various forest areas across Germany to build disturbance response chronologies and examine event-specific patterns. The proposed workflow is based on the R-package “npphen” for non-parametric vegetation phenology reconstruction and anomaly detection using MODIS EVI time series data. Results show the potential to detect distinct disturbance responses in forest ecosystems and reveal event-specific characteristics. Difficulties still exist for the determination of, e.g., scattered wind throw, due to its subpixel resolution, especially in highly fragmented landscapes and small forest patches. However, the demonstrated method shows potential for operational use as a semi-automatic system to augment terrestrial monitoring in the forestry sector. Combining the more robust EVI and the assessment of the phenological series at a pixel-by-pixel level allows for a changing species cover without false classification as forest loss.

RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI

Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and running an experiment, and a lack of standards that would foster collaboration. To address these issues, we have developed RT-Cloud (https://github.com/brainiak/rt-cloud), a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments. RT-Cloud uses standardized data formats and adaptable processing streams to support and expand open science in RT-fMRI research and applications. Cloud computing is a key enabling technology for advancing RT-fMRI because it eliminates the need for on-premise technical expertise and high-performance computing; this allows installation, configuration, and maintenance to be automated and done remotely. Furthermore, the scalability of cloud computing makes it easier to deploy computationally-demanding multivariate analyses in real time. In this paper, we describe how RT-Cloud has been integrated with open standards, including the Brain Imaging Data Structure (BIDS) standard and the OpenNeuro database, how it has been applied thus far, and our plans for further development and deployment of RT-Cloud in the coming years.

Establishing the doctor of nursing practice project ethical review process to improve standardization, efficiency, and timeliness

BackgroundDNP Scholarly projects require review for scientific merit and human subject protection. Rapid growth of DNP programs and projects has increased Institutional Review Board (IRB) burden and increased the length of project approval time when most DNP scholarly projects are quality improvement (QI) projects and not deemed Human Subjects Research (HSR). PurposeDevelop a process and describe the rationale for creating and implementing a Project Ethical Review Committee (PERC) in the School of Nursing and to evaluate the experience of the first cohort of submissions. ProcessCommittee was formed using expert consensus approach, in collaboration with IRB. Standards of Procedures and training materials created. Outcome measure100 projects submitted to committee; 95 deemed QI (95%) and 5 projects considered HSR (5%). First 94 projects were reviewed, and approval letters sent within 8 weeks. DiscussionThis paper discusses how PERC ensures rigorous and ethical review process for standardization, timeliness, and efficiency.

PA clinical postgraduate training: A half-century of development.

Clinical postgraduate training (CPGT) for physician assistants (PAs) began in 1971. A half-century later, the United States has 144 programs in 33 specialties. A growing and aging population, specialization in healthcare, and changes in graduate medical education have created new opportunities for PAs to consider this alternative following graduation. Postgraduate training is attractive to PA graduates who desire advanced skills and deeper understanding in a medical or surgical specialty. This article describes the history of CPGT programs, training, and establishment of an accreditation process for educational standardization.

Experimental Study on Proppant Transport within Complex Fractures

Summary The process of proppant filling within complex fractures is a key factor that determines the effects of volumetric fracturing operation in shale. To address the need of simulating proppant transport within the complex fractures, a large size visual simulation equipment of particle transport within complex fractures was built. The equipment considers roughness and fluid leakoff on the fracture wall and includes branched fractures of different angles. We ensured that the fluid flow and proppant flow were similar to those in the actual fracture and carried out experiments to obtain the influence of operation parameters and fracture parameters on proppant transport, also including injection of special proppants and channel fracturing. Then, we quantified proppant placement through standardized processing of dune shape pictures. The results show that under the influence of perforation, the near-wellbore dune is distributed in a slope shape, and an increase in the perforation density causes dune migration toward the wellbore and proppant accumulation to a certain height within the wellbore. The carrier fluid flow velocity change from 1.0 to 1.5 m/s within the fracture has the greatest effect on the dune equilibrium height (DEH). The dune height only varies slightly after the proppant concentration reaches 12%. Small size proppants have better transport capacity in the slickwater, and the large size proppants are likely to settle down. Combination injection of increasing proppant size improves the filling effect of complex fractures significantly. Pulse injection and stopping pump during fracturing operation have little effect on the transport of conventional proppants. Nevertheless, stopping pump leads to a significant increase in the dune height of the lower density proppants. An increase in the flow rate within the branched fracture leads to the transition in the dune shape from triangle to trapezoid and rectangle. The dune height within the major fracture determines those within the branched fracture, and the proppant volume flowing into the branched fracture determines the dune length. Proppant accumulation within the branched fracture enhances the difficulty in the diversion of carrier fluid to the branched fracture. The proppant tumble area increases within the inclined fracture, which is conductive to proppant transport. The proppants form the dune through bridging within the horizontal fracture, and the dune is less stable than that within the vertical fracture. A reduction in the proppant density and size leads to a significant increase in the proppant transport distance, and the convection effect by concentration difference of the ultralow-density (ULD) proppant is of great significance for propping of the microfractures. In channel fracturing, the proppant and fiber agglomerates are formed at the fracture point with the relatively large fluid leakoff and wall roughness, and the agglomerates grow laterally against the fracture fluid flow direction. A reasonable increase in the injection rate enhances the connectivity between the channels within the fracture. This study provides guidance for the optimization of volumetric fracturing parameters.

Behavior feature extraction method of college students’ social network in sports field based on clustering algorithm

Abstract In order to improve the integrity of the social network behavior feature extraction results for sports college students, this study proposes to be based on the clustering algorithm. This study analyzes the social network information dissemination mechanism in the field of college students’ sports, obtains the real-time social behavior data in the network environment combined with the analysis results, and processes the obtained social network behavior data from two aspects of data cleaning and de-duplication. Using clustering algorithm to determine the type of social network user behavior, setting the characteristics of social network behavior attributes, and finally through quantitative and standardized processing, get the results of college students’ sports field social network behavior characteristics extraction. The experimental results showed that the completeness of the method feature extraction results improved to 9.93%, and the average extraction time cost was 0.344 s, with high result integrity and obvious advantages in the extraction speed.

Method of Cumulative Anomaly Identification for Security Database Based on Discrete Markov chain

There exists an enormous volume of data in the database system, which is accountable for the storage of data and organization of data. The intruders can breach the security system of database and steal the important information. Therefore, it is of great significance to carry out the cumulative anomaly identification of the security database. In view of the shortcomings of traditional anomaly detection methods in detection performance and poor effect of anomaly recognition, this paper proposes a cumulative anomaly recognition method based on discrete Markov chain for security database. First, the sniffer is used to read the user access behaviour data, and then, it is processed, that is, standardized processing. Then, the segmentation method is used to extract the user behaviour features, and the normal feature data and abnormal feature data are obtained. Finally, the state sequence generated by the discrete Markov chain is used to calculate the state probability, which is used to evaluate the abnormal process behaviour. The proposed method in this paper is based on the Markov chain and can be used for better anomaly recognition. The results are obtained in terms of sensitivity score, precision score, and F1-score. The results are also compared with the results obtained by using some of the state-of-the-art traditional techniques. The comparison clearly indicated that the proposed method is more effective as compared to the tradition methods. The proposed method has the highest F1-score of 0.8586, and then the traditional methods have F1-scores of 0.7233, 0.8236, and 0.7562 for methods 1, 2, and 3, respectively.

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data.

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open‐source, containerized, user‐friendly tool that facilitates reproducible analysis of task‐based and resting‐state fMRI data through uniform application of preprocessing, quality assessment, single‐subject feature extraction, and group‐level statistics. It provides state‐of‐the‐art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post‐processing functions at the individual subject level, including calculation of task‐based activation, seed‐based connectivity, network‐template (or dual) regression, atlas‐based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low‐frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed‐effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post‐processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.