Low-quality Data Research Articles

Improved ResNet Models for Chronic Kidney Disease Prediction

Chronic kidney disease (CKD), a consequential health issue that can deeply affect an individual's overall wellness, can be initiated by either kidney cancer or a gradual reduction in kidney function. As the chronic disease advances, it can reach a critical stage where only dialysis or surgery can save lives. Halting its progress becomes crucial. CKD patients also face a heightened risk of premature death. Early detection of associated conditions poses a challenging task for healthcare professionals aiming to prevent their onset. A unique deep learning model is presented in this work for the prediction of CKD. Many existing CKD prediction models have the drawbacks of producing less accuracy, mispredicting, utilizing more computation time, and using low-quality datasets or data with noise and missing values, leading to misprediction. So it is necessary to develop new techniques that give high predictions with less computation time. The objective of this research work is to build improved ResNet models for the prediction of chronic kidney disease and evaluate their performance in comparison to other cutting-edge machine learning and deep learning methods. This research work developed ResNet models such as improved ResNet 152v2 with inception, improved ResNet 101, and improved ResNet50 models that produced 99.90%, 96.53%, and 93.968% accuracy, respectively. The proposed ResNet models for CKD prediction will be useful to nephrologists and other medical professionals.

Physics-inspired transfer learning for ML-prediction of CNT band gaps from limited data

Recent years have seen a drastic increase in the scientific use of machine learning (ML) techniques, yet their applications remain limited for many fields. Here, we demonstrate techniques that allow overcoming two obstacles to the widespread adoption of ML, particularly relevant to nanomaterials and nanoscience fields. Using the prediction of the band gap values of carbon nanotubes as a typical example, we address the representation of the periodic data as well as training on extremely small datasets. We successfully showed that careful choice of the activation function allows capturing periodic tendencies in the datasets that are common in physical data and previously posed significant difficulty for neural networks. In particular, utilization of the recently proposed parametric periodic Snake activation function shows a dramatic improvement. Furthermore, tackling a typical lack of accurate data, we used the transfer learning technique utilizing more abundant low-quality computational data and achieving outstanding accuracy on a significantly expanded dataspace. This strategy was enabled by the use of a combination of the Snake and ReLU layers, capturing data periodicity and amplitude, respectively. Hence, retraining only ReLU layers allowed the transfer of the periodic tendencies captured from low-quality data to the final high-accuracy neural network. Those techniques are expected to expand the usability of ML approaches in application to physical data in general and the fields of nanomaterials in particular.

Fine-grained partial discharge pattern recognition method based on substation equipment knowledge graph

Discharge-type faults are one of the main types of faults in substation equipment, and partial discharge (PD) faults are an important cause of equipment discharge faults. When different types of PD faults occur in equipment, their fault handling plans also vary accordingly. Therefore, the identification of PD types in equipment is very important. Aiming at the problem that the recognition accuracy of the existing methods is not high enough due to the low quality of PD data and unbalanced samples in the process of equipment PD pattern recognition, we propose a fine-grained method to recognize PD patterns based on substation equipment knowledge graph. This model combines a visual encoder with a knowledge graph and uses the fine-grained attribute features and the association feature information of the PD entity provided by the knowledge graph for the research of equipment PD pattern recognition. After experimental verification, the accuracy of PD pattern recognition in our proposed method is significantly higher than that of previous methods.

Evaluation of venous thromboembolism risk assessment models for hospital inpatients: the VTEAM evidence synthesis.

Pharmacological prophylaxis during hospital admission can reduce the risk of acquired blood clots (venous thromboembolism) but may cause complications, such as bleeding. Using a risk assessment model to predict the risk of blood clots could facilitate selection of patients for prophylaxis and optimise the balance of benefits, risks and costs. We aimed to identify validated risk assessment models and estimate their prognostic accuracy, evaluate the cost-effectiveness of different strategies for selecting hospitalised patients for prophylaxis, assess the feasibility of using efficient research methods and estimate key parameters for future research. We undertook a systematic review, decision-analytic modelling and observational cohort study conducted in accordance with Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines. NHS hospitals, with primary data collection at four sites. Medical and surgical hospital inpatients, excluding paediatric, critical care and pregnancy-related admissions. Prophylaxis for all patients, none and according to selected risk assessment models. Model accuracy for predicting blood clots, lifetime costs and quality-adjusted life-years associated with alternative strategies, accuracy of efficient methods for identifying key outcomes and proportion of inpatients recommended prophylaxis using different models. We identified 24 validated risk assessment models, but low-quality heterogeneous data suggested weak accuracy for prediction of blood clots and generally high risk of bias in all studies. Decision-analytic modelling showed that pharmacological prophylaxis for all eligible is generally more cost-effective than model-based strategies for both medical and surgical inpatients, when valuing a quality-adjusted life-year at £20,000. The findings were more sensitive to uncertainties in the surgical population; strategies using risk assessment models were more cost-effective if the model was assumed to have a very high sensitivity, or the long-term risks of post-thrombotic complications were lower. Efficient methods using routine data did not accurately identify blood clots or bleeding events and several pre-specified feasibility criteria were not met. Theoretical prophylaxis rates across an inpatient cohort based on existing risk assessment models ranged from 13% to 91%. Existing studies may underestimate the accuracy of risk assessment models, leading to underestimation of their cost-effectiveness. The cost-effectiveness findings do not apply to patients with an increased risk of bleeding. Mechanical thromboprophylaxis options were excluded from the modelling. Primary data collection was predominately retrospective, risking case ascertainment bias. Thromboprophylaxis for all patients appears to be generally more cost-effective than using a risk assessment model, in hospitalised patients at low risk of bleeding. To be cost-effective, any risk assessment model would need to be highly sensitive. Current evidence on risk assessment models is at high risk of bias and our findings should be interpreted in this context. We were unable to demonstrate the feasibility of using efficient methods to accurately detect relevant outcomes for future research. Further research should evaluate routine prophylaxis strategies for all eligible hospitalised patients. Models that could accurately identify individuals at very low risk of blood clots (who could discontinue prophylaxis) warrant further evaluation. This study is registered as PROSPERO CRD42020165778 and Researchregistry5216. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR127454) and will be published in full in Health Technology Assessment; Vol. 28, No. 20. See the NIHR Funding and Awards website for further award information.

Визуализация в задачах реконструкции данных

Many application tasks of multidimensional data analysis which describe the state of real physical or other systems face with difficulties. This is a consequence of the low-quality source data, including missing values, the probability of errors or unreliability of measurements. Incomplete data can become an obstacle for research using many modern informational methods. The current work examines the potential and capabilities of visual analytics tools for preliminary preparation, correction or complete analysis of primary data volumes. A promising area of application of the approach discussed in the study is the targeted use of visualization capabilities as a data analysis tool. The implementation of specialized visual metaphors is used to solve problems of processing and interpreting data, the sources of which are cyberphysical systems of different complexity levels. Such systems operate in an autonomous or partially controlled mode. A characteristic feature of these systems is the presence of a large number of sensors that collect various types of data. Such data differ in the capacity of the corresponding information channels, their speed and reliability. Examples of such cyberphysical systems are unmanned aerial vehicles (UAVs), robotic stations, and multimodal monitoring systems. These systems can function in conditions where it is difficult to obtain objective observation experience (deep-sea robots). The effective use of data collected by cyberphysical monitoring systems is a condition for solving a large number of application and research tasks.

Quantifying Careless Responses in Student Evaluation of Teaching and Justifying Removal for Data Validity

Surveys are typical for student evaluation of teaching (SET). Survey research consistently confirms the negative impacts of careless responses on research validity, including low data quality and invalid research inferences. SET literature seldom addresses if careless responses are present and how to improve. To improve evaluation practices and validity, the current study proposed a three-step procedure to screen SET data for quantifying careless responses, delete and justify the removal of careless responses, and assess if removing careless responses improved the internal structure of the SET data. For these purposes, a convenience sample was taken from a Chinese university. A web-based survey was administered using a revised version of the Students’ Evaluation of Education Quality. One hundred ninety-nine students evaluated 11 courses with 295 responses. Longstring and Rasch outlier analyses identified 49% of nonrandom and random careless responses. The careless responses impacted evaluation results substantively and were deleted. The subsequent study demonstrated that removal improved data validity, using reliability, separation, and inter-rater agreement from the multi-facet Rasch model and G- and D-coefficients, signal-noise ratios, and error variance from generalizability theory. Removing careless responses improved the data validity in terms of true score variance and discrimination power of the data. Data screening should be a prerequisite to validating SET data based on the research results. Data removal is necessary to improve the research validity only if there is a noticeable change in the estimated teaching abilities. Suggestions and implications were discussed, including developing sound evaluation practices and formative use of SET.

Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions

Gaussian process (GP) regression is a nonparametric Bayesian approach that has been used successfully in various astronomical domains, especially in time-domain astronomy. The most common applications are the smoothing of data for interpolation and the detection of periodicities. The ability to create unbiased data-driven models without a predefined physical model can be a major advantage over conventional regression methods. Prior knowledge can be included by setting boundary conditions or constraining hyperparameter values, while unknown hyperparameters are optimized during the conditioning of the model. We have adapted and transformed previous approaches of GP regression and introduce three new applications for this regression method, especially in the context of stellar occultations: the modeling of occultation light curves, the correction of public JPL ephemerides of minor planets based on publicly available image data of the Zwicky Transient Facility, and the detection of natural satellites. We used data from observations of stellar occultations to validate the models and achieved promising results in all cases, and thus we confirmed the flexibility of GP regression models. Considering various existing use cases in addition to our novel applications, GP regression can be used to model diverse data sets addressing a wide range of problems. The accuracy of the model depends on the input data and on the set boundary conditions. Generally, high-quality data allow the usage of loose boundary conditions, while low-quality data require more restrictive boundary conditions to avoid overfitting.

A Review on Deploying Blockchain Technology for Network Mobility Management

In wireless mobile networks, Mobility Management (MM) is a critical aspect that involves different tasks such as handover management, location management, offloading, resource allocation, and others. Blockchain platform comprising of an array of linked blocks innately supports the incorruptibility, sustaining the authenticity, and sustain pseudonymous privacy. Traditional MM is centralized, in contrast to blockchain-grounded MM that is partially or fully decentralized in its approach. As the inaugural researchers to review on blockchain-grounded MM, we are proud to cluster the blockchain-grounded MM concept under 7 clusters and closely study each framework with respect to different aspects such as blockchain specific parameters, MM specific parameters, network parameters, and more. We gathered a sample of 92 source references by curating the articles for eligibility criteria examined from electronic archives utilizing a comprehensive and enduring way. Grounded on this inspection, blockchain-grounded MM reinforces the overall security, trust, and privacy of location management and resource allocation, blockchain-grounded secure handover, facilitating registration and authentication, automatic MM utilizing smart contracts, reducing vulnerabilities, repairing low quality data, coordination, fair scheduling, in offloading, and blockchain-grounded resource allocation. Close studying proves that out of blockchain grounded MM frameworks, 37.5% utilize blockchain grounded secure handover, 92.5% utilize simple blockchain architecture, 32.5% utilize PoW consensus, 100% distributed MM, 97.5% host grounded MM, 95% packet reordering tolerant MM, 52.5% QoS aware MM, and 12.5% proposed for 5G networks. Finally, we present the openings and obstructions of the concept of blockchain-grounded MM and then catering surmounting advices.

Enhancing action recognition from low-quality skeleton data via part-level knowledge distillation

Skeleton-based action recognition is vital for comprehending human-centric videos and has applications in diverse domains. One of the challenges of skeleton-based action recognition is dealing with low-quality data, such as skeletons that have missing or inaccurate joints. This paper addresses the issue of enhancing action recognition using low-quality skeletons through a general knowledge distillation framework. The proposed framework employs a teacher-student model setup, where a teacher model trained on high-quality skeletons guides the learning of a student model that handles low-quality skeletons. To bridge the gap between heterogeneous high-quality and low-quality skeletons, we present a novel part-based skeleton matching strategy, which exploits shared body parts to facilitate local action pattern learning. An action-specific part matrix is developed to emphasize critical parts for different actions, enabling the student model to distill discriminative part-level knowledge. A novel part-level multi-sample contrastive loss achieves knowledge transfer from multiple high-quality skeletons to low-quality ones, which enables the proposed knowledge distillation framework to include training low-quality skeletons that lack corresponding high-quality matches. Comprehensive experiments conducted on the NTU-RGB+D, Penn Action, and SYSU 3D HOI datasets demonstrate the effectiveness of the proposed knowledge distillation framework.

A trust and privacy-preserving intelligent big data collection scheme in mobile edge-cloud crowdsourcing

As one of important Edge-Cloud solutions, mobile crowd sensing (MCS) platform resides in the cloud, and recruits massive workers in the edge network to sense data, so big data can be collected to build various applications or services to consumers, which is a promising computing paradigm. There are two critical properties that are not well addressed. One is current privacy preserving (PP) scheme pays little attention to quality-ware based data collection, so malicious or low-trust workers can cause false or low-quality data attacks, which will seriously affect the foundation of data-based services. The second is the lack of an intelligent worker selection scheme so to reduce costs and improve data quality under PP condition. To address the above issues, in this paper, we first propose a big data collection framework that not only has the function of PP but also can effectively identify worker trust, which is not achieved by previous studies. In particular, the worker trust identification approach in this paper abandons the past unreasonable assumption that data quality can be identified by the obtained data of data requester (DR). Then, we propose a reinforcement-based worker selection scheme that comprehensively considers workers’ trust, workers’ quotation and workers’ locations, so as to overcome the problem of worker recruitment that cannot be quality-ware in the past location privacy protection. Theoretical analysis and massive experimental results demonstrate that the proposed strategy is better than the state-of-the-art strategies in data quality, system cost and other performance indicator

TB-HQ: An Incentive Mechanism for High-Quality Cooperation in Crowdsensing

Crowdsensing utilizes a range of sensing resources and participants, including mobile device sensors, to achieve collaborative sensing and information fusion. This enables it to handle complex social sensing tasks and provide more intelligent and real-time environment sensing services. Incentive mechanisms in crowdsensing are employed to address issues related to insufficient user participation and low-quality data submission. However, existing mechanisms fail to adequately consider reference points in user decision-making and uncertainty in the decision-making environment. This results in high incentive costs for the platform and limited effectiveness. On the one hand, the probabilities and utilities in the actual decision environment are defined based on user preferences, and uncertainty can lead to unpredictable impacts on users’ future gains or losses. On the other hand, users identify their choices based on certain known values, namely reference points. The factors influencing user decisions are not solely the absolute final result level but rather the relative changes or differences between the final result and the reference point. Therefore, to resolve this problem, we propose TB-HQ, an incentive mechanism for high-quality cooperation in crowdsensing, which simultaneously considers the reference points adopted by users in decision-making and the uncertainty caused by their preferences. This mechanism includes a task bonus-based incentive mechanism (TBIM) and a high quality-driven winner screening mechanism (HQWSM). TBIM motivates users to participate in tasks by offering task bonuses, which alter their reference points. HQWSM enhances data quality by reconstructing utility functions based on user preferences. Simulation results indicate that the proposed incentive mechanism is more effective in improving data quality and platform utility than the comparative incentive mechanisms, with a 32.7% increase in data quality and a 77.3% increase in platform utility.

Robust Distributed Gradient Aggregation Using Projections onto Gradient Manifolds

We study the distributed gradient aggregation problem where individual clients contribute to learning a central model by sharing parameter gradients constructed from local losses. However, errors in some gradients, caused by low-quality data or adversaries, can degrade the learning process when naively combined. Existing robust gradient aggregation approaches assume that local data represent the global data-generating distribution, which may not always apply to heterogeneous (non-i.i.d.) client data. We propose a new algorithm that can robustly aggregate gradients from potentially heterogeneous clients. Our approach leverages the manifold structure inherent in heterogeneous client gradients and evaluates gradient anomaly degrees by projecting them onto this manifold. This algorithm is implemented as a simple and efficient method that accumulates random projections within the subspace defined by the nearest neighbors within a gradient cloud. Our experiments demonstrate consistent performance improvements over state-of-the-art robust aggregation algorithms.

Improving Factual Error Correction by Learning to Inject Factual Errors

Factual error correction (FEC) aims to revise factual errors in false claims with minimal editing, making them faithful to the provided evidence. This task is crucial for alleviating the hallucination problem encountered by large language models. Given the lack of paired data (i.e., false claims and their corresponding correct claims), existing methods typically adopt the ‘mask-then-correct’ paradigm. This paradigm relies solely on unpaired false claims and correct claims, thus being referred to as distantly supervised methods. These methods require a masker to explicitly identify factual errors within false claims before revising with a corrector. However, the absence of paired data to train the masker makes accurately pinpointing factual errors within claims challenging. To mitigate this, we propose to improve FEC by Learning to Inject Factual Errors (LIFE), a three-step distantly supervised method: ‘mask-corrupt-correct’. Specifically, we first train a corruptor using the ‘mask-then-corrupt’ procedure, allowing it to deliberately introduce factual errors into correct text. The corruptor is then applied to correct claims, generating a substantial amount of paired data. After that, we filter out low-quality data, and use the remaining data to train a corrector. Notably, our corrector does not require a masker, thus circumventing the bottleneck associated with explicit factual error identification. Our experiments on a public dataset verify the effectiveness of LIFE in two key aspects: Firstly, it outperforms the previous best-performing distantly supervised method by a notable margin of 10.59 points in SARI Final (19.3% improvement). Secondly, even compared to ChatGPT prompted with in-context examples, LIFE achieves a superiority of 7.16 points in SARI Final.

Data-model-interactive enhancement-based Francis turbine unit health condition assessment using graph driven health benchmark model

As the data-driven Francis turbine units (FTUs) deterioration assessment method is widely investigated, the data quality in actual industrial scene has become an important prerequisite to restrict the method performance. However, existing data augmentation methods often lie in simply mixing real data and simulated data, ignoring the inherent cross-domain state relationships. In this paper, a data-model-interactive enhancement-based FTU health condition assessment using graph driven health benchmark model (HBM) is proposed. First, the pseudo-signals outputs of the mechanism digital twin (DT) model by calculative fluid dynamics (CFD) calculation are modified by critical working condition parameters, extending the theoretical assessment domain. To achieve data-model-interaction through cross-domain state coordination of digital and real data, a digital-reality hybrid graph is constructed from nodes mixing both actual and pseudo through node similarity. The hybrid spatial condition structure created by capture node status association explicitly enhances the sample representation ability. Avoiding excess noise effects, a knowledge-based unsupervised graph pruning regulation is adopted to refine the original graph considering noise and data reality. Finally, the spatial–temporal dependencies hidden in the refined graphs are mined by the designed hybrid graph neural network-based HBM, and output the assessed degradation label values. Verification experiments show that the proposed assessment method can effectively fit FTU deterioration under low-quality onsite data, and is more robust than the comparison methods.

DeepFake detection based on high-frequency enhancement network for highly compressed content

The DeepFake, which generates synthetic content, has sparked a revolution in the fight against deception and forgery. However, most existing DeepFake detection methods mainly focus on improving detection performance with high-quality data while ignoring low-quality synthetic content that suffers from high compression. To address this issue, we propose a novel High-Frequency Enhancement framework, which leverages a learnable adaptive high-frequency enhancement network to enrich weak high-frequency information in compressed content without uncompressed data supervision. The framework consists of three branches, i.e., the Basic branch with RGB domain, the Local High-Frequency Enhancement branch with Block-wise Discrete Cosine Transform, and the Global High-Frequency Enhancement branch with Multi-level Discrete Wavelet Transform. Among them, the local branch utilizes the Discrete Cosine Transform coefficient and channel attention mechanism to indirectly achieve adaptive frequency-aware multi-spatial attention, while the global branch supplements the high-frequency information by extracting coarse-to-fine multi-scale high-frequency cues and cascade-residual-based multi-level fusion by Discrete Wavelet Transform coefficients. In addition, we design a Two-Stage Cross-Fusion module to effectively integrate all information, thereby greatly enhancing weak high-frequency information in low-quality data. Experimental results on FaceForensics++, Celeb-DF, and OpenForensics datasets show that the proposed method outperforms the existing state-of-the-art methods and can effectively improve the detection performance of DeepFakes, especially on low-quality data. The code is available here.11https://github.com/Gina-YUE/HiFE.

Privacy-preserving federated learning based on partial low-quality data

AbstractTraditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in participants’ data. Federated learning provides a method to protect participants’ data privacy by transferring the training process from a centralized server to terminal devices. However, the server may still obtain participants’ privacy through inference attacks and other methods. In addition, the data provided by participants varies in quality, and the excessive involvement of low-quality data in the training process can render the model unusable, which is an important issue in current mainstream federated learning. To address the aforementioned issues, this paper proposes a Privacy Preserving Federated Learning Scheme with Partial Low-Quality Data (PPFL-LQDP). It can achieve good training results while allowing participants to utilize partial low-quality data, thereby enhancing the privacy and security of the federated learning scheme. Specifically, we use a distributed Paillier cryptographic mechanism to protect the privacy and security of participants’ data during the Federated training process. Additionally, we construct composite evaluation values for the data held by participants to reduce the involvement of low-quality data, thereby minimizing the negative impact of such data on the model. Through experiments on the MNIST dataset, we demonstrate that this scheme can complete the model training of federated learning with the participation of partial low-quality data, while effectively protecting the security and privacy of participants’ data. Comparisons with related schemes also show that our scheme has good overall performance.

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33μg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Underwater small target detection based on dynamic convolution and attention mechanism

In ocean observation missions, unmanned autonomous ocean observation platforms play a crucial role, with precise target detection technology serving as a key support for the autonomous operation of unmanned platforms. Among various underwater sensing devices, side-scan sonar (SSS) has become a primary tool for wide-area underwater detection due to its extensive detection range. However, current research on target detection with SSS primarily focuses on large targets such as sunken ships and aircraft, lacking investigations into small targets. In this study, we collected data on underwater small targets using an unmanned boat equipped with SSS and proposed an enhancement method based on the YOLOv7 model for detecting small targets in SSS images. First, to obtain more accurate initial anchor boxes, we replaced the original k-means algorithm with the k-means++ algorithm. Next, we replaced ordinary convolution blocks in the backbone network with Omni-dimensional Dynamic Convolution (ODConv) to enhance the feature extraction capability for small targets. Subsequently, we inserted a Global Attention Mechanism (GAM) into the neck network to focus on global information and extract target features, effectively addressing the issue of sparse target features in SSS images. Finally, we mitigated the harmful gradients produced by low-quality annotated data by adopting Wise-IoU (WIoU) to improve the detection accuracy of small targets in SSS images. Through validation on the test set, the proposed method showed a significant improvement compared to the original YOLOv7, with increases of 5.05% and 2.51% in mAP@0.5 and mAP@0.5: 0.95 indicators, respectively. The proposed method demonstrated excellent performance in detecting small targets in SSS images and can be applied to the detection of underwater mines and small equipment, providing effective support for underwater small target detection tasks.

MtDNA analysis using Mitopore

Mitochondrial DNA (mtDNA) analysis is crucial for diagnosis of mitochondrial disorders, forensic investigations, and basic research, but existing pipelines are complex, expensive, and require specialized personnel. In many cases, including the diagnosis of detrimental single nucleotide variants (SNVs), mtDNA analysis is still carried out using Sanger sequencing. Here, we developed a simple workflow and a publicly available webserver named Mitopore that allows the detection of mtDNA SNVs, indels and haplogroups. To simplify mtDNA analysis, we tailored our workflow to process noisy long-read sequencing data for mtDNA analysis, focusing on sequence alignment and parameter optimization. We implemented Mitopore with eliBQ (eliminate Bad Quality reads), an innovative quality enhancement that allows to increase per base quality of over 20 % for low-quality data. The whole Mitopore workflow and webserver were validated using patient-derived and induced pluripotent stem cells harboring mtDNA mutations. Mitopore streamlines mtDNA analysis as an easy-to-use fast, reliable, and cost-effective analysis method for both long- and short-read sequencing data. This significantly enhances the accessibility of mtDNA analysis and reduces the cost per sample, contributing to the progress of mtDNA-related research and diagnosis.

Too Anecdotal to Be True? Mechanical Turk Is Not All Bots and Bad Data: Response to Webb and Tangney (2022).

In response to Webb and Tangney (2022) we call into question the conclusion that data collected on Amazon's Mechanical Turk (MTurk) was "at best-only 2.6% valid" (p. 1). We suggest that Webb and Tangney made certain choices during the study-design and data-collection process that adversely affected the quality of the data collected. As a result, the anecdotal experience of these authors provides weak evidence that MTurk provides low-quality data as implied. In our commentary we highlight best practice recommendations and make suggestions for more effectively collecting and screening online panel data.