22 papers · ranked by Valyu relevance
Lea Waller, Susanne Erk, Elena Pozzi, Yara J. Toenders + 7 more
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…
Oscar Esteban, Christopher J. Markiewicz, Ross W. Blair, Craig A. Moodie + 12 more
Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed…
Mark M. McAvoy, Lei Liu, Ruiwen Zhou, Benjamin A. Philip
Numerous methods exist to analyze functional MRI (fMRI) data, but no software currently exists to integrate the commonly-used FSL statistical analysis software with alternative preprocessing methods from the Human Connectome Project. Here we developed the Connectome Operations For FSL ExEcution (COFFEE) pipeline to…
Lea Waller, Susanne Erk, Elena Pozzi, Yara J. Toenders + 7 more
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…
Victor Y. Pan, Liang Zhao
- If we append sufficiently many standard Gaussian random rows or columns to any matrix A, such that ||A|| = 1, then the augmented matrix has full rank with probability 1 and is well-conditioned with a probability close to 1, even if the matrix A is rank deficient or ill-conditioned. - We specify and prove these…
Alexander Brown, Saurabh Garg, James Montgomery, Richard Mankin
In this work, we examine the problem of efficiently preprocessing and denoising high volume environmental acoustic data, which is a necessary step in many bird monitoring tasks. Preprocessing is typically made up of multiple steps which are considered separately from each other. These are often resource intensive…
Gökmen Altay, Jose Zapardiel-Gonzalo, Bjoern Peters
Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are…
Jean-Michel Roger, Alexandre Mallet, Federico Marini, Jaan Laane
Even though NIR spectroscopy is based on the Beer-Lambert law, which clearly relates the concentration of the absorbing elements with the absorbance, the measured spectra are subject to spurious signals, such as additive and multiplicative effects. The use of NIR spectra, therefore, requires a preprocessing step. This…
Authors not listed
High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing dataset scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and…
Oswaldo Cadenas, Graham M. Megson, Krishna Garikipati
This paper presents a method to reduce a set of n 2D points to a smaller set of s 2D points with the property that the convex hull on the smaller set is the same as the convex hull of the original bigger set. The paper shows, experimentally, that such reduction accelerates computations; the time it takes to reduce from…
Lydia R Lucchesi, Petra Kuhnert, Jenny Davis, Lexing Xie
Data preprocessing is a crucial stage in the data analysis pipeline, with both technical and social aspects to consider. Yet, the attention it receives is often lacking in research practice and dissemination. We present the Smallset Timeline, a visualisation to help reflect on and communicate data preprocessing…
Keumsun Park, Minah Chae, Jae Hyuk Cho
Even though computer vision has been developing, edge detection is still one of the challenges in that field. It comes from the limitations of the complementary metal oxide semiconductor (CMOS) Image sensor used to collect the image data, and then image signal processor (ISP) is additionally required to understand the…
Authors not listed
In the data-driven discovery of high-performance thermoelectric (TE) materials, the lack of high-quality data remains a key bottleneck. Addressing this issue, we introduce the Systematically Verified Thermoelectric (sysTEm) dataset. Leveraging the physical relationships between transport properties, we curated and…
Baogui Qi, Hao Shi, Yin Zhuang, He Chen + 1 more
With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw…
Adarsh Arun, Jana Weber, Zhen Guo, Alexei Lapkin
As the chemical sector looks to decarbonize, one promising solution is the utilization of bio-feedstocks and biowaste to produce functional molecules. There is, therefore, great interest in understanding how and where to integrate these resources within chemical supply chains. To assist such efforts, screening…
Yi Deng, Chengyue Xing, Ling Cai
At present, data mining technology is continuously researched in science and application. With the rapid development of remote sensing satellite industry, especially the launch of remote sensing satellites with high-resolution sensors, the amount of information obtained from remote sensing images has increased…
Kevin Robben, Christopher Cheatum
We report a comprehensive study of the efficacy of least-squares fitting of multidimensional spectra to generalized Kubo lineshape models and introduce a novel least-squares fitting metric, termed the Scale Invariant Gradient Norm (SIGN), that enables a highly reliable and versatile algorithm. The precision of…
Guzzetta, Gianluca
In this paper, we present a comprehensive study and analysis of the Chan Vese algorithm for image segmentation, using a discretized scheme obtained from the empirical study of Chan-Vese model's functional energy and its partial differential equation based on its level set function. The proof to such result is shown…
Nathan W. Churchill, Grigori Yourganov, Anita Oder, Fred Tam + 3 more
'Simon J. Graham' 'Stephen C. Strother' 'Xi-Nian Zuo'] A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or “pipeline”) may significantly affect fMRI…
Tarini Naravane, Ilias Tagkopoulos
The future of personalized health relies on knowledge of dietary composition. The current analytical methods are impractical to scale up, and the computational methods are inadequate. We propose machine learning models to predict the nutritional profiles of cooked foods given the raw food composition and cooking…
Authors not listed
We present TAPAS (Transient Absorption Processing and Analysis Software), an open-source, Python-based graphical platform that covers the entire transient absorption (TA) workflow, from raw data import and preprocessing to visualization, global and target fitting, and statistical evaluation. Users operate TAPAS through…
Authors not listed
—Many scientific and engineering problems involving multi-physics span a wide range of scales. Understanding the interactions across these scales is essential for fully comprehending such complex problems. However, visualizing multivariate, multiscale data within an integrated view where correlations across space…