Multidimensional Datasets Research Articles

Phippery: a software suite for PhIP-Seq data analysis.

We present the phippery software suite for analyzing data from phage display methods that use immunoprecipitation and deep sequencing to capture antibody binding to peptides, often referred to as PhIP-Seq. It has three main components that can be used separately or in conjunction: (i) a Nextflow pipeline, phip-flow, to process raw sequencing data into a compact, multidimensional dataset format and allows for end-to-end automation of reproducible workflows. (ii) a Python API, phippery, which provides interfaces for tasks such as count normalization, enrichment calculation, multidimensional scaling, and more, and (iii) a Streamlit application, phip-viz, as an interactive interface for visualizing the data as a heatmap in a flexible manner. All software packages are publicly available under the MIT License. The phip-flow pipeline: https://github.com/matsengrp/phip-flow. The phippery library: https://github.com/matsengrp/phippery. The phip-viz Streamlit application: https://github.com/matsengrp/phip-viz.

BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

SCAR: Single-cell and Spatially-resolved Cancer Resources.

Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomicdata from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety oftissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.

Multiview Regularized Discriminant Canonical Correlation Analysis: Sequential Extraction of Relevant Features From Multiblock Data.

One of the important issues associated with real-life high-dimensional data analysis is how to extract significant and relevant features from multiview data. The multiset canonical correlation analysis (MCCA) is a well-known statistical method for multiview data integration. It finds a linear subspace that maximizes the correlations among different views. However, the existing methods to find the multiset canonical variables are computationally very expensive, which restricts the application of the MCCA in real-life big data analysis. The covariance matrix of each high-dimensional view may also suffer from the singularity problem due to the limited number of samples. Moreover, the MCCA-based existing feature extraction algorithms are, in general, unsupervised in nature. In this regard, a new supervised feature extraction algorithm is proposed, which integrates multimodal multidimensional data sets by solving maximal correlation problem of the MCCA. A new block matrix representation is introduced to reduce the computational complexity for computing the canonical variables of the MCCA. The analytical formulation enables efficient computation of the multiset canonical variables under supervised ridge regression optimization technique. It deals with the "curse of dimensionality" problem associated with high-dimensional data and facilitates the sequential generation of relevant features with significantly lower computational cost. The effectiveness of the proposed multiblock data integration algorithm, along with a comparison with other existing methods, is demonstrated on several benchmark and real-life cancer data.

High Spatial Resolution Fractional Vegetation Coverage Inversion Based on UAV and Sentinel-2 Data: A Case Study of Alpine Grassland

Fractional vegetation coverage (FVC) is an important indicator of ecosystem change. At present, FVC products are mainly concentrated at low and medium spatial resolution and lack high temporal and spatial resolution, which brings certain challenges to the fine monitoring of ecological environments. In this study, we evaluated the accuracy of four remote sensing inversion models for FVC based on high-spatial-resolution Sentinel-2 imagery and unmanned aerial vehicle (UAV) field-measured FVC data in 2019. Then the inversion models were optimized by constructing a multidimensional feature dataset. Finally, the Source Region of the Yellow River (SRYR) FVC product was created using the best inversion model, and the spatial-temporal variation characteristics of the FVC in the region were analyzed. The study’s findings revealed that: (1) The accuracies of the four FVC inversion models were as follows: the Gradient Boosting Decision Tree (GBDT) model (R2 = 0.967, RMSE = 0.045) > Random Forest (RF) model (R2 = 0.962, RMSE = 0.049) > Support Vector Machine (SVM) model (R2 = 0.925, RMSE = 0.072) > Pixel Dichotomy (PD) model (R2 = 0.869, RMSE = 0.097). (2) Constructing a multidimensional feature dataset to optimize the driving data can improve the accuracy of the inversion model. NDVI and elevation are important factors affecting the accuracy of machine learning inversion algorithms, and the visible blue band is the most important feature factor of the GBDT model. (3) The FVC in the SRYR gradually increased from west to east and from north to south. The change trajectories of grassland FVC from 2017 to 2022 were not significant. The areas that tend to improve were mainly distributed in the southeast (1.31%), while the areas that tend to degrade were mainly distributed in the central and northwest (1.89%). This study provides a high-spatial-resolution FVC inversion optimization scheme, which is of great significance for the fine monitoring of alpine grassland ecological environments.

Data-driven methods for situation awareness and operational adjustment of sustainable energy integration into power systems

In the context of increasing complexity in power system operations due to the integration of renewable energy sources, two main challenges arise: accurate short-term wind power forecasting and power flow convergence control. Accurate wind power forecasting plays a crucial role in power system scheduling, while controlling power flow convergence is essential for system stability. This study proposes a concise short-term wind power generation prediction model that combines a feature selection-based convolutional neural network-bidirectional long short-term memory network (CNN-BiLSTM) model. By effectively screening multidimensional feature datasets, the model optimizes the selection of highly correlated feature parameters and assigns weights to input data based on feature correlation. The CNN-BiLSTM combination model is then employed to establish a predictive model for wind power generation based on multiple features. Additionally, this study introduces an automatic adjustment model for power flow convergence using the D3QN (Double Dueling Q Network) reinforcement learning algorithm. This addresses the challenge of power imbalance leading to flow non-convergence, enabling effective control of power flow convergence and adaptive adjustment of operating modes. Experiments conducted using the KDD Cup 2022 wind power prediction dataset validate the wind power prediction method. The results demonstrate that the CNN-BiLSTM model effectively utilizes time-series data, surpassing other neural networks in prediction accuracy. Simulation results based on the PYPOWER case39 standard case reveal that the reinforcement learning model’s reward value increases with training rounds and stabilizes at 40. Remarkably, more than 72% of abnormal flow samples achieve rapid convergence within 10 steps, affirming the proposed method's efficacy and computational efficiency. The findings of this study contribute to enhancing the accurate awareness of new energy integration into power systems and provide a novel adaptive control method for power flow.

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging.

Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field.

Mars Global Surveyor's Mars Orbiter Camera (MOC) Wide‐Angle Images (1999–2006): 2. Data Investigation Into North Polar Hood Formation, Broad Brightness Changes in Acidalia, and Seasonal Frost in Hellas

AbstractMars Orbiter Camera Wide Angle images were taken from late 1999 through most of 2006, in both red and blue, providing nearly daily, nearly global coverage of Mars for approximately four Mars Years. Significant work has been published from those data, but there did not exist in a public archive a uniform, full time series of the processed data for easier analysis. The companion work to this (Robbins, 2023a, https://doi.org/10.1029/2022EA002673) presents that processing and quality control of the data, producing the full time series in both colors, in equirectangular and polar stereographic projections, in mosaics that have been binned into four different time intervals (daily, ΔLs = 2°, 5°, and 10° [≈4 through 19 days]) and as approximately true‐color composites. The multidimensional data set can be mined for surface and atmospheric variations on Mars. To demonstrate agreement with past work, several well‐known trends are demonstrated in a review‐style narrative format in Supporting Information S1, including changes after a global dust storm, seasonal frost variations at the poles, and surface variations. The data are analyzed further in the main text to demonstrate new qualitative and quantitative analyses of atmospheric and surface features: the North Polar Hood can form earlier than previously demonstrated, Acidalia has significant seasonal brightness variations that can be quantified, and the seasonal frost deposition and sublimation of Hellas can be quantified and shown to vary from year‐to‐year. These different investigations represent a small slice of what can be done now that these data are made available and easily accessible.

Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population.

Whether the multi-dimensional data of serially measured blood pressure contains information for predicting the future risk of death in elderly individuals in nursing homes is unclear. Of the elderly individuals staying in a nursing home, 19,740 and 40,055 individuals with serially measured blood pressure from day 1 to 365 (for AI-long) and 1 to 90 (for AI-short) along with the death information at day 366 to 730 and 91-365 were included. The neural network-based artificial intelligence (AI) was applied to find the relationship between BP time-series and the future risks of death in both populations. AI-long found a significant relationship between the serially measured BP from day 1 to day 365 days and the risk of death occurring 366-730 days with c-statistics of 0.57 (95% CI: 0.51-0.63). AI-short also found a significant relationship between the serially measured BP from day 1 to day 90 and the rate of death occurring 91-365 days with c-statistics of 0.58 (95%CI: 0.52-0.63). Our results suggest that neural network-based AI could find the hidden subtle relationship between multi-dimensional data of serially measured BP and the future risk of death in apparently healthy elderly Japanese individuals under nursing care.

Accurate Prediction of Alzheimer's Disease Progression Trajectory via a Novel Encoder-Decoder LSTM Architecture.

With an increase in life expectancy, there has been an increase in the aged population globally, and around 10% of this population suffers from Alzheimer's disease. Alzheimer's hugely impacts the quality of life and well-being of older adults and their caregivers. Thus, it is an emerging challenge to improve the early diagnosis and prognosis of the disease. Detecting hidden patterns in complex multidimensional datasets using recent advancements in machine learning provides a tremendous opportunity to meet this crucial need. In this study, using multimodal features and an individual's clinical status on one or more time points, we aimed to predict the individual's cognitive test scores, changes in Magnetic Resonance Imaging features, and the individual's diagnostic status for the next three years. We presented a novel Encoder-Decoder Long Short-Term Memory deep-learning model architecture for implementing our prediction. We applied it to data from the Alzheimer's Disease Neuroimaging Initiative, comprising longitudinal data of 1737 participants and 12,741 instances. The proposed model was found to be competent, with a validation accuracy of 0.941, a multi-class area under the curve of 0.960, and a test accuracy of 0.88 in identifying the various stages of Alzheimer's disease progression in patients with an initially cognitively normal or mild cognitive impairment which is a significant improvement over previous methods.Clinical relevance- The proposed approach can help improve diagnostic understanding of Alzheimer's Disease progression and assist in the early detection of various stages of Alzheimer's Disease based on clinical heterogeneity.

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

Spatial and Temporal Analysis of Climatic Precursors before Major Earthquakes in Iran (2011–2021)

The present study provides a systematic assessment of the relationships between climatic variables and major earthquakes (M > 6) in Iran (2011–2021). These variables include total cloud cover (tcc), low cloud cover (lcc), total precipitation (tp), surface latent heat flux (slhf), and total column rainwater (tcrw). Based on a wider set of variables provided by a multidimensional global dataset (ERA5), the combination of a cross-correlation function (CCF) and receiver operating characteristic (ROC) was used to develop the spatial and temporal analytic relations. Covering maximal values from 0.42 to 0.92, the CCF plots revealed that an increase in climatic parameters could provide valuable information about impending earthquake activity within 8 to 20 days. The mean values of tcc, lcc, tp, slhf, and tcrw were found to increase by 95%, 60%, 80.0 mm, 105 W/m2, and 95 kg−3/m2, respectively. In fact, with the mean AUC (area under the curve) indices ranging from 0.677 (tcc) to 0.810 (tcrw) prior to major earthquakes, the ROC plots allowed for discrimination between seismic and climatic variables ranging from “acceptable” to “excellent”. The changes in the climatic variables under study were due to anomalous air ionization and water condensation in the atmosphere, which can be regarded as short-term precursors to major earthquakes.

Ensemble learning-based applied research on heavy metals prediction in a soil-rice system

Accurate prediction of heavy metal accumulation in soil ecosystems is crucial for maintaining healthy soil environments and ensuring high-quality agricultural products, as well as a challenging scientific task. In this study, we constructed a dataset containing 490 sets of multidimensional environmental covariate data and proposed prediction models for heavy metal concentrations (HMC) in a soil–rice system, EL-HMC (including RF-HMC and GBM-HMC), based on Random Forest (RF) and Gradient Boosting Machine (GBM) ensemble learning (EL) techniques. To reasonably evaluate the effectiveness of each model, Multiple linear and Bayesian regressions were selected as benchmark models (BM), and mean absolute error (MAE), root mean square error (RMSE), and determination coefficient R2 were selected as evaluation indicators. In addition, sensitivity and spatial autocorrelation (SAC) analyses were used to examine the robustness of the model. The results showed that the R2 values of RF-HMC and GBM-HMC for modeling available cadmium (Cd) concentrations in soil were 0.654 and 0.690, respectively, with an average increase of 48.0 % compared to the BMs. The R2 values of RF-HMC and GBM-HMC for predicting Cd, lead (Pb), chromium (Cr), and mercury (Hg) concentrations in rice ranged from 0.618 to 0.824 and 0.645 to 0.850, respectively, with an average increase of 58.2 % compared with the BMs. The corresponding MAEs and RMSEs of RF-HMC and GBM-HMC had low error levels. Sensitivity analysis of the input features and the SAC of the prediction bias showed that the EL-HMC models have excellent robustness. Therefore, the EL technology-based prediction models for HMCs proposed herein are practical and feasible, demonstrating better accuracy and stability than the traditional model. This study verifies the application potential of EL technology in pollution ecology and provides a new perspective and solution for sustainable management and precise prevention of heavy metal pollution in farmland soil at the regional scale.

Applying Machine Learning to investigate metal isotope variations at the watershed scale: A case study with lithium isotopes across the Yukon River Basin

Constraining the multiple climatic, lithological, topographic, and geochemical variables controlling isotope variations in large rivers is often challenging with standard statistical methods. Machine learning (ML) is an efficient method for analyzing multidimensional datasets, resolving correlated processes, and exploring relationships between variables simultaneously. We tested four ML algorithms to elucidate the controls of riverine δ7Li variations across the Yukon River Basin (YRB). We compiled (n = 102) and analyzed new samples (n = 21), producing a dataset of 123 river water samples collected across the basin during the summer including δ7Li and extracted environmental, climatological, and geological characteristics of the drainage area for each sample from open-access geospatial databases. The ML models were trained, tuned, and tested under multiple scenarios to avoid issues such as overfitting. Random Forests (RF) performed best at predicting δ7Li across the basin, with the median model explaining 62 % of the variance. The most important variables controlling δ7Li across the basin are elevation, lithology, and past glacial coverage, which ultimately influence weathering congruence. Riverine δ7Li has a negative dependence on elevation. This reflects congruent weathering in kinetically-limited mountain zones with short residence times. The consistent ranking of lithology, specifically igneous and metamorphic rock cover, as a top feature controlling riverine δ7Li modeled by the RFs is unexpected. Further study is required to validate this finding. Rivers draining areas that were extensively covered during the last glacial maximum tend to have lower δ7Li due to immature weathering profiles resulting in short residence times, less secondary mineral formation and therefore more congruent weathering. We demonstrate that ML provides a fast, simple, visualizable, and interpretable approach for disentangling key controls of isotope variations in river water. We assert that ML should become a routine tool, and present a framework for applying ML to analyze spatial metal isotope data at the catchment scale.

An Enhanced Outlier Detection Approach for Multidimensional Datasets Using a Synergistic Firefly and Grey Wolf Optimization-Based Method

An Enhanced Outlier Detection Approach for Multidimensional Datasets Using a Synergistic Firefly and Grey Wolf Optimization-Based Method

Differentiation of white matter histopathology using b-tensor encoding and machine learning.

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor-valued diffusion encoding schemes (b-tensor) have been developed to enrich the microstructural data that can be obtained through DW-MRI. These advanced methods have proven to be more specific to microstructural properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We achieved this in three steps: 1) We induced different levels of white matter damage in rodent optic nerves. 2) We obtained ex vivo DW-MRI data with b-tensor encoding schemes and calculated quantitative metrics using Q-space trajectory imaging. 3) We used a machine learning model to identify the main contributing features and built a voxel-wise probabilistic classification map of histological damage. Our results show that this model is sensitive to characteristics of microstructural damage. In conclusion, b-tensor encoded DW-MRI data analyzed with machine learning methods, have the potential to be further developed for the detection of histopathology and neurodegeneration.

Multi-algorithm clustering analysis for characterizing cow productivity on automatic milking systems over lactation periods

The introduction of Automated Milking Systems (AMSs), or milking robots, represented a significant advancement in dairy farming techniques. AMSs enable real-time monitoring of udder health and milk quality during each milking episode, which provides a wealth of data that can be utilized to optimize herd management practices. ML algorithms are well-suited for handling large and multi-dimensional datasets, making them a valuable tool for analyzing the vast amount of data generated by AMSs. This study introduces a novel approach to characterize the milk productivity of Holstein Friesians cows milked by AMSs during individual lactation periods and evaluate their stability over time. Four unsupervised ML clustering algorithms were employed to cluster the cows within each lactation period, and a merging index was proposed to combine the clustering results. The dairy cows were grouped into clusters based on their productivity, and the stability of these Productivity Groups (PGs) over time was analyzed. The PGs were found to be weakly stable over time, indicating that selecting cows for insemination based solely on their present or past lactation productivity may not be the most effective strategy. In addition, the results revealed that the High Productivity Group exhibited lower levels of protein, fat, and lactose content in the milk. The proposed methodology was demonstrated using data from one farm with dairy cows that exclusively uses the AMS, however, it can be applied to any context and dataset in which a multi-algorithm clustering analysis is suitable, including data from conventional milking parlors. Understanding milk productivity and its factors in future lactation periods is essential for effective herd management. A comprehensive long-term analysis is of significant importance for the zootechnical sector as it could assists farmers in selecting cows for insemination and making decisions on which ones to retain for future lactation periods.

Indonesian Curriculum 2013 Ten Years On: Impact on Mathematics Teaching

The Indonesian Government introduced the 2013 Curriculum as an attempt to improve education within Indonesia. International comparisons showed that mathematics was a subject in which there was much room for improvement. As it is now ten years since the introduction of the new curriculum, it is time to review its implementation. A key element of the 2013 curriculum is using the Scientific Approach (observing, asking, exploring, associating, and communicating) as the fundamental pedagogy with a general emphasis on active learning. This study explores the extent to which this has been implemented within the classroom by utilizing various data sources, including student focus group discussions, classroom observations, documentation reviews and teacher interviews. This produced a multi-dimensional data set which reveals that ten years on from its introduction, the implementation of the Scientific approach in mathematics is, at best, inconsistent. In practice, there is still a great reliance on teacher-centered pedagogies, although lesson plans indicate an intent to promote more active learning. Students tend to remain passive and individual learners, although they express a strong desire to work together. Teachers report difficulties in finding appropriate learning media to facilitate active learning pedagogies and school infrastructures are often a hindrance to the use of the Scientific Approach.

A multi-dimensional dataset of Ordovician to Silurian graptolite specimens for virtual examination, global correlation, and shale gas exploration

Abstract. Multi-elemental and multi-dimensional data are more and more important in the development of data-driven research, as is the case in modern paleontology, in which, in an examination by experts, or some day artificial intelligence, every fossil specimen plays a fundamental role. We here release a dataset of 1550 graptolite specimens representing 113 Ordovician to Silurian graptolite species or subspecies that are significant in global stratigraphic correlation and shale gas exploration. The dataset contains 2951 high-resolution images and a data table of each specimen's scientific information, including the taxonomic, geologic, and geographic information, comments, and references. Our dataset provides images for specialists or laypeople worldwide, is supported by the tool FSIDvis (Fossil Specimen Image Dataset Visualizer), which we developed to facilitate the interactive exploration of the rich-attribution image dataset, and includes a nonlinear-dimension reduction technique, t-SNE (t-distributed stochastic neighbor embedding), to project image data into a two-dimensional space to visualize and explore the similarities. Our dataset potentially contributes to virtual examinations of specimens, global bio-stratigraphic correlation, and improvement of the shale gas exploration efficiency. All data, images and the spreadsheet are available at https://doi.org/10.5281/zenodo.6688670 (Xu, 2022).

HpMCA: a Python-based graphical program for energy-dispersive X-ray diffraction data collection and analysis

ABSTRACT Energy-dispersive X-ray diffraction (EDXD) at synchrotron beamlines is commonly used for the study of material properties under high pressure and/or high temperature. Experimenters typically rely on the availability of robust data collection and analysis at a beamline, but this has become increasingly difficult, especially with the introduction of multi-element detectors that generate complex, multi-dimensional data sets. These data sets have energy resolution, and they can also be resolved in relation to sample position, diffraction angle, or different external stimuli. We report a new Python-based graphical program, hpMCA, for EDXD data collection and analysis that streamlines the experimental process for the beamline users. The program features a user-friendly interface, capability for online viewing and analyzing data from multi-element energy-dispersive detectors, and includes features useful for working with samples under high pressure and/or high temperature, such as crystal phase identification, real-time unit cell lattice refinement, and pressure determination based on an equation of state.