Open-source Python Tool Research Articles

CIGVis: an open-source python tool for real-time interactive visualization of multidimensional geophysical data

As Python’s role in processing and interpreting geophysical data expands, the need for a Python-based tool tailored for visualizing geophysical data has become increasingly criti- cal. In response, CIGVis was developed - a fully open-source Python tool optimized for researchers and licensed under the MIT. It specializes in real-time, interactive visualiza- tion of multidimensional geophysical data, including 3D seismic, faults, horizons, geological bodies, and well logs. CIGVis enables users to interact with data through operations such as rotation, movement, zooming, and dragging slices. It also supports a multi-canvas func- tionality, allowing simultaneous visualization across multiple sub-canvases with a unified camera perspective. Its ease of use allows for effective visualization with just a few lines of code across all major operating systems, and extends to both desktop and Jupyter environ- ments, facilitating code execution in various settings. The core functionalities of CIGVis are exemplified using straightforward datasets, such as the F3 dataset.

RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension

Abstract. The reduction of in situ observations over the last few decades poses a potential risk of losing important information in regions where local effects dominate the climatology. Reanalyses face challenges in representing climatologies with highly localized effects, especially in regions with complex orography. Empirical downscaling methods offer a cost-effective and easier-to-implement alternative to dynamic downscaling methods and can partially overcome the aforementioned limitations of reanalyses by taking into account the local effects through statistical relationships. This article introduces RASCAL (Reconstruction by AnalogS of ClimatologicAL time series), an open-source Python tool designed to extend time series and fill gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features using the analog method in combination with principal component analysis (PCA). The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allow fast and reproducible downscaling. The evaluations carried out in central Spain, in mountainous and urbanized areas, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis, as expected, in terms of R2, standard deviation, and bias. When analyzing reconstructions against observations, RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations. This confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, as with any other method based on empirical training, this method requires the availability of sufficiently long-term data series. Furthermore, it is susceptible to disruption caused by changes in land use or urbanization processes that might compromise the homogeneity of the training data. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project.

FaultQuake: An open-source Python tool for estimating Seismic Activity Rates in faults

In regions experiencing ongoing aseismic deformation, fault’s Activity Rate (AR) calculations often lead to an overestimation of hazard potential. This study proposes a novel methodology that integrates the Seismic Coupling Coefficient (SCC) into the fault Seismic Activity Rate (SAR) calculation process to discriminate seismic moment rates. We introduce FaultQuake, an open-source Python tool equipped with a Graphical User Interface (GUI), designed to implement this methodology and accurately estimate SAR for faults. These activity rates can be included in Probabilistic Seismic Hazard Assessment (PSHA) frameworks and assist in differentiating the seismic and aseismic deformation. FaultQuake also presents an innovative embedded workflow, the Optimal Value Computation Workflow (OVCW), based on Conflation of Probabilities (CoP), for calculating the Maximum Magnitude (Mmax) from the empirical relationships and the observed magnitudes (Mobs) assigned to a single fault. This enhancement improves the estimation of seismic moment rates and the SAR calculation process. FaultQuake outputs are provided in the format of OpenQuake engine input files to facilitate the PSHA process. We present a sample case study focusing on the PSHA of a region in southern Iran characterized by a substantial aseismic deformation to illustrate the practical application of FaultQuake in seismic hazard analysis. Peak Ground Acceleration (PGA) maps for 10% and 2% Probabilities of Exceedance (PoE) are plotted to compare PGAs with and without applying the FaultQuake algorithm. The results provide an enhanced view of the area’s hazard with mitigation of the overestimation, resulting in more representative hazard maps. The source codes of FaultQuake are available at the FaultQuake GitHub repository, contributing to the computer and geoscience community.

PyLandslide: A Python tool for landslide susceptibility mapping and uncertainty analysis

Mitigating the impacts of landslides and planning resilient infrastructure necessitates assessing the exposure to this hazard through, for example, susceptibility mapping involving the spatial integration of various contributing factors. Here, we introduce PyLandslide, an open-source Python tool that leverages machine learning and sensitivity analysis to quantify the weights of various contributing factors, estimate the associated uncertainties, and generate susceptibility maps. We apply PyLandslide to the case of rainfall-triggered landslides in Italy driven by historical precipitation data (1981–2023) and nine climate projections for the mid-century (2041–2050). Results highlight distance to roads as the most influential factor in determining landslide susceptibility in Italy, followed by slope. Our findings reveal an overall reduction in susceptibility in the mid-century compared to the historical period; however, the directional changes vary spatially. Uncertainty analysis should play a central role in decision-making on landslides, where weights are intricately linked to investments.

Predicting protein retention in ion-exchange chromatography using an open source QSPR workflow.

Protein-based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open-source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open-source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross-validated R2 of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.

Target-decoy false discovery rate estimation using Crema.

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

A data-guided approach for the evaluation of zeolites for hydrogen storage with the aid of molecular simulations.

This study employs a data-guided approach to evaluate zeolites for hydrogen storage, utilizing molecular simulations. The development of efficient and practical hydrogen storage materials is crucial for advancing clean energy technologies. Zeolites have shown promise as potential candidates due to their unique porous structure and tunable properties. However, the selection and design of suitable zeolites for hydrogen storage remain challenging. Therefore, this work aims to address this materials science question by utilizing molecular simulations and data-guided approaches to evaluate zeolites' performance for hydrogen storage. The results obtained from this study provide valuable insights into the evaluation of zeolites for hydrogen storage. Through molecular simulations, we analyze the adsorption behavior of hydrogen molecules in various zeolite structures. The performance of different zeolite frameworks in terms of hydrogen storage capacity, adsorption energy, and diffusion properties is assessed. Linde type A zeolite (LTA) had the highest capacity with a hydrogen capacity of 4.8wt% out of the 233 investigated zeolites. Furthermore, we investigate the influence of different factors such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) on the hydrogen storage performance of zeolites. The results show that Di, D, and M have a negative effect on the percentage weight capacity, while GSA and VSA have the highest positive contribution to the percentage weight. This study, therefore, provides new insights into the factors that affect their hydrogen storage capacity by exhibiting the importance of considering multiple factors when evaluating the performance of zeolites and demonstrates the potential of combining different computational methods to provide a more comprehensive understanding of materials. The current study contributes to the understanding of zeolite-based materials for hydrogen storage applications, aiding in the development of more efficient and practical hydrogen storage systems. Computational techniques were employed to investigate the hydrogen storage properties of zeolites. Molecular simulations were performed using classical force fields and molecular dynamics methods. The calculations were carried out at a force field level of theory with the GGA functional. To accurately capture the thermodynamics and kinetics of hydrogen adsorption, enhanced sampling techniques such as Monte Carlo simulations and molecular dynamics with metadynamics were utilized. We employed Grand Canonical Monte Carlo (GCMC) simulations to model hydrogen adsorption in zeolite structures for hydrogen storage. Our approach involved performing a substantial number of Monte Carlo steps (10,000) to ensure system equilibration and precise results. We defined a cutoff distance for particle interactions as 12.5 Ǻ and considered 0.000e framework charge per cell and 0.000e sorbate charge in energy calculations. The choice of an appropriate simulation cell size (50 × 50 × 50) Ǻ was crucial, mirroring real-world conditions. We specified lower and upper fugacity values (1 to 10atm) to capture the range of gas pressures in the simulations. These methodical steps collectively enabled us to accurately model hydrogen adsorption within zeolites, forming the core of our hydrogen storage evaluation. In this research, we utilized DFT calculations to thoroughly investigate the interactions between zeolites and hydrogen. We employed pseudopotentials to describe electron behavior in zeolite systems, choosing them in line with DFT norms and basis set compatibility. Our simulation cell design replicated zeolite periodicity and eliminated boundary effects. Pre-geometry optimization was performed with HyperChem29, ensuring stable conformations with strict convergence criteria. We utilized 6-31 + G(d) and LanL2DZ basis sets for light and heavy atoms, aligning with field standards for computational efficiency and precision. A machine learning algorithm was used to rank the importance of various structural features such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) and how they affect the capacity of the zeolites. Machine learning analysis was performed with the Scikit-learn library, an open-source Python tool. We employed a range of machine learning models, including SVMs, random forests, and neural networks, primarily for data analysis and feature extraction. Pearson correlation analysis, a classical statistical technique, was used to evaluate linear relationships between variables and assess the strength and direction of these relationships. It served as a complementary tool to understand the interplay of variables in our dataset, distinguishing it from machine learning algorithms. Further quantum chemical calculations were also performed to calculate the adsorption energy, global reactivity electronic descriptors, and natural bond orbital analysis in order to provide insights into the interaction of the zeolites with hydrogen. The simulations and data analysis were performed using BIOVIA material studio software, Gaussian, and Origin Pro software.

Open-source Python module to automate GC-MS data analysis developed in the context of bio-oil analyses

Open-source Python tool automates GC-MS biofuel analysis, promoting sustainable energy research.

An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.

A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research

Abstract. Probability density functions (PDFs) provide information about the probability of a random variable taking on a specific value. In geoscience, data distributions are often expressed by a parametric estimation of their PDF, such as, for example, a Gaussian distribution. At present there is growing attention towards the analysis of non-parametric estimation of PDFs, where no prior assumptions about the type of PDF are required. A common tool for such non-parametric estimation is a kernel density estimator (KDE). Existing KDEs are valuable but problematic because of the difficulty of objectively specifying optimal bandwidths for the individual kernels. In this study, we designed and developed a new implementation of a diffusion-based KDE as an open source Python tool to make diffusion-based KDE accessible for general use. Our new diffusion-based KDE provides (1) consistency at the boundaries, (2) better resolution of multimodal data, and (3) a family of KDEs with different smoothing intensities. We demonstrate our tool on artificial data with multiple and boundary-close modes and on real marine biogeochemical data, and compare our results against other popular KDE methods. We also provide an example for how our approach can be efficiently utilized for the derivation of plankton size spectra in ecological research. Our estimator is able to detect relevant multiple modes and it resolves modes that are located closely to a boundary of the observed data interval. Furthermore, our approach produces a smooth graph that is robust to noise and outliers. The convergence rate is comparable to that of the Gaussian estimator, but with a generally smaller error. This is most notable for small data sets with up to around 5000 data points. We discuss the general applicability and advantages of such KDEs for data–model comparison in geoscience.

Crossing intersections: A tool for investigating road user pathways

The pathways used by cyclists, pedestrians, and users of micromobility to cross intersections do not always align with those planned by traffic engineers. Observing actual usage patterns could lead to a better understanding of the tactical behavior of users of active and micromobility, allowing planners and engineers to create urban environments specifically for these road users. An open-source Python tool is introduced that uses clustering to automatically identify the forms of pathways used by road users. The tool was used to cluster trajectories from five intersections in Germany. The exemplar of each cluster is selected to represent the average shape of each pathway type. The open-source Python tool RoadUserPathways is introduced, the case studies are examined and use cases are presented.

Mikecalsetup: an open-source python tool for automatically creating calibration setups of MIKE SHE models in PEST or OSTRICH

mikecalsetup: an open-source python tool for automatically creating calibration setups of MIKE SHE models in PEST or OSTRICH

Recombulator-X: A fast and user-friendly tool for estimating X chromosome recombination rates in forensic genetics.

Genetic markers (especially short tandem repeats or STRs) located on the X chromosome are a valuable resource to solve complex kinship cases in forensic genetics in addition or alternatively to autosomal STRs. Groups of tightly linked markers are combined into haplotypes, thus increasing the discriminating power of tests. However, this approach requires precise knowledge of the recombination rates between adjacent markers. The International Society of Forensic Genetics recommends that recombination rate estimation on the X chromosome is performed from pedigree genetic data while taking into account the confounding effect of mutations. However, implementations that satisfy these requirements have several drawbacks: they were never publicly released, they are very slow and/or need cluster-level hardware and strong computational expertise to use. In order to address these key concerns we developed Recombulator-X, a new open-source Python tool. The most challenging issue, namely the running time, was addressed with dynamic programming techniques to greatly reduce the computational complexity of the algorithm. Compared to the previous methods, Recombulator-X reduces the estimation times from weeks or months to less than one hour for typical datasets. Moreover, the estimation process, including preprocessing, has been streamlined and packaged into a simple command-line tool that can be run on a normal PC. Where previous approaches were limited to small panels of STR markers (up to 15), our tool can handle greater numbers (up to 100) of mixed STR and non-STR markers. In conclusion, Recombulator-X makes the estimation process much simpler, faster and accessible to researchers without a computational background, hopefully spurring increased adoption of best practices.

Adapting deep learning models to new acoustic environments - A case study on the North Atlantic right whale upcall

Passive acoustic monitoring is increasingly being used for studying marine mammals, leading to the accumulation of large acoustic datasets. Analyzing these datasets becomes impractical without automated detection and classification software. Detectors and classifiers based on deep neural networks have shown great potential, but their performance is often limited by the availability of sufficient quantities of annotated training samples, and their application restricted to the specific acoustic environment(s) from which their training data were collected. We address these limitations by employing transfer learning, a deep learning concept whereby knowledge from a source domain is transferred to a target domain. Specifically, we considered two different underwater acoustic environments as the source and target domains. The objective was to use a deep neural network that had been trained in one environment with abundant annotated training samples, and optimize its performance in the other environment where the annotated training samples were limited. Training and testing were conducted using three acoustic datasets containing North Atlantic right whale (Eubalaena glacialis) upcalls. Experiments show that adapting a trained model to the new environment led to a substantial improvement in recall from 70% to 85%, while maintaining a low false-positive rate of less than 5 per hour. The methodology is implemented as an open-source Python tool to facilitate the creation of more tailored deep learning-based acoustic detectors and classifiers for North Atlantic right whale vocalizations and other stereotyped marine mammal calls.

A FULLY AUTOMATIC FOREST PARAMETERS EXTRACTION AT SINGLE-TREE LEVEL: A COMPARISON OF MLS AND TLS APPLICATIONS

Abstract. Forests are vital for ecological, economic, and social reasons, and adopting sustainable forest management practices is necessary. While traditional forest monitoring techniques provide detailed data, they are time-consuming; conversely, geomatic techniques can provide more detailed data for forest resource management. This study aims to assess the suitability of Mobile Mapping Systems (MMS) with simultaneous localisation and mapping (SLAM) technology for precision forestry purposes in challenging environments. We compared the performance of MMS data with Terrestrial Laser Scanning (TLS) data and evaluated the Forest Structural Complexity Tool (FSCT), which was developed for TLS datasets, on MMS data. The case study area is a highly sloped coniferous forest in the Italian Alps affected by a severe fire in 2017. Data were processed using a fully automated open-source Python tool that detects each tree's position, Diameter at Breast Height (DBH), and height. The validation procedure was conducted with respect to the TLS point cloud manually segmented. The results show that using MMS with SLAM technology is suitable for precision forestry purposes in challenging environments and that FSCT performs well on MMS data.

LipidOz enables automated elucidation of lipid carbon–carbon double bond positions from ozone-induced dissociation mass spectrometry data

Lipids play essential roles in many biological processes and disease pathology, but unambiguous identification of lipids is complicated by the presence of multiple isomeric species differing by fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds. Conventional liquid chromatography-mass spectrometry (LC-MS/MS) analyses enable the determination of fatty acyl chain lengths (and in some cases sn position) and number of double bonds, but not carbon-carbon double bond positions. Ozone-induced dissociation (OzID) is a gas-phase oxidation reaction that produces characteristic fragments from lipids containing double bonds. OzID can be incorporated into ion mobility spectrometry (IMS)-MS instruments for the structural characterization of lipids, including additional isomer separation and confident assignment of double bond positions. The complexity and repetitive nature of OzID data analysis and lack of software tool support have limited the application of OzID for routine lipidomics studies. Here, we present an open-source Python tool, LipidOz, for the automated determination of lipid double bond positions from OzID-IMS-MS data, which employs a combination of traditional automation and deep learning approaches. Our results demonstrate the ability of LipidOz to robustly assign double bond positions for lipid standard mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics.

DFT-continuum characterization of third-order elasticity of sI methane hydrates under pressure

Methane gas hydrates (GHs) are polyhedral crystalline guest-host materials found under high pressure and low-temperature conditions, which can serve as an energy source. Previous work on methane GH material physics was limited to simple linear models, which only involves second-order elasticity. However, this is not fully suited to high-stress load conditions in technological applications and fundamental material physics. For other material systems, it has been demonstrated that third-order elasticity and pressure derivatives of second-order elasticity have a strong and hence significant correlation. To narrow a critical theory-simulation gap in gas hydrates materials research, in this work we expand prior work from second-order elastic constants (SOECs) to third-order elastic constants (TOECs). By using the open-source Python tool Elastic3rd and the DFT calculation software Vienna Ab initio Simulation Package (VASP), we found that the non-linear fitting involving TOECs gave a better overall prediction and a smaller root-mean-square deviation on pressure-strain evaluation when compared with linear fitting. In addition, the non-linear fitting provides robust results on the piezo-effect on the shear constant C44 and the ductile-to-brittle transition (P = −0.5 GPa). These results are not achievable from previous work based on a linear model and these findings prove that non-linear models, including TOECs, are needed under high pressures. In addition, this research includes a detailed analysis of the calculation of TOECs and mechanical properties to study pressure stability limits and ductile-brittle transitions. Together the results, findings, and analyses from this work are a novel and significant contribution to the material physics knowledge of gas hydrates and hydrogen-bonded crystalline materials.

PEMT: a patent enrichment tool for drug discovery.

Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources. Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes. PEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pypi.org/project/PEMT/, respectively. Supplementary data are available at Bioinformatics online.

Volcanic and Seismic Source Modeling: An Open Tool for Geodetic Data Modeling

Volcanic and Seismic source Modeling (VSM) is an open-source Python tool to model ground deformation. VSM allows the user to choose one or more deformation sources of various shapes as a forward model among sphere, spheroid, ellipsoid, rectangular dislocation, and sill. It supports multiple datasets from most satellite and terrestrial geodetic techniques: Interferometric SAR, GNSS, leveling, Electronic Distance Measurements, tiltmeters, and strainmeters. Two sampling algorithms are available: one is a global optimization algorithm based on the Voronoi cells and yields the best-fitting solution and the second follows a probabilistic approach to parameters estimation based on the Bayes theorem and the Markov chain Monte Carlo method. VSM can be executed as Python script, in Jupyter Notebook environments, or by its Graphical User Interface. Its broad applications range from high-level research to teaching, from single studies to near real-time hazard estimates. Potential users range from early-career scientists to experts. It is freely available on GitHub (https://github.com/EliTras/VSM) and is accompanied by step-by-step documentation in Jupyter Notebooks. This study presents the functionalities of VSM and test cases to describe its use and comparisons among possible settings.

Darting across space and time: parametric modulators of sex-biased conditioned fear responses.

Pavlovian fear conditioning is a widely used behavioral paradigm for studying associative learning in rodents. Despite early recognition that subjects may engage in a variety of both conditioned and unconditioned responses, the last several decades have seen the field narrow its focus to measure freezing as the sole indicator of conditioned fear. We previously reported that female rats were more likely than males to engage in darting, an escape-like conditioned response that is associated with heightened shock reactivity. To determine how experimental parameters contribute to the frequency of darting in both males and females, we manipulated factors such as chamber size, shock intensity, and number of trials. To better capture fear-related behavioral repertoires in our animals, we developed ScaredyRat, an open-source custom Python tool that analyzes Noldus Ethovision-generated raw data files to identify darters and quantify both conditioned and unconditioned responses. We found that, like freezing, conditioned darting occurrences scale with experimental alterations. While most darting occurs in females, we found that with an extended training protocol, darting can emerge in males as well. Collectively, our data suggest that darting reflects a behavioral switch in conditioned responding that is a product of an individual animal's sex, shock reactivity, and experimental parameters, underscoring the need for careful consideration of sex as a biological variable in classic learning paradigms.