Open Source Software Solutions Research Articles

Review—Machine Learning-Driven Advances in Electrochemical Sensing: A Horizon Scan

Abstract The burgeoning intersection of machine learning (ML) with electrochemical sensing heralds a transformative era in analytical science, pushing the boundaries of what's possible in detecting and quantifying chemical substances with unprecedented precision and efficiency. This convergence has accelerated a number of discoveries, improving electrochemical sensors' sensitivity, selectivity, and ability to comprehend complicated data streams in real-time. Such advancements are crucial across various applications, from monitoring health biomarkers to detecting environmental pollutants and ensuring industrial safety. Yet, this integration is not without its challenges; it necessitates navigating intricate ethical considerations around data use, ensuring robust data privacy measures, and developing specialized software tools that balance accessibility and security. As the field progresses, addressing these challenges head-on is essential for harnessing the full potential of ML-enhanced electrochemical sensing. This review briefly explores these dimensions, spotlighting the significant technological strides, the ethical landscape, and the dynamic interplay between open-source and proprietary software solutions while also casting a forward gaze at the promising future directions of this interdisciplinary venture.

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package.

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

SendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis.

The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public-private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses. The sendigR package also includes an integrated Python package, xptcleaner, which can be used to harmonize the terminology used in SEND datasets by mapping to CDISC controlled terminologies. The sendigR R package is freely available on the comprehensive R Archive Network (CRAN) and at https://github.com/phuse-org/sendigR. An R Shiny web application was included in the R package to enable toxicologists with no coding experience to perform historical control analyses. Experienced R programmers will be able to integrate the package functions into their own custom scripts/packages and potentially contribute improvements to the functionality of sendigR. sendigR reference manual: https://phuse-org.github.io/sendigR/. sendigR R Shiny demo app: https://phuse-org.shinyapps.io/sendigR/.

Hybrid FFF/CNC: An open source hardware & software system

This paper presents a low-cost milling system composed of spindle mountable on a multi tool 3D printer equipped with maxwell kinematic coupling (E3D “ToolChanger” in this article) as well as two open-source software solutions for implementing a hybrid FFF/CNC manufacturing process. The first solution is the use of a traditional CAM software (FreeCad) for machining programming through the development of a dedicated post-processor. The second is an automatic layer-by-layer hybridization enabled by the software “SuperSlicer”. This method requires no machining knowledge but only allows contouring operations. Results of experiments show that the spindle presented in this work is capable of successfully carrying out a hybrid process that significantly improves the surface roughness parameters, with an improvement factor of 10 for most parameters. An uniformization of surface roughness parameters was also observed in the construction direction and in the deposition/machining direction. The layer-by-layer hybridization yields the better results in terms of surface roughness. This is because the reduced depth of cut (equivalent to a printed layer) minimizes stress and temperature rise, resulting in highly favorable cutting conditions.

Unveiling the Potential of Open-Source Software Integration in Education: Advantages, Challenges, and Effective Strategies

Unveiling the Potential of Open-Source Software Integration in Education: Advantages, Challenges, and Effective Strategies

A practical guide to calculating vocal tract length and scale-invariant formant patterns.

Formants (vocal tract resonances) are increasingly analyzed not only by phoneticians in speech but also by behavioral scientists studying diverse phenomena such as acoustic size exaggeration and articulatory abilities of non-human animals. This often involves estimating vocal tract length acoustically and producing scale-invariant representations of formant patterns. We present a theoretical framework and practical tools for carrying out this work, including open-source software solutions included in R packages soundgen and phonTools. Automatic formant measurement with linear predictive coding is error-prone, but formant_app provides an integrated environment for formant annotation and correction with visual and auditory feedback. Once measured, formants can be normalized using a single recording (intrinsic methods) or multiple recordings from the same individual (extrinsic methods). Intrinsic speaker normalization can be as simple as taking formant ratios and calculating the geometric mean as a measure of overall scale. The regression method implemented in the function estimateVTL calculates the apparent vocal tract length assuming a single-tube model, while its residuals provide a scale-invariant vowel space based on how far each formant deviates from equal spacing (the schwa function). Extrinsic speaker normalization provides more accurate estimates of speaker- and vowel-specific scale factors by pooling information across recordings with simple averaging or mixed models, which we illustrate with example datasets and R code. The take-home messages are to record several calls or vowels per individual, measure at least three or four formants, check formant measurements manually, treat uncertain values as missing, and use the statistical tools best suited to each modeling context.

Chemprop: A Machine Learning Package for Chemical Property Prediction.

Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.

Towards an Open Source-first Praxis in Libraries

In terms of utility and technical quality, open source software solutions have become a common option for many libraries. As barriers to adoption have been reduced and systems such as FOLIO appear poised to change the landscape of LIS technology, it is worth examining how the use of open source can support the normative core values of librarianship, and to outline a strategy for critical engagement with the technology that is beneficial to patrons and libraries. That strategy will require further codification, institutionalization and investigation of open source at many levels.

Classification of dendritic spiking neurons’ shape through clustering and machine learning techniques

A synapse is a specialized region between two adjacent neurons that allows information to be transmitted from one cell to another. The formation of these connections and transfer of signals via electrical impulses is a critical characteristic of neuronal cells. The synapse is formed by the axonal bouton on the axon side and the dendritic spine, a specialized outgrowth of the dendritic membrane, on the dendrite side. Dendritic spines exhibit diverse shapes and sizes, differing significantly across brain regions, cell categories, and animal species. Changes in dendritic spine morphology occur in neurodevelopmental and neurodegenerative ailments while also responding to external stimuli. Although deemed to facilitate synaptic plasticity, further investigation is essential for establishing the correlation between spine construction and its function. To address the issue in modern neurobiology of characterizing synapse morphology on 3D neuron images, the development of effective analytical methods is necessary. Our team has produced an open-source software solution for precise dendritic spine segmentation using 3D dendrite images. This software calculates the 10 most widely used 3D-adapted morphological features [1, 2] and enables classification and clustering of dendritic spine data sets to determine their shape. In addition to numerical features for describing the shape of a dendritic spine, researchers proposed using a histogram of chord lengths, known as the chord length distribution histogram (CLDH). This involves generating a set of random chords within the dendritic spine’s volume, connecting its outer boundaries and forming a histogram. By setting n=30 000, the probabilistic fluctuations of the histogram become insignificant. The derived metrics were then used for clustering and classifying the dataset. Classification based on predetermined morphological groups is a frequently used approach for analyzing the morphology of dendritic spines. This method involves categorizing spines into established groups such as thin, mushroom, and stubby. Experimenters generally perform classification in a semi-automated manner, leading to considerable error. We have created a spine categorization tool using a machine learning algorithm. The tool classifies spines based on a consensus reached by eight experts who manually labeled the training dataset. The accuracy of this method surpasses 77% when using classical morphological features and is comparable to expert labeling. The implementation of this approach reduces classification bias and complexity. Recent studies, including those using live microscopy in vitro and in vivo, indicate that dendritic spine shapes exhibit a continuum rather than distinct categories [3]. Therefore, it is essential to establish a dependable methodology for evaluating and examining the morphology of dendritic spines. We have created a clustering tool that establishes both the number of groups and their content based on data, rather than the experimenter’s discretion. This tool leverages the k-means and DBSCAN algorithms for its representation. Three clustering methods are presented to determine the number of clusters: the silhouette method, the elbow method, and a novel method, developed by the authors, based on the max class divergence criteria. The authors assume that cluster quality improves as clusters differ significantly in the number of mushroom/thin/stubby spine classes, as marked by experts. The benefit of this approach is that it considers the particular data type for clustering. Without this knowledge, assessing the quality of clustering is challenging. The use of the CLDH metric for clustering yielded a consistent and stable number of clusters (n=5) across all three methods described. These clusters contain dendritic spines that share similar shapes and have been validated by experts. In contrast, using classical metrics resulted in a variable cluster count ranging from n=4 to n=14. These findings suggest that the CLDH metric, with its complexity, provides enough information about synapse shape to enable precise clustering.

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

AbstractMap matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.

Furthering Automatic Feature Extraction for Fit-for-Purpose Cadastral Updating: Cases from Peri-Urban Addis Ababa, Ethiopia

Fit-for-purpose land administration (FFPLA) seeks to simplify cadastral mapping via lowering the costs and time associated with conventional surveying methods. This approach can be applied to both the initial establishment and on-going maintenance of the system. In Ethiopia, cadastral maintenance remains an on-going challenge, especially in rapidly urbanizing peri-urban areas, where farmers’ land rights and tenure security are often jeopardized. Automatic Feature Extraction (AFE) is an emerging FFPLA approach, proposed as an alternative for mapping and updating cadastral boundaries. This study explores the role of the AFE approach for updating cadastral boundaries in the vibrant peri-urban areas of Addis Ababa. Open-source software solutions were utilized to assess the (semi-) automatic extraction of cadastral boundaries from orthophotos (segmentation), designation of “boundary” and “non-boundary” outlines (classification), and delimitation of cadastral boundaries (interactive delineation). Both qualitative and quantitative assessments of the achieved results (validation) were undertaken. A high-resolution orthophoto of the study area and a reference cadastral boundary shape file were used, respectively, for extracting the parcel boundaries and validating the interactive delineation results. Qualitative (visual) assessment verified the completed extraction of newly constructed cadastral boundaries in the study area, although non-boundary outlines such as footpaths and artifacts were also retrieved. For the buffer overlay analysis, the interactively delineated boundary lines and the reference cadastre were buffered within the spatial accuracy limits for urban and rural cadastres. As a result, the quantitative assessment delivered 52% correctness and 32% completeness for a buffer width of 0.4 m and 0.6 m, respectively, for the interactively delineated and reference boundaries. The study proposed publicly available software solutions and outlined a workflow to (semi-) automatically extract cadastral boundaries from aerial/satellite images. It further demonstrated the potentially significant role AFE could play in delivering fast, affordable, and reliable cadastral mapping. Further investigation, based on user input and expertise evaluation, could help to improve the approach and apply it to a real-world setting.

Deep Learning within a DICOM WSI Viewer for Histopathology

Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients’ studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.

Digital Public Health Solutions in Response to the COVID-19 Pandemic: Comparative Analysis of Contact Tracing Solutions Deployed in Japan and Germany

Background In response to the COVID-19 pandemic, numerous countries, including the likes of Japan and Germany, initiated, developed, and deployed digital contact tracing solutions in an effort to detect and interrupt COVID-19 transmission chains. These initiatives indicated the willingness of both the Japanese and German governments to support eHealth solution development for public health; however, end user acceptance, trust, and willingness to make use of the solutions delivered through these initiatives are critical to their success. Through a case-based analysis of contact tracing solutions deployed in Japan and Germany during the COVID-19 pandemic we may gain valuable perspectives on the transnational role of digital technologies in crises, while also projecting possible directions for future pandemic technologies. Objective In this study, we investigate (1) which types of digital contact tracing solutions were developed and deployed by the Japanese and German governments in response to the COVID-19 pandemic and (2) how many of these solutions are open-source software (OSS) solutions. Our objective is to establish not only the type of applications that may be needed in response to a pandemic from the perspective of 2 geographically diverse, world-leading economies but also how prevalent OSS pandemic technology development has been in this context. Methods We analyze the official government websites of Japan and Germany to identify digital solutions that are developed and deployed for contact tracing purposes (for any length of time) during the timeframe January-December 2021, specifically in response to the COVID-19 pandemic. We subsequently perform a case-oriented comparative analysis, also identifying which solutions are published as open-source. Results In Japan, a proximity tracing tool (COVID-19 Contact-Confirming Application [COCOA]) and an outbreak management tool (Health Center Real-time Information-sharing System on COVID-19 [HER-SYS]) with an integrated symptom tracking tool (My HER-SYS) were developed. In Germany, a proximity tracing tool (Corona-Warn-App) and an outbreak management tool (Surveillance Outbreak Response Management and Analysis System [SORMAS]) were developed. From these identified solutions, COCOA, Corona-Warn-App, and SORMAS were published as open-source, indicating support by both the Japanese and German governments for OSS pandemic technology development in the context of public health. Conclusions Japan and Germany showed support for developing and deploying not only digital contact tracing solutions but also OSS digital contact tracing solutions in response to the COVID-19 pandemic. Despite the open nature of such OSS solutions’ source code, software solutions (both OSS and non-OSS) are only as transparent as the live or production environment where their processed data is hosted or stored. Software development and live software hosting are thus 2 sides of the same coin. It is nonetheless arguable that OSS pandemic technology solutions for public health are a step in the right direction for enhanced transparency in the interest of the greater public good.

A Rapid Review on the Use of Free and Open Source Technologies and Software Applied to Precision Agriculture Practices

Technology plays a crucial role in the management of natural resources in agricultural production. Free and open-source software and sensor technology solutions have the potential to promote more sustainable agricultural production. The goal of this rapid review is to find exclusively free and open-source software for precision agriculture, available in different electronic databases, with emphasis on their characteristics and application formats, aiming at promoting sustainable agricultural production. A thorough search of the Google Scholar, GitHub, and GitLab electronic databases was performed for this purpose. Studies reporting and/or repositories containing up-to-date software were considered for this review. The various software packages were evaluated based on their characteristics and application formats. The search identified a total of 21 free and open-source software packages designed specifically for precision agriculture. Most of the identified software was shown to be extensible and customizable, while taking into account factors such as transparency, speed, and security, although some limitations were observed in terms of repository management and source control. This rapid review suggests that free and open-source software and sensor technology solutions play an important role in the management of natural resources in sustainable agricultural production, and highlights the main technological approaches towards this goal. Finally, while this review performs a preliminary assessment of existing free and open source solutions, additional research is needed to evaluate their effectiveness and usability in different scenarios, as well as their relevance in terms of environmental and economic impact on agricultural production.

Novel data analysis tool for the evaluation of Coincidence Doppler Broadening Spectra of the positron–electron annihilation line

Coincidence Doppler Broadening Spectroscopy (CDBS) of the 511keV annihilation line reveals the elemental signature at the annihilation site in matter. For this reason, CDBS enables the analysis of foreign atoms in the host matrix, vacancy-solute complexes and precipitates in solids. Due to the lack of a comprehensive program for the analysis of CDBS data we developed a modular python-based open source software solution for the reliable extraction of the annihilation line to provide so-called ratio curves highlighting the elemental signature in the spectra. In particular, the new program allows the readout and spectra averaging of multiple detector pairs. Moreover, we implemented an improved algorithm to further reduce the background by about one order of magnitude at high Doppler shifts.

PyC2MC: An Open-Source Software Solution for Visualization and Treatment of High-Resolution Mass Spectrometry Data.

Complex molecular mixtures are encountered in almost all research disciplines, such as biomedical 'omics, petroleomics, and environmental sciences. State-of-the-art characterization of sample materials related to these fields, deploying high-end instrumentation, allows for gathering large quantities of molecular composition data. One established technological platform is ultrahigh-resolution mass spectrometry, e.g., Fourier-transform mass spectrometry (FT-MS). However, the huge amounts of data acquired in FT-MS often result in tedious data treatment and visualization. FT-MS analysis of complex matrices can easily lead to single mass spectra with more than 10,000 attributed unique molecular formulas. Sophisticated software solutions to conduct these treatment and visualization attempts from commercial and noncommercial origins exist. However, existing applications have distinct drawbacks, such as focusing on only one type of graphic representation, being unable to handle large data sets, or not being publicly available. In this respect, we developed a software, within the international complex matrices molecular characterization joint lab (IC2MC), named "python tools for complex matrices molecular characterization" (PyC2MC). This piece of software will be open-source and free to use. PyC2MC is written under python 3.9.7 and relies on well-known libraries such as pandas, NumPy, or SciPy. It is provided with a graphical user interface developed under PyQt5. The two options for execution, (1) a user-friendly route with a prepacked executable file or (2) running the main python script through a Python interpreter, ensure a high applicability but also an open characteristic for further development by the community. Both are available on the GitHub platform (https://github.com/iC2MC/PyC2MC_viewer).

Experimental and numerical methods to ensure comprehensible and replicable alternating current electrical stimulation experiments

Electrical stimulation has received increasing attention for decades for its application in regenerative medicine. Applications range from bone growth stimulation over cartilage regeneration to deep brain stimulation. Despite all research efforts, translation into clinical use has not yet been achieved in all fields. Recent critical assessments have identified limited documentation and monitoring of preclinical in vitro and in vivo experiments as possible reasons hampering clinical translation. In this work, we present experimental and numerical methods to determine the crucial quantities of electrical stimulation such as the electric field or current density. Knowing the stimulation quantities contributes to comprehending the biological response to electrical stimulation and to finally developing a reliable dose–response curve. To demonstrate the methods, we consider a direct contact electrical stimulation experiment that stands representative for a broad class of stimulation experiments. Electrochemical effects are addressed and methods to integrate them into numerical simulations are evaluated. A focus is laid on affordable lab equipment and reproducible open-source software solutions. Finally, clear guidelines to ensure replicability of electrical stimulation experiments are formulated.

By Pathologists for Pathologists: Solving Common Informatics Problems in Laboratory Medicine with Open-Source Software Solutions.

By Pathologists for Pathologists: Solving Common Informatics Problems in Laboratory Medicine with Open-Source Software Solutions.

No cost but high performance-An alternative open source solution for 3D-visualizations in morphology.

3D-visualization has become common courtesy in science and also found its way into teaching in schools and universities. Nevertheless, the way to high performance 3D-visualization and analyses remains difficult and is often also a matter of budget. Due to the obvious advantages of presenting morphological and anatomical datasets with the help of 3D-figures, all in one software solutions usually come along with high rental and maintenance fees. For that reason, it is more than overdue to establish and use open source software solutions with all their obvious advantages - as other disciplines already do. Here we provide a modular, highly adaptive and freely available software pipeline for high performance 3D-visualizations of (not only) morphological datasets by combining features of ImageJ, MeshLab and Blender, without any additional costs. Exemplarily using serial-block face SEM data as well as serial AZAN-stained histological sections, the herein presented step-by-step protocol allows for a fast and efficient analysis, visualization and animation of large, anatomical datasets. Regardless which type of serial, morphological datasets needs to be analyzed, our open source guide provides an easy to handle and promptly adaptable solution. Therefore, our pipeline for 3D-visualization represents a valuable alternative to conventional, commercial packages. RESEARCH HIGHLIGHTS: We provide a highly modular and easy to learn open source solution for multiple 3D-visualizations. The step-by-step-guide make it easy to start, and advanced users can replace software, add others and such build your own individual software pipeline.

Open Source Solutions in Experimental Design: An Introduction to the Symposium.

The Open Science movement has increased dramatically in popularity with deserved calls to action around transparency, access to resources, and inclusion in our field. However, its practical applications within experimental design have been slow to uptake, with researchers unsure where to even start with the dizzying array of open source hardware and software solutions available. The perceived time investment and unknown cost, especially in implementing open source hardware, has stagnated the implementation of inexpensive experimental solutions, but we sought to increase awareness to lower the barrier to participation in this space. While there are countless technical and financial advantages to integrating open source solutions into every biologist's experimental design, we put an emphasis on the "people" part of the equation in our symposium. This symposium championed innovative experimental designs by early career Society for Integrative and Comparative Biology researchers across all fields of biology, from plants to animals, in the lab or in the field, or even virtually engaging with the public and students. The Open Science movement operates within community norms that champion transparency, continuous development, and collaboration. These values are congruent with the priorities of reducing barriers to participation in science, and we hope our symposium's collection of open source solutions encourages readers to adopt these or other innovative designs into their own experimentation.