Free and Open Source Software organizations: A large-scale analysis of code, comments, and commits frequency.
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ABSTRACT:
SUBMITTER: Chelkowski T
PROVIDER: S-EPMC8460050 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:While researchers are becoming increasingly interested in studying OSS phenomenon, there is still a small number of studies analyzing larger samples of projects investigating the structure of activities among OSS developers. The significant amount of information that has been gathered in the publicly available open-source software repositories and mailing-list archives offers an opportunity to analyze projects structures and participant involvement. In this article, using on commits data from 263 Apache projects repositories (nearly all), we show that although OSS development is often described as collaborative, but it in fact predominantly relies on radically solitary input and individual, non-collaborative contributions. We also show, in the first published study of this magnitude, that the engagement of contributors is based on a power-law distribution.
| S-EPMC4838325 | biostudies-literature
Project description:Visual field impairment affects more than 100 million people globally. However, due to the lack of the access to appropriate ophthalmic healthcare in undeveloped regions as a result of associated costs and expertise this number may be an underestimate. Improved access to affordable diagnostic software designed for visual field examination could slow the progression of diseases, such as glaucoma, allowing for early diagnosis and intervention. We have developed Specvis, a free and open-source application written in Java programming language that can run on any personal computer to meet this requirement (http://www.specvis.pl/). Specvis was tested on glaucomatous, retinitis pigmentosa and stroke patients and the results were compared to results using the Medmont M700 Automated Static Perimeter. The application was also tested for inter-test intrapersonal variability. The results from both validation studies indicated low inter-test intrapersonal variability, and suitable reliability for a fast and simple assessment of visual field impairment. Specvis easily identifies visual field areas of zero sensitivity and allows for evaluation of its levels throughout the visual field. Thus, Specvis is a new, reliable application that can be successfully used for visual field examination and can fill the gap between confrontation and perimetry tests. The main advantages of Specvis over existing methods are its availability (free), affordability (runs on any personal computer), and reliability (comparable to high-cost solutions).
| S-EPMC5640235 | biostudies-literature
Project description:ObjectiveTo assess the adequacy of image agreement regarding uveitis based on color fundus and fluorescein angiography images alone, and to use free and open source applications to conduct an image agreement study.DesignCross-sectional agreement study.ParticipantsBaseline fundus and fluorescein images of patients with panuveitis, posterior, or intermediate uveitis enrolled in the Multi-center Uveitis Steroid Treatment (MUST) trial.MethodsThree fellowship-trained specialists in uveitis independently reviewed patient images using ClearCanvas™ and responded using Epi Info™. The diagnoses of the 3 reviewers were compared with the MUST clinician as a gold standard. A rank transformation adjusted for the possible variation in number of responses per patient. Chance-corrected interobserver agreement among the 3 reviewers was estimated with the ι coefficient. Confidence interval (CI) and SE were bootstrapped.ResultsAgreement between the diagnoses of the respondents and the baseline MUST clinician's diagnosis was poor across all diagnostic categories, ι = 0.09 (95% CI, 0.07-0.11). The agreement among respondents alone also was poor, ι = 0.11 ± 0.02 (95% CI, 0.08-0.13). The specialists requested more patient historical and clinical information to make a diagnosis on all patients.ConclusionsThe role in distinguishing the multiple conditions in uveitis appears to be limited when based on fundus imaging alone. Future studies should investigate different categories of clinical data to supplement image data. Freely available applications have excellent utility in ophthalmic imaging agreement studies.
| S-EPMC3741676 | biostudies-literature
Project description:The sequential analysis of client and clinician speech in psychotherapy sessions can help to identify and characterize potential mechanisms of treatment and behavior change. Previous studies required coding systems that were time-consuming, expensive, and error-prone. Existing software can be expensive and inflexible, and furthermore, no single package allows for pre-parsing, sequential coding, and assignment of global ratings. We developed a free, open-source, and adaptable program to meet these needs: The CASAA Application for Coding Treatment Interactions (CACTI). Without transcripts, CACTI facilitates the real-time sequential coding of behavioral interactions using WAV-format audio files. Most elements of the interface are user-modifiable through a simple XML file, and can be further adapted using Java through the terms of the GNU Public License. Coding with this software yields interrater reliabilities comparable to previous methods, but at greatly reduced time and expense. CACTI is a flexible research tool that can simplify psychotherapy process research, and has the potential to contribute to the improvement of treatment content and delivery.
| S-EPMC3397966 | biostudies-literature
Project description:Minimization is a valuable method for allocating participants between the control and experimental arms of clinical studies. The use of minimization reduces differences that might arise by chance between the study arms in the distribution of patient characteristics such as gender, ethnicity and age. However, unlike randomization, minimization requires real time assessment of each new participant with respect to the preceding distribution of relevant participant characteristics within the different arms of the study. For multi-site studies, this necessitates centralized computational analysis that is shared between all study locations. Unfortunately, there is no suitable freely available open source or free software that can be used for this purpose. OxMaR was developed to enable researchers in any location to use minimization for patient allocation and to access the minimization algorithm using any device that can connect to the internet such as a desktop computer, tablet or mobile phone. The software is complete in itself and requires no special packages or libraries to be installed. It is simple to set up and run over the internet using online facilities which are very low cost or even free to the user. Importantly, it provides real time information on allocation to the study lead or administrator and generates real time distributed backups with each allocation. OxMaR can readily be modified and customised and can also be used for standard randomization. It has been extensively tested and has been used successfully in a low budget multi-centre study. Hitherto, the logistical difficulties involved in minimization have precluded its use in many small studies and this software should allow more widespread use of minimization which should lead to studies with better matched control and experimental arms. OxMaR should be particularly valuable in low resource settings.
| S-EPMC4213009 | biostudies-literature
Project description:Open source software is becoming crucial in the design and testing of quantum algorithms. Many of the tools are backed by major commercial vendors with the goal to make it easier to develop quantum software: this mirrors how well-funded open machine learning frameworks enabled the development of complex models and their execution on equally complex hardware. We review a wide range of open source software for quantum computing, covering all stages of the quantum toolchain from quantum hardware interfaces through quantum compilers to implementations of quantum algorithms, as well as all quantum computing paradigms, including quantum annealing, and discrete and continuous-variable gate-model quantum computing. The evaluation of each project covers characteristics such as documentation, licence, the choice of programming language, compliance with norms of software engineering, and the culture of the project. We find that while the diversity of projects is mesmerizing, only a few attract external developers and even many commercially backed frameworks have shortcomings in software engineering. Based on these observations, we highlight the best practices that could foster a more active community around quantum computing software that welcomes newcomers to the field, but also ensures high-quality, well-documented code.
| S-EPMC6301779 | biostudies-literature
Project description:Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.
| S-EPMC8098009 | biostudies-literature
Project description:13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package.
| S-EPMC5933345 | biostudies-literature
Project description:Teaching the processes of transcription and translation is challenging due to the intangibility of these concepts and a lack of instructional, laboratory-based, active learning modules. Harnessing the genetic code in vitro with cell-free protein synthesis (CFPS) provides an open platform that allows for the direct manipulation of reaction conditions and biological machinery to enable inquiry-based learning. Here, we report our efforts to transform the research-based CFPS biotechnology into a hands-on module called the "Genetic Code Kit" for implementation into teaching laboratories. The Genetic Code Kit includes all reagents necessary for CFPS, as well as a laboratory manual, student worksheet, and augmented reality activity. This module allows students to actively explore transcription and translation while gaining exposure to an emerging research technology. In our testing of this module, undergraduate students who used the Genetic Code Kit in a teaching laboratory showed significant score increases on transcription and translation questions in a post-lab questionnaire compared with students who did not participate in the activity. Students also demonstrated an increase in self-reported confidence in laboratory methods and comfort with CFPS, indicating that this module helps prepare students for careers in laboratory research. Importantly, the Genetic Code Kit can accommodate a variety of learning objectives beyond transcription and translation and enables hypothesis-driven science. This opens the possibility of developing Course-Based Undergraduate Research Experiences (CUREs) based on the Genetic Code Kit, as well as supporting next-generation science standards in 8-12th grade science courses.
| S-EPMC7466673 | biostudies-literature
Project description:BackgroundKnowledge of HLA haplotypes is helpful in many settings as disease association studies, population genetics, or hematopoietic stem cell transplantation. Regarding the recruitment of unrelated hematopoietic stem cell donors, HLA haplotype frequencies of specific populations are used to optimize both donor searches for individual patients and strategic donor registry planning. However, the estimation of haplotype frequencies from HLA genotyping data is challenged by the large amount of genotype data, the complex HLA nomenclature, and the heterogeneous and ambiguous nature of typing records.ResultsTo meet these challenges, we have developed the open-source software Hapl-o-Mat. It estimates haplotype frequencies from population data including an arbitrary number of loci using an expectation-maximization algorithm. Its key features are the processing of different HLA typing resolutions within a given population sample and the handling of ambiguities recorded via multiple allele codes or genotype list strings. Implemented in C++, Hapl-o-Mat facilitates efficient haplotype frequency estimation from large amounts of genotype data. We demonstrate its accuracy and performance on the basis of artificial and real genotype data.ConclusionsHapl-o-Mat is a versatile and efficient software for HLA haplotype frequency estimation. Its capability of processing various forms of HLA genotype data allows for a straightforward haplotype frequency estimation from typing records usually found in stem cell donor registries.
| S-EPMC5450239 | biostudies-literature