Public Code Repositories Research Articles

Hyd1d and hydflood - R packages to compute water levels and annual flood durations along the rivers Rhine and Elbe, Germany

Floodplains are environments characterized by fluctuating water levels. Spatial and temporal heterogeneity of habitat conditions driven by hydrology are the key factor for their high biodiversity because strong environmental filtering either creates favorable conditions for specialized organisms or eliminates competing generalists.hyd1d and hydflood are two R packages for simplified hydrological modelling in two large central European floodplains – along the rivers Rhine and Elbe in Germany – that enable users to simulate the dynamics of water level changes, flood extents and durations with high spatial and temporal resolution over long time periods and vast areas. They are simplified in the sense of not being based on physical equations, but they implement fast computing interpolation of 1d hydraulic data and GIS extrapolation algorithms. Annual and long-term products provide the possibility to predict the distribution of floodplain habitats.All input datasets are highly integrated into the packages and automatically updated. Computationally expensive products such as annual flood durations between 1960 and 2022 are provided as open data. The software itself is provided as open source software through CRAN and in public code repositories. With minimal efforts users can apply the software within the given conditions or integrate other river systems through data and source code contributions.

DeGraphCS : Embedding Variable-based Flow Graph for Neural Code Search

With the rapid increase of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing deep learning-based approaches that provide end-to-end solutions (i.e., accept natural language as queries and show related code fragments), the performance of code search in the large-scale repositories is still low in accuracy because of the code representation (e.g., AST) and modeling (e.g., directly fusing features in the attention stage). In this paper, we propose a novel learnable de ep G raph for C ode S earch (called deGraphCS ) to transfer source code into variable-based flow graphs based on an intermediate representation technique, which can model code semantics more precisely than directly processing the code as text or using the syntax tree representation. Furthermore, we propose a graph optimization mechanism to refine the code representation and apply an improved gated graph neural network to model variable-based flow graphs. To evaluate the effectiveness of deGraphCS , we collect a large-scale dataset from GitHub containing 41,152 code snippets written in the C language and reproduce several typical deep code search methods for comparison. The experimental results show that deGraphCS can achieve state-of-the-art performance and accurately retrieve code snippets satisfying the needs of the users.

Synthesis-based engineering of supervisory controllers for ROS-based applications

An ever-increasing number of robotic applications is being created based on the functionalities and the communication facilities offered by the Robotic Operating System (ROS). When applications involve contact with humans, such as in some transportation tasks, a robot supervisory controller shall guarantee that in every possible circumstance safety and task requirements are satisfied. There is no framework provided by ROS to specify supervisory controllers with formal guarantees about the satisfaction of requirements. This is problematic when the complexity of the controller increases, and it is not viable to test every possible scenario during the validation stage of the robotic application. This paper presents novel work that aims at enabling supervisory controller synthesis for ROS-based applications by (a) proposing a novel mapping between concepts from supervisory control theory and ROS, (b) demonstrating that a supervisory control modeling language exists that can be used to describe plant models and to specify requirements and (c) presenting generic finite-state automata models of ROS components, regarded as plant models, which are input for the synthesis procedure. The approach is demonstrated by synthesizing the controller to coordinate state-of-the-art mobile robot navigation modules. Simulations and real-life experiments show that the generated supervisory controller guarantees the correctness of the model with respect to the requirements and that the execution time of a control loop, including the binding code between the supervisory controller and ROS, is on average short enough to be deployed in high-level control loops. The paper is accompanied by a public code repository that contains the implementation of all methods presented in the paper including simulation environments to ease the replication of the results presented.

Estimation of Neutral Mutation Rates and Quantification of Somatic Variant Selection Using cancereffectsizeR.

An R package provides streamlined, customizable estimation of underlying nucleotide mutation rates and of the oncogenic and epistatic effects of mutations in cancer cohorts.

A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.

A framework for creating knowledge graphs of scientific software metadata

Abstract An increasing number of researchers rely on computational methods to generate or manipulate the results described in their scientific publications. Software created to this end—scientific software—is key to understanding, reproducing, and reusing existing work in many disciplines, ranging from Geosciences to Astronomy or Artificial Intelligence. However, scientific software is usually challenging to find, set up, and compare to similar software due to its disconnected documentation (dispersed in manuals, readme files, websites, and code comments) and the lack of structured metadata to describe it. As a result, researchers have to manually inspect existing tools to understand their differences and incorporate them into their work. This approach scales poorly with the number of publications and tools made available every year. In this paper we address these issues by introducing a framework for automatically extracting scientific software metadata from its documentation (in particular, their readme files); a methodology for structuring the extracted metadata in a Knowledge Graph (KG) of scientific software; and an exploitation framework for browsing and comparing the contents of the generated KG. We demonstrate our approach by creating a KG with metadata from over 10,000 scientific software entries from public code repositories.

Accounting for Geographic Basis Risk in Heat Index Insurance: How Spatial Interpolation Can Reduce the Cost of Risk

AbstractExtreme heat events cause periodic damage to crop yields and may pose a threat to the income of farmers. Weather index insurance provides payouts to farmers in the case of measurable weather extremes to keep production going. However, its viability depends crucially on the accuracy of local weather indices to predict yield damages from adverse weather conditions. So far, extreme heat indices are poorly represented in weather index insurance. In this study, we construct indices of extreme heat using observations at the nearest weather station and estimates for each county using three interpolation techniques: inverse-distance weighting, ordinary kriging, and regression kriging. Applying these indices to insurance against heat damage to corn in Illinois and Iowa, we show that heat index insurance reduces relative risk premiums by 27%–29% and that interpolated indices outperform the nearest-neighbor index by around 2%–3% in terms of relative risk reduction. Further, we find that the advantage of interpolation over a nearest-neighbor index in terms of relative risk reduction increases as the sample of weather stations is reduced. These findings suggest that heat index insurance can work even when weather data are spatially sparse, which delivers important implications for insurance practice and policy makers. Further, our public code repository provides a rich toolbox of methods to be used for other perils, crops, and regions. Our results are therefore not only replicable but also constitute a cornerstone for projects to come.

Replicating dynamic humerus motion using an industrial robot.

Transhumeral percutaneous osseointegrated prostheses provide upper-extremity amputees with increased range of motion, more natural movement patterns, and enhanced proprioception. However, direct skeletal attachment of the endoprosthesis elevates the risk of bone fracture, which could necessitate revision surgery or result in loss of the residual limb. Bone fracture loads are direction dependent, strain rate dependent, and load rate dependent. Furthermore, in vivo, bone experiences multiaxial loading. Yet, mechanical characterization of the bone-implant interface is still performed with simple uni- or bi-axial loading scenarios that do not replicate the dynamic multiaxial loading environment inherent in human motion. The objective of this investigation was to reproduce the dynamic multiaxial loading conditions that the humerus experiences in vivo by robotically replicating humeral kinematics of advanced activities of daily living typical of an active amputee population. Specifically, 115 jumping jack, 105 jogging, 15 jug lift, and 15 internal rotation trials—previously recorded via skin-marker motion capture—were replicated on an industrial robot and the resulting humeral trajectories were verified using an optical tracking system. To achieve this goal, a computational pipeline that accepts a motion capture trajectory as input and outputs a motion program for an industrial robot was implemented, validated, and made accessible via public code repositories. The industrial manipulator utilized in this study was able to robotically replicate over 95% of the aforementioned trials to within the characteristic error present in skin-marker derived motion capture datasets. This investigation demonstrates the ability to robotically replicate human motion that recapitulates the inertial forces and moments of high-speed, multiaxial activities for biomechanical and orthopaedic investigations. It also establishes a library of robotically replicated motions that can be utilized in future studies to characterize the interaction of prosthetic devices with the skeletal system, and introduces a computational pipeline for expanding this motion library.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de

Data Science Recommendation System using Semantic Technology

Data Science is a field in which multidisciplinary blend of data inference, algorithm development, and technology are combined in order to solve analytically complex problems. Data science field tend to focus on a complex and large algorithmic technological problem having data at the core. Students at the starting of the learning phase don’t know all the technological and algorithmic aspects related to data science. Consulting through faculty or knowing from external source helps them to proceed towards the needed expertise that they want to gain. Data science recommendation system using semantic web data-science ontology and service-oriented architecture is proposed in our work to recommend students the appropriate resources for their queries. Recommending student significant information concerning books, on-line documentation, software tools, public code repositories, and experts, tutors to be contacted based on student query or previous exam scores is the main objective of the work. Ontology based service-oriented web platform with a conversational user interface that use NLP (Natural Language Processing) to recommend possible resources is proposed and tested to be efficient

Enhancing C/C++ based OSS development and discoverability with CBRJS: A Rust/Node.js/WebAssembly framework for repackaging legacy codebases

Since the appearance of the C programming language and later C++, a plethora of libraries have been developed in both languages. Unfortunately, discovering such Open Source Software (OSS) components efficiently is not always an easy task. Nonetheless, recent advancements in OSS technologies present an opportunity to improve the status quo. In this paper, we introduce a prototype framework, which utilizes the Rust and JavaScript programming languages, as well as their respective ecosystems, alongside the WebAssembly state-of-the-art Web standard, for achieving boosted exposure for hard-to-find C/C++ OSS components, by taking advantage of their package discovery and delivery channels. By demonstrating how this system works, we show that this methodology is capable of increasing the exposure of such libraries, and providing a modernized stage for further development and maintenance. Provided metrics exhibit a more than twofold increase in downloads for a re-packaged library, superior discoverability compared to standard public OSS code repositories, as well as evidence that Web browser vendors invest heavily in optimizing the underlying runtime.

Using a Predictive Rating System for Computer Programmers to Optimise Recruitment

Using a quantitative assessment system, the number of resumes reviewed to identify a suitable developer was reduced to 3.5% with a successful recruitment decision made in 10 working days of posting the job advertisement. This paper summarises the methodology for developing that rating system. The depth and quality of an available talent pool is a function of demand, which is demonstrated by comparing globally-scaled individual performance metrics. Public code repositories are accessed and the code quality assessed algorithmically. The performance score combines accuracy, timeliness and difficulty from a series of challenges. These three attributes form a meaningful predictive measure of performance by using a non-linear optimisation routine. Bootstrapping is used to validate the approach. This process randomly omitted a scored performance observation per coder in order to calculate the performance score from the retained scores. There was a strong relationship (r = 0.70) between the predicted 1-omitted-performance score with the actual omitted score highlighting the predictive power.

Multi-compartmental modeling in Brian 2

Brian 2 is a fundamental rewrite of the popular Brian [1,2] simulator for spiking neural networks. It is written in the Python programming language and focuses on simplicity and extensibility: neuronal and synaptic models can be described using mathematical formulae and with the use of physical units [3]. Brian has been traditionally used to simulate networks of single-compartment neurons; complex morphologies, modeled as multi-compartment neurons, were not supported. We have recently added support for such models: a neuron's morphology can be either created from a file (in the SWC format [4]) or be created iteratively in the simulation script itself, using a convenient syntax. The flexibility of the Brian simulator applies to multi-compartment models in the same way as to single-compartment models: the model dynamics are governed by arbitrary user-provided differential equations, models can be flexibly parameterized over all the compartments and all variables describing the internal state of the model (voltages, currents , conductances, etc) are accessible to the user. The simulation of multi-compartment models is more complex than that of non-electrically coupled single-compartment models: in addition to the numerical integration of the differential equations governing the change of the membrane potential in a single compartment, the currents flowing across neighboring compartments have to be taken into account. To this end, Brian employs a domain-decomposition method which corrects an initial solution valid for uncoupled compartments, taking the actual coupling into account. This method has the advantage that it is easy to parallelize, since most of computations are performed independently for each compartment. We demonstrate the advantage of this by parallelizing the computation over multiple processor cores using the OpenMP framework. For the future, we are planning to further accelerate the simulation speed by making use of the massively-parallel processing capabilities of graphical processing units. With this addition, we have significantly enlarged the range of research questions that can be investigated using Brian. Brian is made available under a free software license and all development takes place in public code repositories.

Correction to Phylogenomic analyses of deep gastropod relationships reject Orthogastropoda

[ Proc. R. Soc. B 281 , 20141739 (2014; Published 17 September 2014) ([doi:10.1098/rspb.2014.1739][2])][2] In this study [[1][2]], we investigated the phylogenetic relationships among and within major clades of gastropods using new transcriptome data for 40 species and publicly available genomes

Brian 2: neural simulations on a variety of computational hardware

Brian 2 is a fundamental rewrite of the Brian [1,2] simulator for spiking neural networks. It is written in the Python programming language and focuses on simplicity and extensibility: neuronal and synaptic models can be described using mathematical formulae and with the use of physical units [3]. The same formalism can also be used to specify connectivity patterns (e.g. spatial connectivity), using mathematical expressions to define connections, probabilities of connections, number of synapses between neurons, and synaptic delays. Brian 2 offers two modes of operation: a “runtime mode”, where executable code is generated from the model descriptions on the fly and executed from Python and a “standalone mode”, where a set of source code files is generated that can then be compiled and executed with no dependency on Brian or Python. The runtime mode is ideal for rapid prototyping and interactive exploration, e.g. from a Python console. The standalone mode on the other hand is designed for maximum of performance and for simulating models on a variety of hardware and platforms. We show a number of example applications for the standalone mode, generating code for a wide range of devices: • C++ code that is completely independent of Brian, Python or Python libraries. Optionally, this code can be parallelized over multiple CPU cores using the OpenMP libraries. • Java/Renderscript for Android-based devices, enabling Brian to run neural models on commodity hardware (e.g. phones) for robotic applications [5]. • The GPU enhanced Neuronal Networks (GeNN) framework [6,7], a neural simulator using GPUs to accelerate neural simulations. • The same approach would also allow the generation of code targeted at neuromorphic computing architectures such as the SpiNNaker platform [8] for which we have started preliminary work. Brian is made available under a free software license and all development takes place in public code repositories [9].

Brian 2 - the second coming: spiking neural network simulation in Python with code generation

Brian 2 is a fundamental rewrite of the Brian [1,2] simulator for spiking neural networks. Brian is written in the Python programming language and focuses on simplicity and extensibility: neuronal models can be described using mathematical formulae (differential equations) and with the use of physical units. Depending on the model equations, several integration methods are available, ranging from exact integration for linear differential equations to numerical integration for arbitrarily complex equations. The same formalism can also be used to specify synaptic models, allowing the user to easily define complex synapse models. Brian 2 keeps most of the syntax and functionality consistent with previous versions of Brian, but achieves more consistency and modularity as well as adding new features such as a simpler and more general new formulation of refractoriness. A consistent interface centered around human-readable descriptions using mathematical notation allows the specification of neuronal models (including complex reset, threshold and refractory conditions), synaptic models (including complex plasticity rules) and synaptic connections. Every aspect of Brian 2 has been designed with extensibility and adaptability in mind, which, for example, makes it straightforward to implement new numerical integration methods. Even though Brian 2 benefits from the ease of use and the flexibility of the Python programming language, its performance is not limited by the speed of Python: At the core of the simulation machinery Brian 2 makes use of fully automated runtime code generation [3], allowing the same model to be run in the Python interpreter, in compiled C++ code or on a GPU using CUDA libraries[4]. The code generation system is designed to be extensible to new target languages and its output can also be used on its own: for situations where high performance is necessary and/or where a Python interpreter is not available (for example for robotics applications), Brian 2 offers tools to assist in assembling the generated code into a stand-alone version that runs independently of Brian or a Python interpreter. To ensure the correctness and maintainability of the software, Brian 2 includes an extensive, full coverage test suite. Debugging of simulation scripts is supported by a configurable logging system, allowing simple monitoring of the internal details of the simulation process. Brian is made available under a free software license and all development takes place in public code repositories [5].

Phylotastic! Making tree-of-life knowledge accessible, reusable and convenient.

BackgroundScientists rarely reuse expert knowledge of phylogeny, in spite of years of effort to assemble a great “Tree of Life” (ToL). A notable exception involves the use of Phylomatic, which provides tools to generate custom phylogenies from a large, pre-computed, expert phylogeny of plant taxa. This suggests great potential for a more generalized system that, starting with a query consisting of a list of any known species, would rectify non-standard names, identify expert phylogenies containing the implicated taxa, prune away unneeded parts, and supply branch lengths and annotations, resulting in a custom phylogeny suited to the user’s needs. Such a system could become a sustainable community resource if implemented as a distributed system of loosely coupled parts that interact through clearly defined interfaces.ResultsWith the aim of building such a “phylotastic” system, the NESCent Hackathons, Interoperability, Phylogenies (HIP) working group recruited 2 dozen scientist-programmers to a weeklong programming hackathon in June 2012. During the hackathon (and a three-month follow-up period), 5 teams produced designs, implementations, documentation, presentations, and tests including: (1) a generalized scheme for integrating components; (2) proof-of-concept pruners and controllers; (3) a meta-API for taxonomic name resolution services; (4) a system for storing, finding, and retrieving phylogenies using semantic web technologies for data exchange, storage, and querying; (5) an innovative new service, DateLife.org, which synthesizes pre-computed, time-calibrated phylogenies to assign ages to nodes; and (6) demonstration projects. These outcomes are accessible via a public code repository (GitHub.com), a website (http://www.phylotastic.org), and a server image.ConclusionsApproximately 9 person-months of effort (centered on a software development hackathon) resulted in the design and implementation of proof-of-concept software for 4 core phylotastic components, 3 controllers, and 3 end-user demonstration tools. While these products have substantial limitations, they suggest considerable potential for a distributed system that makes phylogenetic knowledge readily accessible in computable form. Widespread use of phylotastic systems will create an electronic marketplace for sharing phylogenetic knowledge that will spur innovation in other areas of the ToL enterprise, such as annotation of sources and methods and third-party methods of quality assessment.