Sire: An interoperability engine for prototyping algorithms and exchanging information between molecular simulation programs.

Software design for a highly parallel molecular dynamics simulation framework in chemical engineering

New Developments in the Collective Variables Module: More Flexible, More Interactive

With the constant increase in the number and variety of small molecule chemical compounds, drug discovery is becoming a very resource intensive endeavor. Performing molecular simulations of ligand-protein binding by virtual screening has become an integral part of the discovery process. Cloud computing is an efficient choice to execute these large-scale screenings, given that large compute allocations are not accessible to many researchers. This research focused on developing a multi-site cloud environment that combines small allocations of virtual machines in multiple locations connected through a virtual networking system (ViNe), and compared two parallelization approaches: Message Passing Interface (MPI) and MapReduce using Hadoop. Virtual screenings were conducted using DOCK, a protein-ligand molecular interaction simulation program. Multiple DOCK test simulations through MPI and Hadoop were run to assess the performance and flexibility of the environment. These tests indicated that MPI and MapReduce offer comparable scalability performance, and that network latency has a significant influence on low accuracy simulations. Furthermore, differences in performance at individual cloud resource sites were reduced on average because of the larger combined pool of resources. This project prototyped and assessed a fully functional multi-site cloud environment for virtual screenings, which can be used to guide small laboratories in deploying their own cloud-based screenings.

Computer Algebra Systems A computer algebra system (CAS) is a computer program designed to manipulate mathematical expressions in symbolic rather than numeric form, a task carried out routinely by theoretical scientists. There are a number of specialist programs, and some general purpose systems applicable to a wide range of problems, and it is the latter which we consider here. It is perhaps remarkable that two of the very oldest, Reduce and Maxima , which originated in the 1960s are still available as open source software. Subsequently various proprietary packages appeared. Perhaps the best known are Maple and Mathematica , which originated in the 1980s. Two more recent entries this century are Sage and SymPy . Both are open source projects. Besides symbolic manipulation, they all offer the ability to do numerics and create graphical output. Also, just like Python, they consist of a relatively small core and an extensive set of libraries which can be added as needed for specialist tasks. So which of these four open source systems should one use? The most striking feature about SymPy is that it is written entirely in Python, and indeed is just an add-on module. It is small, and works in any Python system. It interfaces well to NumPy for numerics and to Matplotlib for graphics. The standard terminal mode has unsophisticated output, but naturally it can be used within the Jupyter notebook, and there it offers perhaps the best formatting currently available of complicated symbolic output. In comparison with the others it is incredibly slow, and many of the features are still in beta form, needing further development. I recommend it unreservedly for (a) learning about CASs and (b) for lightweight tasks. But what of the other open source alternatives? Since 2008 Reduce has been available as open source software. The users' manual is a mere 1006 pages, but the core language is covered in the first 184 pages. It was developed at a time when fancy output displays did not exist, and this shows in terminal mode, which interfaces well with advanced editors such as emacs . However, recent incarnations offer an enhanced terminal mode which, if LATEX is available, offers fancy output up to the standard of the opposition.

Groundwater represents more than 97% of the globally available freshwater resources. Groundwater is situated in geological structures in the subsurface and is therefore not visible, and difficult to characterize and manage. As a consequence, it is often not adequately considered by authorities, the general public – and in education. However, teaching and learning Hydrogeology and Groundwater Management at universities, as well as continuing education training for professionals, is essential to deal with future challenges. For this reason, it is important to use suitable educational materials to improve understanding of the complex topic of groundwater among these target groups. An ongoing Erasmus+ cooperation project named iNUX – interactive understanding of groundwater hydrogeology aims to address the need for digital teaching material (www.gw-inux.org). The iNUX project aims to achieve an interactive and digital learning environment in hydrogeology and groundwater management with a European but also global target of teachers and students.Existing experience in teaching relevant groundwater subjects from highly reputable European universities is used to develop provide open source interactive and digital teaching material focusing on fundamental and applied hydrogeology. The teaching material covers basic theory in combination with field and laboratory applications in different European environments (Northern Europe, Central Europe, and the Mediterranean). The teaching material comprises (1) various types of videos (e.g., field experiments, lab experiments, screencasts of calculations and software use), (2) interactive educational documents based on Python  like Streamlit Apps and  Jupyter notebooks that combine explanation with live code, (3) various types of questions and problems that allow different assessments to enhance self-controlled learning of students. All materials are intended as open source and publicly available. The iNUX activities also comprise initiatives to establish interest groups to combine efforts towards larger pools of commonly developed digital teaching material (e.g., open source repositories like https://github.com/gw-inux/, question pools, and more) and to link with other activities like the 'Groundwater project' (https://gw-project.org/). The presentation will include existing examples of digital teaching materials and initial evaluation results to investigate the effect on student learning.

Collection Eye is the name of the collection management software developed in-house by EYE. It is a web-based application, optimized for Mozilla Firefox, which currently can only be consulted on-site at all the different locations of EYE and online via the EYE website. Collection Eye runs on an PostGresSQL database that stores the data, and uses Apache SOLR, a powerful open source search engine. This is a flexible full-text indexing search engine, that searches across all collections (the Google approach). This environment is easy to adjust: you can add index paths, develop queries, and adjust the ranking of search results. The interface is developed with PHP and ExtJS (Javascript). Configuration Tools are developed in McAccess. The metadata model that EYE implemented in Collection Eye is centered around the concept of Filmwork (Filmwerk). All different item types are linked to the concept of Filmwork. Examples of item types are posters, photos, filmcopy information (analog and digital), and film equipment. This is EYE’s translation of the Freebase entity-relationship model. The database model in the underlying PostGreSQL is structured around item types (entities) with their fields, item-type views (views configured on the item types), and index configuration (configuration of the search engine).

<p>Snow cover is an essential climate variable directly affecting the Earth’s energy balance, therefore estimating the snow parameters play an important role in hydrological, land surface, meteorological and climate models. Remote sensing provides a good understanding of snow cover monitoring thus several satellite snow products have been developed and disseminated so far.  In this study, Jupyter Notebook as an open source interactive satellite snow products retrieval, visualization and analysis tool has been developed by using Python language. Jupyter Notebook allows easy and straightforward data analysis with the possibility of live interaction and requires little programming knowledge.</p><p>The developed tool provides the capabilities of downloading the satellite snow products, georeferencing them and performing spatial analysis like zonal statistics. In this study EUMETSAT HSAF snow products, namely H10 (Snow detection), H13 (Snow Water Equivalent) and H34 (Snow cover) are used. The tool allows user to upload their own region in ESRI shapefile format for spatial and temporal analysis and the uploaded region can be visualized on interactive map via custom interactive widget like ipyleaflet. The cloud percentage for the snow cover product can be selected and daily snow covered area or snow water equivalent change for the uploaded region can be calculated for the selected period. With this tool, it is aimed to retrieve the satellite snow products easily and perform spatial and temporal analysis of snow cover for the area of interest without getting lost in data formats. Therefore, users with little or no knowledge about programming can interact easily with EUMETSAT HSAF snow products. Furthermore, with the high extensibility of Jupyter Notebook, it can also be improved or modified in accordance with the need of the end users.</p>

Jupyter notebooks [ JN ] are rapidly becoming the tool of choice of rmodelling the mechanics of physics calculation and investigation. This open source tool is used in research, engineering as well as compter and data science.  It can also be an effective tool for classroom use at the high school level as well.  A framework for introducing and using using jupyter notebooks as a model for student engagement in topics presented at the high school level will be presented.  In the form of Google's CoLab (a cloud based implementation of JN ) it can also allow remote interaction with distance learning students through shared documents as well.

Molecular dynamics simulations provide detailed insights into the structure and function of biomolecular systems. Thus, they complement experimental measurements by giving access to experimentally inaccessible regimes. Among the different molecular dynamics techniques, native structure-based models (SBMs) are based on energy landscape theory and the principle of minimal frustration. Typically used in protein and RNA folding simulations, they coarse-grain the biomolecular system and/or simplify the Hamiltonian resulting in modest computational requirements while achieving high agreement with experimental data. eSBMTools streamlines running and evaluating SBM in a comprehensive package and offers high flexibility in adding experimental- or bioinformatics-derived restraints. We present a software package that allows setting up, modifying and evaluating SBM for both RNA and proteins. The implemented workflows include predicting protein complexes based on bioinformatics-derived inter-protein contact information, a standardized setup of protein folding simulations based on the common PDB format, calculating reaction coordinates and evaluating the simulation by free-energy calculations with weighted histogram analysis method or by phi-values. The modules interface with the molecular dynamics simulation program GROMACS. The package is open source and written in architecture-independent Python2. http://sourceforge.net/projects/esbmtools/. alexander.schug@kit.edu. Supplementary data are available at Bioinformatics online.

We present the newest version of the GROningen MOlecular Simulation program package, GROMOS96. GROMOS96 has been developed for the dynamic modelling of (bio)molecules using the methods of molecular dynamics, stochastic dynamics, and energy minimization as well as the path-integral formalism. An overview of its functionality is given, highlighting methodology not present in the last major release, GROMOS87. The organization of the code is outlined, and reliability, testing, and efficiency issues involved in the design of this large (73 000 lines of FORTRAN77 code) and complex package are discussed. Finally, we present two applications illustrating new functionality: local elevation simulation and molecular dynamics in four spatial dimensions.

Newer satellite platforms, such as NISAR, are poised to produce huge amounts of data that require large computational resources. Currently, researchers typically download datasets for analysis on local computer resources. This paradigm is no longer practical given the volumes of data from new sensing platforms. While cloud computing services offer a potential solution for accessing and managing large computational resources, there remains a significant barrier to entry. Levering cloud services requires users to: navigate new terminology without appropriate documentation; optimize settings for services to reduce costs; and maintain software dependencies, upgrades, and allocated hardware resources. A more accessible approach for migrating earth scientists to the cloud is needed. To address this problem, we are developing the open source Python library PODPAC (Pipeline for Observational Data Processing Analysis and Collaboration), with the goal of helping to address NASA’s rapidly growing observational data volume and variety needs. PODPAC enables earth scientists to seamlessly transition between processing on a local workstation (their current paradigm) to distributed remote processing on the cloud. It does this by leveraging a text-based JSON format automatically generated for any plug-and-play algorithm developed using PODPAC (e.g., in a Jupyter Notebook). This text format describes data provenance, and is used in RESTful web requests to preconfigured PODPAC cloud deployments, allowing scalable, massively distributed processing. We demonstrate the seamless transition to the cloud by developing a simplified soil moisture downscaling algorithm in Python using PODPAC. Data for this algorithm uses NASA Soil Moisture Active Passive (SMAP) sensor retrieved from the National Snow and Ice Data Center using OpenDAP, and fine-scale topographic data retrieved via Open Geospatial Consortium (OGC) Web Coverage Service (WCS) calls. We then use a serverless AWS Lambda function to run the same algorithm using the automatically-generated text format. Our generic preconfigured environment can handle a wide variety of processing pipelines, and scale up to 1024 parallel processes. This approach enables incremental adoption of cloud services by researchers, significantly lowering the barrier to entry.

The Penguin program is an open-source Python program that enables molecular property and spectroscopy calculations using coupled cluster theory and perturbation theory approaches, such as cluster perturbation theory and Mo̷ller-Plesset perturbation theory. This is the first time that cluster perturbation theory approaches have been made available in open source software. The program is written in a modular and object-oriented fashion and features a matrix element library for evaluating commonly encountered terms in coupled cluster theory. The Penguin program supports interactive usage through Jupyter notebooks, making it ideal for educational purposes as well as rapid prototyping of novel coupled cluster and perturbation theory methods. In the current version, the program also features an OpenMP/MPI parallel version of the CC2 model highlighting the high performance computing potential that can be realized using this strategy of software design.

In this paper, I will present a new method of applying open source digital resources for investigating patterns of participants in the legal material of the so-called Holiness Code (Lev 17-26). Although several scholars have tried to explain the internal relationships between the participants in the Holiness Code, the large corpus complicates a systematic and consistent analysis. Developments within computational corpus-linguistics, however, provide new ways of systematic investigations into the discourse of Biblical legal texts. Accordingly, this paper will demonstrate how open source technologies and resources, such as the ETCBC database, text-fabric, R, and Jupyter Notebook, can benefit discourse analysis of the Holiness Code. Using Leviticus 25:23-28 as a case-study, the paper presents a three-step method of creating a semantic map of the participants and their internal relationships. First, semantic role labels are distributed to all participants according to the Role and Reference Grammar theory of the thematic relationship between verb and arguments. Second, all linguistic participant references are tracked and linked to their respective referents. Finally, the formal, linguistic participant tracking and the semantic role labelling are combined to create a semantic map of participants.

Grid Inhomogeneous Solvation Theory (GIST) is a method to compute the free energy of solvation of a solute molecule on a three-dimensional grid based on sampling from molecular dynamics (MD) simulations. The high spatial resolution of the GIST output, as well as the decomposition into energy and entropy contributions, allow for highly detailed analyses of solvation around both proteins and small molecules. However, this versatility also comes with a significant entry barrier for new users. In this tutorial, we aim to guide the reader through the most common steps involved in a GIST analysis using the streptavidin-biotin complex as a demonstrative system. To this end, Jupyter notebooks and a Python package (gisttools) are provided to simplify the analysis. Furthermore, we discuss the theory of GIST with a focus on practical aspects. We highlight potential pitfalls and provide strategies to avoid technical difficulties. This tutorial assumes familiarity with molecular dynamics simulations and the AmberTools package.

PurposeThis paper aims to provide a systematic review of extant literature on supplier selection by identifying drivers of integration of Environmental Sustainability (ES) into supplier selection, with the aim of classifying them under broad categories for the development of a framework showing the interrelationships among them.Design/methodology/approachA search was ran for selected keyword themes using three powerful and rigorous search engines: Scopus, Google Scholar and Science Direct, to identify relevant articles from 12 peer-reviewed journals. These were desk reviewed through manual filtering to select drivers of integration of ES into supplier selection.FindingsThirty-one drivers identified from 41 relevant articles as propelling integration of ES into supplier selection were then classified into five categories: strong policy direction, high level of commitment, desire for high reputation, robust technology and availability of green products. This yielded a framework showing the interconnectedness among the drivers.Research limitations/implicationsThe study contributes to the body of knowledge by developing a classification of drivers of integration of ES into supplier selection. The interconnectedness brought to the fore a more subtle appreciation of the drivers of green integration, which could help expand current knowledge outside the narrow scope of isolated drivers. This study provides detailed analyses of drivers of green integration into supplier selection.Originality/valueThis paper provides a comprehensive review of drivers of integrating green into supplier selection, which is lacking in the body of knowledge on ES.