Creating a more fluent conversation between data, model and users through interactive Jupyter Notebooks

2021 ◽  
Author(s):  
Andres Peñuela ◽  
Francesca Pianosi
Keyword(s):  
Data Model ◽  
Global Sensitivity Analysis ◽  
Numerical Code ◽  
Programming Environment ◽  
Technical Documentation ◽  
Computing Environment ◽  
Web Browser ◽  
Interactive Computing ◽  
Helping Others ◽  
Access To Data

<p>Reproducibility and re-usability of research requires giving access to data and numerical code but, equally importantly, helping others to understand how inputs, models and outputs are linked together. Jupyter Notebooks is a programming environment that dramatically facilitates this task, by enabling to create stronger and more transparent links between data, model and results. Within a single document where all data, code, comments and results are brought together, Jupyter Notebooks provide an interactive computing environment in which users can read, run or modify the code, and visualise the resulting outputs. In this presentation, we will explain the philosophy that we have applied for the development of interactive Jupyter Notebooks for two Python toolboxes, iRONS (a package of functions for reservoir modelling and optimisation) and SAFE (a package of functions for global sensitivity analysis). The purposes of the Jupyter Notebooks are two: some Notebooks target current users by demonstrating the key functionalities of the toolbox (‘how’ to use it), effectively replacing the technical documentation of the software; other Notebooks target potential users by demonstrating the general value of the methodologies implemented in the toolbox (‘why’ use it). In all cases, the Notebooks integrate the following features: 1) the code is written in a math-like style to make it readable to a wide variety of users, 2) they integrate interactive results visualization to facilitate the conversation between the data, the model and the user, even when the user does not have the time or expertise to read the code, 3) they can be run on the cloud by using online computational environments, such as Binder, so that they are accessible by a web browser without requiring the installation of Python. We will discuss the feedback received from users and our preliminary results of measuring the effectiveness of the Notebooks in transferring knowledge of the different modelling tasks.</p>

scholarly journals A programming environment for ubiquitous computing environment

2007 ◽  
Vol 42 (4) ◽  
pp. 14-22 ◽  
Author(s):  
Minkyoung Oh ◽  
Jiyeon Lee ◽  
Byeong-Mo Chang ◽  
Joonseon Ahn ◽  
Kyung-Goo Doh
Keyword(s):  
Ubiquitous Computing ◽  
Programming Environment ◽  
Computing Environment ◽  
Ubiquitous Computing Environment

scholarly journals Shifting from closed-source graphical-interface to open-source programming environment: a brief tutorial on running Maxent in R

2017 ◽  
Author(s):  
Xiao Feng ◽  
Fikirte Gebresenbet ◽  
Cassondra Walker
Keyword(s):  
Open Source ◽  
Ecological Niche Modeling ◽  
Programming Environment ◽  
Graphical Interface ◽  
Limited Information ◽  
Computing Environment ◽  
New Methods ◽  
Software Algorithms ◽  
The Relationship ◽  
Closed Source

Ecological niche modeling (ENM) is increasingly being used in studying the relationship between species distributions and environmental conditions. The development of ENM software/algorithms is heading toward open-source programming, for the advantage of efficiency in handling big data and incorporating new methods. Maxent is one of the commonly used ENM algorithms, but there has been limited information and efforts in implementing Maxent in an open-source programming environment (e.g., R). Therefore, we aim to fill the gap of knowledge for using Maxent in R. More specifically, we demonstrate the general implementation of Maxent in R based on a commonly used ENM procedure, provide a function that bridges the Maxent algorithm and R computing environment for easier use, and demonstrate the manipulation of a few crucial Maxent parameters in R. We expect our efforts will promote a shift of the Maxent user community from a graphical-interface to open-source programming.

scholarly journals RaDMaX online: a web-based program for the determination of strain and damage profiles in irradiated crystals using X-ray diffraction

2020 ◽  
Vol 53 (2) ◽  
pp. 587-593
Author(s):  
A. Boulle ◽  
V. Mergnac
Keyword(s):  
Web Application ◽  
The Internet ◽  
Programming Environment ◽  
X Ray Diffraction ◽  
Web Browser ◽  
Web Based ◽  
X Ray ◽  
Depth Profiles ◽  
User Friendly

RaDMaX online is a major update to the previously published RaDMaX (radiation damage in materials analysed with X-ray diffraction) software [Souilah, Boulle & Debelle (2016). J. Appl. Cryst. 49, 311–316]. This program features a user-friendly interface that allows retrieval of strain and disorder depth profiles in irradiated crystals from the simulation of X-ray diffraction data recorded in symmetrical θ/2θ mode. As compared with its predecessor, RaDMaX online has been entirely rewritten in order to be able to run within a simple web browser, therefore avoiding the necessity to install any programming environment on the users' computers. The RaDMaX online web application is written in Python and developed within a Jupyter notebook implementing graphical widgets and interactive plots. RaDMaX online is free and open source and can be accessed on the internet at https://aboulle.github.io/RaDMaX-online/.

WMO Hydrological Observing System (WHOS) broker: implementation progress and outcomes

2020 ◽  
Author(s):  
Enrico Boldrini ◽  
Paolo Mazzetti ◽  
Stefano Nativi ◽  
Mattia Santoro ◽  
Fabrizio Papeschi ◽  
...  
Keyword(s):  
Real Time ◽  
Data Model ◽  
Meteorological Data ◽  
Heterogeneous Systems ◽  
Data Sources ◽  
Technical Documentation ◽  
Time Data ◽  
Hydrological Data ◽  
Real Time Data ◽  
Rest Api

<p>The WMO Hydrological Observing System (WHOS) is a service-oriented System of Systems (SoS) linking hydrological data providers and users by enabling harmonized and real time discovery and access functionalities at global, regional, national and local scale. WHOS is being realized through a coordinated and collaborative effort amongst:</p><ul><li>National Hydrological Services (NHS) willing to publish their data to the benefit of a larger audience,</li> <li>Hydrologists, decision makers, app and portal authors willing to gain access to world-wide hydrological data,</li> <li>ESSI-Lab of CNR-IIA responsible for the WHOS broker component: a software framework in charge of enabling interoperability amongst the distributed heterogeneous systems belonging to data providers (e.g. data publishing services) and data consumers (e.g. web portals, libraries and apps),</li> <li>WMO Commission of Hydrology (CHy) providing guidance to WMO Member countries in operational hydrology, including capacity building, NHSs engagement and coordination of WHOS implementation.</li> </ul><p>In the last years two additional WMO regional programmes have been targeted to benefit from WHOS, operating as successful applications for others to follow:</p><ul><li>Plata river basin,</li> <li>Arctic-HYCOS.</li> </ul><p>Each programme operates with a “view” of the whole WHOS, a virtual subset composed only by the data sources that are relevant to its context.</p><p><strong>WHOS-Plata</strong> is currently brokering data sources from the following countries:</p><ul><li>Argentina (hydrological & meteorological data),</li> <li>Bolivia (meteorological data; hydrological data expected in the near future),</li> <li>Brazil (hydrological & meteorological data),</li> <li>Paraguay (meteorological data; hydrological data in process),</li> <li>Uruguay (hydrological & meteorological data).</li> </ul><p><strong>WHOS-Arctic</strong> is currently brokering data sources from the following countries:</p><ul><li>Canada (historical and real time data),</li> <li>Denmark (historical data),</li> <li>Finland (historical and real time data),</li> <li>Iceland (historical and real time data),</li> <li>Norway (historical and real time data),</li> <li>Russian (historical and real time data),</li> <li>United States (historical and real time data).</li> </ul><p>Each data source publishes its data online according to specific hydrological service protocols and/or APIs (e.g. CUAHSI HydroServer, USGS Water Services, FTP, SOAP, REST API, OData, WAF, OGC SOS, …). Each service protocol and API in turn implies support for a specific metadata and data model (e.g. WaterML, CSV, XML , JSON, USGS RDB, ZRXP, Observations & Measurements, …).</p><p>WHOS broker implements mediation and harmonization of all these heterogeneous standards, in order to seamlessly support discovery and access of all the available data to a growing set of data consumer systems (applications and libraries) without any implementation effort for them:</p><ul><li>52North Helgoland (through SOS v.2.0.0),</li> <li>CUAHSI HydroDesktop (through CUAHSI WaterOneFlow),</li> <li>National Water Institute of Argentina (INA) node.js WaterML client (through CUAHSI WaterOneFlow),</li> <li>DAB JS API (through DAB REST API),</li> <li>USGS GWIS JS API plotting library (through RDB service),</li> <li>R scripts (through R WaterML library),</li> <li>C# applications (through CUAHSI WaterOneFlow),</li> <li>UCAR jOAI (through OAI-PMH/WIGOS metadata).</li> </ul><p>In particular, the support of WIGOS metadata standard provides a set of observational metadata elements for the effective interpretation of observational data internationally.</p><p>In addition to metadata and data model heterogeneity, WHOS needs to tackle also semantics heterogeneity. WHOS broker makes use of a hydrology ontology (made available as a SPARQL endpoint) to augment WHOS discovery capabilities (e.g. to obtain translation of a hydrology search parameter in multiple languages).</p><p>Technical documentation to exercise WHOS broker is already online available, while the official public launch with a dedicated WMO WHOS web portal is expected shortly.</p>

scholarly journals SciPy and OpenCV as an interactive computing environment for computer vision

2015 ◽  
Vol 22 (1) ◽  
pp. 154
Author(s):  
Thiago Teixeira Santos
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Vision Research ◽  
Data Exploration ◽  
Computing Environment ◽  
Interactive Computing ◽  
Python Programming Language ◽  
Computing Environments ◽  
Python Programming

In research and development (R&D), interactive computing environments are a frequently employed alternative for data exploration, algorithm development and prototyping. In the last twelve years, a popular scientific computing environment flourished around the Python programming language. Most of this environment is part of (or built over) a software stack named SciPy Stack. Combined with OpenCV’s Python interface, this environment becomes an alternative for current computer vision R&D. This tutorial introduces such an environment and shows how it can address different steps of computer vision research, from initial data exploration to parallel computing implementations. Several code examples are presented. They deal with problems from simple image processing to inference by machine learning. All examples are also available as IPython notebooks.

scholarly journals Development of Word Game Algorithm for Learning Javanese Script

2018 ◽  
Vol 12 (2) ◽  
pp. 73
Author(s):  
Ozzi Suria
Keyword(s):  
Success Rate ◽  
Data Model ◽  
Web Application ◽  
Development Process ◽  
Use Case ◽  
Web Browser ◽  
Activity Diagram ◽  
Word Game ◽  
Average Success Rate ◽  
The Web

The students consider learning Javanese script to be difficult particularly in distinguishing and memorizing Carakan, and memorizing Sandangan and Pasangan with its writing rules. This work intends to develop a supporting medium for learning Javanese script. The development process is started by defining the game functionalities by using the use-case diagrams, and then, the activity diagram is created to describe the workflow of the game algorithm. The database to support the game is also created and displayed by using the physical data model. Afterward, the game algorithm script is created using JavaScript so that the game can be played through a web browser. There are 27 respondents requested to test the game and to fill in questionnaires about the web application. The results suggest that 100%of respondents agree that the web application is necessary and useful to learn Javanese script. The application provides positive benefit to the users such as students who still need to learn Javanese script in schools with 97% average success rate to run the game 

scholarly journals Data-dependent visualization of biological networks in the web-browser with NDExEdit

2021 ◽  
Author(s):  
Florian Auer ◽  
Simone Mayer ◽  
Frank Kramer
Keyword(s):  
Biological Networks ◽  
Data Model ◽  
Biological Knowledge ◽  
Network Data ◽  
Web Tool ◽  
Bioinformatics Analyses ◽  
Web Browser ◽  
Web Based ◽  
Biological Entities ◽  
The Web

Networks are a common methodology used to capture increasingly complex associations between biological entities. They serve as a resource of biological knowledge for bioinformatics analyses, and also comprise the subsequent results. However, the interpretation of biological networks is challenging and requires suitable visualizations dependent on the contained information. The most prominent software in the field for the visualization of biological networks is Cytoscape, a desktop modeling environment also including many features for analysis. A further challenge when working with networks is their distribution. Within a typical collaborative workflow, even slight changes of the network data force one to repeat the visualization step as well. Also, just minor adjustments to the visual representation not only need the networks to be transferred back and forth. Collaboration on the same resources requires specific infrastructure to avoid redundancies, or worse, the corruption of the data. A well-established solution is provided by the NDEx platform where users can upload a network, share it with selected colleagues or make it publicly available. NDExEdit is a web-based application where simple changes can be made to biological networks within the browser, and which does not require installation. With our tool, plain networks can be enhanced easily for further usage in presentations and publications. Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. The web tool is designed to conform to the Cytoscape Exchange (CX) format as a data model, which is used for the data transmission by both tools, Cytoscape and NDEx. Therefore the modified network can be exported as a compatible CX file, additionally to standard image formats like PNG and JPEG.

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