scholarly journals Towards computational reproducibility: researcher perspectives on the use and sharing of software

2018 ◽  
Vol 4 ◽  
pp. e163 ◽  
Author(s):  
Yasmin AlNoamany ◽  
John A. Borghi
Keyword(s):  
Computer Science ◽  
Programming Languages ◽  
Source Code ◽  
Analysis Data ◽  
Research Process ◽  
Long Tail ◽  
Research Software ◽  
Scholarly Communications ◽  
Collection Data ◽  
Write Source

Research software, which includes both source code and executables used as part of the research process, presents a significant challenge for efforts aimed at ensuring reproducibility. In order to inform such efforts, we conducted a survey to better understand the characteristics of research software as well as how it is created, used, and shared by researchers. Based on the responses of 215 participants, representing a range of research disciplines, we found that researchers create, use, and share software in a wide variety of forms for a wide variety of purposes, including data collection, data analysis, data visualization, data cleaning and organization, and automation. More participants indicated that they use open source software than commercial software. While a relatively small number of programming languages (e.g., Python, R, JavaScript, C++, MATLAB) are used by a large number, there is a long tail of languages used by relatively few. Between-group comparisons revealed that significantly more participants from computer science write source code and create executables than participants from other disciplines. Differences between researchers from computer science and other disciplines related to the knowledge of best practices of software creation and sharing were not statistically significant. While many participants indicated that they draw a distinction between the sharing and preservation of software, related practices and perceptions were often not aligned with those of the broader scholarly communications community.

scholarly journals Towards computational reproducibility: researcher perspectives on the use and sharing of software

2018 ◽  
Author(s):  
Yasmin Alnoamany ◽  
John A. Borghi
Keyword(s):  
Programming Languages ◽  
Source Code ◽  
Analysis Data ◽  
Research Process ◽  
Long Tail ◽  
Research Software ◽  
Scholarly Communications ◽  
Collection Data ◽  
Write Source ◽  
Group Comparisons

Research software, which includes both the source code and executables used as part of the research process, presents a significant challenge for efforts aimed at ensuring reproducibility. In order to inform such efforts, we conducted a survey to better understand the characteristics of research software as well as how it is created, used, and shared by researchers. Based on the responses of 215 participants, representing a range of research disciplines, we found that researchers create, use, and share software in a wide variety of forms for a wide variety of purposes, including data collection, data analysis, data visualization, data cleaning and organization, and automation. More participants indicated that they use open source software than commercial software. While a relatively small number of programming languages (e.g. Python, R, JavaScript, C++, Matlab) are used by a large number, there is a long tail of languages used by relatively few. Between group comparisons revealed that significantly more participants from computer science write source code and create executables than participants from other disciplines. Group comparisons related to knowledge of best practices related to software creation or sharing were not significant. While many participants indicated that they draw a distinction between the sharing and preservation of software, related practices and perceptions were often not aligned with those of the broader scholarly communications community.

scholarly journals Towards computational reproducibility: researcher perspectives on the use and sharing of software

2018 ◽  
Author(s):  
Yasmin Alnoamany ◽  
John A. Borghi
Keyword(s):  
Programming Languages ◽  
Source Code ◽  
Analysis Data ◽  
Research Process ◽  
Long Tail ◽  
Research Software ◽  
Scholarly Communications ◽  
Collection Data ◽  
Write Source ◽  
Group Comparisons

Research software, which includes both the source code and executables used as part of the research process, presents a significant challenge for efforts aimed at ensuring reproducibility. In order to inform such efforts, we conducted a survey to better understand the characteristics of research software as well as how it is created, used, and shared by researchers. Based on the responses of 215 participants, representing a range of research disciplines, we found that researchers create, use, and share software in a wide variety of forms for a wide variety of purposes, including data collection, data analysis, data visualization, data cleaning and organization, and automation. More participants indicated that they use open source software than commercial software. While a relatively small number of programming languages (e.g. Python, R, JavaScript, C++, Matlab) are used by a large number, there is a long tail of languages used by relatively few. Between group comparisons revealed that significantly more participants from computer science write source code and create executables than participants from other disciplines. Group comparisons related to knowledge of best practices related to software creation or sharing were not significant. While many participants indicated that they draw a distinction between the sharing and preservation of software, related practices and perceptions were often not aligned with those of the broader scholarly communications community.

scholarly journals Sistem Informasi Perpustakaan Menggunakan Barcode Berbasis Web

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Yati Nurhayati
Keyword(s):  
Information System ◽  
Computer Science ◽  
Programming Languages ◽  
Scientific Research ◽  
Practical Work ◽  
Work Unit ◽  
Sources Of Knowledge ◽  
Collection Data ◽  
The Media ◽  
The University

The library as one of the media that provides a variety of reliable sources of knowledge in the form of books, journals and other scientific research. To increase interest in reading, especially in students and lecturers, the library needs to be supported by a system that can manage library data quickly and accurately. At this time, especially in the University of Kuningan Faculty of Computer Science Library the existing library application still has several obstacles, namely the system cannot display the existing book stock and the number of books borrowed making it difficult for librarians and students to check stock, for input and search books are still input by typing it so that it is inefficient, to search for books in other faculties' libraries, they must choose the first keywords that are searched so that they must check one by one in various faculties, there is no division of categories between books, journals or theses and the resulting reports are not optimal . The results of this study are in the form of an information system in which librarians can manage collection data in the form of books, journals, student / lecturer research results (practical work reports / thesis proposals / final assignments / theses / dissertations), other collections (such as magazines , newspapers, etc.), management of borrowing and returning books, management of library members, guest books and reports (available books, borrowed, etc.). The system developed can read book codes and member codes in the form of barcodes to facilitate data search when borrowing / returning transactions occur. Each book has one barcode, where the same title (including the author, publisher and published year) has the same code, the code is only the back for each book, so that the book can be borrowed / available / lost. even though the title is the same. This system is designed using DFD and developed using the RAD method. The design results are applied to the PHP and MySQL programming languages. This information system can be used by all librarians in all faculties in Kuningan University and Kuningan University librarians, where each admin / user can only manage data in each work unit..�Keywords� Library, DFD, Barcode, MySQL, Information System

scholarly journals An Algorithm-Adaptive Source Code Converter to Automate the Translation from Python to Java

2021 ◽  
Author(s):  
Eric Jin ◽  
Yu Sun
Keyword(s):  
United States ◽  
Computer Science ◽  
Programming Languages ◽  
Error Rate ◽  
Source Code ◽  
Qualitative Evaluation ◽  
General Purpose ◽  
Huge Number ◽  
Code Converter

In the fields of computer science, there exist hundreds of different programming languages. They often have different usage and strength but also have a huge number of overlapping abilities [1]. Especially the kind of general-purpose coding language that is widely used by people, for example Java, Python and C++ [2]. However, there is a lack of comprehensive methods for the conversion for codes from one language to another [3], making the task of converting a program in between multiple coding languages hard and inconvenient. This paper thoroughly explained how my team designs a tool that converts Python source code into Java which has the exact same function and features. We applied this converter, or transpiler, to many Python codes, and successfully turned them into Java codes. Two qualitative experiments were conducted to test the effectiveness of the converter. 1. Converting Python solutions of 5 United States Computer Science Olympic (USACO) problems into Java solutions and conducting a qualitative evaluation of the correctness of the produced solution; 2. converting codes of various lengths from 10 different users to test the adaptability of this converter with randomized input. The results show that this converter is capable of an error rate less than 10% out of the entire code, and the translated code can perform the exact same function as the original code.

PERANGKAT LUNAK KOMPUTER

2020 ◽  
Author(s):  
Cut Nabilah Damni
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Operating Systems ◽  
Source Code ◽  
Computer Software ◽  
Computer Programs ◽  
Application Systems ◽  
Executable Programs

AbstrakSoftware komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program atau prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan intruksi (data) yang diberikan. (Yahfizham, 2019 : 19) Sebagian besar dari software komputer dibuat oleh (programmer) dengan menggunakan bahasa pemprograman. Orang yang membuat bahasa pemprograman menuliskan perintah dalam bahasa pemprograman seperti layaknya bahasa yang digunakan oleh orang pada umumnya dalam melakukan perbincangan. Perintah-perintah tersebut dinamakan (source code). Program komputer lainnya dinamakan (compiler) yang digunakan pada (source code) dan kemudian mengubah perintah tersebut kedalam bahasa yang dimengerti oleh komputer lalu hasilnya dinamakan program executable (EXE). Pada dasarnya, komputer selalu memiliki perangkat lunak komputer atau software yang terdiri dari sistem operasi, sistem aplikasi dan bahasa pemograman.AbstractComputer software or computer software is a collection of instructions (programs or procedures) to be able to carry out work automatically by processing or processing the collection of instructions (data) provided. (Yahfizham, 2019: 19) Most of the computer software is made by (programmers) using the programming language. People who make programming languages write commands in the programming language like the language used by people in general in conducting conversation. The commands are called (source code). Other computer programs called (compilers) are used in (source code) and then change the command into a language understood by the computer and the results are called executable programs (EXE). Basically, computers always have computer software or software consisting of operating systems, application systems and programming languages.

scholarly journals Shaping the foundations of programming languages

2021 ◽  
Vol 64 (6) ◽  
pp. 120
Author(s):  
Leah Hoffmann
Keyword(s):  
Science Education ◽  
Computer Science ◽  
Programming Languages ◽  
Computer Science Education ◽  
The Future ◽  
Turing Award

ACM A.M. Turing Award recipients Alfred Aho and Jeffrey Ullman discuss their early work, the 'Dragon Book,' and the future of 'live' computer science education.

scholarly journals A Review of Qualitative Data Analysis Practices in Health Education and Health Behavior Research

2018 ◽  
Vol 46 (1) ◽  
pp. 32-39 ◽  
Author(s):  
Ilana G. Raskind ◽  
Rachel C. Shelton ◽  
Dawn L. Comeau ◽  
Hannah L. F. Cooper ◽  
Derek M. Griffith ◽  
...  
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Health Education ◽  
Qualitative Analysis ◽  
Analysis Data ◽  
Research Process ◽  
Common Coding ◽  
Education And Health ◽  
Analytic Process ◽  
Member Checks

Data analysis is one of the most important, yet least understood, stages of the qualitative research process. Through rigorous analysis, data can illuminate the complexity of human behavior, inform interventions, and give voice to people’s lived experiences. While significant progress has been made in advancing the rigor of qualitative analysis, the process often remains nebulous. To better understand how our field conducts and reports qualitative analysis, we reviewed qualitative articles published in Health Education & Behavior between 2000 and 2015. Two independent reviewers abstracted information in the following categories: data management software, coding approach, analytic approach, indicators of trustworthiness, and reflexivity. Of the 48 ( n = 48) articles identified, the majority ( n = 31) reported using qualitative software to manage data. Double-coding transcripts was the most common coding method ( n = 23); however, nearly one third of articles did not clearly describe the coding approach. Although the terminology used to describe the analytic process varied widely, we identified four overarching trajectories common to most articles ( n = 37). Trajectories differed in their use of inductive and deductive coding approaches, formal coding templates, and rounds or levels of coding. Trajectories culminated in the iterative review of coded data to identify emergent themes. Few articles explicitly discussed trustworthiness or reflexivity. Member checks ( n = 9), triangulation of methods ( n = 8), and peer debriefing ( n = 7) were the most common procedures. Variation in the type and depth of information provided poses challenges to assessing quality and enabling replication. Greater transparency and more intentional application of diverse analytic methods can advance the rigor and impact of qualitative research in our field.

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