A Vision on Open Science for the Evolution of Software Engineering Research and Practice
- URL: http://arxiv.org/abs/2405.12132v1
- Date: Mon, 20 May 2024 15:51:23 GMT
- Title: A Vision on Open Science for the Evolution of Software Engineering Research and Practice
- Authors: Edson OliveiraJr, Fernanda Madeiral, Alcemir Rodrigues Santos, Christina von Flach, Sergio Soares,
- Abstract summary: Open Science aims to foster openness and collaboration in research, leading to more significant scientific and social impact.
practicing Open Science comes with several challenges and is currently not properly rewarded.
- Score: 40.07325268305058
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Open Science aims to foster openness and collaboration in research, leading to more significant scientific and social impact. However, practicing Open Science comes with several challenges and is currently not properly rewarded. In this paper, we share our vision for addressing those challenges through a conceptual framework that connects essential building blocks for a change in the Software Engineering community, both culturally and technically. The idea behind this framework is that Open Science is treated as a first-class requirement for better Software Engineering research, practice, recognition, and relevant social impact. There is a long road for us, as a community, to truly embrace and gain from the benefits of Open Science. Nevertheless, we shed light on the directions for promoting the necessary culture shift and empowering the Software Engineering community.
Related papers
- Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.
Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - Open Problems in Mechanistic Interpretability [61.44773053835185]
Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities.
Despite recent progress toward these goals, there are many open problems in the field that require solutions.
arXiv Detail & Related papers (2025-01-27T20:57:18Z) - MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects.
MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years.
We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
arXiv Detail & Related papers (2024-06-10T15:19:09Z) - Infrastructure Engineering: A Still Missing, Undervalued Role in the Research Ecosystem [0.0]
Research has become increasingly reliant on software.
The need for such a role is not just ideal, but essential for the continued success of science.
In this article we will highlight the importance of this missing layer, providing examples of how a missing role of infrastructure engineer has led to inefficiencies.
arXiv Detail & Related papers (2024-05-17T00:15:43Z) - Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)
We identify benefits, harms, and future research directions for the four main themes.
We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Search and Society: Reimagining Information Access for Radical Futures [3.909878683245887]
Information retrieval research must understand and contend with the social implications of the technology it produces.
The community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries.
arXiv Detail & Related papers (2024-03-26T17:43:08Z) - How to Sustain a Scientific Open-Source Software Ecosystem: Learning
from the Astropy Project [9.049664874474736]
This study examines the challenges and opportunities to enhance the sustainability of scientific OSS.
We conducted a case study on a widely-used software ecosystem in the astrophysics domain, the Astropy Project.
arXiv Detail & Related papers (2024-02-23T03:54:53Z) - Academic competitions [61.592427413342975]
This chapter provides a survey of academic challenges in the context of machine learning and related fields.
We review the most influential competitions in the last few years and analyze challenges per area of knowledge.
The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed.
arXiv Detail & Related papers (2023-12-01T01:01:04Z) - Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
New area of research known as AI for science (AI4Science)
Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales.
Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
arXiv Detail & Related papers (2023-07-17T12:14:14Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - AI for Science: An Emerging Agenda [30.260160661295682]
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling"
The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains.
Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers.
arXiv Detail & Related papers (2023-03-07T20:21:43Z) - Empowering Local Communities Using Artificial Intelligence [70.17085406202368]
It has become an important topic to explore the impact of AI on society from a people-centered perspective.
Previous works in citizen science have identified methods of using AI to engage the public in research.
This article discusses the challenges of applying AI in Community Citizen Science.
arXiv Detail & Related papers (2021-10-05T12:51:11Z) - The Right Tools for the Job: The Case for Spatial Science Tool-Building [0.0]
This paper presents the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC.
It discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this.
The paper concludes with paths forward, emphasizing open-source software and reusable computational data science.
arXiv Detail & Related papers (2020-08-12T20:15:39Z) - Augmented reality as a tool for open science platform by research
collaboration in virtual teams [0.0]
The provision of open science is defined as a general policy aimed at overcoming the barriers that hinder the implementation of the European Research Area (ERA)
Managing shared resources for the community of scholars maximizes the benefits to society.
It has been shown that the structure of the cloud of open science includes an augmented reality as an open-science platform.
arXiv Detail & Related papers (2020-02-28T07:32:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.