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Open science : knowledge for everyone
\"Part of the nonfiction Orca Think series for middle-grade readers, this book explores the concept of open science and how scientists around the world are working together to make research available to everyone.\"-- Provided by publisher.
Book
The Open International Soccer Database for machine learning
How well can machine learning predict the outcome of a soccer game, given the most commonly and freely available match data? To help answer this question and to facilitate machine learning research in soccer, we have developed the Open International Soccer Database. Version v1.0 of the Database contains essential information from 216,743 league soccer matches from 52 leagues in 35 countries. The earliest entries in the Database are from the year 2000, which is when football leagues generally adopted the “three points for a win” rule. To demonstrate the use of the Database for machine learning research, we organized the 2017 Soccer Prediction Challenge. One of the goals of the Challenge was to estimate where the limits of predictability lie, given the type of match data contained in the Database. Another goal of the Challenge was to pose a real-world machine learning problem with a fixed time line and a genuine prediction task: to develop a predictive model from the Database and then to predict the outcome of the 206 future soccer matches taking place from 31 March 2017 to the end of the regular season. The Open International Soccer Database is released as an open science project, providing a valuable resource for soccer analysts and a unique benchmark for advanced machine learning methods. Here, we describe the Database and the 2017 Soccer Prediction Challenge and its results.
Journal Article
Research e-infrastructures for open science: The national example of CSTCloud in China
This paper focuses on research e-infrastructures in the open science era. We analyze some of the challenges and opportunities of cloud-based science and introduce an example of a national solution in the China Science and Technology Cloud (CSTCloud). We selected three CSTCloud use cases in deploying open science modules, including scalable engineering in astronomical data management, integrated Earth-science resources for SDG-13 decision making, and the coupling of citizen science and artificial intelligence (AI) techniques in biodiversity. We conclude with a forecast on the future development of research e-infrastructures and introduce the idea of the Global Open Science Cloud (GOSC). We hope this analysis can provide some insights into the future development of research e-infrastructures in support of open science.
Journal Article
FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units
Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).
Journal Article
Factors influencing open science participation through research data sharing and reuse among researchers: a systematic literature review
This systematic literature review investigates the influential factors guiding researchers’ active engagement in open science through research data sharing and subsequent reuse, spanning various scientific disciplines. The review addresses key objectives and questions, including identifying distinct sample types, data collection methods, critical factors, and existing gaps within the body of literature concerning data sharing and reuse in open science. The methodology employed in the review was detailed, outlining a series of systematic steps. These steps encompass the systematic search and selection of relevant studies, rigorous data extraction and analysis, comprehensive evaluation of selected studies, and transparent reporting of the resulting findings. The review’s evaluation process was governed by well-defined inclusion and exclusion criteria, encompassing publication dates, language, study design, and research outcomes. Furthermore, it adheres to the PRISMA 2020 flow diagram, effectively illustrating the progression of records through the review stages, highlighting the number of records identified, screened, included, and excluded. The findings include a concise tabular representation summarizing data extracted from the 51 carefully selected studies incorporated within the review. The table provides essential details, including study citations, sample sizes, data collection methodologies, and key factors influencing open science data sharing and reuse. Additionally, common themes and categories among these influential factors are identified, shedding light on overarching trends in the field. In conclusion, this systematic literature review offers valuable insights into the multifaceted landscape of open science participation, emphasizing the critical role of research data sharing and reuse. It is a comprehensive resource for researchers and practitioners interested in further understanding the dynamics and factors shaping the open science ecosystem.
Journal Article
Absolute beginners guide to computing
Learn and understand how you can perform a wide range of tasks on your new Windows computer, including managing files, browsing the internet, and protecting yourself, as well as interacting with Cortana. Using Absolute Beginners Guide to Computing you will see how to use Windows, and how you can connect and communicate with others. You will learn the basics of browsing the web, how to send email, and sign up for services. You will learn about some of the social media sites such as Facebook and Twitter. You will also learn how to connect and use external hardware, and process digital music, photos, and video. Written by an author who has written multiple computing titles, this book is friendly and approachable, and can teach anyone how to use a computer. With simple steps, easy troubleshooting, and online resources, it's the best place to learn how to make computing a part of your life. What You'll Learn: * Get pictures onto your computer to share* Listen to digital music* What clubs, groups, and other resources there are to help Who this Book Is For Anyone that wants to learn all the latest Windows features. Beginners who want to use their new Windows computer to share pictures or video clips on YouTube or Facebook to those seeking a common sense approach to safe computing.
Book
Open science policies as regarded by the communities of researchers from the basic sciences in the scientific periphery
PurposeThis paper explores the different open science policy effects on the knowledge generation process of researchers in basic sciences: biology, chemistry and physics.Design/methodology/approachThis paper uses a qualitative methodology with a content analysis approach. It uses seventeen semi-directed interviews.FindingsThe main perceived effect of open science is access to research inputs, with open access, open research data and code reuse as primary sources. Another issue is the increase of collaboration with other colleagues in terms of the ability to collaborate faster and encouraging the exchange of ideas. However, this benefit does not translate to the division of labor in large transnational teams. Time spent on tasks like cleaning up data and code, scooping and other ethical issues are unfavorable aspects noted.Practical implicationsPolicymakers could use this study to enhance current open science policies in the countries.Originality/valueThis study analyzes the perspectives of basic sciences researchers from two countries about open science policies. The main conclusion is the fact that open science policies should focus on the research process itself – rather than research outputs – in order to effectively tackle inequalities in science.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2023-0135
Journal Article