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Numerous metascience studies and other initiatives have begun to monitor the prevalence of open science practices when it is more important to understand the 'downstream' effects or impacts of open science. PLOS and DataSeer have developed a new LLM-based indicator to measure an important effect of open science: the reuse of research data. Our results show a data reuse rate of 43%, which is higher than established bibliometric techniques. We show that data reuse can be measured at scale using LLMs and generative artificial intelligence. The positive effects of research data sharing and reuse may currently be underestimated.
Open data are characterized by a number of economic, technological, innovative and social benefits. They are seen as a significant contributor to the city's transformation into Smart City. This is all the more so when the society is on the border of Society 5.0, i.e., shift from the information society to a super smart society or society of imagination takes place. However, the question constantly asked by open data experts is, what are the key factors to be met and satisfied in order to achieve promised benefits? The current trend of openness suggests that the principle of openness should be followed not only by data but also research, education, software, standard, hardware etc., it should become a philosophy to be followed at different levels, in different domains. This should ensure greater transparency, eliminating inequalities, promoting, and achieving sustainable development goals. Therefore, many agendas now have openness as a prerequisite. This chapter deals with concepts of open (government) data and Society 5.0 pointing to their common objectives, providing some success stories of open data use in smart cities or transformation of cities towards smart cities, mapping the
AI for Science (AI4Science), particularly in the form of self-driving labs, has the potential to sideline human involvement and hinder scientific discovery within the broader community. While prior research has focused on ensuring the responsible deployment of AI applications, enhancing security, and ensuring interpretability, we also propose that promoting openness in AI4Science discoveries should be carefully considered. In this paper, we introduce the concept of AI for Open Science (AI4OS) as a multi-agent extension of AI4Science with the core principle of maximizing open knowledge translation throughout the scientific enterprise rather than a single organizational unit. We use the established principles of Knowledge Discovery and Data Mining (KDD) to formalize a language around AI4OS. We then discuss three principle stages of knowledge translation embedded in AI4Science systems and detail specific points where openness can be applied to yield an AI4OS alternative. Lastly, we formulate a theoretical metric to assess AI4OS with a supporting ethical argument highlighting its importance. Our goal is that by drawing attention to AI4OS we can ensure the natural consequence of AI4Scie
This paper reviews research literature on Diamond Open Access (DOA) journals - sometimes also called Platinum Open Access - that was produced after this journal segment started to become a priority in European research policy around 2020. It contextualizes the current science policy debate, critically examines different understandings of DOA, and reviews studies on the role of such journals in scholarly communication. Most existing research consists of quantitative studies focusing on aspects such as the number of DOA journals, their publication output, the diversity of the landscape in terms of subject areas, languages, publishing entities, indexing in major databases, awareness and perception among scholars, cost analyses, as well as insights into the internal operations of DOA journals. The review shows that research on DOA journals is partly influenced by the science policy discourse in at least two ways: first, through the normativity inherent in that discourse, and second, through the temporality of policy-driven research of practical relevance, which leaves important aspects of the phenomenon understudied. Moreover, research on the DOA journal landscape has implications beyo
This paper calls on the research community not only to investigate how human biases are inherited by large language models (LLMs) but also to explore how these biases in LLMs can be leveraged to make society's "unwritten code" - such as implicit stereotypes and heuristics - visible and accessible for critique. We introduce a conceptual framework through a case study in science: uncovering hidden rules in peer review - the factors that reviewers care about but rarely state explicitly due to normative scientific expectations. The idea of the framework is to push LLMs to speak out their heuristics through generating self-consistent hypotheses - why one paper appeared stronger in reviewer scoring - among paired papers submitted to 46 academic conferences, while iteratively searching deeper hypotheses from remaining pairs where existing hypotheses cannot explain. We observed that LLMs' normative priors about the internal characteristics of good science extracted from their self-talk, e.g., theoretical rigor, were systematically updated toward posteriors that emphasize storytelling about external connections, such as how the work is positioned and connected within and across literatures.
Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis's (2012) statement that, "the scientific process demands the highest standards of quality, ethics and rigour," they are worth implementing. More importantly, they are feasible to work on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source.
We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset. Our toolkit provides: 1. A scalable machine learning workflow for materials science leveraging PyTorch Lightning, which enables seamless scaling across different computation capabilities (laptop, server, cluster) and hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for rapid graph neural network prototyping and development. By publishing and sharing this toolkit with the research community via open-source release, we hope to: 1. Lower the entry barrier for new machine learning researchers and practitioners that want to get started with the OpenCatalyst dataset, which presently comprises the largest computational materials science dataset. 2. Enable the scientific community to apply advanced machine learning tools to high-impact scientific challenges, such as modeling of materials behavior for clean energy applications. We demonstrate the capabilities of our framework by enabling three new equivariant neural network models for
Authorship of scientific articles has profoundly changed from early science until now. While once upon a time a paper was authored by a handful of authors, scientific collaborations are much more prominent on average nowadays. As authorship (and citation) is essentially the primary reward mechanism according to the traditional research evaluation frameworks, it turned out to be a rather hot-button topic from which a significant portion of academic disputes stems. However, the novel Open Science practices could be an opportunity to disrupt such dynamics and diversify the credit of the different scientific contributors involved in the diverse phases of the lifecycle of the same research effort. In fact, a paper and research data (or software) contextually published could exhibit different authorship to give credit to the various contributors right where it feels most appropriate. As a preliminary study, in this paper, we leverage the wealth of information contained in Open Science Graphs, such as OpenAIRE, and conduct a focused analysis on a subset of publications with supplementary material drawn from the European Marine Science (MES) research community. The results are promising an
Current definitions of Information Science are inadequate to comprehensively describe the nature of its field of study and for addressing the problems that are arising from intelligent technologies. The ubiquitous rise of artificial intelligence applications and their impact on society demands the field of Information Science acknowledge the sociotechnical nature of these technologies. Previous definitions of Information Science over the last six decades have inadequately addressed the environmental, human, and social aspects of these technologies. This perspective piece advocates for an expanded definition of Information Science that fully includes the sociotechnical impacts information has on the conduct of research in this field. Proposing an expanded definition of Information Science that includes the sociotechnical aspects of this field should stimulate both conversation and widen the interdisciplinary lens necessary to address how intelligent technologies may be incorporated into society and our lives more fairly.
Access to the work of others is something that is too often taken for granted, yet problematic and difficult to be obtained unless someone pays for it. Green and gold open access are claimed to be a solution to this problem. While open access is gaining momentum in some fields, there is a limited and seasoned knowledge about self-archiving in computer science. In particular, there is an inadequate understanding of author-based self-archiving awareness, practice, and inhibitors. This article reports an exploratory study of the awareness of self-archiving, the practice of self-archiving, and the inhibitors of self-archiving among authors in an Italian computer science faculty. Forty-nine individuals among interns, PhD students, researchers, and professors were recruited in a questionnaire (response rate of 72.8%). The quantitative and qualitative responses suggested that there is still work needed in terms of advocating green open access to computer science authors who seldom self-archive and when they do, they often infringe the copyright transfer agreements (CTAs) of the publishers. In addition, tools from the open-source community are needed to facilitate author-based self-archivi
It is widely recognised nowadays that there is no single, accepted, unified definition of Open Science, which motivates our proposal of an Open Science definition as a political and legal framework where research outputs are shared and disseminated in order to be rendered visible, accessible, reusable is developed, standing over the concepts enhanced by the Budapest Open Science Initiative (BOAI), and by the Free/Open Source Software (FOSS) and Open data movements. We elaborate this proposal through a detailed analysis of some selected EC policies and laws as well as of the function of research evaluation practices. The legal aspects considered in our examination include, in particular, the study of the role of licenses in the context of the dissemination of research outputs.
Symbolic regression (SR) has emerged as a powerful method for uncovering interpretable mathematical relationships from data, offering a novel route to both scientific discovery and efficient empirical modelling. This article introduces the Special Issue on Symbolic Regression for the Physical Sciences, motivated by the Royal Society discussion meeting held in April 2025. The contributions collected here span applications from automated equation discovery and emergent-phenomena modelling to the construction of compact emulators for computationally expensive simulations. The introductory review outlines the conceptual foundations of SR, contrasts it with conventional regression approaches, and surveys its main use cases in the physical sciences, including the derivation of effective theories, empirical functional forms and surrogate models. We summarise methodological considerations such as search-space design, operator selection, complexity control, feature selection, and integration with modern AI approaches. We also highlight ongoing challenges, including scalability, robustness to noise, overfitting and computational complexity. Finally we emphasise emerging directions, particula
The Open Science Grid (OSG) includes work to enable new science, new scientists, and new modalities in support of computationally based research. There are frequently significant sociological and organizational changes required in transformation from the existing to the new. OSG leverages its deliverables to the large scale physics experiment member communities to benefit new communities at all scales through activities in education, engagement and the distributed facility. As a partner to the poster and tutorial at SciDAC 2008, this paper gives both a brief general description and some specific examples of new science enabled on the OSG. More information is available at the OSG web site: (http://www.opensciencegrid.org).
The large instantaneous sensitivity, a wide frequency coverage and flexible observation modes with large number of beams in the sky are the main features of the SKA observatory's two telescopes, the SKA-Low and the SKA-Mid, which are located on two different continents. Owing to these capabilities, the SKAO telescopes are going to be a game-changer for radio astronomy in general and pulsar astronomy in particular. The eleven articles in this special issue on pulsar science with the SKA Observatory describe its impact on different areas of pulsar science. In this lead article, a brief description of the two telescopes highlighting the relevant features for pulsar science is presented followed by an overview of each accompanying article, exploring the inter-relationship between different pulsar science use cases.
Mauve is a low-cost small satellite developed and operated by Blue Skies Space Ltd. The payload features a 13 cm telescope connected with a fibre that feeds into a UV-Vis spectrometer. The detector covers the 200-700 nm range in a single shot, obtaining low resolution spectra at R~20-65. Mauve has launched on 28th November 2025, reaching a 510 km Low-Earth Sun-synchronous orbit. The satellite will enable UV and visible observations of a variety of stellar objects in our Galaxy, filling the gaps in the ultraviolet space-based data. The researchers that have already joined the mission have defined the science themes, observational strategy and targets that Mauve will observe in the first year of operations. To date 10 science themes have been developed by the Mauve science collaboration for year 1, with observational strategies that include both long duration monitoring and short cadence snapshots. Here, we describe these themes and the science that Mauve will undertake in its first year of operations.
We shall discuss some aspects of science and technology, their increasing role in the society, the fast advances in modern science, the apparent decrease of interest of the young generation in basic sciences, the importance of proper science popularization for better public education and awareness in scientific fields.
The impact of Machine Learning (ML) algorithms in the age of big data and platform capitalism has not spared scientific research in academia. In this work, we will analyse the use of ML in fundamental physics and its relationship to other cases that directly affect society. We will deal with different aspects of the issue, from a bibliometric analysis of the publications, to a detailed discussion of the literature, to an overview on the productive and working context inside and outside academia. The analysis will be conducted on the basis of three key elements: the non-neutrality of science, understood as its intrinsic relationship with history and society; the non-neutrality of the algorithms, in the sense of the presence of elements that depend on the choices of the programmer, which cannot be eliminated whatever the technological progress is; the problematic nature of a paradigm shift in favour of a data-driven science (and society). The deconstruction of the presumed universality of scientific thought from the inside becomes in this perspective a necessary first step also for any social and political discussion. This is the subject of this work in the case study of ML.
In science and beyond, numbers are omnipresent when it comes to justifying different kinds of judgments. Which scientific author, hiring committee-member, or advisory board panelist has not been confronted with page-long "publication manuals", "assessment reports", "evaluation guidelines", calling for p-values, citation rates, h-indices, or other statistics in order to motivate judgments about the "quality" of findings, applicants, or institutions? Yet, many of those relying on and calling for statistics do not even seem to understand what information those numbers can actually convey, and what not. Focusing on the uninformed usage of bibliometrics as worrysome outgrowth of the increasing quantification of science and society, we place the abuse of numbers into larger historical contexts and trends. These are characterized by a technology-driven bureaucratization of science, obsessions with control and accountability, and mistrust in human intuitive judgment. The ongoing digital revolution increases those trends. We call for bringing sanity back into scientific judgment exercises. Despite all number crunching, many judgments - be it about scientific output, scientists, or research
With the rise of Wikipedia as a first-stop source for scientific knowledge, it is important to compare its representation of that knowledge to that of the academic literature. Here we identify the 250 most heavily used journals in each of 26 research fields (4,721 journals, 19.4M articles in total) indexed by the Scopus database, and test whether topic, academic status, and accessibility make articles from these journals more or less likely to be referenced on Wikipedia. We find that a journal's academic status (impact factor) and accessibility (open access policy) both strongly increase the probability of it being referenced on Wikipedia. Controlling for field and impact factor, the odds that an open access journal is referenced on the English Wikipedia are 47% higher compared to paywall journals. One of the implications of this study is that a major consequence of open access policies is to significantly amplify the diffusion of science, through an intermediary like Wikipedia, to a broad audience.
The CamCAN Lifespan Neuroimaging Dataset, Cambridge (UK) Centre for Ageing and Neuroscience, was acquired and processed beginning in December, 2016. The referee consensus solver deployed to the Open Science Grid was used for this task. The dataset includes demographic and screening measures, a high-resolution MRI scan of the brain, and whole-head magnetoencephalographic (MEG) recordings during eyes closed rest (560 sec), a simple task (540 sec), and passive listening/viewing (140 sec). The data were collected from 619 neurologically normal individuals, ages 18-87. The processed results from the resting recordings are completed and available online. These constitute 1.7 TBytes of data including the location within the brain (1 mm resolution), time stamp (1 msec resolution), and 80 msec time course for each of 3.7 billion validated neuroelectric events, i.e. mean 6.1 million events for each of the 619 participants. The referee consensus solver provides high yield (mean 11,000 neuroelectric currents/sec; standard deviation (sd): 3500/sec) high confidence (p < 10-12 for each identified current) measures of the neuroelectric currents whose magnetic fields are detected in the MEG reco