Opportunities in Open Science With AI.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang K
PROVIDER: S-EPMC7931916 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Bolstered by ever affordable computational power and open big datasets, artificial intelligence (AI) technologies are bringing revolutionary changes to our lives. This article examines the current trends and elaborates the future potentials of AI in its role for making science more open and accessible. Based on the experience derived from a research project called Microsoft Academic, the advocates have reasons to be optimistic about the future of open science as the advanced discovery, ranking, ...[more]
Similar Datasets
Project description:This perspective article was written by invitation of the editors in chief as a summary and extension of the symposium entitled Psychoneuroendocrine Research in the Era of the Replication Crisis which was held at the virtual meeting of the International Society of Psychoneuroendocrinology 2021. It highlights the opportunities presented by the application of open and reproducible scientific practices in psychoneuroendocrinology (PNE), an interdisciplinary field at the intersection of psychology, endocrinology, immunology, neurology, and psychiatry. It conveys an introduction to the topics preregistration, registered reports, quantifying the impact of equally-well justifiable analysis decisions, and open data and scripts, while emphasizing 'selfish' reasons to adopt such practices as individual researcher. Complementary to the call for adoption of open science practices, we highlight the need for methodological best practice guidelines in the field of PNE, which could further contribute to enhancing replicability of results. We propose concrete steps for future actions and provide links to additional resources for those interested in adopting open and reproducible science practices in future studies.
| S-EPMC9216702 | biostudies-literature
Project description:The Open Science Prize was established with the following objectives: first, to encourage the crowdsourcing of open data to make breakthroughs that are of biomedical significance; second, to illustrate that funders can indeed work together when scientific interests are aligned; and finally, to encourage international collaboration between investigators with the intent of achieving important innovations that would not be possible otherwise. The process for running the competition and the successes and challenges that arose are presented.
| S-EPMC5538631 | biostudies-other
Project description:Neurolinguistic assessments play a vital role in neurological examinations, revealing a wide range of language and communication impairments associated with developmental disorders and acquired neurological conditions. Yet, a thorough neurolinguistic assessment is time-consuming and laborious and takes valuable resources from other tasks. To empower clinicians, healthcare providers, and researchers, we have developed Open Brain AI (OBAI). The aim of this computational platform is twofold. First, it aims to provide advanced AI tools to facilitate spoken and written language analysis, automate the analysis process, and reduce the workload associated with time-consuming tasks. The platform currently incorporates multilingual tools for English, Danish, Dutch, Finnish, French, German, Greek, Italian, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, and Swedish. The tools involve models for (i) audio transcription, (ii) automatic translation, (iii) grammar error correction, (iv) transcription to the International Phonetic Alphabet, (v) readability scoring, (vi) phonology, morphology, syntax, semantic measures (e.g., counts and proportions), and lexical measures. Second, it aims to support clinicians in conducting their research and automating everyday tasks with "OBAI Companion," an AI language assistant that facilitates language processing, such as structuring, summarizing, and editing texts. OBAI also provides tools for automating spelling and phonology scoring. This paper reviews OBAI's underlying architectures and applications and shows how OBAI can help professionals focus on higher-value activities, such as therapeutic interventions.
| S-EPMC11333242 | biostudies-literature
Project description:BackgroundThe open science movement is transforming scientific practice with the goal of enhancing the transparency, productivity, and reproducibility of research. Nevertheless, transparency is a complex concept, and efforts to promote some forms of transparency may do relatively little to advance other important forms of transparency.ObjectivesDrawing from the literature in history, philosophy, and sociology of science, we aim to distinguish between different forms of scientific transparency. Our goal is to identify strategies for achieving forms of transparency that are relevant not only to scientists but also to decision makers and members of the public.DiscussionWe draw a distinction between "scientifically relevant transparency" and "socially relevant transparency." Most of the prominent strategies associated with the open science movement (e.g., making data publicly available and registering studies) are designed primarily to promote scientifically relevant transparency. To achieve socially relevant transparency, which is particularly important in fields like environmental health, further steps are needed to provide scientific information in ways that are relevant to decision makers and members of the public.ConclusionsPromoting socially relevant transparency will require a range of activities by many different individuals and institutions. We propose an array of strategies that can be pursued by scientists and other scholars, journals, universities, funders, government agencies, and members of the public. https://doi.org/10.1289/EHP4808.
| S-EPMC6792383 | biostudies-literature
Project description:Mounting evidence indicates that worldwide, innovation systems are increasing unsustainable. Equally, concerns about inequities in the science and innovation process, and in access to its benefits, continue. Against a backdrop of growing health, economic and scientific challenges global stakeholders are urgently seeking to spur innovation and maximize the just distribution of benefits for all. Open Science collaboration (OS) - comprising a variety of approaches to increase open, public, and rapid mobilization of scientific knowledge - is seen to be one of the most promising ways forward. Yet, many decision-makers hesitate to construct policy to support the adoption and implementation of OS without access to substantive, clear and reliable evidence. In October 2017, international thought-leaders gathered at an Open Science Leadership Forum in the Washington DC offices of the Bill and Melinda Gates Foundation to share their views on what successful Open Science looks like. Delegates from developed and developing nations, national governments, science agencies and funding bodies, philanthropy, researchers, patient organizations and the biotechnology, pharma and artificial intelligence (AI) industries discussed the outcomes that would rally them to invest in OS, as well as wider issues of policy and implementation. This first of two reports, summarizes delegates' views on what they believe OS will deliver in terms of research, innovation and social impact in the life sciences. Through open and collaborative process over the next months, we will translate these success outcomes into a toolkit of quantitative and qualitative indicators to assess when, where and how open science collaborations best advance research, innovation and social benefit. Ultimately, this work aims to develop and openly share tools to allow stakeholders to evaluate and re-invent their innovation ecosystems, to maximize value for the global public and patients, and address long-standing questions about the mechanics of innovation.
| S-EPMC5852639 | biostudies-literature
Project description:Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.
| S-EPMC4973366 | biostudies-other
Project description:The way science and research is done is rapidly becoming more open and collaborative. The traditional way of publishing new findings in journals is becoming increasingly outdated and no longer serves the needs of much of science. Whilst preprints can bring significant benefits of removing delay and selection, they do not go far enough if simply implemented alongside the existing journal system. We propose that we need a new approach, an Open Science Platform, that takes the benefits of preprints but adds formal, invited, and transparent post-publication peer review. This bypasses the problems of the current journal system and, in doing so, moves the evaluation of research and researchers away from the journal-based Impact Factor and towards a fairer system of article-based qualitative and quantitative indicators. In the long term, it should be irrelevant where a researcher publishes their findings. What is important is that research is shared and made available without delay within a framework that encourages quality standards and requires all players in the research community to work as collaborators.
| S-EPMC4768651 | biostudies-other
Project description:Openly shared low-cost electronic hardware applications, known as open electronics, have sparked a new open-source movement, with much untapped potential to advance scientific research. Initially designed to appeal to electronic hobbyists, open electronics have formed a global "maker" community and are increasingly used in science and industry. In this perspective article, we review the current costs and benefits of open electronics for use in scientific research ranging from the experimental to the theoretical sciences. We discuss how user-made electronic applications can help (I) individual researchers, by increasing the customization, efficiency, and scalability of experiments, while improving data quantity and quality; (II) scientific institutions, by improving access to customizable high-end technologies, sustainability, visibility, and interdisciplinary collaboration potential; and (III) the scientific community, by improving transparency and reproducibility, helping decouple research capacity from funding, increasing innovation, and improving collaboration potential among researchers and the public. We further discuss how current barriers like poor awareness, knowledge access, and time investments can be resolved by increased documentation and collaboration, and provide guidelines for academics to enter this emerging field. We highlight that open electronics are a promising and powerful tool to help scientific research to become more innovative and reproducible and offer a key practical solution to improve democratic access to science.
| S-EPMC9617215 | biostudies-literature
Project description:ObjectivesThe aim is to offer an overview of the existing training programs and critically examine them and suggest avenues for further development of AI training programs for radiologists.MethodsDeductive thematic analysis of 100 training programs offered in 2019 and 2020 (until June 30). We analyze the public data about the training programs based on their "contents," "target audience," "instructors and offering agents," and "legitimization strategies."ResultsThere are many AI training programs offered to radiologists, yet most of them (80%) are short, stand-alone sessions, which are not part of a longer-term learning trajectory. The training programs mainly (around 85%) focus on the basic concepts of AI and are offered in passive mode. Professional institutions and commercial companies are active in offering the programs (91%), though academic institutes are limitedly involved.ConclusionsThere is a need to further develop systematic training programs that are pedagogically integrated into radiology curriculum. Future training programs need to further focus on learning how to work with AI at work and be further specialized and customized to the contexts of radiology work.Key points• Most of AI training programs are short, stand-alone sessions, which focus on the basics of AI. • The content of training programs focuses on medical and technical topics; managerial, legal, and ethical topics are marginally addressed. • Professional institutions and commercial companies are active in offering AI training; academic institutes are limitedly involved.
| S-EPMC8270863 | biostudies-literature
Project description:Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.
| S-EPMC10302890 | biostudies-literature