The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory,
As social media became major platforms for political campaigns and discussions of other important issues, concerns have been growing about manipulation of the information ecosystem by bad actors. Typical techniques used by the bad actors vary from astroturf (Ratkiewicz,
Big bibliographic datasets hold promise for revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Yet, hosting proprietary and open big bibliographic datasets poses significant difficulties for libraries, both large and small. Libraries face significant barriers to hosting such assets, including cost and expertise, which has limited their ability to provide stewardship for big datasets, and thus has hampered researchers' access to them. What is needed is a solution to address the libraries' and researchers’ joint needs. This article outlines the theoretical framework that underpins the Collaborative Archive and Data Research Environment project. We recommend a shared cloud-based infrastructure to address this need built on five pillars: 1) Community–a community of libraries and industry partners who support and maintain the platform and a community of researchers who use it; 2) Access–the sharing platform should be accessible and affordable to both proprietary data customers and the general public; 3) Data-Centric–the platform is optimized for efficient and high-quality bibliographic data services, satisfying diverse data needs; 4) Reproducibility–the platform should be designed to foster and encourage reproducible research; 5) Empowerment—the platform should empower researchers to perform big data analytics on the hosted datasets. In this article, we describe the many facets of the problem faced by American academic libraries and researchers wanting to work with big datasets. We propose a practical solution based on the five pillars: The Collaborative Archive and Data Research Environment. Finally, we address potential barriers to implementing this solution and strategies for overcoming them.
BACKGROUNDIdentifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre‐symptomatic risk assessment but also for building personalized therapeutic strategies.METHODSWe implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD‐risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.RESULTSRs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD‐risk SNPs were significant predictors of AD progression.DISCUSSIONThe model successfully estimated the contribution of AD‐risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.
With funding from the National Science Foundation, the Center for Open Science (COS) and Indiana University will create a dynamic, distributed, and heterogeneous data source for the advancement of science of science research. This will be achieved by using, enhancing, and combining the capabilities of the Open Science Framework (OSF) and the Collaborative Archive & Data Research Environment (CADRE). With over 200,000 users (currently growing by >220 per day), many thousands of projects, registrations, and papers, millions of files stored and managed, and rich metadata tracking researcher actions, the OSF is already a very rich dataset for investigating the research lifecycle, researcher behaviors, and how those behaviors evolve in the social network. As a cross-university effort, CADRE provides an integrated data mining and collaborative environment for big bibliographic data sets. While still under development, the CADRE platform has already attracted long-term financial commitments from 10 research intensive universities with additional support from multiple infrastructure and industry partners. Connecting these efforts will catalyze transformative research of human networks in the science of science.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.