30,360 research outputs found
Maturation trajectories of cortical resting-state networks depend on the mediating frequency band
The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13â30âŻHz) and gamma (31â80âŻHz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.This work was supported by grants from the Nancy Lurie Marks Family Foundation (TK, SK, MGK), Autism Speaks (TK), The Simons Foundation (SFARI 239395, TK), The National Institute of Child Health and Development (R01HD073254, TK), National Institute for Biomedical Imaging and Bioengineering (P41EB015896, 5R01EB009048, MSH), and the Cognitive Rhythms Collaborative: A Discovery Network (NFS 1042134, MSH). (Nancy Lurie Marks Family Foundation; Autism Speaks; SFARI 239395 - Simons Foundation; R01HD073254 - National Institute of Child Health and Development; P41EB015896 - National Institute for Biomedical Imaging and Bioengineering; 5R01EB009048 - National Institute for Biomedical Imaging and Bioengineering; NFS 1042134 - Cognitive Rhythms Collaborative: A Discovery Network
Creating business value from big data and business analytics : organizational, managerial and human resource implications
This paper reports on a research project, funded by the EPSRCâs NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be âbigâ. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organizationâs business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as âbricoleurâ, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model
Controlling a mobile robot with a biological brain
The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robotïżœthereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots
What is the âSourceâ of Open Source Hardware?
What âopen sourceâ means once applied to tangible products has been so far mostly addressed through the light of licensing. While this approach is suitable for software, it appears to be over-simplistic for complex hardware products. Whether such a product can be labelled as open source is not only a question of licence but a question of documentation, i.e. what is the information that sufficiently describes it? Or in other words, what is the âsourceâ of open source hardware? To date there is no simple answer to this question, leaving large room for interpretation in the usage of the term. Based on analysis of public documentation of 132 products, this paper provides an overview of how practitioners tend to interpret the concept of open source hardware. It specifically focuses on the recent evolution of the open source movement outside the domain of electronics and DIY to that of non-electronic and complex open source hardware products. The empirical results strongly indicate the existence of two main usages of open source principles in the context of tangible products: publication of product-related documentation as a means to support community-based product development and to disseminate privately developed innovations. It also underlines the high variety of interpretations and even misuses of the concept of open source hardware. This reveals in turn that this concept may not even be clear to practitioners and calls for more narrowed down definitions of what has to be shared for a product to be called open source. This article contributes towards this effort through the definition of an open source hardware lifecycle summarizing the observed approaches to open source hardware.DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische UniversitĂ€t Berli
Big data-driven investigation into the maturity of library research data services (RDS)
Research data management (RDM) poses a significant challenge for academic organizations. The creation of library research data services (RDS) requires assessment of their maturity, i.e., the primary objective of this study. Its authors have set out to probe the nationwide level of library RDS maturity, based on the RDS maturity model, as proposed by Cox et al. (2019), while making use of natural language processing (NLP) tools, typical for big data analysis. The secondary objective consisted in determining the actual suitability of the above-referenced tools for this particular type of assessment. Web scraping, based on 72 keywords, and completed twice, allowed the authors to select from the list of 320 libraries that run RDS, i.e., 38 (2021) and 42 (2022), respectively. The content of the websites run by the academic libraries offering a scope of RDM services was then appraised in some depth. The findings allowed the authors to identify the geographical distribution of RDS (academic centers of various sizes), a scope of activities undertaken in the area of research data (divided into three clusters, i.e., compliance, stewardship, and transformation), and overall potential for their prospective enhancement. Although the present study was carried within a single country only (Poland), its protocol may easily be adapted for use in any other countries, with a view to making a viable comparison of pertinent findings
Open Data Ecosystems - an empirical investigation into an emerging industry collaboration concept
Software systems are increasingly depending on data, particularly with the rising use of machine learning, and developers are looking for new sources of data. Open Data Ecosystems (ODE) is an emerging concept for data sharing under public licenses in software ecosystems, similar to Open Source Software (OSS). It has certain similarities to Open Government Data (OGD), where public agencies share data for innovation and transparency.We aimed to explore open data ecosystems involving commercial actors. Thus, we organized five focus groups with 27 practitioners from 22 companies, public organizations, and research institutes. Based on the outcomes, we surveyed three cases of emerging ODE practice to further understand the concepts and to validate the initial findings. The main outcome is an initial conceptual model of ODEsâ value, intrinsics, governance, and evolution, and propositions for practice and further research.We found that ODE must be value driven. Regarding the intrinsics of data, we found their type, meta-data, and legal frameworks influential for their openness. We also found the characteristics of ecosystem initiation, organization, data acquisition and openness be differentiating, which we advise research and practice to take into consideration
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