scholarly journals Big data, open science and the brain: lessons learned from genomics

Author(s):  
Suparna Choudhury ◽  
Jennifer R. Fishman ◽  
Michelle L. McGowan ◽  
Eric T. Juengst
2017 ◽  
Author(s):  
Michael P. Milham ◽  
R. Cameron Craddock ◽  
Arno Klein

AbstractDespite decades of research, visions of transforming neuropsychiatry through the development of brain imaging-based ‘growth charts’ or ‘lab tests’ have remained out of reach. In recent years, there is renewed enthusiasm about the prospect of achieving clinically useful tools capable of aiding the diagnosis and management of neuropsychiatric disorders. The present work explores the basis for this enthusiasm. We assert that there is no single advance that currently has the potential to drive the field of clinical brain imaging forward. Instead, there has been a constellation of advances that, if combined, could lead to the identification of objective brain imaging-based markers of illness. In particular, we focus on advances that are helping to: 1) elucidate the research agenda for biological psychiatry (e.g., neuroscience focus, precision medicine), 2) shift research models for clinical brain imaging (e.g., big data exploration, standardization), 3) break down research silos (e.g., open science, calls for reproducibility and transparency), and 4) improve imaging technologies and methods. While an arduous road remains ahead, these advances are repositioning the brain imaging community for long-term success.


2020 ◽  
Vol 5 (1) ◽  
pp. 88-96
Author(s):  
Mary R. T. Kennedy

Purpose The purpose of this clinical focus article is to provide speech-language pathologists with a brief update of the evidence that provides possible explanations for our experiences while coaching college students with traumatic brain injury (TBI). Method The narrative text provides readers with lessons we learned as speech-language pathologists functioning as cognitive coaches to college students with TBI. This is not meant to be an exhaustive list, but rather to consider the recent scientific evidence that will help our understanding of how best to coach these college students. Conclusion Four lessons are described. Lesson 1 focuses on the value of self-reported responses to surveys, questionnaires, and interviews. Lesson 2 addresses the use of immediate/proximal goals as leverage for students to update their sense of self and how their abilities and disabilities may alter their more distal goals. Lesson 3 reminds us that teamwork is necessary to address the complex issues facing these students, which include their developmental stage, the sudden onset of trauma to the brain, and having to navigate going to college with a TBI. Lesson 4 focuses on the need for college students with TBI to learn how to self-advocate with instructors, family, and peers.


2016 ◽  
Vol 34 (10) ◽  
pp. 1366-1372 ◽  
Author(s):  
John P. Prybylski ◽  
Erin Maxwell ◽  
Carla Coste Sanchez ◽  
Michael Jay

NeuroImage ◽  
2019 ◽  
Vol 190 ◽  
pp. 79-93 ◽  
Author(s):  
David Meder ◽  
Damian Marc Herz ◽  
James Benedict Rowe ◽  
Stéphane Lehéricy ◽  
Hartwig Roman Siebner

Author(s):  
Yupo Chan

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: ( a) detecting and resolving an incident (possibly through crowdsourcing), ( b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), ( c) advising motorists about routing alternatives (possibly through real-time information), and ( d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.


2016 ◽  
Vol 12 (9) ◽  
pp. 1014-1021 ◽  
Author(s):  
Hugo Geerts ◽  
Penny A. Dacks ◽  
Viswanath Devanarayan ◽  
Magali Haas ◽  
Zaven S. Khachaturian ◽  
...  

Author(s):  
Andrew N. Pilny ◽  
Marshall Scott Poole

The exponential growth of “Big Data” has given rise to a field known as computational social science (CSS). The authors view CSS as the interdisciplinary investigation of society that takes advantage of the massive amount of data generated by individuals in a way that allows for abductive research designs. Moreover, CSS complicates the relationship between data and theory by opening the door for a more data-driven approach to social science. This chapter will demonstrate the utility of a CSS approach using examples from dynamic interaction modeling, machine learning, and network analysis to investigate organizational communication (OC). The chapter concludes by suggesting that lessons learned from OC's history can help deal with addressing several current issues related to CSS, including an audit culture, data collection ethics, transparency, and Big Data hubris.


Author(s):  
A. Jayanthiladevi ◽  
S. Murugan ◽  
K. Manivel

Today, images and image sequences (videos) make up about 80% of all corporate and public unstructured big data. As growth of unstructured data increases, analytical systems must assimilate and interpret images and videos as well as they interpret structured data such as text and numbers. An image is a set of signals sensed by the human eye and processed by the visual cortex in the brain creating a vivid experience of a scene that is instantly associated with concepts and objects previously perceived and recorded in one's memory. To a computer, images are either a raster image or a vector image. Simply put, raster images are a sequence of pixels with discreet numerical values for color; vector images are a set of color-annotated polygons. To perform analytics on images or videos, the geometric encoding must be transformed into constructs depicting physical features, objects and movement represented by the image or video. This chapter explores text, images, and video analytics in fog computing.


2020 ◽  
Vol 10 (4) ◽  
pp. 1-20
Author(s):  
Swati Kamthekar ◽  
Prachi Deshpande ◽  
Brijesh Iyer

The article reports the effect of Tratak Sadhana (meditation) on humans using electroencephalograph (EEG) signals. EEG represents the brain activities in the form of electrical signals. Due to non-stationary nature of the EEG signals, nonlinear parameters like approximate entropy, wavelet entropy and Higuchi' fractal dimensions are used to assess the variations in EEG rest as well as during Tratak Sadhana, i.e. at a rest state with eyes closed and during Tratak meditation. EEG signals are captured using EPOC Emotive EEG sensor. The sensor has 14 electrodes covering human scalp. Results shows that new practitioners can also achieve a rapid meditative state as compared to other meditation techniques. Further, the Big Data perspective of the present study is discussed. The present study shows that Tratak Sadhana meditation is an effective tool for rapid stress relief in humans.


2019 ◽  
Vol 11 (3) ◽  
pp. 255-273 ◽  
Author(s):  
Vicki Xafis ◽  
Markus K. Labude

Abstract There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.


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