scholarly journals Improving data availability for brain image biobanking in healthy subjects: practice-based suggestions from an international multidisciplinary working group

2017 ◽  
Author(s):  
◽  
Susan D Shenkin ◽  
Cyril Pernet ◽  
Thomas E Nichols ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Data Storage ◽  
Healthy Subjects ◽  
Image Data ◽  
Lessons Learned ◽  
Data Availability ◽  
Brain Diseases ◽  
Linkage Data ◽  
Brain Image ◽  
Wide Range ◽  
Computer Scientists

AbstractBrain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining ‘normality’); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.

scholarly journals Distribution-preserving data augmentation

2021 ◽  
Vol 7 ◽  
pp. e571
Author(s):  
Nurdan Ayse Saran ◽  
Murat Saran ◽  
Fatih Nar
Keyword(s):  
Data Augmentation ◽  
Image Data ◽  
Large Data ◽  
Data Availability ◽  
Superior Performance ◽  
Color Distribution ◽  
Spatial Transformations ◽  
Wide Range ◽  
Dataset Size ◽  
Existing Data

In the last decade, deep learning has been applied in a wide range of problems with tremendous success. This success mainly comes from large data availability, increased computational power, and theoretical improvements in the training phase. As the dataset grows, the real world is better represented, making it possible to develop a model that can generalize. However, creating a labeled dataset is expensive, time-consuming, and sometimes not likely in some domains if not challenging. Therefore, researchers proposed data augmentation methods to increase dataset size and variety by creating variations of the existing data. For image data, variations can be obtained by applying color or spatial transformations, only one or a combination. Such color transformations perform some linear or nonlinear operations in the entire image or in the patches to create variations of the original image. The current color-based augmentation methods are usually based on image processing methods that apply color transformations such as equalizing, solarizing, and posterizing. Nevertheless, these color-based data augmentation methods do not guarantee to create plausible variations of the image. This paper proposes a novel distribution-preserving data augmentation method that creates plausible image variations by shifting pixel colors to another point in the image color distribution. We achieved this by defining a regularized density decreasing direction to create paths from the original pixels’ color to the distribution tails. The proposed method provides superior performance compared to existing data augmentation methods which is shown using a transfer learning scenario on the UC Merced Land-use, Intel Image Classification, and Oxford-IIIT Pet datasets for classification and segmentation tasks.

scholarly journals Chronopolis Digital Preservation Network

2010 ◽  
Vol 5 (1) ◽  
pp. 119-133 ◽  
Author(s):  
David Minor ◽  
Don Sutton ◽  
Ardys Kozbial ◽  
Brad Westbrook ◽  
Michael Burek ◽  
...  
Keyword(s):  
Data Storage ◽  
High Speed ◽  
San Diego ◽  
Digital Preservation ◽  
Data Grid ◽  
Lessons Learned ◽  
First Year ◽  
Redundant Data ◽  
Wide Range

The Chronopolis Digital Preservation Initiative, one of the Library of Congress’ latest efforts to collect and preserve at-risk digital information, has completed its first year of service as a multi-member partnership to meet the archival needs of a wide range of domains.Chronopolis is a digital preservation data grid framework developed by the San Diego Supercomputer Center (SDSC) at UC San Diego, the UC San Diego Libraries (UCSDL), and their partners at the National Center for Atmospheric Research (NCAR) in Colorado and the University of Maryland's Institute for Advanced Computer Studies (UMIACS).Chronopolis addresses a critical problem by providing a comprehensive model for the cyberinfrastructure of collection management, in which preserved intellectual capital is easily accessible, and research results, education material, and new knowledge can be incorporated smoothly over the long term. Integrating digital library, data grid, and persistent archive technologies, Chronopolis has created trusted environments that span academic institutions and research projects, with the goal of long-term digital preservation.A key goal of the Chronopolis project is to provide cross-domain collection sharing for long-term preservation. Using existing high-speed educational and research networks and mass-scale storage infrastructure investments, the partnership is leveraging the data storage capabilities at SDSC, NCAR, and UMIACS to provide a preservation data grid that emphasizes heterogeneous and highly redundant data storage systems.In this paper we will explore the major themes within Chronopolis, including:a) The philosophy and theory behind a nationally federated data grid for preservation. b) The core tools and technologies used in Chronopolis. c) The metadata schema that is being developed within Chronopolis for all of the data elements. d) Lessons learned from the first year of the project.e) Next steps in digital preservation using Chronopolis: how we plan to strengthen and broaden our network with enhanced services and new customers.

scholarly journals Data collection in urban drainage and stormwater management systems – case studies

2021 ◽  
pp. 415-469
Author(s):  
Alma Schellart ◽  
Frank Blumensaat ◽  
Francois Clemens-Meyer ◽  
Job van der Werf ◽  
Wan Hanna Melina Wan Mohtar ◽  
...  
Keyword(s):  
Data Collection ◽  
Case Studies ◽  
Data Storage ◽  
Stormwater Management ◽  
Lessons Learned ◽  
Management Systems ◽  
Urban Drainage ◽  
Data Validation ◽  
Water Industry ◽  
Wide Range

Abstract Data collection in urban drainage systems comes with many challenges. However, many examples already exist, containing numerous useful lessons learned. This chapter therefore contains several urban drainage and stormwater management metrology case studies, selected to cover a wide range of scopes, scales, objectives, climates, data validation methods, and data storage approaches. The case studies are initiated by academics as well as by institutions from the water industry.

scholarly journals Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training

2020 ◽  
Author(s):  
Caroline Bivik Stadler ◽  
Martin Lindvall ◽  
Claes Lundström ◽  
Anna Bodén ◽  
Karin Lindman ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Data ◽  
Ground Truth ◽  
Lessons Learned ◽  
Training Data ◽  
Imaging Data ◽  
High Quality ◽  
High Quality Image ◽  
Specific Objective ◽  
Wide Range

Abstract Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.

scholarly journals Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group

NeuroImage ◽  
2017 ◽  
Vol 153 ◽  
pp. 399-409 ◽  
Author(s):  
Susan D. Shenkin ◽  
Cyril Pernet ◽  
Thomas E. Nichols ◽  
Jean-Baptiste Poline ◽  
Paul M. Matthews ◽  
...  
Keyword(s):  
Working Group ◽  
Healthy Subjects ◽  
Data Availability ◽  
Brain Image ◽  
Multidisciplinary Working

scholarly journals The Role of IoT and Cloud Computing in Health Monitoring Systems

2020 ◽  
Author(s):  
Renta Tsiachri ◽  
Soultanopoulos Sotiriadis
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Health Monitoring ◽  
The Elderly ◽  
Lessons Learned ◽  
Time Data ◽  
Chronic Patients ◽  
Wireless Protocols ◽  
Wide Range ◽  
Remote Patient

Cloud computing emerges as the key platform for IoT data storage, processing and analytics due to its simplicity, scalability and affordability (i.e. no up-front investment, low operation costs). Remote patient monitoring in particular can benefit from for this technology in many ways: (a) the new solution is acceptable by many user categories and provides invaluable assistance to chronic patients and the elderly, (b) it is expected to increase users autonomy and confidence and enable self-managing of their condition with the help of caregivers remotely, (c) it reduces the need for face-to-face appointments with doctors and days in hospital. This work reviews key challenges for reliable and secure remote health monitoring based on experience and lessons learned from applying the above technology to the problem of real-time data collection using both wide-range and short-rage wireless protocols and health sensors.

scholarly journals CyVerse for Reproducible Research: RNA-Seq Analysis

2022 ◽  
pp. 57-79
Author(s):  
Jason Williams
Keyword(s):  
Data Storage ◽  
High Performance ◽  
Lessons Learned ◽  
Data Availability ◽  
Reproducible Research ◽  
Rna Seq ◽  
Data Intensive ◽  
Interactive Computing ◽  
Computing Environments ◽  
Performance Computing

AbstractPosing complex research questions poses complex reproducibility challenges. Datasets may need to be managed over long periods of time. Reliable and secure repositories are needed for data storage. Sharing big data requires advance planning and becomes complex when collaborators are spread across institutions and countries. Many complex analyses require the larger compute resources only provided by cloud and high-performance computing infrastructure. Finally at publication, funder and publisher requirements must be met for data availability and accessibility and computational reproducibility. For all of these reasons, cloud-based cyberinfrastructures are an important component for satisfying the needs of data-intensive research. Learning how to incorporate these technologies into your research skill set will allow you to work with data analysis challenges that are often beyond the resources of individual research institutions. One of the advantages of CyVerse is that there are many solutions for high-powered analyses that do not require knowledge of command line (i.e., Linux) computing. In this chapter we will highlight CyVerse capabilities by analyzing RNA-Seq data. The lessons learned will translate to doing RNA-Seq in other computing environments and will focus on how CyVerse infrastructure supports reproducibility goals (e.g., metadata management, containers), team science (e.g., data sharing features), and flexible computing environments (e.g., interactive computing, scaling).

scholarly journals Image-based surface reconstruction in geomorphometry – merits, limits and developments

2016 ◽  
Vol 4 (2) ◽  
pp. 359-389 ◽  
Author(s):  
Anette Eltner ◽  
Andreas Kaiser ◽  
Carlos Castillo ◽  
Gilles Rock ◽  
Fabian Neugirg ◽  
...  
Keyword(s):  
High Performance ◽  
Image Data ◽  
Ground Truth ◽  
Limited Range ◽  
Lessons Learned ◽  
Data Sets ◽  
Scale Invariant ◽  
High Quality ◽  
Research Fields ◽  
Wide Range

Abstract. Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost.The increasing computing capabilities during the last decade, together with the development of high-performance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice.This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

Using Motion Detection to Measure Social Polarization in the U.S. House of Representatives

2020 ◽  
pp. 1-10
Author(s):  
Bryce J. Dietrich
Keyword(s):  
Motion Detection ◽  
House Of Representatives ◽  
Social Science Research ◽  
Image Data ◽  
Science Research ◽  
Video Data ◽  
Social Scientists ◽  
Wide Range ◽  
Party Voting ◽  
The Many

Abstract Although previous scholars have used image data to answer important political science questions, less attention has been paid to video-based measures. In this study, I use motion detection to understand the extent to which members of Congress (MCs) literally cross the aisle, but motion detection can be used to study a wide range of political phenomena, like protests, political speeches, campaign events, or oral arguments. I find not only are Democrats and Republicans less willing to literally cross the aisle, but this behavior is also predictive of future party voting, even when previous party voting is included as a control. However, this is one of the many ways motion detection can be used by social scientists. In this way, the present study is not the end, but the beginning of an important new line of research in which video data is more actively used in social science research.

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