An EMF-based Toolkit for Creation of Domain-specific Data Services

2014 ◽  
Keyword(s):  
Specific Data ◽  
Data Services ◽  
Domain Specific

Linking Domain Repositories to Build Cyberinfrastructure for Interdisciplinary Critical Zone Research

2021 ◽  
Author(s):  
Jeffery S. Horsburgh ◽  
Kerstin Lehnert ◽  
Jerad Bales
Keyword(s):  
Science Studies ◽  
Critical Zone ◽  
Scientific Data ◽  
Data Repositories ◽  
Specific Data ◽  
Data Services ◽  
Domain Specific ◽  
Geological Processes ◽  
Network Cluster ◽  
Digital Management

<p>Critical Zone science studies the system of coupled chemical, biological, physical, and geological processes operating together across all scales to support life at the Earth's surface (Brantley et al., 2007). In 2020, the  U.S. National Science Foundation funded 10 Critical Zone Collaborative Network awards. These 5-year projects will collaboratively work to answer scientific questions relevant to understanding processes in the Critical Zone such as the effects of urbanization on Critical Zone processes; Critical Zone function in semi-arid landscapes and the role of dust in sustaining these ecosystems; processes in deep bedrock and their relationship to Critical Zone evolution; the recovery of the Critical Zone from disturbances such as fire and flooding; and changes in the coastal Critical Zone related to rising sea level. In order to support community data collection, access, and archival  for the Critical Zone Network community, the development of new cyberinfrastructure (CI) is now underway that leverages prior investments in domain-specific data repositories that are already operational and delivers data services to established communities. The goal is to create the infrastructure required for managing, curating, disseminating, and preserving data from the new network of Critical Zone Cluster projects, along with legacy datasets from the existing Critical Zone Observatory Network, including digital management of physical samples. This CI will have a distributed architecture that links existing data facilities and services, including HydroShare, EarthChem, SESAR (System for Earth Sample Registration), and eventually other systems like OpenTopography as needed, via a central CZ Hub that provides tools and services for simplified data submission, integrated data discovery and access, and links to computational resources for data analysis and visualization in support of CZ synthesis efforts. Our goal is to make data, samples, and software collected by the CZ Network Cluster projects Findable, Accessible, Interoperable, and Reusable following the FAIR guiding principles for scientific data management and stewardship, by taking advantage of existing, FAIR compliant, domain-specific data repositories. This collaboration among domain repositories to deliver integrated data services for an interdisciplinary science program will provide a template for future development of integrated interdisciplinary data services.</p>

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

Domain-specific data mining for residents' transit pattern retrieval from incomplete information

2019 ◽  
Vol 134 ◽  
pp. 62-71 ◽  
Author(s):  
Yongxin Liu ◽  
Jianqiang Li ◽  
Zhong Ming ◽  
Houbing Song ◽  
Xiaoxiong Weng ◽  
...  
Keyword(s):  
Data Mining ◽  
Incomplete Information ◽  
Specific Data ◽  
Domain Specific ◽  
Pattern Retrieval

Status and challenges of FAIR data principles for a long-term repository

2020 ◽  
Author(s):  
Chad Trabant ◽  
Rick Benson ◽  
Rob Casey ◽  
Gillian Sharer ◽  
Jerry Carter
Keyword(s):  
Scientific Data ◽  
Current Status ◽  
Data Accessibility ◽  
Data Services ◽  
Domain Specific ◽  
Rich Domain ◽  
Fair Principles ◽  
High Degree ◽  
Iris Data

<p>The data center of the National Science Foundation’s Seismological Facility for the Advancement of Geoscience (SAGE), operated by IRIS Data Services, has evolved over the past 30 years to address the data accessibility needs of the scientific research community.  In recent years a broad call for adherence to FAIR data principles has prompted repositories to increased activity to support them. As these principles are well aligned with the needs of data users, many of the FAIR principles are already supported and actively promoted by IRIS.  Standardized metadata and data identifiers support findability. Open and standardized web services enable a high degree of accessibility. Interoperability is ensured by offering data in a combination of rich, domain-specific formats in addition to simple, text-based formats. The use of open, rich (domain-specific) format standards enables a high degree of reuse.  Further advancement towards these principles includes: an introduction and dissemination of DOIs for data; and an introduction of Linked Data support, via JSON-LD, allowing scientific data brokers, catalogers and generic search systems to discover data. Naturally, some challenges remain such as: the granularity and mechanisms needed for persistent IDs for data; the reality that metadata is updated with corrections (having implications for FAIR data principles); and the complexity of data licensing in a repository with data contributed from individual PIs, national observatories, and international collaborations.  In summary, IRIS Data Services is well along the path of adherence of FAIR data principles with more work to do. We will present the current status of these efforts and describe the key challenges that remain.</p>

Visualizing Model-Based Product Definitions in Augmented Reality

2021 ◽  
Author(s):  
Teodor Vernica ◽  
Robert Lipman ◽  
William Z. Bernstein
Keyword(s):  
Augmented Reality ◽  
Three Dimensional ◽  
Major Barrier ◽  
Specific Data ◽  
Domain Specific ◽  
Engineering Data ◽  
Data Representations ◽  
Design And Manufacturing ◽  
Product Manufacturing ◽  
Immense Potential

Abstract Augmented reality (AR) technologies present immense potential for the design and manufacturing communities. However, coordinating traditional engineering data representations into AR systems without loss of context and information remains a challenge. A major barrier is the lack of interoperability between manufacturing-specific data models and AR-capable data representations. In response, we present a pipeline for porting standards-based Product Manufacturing Information (PMI) with three-dimensional (3D) model data into an AR scene. We demonstrate our pipeline by interacting with annotated parts while continuously tracking their pose and orientation. Our work provides insight on how to address fundamental issues related to interoperability between domain-specific models and AR systems.

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