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Share Data with OPeNDAP Hyrax: New Features and Improvements
During the upcoming Summer 2016 meeting of the ESIP Federation (July 19-22), OpenDAP will hold a Developers and Users Workshop. While a broad set of topics will be covered, a key focus is capitalizing on recent EOSDIS-sponsored advances in Hyrax, OPeNDAPs own software for server-side realization of the DAP2 and DAP4 protocols. These Hyrax advances are as important to data users as to data providers, and the workshop will include hands-on experiences of value to both. Specifically, a balanced set of presentations and hands-on tutorials will address advances in 1. server installation, 2. server configuration, 3. Hyrax aggregation capabilities, 4. support for data-access from clients that are HTTP-based, JSON-based or OGC-compliant (especially WCS and WMS), 5. support for DAP4,6.use and extension of server-side computational capabilities, and7.several performance-affecting matters. Topics 2 through 7 will be relevant to data consumers, data providers and notably, due to the open-source nature of all OPeNDAP software to developers wishing to extend Hyrax, to build compatible clients and servers, and/or to employ Hyrax as middleware that enables interoperability across a variety of end-user and source-data contexts. A session for contributed talks will elaborate the topics listed above and embrace additional ones.
Human Research Program Data Sharing Process
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CHORD: Distributed Data-sharing via Hybrid ROS 1 and 2 for Multi-robot Exploration of Large-scale Complex Environments
No abstract provided
Windchill 9.1 Developing Requirements for Sharing Data between NSE Sites.
Abstract Not Provided
Windchill 9.1 Developing Requirements for Sharing Data between NSE Sites.
Abstract not provided.
Federated Data Sharing to Support Distributed Incident Response.
Abstract not provided.
The Value of Marine Renewable Energy Data Sharing and Products Outreach.
Abstract not provided.
Physics-Guided Data-Driven Seismic Full-Waveform Inversion: Recent Progress on Model Development and Data Sharing
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A browser-based tool for visualization and analysis of diffusion MRI data
Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.
Adversarial Training for Privacy-Preserving Deep Learning Model Distribution
Collaboration among cancer registries is essential to develop accurate, robust, and generalizable deep learning models for automated information extraction from cancer pathology reports. Sharing data presents a serious privacy issue, especially in biomedical research and healthcare delivery domains. Distributing pretrained deep learning (DL) models has been proposed to avoid critical data sharing. However, there is growing recognition that collaboration among clinical institutes through DL model distribution exposes new security and privacy vulnerabilities. These vulnerabilities increase in natural language processing (NLP) applications, in which the dataset vocabulary with word vector representations needs to be associated with the other model parameters. In this paper, we propose a novel privacy-preserving DL model distribution across cancer registries for information extraction from cancer pathology reports with privacy and confidentiality considerations. The proposed approach exploits the adversarial training framework to distinguish private features from shared features among different datasets. It only shares registry-invariant model parameters, without sharing raw data nor registry-specific model parameters among cancer registries. Thus, it protects both the data and the trained model simultaneously. We compare our proposed approach to single-registry models, and a model trained on centrally hosted data from different cancer registries. The results show that the proposed approach significantly outperforms the single-registry models and achieves statistically indistinguishable micro and macro F1-score as compared to the centralized model.
Decommissioning costs - some international perspectives and initiatives. Presentation for Panel discussion. Session No. 024: 'Getting a Handle on Decommissioning Costs - a Global Perspective'
Financing for decommissioning: Are the cost estimates valid? Are the financing arrangements suitable? Are the fund management systems appropriate? Cost estimates are estimated, analysed and updated on a regular basis, but cost comparisons are not straight-forward. There is an incomplete understanding of variability between estimates. Multiple internal and external factors impact estimates: Institutional, legal and policy, purpose, Strategies and plans, overall context, Facility characteristics and history Contingencies and risk, Limited actual cost data. The financing arrangements: there exists a range of systems in place for the implementation of Funding Policy and Strategy, but there are Considerable variations in: Calculating funding requirements, contributions/provisions, Allocating risks and requirements for securities Managing funds and accounting, adjusting the system, Managing disbursements, Reporting and supervision. All cost estimates for future decommissioning activities include considerable uncertainties. These uncertainties will gradually diminish with growing experience from current and planned projects. Different costing methods have different data requirements. Their reliability depends on the extent to which various data are available and applicable to the specific case being considered. Independent of the assessment method, some uncertainty is inevitable in all estimates of future costs. Analysis of the costing method may be useful in order to locate the key uncertainties in each specific estimate. NEA's Decommissioning Cost Estimation Group (DCEG) was Created in 2008 to foster the exchange of information and experience. It Aims to enhance the credibility, reliability, and auditability of the cost estimation process. It Helps define international good practices in decommissioning cost estimation. However, some major challenges remain. Weaknesses in: Understanding relations between estimates and actual costs, Lack of relevant cost data, sharing data. How might be addressed: Improved sharing of cost data, Systematic analyses of estimates and actual costs, Developing benchmarking approaches.
2025 TEM Workshop
The TEM Data Management Workshop will take place on August 26 from 9 a.m. to 12 p.m. MT, and will be held virtually on TEAMS. The primary goal of this workshop is to engage NSUF users and stakeholders in discussions about the data needs for the utilization of AI and ML in the analysis of TEM data. Key topics to be covered include data storage, data sharing, data tagging, metadata inclusion, standardized data formats, data augmentation, and annotated training datasets. Additionally, the workshop will provide valuable insights into resources such as the Nuclear Research Data System (NRDS) for data storage and sharing, as well as open-source codes for data analysis.
CLIPS, AppleEvents, and AppleScript: Integrating CLIPS with commercial software
Many of today's intelligent systems are comprised of several modules, perhaps written in different tools and languages, that together help solve the user's problem. These systems often employ a knowledge-based component that is not accessed directly by the user, but instead operates 'in the background' offering assistance to the user as necessary. In these types of modular systems, an efficient, flexible, and eady-to-use mechanism for sharing data between programs is crucial. To help permit transparent integration of CLIPS with other Macintosh applications, the AI Research Branch at NASA Ames Research Center has extended CLIPS to allow it to communicate transparently with other applications through two popular data-sharing mechanisms provided by the Macintosh operating system: Apple Events (a 'high-level' event mechanism for program-to-program communication), and AppleScript, a recently-released scripting language for the Macintosh. This capability permits other applications (running on either the same or a remote machine) to send a command to CLIPS, which then responds as if the command were typed into the CLIPS dialog window. Any result returned by the command is then automatically returned to the program that sent it. Likewise, CLIPS can send several types of Apple Events directly to other local or remote applications. This CLIPS system has been successfully integrated with a variety of commercial applications, including data collection programs, electronics forms packages, DBMS's, and email programs. These mechanisms can permit transparent user access to the knowledge base from within a commercial application, and allow a single copy of the knowledge base to service multiple users in a networked environment.