Human Research Program Data Sharing Process
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William B Vessey↗
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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
Agha-mohammadi, Ali-akbar↗
Physics-Guided Data-Driven Seismic Full-Waveform Inversion: Recent Progress on Model Development and Data Sharing
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Computer Science↗
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.
Alawad, Mohammed↗
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.
Bachhav, Mukesh↗
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.
Compton, Michael M.↗
OSCAR-Mike: Why not ATAK?
The Office of Nuclear Smuggling Detection and Deterrence (NSDD) is evaluating methods to increase the probability that international partners will detect radioactive materials. One proposed method to accomplish this goal is to improve communications within teams operating in challenging environments. The goal of these improvements would be to enable remote monitoring of detection equipment, share data among end users in the field and subject matter experts, and integrate data from radiation detectors with other types of sensors and camera systems. Accomplishing this goal has the potential to improve the capabilities of currently deployed NSDD equipment. During FY 2021, Oak Ridge National Laboratory demonstrated some core and expanded capabilities of the Android Team Awareness Kit (ATAK), a situational awareness application developed by the US Department of Defense, to enable precision targeting, navigation, and data sharing. During FY 2022, Oak Ridge National Laboratory developed software requirements for an NSDD team awareness kit–based system. The requirements were developed by liaising with NSDD management and subject matter experts to determine NSDD’s needs and by reviewing available software and hardware solutions with US Department of Defense team awareness kit program managers and developers and radiation detection equipment vendors.
97 MATHEMATICS AND COMPUTING↗
Jell-Molds and Cookie-cutters: Shrinkwrap Isn't Just for Leftovers Anymore
So what is Shrinkwrap all about? For those of you who may not know about it, Shrinkwrap is a type of data structure that can manifest itself as a feature or model. It is cleverly covered up, almost hidden, and doesn't get the press or widespread use of a solid or surface. The shrinkwrap feature is located under the data sharing submenu of the feature menu. The shrinkwrap feature, as described by PTC, is a collection of surfaces and datum features of a model that represents the exterior of the model . The advantages and applications of the shrinkwrap feature are in the creation of minimal memory guzzling representations of assemblies. These can be used to represent subassemblies in parent assemblies, and can handle control of dependency issues, geometry represented, and additional references through the use of the shrinkwrap feature options. The shrinkwrap model is an option available under the save as umbrella. Its function, as described by PTC, is to share data with internal and external design groups and improve performance in large assembly design . Some of the benefits of the shrinkwrap model include being able to represent complex assemblies with a single, lightweight part that protects design intent and parametric data, and the ability to improve performance of large assembly modeling in the area of less load time. The proper-scale models can be saved as IGES, STEP, and VRML (for fly-throughs).
Randazzo, John↗
Perspectives on flood forecast-based early action and opportunities for Earth observations
This paper seeks to identify opportunities to integrate Earth observations (EO) into flood forecast-based early action and propose future directions for research and collaboration between EO and humanitarian communities. Forecast-based early action (FbA) is an approach to shift disaster response toward anticipation to mitigate impacts to at-risk communities; however, timely and accurate information is needed in the development of data-based triggers and thresholds for action. Therefore, this paper considers the readiness of a wide range of EO for flood monitoring and forecasting in the design, operations, and evaluation phases of FbA. The most significant opportunities for EO to inform FbA efforts lie in the design and evaluation phases, as EO can aid in the development of impact-based triggers. The EO products most readily applicable include precipitation, streamflow estimates, and exposure mapping, and those requiring the greatest amount of further research include vulnerability and impact assessments. This paper identifies collaboration opportunities for the EO and humanitarian communities to create tailored products, such as overlays combining flood extents with exposure maps. Such collaboration opportunities can be fostered by open data sharing, data verification efforts, and incentives for supporting boundary organizations capable of enabling the use of EO for FbA.
disasters↗
Comparative Performance Evaluation of High-performance Data Transfer Tools
Data transfer in wide-area networks has been long studied in different contexts, from data sharing among data centers to online access to scientific data. Many software tools and platforms have been developed to facilitate easy, reliable, fast, and secure data transfer over wide area networks, such as GridFTP, FDT, bbcp, mdtmFTP, and XDD. However, few studies have shown the full capabilities of existing data transfer tools from the perspective of whether such tools have fully adopted state-of-the-art techniques through meticulous comparative evaluations. In this paper, we evaluate the performance of the four highperformance data transfer tools (GridFTP, FDT, mdtmFTP, and XDD) in various environments. Our evaluation suggests that each tool has strengths and weaknesses. FDT and GridFTP perform consistently in diverse environments. XDD and mdtmFTP show improved performance in limited environments and datasets during our evaluation. Unlike other studies on data transfer tools, we also evaluate the predictability of the tools' performance, an important factor for scheduling different stages of science workflows. Performance predictability also helps in (auto)tuning the configurable parameters of the data transfer tool. We apply statistical learning techniques such as linear/polynomial regression, and k-nearest neighbors (kNN), to assess the performance predictability of each tool using its control parameters. Our results show that we can achieve good prediction performance for GridFTP and mdtmFTP using linear regression and kNN, respectively.
Nadig, Deepak↗
EBR-II Driver Fuel Cladding Hull Analytical Data
Sharing EBR-II driver fuel cladding hull data with General Atomics under NDA No. 20NDA155. Data share is needed to advance discussions regarding process technologies to decontaminate separated cladding hulls
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Visualization of International Solar-Terrestrial Physics Program (ISTP) data
The International Solar-Terrestrial Physics Program (ISTP) is a multispacecraft, multinational program whose objective is to promote further understanding of the Earth's complex plasma environment. Extensive data sharing and data analysis will be needed to ensure the success of the overall ISTP program. For this reason, there has been a special emphasis on data standards throughout ISTP. One of the key tools will be the common data format (CDF), developed, maintained, and evolved at the National Space Science Data Center (NSSDC), with the set of ISTP implementation guidelines specially designed for space physics data sets by the Space Physics Data Facility (associated with the NSSDC). The ISTP guidelines were developed to facilitate searching, plotting, merging, and subsetting of data sets. We focus here on the plotting application. A prototype software package was developed to plot key parameter (KP) data from the ISTP program at the Science Planning and Operations Facility (SPOF). The ISTP Key Parameter Visualization Tool is based on the Interactive Data Language (IDL) and is keyed to the ISTP guidelines, reading data stored in CDF. With the combination of CDF, the ISTP guidelines, and the visualization software, we can look forward to easier and more effective data sharing and use among ISTP scientists.
Kessel, Ramona L.↗
Data Interfaces for Automated Vehicle Services - A Municipality Perspective
As Automated Vehicle (AV) services proliferate, data sharing between AV operators and municipal agents is assuming greater importance. Information on the dynamic nature of the road system such as incidents to avoid, weather hazards (such as flooding), construction and detours, as well as active safety concerns (e.g. - riots) is important for AV operators. Such information cannot be directly sensed from a vehicle's sensor array, but instead must be communicated in a timely and trustworthy channel. Municipalities are interested in pushing this information to AV operators to support emergency response efforts, reduce traffic in construction zones, and generally improve operation of the system. Similarly, information on vehicle safety such as disengagements, as well as critical information on the use of roadway system (trips, origin and destination patterns) are important performance factors for municipalities to understand utilization and plan for appropriate infrastructure. As mobility shifts to on-demand options, the need for safe and coordinated pick-up and drop-off zones will increase (potentially reducing parking needs). For all of these reasons, communication flows between AV operators and municipalities are becoming increasingly important. This paper investigates the functions, emerging practices and protocols for sharing of such critical data, and identifies gaps in and challenges in existing practices. Additionally, case studies are used to highlight the impacts of data sharing between AV operators and municipalities.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Standard measures in Analogs of Spaceflight
Standard measures are defined as research measures (physiological, performance, or otherwise) that quantify the subject’s health and performance before, during, and after a spaceflight or a spaceflight analog mission. The Standard Measures Cross-Cutting Project (SMCCP) collects and analyzes standard measures that support assessment and quantification of health risks and efficacy of countermeasures and technologies required to prevent or mitigate adverse outcomes from exposure to the spaceflight environment. SMCCP uses the following spaceflight analogs: - Human Exploration Research Analog (HERA) in Houston, TX: 4-person isolation/confinement; nominal mission duration of 45 days; run by the Human Research Program (HRP). SMCCP uses weekly questionnaires to gather data on the crewmembers’ emotional and mental state and collects blood samples biweekly to measure crewmembers’ serum cortisol levels at the start, middle, and end of the mission. - Institute for Biomedical Problems (IBMP) Ground-Based Experimental Complex, called NEK, in Moscow, Russia: 6-person isolation/confinement; duration of 8 months; run by IBMP. Standard measures data collected during NEK are the same as for HERA above. - :envihab in Cologne, Germany: 12-person bed rest facility; closed environment; 30 to 60-day campaigns; run by the German Aerospace Center (DLR). For bedrest studies, SMCCP uses the international standard measures as documented in the International Academy of Astronautics publication titled Guidelines for Standardization of Bed Rest Studies in the Spaceflight Context (June 2014). These international standard measures evaluate potential changes in bone, muscle, cardiovascular, and visual systems, as well as changes in psychology and nutrition. The international standard measures complement those collected during studies by individual investigators and allow comparison of responses across different bed rest and countermeasures durations. In this presentation, we will review available SMCCP data collected during the recent isolation studies in HERA (C6) and NEK (SIRIUS-21), and during the SANS (SM1, SM2) bed rest studies in :envihab. These standard measures datasets are made available to concurrent investigators in the given mission or campaign via data sharing agreements and/or data sharing plans and to other investigators that have been granted retrospective access to the data (researchers with IRB approved protocols, risk custodians) to advance understanding of physiological and behavioral risks associated with these spaceflight analogs.
Standard Measures↗
US-EUSST SSA Data Exchange for Improved Orbital Safety
Data sharing and exchange among different Space Situational Awareness (SSA) data collectors/providers is frequently discussed as a mechanism to improve the accuracy and precision of orbital safety products. Indeed, at the conceptual level this claim is quite sensible: objects with more tracking, and thus presumably better distribution of that tracking about their orbit, should produce updates with smaller epoch errors and more realistic covariances at epoch, which should then translate into better performance in prediction and thus improved close approach calculations. However, this thesis has not been verified with any large study involving two different data providers, through which the amount of realized improvement can be quantified and, perhaps more importantly, the logistics of effecting observation data sharing in a regularized way can be established and circulated to assist others. To create rubrics for helpful data sharing for conjunction assessment applications, and to collect empirical data on the improvements achieved through sharing of measurement data, the US Office of Space Commerce and the European Union Space Surveillance and Tracking (EUSST) Support Framework have embarked on a joint experiment to collect and exchange satellite metric observations on selected satellites of interest over an experimental period. Each entity will perform orbit determination with their own observations, the other entity’s observations, and with both observation data sets combined. The product results will be compared with each other, showing differences attributable both to the three different data groups and to the two entities’ different approaches to orbit determination. The exemplar satellites include both objects that have external precision reference ephemerides and those that do not, allowing the experiment to exercise both precision-comparison and assimilative-comparison techniques. The present reporting will give preliminary results and a set of lessons learned for data sharing; a second presentation later in 2023 will provide a full accounting of the experiment’s results. The hope is that, through this work, a pathway can be established for the expeditious and fruitful sharing among collection entities, both commercial and governmental.
space situational awareness↗