A Decentralized Authorization and Security Framework for Distributed Research Workflows

Research challenges such as climate change and the search for habitable planets increasingly use academic and commercial computing resources distributed across different institutions and physical sites. Furthermore, such analyses often require a level of automation that precludes direct human interaction, and securing these workflows involves adherence to security policies across institutions. In this paper, we present a decentralized authorization and security framework that enables researchers to utilize resources across different sites while allowing service providers to maintain autonomy over their secrets and authorization policies. We describe this framework as part of the Tapis platform, a web-based, hosted API used by researchers from multiple institutions, and we measure the performance of various authorization and security queries, including cross-site queries. We conclude with two use case studies -- a project at the University of Hawaii to study climate change and the NASA NEID telescope project that searches the galaxy for exoplanets.Comment: 10 pages. Short version of this paper to be published on COMPSAC 2023 proceeding

Sustainability in the Tapis Framework

As more research depends fundamentally on software, sustainability becomes increasingly critical. Nevertheless, despite valiant efforts from a growing number of researchers and practitioners, a basic understanding of best-practices for sustainable software remains elusive. In this paper, we review the specific practices and strategies that have helped to sustain Tapis, a cyberinfastructure project that has been in use for over a decade. The Tapis framework is an open-source, software-as-a-service Application Programming Interface (API) for collaborative, automated, reproducible computational research which began as the Foundation API for the iPlant Collaborative Project in 2008, and today is used by tens of thousands of individuals across more than a dozen active projects. This paper describes our multi-faceted approach to sustaining an increasingly complex ecosystem of software, documentation and other digital assets, including both technical and organizational strategies for minimizing the cost of sustainment while maximizing available resources for sustainment activities

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the “last mile” implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain

Tapis-CHORDS Integration: Time-Series DataSupport in Science Gateway Infrastructure

The explosion of IoT devices and sensors in recent years has led to a demand for efficiently storing, processing and analyzing time-series data. Geoscience researchers use time-series data stores such as Hydroserver, VOEIS and CHORDS. Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. Tapis's (formerly known as Agave) platform as a service provides a way to support researchers in a way that they are not responsible for the infrastructure and can focus on the science. The University of Hawai‘i (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop a new API integration that combines Tapis with the CHORDS time series data service to support projects at both institutions for storing, annotating and querying time-series data. This new Streams API leverages the strengths of both the Tapis platform and CHORDS service to enable capabilities for supporting time-series data streams not available in either tool alone. These new capabilities may be leveraged by Tapis powered science gateways with needs for handling spatially indexed time-series data-sets for their researchers as they have been at UH and TACC.Office of the VP for Researc

Participation of Renewable Energy Sources in the Frequency Regulation Issues of a Five-Area Hybrid Power System Utilizing a Sine Cosine-Adopted African Vulture Optimization Algorithm

In this article, a novel methodology is proposed by utilizing a technique which, in light of the change in the African vulture optimization known as Sine Cosine, adopted an African vulture optimization algorithm (SCaAVOA)-based tilt integral derivative (TID) regulator for the load frequency control (LFC) of a five-area power system with multi-type generations. At first, the execution of the Sine Cosine-adopted calculation is tried by contrasting it with the standard AVOA calculation while considering different standard benchmark functions. To demonstrate the superiority of the proposed SCaAVOA algorithm, the results are contrasted using different standard approaches. In the next stage, the proposed method is used in a five-area thermal power system and is likewise applied to a five-area, ten-unit system comprising different conventional sources as well as some renewable energy sources. The performance analysis of the planned regulator is completed for various system boundaries and loading conditions. It is seen that the said regulator is more viable in comparison to the other standard controllers

Development of an Ontology for the INCF Neuroimaging Data Model (NIDM)

International audienceThe successful reuse of shared data relies on the existence of easily-available well-described metadata [1]. The metadata, as a rich description of the data, must capture information on how the data was acquired, processed and analyzed. The terms used to describe the data should be chosen with a logical, consistent framework in mind and include definitions to avoid ambiguity. In addition, a lexicon or ontology should reuse terms from existing efforts as much as possible [2]

Tools for FAIR Neuroimaging Experiment Metadata Annotation with NIDM Experiment

International audienc

Tools for FAIR Neuroimaging Experiment Metadata Annotation with NIDM Experiment

International audienc

Capturing and Reusing Computation Details with the Neuroimaging Data Model (NIDM)

International audienc

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the ‘last mile’ implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain