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1. Integrity Protection for Research Artifacts using Open Science Chain’s Command Line Utility

   https://doi.org/10.1145/3437359.3465587  

   Shantharam, Manu; Lin, Kai; Sakai, Scott; Sivagnanam, Subhashini (July 2021, PEARC '21: Practice and Experience in Advanced Research Computing)

   null (Ed.)

   Scientific data, its analysis, accuracy, completeness, and reproducibility play a vital role in advancing science and engineering. Open Science Chain (OSC) is a cyberinfrastructure platform built using the Hyperledger Fabric (HLF) blockchain technology to address issues related to data reproducibility and accountability in scientific research. OSC preserves the integrity of research datasets and enables different research groups to share datasets with the integrity information. Additionally, it enables quick verification of the exact datasets that were used for a particular published research and tracks its provenance. In this paper, we describe OSC’s command line utility that will preserve the integrity of research datasets from within the researchers’ environment or from remote systems such as HPC resources or campus clusters used for research. The Python-based command line utility can be seamlessly integrated within research workflows and provides an easy way to preserve the integrity of research data in OSC blockchain platform. 

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2. Towards building a Fault Tolerant and Secure Open Science Chain

   https://doi.org/10.17605/OSF.IO/MJHK8  

   Shantharam, Manu; Sakai, Scott; Lin, Kai; Sivagnanam, Subhashini (October 2020, Gateways2020)

   null (Ed.)

   Scientific data, along with its analysis, accuracy, completeness, and reproducibility, plays a vital role in advancing science and engineering. Open Science Chain (OSC) provides a Cyberinfrastructure platform, built using distributed ledger technologies, where verification information about scientific dataset is stored and managed in a consortium blockchain. Researchers have the ability to independently verify the authenticity of scientific results using the information stored with OSC. Researchers can also build research workflows by linking data entries in the ledger and external repositories such as GitHub that will allow for detailed provenance tracking. OSC enables answers to questions such as: how can we ensure research integrity when different research groups share and work on the same datasets across the world? Is it possible to enable quick verification of the exact data sets that were used for particular published research? Can we check the provenance of the data used in the research? In this poster, we highlight our work in building a secure, scalable architecture for OSC including developing a security module for storing identities that can be used by the researchers of science gateways communities to increase the confidence of their scientific results. 

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3. Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain

   https://doi.org/10.1093/database/baae023  

   Sivagnanam, S.; Yeu, S.; Lin, K.; Sakai, S.; Garzon, F.; Yoshimoto, K.; Prantzalos, K.; Upadhyaya, D_P; Majumdar, A.; Sahoo, S_S; et al (April 2024, Database)

   Abstract When the scientific dataset evolves or is reused in workflows creating derived datasets, the integrity of the dataset with its metadata information, including provenance, needs to be securely preserved while providing assurances that they are not accidentally or maliciously altered during the process. Providing a secure method to efficiently share and verify the data as well as metadata is essential for the reuse of the scientific data. The National Science Foundation (NSF) funded Open Science Chain (OSC) utilizes consortium blockchain to provide a cyberinfrastructure solution to maintain integrity of the provenance metadata for published datasets and provides a way to perform independent verification of the dataset while promoting reuse and reproducibility. The NSF- and National Institutes of Health (NIH)-funded Neuroscience Gateway (NSG) provides a freely available web portal that allows neuroscience researchers to execute computational data analysis pipeline on high performance computing resources. Combined, the OSC and NSG platforms form an efficient, integrated framework to automatically and securely preserve and verify the integrity of the artifacts used in research workflows while using the NSG platform. This paper presents the results of the first study that integrates OSC–NSG frameworks to track the provenance of neurophysiological signal data analysis to study brain network dynamics using the Neuro-Integrative Connectivity tool, which is deployed in the NSG platform. Database URL: https://www.opensciencechain.org. 

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4. Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows

   https://doi.org/10.3390/ijgi9020119  

   Sun, Ziheng; Di, Liping; Burgess, Annie; Tullis, Jason A.; Magill, Andrew B. (February 2020, ISPRS International Journal of Geo-Information)

   AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research. 

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5. WfCommons: Data Collection and Runtime Experiments using Multiple Workflow Systems

   Casanova, H.; K. Berney, K.; Chastel, S.; R. Ferreira da Silva, Rafael (June 2023, 1st IEEE International Workshop on Workflows in Distributed Environments (WiDE))

   Scientific workflows have become ubiquitous across scientific fields, and their execution methods and systems continue to be the subject of research and development. Most experimental evaluations of these workflows rely on workflow instances, which can be either real-world or synthetic, to ensure relevance to current application domains or explore hypothetical/future scenarios. The WfCommons project addresses this need by providing data and tools to support such evaluations. In this paper, we present an overview of WfCommons and describe two recent developments. Firstly, we introduce a workflow execution "tracer" for NextFlow, which significantly enhances the set of real-world instances available in WfCommons. Secondly, we describe a workflow instance "translator" that enables the execution of any real-world or synthetic WfCommons workflow instance using Dask. Our contributions aim to provide researchers and practitioners with more comprehensive resources for evaluating scientific workflows. 

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