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1. An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

   https://doi.org/10.3390/data5020039  

   Sha, Dexuan; Miao, Xin; Xu, Mengchao; Yang, Chaowei; Xie, Hongjie; Mestas-Nuñez, Alberto M.; Li, Yun; Liu, Qian; Yang, Jingchao (June 2020, Data)

   null (Ed.)

   Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects. 

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2. CiteSeerX: 20 years of service to scholarly big data

   https://doi.org/10.1145/3359115.3359119  

   Wu, Jian; Kim, Kunho; Giles, C. Lee (October 2019, Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse, AIDR 2019)

   We overview CiteSeerX, the pioneer digital library search engine, that has been serving academic communities for more than 20 years (first released in 1998), from three perspectives. The system perspective summarizes its architecture evolution in three phases over the past 20 years. The data perspective describes how CiteSeerX has created searchable scholarly big datasets and made them freely available for multiple purposes. In order to be scalable and effective, AI technologies are employed in all essential modules. To effectively train these models, a sufficient amount of data has been labeled, which can then be reused for training future models. Finally, we discuss the future of CiteSeerX. Our ongoing work is to make Cite- SeerX more sustainable. To this end, we are working to ingest all open access scholarly papers, estimated to be 30-40 million. Part of the plan is to discover dataset mentions and metadata in scholarly articles and make them more accessible via search interfaces. Users will have more opportunities to explore and trace datasets that can be reused and discover other datasets for new research projects. We summarize what was learned to make a similar system more sustainable and useful. 

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3. A FAIR Resource Recommender System for Smart Open Scientific Inquiries

   https://doi.org/10.3390/app15158334  

   Sakib, Syed N; Rubaiat, Sajratul Y; Naha, Kallol; Rahman, Hasan H; Jamil, Hasan M (July 2025, Applied Sciences)

   A vast proportion of scientific data remains locked behind dynamic web interfaces, often called the deep web—inaccessible to conventional search engines and standard crawlers. This gap between data availability and machine usability hampers the goals of open science and automation. While registries like FAIRsharing offer structured metadata describing data standards, repositories, and policies aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, they do not enable seamless, programmatic access to the underlying datasets. We present FAIRFind, a system designed to bridge this accessibility gap. FAIRFind autonomously discovers, interprets, and operationalizes access paths to biological databases on the deep web, regardless of their FAIR compliance. Central to our approach is the Deep Web Communication Protocol (DWCP), a resource description language that represents web forms, HyperText Markup Language (HTML) tables, and file-based data interfaces in a machine-actionable format. Leveraging large language models (LLMs), FAIRFind combines a specialized deep web crawler and web-form comprehension engine to transform passive web metadata into executable workflows. By indexing and embedding these workflows, FAIRFind enables natural language querying over diverse biological data sources and returns structured, source-resolved results. Evaluation across multiple open-source LLMs and database types demonstrates over 90% success in structured data extraction and high semantic retrieval accuracy. FAIRFind advances existing registries by turning linked resources from static references into actionable endpoints, laying a foundation for intelligent, autonomous data discovery across scientific domains. 

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4. 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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5. Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets.

   Athar Sefid, Jian Wu (January 2019, Proceedings of the ... Innovative Applications of Artificial Intelligence Conference)

   Automatically extracted metadata from scholarly documents in PDF formats is usually noisy and heterogeneous, often containing incomplete fields and erroneous values. One common way of cleaning metadata is to use a bibliographic reference dataset. The challenge is to match records between corpora with high precision. The existing solution which is based on information retrieval and string similarity on titles works well only if the titles are cleaned. We introduce a system designed to match scholarly document entities with noisy metadata against a reference dataset. The blocking function uses the classic BM25 algorithm to find the matching candidates from the reference data that has been indexed by ElasticSearch. The core components use supervised methods which combine features extracted from all available metadata fields. The system also leverages available citation information to match entities. The combination of metadata and citation achieves high accuracy that significantly outperforms the baseline method on the same test dataset. We apply this system to match the database of CiteSeerX against Web of Science, PubMed, and DBLP. This method will be deployed in the CiteSeerX system to clean metadata and link records to other scholarly big datasets. 

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