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The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigate this problem, which offers data access with on-demand precision. However,...

The reproducibility of scientific articles is central to the advancement of science. Despite this importance, evaluating reproducibility remains challenging due to the scarcity of ground truth data. Predictive models can address this limitation by ...

Extensive data processing is becoming commonplace in many fields of science. Distributing data to processing sites and providing methods to share the data with collaborators efficiently has become essential. The Open Science Data Federation (OSDF) ...

The rapid expansion of computational capabilities and the evergrowing scale of modern HPC systems present formidable challenges in managing exascale scientific data. Faced with such vast datasets, traditional lossless compression techniques prove ...

Text summarization refers to the procedure of condensing the key ideas present in a single document or a group of related documents. The fundamental advantage of text summarization is that it saves the reader’s time by extracting the most crucial ...

Today's large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, data compression is becoming a critical technique to mitigate the storage burden and data-movement cost. However, existing ...

Dynamic graphs are increasingly being utilized in domain knowledge modeling and large-scale scientific data management. Managing dynamic graph data requires a graph database system that can handle constantly changing volumes and data versions, while ...

This paper presents the results of an analysis of trends in the development of the information ecosystem of open science based on the study of the practices of the main actors (research and educational organizations, publishers, sponsors of ...

As HPC systems continue to grow to exascale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O ...

cData provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance ...

Today's scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range of domains, which impose a serious burden on data transfer and storage. Error-bounded lossy compression has been ...

Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most ...

The results of an analysis of trends in the development of the information ecosystem of open science based on the study of the global document flow, open access resources, and scientific data repositories, as well as initiatives in the field of ...

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity for postanalysis. Because supercomputers and HPC ...

To understand and properly use scientific data, it is important that the metadata, which describes information related to data, contains sufficient information. Keywords are one of the metadata items, and for assigning keywords to the earth science ...

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors ...

Because of the vast volume of data being produced by today’s scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. ...

A Retrieval System requires several components that define its functionality and behavior. In the case of a meta-search engine for the retrieval of scientific data, a schema that defines the way to store such data is considered a necessary element ...

In1 the big data era, scientific data plays a crucial role in scientific research. Data sharing, retrieval and usage has become an inevitable trend. We study how the users of scientific data select relevant data from the data sharing platform. The study ...

Genomics datasets are currently managed by iRODS, the Integrated Rule-Oriented Data System, which is an open source data management software. iRODS provides several services, including indexing, publishing, integrity, storage, and provenance. In this ...