Related papers: DAS: a data management system for instrument tests…
Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e. platforms). We call this data multi-block, multi-view, or multi-omics data. The emergent…
Data science (DS) projects often follow a lifecycle that consists of laborious tasks for data scientists and domain experts (e.g., data exploration, model training, etc.). Only till recently, machine learning(ML) researchers have developed…
The growing demand for mid-band spectrum necessitates efficient Dynamic Spectrum Sharing (DSS) to ensure coexistence between cellular networks and incumbent radar systems. Existing Spectrum Access System (SAS) frameworks rely on fixed…
Four years after the last LISA meeting, the NASA Astrophysics Data System (ADS) finds itself in the middle of major changes to the infrastructure and contents of its database. In this paper we highlight a number of features of great…
New radio interferometers such as MeerKAT, SKA, ngVLA, and DSA-2000 drive advancements in software for two key reasons. First, handling the vast data from these instruments requires subdivision and multi-node processing. Second, their…
Maintaining high data quality is crucial for reliable data analysis and machine learning (ML). However, existing data quality management tools often lack automation, interactivity, and integration with ML workflows. This demonstration paper…
Deriving reliable conclusions and insights from environmental observational data urgently requires the enrichment with consistent and comprehensive metadata, including time-resolved context such as changing deployments, configurations, and…
The increasingly collaborative, globalized nature of scientific research combined with the need to share data and the explosion in data volumes present an urgent need for a scientific data management system (SDMS). An SDMS presents a…
The LIGO Control and Data system (CDS) features a tightly coupled and highly integrated control and data acquisition system. Control of the interferometers requires many Multiple Input Multiple Output (MIMO) control loops closed both…
Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These…
Big Data is defined as high volume of variety of data with an exponential data growth rate. Data are amalgamated to generate revenue, which results a large data silo. Data are the oils of modern IT industries. Therefore, the data are…
The ATLAS experiment at the Large Hadron Collider has implemented a new system for recording information on detector status and data quality, and for transmitting this information to users performing physics analysis. This system revolves…
CHARIS is an IFS designed for imaging and spectroscopy of disks and sub-stellar companions. To improve ease of use and efficiency of science production, we present progress on a fully-automated backend for CHARIS. This Automated Data…
The present paper describes the design of a cost effective, better resolution data acquisition system (DAS) which is compatible to most of the PC and laptops. A low cost DAS has been designed using PIC12F675 having 4-channel analog input…
Many scientific-software projects test their codes inadequately, or not at all. Despite its well-known benefits, adopting routine testing is often not easy. Development teams may have doubts about establishing effective test procedures,…
Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. The recorded seismic signals by DAS have several distinct characteristics, such as unknown coupling effects, strong anthropogenic…
Machine learning (ML) methods are widely used in industrial applications, which usually require a large amount of training data. However, data collection needs extensive time costs and investments in the manufacturing system, and data…
Big data analytics (BDA) applications use machine learning algorithms to extract valuable insights from large, fast, and heterogeneous data sources. New software engineering challenges for BDA applications include ensuring performance…
Contemporary businesses operate in dynamic environments requiring rapid adaptation to achieve goals and maintain competitiveness. Existing data platforms often fall short by emphasizing tools over alignment with business needs, resulting in…
In this paper we provide an update concerning the operations of the NASA Astrophysics Data System (ADS), its services and user interface, and the content currently indexed in its database. As the primary information system used by…