Astronomical research in the next decade: trends, barriers and needs in data access, management, visualization and analysis

TL;DR

This paper surveys the astrophysics community to identify current practices, challenges, and needs in data access, management, visualization, and analysis, highlighting gaps in reproducibility, tools, and machine learning adoption.

Contribution

It provides a comprehensive analysis of community needs and barriers in astronomical data research, informing future developments in the field.

Findings

Significant gaps in results reproducibility.

Limited availability of visual analytics tools.

Low adoption of Machine Learning techniques.

Abstract

We report the outcomes of a survey that explores the current practices, needs and expectations of the astrophysics community, concerning four research aspects: open science practices, data access and management, data visualization, and data analysis. The survey, involving 329 professionals from several research institutions, pinpoints significant gaps in matters such as results reproducibility, availability of visual analytics tools and adoption of Machine Learning techniques for data analysis. This research is conducted in the context of the H2020 NEANIAS project.