Ten Research Challenge Areas in Data Science

• URL: http://arxiv.org/abs/2002.05658v1
• Date: Mon, 27 Jan 2020 21:39:57 GMT
• Title: Ten Research Challenge Areas in Data Science
• Authors: Jeannette M. Wing
• Abstract summary: Data science builds on knowledge from computer science, mathematics, statistics, and other disciplines. This article starts with meta-questions about data science as a discipline and then elaborates on ten ideas for the basis of a research agenda for data science.
• Score: 4.670305538969914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although data science builds on knowledge from computer science, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: challenging scientific questions and pressing questions of societal importance. This article starts with meta-questions about data science as a discipline and then elaborates on ten ideas for the basis of a research agenda for data science.

Related papers

• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• A data science axiology: the nature, value, and risks of data science [0.0]
  Data science is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery. This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving.
  arXiv  Detail & Related papers  (2023-07-19T21:12:04Z)
• Defining data science: a new field of inquiry [0.0]
  Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is one of the most active, powerful, and rapidly evolving 21st century innovations. Due to its value, power, and applicability, it is emerging in over 40 disciplines, hundreds of research areas, and thousands of applications. This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework.
  arXiv  Detail & Related papers  (2023-06-28T12:58:42Z)
• Modeling Information Change in Science Communication with Semantically Matched Paraphrases [50.67030449927206]
  SPICED is the first paraphrase dataset of scientific findings annotated for degree of information change. SPICED contains 6,000 scientific finding pairs extracted from news stories, social media discussions, and full texts of original papers. Models trained on SPICED improve downstream performance on evidence retrieval for fact checking of real-world scientific claims.
  arXiv  Detail & Related papers  (2022-10-24T07:44:38Z)
• Data Science: A Comprehensive Overview [42.98602883069444]
  The twenty-first century has ushered in the age of big data and data economy, in which data DNA has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons and thinking about data science and analytics.
  arXiv  Detail & Related papers  (2020-07-01T02:33:58Z)
• Data Science: Nature and Pitfalls [42.98602883069444]
  A critical matter for the healthy development of data science in its early stages is to deeply understand the nature of data and data science. These important issues motivate the discussions in this article.
  arXiv  Detail & Related papers  (2020-06-28T02:06:54Z)
• Data Science: Challenges and Directions [42.98602883069444]
  We review hundreds of pieces of literature which include data science in their titles. We find that the majority of the discussions essentially concern statistics, data mining, machine learning, big data, or broadly data analytics. We focus on the research and innovation challenges inspired by the nature of data science problems as complex systems.
  arXiv  Detail & Related papers  (2020-06-28T01:49:00Z)
• A Data Scientist's Guide to Streamflow Prediction [55.22219308265945]
  We focus on the element of hydrologic rainfall--runoff models and their application to forecast floods and predict streamflow. This guide aims to help interested data scientists gain an understanding of the problem, the hydrologic concepts involved, and the details that come up along the way.
  arXiv  Detail & Related papers  (2020-06-05T08:04:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.