Dataset Reuse: Toward Translating Principles to Practice

TY - JOUR

T1 - Dataset Reuse

T2 - Toward Translating Principles to Practice

AU - Koesten, Laura

AU - Vougiouklis, Pavlos

AU - Simperl, Elena

AU - Groth, Paul

PY - 2020/11/13

Y1 - 2020/11/13

N2 - The web provides access to millions of datasets that can have additional impact when used beyond their original context. We have little empirical insight into what makes a dataset more reusable than others and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data. We describe a corpus of more than 1.4 million data files, from over 65,000 repositories. Using GitHub's engagement metrics as proxies for dataset reuse, we relate them to reuse features from the literature and devise an initial model, using deep neural networks, to predict a dataset's reusability. This demonstrates the practical gap between principles and actionable insights that allow data publishers and tools designers to implement functionalities that provably facilitate reuse. The web provides access to millions of datasets. These data can have additional impact when it is used beyond the context for which it was originally created. We have little empirical insight into what makes a dataset more reusable than others, and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data. We describe a corpus of more than 1.4 million data files, from over 65,000 repositories. Using GitHub's engagement metrics as proxies for dataset reuse, we relate them to reuse features from the literature and devise an initial model, using deep neural networks, to predict a dataset's reusability. This work demonstrates the practical gap between principles and actionable insights that allow data publishers and tools designers to implement functionalities that provably facilitate reuse. There is plenty of advice on how to make a dataset easier to reuse, including technical standards, legal frameworks, and guidelines. This paper begins to address the gap between this advice and practice. To do so, a compilation of reuse features from literature is presented. To understand how they look like in data projects, we carried out a case study of datasets published and shared on GitHub, a large online platform to share code and data.

AB - The web provides access to millions of datasets that can have additional impact when used beyond their original context. We have little empirical insight into what makes a dataset more reusable than others and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data. We describe a corpus of more than 1.4 million data files, from over 65,000 repositories. Using GitHub's engagement metrics as proxies for dataset reuse, we relate them to reuse features from the literature and devise an initial model, using deep neural networks, to predict a dataset's reusability. This demonstrates the practical gap between principles and actionable insights that allow data publishers and tools designers to implement functionalities that provably facilitate reuse. The web provides access to millions of datasets. These data can have additional impact when it is used beyond the context for which it was originally created. We have little empirical insight into what makes a dataset more reusable than others, and which of the existing guidelines and frameworks, if any, make a difference. In this paper, we explore potential reuse features through a literature review and present a case study on datasets on GitHub, a popular open platform for sharing code and data. We describe a corpus of more than 1.4 million data files, from over 65,000 repositories. Using GitHub's engagement metrics as proxies for dataset reuse, we relate them to reuse features from the literature and devise an initial model, using deep neural networks, to predict a dataset's reusability. This work demonstrates the practical gap between principles and actionable insights that allow data publishers and tools designers to implement functionalities that provably facilitate reuse. There is plenty of advice on how to make a dataset easier to reuse, including technical standards, legal frameworks, and guidelines. This paper begins to address the gap between this advice and practice. To do so, a compilation of reuse features from literature is presented. To understand how they look like in data projects, we carried out a case study of datasets published and shared on GitHub, a large online platform to share code and data.

KW - data portals

KW - dataset reuse

KW - DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem

KW - human-data interaction

KW - neural networks

KW - reuse prediction

UR - http://www.scopus.com/inward/record.url?scp=85097431688&partnerID=8YFLogxK

U2 - 10.1016/j.patter.2020.100136

DO - 10.1016/j.patter.2020.100136

M3 - Article

AN - SCOPUS:85097431688

SN - 2666-3899

VL - 1

JO - Patterns

JF - Patterns

IS - 8

M1 - 100136

ER -