Related papers: How are research data referenced? The use case of …
In today's data-driven research landscape, dataset visibility and accessibility play a crucial role in advancing scientific knowledge. At the same time, data citation is essential for maintaining academic integrity, acknowledging…
Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from…
Data reuse is a common practice in the social sciences. While published data play an essential role in the production of social science research, they are not consistently cited, which makes it difficult to assess their full scholarly…
Data sharing is fundamental to scientific progress, enhancing transparency, reproducibility, and innovation across disciplines. Despite its growing significance, the variability of data-sharing practices across research fields remains…
Data sharing by researchers is a centerpiece of Open Science principles and scientific progress. For a sample of 6019 researchers, we analyze the extent/frequency of their data sharing. Specifically, the relationship with the following four…
We present an analysis of data citation practices based on the Data Citation Index from Thomson Reuters. This database launched in 2012 aims to link data sets and data studies with citations received from the other citation indexes. The DCI…
Open data is receiving increased attention and support in academic environments, with one justification being that shared data may be re-used in further research. But what evidence exists for such re-use, and what is the relationship…
The sharing and citation of research data is becoming increasingly recognized as an essential building block in scientific research across various fields and disciplines. Sharing research data allows other researchers to reproduce results,…
We report evidence of a new set of sneaked references discovered in the scientific literature. Sneaked references are references registered in the metadata of publications without being listed in reference section or in the full text of the…
Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent…
Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data…
Context: The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. Objective: Examine the usage of data papers…
In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets. While a growing body of work establishes guidelines for -- and levies criticisms at -- data and benchmarking practices…
Scientific full text papers are usually stored in separate places than their underlying research datasets. Authors typically make references to datasets by mentioning them for example by using their titles and the year of publication.…
Recommender-system datasets are used for recommender-system evaluations, training machine-learning algorithms, and exploring user behavior. While there are many datasets for recommender systems in the domains of movies, books, and music,…
Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data…
We report evidence of an undocumented method to manipulate citation counts involving 'sneaked' references. Sneaked references are registered as metadata for scientific articles in which they do not appear. This manipulation exploits trusted…
Data repositories have accumulated a large number of tabular datasets from various domains. Machine Learning researchers are actively using these datasets to evaluate novel approaches. Consequently, data repositories have an important…
Medical imaging papers often focus on methodology, but the quality of the algorithms and the validity of the conclusions are highly dependent on the datasets used. As creating datasets requires a lot of effort, researchers often use…
Academic data sharing is a way for researchers to collaborate and thereby meet the needs of an increasingly complex research landscape. It enables researchers to verify results and to pursuit new research questions with "old" data. It is…