Étiquette : data papers

Catégories
EN

Publish-Review-Curate Modelling for Data Paper and Dataset: A Collaborative Approach

Authors : Youngim Jung, Sungsoo Robert Ahn

Research datasets—capturing natural, societal, or artificial phenomena—are critical in generating new scientific insights, validating research models, and supporting data-intensive discovery. Data papers that describe and contextualise these datasets aim to ensure their findability, accessibility, interoperability, and reusability (FAIR) while providing academic credit to data creators.

However, the peer review of data papers and associated datasets presents considerable challenges, requiring reviewers to assess both the syntactic and semantic integrity of the data, metadata quality, and domain-specific scientific relevance. Furthermore, the coordination between journal editors, reviewers, and curators demands substantial effort, often leading to publication delays in the conventional review and then publishing framework.

This study proposes a novel Publish-Review-Curate (PRC) model tailored to the synchronised publication and review of data papers and their underlying datasets. Building on preprint and open science practices, the model defines a collaborative, multi-stakeholder workflow involving authors, peer reviewers, data experts, and journal editors.

The PRC model integrates open feedback, transparent peer review, and structured curation to improve research data’s quality, discoverability, and impact. By articulating conceptual and operational workflows, this study contributes a practical framework for modernising data publishing infrastructures and supporting the co-evaluation of narrative and data artefacts.

URL : Publish-Review-Curate Modelling for Data Paper and Dataset: A Collaborative Approach

DOI : https://doi.org/10.1002/leap.2024

Catégories
EN

Who funds what: An assessment of research funding networks in data papers

Authors : Yurdagül Ünal, Müge Akbulut

This study examines the role of funding collaborations in shaping the production and dissemination of scientific information through data papers, a rapidly growing academic publication format.

To the best of our knowledge, there are no studies investigating, and evaluating the data paper-funder relationship. The goal of this study was, therefore, to evaluate data papers and funder information in detail, extracted from the data papers themselves, in order to reveal the collaborative characteristics of funders, and to provide guidance to researchers and funding agencies.

Data papers published between 2006–2017 were downloaded from the Web of Science database. The same papers were retrieved from Dimension, which offered more detailed category classifications. These classifications were then utilized for further analysis based on categories. The names of funders were standardized by matching them using the Crossref funder registry, and associated funding metadata.

A statistical, and social network analysis were performed. The top funding country was the USA; the top funding institution was the U.S. Department of Health and Human Services, National Institutes of Health. The collaboration network among funders exhibited relatively low density.

A collaboration network of 1197 links between 69 countries was created. The USA had connections with 62 countries. Our study is important because it standardizes the funding data for data papers by associating them with Crossref funding metadata.

The widespread increase of data papers, and their relatively dispersed funding among a variety of funders points to the need for research evaluating collaborations between funders, as important both for the funded researchers, and for understanding and optimizing the shortcomings of current funding management.

URL : Who funds what: An assessment of research funding networks in data papers

DOI : https://doi.org/10.1177/02666669251352185

Catégories
EN

Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

Authors : Barbara McGillivray, Paola Marongiu, Nilo Pedrazzini, Marton Ribary, Mandy Wigdorowitz, Eleonora Zordan

The humanities and social sciences (HSS) have recently witnessed an exponential growth in data-driven research. In response, attention has been afforded to datasets and accompanying data papers as outputs of the research and dissemination ecosystem.

In 2015, two data journals dedicated to HSS disciplines appeared in this landscape: Journal of Open Humanities Data (JOHD) and Research Data Journal for the Humanities and Social Sciences (RDJ).

In this paper, we analyse the state of the art in the landscape of data journals in HSS using JOHD and RDJ as exemplars by measuring performance and the deep impact of data-driven projects, including metrics (citation count; Altmetrics, views, downloads, tweets) of data papers in relation to associated research papers and the reuse of associated datasets.

Our findings indicate: that data papers are published following the deposit of datasets in a repository and usually following research articles; that data papers have a positive impact on both the metrics of research papers associated with them and on data reuse; and that Twitter hashtags targeted at specific research campaigns can lead to increases in data papers’ views and downloads.

HSS data papers improve the visibility of datasets they describe, support accompanying research articles, and add to transparency and the open research agenda.

URL : Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

DOI : https://doi.org/10.3390/publications10040039

Catégories
EN

Data papers as a new form of knowledge organization in the field of research data

Authors : Joachim Schöpfel, Dominic Farace, Hélène Prost, Antonella Zane

Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e. disciplines, publishers and business models, and about their structure, length, formats, metadata and licensing.

Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable.

Data papers are essentially information, i.e. description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing.

URL : https://halshs.archives-ouvertes.fr/ISKOFRANCE2019/halshs-02284548

Catégories
EN

Data objects and documenting scientific processes: An analysis of data events in biodiversity data papers

Authors : Kai Li, Jane Greenberg, Jillian Dunic

The data paper, an emerging scholarly genre, describes research datasets and is intended to bridge the gap between the publication of research data and scientific articles. Research examining how data papers report data events, such as data transactions and manipulations, is limited.

The research reported on in this paper addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility (GBIF).

Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space.

The findings challenge the degrees to which data papers are a distinct genre compared to research papers and they describe data-centric research processes in a through way.

This paper also discusses how our results could inform a better data publication ecosystem in the future.

URL : Data objects and documenting scientific processes: An analysis of data events in biodiversity data papers

Alternative location : https://arxiv.org/abs/1903.06215

Catégories
EN

Publishing science without results and recycling research

Author : Eric Lichtfouse

Most scientists focus too much on publishing original articles. In doing so, scientists are restricting their writing skills to this form of highly specialised publication, which is poorly readable by scientists from other disciplines.

In the context of rising interdisciplinary research and data abundance, there is a need for more publications that recycle existing research and communicate to a wider audience. Therefore, I present here five types of publications that do not require additional experiments , namely reviews, methods, data papers, meta-analyses and videos.

Benefits include more citations, larger visibility, wider dissemination, easier job finding, grant success and better recycling of research.

URL : https://hal.archives-ouvertes.fr/hal-01711017

Catégories
FR

Améliorer l’exposition des données de la recherche : la publication de data papers

Auteur/Author : Nathalie Reymonet

Les données de la recherche sont l’objet de l’intérêt des financeurs de la recherche publique, qui incitent les chercheurs à partager ces données, afin de répondre à des enjeux financiers comme de circulation des savoirs.

Parmi les différentes modalités de la communication scientifique, la publication d’un « data paper » est une démarche relativement nouvelle. Le « data paper », ou article sur des données, décrit des données scientifiques et propose un lien vers un entrepôt de données qui les stocke.

La description est en particulier très précise sur les points techniques et la méthodologie de production des données. Cette démarche va dans le sens de l’exposition des données, de leur accessibilité, leur interopérabilité et leur réutilisabilité, répondant ainsi aux recommandations des communautés d’intérêt de la recherche académique.

Ce texte présente la structure et le contenu d’un « data paper » ainsi que des exemples de revues qui publient de tels articles.

URL : Améliorer l’exposition des données de la recherche : la publication de data papers

Alternative location : https://archivesic.ccsd.cnrs.fr/sic_01427978