This paper carries out a lightweight review to explore the potentials of data science in the last two decades and especially focuses on the four essential components: data resources, technologies, data infrastructures, and data education. Considering the barriers of data science, the analysis has been mapped into four essential components, highlighting priorities and challenges in social and cultural, epistemological, scientific and technical, economic, legal, and ethical aspects. As a result, the future development of data science tends to shift toward datafication, data technicity, infrastru ..read more

This essay reflects on the shifting attention to the “social” and the “cultural” in data science communities. While recently the “social” and the “cultural” have been prioritized in data science discourse, social and cultural concerns that get raised in data science are almost always outwardly focused – applying to the communities that data scientists seek to support more so than more computationally-focused data science communities. I argue that data science communities have a responsibility to attend not only to the cultures that orient the work of domain communities, but also to the culture ..read more

Persistent identifiers are applied to an ever-increasing variety of research objects, including software, samples, models, people, instruments, grants, and projects, and there is a growing need to apply identifiers at a finer and finer granularity. Unfortunately, the systems developed over two decades ago to manage identifiers and the metadata describing the identified objects no longer scale. Communities working with physical samples have grappled with these three challenges of the increasing volume, variety, and variability of identified objects for many years. To address this dual challenge ..read more

This article details a correction to the article: Caracciolo, C., Aubin, S., Jonquet, C., Amdouni, E., David, R., Garcia, L., Whitehead, B., Roussey, C., Stellato, A. and Villa, F., 2020. 39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition. Data Science Journal, 19(1), p.47. DOI: http://doi.org/10.5334/dsj-2020-047 Published on 2023-02-14 10:19:51 ..read more

The article “39 Hints to Facilitate the Use of Semantics for Data on Agriculture and Nutrition (Caracciolo et al. 2020) cites an article by Dury et al. (Dury et al, 2019) but did not list this within the reference list. The following is the correct reference for the article by Dury et al. Drury, B., Fernades, R., Moura, M. and de Andrade Lopes, A., 2019. A survey of semantic web technology for agriculture. Information Processing in Agriculture, 6(4), p.487-501.  DOI: https://doi.org/10.1016/j.inpa.2019.02.001 Published on 2023-02-14 10:19:51 ..read more

Data discovery is important to facilitate data re-use. In order to help frame the development and improvement of data discovery tools, we collected a list of requirements and users’ wishes. This paper presents the analysis of these 101 use cases to examine data discovery requirements; these cases were collected between 2019 and 2020. We categorized the information across 12 ‘topics’ and eight types of users. While the availability of metadata was an expected topic of importance, users were also keen on receiving more information on data citation and a better overview of their field. We conduct ..read more

Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements. This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements. The automated analysis revealed that terms related ..read more

Small Uncrewed Aircraft Systems (sUAS) are an increasingly common tool for data collection in many scientific fields. However, there are few standards or best practices guiding the collection, sharing, or publication of data collected with these tools. This makes collaboration, data quality control, and reproducibility challenging. To that end, we have used iterative rounds of data modeling and user engagement to develop a Minimum Information Framework (MIF) to guide sUAS users in collecting the metadata necessary to ensure that their data is trust-worthy, shareable and reusable. This paper br ..read more

The University of Cologne (UoC) has historically grown in highly decentralised structures. This is reflected by a two-layered library structure as well as by a number of decentralised research data management (RDM) activities established on the faculty and research consortium level. With the aim to foster networking, cooperation, and synergies between existing activities, a university-wide RDM will be established. A one-year feasibility study was commissioned by the Rectorate in 2016 and carried out by the department research management, library and computing centre. One study outcome was the ..read more

Persistent identifiers (PIDs) for scientific organizations such as research institutions and research funding agencies are a further decisive piece of the puzzle to promote standardization in the scholarly publication process—especially in light of the already established digital object identifiers (DOIs) for research outputs and ORCID iDs for researchers. The application of these PIDs enables automated data flows and guarantees the persistent linking of information objects. Moreover, PIDs are fundamental components for the implementation of open science. For example, the application of PIDs f ..read more