Abstract

Background: Digital tools that support open science practices play a key role in the seamless accumulation, archiving and dissemination of scholarly data, outcomes and conclusions. Despite their integration into open science practices, the providence and design of these digital tools are rarely explicitly scrutinized. This means that influential factors, such as the funding models of the parent organizations, their geographic location, and the dependency on digital infrastructures are rarely considered. Suggestions from literature and anecdotal evidence already draw attention to the impact of these factors, and raise the question of whether the open science ecosystem can realise the aspiration to become a truly “unlimited digital commons” in its current structure.

Methods: In an online research approach, we compiled and analysed the geolocation, terms and conditions as well as funding models of 242 digital tools increasingly being used by researchers in various disciplines.

Results: Our findings indicate that design decisions and restrictions are biased towards researchers in North American and European scholarly communities. In order to make the future open science ecosystem inclusive and operable for researchers in all world regions including Africa, Latin America, Asia and Oceania, those should be actively included in design decision processes.

Conclusions: Digital open science tools carry the promise of enabling collaboration across disciplines, world regions and language groups through responsive design. We therefore encourage long-term funding mechanisms and ethnically as well as culturally inclusive approaches serving local prerequisites and conditions to tool design and construction allowing a globally connected digital research infrastructure to evolve in a regionally balanced manner.

Digital tools for open science practices are readily available but often lack seamless interoperability. Key components of an open and effective digital scholarly ecosystem are persistent identifiers for researchers, research institutions, research items, and data points. Once we manage to align the existing infrastructure tools and services with each other through PIDs across all service providers, we will have a sustainable and reliable scholarly infrastructure that enables global research equity.

Dr Jo Havemann

Cite as

Bezuidenhout L and Havemann J. The varying openness of digital open science tools [version 2]. F1000Research 2021, 9:1292 (https://doi.org/10.12688/f1000research.26615.2)

DOST Visual Mapping

To view the map on the Kumu website go to kumu.io/access2perspectives/dost#dataset/

Underlying dataset: docs.google.com/spreadsheets/d/1vnA1oaO87WLxRpmqRmub3YLJcojQJkLmrOcv-Em2IAA/

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Figures & tables from the manuscript

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Figure 1.: A) Diagram from Kramer & Bosman (2016)13 demonstrating diversity of DOSTs, linkages between tools at different stages of workflow. Green line demonstrates a potential research workflow involving DOSTs. Image shared under CC-BY license. B) Pictogram of a random digital tool representing the tools displayed in 1A with influencing aspects addressed in this paper: underlying values, financial models, language choices, geographical location, user communities.

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Figure 2. Visual map using the software Kumu.io: A) Clustering overview of all tools sorted by workflow step (url: https://kumu.io/a2p/dost#dataset/workflow-step); B) Clustering overview by geographical location of the tool or the respective host institution (url: https://kumu.io/a2p/dost#dataset/workflow-step); C) Clustering overview by host institution for the tool (url: https://kumu.io/a2p/dost#dataset/host); D) Focus view on hist self-hosted tools – closeup from square in C).

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Figure 3. Number of tools per host location: Regions displayed are the United States of America (US), the European Union (EU), the United Kingdom (UK) and other parts of the world with concentration on US territory. ‘Other’ includes Argentina (n=1), Australia (n=2), Brazil (n=1), Canada (n=7), Colombia (n=1), Mexico (n=1), South Africa (n=1), Switzerland (n=5), with a total of n=242.
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Figure 4. Illustration of the funding models of DOSTs: The funding sources for the respective tools were classified as a) Commercial (n=56, 23.1%); b) Grant (n=19, 7.9%); c) mixed (commercial and grant, n=122, 50.4%), and d) Institutional (n=44, 18.2%). 0.4% of the tools (n=1) had no funding source specified. n=242.

Figure 5. Tool providers across workflow showing the number of tools per workflow step.
Table 1. Key pressures on the DOST ecosystem.

Table 2. Example T&Cs of two entities within the OS ecosystem.