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author-meta: "V.A. Traag, etc..."
callout-appearance: simple
zotero: PathOS
bibliography: references.bib


editor: visual
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country: the Netherlands

zotero: PathOS
bibliography: references.bib
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city: Athena
country: Greece
---

# APC Costs {#apc-costs .unnumbered}

::: {}
<div>

## History

| Version | Revision date | Revision | Author |
|---------|---------------|-------------------------------|---------------------------------------------------------|
| 1.1 | 2023-08-28 | Draft for initial publication | I. Grypari |
| 1.0 | 2023-05-09 | First draft | I. Grypari, N. Manola, H. Papageorgiou, P. Stavropoulos |
| Version | Revision date | Revision | Author |
|-------------|-------------|---------------|-------------------------------|
| 1.1 | 2023-08-28 | Draft for initial publication | I. Grypari |
| 1.0 | 2023-05-09 | First draft | I. Grypari, N. Manola, H. Papageorgiou, P. Stavropoulos |

:::
</div>

## Description

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### Estimating unknown APCs

An APC extrapolation exercise was conducted for the purpose of an EC study (European Commission, 2021). The authors defined groupings, and imputed the average APC of the group to each publication in the group for which the APC is unknown. The groupings were based on the following variables, similar to the correlates above:
- quantile of the Source Normalised Impact per Paper (SNIP) score in the [CWTS journal indicators](https://www.journalindicators.com/),
- whether the publication is pure ‘gold’ open access or ‘hybrid’,
- the year of publication.

The [French Open Science Monitor](https://frenchopensciencemonitor.esr.gouv.fr/) also uses several extrapolations when the APC for a DOI is not available in OpenAPC (Bracco et al., 2022). They are listed here in order of preference (only when one computation is not possible is the following used):
- average APC for the same journal and the same year in OpenAPC (provided $n \geq 10$)
- average APC for the same publisher in OpenAPC, for the same year as the article (provided $n \geq 10$)
- average APC for the same journal in OpenAPC, for all years available
- average APC for the same publisher in OpenAPC, for all years available
- APC for the journal in DOAJ



### References

Bracco, Laetitia, Anne L'Hôte, Eric Jeangirard, Didier Torny. *Extending the open monitoring of open science: A new framework for the French Open Science Monitor (BSO)*, 2022, <https://hal.science/hal-03651518>

European Commission, Directorate-General for Research and Innovation, *Monitoring the open access policy of Horizon 2020 – Final report*, Publications Office, 2021, <https://data.europa.eu/doi/10.2777/268348>
An APC extrapolation exercise was conducted for the purpose of an EC study [@monitori2021]. The authors defined groupings, and imputed the average APC of the group to each publication in the group for which the APC is unknown. The groupings were based on the following variables, similar to the correlates above: - quantile of the Source Normalised Impact per Paper (SNIP) score in the [CWTS journal indicators](https://www.journalindicators.com/), - whether the publication is pure ‘gold’ open access or ‘hybrid’, - the year of publication.

Schönfelder, Nina. "Article processing charges: Mirroring the citation impact or legacy of the subscription-based model?." *Quantitative Science Studies* 1.1 (2020): 6-27.
The [French Open Science Monitor](https://frenchopensciencemonitor.esr.gouv.fr/) also uses several extrapolations when the APC for a DOI is not available in OpenAPC [@bracco2022]. They are listed here in order of preference (only when one computation is not possible is the following used): - average APC for the same journal and the same year in OpenAPC (provided $n \geq 10$) - average APC for the same publisher in OpenAPC, for the same year as the article (provided $n \geq 10$) - average APC for the same journal in OpenAPC, for all years available - average APC for the same publisher in OpenAPC, for all years available - APC for the journal in DOAJ.
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# Availability of data repositories {#availability-of-data-repositories .unnumbered}

::: {}
<div>

## History

| Version | Revision date | Revision | Author |
|---------|---------------|------------------------------|--------------|
| 1.1 | 2023-07-20 | Edited & revised | T. Willemse |
| 1.0 | 2023-04-13 | First draft | T. Willemse |
| Version | Revision date | Revision | Author |
|---------|---------------|------------------|-------------|
| 1.1 | 2023-07-20 | Edited & revised | T. Willemse |
| 1.0 | 2023-04-13 | First draft | T. Willemse |

:::
</div>

## Description

In the context of Open Science, the availability of research data is an important topic. Open Data is often, but not exclusively, made available through data repositories, which can store and archive data for long-term preservation. More and more Open Data repositories are initiated and efforts to establish the needed infrastructure are undertaken. However, these repositories differ vastly in their nature and accessibility. It is therefore important to get an overview of the accessibility of these different data sources for the assessment and practice of Open Science.

Governments and governmental agencies, individual universities and research communities are types of organisations involved in setting up data sources in various fields (Goben & Sandusky, 2020). There are also publisher-driven data repositories that stimulate cooperation and can be openly accessible to a certain extent. Lastly, there exist non-institution affiliated data repositories, ranging from field specific (e.g. gene databanks, such as International Nucleotide Sequence Database Collaboration) to more general repositories including multiple topics (e.g. Zenodo, Dryad, figshare).
Governments and governmental agencies, individual universities and research communities are types of organisations involved in setting up data sources in various fields [@goben2020]. There are also publisher-driven data repositories that stimulate cooperation and can be openly accessible to a certain extent. Lastly, there exist non-institution affiliated data repositories, ranging from field specific (e.g. gene databanks, such as International Nucleotide Sequence Database Collaboration) to more general repositories including multiple topics (e.g. Zenodo, Dryad, figshare).

The wide variety of data repositories out there present a number of opportunities and challenges (Goben & Sandusky, 2020). A clear opportunity is the large increase in accessible data by an increasing number of repositories. However, the wide variety of repositories and infrastructure also presents a challenge in finding the right data repository or dataset that one is looking for. The increase in variety also leads to a risk of data misinterpretation or misuse and can lead to data loss.
The wide variety of data repositories out there present a number of opportunities and challenges [@goben2020]. A clear opportunity is the large increase in accessible data by an increasing number of repositories. However, the wide variety of repositories and infrastructure also presents a challenge in finding the right data repository or dataset that one is looking for. The increase in variety also leads to a risk of data misinterpretation or misuse and can lead to data loss.

Given the potential for Open Data repositories it can be very helpful to get an indication of the accessibility of these resources and how they link up with research. It must be noted however that this indicator is not meant to be solely used to rank data repositories or scientific entities. To do this, other indicators and measures should be taken into account, as well as relevant contextual factors that are difficult to capture in quantitative data.

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###### DataCite

[DataCite](https://datacite.org/value.html) is a non-profit organisation that provides DOI's for research data and research output. Within their services DataCite also produces an [overview](https://search.datacite.org/repositories) of data repositories. These include a wide variety of data repositories that are associated with the data that is documented by DataCite. Although, many data repositories and associated datasets are documented here, the catalogue is somewhat limited in filtering for the openness of the data repositories themselves. It can therefore mainly serve as an information source on what type of data repositories are out there. An overview on the analytical possibilities of DataCite can be found in (Robinson-Garcia et al., 2017).
[DataCite](https://datacite.org/value.html) is a non-profit organisation that provides DOI's for research data and research output. Within their services DataCite also produces an [overview](https://search.datacite.org/repositories) of data repositories. These include a wide variety of data repositories that are associated with the data that is documented by DataCite. Although, many data repositories and associated datasets are documented here, the catalogue is somewhat limited in filtering for the openness of the data repositories themselves. It can therefore mainly serve as an information source on what type of data repositories are out there. An overview on the analytical possibilities of DataCite can be found in [@robinson-garcia2017].

###### FAIRsharing.org (<https://fairsharing.org/>)

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###### Core Trust Seal

Core Trust Seal is a non-profit organisation that labels data sources with their seal if data sources adhere to the FAIR principles. On the website a [list](https://amt.coretrustseal.org/certificates/) is maintained with all the data sources that the seal has been assigned to. Data stored in these sources can thus be considered to be produced in accordance with the FAIR principles. When performing research related to the availability of data repositories, one can consider repositories that have received the CoreTrustSeal, the Nestor Seal DIN31644, the ISO16363 certification, or similar, to be automatically trusted (Jahn et al., 2023).

## References

Goben, A., & Sandusky, R. J. (2020). *Open data repositories: Current risks and opportunities \| Goben \| College & Research Libraries News*. https://doi.org/10.5860/crln.81.1.62

Jahn, N., Laakso, M., Lazzeri, E., & McQuilton, P. (2023). *Study on the readiness of research data and literature repositories to facilitate compliance with the Open Science Horizon Europe MGA requirements*. Zenodo. https://zenodo.org/record/7728016

Robinson-Garcia, N., Mongeon, P., Jeng, W., & Costas, R. (2017). DataCite as a novel bibliometric source: Coverage, strengths and limitations. *Journal of Informetrics*, *11*(3), 841–854. https://doi.org/10.1016/j.joi.2017.07.003
Core Trust Seal is a non-profit organisation that labels data sources with their seal if data sources adhere to the FAIR principles. On the website a [list](https://amt.coretrustseal.org/certificates/) is maintained with all the data sources that the seal has been assigned to. Data stored in these sources can thus be considered to be produced in accordance with the FAIR principles. When performing research related to the availability of data repositories, one can consider repositories that have received the CoreTrustSeal, the Nestor Seal DIN31644, the ISO16363 certification, or similar, to be automatically trusted [@jahn2023].
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