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Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

Author : Daniel Spichtinger

Background

Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU’s Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance.

Methods

As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs.

Results & Conclusions

We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being “just” an European Commission (EC) requirement.

DMPs are most prominently developed within a project’s Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach.

The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk.

If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs.

The study identifies IP and licensing arrangements for DMPs as promising areas for further research.

URL : Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

DOI : https://doi.org/10.12688/openreseurope.13342.1

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EN

The Changing Landscape of Open Access Publishing: Can Open Access Publishing Make the Scholarly World More Equitable and Productive?

Author : Richard G. Dudley

Almost 50% of scholarly articles are now open access in some form. This greatly benefits scholars at most institutions and is especially helpful to independent scholars and those without access to libraries. It also furthers the long-standing idea of knowledge as a public good.

The changing dynamics of open access (OA) threaten this positive development by solidifying the pay-to-publish OA model which further marginalizes peripheral scholars and incentivizes the development of sub-standard and predatory journals. Causal loop diagrams (CLDs) are used to illustrate these interactions.

URL : The Changing Landscape of Open Access Publishing: Can Open Access Publishing Make the Scholarly World More Equitable and Productive?

DOI : https://doi.org/10.7710/2162-3309.2345

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EN

Transparency, provenance and collections as data: the National Library of Scotland’s Data Foundry

Author : Sarah Ames

‘Collections as data’ has become a core activity for libraries in recent years: it is important that we make collections available in machine-readable formats to enable and encourage computational research. However, while this is a necessary output, discussion around the processes and workflows required to turn collections into data, and to make collections data available openly, are just as valuable.

With libraries increasingly becoming producers of their own collections – presenting data from digitisation and digital production tools as part of datasets, for example – and making collections available at scale through mass-digitisation programmes, the trustworthiness of our processes comes into question.

In a world of big data, often of unclear origins, how can libraries be transparent about the ways in which collections are turned into data, how do we ensure that biases in our collections are recognised and not amplified, and how do we make these datasets available openly for reuse?

This paper presents a case study of work underway at the National Library of Scotland to present collections as data in an open and transparent way – from establishing a new Digital Scholarship Service, to workflows and online presentation of datasets.

It considers the changes to existing processes needed to produce the Data Foundry, the National Library of Scotland’s open data delivery platform, and explores the practical challenges of presenting collections as data online in an open, transparent and coherent manner.

URL : Transparency, provenance and collections as data: the National Library of Scotland’s Data Foundry

Original location : https://www.liberquarterly.eu/article/10.18352/lq.10371/

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Prevalence of nonsensical algorithmically generated papers in the scientific literature

Authors : Guillaume Cabanac, Cyril Labbé

In 2014 leading publishers withdrew more than 120 nonsensical publications automatically generated with the SCIgen program. Casual observations suggested that similar problematic papers are still published and sold, without follow-up retractions.

No systematic screening has been performed and the prevalence of such nonsensical publications in the scientific literature is unknown. Our contribution is 2-fold.

First, we designed a detector that combs the scientific literature for grammar-based computer-generated papers. Applied to SCIgen, it has a 83.6% precision. Second, we performed a scientometric study of the 243 detected SCIgen-papers from 19 publishers.

We estimate the prevalence of SCIgen-papers to be 75 per million papers in Information and Computing Sciences. Only 19% of the 243 problematic papers were dealt with: formal retraction (12) or silent removal (34).

Publishers still serve and sometimes sell the remaining 197 papers without any caveat. We found evidence of citation manipulation via edited SCIgen bibliographies. This work reveals metric gaming up to the point of absurdity: fraudsters publish nonsensical algorithmically generated papers featuring genuine references.

It stresses the need to screen papers for nonsense before peer-review and chase citation manipulation in published papers. Overall, this is yet another illustration of the harmful effects of the pressure to publish or perish.

URL : Prevalence of nonsensical algorithmically generated papers in the scientific literature

DOI : https://doi.org/10.1002/asi.24495

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EN

Digital Object Identifier (DOI) Under the Context of Research Data Librarianship

Author : Jia Liu

A digital object identifier (DOI) is an increasingly prominent persistent identifier in finding and accessing scholarly information. This paper intends to present an overview of global development and approaches in the field of DOI and DOI services with a slight geographical focus on Germany.

At first, the initiation and components of the DOI system and the structure of a DOI name are explored. Next, the fundamental and specific characteristics of DOIs are described and DOIs for three (3) kinds of typical intellectual entities in the scholar communication are dealt with; then, a general DOI service pyramid is sketched with brief descriptions of functions of institutions at different levels.

After that, approaches of the research data librarianship community in the field of RDM, especially DOI services, are elaborated. As examples, the DOI services provided in German research libraries as well as best practices of DOI services in a German library are introduced; and finally, the current practices and some issues dealing with DOIs are summarized. It is foreseeable that DOI, which is crucial to FAIR research data, will gain extensive recognition in the scientific world.

URL : Digital Object Identifier (DOI) Under the Context of Research Data Librarianship

DOI : https://doi.org/10.7191/jeslib.2021.1180

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EN

Open access book usage data – how close is COUNTER to the other kind?

Author : Ronald Snijder

In April 2020, the OAPEN Library moved to a new platform, based on DSpace 6. During the same period, IRUS-UK started working on the deployment of Release 5 of the COUNTER Code of Practice (R5). This is, therefore, a good moment to compare two widely used usage metrics – R5 and Google Analytics (GA).

This article discusses the download data of close to 11,000 books and chapters from the OAPEN Library, from the period 15 April 2020 to 31 July 2020. When a book or chapter is downloaded, it is logged by GA and at the same time a signal is sent to IRUS-UK.

This results in two datasets: the monthly downloads measured in GA and the usage reported by R5, also clustered by month. The number of downloads reported by GA is considerably larger than R5. The total number of downloads in GA for the period is over 3.6 million.

In contrast, the amount reported by R5 is 1.5 million, around 400,000 downloads per month. Contrasting R5 and GA data on a country-by-country basis shows significant differences. GA lists more than five times the number of downloads for several countries, although the totals for other countries are about the same.

When looking at individual tiles, of the 500 highest ranked titles in GA that are also part of the 1,000 highest ranked titles in R5, only 6% of the titles are relatively close together. The choice of metric service has considerable consequences on what is reported.

Thus, drawing conclusions about the results should be done with care. One metric is not better than the other, but we should be open about the choices made. After all, open access book metrics are complicated, and we can only benefit from clarity.

URL : Open access book usage data – how close is COUNTER to the other kind?

DOI : http://doi.org/10.1629/uksg.539

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EN

Affiliation Information in DataCite Dataset Metadata: a Flemish Case Study

Author/Auteur : Niek Van Wettere

This article aims to evaluate how and to what extent metadata of datasets indexed in DataCite offer clear human- or machine-readable information that enables the research data to be linked to a particular research institution.

Two main pathways are explored. First, researchers can encode their affiliation information at the moment of data submission. This can be done by means of free-text metadata fields or via the inclusion of identifiers such as GRID/ROR and ORCID. Second, affiliation information can be traced indirectly through linking between a dataset and associated publications, given that the metadata of publications is often more explicit about affiliation information than the metadata of datasets.

Both pathways of affiliation information encoding are evaluated on the basis of metadata pertaining to datasets created at the five Flemish universities. It is shown that good practices such as encoding of affiliation information in a dedicated metadata field or inclusion of ORCID in the metadata are on the rise, but could be expanded further.

Finally, the establishment of links between datasets and related publications is often lacking in dataset metadata, although there are important differences between data repositories, as is also demonstrated in a more data-intensive follow-up analysis based on random samples of metadata records.

It is important that data repositories address this issue by providing a metadata field clearly dedicated to associated publications, prominently displayed on the landing page of the dataset.

URL : Affiliation Information in DataCite Dataset Metadata: a Flemish Case Study

DOI : http://doi.org/10.5334/dsj-2021-013