Category Archives: oa.dataseer

PLOS Release Results from New Scheme & Springer Nature Launches OA Initiative | The Hub by The London Book Fair | Publishing News

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The Public Library of Science (PLOS) has released the first results from its new initiative, launched in partnership with AI-driven data sharing support body DataSeer, to measure researchers’ Open Science practices across published literature.

The two organisations have released data on three of the numerical indicators they have developed together – on data sharing, code sharing, and preprint posting – to show that good practices in research data and code sharing, along with the use of preprints, are becoming increasingly prevalent in the research community….”

Explore the first Open Science Indicators dataset—and share your thoughts – The Official PLOS Blog

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“Open Science is on the rise. We can infer as much from the proliferation of Open Access publishing options; the steady upward trend in bioRxiv postings; the periodic rollout of new national, institutional, or funder policies. 

But what do we actually know about the day-to-day realities of Open Science practice? What are the norms? How do they vary across different research subject areas and regions? Are Open Science practices shifting over time? Where might the next opportunity lie and where do barriers to adoption persist? 

To even begin exploring these questions and others like them we need to establish a shared understanding of how we define and measure Open Science practices. We also need to understand the current state of adoption in order to track progress over time. That’s where the Open Science Indicators project comes in. PLOS conceptualized a framework for measuring Open Science practices according to the FAIR principles, and partnered with DataSeer to develop a set of numerical “indicators” linked to specific Open Science characteristics and behaviors observable in published research articles. Our very first dataset, now available for download at Figshare, focuses on three Open Science practices: data sharing, code sharing, and preprint posting….”

PLOS Open Science Indicators

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“This dataset contains article metadata and information about Open Science Indicators for approximately 61,000 research articles published in PLOS from 1 January 2019 to 30 June 2022 and a set of approximately 6,500 comparator articles published in non-PLOS journals. This is the first release of this dataset, which will be updated with new versions as newly published content is analysed.

This version of the Open Science Indicators dataset focuses on detection of three Open Science practices by analysing the XML of published research articles:

Sharing of research data, in particular data shared in data repositories
Sharing of code
Posting of preprints

The dataset provides data and code generation and sharing rates, the location of shared data and code (whether in Supporting Information or in an online repository). It also provides preprint sharing rates as well as details of the shared preprint, such as publication date, URL and preprint server used. Additional data fields are also provided for each article analysed, such as geographic information (‘Country’) and research topics (‘Discipline’)….”

Explore the first Open Science Indicators dataset—and share your thoughts – The Official PLOS Blog

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“But what do we actually know about the day-to-day realities of Open Science practice? What are the norms? How do they vary across different research subject areas and regions? Are Open Science practices shifting over time? Where might the next opportunity lie and where do barriers to adoption persist? 

To even begin exploring these questions and others like them we need to establish a shared understanding of how we define and measure Open Science practices. We also need to understand the current state of adoption in order to track progress over time. That’s where the Open Science Indicators project comes in. PLOS conceptualized a framework for measuring Open Science practices according to the FAIR principles, and partnered with DataSeer to develop a set of numerical “indicators” linked to specific Open Science characteristics and behaviors observable in published research articles. Our very first dataset, now available for download at Figshare, focuses on three Open Science practices: data sharing, code sharing, and preprint posting….”

PLOS partners with DataSeer to develop Open Science Indicators – The Official PLOS Blog

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“To provide richer and more transparent information on how PLOS journals support best practice in Open Science, we’re going to begin publishing data on ‘Open Science Indicators’ observed in PLOS articles. These Open Science Indicators will initially include (i) sharing of research data in repositories, (ii) public sharing of code and, (iii) preprint posting, for all PLOS articles from 2019 to present. These indicators – conceptualized by PLOS and developed with DataSeer, using an artificial intelligence-driven approach – are increasingly important to PLOS achieving its mission. We plan to share the results openly to support Open Science initiatives by the wider community.”

B2X – a new pipeline for author services

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“For several years, bioRxiv has made life easier for authors by enabling them to send their papers directly from bioRxiv to journals. This B2J (bioRxiv-to-journal) technology saves people time by automatically transferring their PDF, metadata and any source files to journal submission systems so they don’t have to upload these again at the journal website and re-enter all the information. Around 200 journals now participate in B2J, and portable peer review services such as Review Commons also participate.

We are now introducing a new delivery pipeline – B2X – that will enable authors to send their manuscripts to a variety of third-party services. These services are completely independent of bioRxiv and may include groups that assess particular aspects of manuscripts, help authors improve them, or check for compliance with specific funder requirements. The first organization to join B2X is DataSeer, a service that helps researchers navigate open data policies.

DataSeer scans articles for datasets collected and provides recommendations for how these should be shared. Authors receive a brief report on the data that should be shared and advice on metadata, file formats, and appropriate repositories. They can also obtain an Open Data certificate documenting data deposited in public repositories….”

DataSeer

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“DataSeer scans scientific texts for sentences describing data collection, then gives best-practice advice for sharing that type of data.

Researchers can use DataSeer to ensure that their data sharing is complete and follows best practice.

Funders, journals, and institutions can use DataSeer to find all of the data associated with a corpus of articles, or use it to promote compliance with their data sharing policies….”

Start-up Stories: Bringing DataSeer, A New Data-sharing Toolkit, From Idea to Launch – The Scholarly Kitchen

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“DataSeer is a newly launched tool, developed by Scholarly Kitchen writer Tim Vines and colleagues. It scans through articles and other texts to look for mentions of related research data; it then annotates the text with suggestions for sharing that data, for example, which repositories might be appropriate. The idea is to substantially improve levels of data sharing by providing much more specific, easy-to-follow guidance about what should be shared, where, and how (e.g., formats). It should help researchers to comply better with data policies, and help editors, publishers, and funders drive compliance more efficiently….”

Start-up Stories: Bringing DataSeer, A New Data-sharing Toolkit, From Idea to Launch – The Scholarly Kitchen

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“DataSeer is a newly launched tool, developed by Scholarly Kitchen writer Tim Vines and colleagues. It scans through articles and other texts to look for mentions of related research data; it then annotates the text with suggestions for sharing that data, for example, which repositories might be appropriate. The idea is to substantially improve levels of data sharing by providing much more specific, easy-to-follow guidance about what should be shared, where, and how (e.g., formats). It should help researchers to comply better with data policies, and help editors, publishers, and funders drive compliance more efficiently….”

Coko Open Science – achieving FAIR data : Collaborative Knowledge Foundation

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“The European Commission has identified the opportunity to save €10.2 billion per year by using FAIR data (Findable, Accessible, Interoperable, Reusable). As policies begin to emerge requiring FAIR data, it’s timely to consider the open infrastructure needed to make embed FAIRness into the research and research communication workflows and outputs.  

Coko recently received a grant from the Sloan Foundation to build DataSeer, an web service that uses Natural Language Processing to identify and call out datasets associated with research articles. Datasets are often not explicitly identified, let alone made FAIR and accessible. The first step is knowing how many datasets were used in a body of work. DataSeer “reads” documents and finds mentions of dataset creation and use. Based on the context, DataSeer can offer recommendations to curate, deposit, add metadata too, or otherwise better handle datasets. DataSeer can fit into the workflows of researchers, publishers, aggregators, funders, and institutions….

Before FAIR compliance can be assessed, the full range of datasets associated with a research project must first be identified. There are often ‘hidden’ datasets mentioned in the text that are included among the ‘official’ outputs. DataSeer finds these mentions and help  to authors to identify and share all of the datasets involved in their work. …”

Open data: growing pains | Research Information

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“In its latest State of Open Data survey, Figshare revealed that a hefty 64 per cent of respondents made their data openly available in 2018.

The percentage, up four per cent from last year and seven per cent from 2016, indicates a healthy awareness of open data and for Daniel Hook, chief executive of Figshare’s parent company, Digital Science, it spells good news….

For example, the majority of respondents – 63 per cent – support national mandates for open data, an eight  per cent rise from 2017. And, at the same time, nearly half of the respondents – 46 per cent – reckon data citations motivate them to make data openly available. This figure is up seven per cent from last year….

Yet, amid the data-sharing success stories, myriad worries remain. Top of the pile is the potential for data misuse….

Inappropriate sharing of data is another key concern….

Results indicated that a mighty 58 per cent of respondents felt they do not receive sufficient credit for sharing data, while only nine per cent felt they do….

Coko recently won funding from the Sloan Foundation to build DataSeer, an online service that will use Natural Language Processing to identify datasets that are associated with a particular article. …”