Europe PMC in 2017

Levchenko, Maria; Gou, Yuci; Graef, Florian; Hamelers, Audrey; Huang, Zeping; Ide-Smith, Michele; Iyer, Anand; Kilian, Oliver; Katuri, Jyothi; Kim, Jee-Hyub; Marinos, Nikos; Nambiar, Rakesh; Parkin, Michael; Pi, Xingjun; Rogers, Frances; Talo, Francesco; Vartak, Vid; Venkatesan, Aravind; McEntyre, Johanna

doi:10.1093/nar/gkx1005

Cited by 43 publications

(40 citation statements)

References 7 publications

(6 reference statements)

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“…Actually, it is more common that citations of papers reference results, claims and biological conclusions rather than the actual dataset. In 2013, Piwowar et al proposed the use of direct citations of datasets in the literature as a metric [16], based on the counts generated using the EuropePMC API [17]. EuropePMC stores scientific publications (and also pre-prints, if they have been manually added by the authors) that are open access, so the whole text of the manuscript can be used for performing text mining, enabling this functionality.…”

Section: Methodsmentioning

confidence: 99%

“…The implementation of the reanalysis metric is possible because the OmicsDI XML schema (https://github.com/OmicsDI/specifications) provides a mechanism to define when a dataset is based on another one. Finally, the citations metric is computed by parallel calls to the EuropePMC API (europepmc.org/RestfulWebService) [17] and the annotation of the final counts for each dataset. All these pipelines and software components can scale to handle thousands of datasets and systematically compute and update the metrics on a weekly basis (github.com/OmicsDI/index-pipeline).…”

Section: Proposed Platform To Quantify Dataset Impactmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying the impact of public omics data

Perez‐Riverol

Zorin

Dass

et al. 2018

Preprint

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The amount of omics data in the public domain is increasing every year [1, 2]. Public availability of datasets is growing in all disciplines, because it is considered to be a good scientific practice (e.g. to enable reproducibility), and/or it is mandated by funding agencies, scientific journals, etc. Science is now a data intensive discipline and therefore, new and innovative ways for data management, data sharing, and for discovering novel datasets are increasingly required [3, 4]. However, as data volumes grow, quantifying its impact becomes more and more important. In this context, the FAIR (Findable, Accessible, Interoperable, Reusable) principles have been developed to promote good scientific practises for scientific data and data resources [5]. In fact, recently, several resources [1, 2, 6] have been created to facilitate the Findability (F) and Accessibility (A) of biomedical datasets. These principles put a specific emphasis on enhancing the ability of both individuals and software to discover and re-use digital objects in an automated fashion throughout their entire life cycle [5]. While data resources typically assign an equal relevance to all datasets (e.g. as results of a query), the usage patterns of the data can vary enormously, similarly to other "research products" such as publications. How do we know which datasets are getting more attention? More generally, how can we quantify the scientific impact of datasets?Recently, several authors [7][8][9] and resources [10] pointed out the importance of evaluating the impact of each research product, including datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Proposed Platform To Quantify Dataset Impactmentioning

confidence: 99%

Quantifying the impact of public omics data

Perez‐Riverol

Zorin

Dass

et al. 2018

Preprint

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“…Disease annotations for 29,187 papers were available from Europe PMC and collected using the europepmc R package 56 . A less redundant list of top 10 terms was made by excluding previously counted papers and re-sorting by disease term frequency.…”

Section: Characterization Of Pathway Figuresmentioning

confidence: 99%

25 Years of Pathway Figures

Hanspers

Riutta

Kutmon

et al. 2020

Preprint

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BackgroundPathway diagrams are fundamental tools for describing biological processes in all aspects of science, including training, generating hypotheses, describing new knowledge and ultimately as communication tools in published work. Thousands of pathway diagrams are published each year as figures in papers. But as static images the pathway knowledge represented in figures is not accessible to researchers for computational queries and analyses. In this study, we aimed to identify pathway figures published in the past 25 years, to characterize the human gene content in figures by optical character recognition, and to describe their utility as a resource for pathway knowledge. ApproachTo identify pathway figures representing 25 years of published research, we trained a machine learning service on manually-classified figures and applied it to 235,081 image query results from PubMed Central. Our previously described pipeline 1 was utilized to extract human genes from the pathway figure images. These figures were characterized in terms of their parent papers, human gene content and enriched disease terms. Diverse use cases were explored for this newly accessible pathway resource. ResultsWe identified 64,643 pathway figures published between 1995 and 2019, depicting 1,112,551 instances of human genes (13,464 unique NCBI Genes) in various interactions and contexts. This represents more genes than found in the text of the same papers, as well as genes not found in any pathway database. We developed an interactive web tool to explore the results from the 65k set of figures, and used this tool to explore the history of scientific discovery of the Hippo Signaling pathway. We also defined a filtered set of 32k pathway figures useful for enrichment analysis.

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“…Promoting this, SUB Göttingen makes its code and, if possible, data sources openly available and actively engages in open science working groups. For instance, code libraries, which facilitate access to Europe PubMed Central [25] and Unpaywall Data [24], a DOI linking service for open access full-texts, have been developed at the SUB and were contributed to the rOpenSci initiative through its on-boarding mechanism, an open peer review process [26]. These software clients enable automated access to data sources that are instrumental for analyzing the uptake of open access, as well as for obtaining full-texts for text-mining purposes.…”

Section: Analyzing Scholarly Communicationmentioning

confidence: 99%

“…The Deep Green project 25 focuses on so-called "alliance licenses" containing an open access clause that enables authors from authorized institutions to deposit articles that were published in such licensed journals in an open access repository of their choice with no or only a short embargo period. These licenses are negotiated on a national level and are co-funded by the DFG.…”

Section: Transforming Acquisition Budgets From Subscriptions To Finanmentioning

confidence: 99%

Open Science Support as a Portfolio of Services and Projects: From Awareness to Engagement

et al. 2018

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Together with many other universities worldwide, the University of Göttingen has aimed to unlock the full potential of networked digital scientific communication by strengthening open access as early as the late 1990s. Open science policies at the institutional level consequently followed and have been with us for over a decade. However, for several reasons, their adoption often is still far from complete when it comes to the practices of researchers or research groups. To improve this situation at our university, there is dedicated support at the infrastructural level: the university library collaborates with several campus units in developing and running services, activities and projects in support of open access and open science. This article outlines our main activity areas and aligns them with the overall rationale to reach higher uptake and acceptance of open science practice at the university. The mentioned examples of our activities highlight how we seek to advance open science along the needs and perspectives of diverse audiences and by running it as a multi-stakeholder endeavor. Therefore, our activities involve library colleagues with diverse backgrounds, faculty and early career researchers, research managers, as well as project and infrastructure staff. We conclude with a summary of achievements and challenges to be faced.

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Europe PMC in 2017

Cited by 43 publications

References 7 publications

Quantifying the impact of public omics data

Quantifying the impact of public omics data

25 Years of Pathway Figures

Open Science Support as a Portfolio of Services and Projects: From Awareness to Engagement

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