Elsevier BV Showcase

Automatic extraction of funding information from academic articles adds significant value to industry and research communities, such as tracking research outcomes by funding organizations, profiling researchers and universities based on the received funding, and supporting open access policies. This dataset collected by Elsevier in 2019. Sentences talking about the funding information are extracted from 30000 scientific articles. Name of organisation which financially supported the research is highlighted and linked to Crossref Taxonomy. Each document is annotated by two human and harmonized by an independent SME. For more information please read our paper "Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases" presented in COLING2022. In addition to EDFund, we also release ELFund. This dataset only includes the sentences which have been classified as positive by our sentence classifier, as explained in the aforementioned paper. ELFund: #Articles: 53864 #Links: 177111 NILs%: 19.23% EDFund: #Articles: 53243 #Links: 172031 NILs%: 18.95% - We thank Ramadurai Petchiappan and Georgios Cheirmpos for their assistance in preparing the 2nd version of the datasets.

Dataset description Dataset description Dataset description Dataset description

Dataset descriptionDataset descriptionDataset descriptionDataset descriptionDataset description

Dataset published with file Dataset published with file Dataset published with file Dataset published with file

You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

Restricted dataset description. Restricted dataset description Restricted dataset description Restricted dataset description Restricted dataset description

New collection Infraa

This dataset includes author-level metrics for over 1.2 anonymized author profiles which have been used for the analysis proposed in our article titled "Structure, Status, and Span: Global Gender Differences in Disciplinary Authorship Networks Across a Global Landscape". The set of metrics are meant to measure the structure, status and span of the collaborative research activity of women and men authors active in four regions and four fields during the period 2009-2013.

Herewith, we present the files that can be used to open the term maps in VOSviewer to inspect the maps presented in our paper, 'Sustainability through a gender lens: The extent to which research on UN Sustainable Development Goals includes sex and gender consideration'. To open and inspect the maps, the user will need access to VOSviewer (available at https://www.vosviewer.com). A pair of .csv files is provided for each SDG 1 - 16: . From within the VOSviewer tool, the pair of .csv files can be used to Open the map file. To generate the sex and gender overlay of the map, which is not typically available in a Scopus-generated VOSviewer map of a publication set, we set up a small work-around to ensure that VOSviewer could read the original underlying data files in such a way to devlier the overlay we were presenting. In this case, we re-used the Cited by column generated by the Scopus extract for the purpose of sex and gender consideration. Therefore, the sex and gender overlay maps can be viewed by selecting the 'Overlay Visualisation' tab within the mapping tool and then selecting "Avg. citations": this actually reflects the extent to which the terms consider sex and gender.

The United Nations Sustainable Development Goals (SDGs) challenge the global community to build a world where no one is left behind. Since 2018, Elsevier has generated SDG search queries to help researchers and institutions track and demonstrate progress toward the SDG targets. In the past 3 years, these queries, along with the university’s own data and evidence supporting progress and contributions to the particular SDG outside of research-based metrics, are used for the THE Impact Rankings. For 2022, all SDGs used the exact same search query and ML algorithm as the Elsevier 2021 SDG mapping, except SDG 3. Working in conjunction with our university partners, we agreed that Covid related search terms should be added to SDG 3 - Good health and well-being. We made no changes to the ML algorithm. The newly added Covid query adds a little more than 190,000 publications to SDG 3, out of about 300,000 publications containing Covid related terms. We focused on public health, clinical medicine, and biomedical research, avoiding publications related to the impact of the pandemic on research spheres outside health research (e.g., economic, political, etc.).