Elsevier BV Showcase

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Dr. Angela Williams, a sociologist from the United Kingdom, defines leisure space-time as "the temporal and spatial coalescence of activities and emotions within a designed environment." Dr. Wei Chen, from China's Leisure Studies Association defines leisure space-time as "the intersection of leisure experiences with the physical spaces they occur in." Dr. Hari Seldon. (United States, 2023): "Leisure Space-Time refers to a suitable location or place or environment for spending personal time in entertainment, whether individually or with friends and family." Leisure space-time discusses the essence of the concept as a harmonious blend of space, time, and experiences of happiness feelings. It's not mandatory for leisure space-time to be a luxury at all.

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The data provided on the 'Matrix' tab of this excel workbook is the data used for analyses conducted in the whitepaper 'Demystifying sustainability assessment and reporting frameworks https://assets.ctfassets.net/o78em1y1w4i4/vsnNF0bJcJgdMSWUJuHL0/5abac6ecfc7268caacc7d83aac4b2fbf/Elsevier_Sustainability_Frameworks_Whitepaper.pdf. An analysis of six of the most popular frameworks used by higher education institutions (HEIs) and a step-by-step guide to help HEIs get started' (2023). The whitepaper aims to provide a comprehensive and objective analysis of six of the most popular sustainability frameworks used by HEIs. Our hope is that whitepaper will demystify sustainability assessment frameworks and help institutional leaders identify which of them is best placed to help their organizations drive action and achieve sustainable outcomes. We also hope the guide will prove a valuable resource for sustainability practitioners and students with an interest in this topic. The matrix is a searchable database of indicators that can be analysed. Please note that the frameworks vary considerably in their methods and this needs to be taken into consideration when conducting analyses with the matrix. Use our infographic to understand what each sustainability and reporting framework measures https://assets.ctfassets.net/o78em1y1w4i4/57Fu7d8NByerd7UEoSn2fv/eae1d30f2bd3e75ba9350d8a30e97b63/Elsevier_SDG-GRI-Infographic-.pdf

2023 survey of 2,607 clinicians from 116 countries (building on a 2022 survey conducted with IPSOS). Fieldwork April and May 2023. Methodology: a random sample of clinicians (doctors and nurses in primary and secondary care) from a variety of sources. Survey sent branded on behalf of Elsevier. Survey tool: online survey available in English, Spanish, Chinese (simplified), French, German, Japanese and Portuguese.

A look at gender imbalance amongst top cited authors. The term "breakdown" as it appears here means aggregate counts in the following list: author_count_total, author_count_top_2_pct, author_count_top_2_pct_sy, author_count_total_male, author_count_top_2_pct_male, author_count_top_2_pct_sy_male, author_count_total_female, author_count_top_2_pct_female, author_count_top_2_pct_sy_female, author_count_total_unknown, author_count_top_2_pct_unknown, author_count_top_2_pct_sy_unknown. The analysis done produces the following computed field: Femaleprop: The number of female authors in the top 2pct of cited authors over all genderized authors in the top 2pct of cited author for entire career Femalepropsy: The number of female authors in the top 2pct of cited authors in 2021 over all genderized authors in the top 2pct of cited authors for entire career calculated for 2021 only Difference: Femalepropsy - Femaleprop Fmtoppropensity: For each cohort and subfield the product of (total male authors/total number of female authors) * (total female authors in the top 2pct of cited authors in 2021/ total male authors in the top 2pct of cited authors in 2021) or more simply. total male to female ratio * ratio of female to male authors int the top 2pct of cited authors in 2021.

This survey was part of a joint project between Humboldt-Universität zu Berlin and Elsevier to understand research data sharing and re-use practices, and the drivers (and/or barriers) acting on researchers in this regard. For comparability, the individual items were constructed in accordance with the Open Data Survey (Centre for Science and Technology Studies, Elsevier and Leiden University, 2017) conducted in the year 2016. The online survey was from 30 September 2020 till 5 November 2020 to researchers worldwide, in all scientific fields. 99,667 individuals randomly selected from Scopus author database, with added 801 individuals picked from Peru (as one of the target group for Katarzyna Biernacka’s thesis) were contacted via e-mail. A personalised link guided the researchers to the Confirmit survey website. Upload content: - Readme.txt - OpenDataSharingReuse2020_Questionnaire.pdf - the survey questions including question codes - OpenDataSharingReuse2020_Responses_anonymised.csv - the anonymised responses to the survey

Dataset of Women Directors’ Engagement and Carbon Information Disclosures of Global Energy Companies

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 5 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 2023, the SDGs use the exact same search query and ML algorithm as the Elsevier 2022 SDG mappings, with only minor modifications to five SDGs, namely SDG 1, 4, 5, 7 and 14. In these cases, the queries were shortened by removing exclusion lists based on journal identifiers. These exclusion lists often contained thousands of items to filter out content in journals that were not core to the SDGs. To replicate the effect of these journal exclusions, sets of keywords were used to closely mimic the effects the journal exclusions had on the SDG content, while greatly reducing the overall query size and complexity. By following this approach, we were able to limit the changes to the publications in each SDG by less than 2 percent for most SDGs, while reducing the query size by 50 percent or more. These shortened queries also have the added benefit of running faster in Scopus, allowing further analysis of the SDG data to be done more easily. For each SDG, the full search query, along with further details about the top keyphrases, subfields, journals and keyphrases are available for download.

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.