The Semantic Scholar Open Data Platform

Kinney, R.; Anastasiades, Chloe; Authur, Russell; Beltagy, Iz; Bragg, Jonathan; Buraczynski, Alexandra; Cachola, Isabel; Candra, Stefan; Chandrasekhar, Yoganand; Cohan, Arman; Crawford, Miles; Downey, Doug; Dunkelberger, Jason; Etzioni, Oren; Evans, Robert; Feldman, Sergey; Gorney, Joseph; Graham, David Y.; Hu, Fangzhou; Huff, Regan; King, Daniel L.; Kohlmeier, Sebastian; Kuehl, Bailey; Langan, Michael; Lin, Daniel W.; Liu, Haokun; Lo, Kyle; Lochner, Jaron; Kelsey, MacMillan,; Murray, Tyler; Newell, Christopher; Rao, Smita; Rohatgi, Shaurya; Sayre, Paul L; Shen, Zejiang; Singh, Amanpreet; Soldaini, Luca; Subramanian, Shivashankar; Tanaka, Amber; Wade, Alex D.; L, Wagner; Wang, Lucy Lu; Wilhelm, Chris; Wu, Caroline; Yang, Jiangjiang; Zamarron, Angele; Zuylen, Madeleine Van; Weld, Daniel S.

doi:10.48550/arxiv.2301.10140

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…A major aspect of Semantic Scholar is to integrate machine learning methods to enhance data quality and search. They did, for instance, develop a system for publication deduplication named S2APLER and perform citation linking based on fuzzy text-matching heuristics (Kinney et al, 2023). Data is provided free and open by Semantic Scholar and can be accessed via API.…”

Section: Related Workmentioning

confidence: 99%

“…Bibliometric and citation analyses are performed with the aid of providers of bibliometric data and are based on the structured data they provide. Semantic Scholar (Kinney et al, 2023) or Crossref (Hendricks, Tkaczyk, Lin, & Feeney, 2020), for instance, provide powerful application programming interfaces (API) to access the already pre-processed data about millions of articles. Such infrastructure should also build the basis to integrate software in bibliometric analyses.…”

Section: Introductionmentioning

confidence: 99%

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A multilevel analysis of data quality for formal software citation

Schindler,

Hossain,

Spors

et al. 2024

Quantitative Science Studies

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Software is a central part of modern science, and knowledge of its use is crucial for the scientific community with respect to reproducibility and attribution of its developers. Several studies have investigated in-text mentions of software and its quality, while the quality of formal software citations has only been analyzed superficially. This study performs an in-depth evaluation of formal software citation based on a set of manually annotated software references. It examines which resources are cited for software usage, to what extend they allow proper identification of software and its specific version, how this information is made available by scientific publishers, and how well it is represented in large-scale bibliographic databases. The results show that software articles are the most cited resource for software, while direct software citations are better suited for identification of software versions. Moreover, we found current practices by both, publishers and bibliographic databases, to be unsuited to represent these direct software citations, hindering large-scale analyses such as assessing software impact. We argue that current practices for representing software citations—the recommended way to cite software by current citation standards—stand in the way of their adaption by the scientific community, and urge providers of bibliographic data to explicitly model scientific software. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00309

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multilevel analysis of data quality for formal software citation

Schindler,

Hossain,

Spors

et al. 2024

Quantitative Science Studies

View full text Add to dashboard Cite

show abstract

“…The Web of Science was the database selected to obtain the papers. There are several other options, such as dimensions (Hook et al, 2018), Semantic Scholar (Kinney et al, 2023), OpenAlex (Priem et al, 2022), and Scopus. Among these options, the Web of Science and Scopus include many multidisciplinary, international, and peer-reviewed journals and conferences.…”

Section: Information Sourcesmentioning

confidence: 99%

Are there literature reviews about gamification to foster Inclusive Teaching? A scoping review of gamification literature reviews

Ruiz-Navas,

Ackaradejraungsri,

Dijk

2024

Front. Educ.

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IntroductionGamification can support the practical application of Inclusive Teaching. However, gamification literature reviews to implement Inclusive Teaching are scarce or not existent. Therefore, we conducted a scoping review of gamification literature reviews to identify what themes are covered and specifically if Inclusive Teaching has been explored.MethodThe scoping literature review comprises network and content analyses of gamification literature reviews retrieved from the Web of Science. We analyzed a multimode network of papers and keywords and used their eigenvector centrality to identify themes. The content analysis comprised of a human and automatic tagging process to identify each paper’s discipline/context.ResultsWe mapped the themes explored in 125 gamification literature reviews to answer our first research question, what are the areas of knowledge covered by gamification literature reviews? The central topic is gamification and education to increase motivation, followed by gamification itself and understanding the implementation of gamification in various contexts. We identified 12 contexts and the top five frequent were Education, Business, Gamification, and Political Science. From the year-by-year analysis, we separated the themes into four periods: beginning (2014–2015), understanding (2016–2017), focus 2018 and focus and emergence (2019–2022). Regarding our second research question, how is the topic of Inclusive Teaching explored in gamification literature reviews? We did not find literature reviews about gamification to support Inclusive Teaching in the existing dataset.DiscussionWe report on the benefits of organizing central keywords by quartiles and using multimode networks to support scoping reviews; and disadvantages and advantages of using literature reviews as data sources for scoping reviews. We invite researchers to create more gamification literature reviews, to investigate gamification ethics in the light of recent technological developments such as generative models, and to reconnect gamification to the game design elements part of its definition, which goes beyond game elements.

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“…To generate the replay-captions, we first parse titles of ecology papers corresponding to the query “Serengeti+Wildlife” with the Semantic Scholar API (55). We then use the Rapid Automatic Keyword Extraction (Rake (56)) on all titles to keep only keywords from them.…”

Section: Caption Templatesmentioning

confidence: 99%

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

Gabeff,

Rußwurm,

Tuia

et al. 2023

Preprint

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Wildlife observation with camera traps has great potential for ethology and ecology, as it gathers data non-invasively in an automated way. However, camera traps produce large amounts of uncurated data, which is time-consuming to annotate. Existing methods to label these data automatically commonly use a fixed pre-defined set of distinctive classes and require many labeled examples per class to be trained. Moreover, the attributes of interest are sometimes rare and difficult to find in large data collections. Large pretrained vision-language models, such as Contrastive Language Image Pretraining (CLIP), offer great promises to facilitate the annotation process of camera-trap data. Images can be described with greater detail, the set of classes is not fixed and can be extensible on demand and pretrained models can help to retrieve rare samples. In this work, we explore the potential of CLIP to retrieve images according to environmental and ecological attributes. We create WildCLIP by fine-tuning CLIP on wildlife camera-trap images and to further increase its flexibility, we add an adapter module to better expand to novel attributes in a few-shot manner. We quantify WildCLIP’s performance and show that it can retrieve novel attributes in the Snapshot Serengeti dataset. Our findings outline new opportunities to facilitate annotation processes with complex and multi-attribute captions. The code will be made available athttps://github.com/amathislab/wildclip.

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The Semantic Scholar Open Data Platform

Cited by 8 publications

References 9 publications

A multilevel analysis of data quality for formal software citation

A multilevel analysis of data quality for formal software citation

Are there literature reviews about gamification to foster Inclusive Teaching? A scoping review of gamification literature reviews

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

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