Google Scholar provides a simple way to broadly search for scholarly literature. From one place, you can search across many disciplines and sources: articles, theses, books, abstracts and court opinions, from academic publishers, professional societies, online repositories, universities and other web sites. Google Scholar helps you find relevant work across the world of scholarly research. Features of Google Scholar * Search diverse sources from one convenient place * Find articles, theses, books, abstracts or court opinions * Locate the complete document through your library or on the web * Learn about key scholarly literature in any area of research How are documents ranked? Google Scholar aims to rank documents the way researchers do, weighing the full text of each document, where it was published, who it was written by, as well as how often and how recently it has been cited in other scholarly literature. * Publishers - Include your publications in Google Scholar * Librarians - Help patrons discover your library''s resources

The number of articles related to edge computing on Google Scholar increase dramatically in the last decade. 2010 only recorded 240 articles related to edge computing on Google Scholar, whereas 2023 saw more than 42,700 scholarly papers on this topic.

Google scholar

Dataset

This dataset was created by Aditya Kumar Dubey

Contents

This dataset offers the list of sources included in our critical review of the term 'just transition' (in English), from 1990 to 2021 in the Global North and South Africa, using Google and Google Scholar as search engines. The publications retrieved include both peer-reviewed literature and publicly available reports and documents. Results (with full citations) are categorized by actor group and type, location, and year of publication.

n/a corresponds to search results that were too voluminous to download in full. See Table 2 for case study explanations.

Correlation between scientific production (as captured by Google Scholar and PubMed), news coverage (as captured by Google News), web queries (as captured by Google Trends), access to Wikipedia page and Internet activities (as captured by Twitter and YouTube).

This second version (V2) provides additional data cleaning compared to V1, additional data collection (mainly to include data from 2019), and more metadata for nodes. Please see NETWORKv2README.txt for more detail.

This dataset contains all the results for the term "Mobile Cloud Computing" on Google Scholar until Nov. 15, 2017. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice.

Data on citations made by SMU faculty from School of Economics and Social Sciences. Is the full text findable in Google Scholar?

Data used for network analyses. deduplicated nodes.csv - List of all nodes in the network. Each node represents a paper. Column headings: ID, Authors, Title, Year Descriptions of column headings: ID - ID for the paper/node. IDs follow the following conventions: - A000 represents the retracted Matsuyama paper. - F### represents a first-generation citation that directly cites the retracted Matsuyama paper. - F###S### represents a second-generation citation that does not cite the retracted Matsuyama paper but that cites some first-generation citation (where F### is one of the first-generation articles it cites). Authors - Authors of the paper Title - Title of the paper (some in Unicode) Year - Year of publication of the paper; Either a 4-digit year or NA (which indicates that the first data source we got it from either did not provide a year, or provided a year that we deemed unreliable) NOTE: Authors/Title/Year were taken primarily from Google Scholar (since it had the larger number of items) with unique items from Web of Science added. ------- deduplicated edges.csv - List of all edges in the network. Each edge represents a citation between two papers. Column headings: from, to Descriptions of column headings: from - ID for the cited paper. This is what the citation goes FROM. to - ID for the citing paper. This is what the citation goes TO. NOTE: All IDs are from deduplicated nodes.csv and follow the conventions above. ------- nodesFG.txt - List of the IDs for the 135 first-generation citations, from 2005 (when Matsuyama was published) through 2018. ------- nodesSGnotFG.txt - List of the IDs for the 2559 second-generation citations that are not first-generation citations from 2005 (when Matsuyama was published), through 2018 -------

This dataset contains all the results for the term "Edge Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice.

Search strings used to generate citation counts for three data sets in WoS, publishers' full text websites, and Google Scholar.

This dataset contains all the results for the term "Mobile Cloud Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice.

Basic statistics for various indices for the Google Scholar data set.

This dataset contains publications collected from Google Scholar with the following queries: 1. "as of my last knowledge update" 2. "I don't have access to real-time data" 3. "as of my last knowledge update" AND "I don't have access to real-time data" It contains the following: variable, description id, ID title, Title of paper author, Author(s) of paper pub_year, Year of publication venue, Venue of publication abstract, Abstract snippet or sample text with exact string match - if found pub_url, URL for publication query, Query that matches publication chatgpt_method, Declared justified or otherwise explainable in-text mention or use of GPT/ChatGPT Example Text, Sample text with exact string match - if found

Document in .xlsx format. Contains 2 sheets. The first, entitled "profiles", has 10 columns (Orcid no.; id; Name; Domain; Citations; Description; Keywords; Field; Gender and Type) and 1032 rows (number of authors' profiles). The second sheet contains the discarded profiles. To protect personal data, Name column data values (Profiles Sheet: C Column; Discarded Sheet: B Column) have been replaced by a correlation number and Citations column data values (Profiles Sheet: E Column; Discarded Sheet: D Column) have been replaced by X value. The purpose of this work is to determine the use of Research Organizations Registry (ROR) IDs in author academic profiles, specifically in Google Scholar Profiles (GSP). To do this, all the Google Scholar profiles including the term ROR in any of the public descriptive fields were collected and analyzed. The results evidence a low use of ROR IDs (1,033 profiles), mainly from a few institutions.

This dataset contains the list of articles resulting from the Google Scholar search “graph based author name disambiguation” published after 1/1/2021. The list is provided for reproducibility of the survey article “Graph-based Methods for Author Name Disambiguation: A Survey” and it was obtained using the following Python script available at https://github.com/WittmannF/sort-google-scholar:

$ python sortgs.py --kw “graph based author name disambiguation” --startyear 2021

The command returned the CSV file that contains the first 94 publications matching the query (articles with corrupted metadata have been excluded), each with metadata about Title, Number of Citations, and Rank. The CSV contains a column that specified which articles have been eventually selected for the survey.

This dataset contains: - A sample of 64,000 highly cited documents published in the period 1950-2013, collected from Google Scholar on the 28th of May, 2014. - List of clean references of the top 1% most cited documents in Google Scholar (640 documents) - Study case: different versions (detected and undetected by Google Scholar) for the work "A Mathematical Theory of Communication", by Claude Shannon.- Frequency table: number of highly-cited documents in our sample published in WoS-covered journals

The transition from analog to digital archives and the recent explosion of online content offers researchers novel ways of engaging with data. The crucial question for ensuring a balance between the supply and demand-side of data, is whether this trend connects to existing scholarly practices and to the average search skills of researchers. To gain insight into this process a survey was conducted among nearly three hundred (N= 288) humanities scholars in the Netherlands and Belgium with the aim of finding answers to the following questions: 1) To what extent are digital databases and archives used? 2) What are the preferences in search functionalities 3) Are there differences in search strategies between novices and experts of information retrieval? Our results show that while scholars actively engage in research online they mainly search for text and images. General search systems such as Google and JSTOR are predominant, while large-scale collections such as Europeana are rarely consulted. Searching with keywords is the dominant search strategy and advanced search options are rarely used. When comparing novice and more experienced searchers, the first tend to have a more narrow selection of search engines, and mostly use keywords. Our overall findings indicate that Google is the key player among available search engines. This dominant use illustrates the paradoxical attitude of scholars toward Google: while transparency of provenance and selection are deemed key academic requirements, the workings of the Google algorithm remain unclear. We conclude that Google introduces a black box into digital scholarly practices, indicating scholars will become increasingly dependent on such black boxed algorithms. This calls for a reconsideration of the academic principles of provenance and context.