The authors would like to thank the Deanship of Scientific Research for its fund to support the video production for this study. The authors would also like to thank Dr. Aseel Zabin, Department of English Language and Literature, The University of Jordan for English language review of this study.

NOTE: The following are search methods and an example search for each method is provided. Note that the part related specifically to bibliometric analysis is also supplied.
1. PubMed
<ol>
	<li>Choose Advanced Search from PubMed homepage (www.ncbi.nlm.nih.gov/pubmed).</li>
	<li>Enter the desired search term in the search field. Choose the search terms from the medical subject heading (MeSH) database. The following example details how to assess research in &#34;cancer&#34;.
	<ol>
		<li>Open the MeSH database:&#160;https://www.ncbi.nlm.nih.gov/mesh.</li>
		<li>Enter the word &#34;cancer&#34; in the search field.</li>
		<li>Ensure that the results show the word &#34;neoplasms&#34; at the top of the search, where it notes that using the word &#34;neoplasms&#34; is more suitable, as &#34;neoplasms&#34; is the word used to index relevant topics.</li>
		<li>Further check other terms listed under the term &#34;neoplasms&#34; by clicking on it. The results will show other terms used to describe similar topics (e.g., tumor), and will also list other subcategories (subheadings) under the term neoplasm.</li>
		<li>Use the drop-down lists in the Builder section to specify the field of the article that PubMed will search for the term in. Note that the following search fields are available: all fields, title, abstract, authors, affiliation, conflict of interest, language, journal, publisher, publication type, grant number, ISBN, and MeSH terms.</li>
		<li>Add as many fields as needed and choose the relation between these fields (AND, OR, or NOT). See Table 1 for further details.</li>
	</ol>
	</li>
	<li>Click Search.</li>
	<li>Refine the search results further in the results using different filters available as detailed in Table 2.&#160;Note that from now on, the final search done will be saved in the history of the Advanced Search, which was accessed in the first step. This means that the search can be paused at this step and resumed later.</li>
	<li>Ensure that each new search is saved in the Advanced Search history screen where it will be assigned a number (e.g., #2). Use this number in the search field above to subtract searched queries (e.g., &#34;#1 NOT #2&#34; to subtract results in search #2 from results of search #1).</li>
	<li>Export the results to further analyze them.
	<ol>
		<li>Use the FLink tool (https://www.ncbi.nlm.nih.gov/Structure/flink/flink.cgi) to export the results in a comma separated values (CSV) format, and choose PubMed from the Please choose a database to start drop-down list.</li>
		<li>Choose Input From Entrez History from the input screen, and an advanced PubMed search history will appear at the drop-down list.</li>
		<li>Choose the search performed in the previous steps at the PubMed advanced search and click Submit.</li>
		<li>View the resulting report since it provides the option of exporting the search results in a CSV format file.</li>
	</ol>
	</li>
	<li>Perform the following steps to analyze Jordanian research output during a 5-year period between 1/1/2013 and 31/12/2017 using PubMed.
	<ol>
		<li>Open the Advanced search form in the document search form at the PubMed website (www.ncbi.nlm.nih.gov/pubmed).&#160;</li>
		<li>Use Jordan as the search term and specify Affiliation as the search field. Note that PubMed interprets affiliation as all information related to the author (i.e., author&#39;s name, address, affiliation), so exclude any document authored by an author named &#34;Jordan&#34; in which the affiliation country is not Jordan. Follow the steps below to avoid including such irrelevant results.</li>
		<li>Type Jordan in another field and choose the field type Author.</li>
		<li>Choose the operator NOT as the relation between the two fields and click on Search.</li>
		<li>Specify Publication Dates from 1/1/2013 to 31/12/2017 in the results window, and choose Journal Article and Review from Article Types.</li>
		<li>Open FLink (https://www.ncbi.nlm.nih.gov/Structure/flink/flink.cgi), and choose PubMed as the database.</li>
		<li>Choose Input From Entrez History from the input screen, and choose the search from the drop-down list.</li>
		<li>Click Download CSV. 
		NOTE: Figure 1 details the PubMed search report with annotation for each section of the report.</li>
	</ol>
	</li>
</ol>
2. Scopus
<ol>
	<li>Register in order to access the full search capabilities of the Scopus database. Check if the local institution is already registered and has access to the database since academic institutions are usually registered in Scopus.</li>
	<li>Go to the website (www.scopus.com) and by default, Scopus opens the document search form screen.</li>
	<li>Enter the search term desired in the search field available.</li>
	<li>Specify the fields in the article to be searched for. Note that the following search fields are available: all fields, title, abstract, keywords, authors, affiliations, funding information, language, references, conference, ISSN, CODEN, DOI, ORCID, and CAS number.</li>
	<li>Add other fields to search for and indicate the relation between the newly added field and the other field already entered (AND, OR, or AND NOT). See ( Table 1) for further details.</li>
	<li>Use the Limit option to limit the search based on the options provided by Scopus, as detailed in Table 2.After executing the search, save the search and continue later if needed.
	<ol>
		<li>In this case, set an alert using the option Set alert, where an email will be sent when an article satisfying the searching criteria is added.</li>
	</ol>
	</li>
	<li>Refine the search results further from the results directly by choosing from the options provided by Scopus ( Table 2), where Scopus shows the number of documents included for each option.</li>
	<li>Choose to either analyze the results directly on the Scopus website (click Analyze Search Results), or to export the results in either zip or CSV formats after completing the search criteria.</li>
	<li>Perform the following steps to analyze Jordanian research output during a 5-year period between 1/1/2013 and 31/12/2017 using Scopus.
	<ol>
		<li>Go to the website (www.scopus.com) and by default, Scopus opens the document search form screen.</li>
		<li>Type Jordan as the searching term in the document search form.</li>
		<li>Specify Affiliation Country as the search field.</li>
		<li>Limit the search duration from 2013 to 2017. Note that from 2013 means from 1/1/2013, and to 2017 means to 31/12/2017.</li>
		<li>Limit the document type to Article or Review, and then click Search. 
		NOTE: Figure 2 details the Scopus search report with annotation for each section of the report.</li>
	</ol>
	</li>
</ol>
3. Web of Science
<ol>
	<li>Register in order to access the full search capabilities of the Web of Science database. Check if the local institution is already registered and has access to the database as academic institutions are usually registered in Web of Science.</li>
	<li>Go to the Web of Science home page&#160;(www.webofknowledge.com). The website opens the basic search and includes the Web of Science Core Collection as the selected database for search.</li>
	<li>Search for the fields as detailed in Table 2.</li>
	<li>Add another field (if needed) to connect both fields by either AND, OR, or NOT. See Table 1.</li>
	<li>Define the duration that is searched down to 1945. After completing the search, the results are saved in the history and can be returned to at any time. If needed, set an alert if any new document is added to the search report.</li>
	<li>Sort the results according to either data, times cited, usage counts, or to other categories from the drop-down list provided.</li>
	<li>Refine the search results further from the results directly by choosing from the options provided by Web of Science ( Table 2), where Web of Science shows the number of documents included for each option.</li>
	<li>View the results and analyze them via a tree map or bar graph. Note that there is a table showing the count in each category.</li>
	<li>Download the results. Note that unlike Scopus and PubMed, Web of Science only allows the downloading of 5,000 records at a time (e.g., a 10,000 result search is downloaded in two batches, the first batch for the first 5,000 records, and a second batch for the next 5,000 records).</li>
	<li>Perform the following steps to analyze Jordanian research output during a 5-year period between 1/1/2013 and 31/12/2017 using Web of Science.
	<ol>
		<li>Type Jordan in the search field and specify Address as the search field.</li>
		<li>Identify the duration of search between 2013 and 2017 and click Search.</li>
		<li>Restrict the search using Article and Review filters.</li>
		<li>Choose to analyze the results that are saved in Search History now or later.</li>
		<li>Choose to analyze the results in the form of tables or visual tree map and bars. 
		NOTE: Figure 3 details the Web of Science search report with annotation for each section of the report.</li>
	</ol>
	</li>
</ol>

<ol><li>McGowan, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((PRESS+peer+review+of+electronic+search+strategies%3A+2015+guideline+statement%5BTitle%5D)+AND+%22Journal+of+Clinical+Epidemiology%22%5BJournal%5D)">PRESS peer review of electronic search strategies: 2015 guideline statement.</a> Journal of Clinical Epidemiology. 75, 40-46 (2016).</li>
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<li>Falagas, M. E., Karavasiou, A. I., Bliziotis, I. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Estimates+of+global+research+productivity+in+virology%5BTitle%5D)+AND+%22Journal+of+Medical+Virology%22%5BJournal%5D)">Estimates of global research productivity in virology.</a> Journal of Medical Virology. 76 (2), 223-229 (2005).</li>
<li>Young, H. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aYoung%2C+H+%22The+ALA+glossary+of+library+and+information+science%22">The ALA glossary of library and information science</a>. , Ediciones Díaz de Santos. (1983).</li>
<li>Broadus, R. <a target="_blank" href="https://link.springer.com/article/10.1007/BF02016680">Toward a definition of bibliometrics.</a> Scientometrics. 12 (5-6), 373-379 (1987).</li>
<li>Falagas, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comparison+of+PubMed%2C+Scopus%2C+web+of+science%2C+and+Google+scholar%3A+strengths+and+weaknesses%5BTitle%5D)+AND+%22The+FASEB+Journal%22%5BJournal%5D)">Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses.</a> The FASEB Journal. 22 (2), 338-342 (2008).</li>
<li>Guz, A. N., Rushchitsky, J. J. <a target="_blank" href="https://link.springer.com/article/10.1007/s10778-009-0189-4">Scopus: A system for the evaluation of scientific journals.</a> International Applied Mechanics. 45 (4), 351(2009).</li>
<li>Jacso, P. <a target="_blank" href="https://www.researchgate.net/publication/234144770_As_we_may_search-Comparison_of_major_features_of_the_Web_of_Science_Scopus_and_Google_Scholar_citation-based_and_citation-enhanced_databases">As we may search-comparison of major features of the Web of Science, Scopus, and Google Scholar citation-based and citation-enhanced databases.</a> CurrentScience. 89 (9), 1537-1547 (2005).</li>
<li>Li, J. <a target="_blank" href="https://www.tandfonline.com/doi/abs/10.1080/15424065.2010.505518">Citation analysis: Comparison of Web of Science, Scopus, Scifinder, And Google Scholar.</a> Journal of Electronic Resources in Medical Libraries. 7 (3), 196-217 (2010).</li>
<li>Levine-Clark, M., Kraus, J. <a target="_blank" href="https://www.tandfonline.com/doi/abs/10.1300/J122v27n04_02">Finding chemistry information using Google Scholar: a comparison with Chemical Abstracts Service.</a> Science & Technology Libraries. 27 (4), 3-17 (2007).</li>
<li>Gavel, Y., Iselid, L. <a target="_blank" href="https://www.emeraldinsight.com/doi/abs/10.1108/14684520810865958">Web of Science and Scopus: a journal title overlap study.</a> Online Information Review. 32 (1), 8-21 (2008).</li>
<li>Harzing, A. W., Alakangas, S. <a target="_blank" href="https://link.springer.com/article/10.1007/s11192-015-1798-9">Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison.</a> Scientometrics. 106 (2), 787-804 (2016).</li>
<li>Aghaei Chadegani, A., et al. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aAghaei+Chadegani%2C+A+%22A+comparison+between+two+main+academic+literature+collections%3A+Web+of+Science+and+Scopus+databases%22">A comparison between two main academic literature collections: Web of Science and Scopus databases</a>. , (2013).</li>
<li>Testa, J. <a target="_blank" href="https://www.tandfonline.com/doi/abs/10.1080/19186444.2009.11658213?journalCode=rncr20">The Thomson Reuters journal selection process.</a> Transnational Corporations Review. 1 (4), 59-66 (2009).</li>
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<li>Burnham, J. F. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aBurnham%2C+JF+%22Scopus+database%3A+a+review%22">Scopus database: a review</a>. 3 (1), Biomedical Digital Libraries. 1(2006).</li>
<li>Pubmed. , 
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<li>Scopus. , 
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<li>Web of Science. , 
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<li>Cobo, M. J. <a target="_blank" href="https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.21525">Science mapping software tools: Review, analysis, and cooperative study among tools.</a> Journal of the American Society for Information Science and Technology. 62 (7), 1382-1402 (2011).</li>
<li>World bank. , 
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<li>Statistical Yearbook – 60th issue. , 
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<li>Mongeon, P., Paul-Hus, A. <a target="_blank" href="https://link.springer.com/article/10.1007/s11192-015-1765-5">The journal coverage of Web of Science and Scopus: a comparative analysis.</a> Scientometrics. 106 (1), 213-228 (2016).</li>
</ol>

All authors disclose no potential conflicts of interest.

In this study, the steps through which PubMed, Scopus, and Web of Science databases are used to perform a bibliometric analysis were provided. It was indicated that the friendliest and the easiest tool to use for bibliometric analysis services is Web of Science; however, its drawback is that its services are not available for free. PubMed is devoted for biomedical sciences and is affiliated with several other National Library of Medicine (NLM) tools that can help to optimize analysis of biomedical subjects. Medical Subject Heading (MeSH) is a professional indexing tool, where upon adding a new article to PubMed database, the article will be searched by experts for the main topics it discusses, and a list of MeSH will be assigned for each article. On the other hand, its main drawback is that it requires good knowledge on how to use it. Searching the Web of Science core collection will yield all articles that are published in journals indexed in the Science Citation Index Expanded (SCIE), the Social Science Citation Index (SSCI), the Arts and Humanities Citation Index (AHCI), and the newly added Emerging Source Citation Index (ESCI), where authors can choose the database within Web of Science to search in18. In addition, two other databases for books and conferences are also included19. Scopus is generally easy to use and has a database that covers more journals than the other two services20, but it is still a paid service. Table 3 further details and compares the characteristics of PubMed, Scopus, and Web of Science.
As shown in the results, each of Scopus and Web of Science database search provided different disciplines as the most common disciplines Jordanians publish in. The reasons behind these discrepancies were examined by analyzing the research area (discipline) classification for each database. It was found that Scopus search yielded 27 research areas, where publications are classified into one or more of them. On the other hand, Web of Science search yielded 140 research areas. However, Web of Science publications are classified into only one of them (no publication is classified into more than one research area). For example, the single research area &#34;Medicine&#34; in Scopus corresponds with 27 research areas in Web of Science, which are (numbers correspond to the contribution of each research area in the total 10,936 publications which resulted from Web of Science search):
Internal medicine (2.5%), neurology (2.2%), oncology (2.2%), surgery (1.4%), endocrinology (1.1%), pediatrics (1.1%), psychiatry (1%), experimental medicine (1%), cardiovascular system (0.9%), infectious diseases (0.9%), radiology (0.9%), orthopedics (0.7%), obstetrics and gynecology (0.7%), immunology (0.6%), rehabilitation (0.6%), hematology (0.6%), urology (0.5%), respiratory (0.4%), ophthalmology (0.3%), gastroenterology (0.3%), complementary medicine (0.3%), dermatology (0.2%), morphology (0.2%), rheumatology (0.2%), anesthesiology (0.2%), emergency medicine (0.1%), allergy (0.1%).
As explained earlier in the protocol, researchers may download search results in a CSV or XLSX format, where several tools are available to further analyze and map the results. These tools apply the concept of science mapping or bibliometric mapping, which is a spatial representation of how disciplines, fields, documents, or authors are related24,25:
<ul>
	<li>The Sci2 Tool (https://sci2.cns.iu.edu/user/index.php ): A freely available tool to perform analysis on data extracted from Scopus, Web of Science, or even PubMed.</li>
	<li>BibExcel (http://homepage.univie.ac.at/juan.gorraiz/bibexcel/ ): A freely available tool that manages and builds maps for data extracted from different bibliometric software.</li>
	<li>CiteSpace (http://cluster.cis.drexel.edu/~cchen/citespace/ ): A freely available tool for visualizing and analyzing trends and patterns in scientific literature.</li>
	<li>UCINET (https://sites.google.com/site/ucinetsoftware/home ): A s ubscription-based tool for the analysis of social network data and drawing visualized maps.</li>
	<li>Pajek (http://mrvar.fdv.uni-lj.si/pajek/ ): A freely available tool for the analysis and visualization of large networks.</li>
	<li>Leydesdorff&#8217; s Software (https://www.leydesdorff.net/software.htm ): A freely available tool to analze output from bibliometric databases and to draw mappings of the results.</li>
	<li>Network Workbench Tool (http://nwb.cns.iu.edu ): A freely available tool that provides specific algorithms to deal with publications data to construct and analyze bibliometric networks and maps.&#160;</li>
	<li>VintagePoint (https://www.thevantagepoint.com ): A subscription-based tool to analyze and science map large volumes of structured text to discover patterns and relationships&#160;</li>
	<li>VOSviewer (http://www.vosviewer.com/download ): A freely available tool specifically designed to construct and visualize bibliometric maps, paying special attention to the graphical representation of such maps.&#160;</li>
</ul>
In addition, researchers can use data obtained from the three databases (Pubmed, Scopus and Web of Science) and calculate several other valuable indices using data from other sources, including World Bank and the Organization for Economic Co-operation and Development (OECD). As yearly publications and the author&#39;s country of affiliation are available as outcome measurements in the three databases, the following indices can thus be measured:
<ul>
	<li>Population index&#160;</li>
	<li>Number of publications per million inhabitants, where populations can be obtained from World Bank database26.</li>
	<li>Publications per gross domestic product (GDP)</li>
	<li>Number of publications per billion-dollar GDP, where GDP can be obtained from &#8220;World Development Indicators&#8221; from World Bank database26.</li>
	<li>Annual scientific growth rate (Research productivity)</li>
	<li>(Publications in the year (n) &#8211; Publications in the year (n-1))/ Publications in the year (n-1)</li>
	<li>Publications per region</li>
</ul>
As the world is divided into 9 regions&#160;according to United Nations Statistical Year Book by the United Nations27, these divisions are based on geographical, scientific, and economical considerations. These regions are: Western Europe, Eastern Europe, the United States of America (USA), Canada, Latin America and the Caribbean, Africa, Japan, Asia (excluding Japan), and Oceania.
Researchers aiming to do bibliometric analysis using the aforementioned databases should be aware of their limitations; journal coverage by Scopus and Web of Science in almost all disciplines does not reach half of the journals in Ulrich&#39;s periodicals dictionary28. This means that although Scopus and Web of Science indexed journals are based on quality, they do not cover all journals in any discipline. Moreover, non-English language journals are under-represented, as the focus of these databases are English-language journals28. One of the limitations one can encounter during the analysis is the unavailability of complete information about an article (e.g., missing author&#39;s country of affiliation), which might lead to some sort of error in the results. This can be avoided by performing a manual search for the author. However, this issue was not discussed in the analysis conducted in this study since previous studies have estimated the missing information caused by this issue to be insignificant (less than 5%)6.

Classically, researchers have used literature databases to perform literature review for their studies1. Another use of these literature databases arose at the end of the 19th century, where researchers analyzed the body of literature, a use that has slowly grown since2. In the last few decades, digitizing literature and the formation of online literature databases provided an opportunity to researchers to analyze the body of literature and research performance easily and efficiently. An example would be analyzing the research performance for a document3, a subject4, a discipline5, a country6, or even a region in the world7. This type of analysis is known as bibliometric analysis. Heartsill Young defined bibliometric analysis as the use of statistical methods to analyze a body of literature to reveal historical development8. In other words, bibliometrics is the quantitative study of published units on the basis of citation and text analysis9.
Different databases are used to do bibliometric analysis and each database has different characteristics and can provide different services10. Currently, the most commonly used literature databases are the Web of Science and Scopus for almost all disciplines, both only available on a subscription basis11, and PubMed for biomedical and life sciences, a freely available database10. There is also Google Scholar, which might be an easy tool to handle, but it should not be used as a bibliometric analysis tool currently due to some deficiencies such as its unclear scope and coverage, its lack of citation analysis tools, and its inclusion of non-peer reviewed non-scientific contents12,13. Moreover, Google Scholar lacks the tools for performing advanced search and keyword optimization14.
Several previous studies have compared the features of the previously mentioned literature databases for literature review purposes3,5,10,12,13,15,16,17. However, in this study, the means by which PubMed, Scopus, and Web of Science databases are used to perform a bibliometric analysis will be provided, and the pros and cons for each of them will be compared. Bibliometric analysis can be used to analyze the research output in almost any discipline, so the target audience would be any researcher who intends to analyze publication trends. An example of analyzing a publication trend in Jordan as a country will be presented using each database. Jordan was chosen because doing a bibliometric analysis for a country (in contrast to a subject) is not very straightforward. In addition, Jordan, specifically, is poorly studied in a bibliometric way as it can be both an author name and a country name. We explain how to overcome such a challenge in the search.

<table><tbody><tr><td></td><td>clarivate</td><td>N/A</td><td>Web of Science provider, where the access was provided by the subscription made by the University of Jordan.</td></tr><tr><td></td><td>Elsevier</td><td>N/A</td><td>Scopus provider, where the access was provided by the subscription made by the University of Jordan.</td></tr></tbody></table>

comparing bibliometric analysis using pubmed, scopus, and web of science databases

Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly on life sciences and biomedical disciplines, whereas Scopus and Web of Science are multidisciplinary. The protocol described in the current study was used to search for publications from Jordanian authors in the years 2013-2017. In this protocol, how to use each database to conduct this type of search is explained in detail. A Scopus search resulted in the highest number of documents (11,444 documents), followed by a Web of Science search (10,943 documents). PubMed resulted in a smaller number of documents due to its narrower scope and coverage (4,363 documents). The results also show a yearly trend in: (1) the number of publications, (2) the disciplines that have the most publications, (3) the countries of collaboration, and (4) the number of open access publications. In contrast, PubMed has a sophisticated keyword optimization service (i.e., Medical Subject Heading, or MeSH), while both Scopus and Web of Science provide search analysis tools that can produce representative figures. Finally, the features of each database are explained in detail and several indices that can be extracted using the search results are provided. This study provides a base for using literature databases for bibliometric analysis.

Results from PubMed search
A total of 4,363 documents were retrieved based on the search conducted in this study. Free full text was available for 1,767 documents (40.5%). In 2013, a total of 532 documents were published, 663 documents in 2014, 811 documents in 2015, 952 documents in 2016, and 1,405 documents in 2017.
The results reveal that 1,008 (23.8%) documents discussed issues related to cancer, while only 53 (1.2%) documents discussed AIDS related topics. The results also show that 150 (3.5%) documents were published in dentistry related journals, while 275 (6.5%) documents were published in nursing journals.
Results from Scopus search
A total of 11,444 documents resulted from the search conducted in the current study, including 10,974 (95.9%) articles and 470 (4.1%) reviews. Only 652 (5.7%) of the documents were open access.
Figure 4&#160;shows the yearly trend in Jordanian publications during the 5-year interval. According to the country of collaboration in the Scopus search (Figure 5), the United States of America (USA) is the most common country Jordanian researchers collaborate with (1,553 publications), followed by Saudi Arabia with 1,176 publications, and United Kingdom with 723 publications.
Figure 6&#160;details the 10 most common disciplines Jordanians have published in. Based on the Scopus search, medicine is the most common discipline published in (2,441 publications), followed by engineering (1,837 publications), and social sciences (1,468 publications). The University of Jordan has contributed to 3,346 (29.3%) publications of the total five year publications, followed by Jordan University of Science and Technology with 2,396 publications (21.0%), and Hashemite University by 1,347 publications (11.8%).&#160;
Results from Web of Science search
A total of 10,943 documents were published in Jordan. 87 are highly cited papers and 14 are considered to be hot papers. The results show that 2,879 documents were Open Access, 2,547 documents were Gold open access, 170 documents were Green published, and 162 documents were Green accepted (manuscript deposited in repositories upon acceptance before publication).
Figure 7&#160;shows the yearly trend in Jordanian publications during the 5-year interval. Figure 8 details the country of collaboration. According to the Web of Science search, the USA is the most common country Jordanians collaborate with (929 publications), followed by France with 860 publications, and Austria with 429 publications. Figure 9 details the 10 most common disciplines Jordanians published in. According to the Web of Science search, engineering is the most common discipline published in (1,315 publications), followed by mathematics (1,263 publications), and computer sciences (828 publications).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58494/58494fig1.jpg" /> 
Figure 1: The report for the PubMed search with color annotation for each section in the report. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58494/58494fig2.jpg" /> 
Figure 2: The report for the Scopus search with color annotation for each section in the report. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58494/58494fig3.jpg" /> 
Figure 3: The report for the Web of Science search with color annotation for each section in the report. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58494/58494fig4.jpg" /> 
Figure 4: The yearly trend in publications in Jordan during the 5-years period, as extracted from Scopus. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58494/58494fig5.jpg" /> 
Figure 5: The countries Jordanians tend to author publications with, as extracted from Scopus. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58494/58494fig6.jpg" /> 
Figure 6: The disciplines Jordanian publications are generally about, as extracted from Scopus. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/58494/58494fig7.jpg" /> 
Figure 7: A bar chart showing the yearly publication trend in the years 2013-2017 in Jordan, as extracted from Web of Science. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/58494/58494fig8.jpg" /> 
Figure 8: A bar chart showing the countries Jordanians tend to collaborate with in the years 2013-2017, as extracted from Web of Science. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/58494/58494fig9.jpg" /> 
Figure 9: A tree map showing the 10 disciplines which most Jordanian publish in during the years 2013-2017, as extracted from Web of Science. <a href="https://www.jove.com/files/ftp_upload/58494/58494fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Operator function</td>
			<td>PubMed</td>
			<td>Scopus</td>
			<td>Web of Science</td>
			<td>Example</td>
		</tr>
		<tr>
			<td>Both terms must appear</td>
			<td>AND</td>
			<td>AND</td>
			<td>AND</td>
			<td></td>
		</tr>
		<tr>
			<td>At least one of the terms must appear</td>
			<td>OR</td>
			<td>OR</td>
			<td>OR</td>
			<td></td>
		</tr>
		<tr>
			<td>The term after it must not appear</td>
			<td>NOT</td>
			<td>AND NOT</td>
			<td>NOT</td>
			<td></td>
		</tr>
		<tr>
			<td>You want to find two words within an &#8220;n&#8221; distance from each other regardless of their order</td>
			<td>X</td>
			<td>W/n</td>
			<td>NEAR/n</td>
			<td>Jordan W/2 Cancer &#8594;&#160;finding a result with the words &#34;Jordan&#34; and &#34;Cancer&#34; within 2 words from each other</td>
		</tr>
		<tr>
			<td>You want to find a word within an &#8220;n&#8221; distance prior to the other word (order respected)</td>
			<td>X</td>
			<td>Pre/n</td>
			<td>X</td>
			<td>Jordan Pre/2 Cancer&#160;&#8594; finding a result with the words &#34;Jordan&#34;&#160; is preceding &#34;Cancer&#34; by 2 words</td>
		</tr>
		<tr>
			<td>You want to find the words with the specified stem, regardless of the other part of the word</td>
			<td>X</td>
			<td>* or ?</td>
			<td>*</td>
			<td>Jordan* or Jordan?&#160;&#8594; will return also the results for &#8220;Jordanian&#8221;</td>
		</tr>
		<tr>
			<td>You want to find a word with the specified stem and with a maximum of just one more letter after it</td>
			<td>X</td>
			<td>X</td>
			<td>$ or ?</td>
			<td>Jordan$ or Jordan?&#160;&#8594; will give results for &#8220;Jordans&#8221; but not for &#34;Jordanian&#34;</td>
		</tr>
		<tr>
			<td>Searches for the exact phrase within the quotation marks, will respect the meaning of any operators mentioned within the quotations</td>
			<td>X</td>
			<td>&#8220;&#8221;</td>
			<td>&#8220;&#8221;</td>
			<td>&#8220;Cancer in Jordan?&#8221;&#160;&#8594; will search for &#8220;cancer in Jordan&#8221; or &#8220;cancer in Jordans&#8221;</td>
		</tr>
		<tr>
			<td>Searches for the exact phrase within the quotation marks, without respecting the meaning of any operators mentioned within the quotations</td>
			<td>X</td>
			<td>{}</td>
			<td>X</td>
			<td>{Cancer in Jordan?}&#160;&#8594; will search for &#8220;cancer in Jordan?&#8221; only, that is it will interpret the question mark as a question mark</td>
		</tr>
	</tbody>
</table>
Table 1: Operators to perform the specified functions for each database. Operators in PubMed must be in upper case, unlike those for Scopus and Web of Science. X=not present.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Outcome measures&#160;</td>
			<td>PubMed</td>
			<td>Scopus</td>
			<td>Web of Science</td>
		</tr>
		<tr>
			<td>Documents each year</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Publications in specific Journal</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Publications per author</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Institutional affiliation</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Country of authors</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Number of open access publications (Golden OA)</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Number of open access publications (Green OA)</td>
			<td>X</td>
			<td>X</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Publications per each document type</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Subject area</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Publications in specific publishers</td>
			<td>&#8730;</td>
			<td>X</td>
			<td>X</td>
		</tr>
		<tr>
			<td>Publications for specific MeSH terms</td>
			<td>&#8730;</td>
			<td>X</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Web of Science categories</td>
			<td>X</td>
			<td>X</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Funding agency</td>
			<td>X</td>
			<td>X</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Publications on specific gender</td>
			<td>&#8730;</td>
			<td>X</td>
			<td>X</td>
		</tr>
		<tr>
			<td>Publications on specific age group</td>
			<td>&#8730;</td>
			<td>X</td>
			<td>X</td>
		</tr>
		<tr>
			<td>Publications by a unique PubMed ID</td>
			<td>&#8730;</td>
			<td>X</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Publications managed by specific editor</td>
			<td>X</td>
			<td>X</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Highly cited papers: Papers in top 1% in each subject area in terms of highest citations in the last 10 years.</td>
			<td>X</td>
			<td>X</td>
			<td>&#8730;</td>
		</tr>
		<tr>
			<td>Hot papers in the field: Papers that have been highly cited in the latest two months compared to the norm (average citations in peer papers).</td>
			<td>X</td>
			<td>X</td>
			<td>&#8730;</td>
		</tr>
	</tbody>
</table>
Table 2: Outcome measures and search filters that are available for each literature database. Researchers may refer to each database&#39;s instructions for further details on using each filter.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>PubMed</td>
			<td>Scopus</td>
			<td>Web of Science</td>
		</tr>
		<tr>
			<td>Covered disciplines</td>
			<td>Life sciences and biomedical disciplines</td>
			<td>All disciplines</td>
			<td>All disciplines</td>
		</tr>
		<tr>
			<td>Focus</td>
			<td>Life sciences and biomedical disciplines</td>
			<td>Physical, health, life, and social sciences</td>
			<td>Science, technology, social sciences, arts and humanities.</td>
		</tr>
		<tr>
			<td>Covered period</td>
			<td>1966</td>
			<td>1970</td>
			<td>1900*</td>
		</tr>
		<tr>
			<td>Free/Paid</td>
			<td>Free</td>
			<td>Paid</td>
			<td>Paid</td>
		</tr>
		<tr>
			<td>Ownership</td>
			<td>National Institute of Health</td>
			<td>Elsevier</td>
			<td>Clarivate</td>
		</tr>
		<tr>
			<td>Professional term indexing</td>
			<td>Yes</td>
			<td>No</td>
			<td>No</td>
		</tr>
		<tr>
			<td>Associated data search</td>
			<td>No</td>
			<td>No</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Old data coverage</td>
			<td>No</td>
			<td>No</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Figure production</td>
			<td>No</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Open access assessment</td>
			<td>Gold open access</td>
			<td>Gold open access</td>
			<td>Green and gold open access</td>
		</tr>
		<tr>
			<td>Friendly interface</td>
			<td>+</td>
			<td>++</td>
			<td>+++</td>
		</tr>
		<tr>
			<td>Availability of operators</td>
			<td>+</td>
			<td>+++</td>
			<td>++</td>
		</tr>
		<tr>
			<td colspan="4">* Coverage depend on institutional subscription</td>
		</tr>
	</tbody>
</table>
Table 3: Comparing the characteristics of PubMed, Scopus, and Web of Science. Information in this table is based on this study&#39;s data and the information provided by each database10,22,23,24.

Watch this Scientific Journal Video about Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases at JoVE.com

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Literature databases are commonly used to assess publications in a certain subject, discipline, country, or region of the world, a practice known as bibliometric analysis. The current protocol details how to use PubMed, Scopus, and Web of Science databases to do bibliometric analysis.

Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly ...

comparing-bibliometric-analysis-using-pubmed-scopus-web-science

Nanyang Technological University

Research

JoVE Journal

Medicine

31.1K Views.  University of Jordan. Literature databases are commonly used to assess publications in a certain subject, discipline, country, or region of the world, a practice known as bibliometric analysis. The current protocol details how to use PubMed, Scopus, and Web of Science databases to do bibliometric analysis.

Video: Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

In recent years, emerging databases were designed to lower the barriers for approaching the intricate cancer genomic datasets, thereby, facilitating investigators to analyze and interpret genes, samples and clinical data across different types of cancer. Herein, we describe a practical operation procedure, taking ID1 (Inhibitor of DNA binding proteins 1) as an example, to characterize the expression patterns of biomarker and survival predictors of breast cancer based on pooled clinical datasets derived from online accessible databases, including ONCOMINE, bcGenExMiner v4.0 (Breast cancer gene-expression miner v4.0), GOBO (Gene expression-based Outcome for Breast cancer Online), HPA (The human protein atlas), and Kaplan-Meier plotter. The analysis began with querying the expression pattern of the gene of interest (e.g., ID1) in cancerous samples vs. normal samples. Then, the correlation analysis between ID1 and clinicopathological characteristics in breast cancer was performed. Next, the expression profiles of ID1 was stratified according to different subgroups. Finally, the association between ID1 expression and survival outcome was analyzed. The operation procedure simplifies the concept to integrate multidimensional data types at the gene level from different databases and test hypotheses regarding recurrence and genomic context of gene alteration events in breast cancer. This method can improve the credibility and representativeness of the conclusions, thereby, present informative perspective on a gene of interest.

Here, we present a protocol to explore the biomarker and survival predictor of breast cancer based on the comprehensive analysis of pooled clinical datasets derived from a variety of publicly accessible databases, using the strategy of expression, correlation and survival analysis step by step.

In recent years, emerging databases were designed to lower the barriers for approaching the intricate cancer genomic datasets, thereby, facilitating ...

Cancer Research

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to interference with proper development and may even result in initiation or progression of cancers in certain cases. Because this pathway serves complex and versatile functions, it can be studied extensively through many different approaches. Of these, bioinformatics provides an undeniably cost-efficient, approachable, and user-friendly method of study. Bioinformatics is a useful way to extract smaller pieces of information from large-scale datasets. Through the implementation of various bioinformatics approaches, researchers can quickly, reliably, and efficiently interpret these large datasets, yielding insightful applications and scientific discoveries. Here, a protocol is presented for integration of bioinformatics approaches to investigate the role of Notch signaling in ovarian cancer. Furthermore, bioinformatics findings are validated through experimentation.

Bioinformatics is a useful way to process large-scale datasets. Through the implementation of bioinformatics approaches, researchers can quickly, reliably, and efficiently obtain insightful applications and scientific discoveries. This article demonstrates the utilization of bioinformatics in ovarian cancer research. It also successfully validates bioinformatics findings through experimentation.

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to ...

Pipeline for Analyzing Microarray Data in Esophageal Cancer Research

Development of Compendium for Esophageal Squamous Cell Carcinoma

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early detection. EC manifests in two major histological forms - adenocarcinoma (EAD) and squamous cell carcinoma (ESCC) - both exhibiting variations in incidence across geographically distinct populations. High-throughput technologies are transforming the understanding of diseases, including cancer. A significant challenge for the scientific community is dealing with scattered data in the literature. To address this, a simple pipeline is proposed for the analysis of publicly available microarray datasets and the collection of differentially regulated molecules between cancer and normal conditions. The pipeline can serve as a standard approach for differential gene expression analysis, identifying genes differentially expressed between cancer and normal tissues or among different cancer subtypes. The pipeline involves several steps, including Data preprocessing (involving quality control and normalization of raw gene expression data to remove technical variations between samples), Differential expression analysis (identifying genes differentially expressed between two or more groups of samples using statistical tests such as t-tests, ANOVA, or linear models), Functional analysis (using bioinformatics tools to identify enriched biological pathways and functions in differentially expressed genes), and Validation (involving validation using independent datasets or experimental methods such as qPCR or immunohistochemistry). Using this pipeline, a collection of differentially expressed molecules (DEMs) can be generated for any type of cancer, including esophageal cancer. This compendium can be utilized to identify potential biomarkers and drug targets for cancer and enhance understanding of the molecular mechanisms underlying the disease. Additionally, population-specific screening of esophageal cancer using this pipeline will help identify specific drug targets for distinct populations, leading to personalized treatments for the disease.

This protocol describes a useful tool for identifying significant molecular changes in cancer and leads to the development of new diagnostic and therapeutic approaches for esophageal squamous cell carcinoma.

Esophageal cancer (EC) ranks as the 8th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early ...

TMOD3 Expression and Its Role in Ovarian Cancer Progression

Tropomodulin 3 Overexpression as a Marker for Platinum Resistance and Immune Infiltration in Ovarian Cancer

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, but its role in the drug resistance of ovarian cancer remains unexplored. By analyzing data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, this study compared TMOD3 expression in ovarian cancer and normal tissues, and examined the expression of TMOD3 after platinum treatment in platinum-sensitive and platinum-resistant ovarian cancers. The Kaplan-Meier method was used to assess the effect of TMOD3 on overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients. microRNAs (miRNAs) targeting TMOD3 were predicted using TargetScan and analyzed using the TCGA database. Tumor Immune Estimation Resource (TIMER) and an integrated repository portal for tumor-immune system interactions (TISIDB) were used to determine the relationship between TMOD3 expression and immune infiltration. TMOD3 coexpression networks in ovarian cancer were explored using LinkedOmics, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and The Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics. The results showed that TMOD3 was highly expressed in ovarian cancer and was associated with the grading, staging, and metastasis of ovarian cancer. TMOD3 expression was significantly reduced in platinum-treated ovarian cancer cells and patients. However, TMOD3 expression was higher in platinum-resistant ovarian cancer cells and tissues compared to platinum-sensitive ones. Higher TMOD3 expression was significantly associated with lower OS and PFS in ovarian cancer patients treated with platinum-based chemotherapy. miRNA-mediated post-transcriptional regulation is likely responsible for high TMOD3 expression in ovarian cancer and platinum-resistant ovarian tissues. The expression of TMOD3 mRNA was associated with immune infiltration in ovarian cancer. These findings indicate that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration.

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

Tropomodulin 3 (TMOD3) has been increasingly studied in tumors in recent years. This study is the first to report that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration. These results could help improve the therapeutic outcomes for ovarian cancer.

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, ...

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for biomedical text-mining applications. These tools enable investigators to integrate, conceptualize, and translate these discoveries to uncover new insights into disease pathology and therapeutics. In this protocol, we present CaseOLAP LIFT, a new computational pipeline to investigate cellular components and their disease associations by extracting user-selected information from text datasets (e.g., biomedical literature). The software identifies sub-cellular proteins and their functional partners within disease-relevant documents. Additional disease-relevant documents are identified via the software's label imputation method. To contextualize the resulting protein-disease associations and to integrate information from multiple relevant biomedical resources, a knowledge graph is automatically constructed for further analyses. We present one use case with a corpus of ~34 million text documents downloaded online to provide an example of elucidating the role of mitochondrial proteins in distinct cardiovascular disease phenotypes using this method. Furthermore, a deep learning model was applied to the resulting knowledge graph to predict previously unreported relationships between proteins and disease, resulting in 1,583 associations with predicted probabilities &gt;0.90 and with an area under the receiver operating characteristic curve (AUROC) of 0.91 on the test set. This software features a highly customizable and automated workflow, with a broad scope of raw data available for analysis; therefore, using this method, protein-disease associations can be identified with enhanced reliability within a text corpus.

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A computational protocol, CaseOLAP LIFT, and a use case are presented for investigating mitochondrial proteins and their associations with cardiovascular disease as described in biomedical reports. This protocol can be easily adapted to study user-selected cellular components and diseases.

The rapidly increasing and vast quantities of biomedical reports, each containing numerous entities and rich information, represent a rich resource for ...

Biochemistry

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Cancer poses a significant challenge to human health due to its complex biological systems, necessitating in-depth analysis. Single-cell sequencing has become an essential tool for investigating these systems, enabling the detection of gene expression and epigenetic modifications at the single-cell level. To elucidate research trends, collaboration networks, and knowledge dissemination in this field, a bibliometric analysis was conducted using the Web of Science Core Collection database, covering publications from January 1, 2010, to December 31, 2023. The Bibliometrix package in R was used to extract and analyze key publication data, including document types, countries, institutions, authors, and keywords. Additionally, CiteSpace, VOSviewer, and the Online Analysis Platform of Literature Metrology were employed for data compilation and visualization. The analysis identified 34,074 authors from 3,129 institutions across 75 countries and regions, contributing to 5,680 publications on single-cell sequencing in cancer, published in 788 academic journals. China and the United States emerged as the leading nations in publication volume. Harvard University produced the highest number of publications (320), with Aviv Regev, affiliated with Harvard, recognized as a key contributor. Leading journals, such as Frontiers in Immunology and Nature Communications, highlight both established and emerging research areas, including the immune microenvironment and immunotherapy. Key trends and potential areas for future research include intra-tumor heterogeneity, clonal evolution, and drug delivery systems. This study provides a comprehensive overview of single-cell sequencing research in oncology, emphasizing its rapid progress, driven by technological advancements and international collaborations. Strengthening global partnerships, developing integrative analytical tools, and addressing data complexities will be crucial for advancing personalized cancer therapies and deepening insights into cancer biology.

This bibliometric analysis of single-cell sequencing in cancer research indicates that China and the USA produce significantly more scholarly articles than other nations. Burst detection identifies emerging terms such as 'intra-tumor heterogeneity,' 'clonal evolution,' and 'drug delivery systems,' which are expected to influence future research.

Cancer poses a significant challenge to human health due to its complex biological systems, necessitating in-depth analysis. Single-cell sequencing has ...

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Science relies on increasingly complex data sets for progress, but common data management methods such as spreadsheet programs are inadequate for the growing scale and complexity of this information. While database management systems have the potential to rectify these issues, they are not commonly utilized outside of business and informatics fields. Yet, many research labs already generate "medium sized", low velocity, multi-dimensional data that could greatly benefit from implementing similar systems. In this article, we provide a conceptual overview explaining how databases function and the advantages they provide in tissue engineering applications. Structural fibroblast data from individuals with a lamin A/C mutation was used to illustrate examples within a specific experimental context. Examples include visualizing multidimensional data, linking tables in a relational database structure, mapping a semi-automated data pipeline to convert raw data into structured formats, and explaining the underlying syntax of a query. Outcomes from analyzing the data were used to create plots of various arrangements and significance was demonstrated in cell organization in aligned environments between the positive control of Hutchinson-Gilford progeria, a well-known laminopathy, and all other experimental groups. In comparison to spreadsheets, database methods were enormously time efficient, simple to use once set up, allowed for immediate access of original file locations, and increased data rigor. In response to the National Institutes of Health (NIH) emphasis on experimental rigor, it is likely that many scientific fields will eventually adopt databases as common practice due to their strong capability to effectively organize complex data.

Many researchers generate "medium-sized", low-velocity, and multi-dimensional data, which can be managed more efficiently with databases rather than spreadsheets. Here we provide a conceptual overview of databases including visualizing multi-dimensional data, linking tables in relational database structures, mapping semi-automated data pipelines, and using the database to elucidate data meaning.

Science relies on increasingly complex data sets for progress, but common data management methods such as spreadsheet programs are inadequate for the ...

Bioengineering

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications.
We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm.
Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents.
CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

We present a protocol and associated programming code as well as metadata samples to support a cloud-based automated identification of phrases-category association representing unique concepts in user selected knowledge domain in biomedical literature. The phrase-category association quantified by this protocol can facilitate in depth analysis in the selected knowledge domain.

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining ...

Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia

A systematic review and meta-analysis were conducted to evaluate the clinical effectiveness and safety of Shugan Jieyu capsules for treating insomnia by searching seven databases up to February 21, 2022. The study was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of the studies was assessed using the risk of bias assessment tool. This article describes in detail how to retrieve and screen the literature. The detailed steps for conducting the meta-analysis are also included in the protocol. Fourteen studies were found to be eligible, including 1,283 insomnia patients (644 with and 639 without Shugan Jieyu capsules at baseline). The meta-analysis showed a better total clinical effectiveness (odds ratio [OR]: 5.71, 95% confidence interval [CI]: 3.56 to 9.15) and a lower Pittsburgh Sleep Quality Index (PSQI) score (mean difference [MD]: &#8722;2.95, 95% CI: &#8722;4.97 to &#8722;0.93) with combined Shugan Jieyu capsules and Western medicine compared to Western medicine alone. The secondary outcomes showed that the Shugan Jieyu capsule group had significantly reduced adverse reactions and improvements in sleep duration, night awakening, nightmares with excessive dreaming, daytime sleepiness, and low energy. Further multicenter randomized trials must be encouraged to provide more concrete evidence on whether Shugan Jieyu capsules are beneficial in routine clinical practice.

The presented protocol describes an investigation strategy and systematic review to evaluate the clinical effectiveness and safety of Shugan Jieyu capsules for treating insomnia.

A systematic review and meta-analysis were conducted to evaluate the clinical effectiveness and safety of Shugan Jieyu capsules for treating insomnia by ...

Addressing Metabolomics Data Analysis Using Advanced Bioinformatics Tools

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis 

We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of ...

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

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