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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.

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The top list of academic search engines

academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

  • Coverage: approx. 200 million articles
  • Abstracts: only a snippet of the abstract is available
  • Related articles: ✔
  • References: ✔
  • Cited by: ✔
  • Links to full text: ✔
  • Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX

Search interface of Google Scholar

BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

  • Coverage: approx. 136 million articles (contains duplicates)
  • Abstracts: ✔
  • Related articles: ✘
  • References: ✘
  • Cited by: ✘
  • Export formats: RIS, BibTeX

Search interface of Bielefeld Academic Search Engine aka BASE

CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

  • Coverage: approx. 136 million articles
  • Links to full text: ✔ (all articles in CORE are open access)
  • Export formats: BibTeX

Search interface of the CORE academic search engine

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

  • Coverage: approx. 200 million articles and reports
  • Links to full text: ✔ (available for some databases)
  • Export formats: APA, MLA, RIS, BibTeX (available for some databases)

Search interface of Science.gov

Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

  • Coverage: approx. 40 million articles
  • Export formats: APA, MLA, Chicago, BibTeX

Search interface of Semantic Scholar

Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

  • Coverage: no detailed statistics available, approx. 100 million articles
  • Abstracts: only snippets of the abstract are available
  • Export formats: APA, MLA, RIS, BibTeX

Search interface of Baidu Scholar

RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

  • Coverage: no detailed statistics available, approx. 1 billion documents
  • Abstracts: only snippets of the article are available
  • Export formats: not available

Search interface of RefSeek

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

google search for research papers

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Google publishes hundreds of research papers each year. Publishing is important to us; it enables us to collaborate and share ideas with, as well as learn from, the broader scientific community. Submissions are often made stronger by the fact that ideas have been tested through real product implementation by the time of publication.

We believe the formal structures of publishing today are changing - in computer science especially, there are multiple ways of disseminating information.  We encourage publication both in conventional scientific venues, and through other venues such as industry forums, standards bodies, and open source software and product feature releases.

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Google Scholar

Using Google Scholar with your HarvardKey allows you to make the most of provided links, granting access to full text available through Harvard Library subscriptions.

Google Scholar can quickly surface highly cited peer-reviewed articles, abstracts, books, patents, scholarly web pages, and more. 

Explore Google Scholar

Connect Google Scholar To Your Library Access

Connecting Google Scholar to your Harvard Library access is a good way to make sure you get access to articles that Harvard Library subscribes to.

Here's how: 

  • Go to Google Scholar and sign in to your Google account
  • Look for the menu options
  • Go into the settings and select "Library links"
  • Type in Harvard and select: Harvard University - Try Harvard Library
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  • Search your topic and look for the "Try Harvard Library" links to the right of the articles. This link should take you to Harvard's access to that item

Google Scholar Tips

  • Like Google, Google Scholar allows searching of metadata terms, but unlike Google, it also indexes full text. 
  • Choose the default search or select “Advanced search” to search by title, author, journal, and date.
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  • J Med Libr Assoc
  • v.106(4); 2018 Oct

A systematic approach to searching: an efficient and complete method to develop literature searches

Associated data.

Creating search strategies for systematic reviews, finding the best balance between sensitivity and specificity, and translating search strategies between databases is challenging. Several methods describe standards for systematic search strategies, but a consistent approach for creating an exhaustive search strategy has not yet been fully described in enough detail to be fully replicable. The authors have established a method that describes step by step the process of developing a systematic search strategy as needed in the systematic review. This method describes how single-line search strategies can be prepared in a text document by typing search syntax (such as field codes, parentheses, and Boolean operators) before copying and pasting search terms (keywords and free-text synonyms) that are found in the thesaurus. To help ensure term completeness, we developed a novel optimization technique that is mainly based on comparing the results retrieved by thesaurus terms with those retrieved by the free-text search words to identify potentially relevant candidate search terms. Macros in Microsoft Word have been developed to convert syntaxes between databases and interfaces almost automatically. This method helps information specialists in developing librarian-mediated searches for systematic reviews as well as medical and health care practitioners who are searching for evidence to answer clinical questions. The described method can be used to create complex and comprehensive search strategies for different databases and interfaces, such as those that are needed when searching for relevant references for systematic reviews, and will assist both information specialists and practitioners when they are searching the biomedical literature.

INTRODUCTION

Librarians and information specialists are often involved in the process of preparing and completing systematic reviews (SRs), where one of their main tasks is to identify relevant references to include in the review [ 1 ]. Although several recommendations for the process of searching have been published [ 2 – 6 ], none describe the development of a systematic search strategy from start to finish.

Traditional methods of SR search strategy development and execution are highly time consuming, reportedly requiring up to 100 hours or more [ 7 , 8 ]. The authors wanted to develop systematic and exhaustive search strategies more efficiently, while preserving the high sensitivity that SR search strategies necessitate. In this article, we describe the method developed at Erasmus University Medical Center (MC) and demonstrate its use through an example search. The efficiency of the search method and outcome of 73 searches that have resulted in published reviews are described in a separate article [ 9 ].

As we aimed to describe the creation of systematic searches in full detail, the method starts at a basic level with the analysis of the research question and the creation of search terms. Readers who are new to SR searching are advised to follow all steps described. More experienced searchers can consider the basic steps to be existing knowledge that will already be part of their normal workflow, although step 4 probably differs from general practice. Experienced searchers will gain the most from reading about the novelties in the method as described in steps 10–13 and comparing the examples given in the supplementary appendix to their own practice.

CREATING A SYSTEMATIC SEARCH STRATEGY

Our methodology for planning and creating a multi-database search strategy consists of the following steps:

  • Determine a clear and focused question
  • Describe the articles that can answer the question
  • Decide which key concepts address the different elements of the question
  • Decide which elements should be used for the best results
  • Choose an appropriate database and interface to start with
  • Document the search process in a text document
  • Identify appropriate index terms in the thesaurus of the first database
  • Identify synonyms in the thesaurus
  • Add variations in search terms
  • Use database-appropriate syntax, with parentheses, Boolean operators, and field codes
  • Optimize the search
  • Evaluate the initial results
  • Check for errors
  • Translate to other databases
  • Test and reiterate

Each step in the process is reflected by an example search described in the supplementary appendix .

1. Determine a clear and focused question

A systematic search can best be applied to a well-defined and precise research or clinical question. Questions that are too broad or too vague cannot be answered easily in a systematic way and will generally result in an overwhelming number of search results. On the other hand, a question that is too specific will result into too few or even zero search results. Various papers describe this process in more detail [ 10 – 12 ].

2. Describe the articles that can answer the question

Although not all clinical or research questions can be answered in the literature, the next step is to presume that the answer can indeed be found in published studies. A good starting point for a search is hypothesizing what the research that can answer the question would look like. These hypothetical (when possible, combined with known) articles can be used as guidance for constructing the search strategy.

3. Decide which key concepts address the different elements of the question

Key concepts are the topics or components that the desired articles should address, such as diseases or conditions, actions, substances, settings, domains (e.g., therapy, diagnosis, etiology), or study types. Key concepts from the research question can be grouped to create elements in the search strategy.

Elements in a search strategy do not necessarily follow the patient, intervention, comparison, outcome (PICO) structure or any other related structure. Using the PICO or another similar framework as guidance can be helpful to consider, especially in the inclusion and exclusion review stage of the SR, but this is not necessary for good search strategy development [ 13 – 15 ]. Sometimes concepts from different parts of the PICO structure can be grouped together into one search element, such as when the desired outcome is frequently described in a certain study type.

4. Decide which elements should be used for the best results

Not all elements of a research question should necessarily be used in the search strategy. Some elements are less important than others or may unnecessarily complicate or restrict a search strategy. Adding an element to a search strategy increases the chance of missing relevant references. Therefore, the number of elements in a search strategy should remain as low as possible to optimize recall.

Using the schema in Figure 1 , elements can be ordered by their specificity and importance to determine the best search approach. Whether an element is more specific or more general can be measured objectively by the number of hits retrieved in a database when searching for a key term representing that element. Depending on the research question, certain elements are more important than others. If articles (hypothetically or known) exist that can answer the question but lack a certain element in their titles, abstracts, or keywords, that element is unimportant to the question. An element can also be unimportant because of expected bias or an overlap with another element.

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Schema for determining the optimal order of elements

Bias in elements

The choice of elements in a search strategy can introduce bias through use of overly specific terminology or terms often associated with positive outcomes. For the question “does prolonged breastfeeding improve intelligence outcomes in children?,” searching specifically for the element of duration will introduce bias, as articles that find a positive effect of prolonged breastfeeding will be much more likely to mention time factors in their titles or abstracts.

Overlapping elements

Elements in a question sometimes overlap in their meaning. Sometimes certain therapies are interventions for one specific disease. The Lichtenstein technique, for example, is a repair method for inguinal hernias. There is no need to include an element of “inguinal hernias” to a search for the effectiveness of the Lichtenstein therapy. Likewise, sometimes certain diseases are only found in certain populations. Adding such an overlapping element could lead to missing relevant references.

The elements to use in a search strategy can be found in the plot of elements in Figure 1 , by following the top row from left to right. For this method, we recommend starting with the most important and specific elements. Then, continue with more general and important elements until the number of results is acceptable for screening. Determining how many results are acceptable for screening is often a matter of negotiation with the SR team.

5. Choose an appropriate database and interface to start with

Important factors for choosing databases to use are the coverage and the presence of a thesaurus. For medically oriented searches, the coverage and recall of Embase, which includes the MEDLINE database, are superior to those of MEDLINE [ 16 ]. Each of these two databases has its own thesaurus with its own unique definitions and structure. Because of the complexity of the Embase thesaurus, Emtree, which contains much more specific thesaurus terms than the MEDLINE Medical Subject Headings (MeSH) thesaurus, translation from Emtree to MeSH is easier than the other way around. Therefore, we recommend starting in Embase.

MEDLINE and Embase are available through many different vendors and interfaces. The choice of an interface and primary database is often determined by the searcher’s accessibility. For our method, an interface that allows searching with proximity operators is desirable, and full functionality of the thesaurus, including explosion of narrower terms, is crucial. We recommend developing a personal workflow that always starts with one specific database and interface.

6. Document the search process in a text document

We advise designing and creating the complete search strategies in a log document, instead of directly in the database itself, to register the steps taken and to make searches accountable and reproducible. The developed search strategies can be copied and pasted into the desired databases from the log document. This way, the searcher is in control of the whole process. Any change to the search strategy should be done in the log document, assuring that the search strategy in the log is always the most recent.

7. Identify appropriate index terms in the thesaurus of the first database

Searches should start by identifying appropriate thesaurus terms for the desired elements. The thesaurus of the database is searched for matching index terms for each key concept. We advise restricting the initial terms to the most important and most relevant terms. Later in the process, more general terms can be added in the optimization process, in which the effect on the number of hits, and thus the desirability of adding these terms, can be evaluated more easily.

Several factors can complicate the identification of thesaurus terms. Sometimes, one thesaurus term is found that exactly describes a specific element. In contrast, especially in more general elements, multiple thesaurus terms can be found to describe one element. If no relevant thesaurus terms have been found for an element, free-text terms can be used, and possible thesaurus terms found in the resulting references can be added later (step 11).

Sometimes, no distinct thesaurus term is available for a specific key concept that describes the concept in enough detail. In Emtree, one thesaurus term often combines two or more elements. The easiest solution for combining these terms for a sensitive search is to use such a thesaurus term in all elements where it is relevant. Examples are given in the supplementary appendix .

8. Identify synonyms in the thesaurus

Most thesauri offer a list of synonyms on their term details page (named Synonyms in Emtree and Entry Terms in MeSH). To create a sensitive search strategy for SRs, these terms need to be searched as free-text keywords in the title and abstract fields, in addition to searching their associated thesaurus terms.

The Emtree thesaurus contains more synonyms (300,000) than MeSH does (220,000) [ 17 ]. The difference in number of terms is even higher considering that many synonyms in MeSH are permuted terms (i.e., inversions of phrases using commas).

Thesaurus terms are ordered in a tree structure. When searching for a more general thesaurus term, the more specific (narrower) terms in the branches below that term will also be searched (this is frequently referred to as “exploding” a thesaurus term). However, to perform a sensitive search, all relevant variations of the narrower terms must be searched as free-text keywords in the title or abstract, in addition to relying on the exploded thesaurus term. Thus, all articles that describe a certain narrower topic in their titles and abstracts will already be retrieved before MeSH terms are added.

9. Add variations in search terms (e.g., truncation, spelling differences, abbreviations, opposites)

Truncation allows a searcher to search for words beginning with the same word stem. A search for therap* will, thus, retrieve therapy, therapies, therapeutic, and all other words starting with “therap.” Do not truncate a word stem that is too short. Also, limitations of interfaces should be taken into account, especially in PubMed, where the number of search term variations that can be found by truncation is limited to 600.

Databases contain references to articles using both standard British and American English spellings. Both need to be searched as free-text terms in the title and abstract. Alternatively, many interfaces offer a certain code to replace zero or one characters, allowing a search for “pediatric” or “paediatric” as “p?ediatric.” Table 1 provides a detailed description of the syntax for different interfaces.

Field codes in five most used interfaces for biomedical literature searching

Searching for abbreviations can identify extra, relevant references and retrieve more irrelevant ones. The search can be more focused by combining the abbreviation with an important word that is relevant to its meaning or by using the Boolean “NOT” to exclude frequently observed, clearly irrelevant results. We advise that searchers do not exclude all possible irrelevant meanings, as it is very time consuming to identify all the variations, it will result in unnecessarily complicated search strategies, and it may lead to erroneously narrowing the search and, thereby, reduce recall.

Searching partial abbreviations can be useful for retrieving relevant references. For example, it is very likely that an article would mention osteoarthritis (OA) early in the abstract, replacing all further occurrences of osteoarthritis with OA . Therefore, it may not contain the phrase “hip osteoarthritis” but only “hip oa.”

It is also important to search for the opposites of search terms to avoid bias. When searching for “disease recurrence,” articles about “disease free” may be relevant as well. When the desired outcome is survival , articles about mortality may be relevant.

10. Use database-appropriate syntax, with parentheses, Boolean operators, and field codes

Different interfaces require different syntaxes, the special set of rules and symbols unique to each database that define how a correctly constructed search operates. Common syntax components include the use of parentheses and Boolean operators such as “AND,” “OR,” and “NOT,” which are available in all major interfaces. An overview of different syntaxes for four major interfaces for bibliographic medical databases (PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest) is shown in Table 1 .

Creating the appropriate syntax for each database, in combination with the selected terms as described in steps 7–9, can be challenging. Following the method outlined below simplifies the process:

  • Create single-line queries in a text document (not combining multiple record sets), which allows immediate checking of the relevance of retrieved references and efficient optimization.
  • Type the syntax (Boolean operators, parentheses, and field codes) before adding terms, which reduces the chance that errors are made in the syntax, especially in the number of parentheses.
  • Use predefined proximity structures including parentheses, such as (() ADJ3 ()) in Ovid, that can be reused in the query when necessary.
  • Use thesaurus terms separately from free-text terms of each element. Start an element with all thesaurus terms (using “OR”) and follow with the free-text terms. This allows the unique optimization methods as described in step 11.
  • When adding terms to an existing search strategy, pay close attention to the position of the cursor. Make sure to place it appropriately either in the thesaurus terms section, in the title/abstract section, or as an addition (broadening) to an existing proximity search.

The supplementary appendix explains the method of building a query in more detail, step by step for different interfaces: PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest. This method results in a basic search strategy designed to retrieve some relevant references upon which a more thorough search strategy can be built with optimization such as described in step 11.

11. Optimize the search

The most important question when performing a systematic search is whether all (or most) potentially relevant articles have been retrieved by the search strategy. This is also the most difficult question to answer, since it is unknown which and how many articles are relevant. It is, therefore, wise first to broaden the initial search strategy, making the search more sensitive, and then check if new relevant articles are found by comparing the set results (i.e., search for Strategy #2 NOT Strategy #1 to see the unique results).

A search strategy should be tested for completeness. Therefore, it is necessary to identify extra, possibly relevant search terms and add them to the test search in an OR relationship with the already used search terms. A good place to start, and a well-known strategy, is scanning the top retrieved articles when sorted by relevance, looking for additional relevant synonyms that could be added to the search strategy.

We have developed a unique optimization method that has not been described before in the literature. This method often adds valuable extra terms to our search strategy and, therefore, extra, relevant references to our search results. Extra synonyms can be found in articles that have been assigned a certain set of thesaurus terms but that lack synonyms in the title and/or abstract that are already present in the current search strategy. Searching for thesaurus terms NOT free-text terms will help identify missed free-text terms in the title or abstract. Searching for free-text terms NOT thesaurus terms will help identify missed thesaurus terms. If this is done repeatedly for each element, leaving the rest of the query unchanged, this method will help add numerous relevant terms to the query. These steps are explained in detail for five different search platforms in the supplementary appendix .

12. Evaluate the initial results

The results should now contain relevant references. If the interface allows relevance ranking, use that in the evaluation. If you know some relevant references that should be included in the research, search for those references specifically; for example, combine a specific (first) author name with a page number and the publication year. Check whether those references are retrieved by the search. If the known relevant references are not retrieved by the search, adapt the search so that they are. If it is unclear which element should be adapted to retrieve a certain article, combine that article with each element separately.

Different outcomes are desired for different types of research questions. For instance, in the case of clinical question answering, the researcher will not be satisfied with many references that contain a lot of irrelevant references. A clinical search should be rather specific and is allowed to miss a relevant reference. In the case of an SR, the researchers do not want to miss any relevant reference and are willing to handle many irrelevant references to do so. The search for references to include in an SR should be very sensitive: no included reference should be missed. A search that is too specific or too sensitive for the intended goal can be adapted to become more sensitive or specific. Steps to increase sensitivity or specificity of a search strategy can be found in the supplementary appendix .

13. Check for errors

Errors might not be easily detected. Sometimes clues can be found in the number of results, either when the number of results is much higher or lower than expected or when many retrieved references are not relevant. However, the number expected is often unknown, and very sensitive search strategies will always retrieve many irrelevant articles. Each query should, therefore, be checked for errors.

One of the most frequently occurring errors is missing the Boolean operator “OR.” When no “OR” is added between two search terms, many interfaces automatically add an “AND,” which unintentionally reduces the number of results and likely misses relevant references. One good strategy to identify missing “OR”s is to go to the web page containing the full search strategy, as translated by the database, and using Ctrl-F search for “AND.” Check whether the occurrences of the “AND” operator are deliberate.

Ideally, search strategies should be checked by other information specialists [ 18 ]. The Peer Review of Electronic Search Strategies (PRESS) checklist offers good guidance for this process [ 4 ]. Apart from the syntax (especially Boolean operators and field codes) of the search strategy, it is wise to have the search terms checked by the clinician or researcher familiar with the topic. At Erasmus MC, researchers and clinicians are involved during the complete process of structuring and optimizing the search strategy. Each word is added after the combined decision of the searcher and the researcher, with the possibility of directly comparing results with and without the new term.

14. Translate to other databases

To retrieve as many relevant references as possible, one has to search multiple databases. Translation of complex and exhaustive queries between different databases can be very time consuming and cumbersome. The single-line search strategy approach detailed above allows quick translations using the find and replace method in Microsoft Word (<Ctrl-H>).

At Erasmus MC, macros based on the find-and-replace method in Microsoft Word have been developed for easy and fast translation between the most used databases for biomedical and health sciences questions. The schema that is followed for the translation between databases is shown in Figure 2 . Most databases simply follow the structure set by the Embase.com search strategy. The translation from Emtree terms to MeSH terms for MEDLINE in Ovid often identifies new terms that need to be added to the Embase.com search strategy before the translation to other databases.

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Schematic representation of translation between databases used at Erasmus University Medical Center

Dotted lines represent databases that are used in less than 80% of the searches.

Using five different macros, a thoroughly optimized query in Embase.com can be relatively quickly translated into eight major databases. Basic search strategies will be created to use in many, mostly smaller, databases, because such niche databases often do not have extensive thesauri or advanced syntax options. Also, there is not much need to use extensive syntax because the number of hits and, therefore, the amount of noise in these databases is generally low. In MEDLINE (Ovid), PsycINFO (Ovid), and CINAHL (EBSCOhost), the thesaurus terms must be adapted manually, as each database has its own custom thesaurus. These macros and instructions for their installation, use, and adaptation are available at bit.ly/databasemacros.

15. Test and reiterate

Ideally, exhaustive search strategies should retrieve all references that are covered in a specific database. For SR search strategies, checking searches for their recall is advised. This can be done after included references have been determined by the authors of the systematic review. If additional papers have been identified through other non-database methods (i.e., checking references in included studies), results that were not identified by the database searches should be examined. If these results were available in the databases but not located by the search strategy, the search strategy should be adapted to try to retrieve these results, as they may contain terms that were omitted in the original search strategies. This may enable the identification of additional relevant results.

A methodology for creating exhaustive search strategies has been created that describes all steps of the search process, starting with a question and resulting in thorough search strategies in multiple databases. Many of the steps described are not new, but together, they form a strong method creating high-quality, robust searches in a relatively short time frame.

Our methodology is intended to create thoroughness for literature searches. The optimization method, as described in step 11, will identify missed synonyms or thesaurus terms, unlike any other method that largely depends on predetermined keywords and synonyms. Using this method results in a much quicker search process, compared to traditional methods, especially because of the easier translation between databases and interfaces (step 13). The method is not a guarantee for speed, since speed depends on many factors, including experience. However, by following the steps and using the tools as described above, searchers can gain confidence first and increase speed through practice.

What is new?

This method encourages searchers to start their search development process using empty syntax first and later adding the thesaurus terms and free-text synonyms. We feel this helps the searcher to focus on the search terms, instead of on the structure of the search query. The optimization method in which new terms are found in the already retrieved articles is used in some other institutes as well but has to our knowledge not been described in the literature. The macros to translate search strategies between interfaces are unique in this method.

What is different compared to common practice?

Traditionally, librarians and information specialists have focused on creating complex, multi-line (also called line-by-line) search strategies, consisting of multiple record sets, and this method is frequently advised in the literature and handbooks [ 2 , 19 – 21 ]. Our method, instead, uses single-line searches, which is critical to its success. Single-line search strategies can be easily adapted by adding or dropping a term without having to recode numbers of record sets, which would be necessary in multi-line searches. They can easily be saved in a text document and repeated by copying and pasting for search updates. Single-line search strategies also allow easy translation to other syntaxes using find-and-replace technology to update field codes and other syntax elements or using macros (step 13).

When constructing a search strategy, the searcher might experience that certain parentheses in the syntax are unnecessary, such as parentheses around all search terms in the title/abstract portion, if there is only one such term, there are double parentheses in the proximity statement, or one of the word groups exists for only one word. One might be tempted to omit those parentheses for ease of reading and management. However, during the optimization process, the searcher is likely to find extra synonyms that might consist of one word. To add those terms to the first query (with reduced parentheses) requires adding extra parentheses (meticulously placing and counting them), whereas, in the latter search, it only requires proper placement of those terms.

Many search methods highly depend on the PICO framework. Research states that often PICO or PICOS is not suitable for every question [ 22 , 23 ]. There are other acronyms than PICO—such as sample, phenomenon of interest, design, evaluation, research type (SPIDER) [ 24 ]—but each is just a variant. In our method, the most important and specific elements of a question are being analyzed for building the best search strategy.

Though it is generally recommended that searchers search both MEDLINE and Embase, most use MEDLINE as the starting point. It is considered the gold standard for biomedical searching, partially due to historical reasons, since it was the first of its kind, and more so now that it is freely available via the PubMed interface. Our method can be used with any database as a starting point, but we use Embase instead of MEDLINE or another database for a number of reasons. First, Embase provides both unique content and the complete content of MEDLINE. Therefore, searching Embase will be, by definition, more complete than searching MEDLINE only. Second, the number of terms in Emtree (the Embase thesaurus) is three times as high as that of MeSH (the MEDLINE thesaurus). It is easier to find MeSH terms after all relevant Emtree terms have been identified than to start with MeSH and translate to Emtree.

At Erasmus MC, the researchers sit next to the information specialist during most of the search strategy design process. This way, the researchers can deliver immediate feedback on the relevance of proposed search terms and retrieved references. The search team then combines knowledge about databases with knowledge about the research topic, which is an important condition to create the highest quality searches.

Limitations of the method

One disadvantage of single-line searches compared to multi-line search strategies is that errors are harder to recognize. However, with the methods for optimization as described (step 11), errors are recognized easily because missed synonyms and spelling errors will be identified during the process. Also problematic is that more parentheses are needed, making it more difficult for the searcher and others to assess the logic of the search strategy. However, as parentheses and field codes are typed before the search terms are added (step 10), errors in parentheses can be prevented.

Our methodology works best if used in an interface that allows proximity searching. It is recommended that searchers with access to an interface with proximity searching capabilities select one of those as the initial database to develop and optimize the search strategy. Because the PubMed interface does not allow proximity searches, phrases or Boolean “AND” combinations are required. Phrase searching complicates the process and is more specific, with the higher risk of missing relevant articles, and using Boolean “AND” combinations increases sensitivity but at an often high loss of specificity. Due to some searchers’ lack of access to expensive databases or interfaces, the freely available PubMed interface may be necessary to use, though it should never be the sole database used for an SR [ 2 , 16 , 25 ]. A limitation of our method is that it works best with subscription-based and licensed resources.

Another limitation is the customization of the macros to a specific institution’s resources. The macros for the translation between different database interfaces only work between the interfaces as described. To mitigate this, we recommend using the find-and-replace functionality of text editors like Microsoft Word to ease the translation of syntaxes between other databases. Depending on one’s institutional resources, custom macros can be developed using similar methods.

Results of the method

Whether this method results in exhaustive searches where no important article is missed is difficult to determine, because the number of relevant articles is unknown for any topic. A comparison of several parameters of 73 published reviews that were based on a search developed with this method to 258 reviews that acknowledged information specialists from other Dutch academic hospitals shows that the performance of the searches following our method is comparable to those performed in other institutes but that the time needed to develop the search strategies was much shorter than the time reported for the other reviews [ 9 ].

CONCLUSIONS

With the described method, searchers can gain confidence in their search strategies by finding many relevant words and creating exhaustive search strategies quickly. The approach can be used when performing SR searches or for other purposes such as answering clinical questions, with different expectations of the search’s precision and recall. This method, with practice, provides a stepwise approach that facilitates the search strategy development process from question clarification to final iteration and beyond.

SUPPLEMENTAL FILE

Acknowledgments.

We highly appreciate the work that was done by our former colleague Louis Volkers, who in his twenty years as an information specialist in Erasmus MC laid the basis for our method. We thank Professor Oscar Franco for reviewing earlier drafts of this article.

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Best Academic Search Engines [2023]

Sumalatha G

Table of Contents

Gone are the days when researchers used to spend hours in the library skimming through endless reference books and resources. Now, thanks to academic search engines — with just a few clicks, researchers can access an extensive amount of information at their fingertips.

However, not all search engines are designed to make the research discovery process easier. It varies from one search engine to another, few might not have updated their database to the latest articles, while others might still provide older articles as a result of your search keyword or topic, and so on. This way, half of the researcher’s time is consumed shortlisting the best academic search engines.

Therefore, to help you choose the best search engine for academic research, we’ve crafted this blog. In this article, we will explore the best academic search engines available and why they are essential for scholars, researchers, and students alike.

Introduction to Academic Search Engines

Academic search engines are online repositories or databases that host millions of research articles and allow users to find relevant scholarly articles, research publications, conference proceedings, and other academic resources. Unlike web search engines like Google or Bing, these platforms are specifically designed to provide accurate, reliable, and relevant academic content.

These search engines often have advanced features that help users filter their search results based on specific criteria. For example, SciSpace helps you filter the results based on author, publication date, PDF, open-access, and more. In addition, it also provides citation information, abstracts, and full-text access to research papers and other scholarly literature, making them invaluable tools for scholars and researchers.

Academic search engines play a crucial role in the research process by providing scholars with easy access to relevant and reliable information. They save researchers valuable time by eliminating the need to sift through irrelevant search results and provide them with free access to a focused pool of academic resources.

With their advanced features and comprehensive coverage, these academic databases empower researchers to stay at the forefront of their fields and contribute to the advancement of knowledge.

Benefits of using reliable academic search engines for research

When it comes to academic research, using reliable search engines is of utmost importance. The credibility and quality of the sources you rely on can significantly impact the results of your research findings and conclusions.

Here are the potential advantages of using a popular search engine!

1. Reliable scholarly source: By using an academic search engine, researchers can ensure that the information they find is from reputable sources. These academic databases typically index content from scholarly journals, universities, research institutions, and other reliable and cited sources. As a result, the risk of using incorrect or biased information, which is prevalent on the open web, is significantly reduced.

2. Increased exposure to enormous articles: With a reliable academic search engine, you can access a vast array of scholarly articles and research publications. These search engines have extensive academic databases that include articles from various disciplines, including science and social sciences, allowing researchers to explore a wide range of topics and find relevant studies to support their research.

3. Advanced search filters: Reliable academic search engines often provide advanced search features that enable researchers to refine their search queries and narrow down the results to find the most relevant and latest information. These features may include filters for publication date, author, journal, and citation count, among others. By utilizing these advanced search terms and options, researchers can save time and effort by quickly finding the most pertinent resources.

4. Access full-text journal articles: Another advantage of using search engine for academic research is the ability to access full-text scientific articles. Many academic search engines provide direct links to the full text of articles, either freely available or through institutional subscriptions. This ensures that researchers can read and analyze the complete article, rather than relying on abstracts or TL;DR summaries.

5. Additional tools support: The most reliable search engines for research like SciSpace offer additional tools and features to enhance the research workflow. These may include citation generators, reference management systems, and options to save and organize search results. These tools can greatly facilitate the organization and the citation analysis of sources, making the research process more efficient and systematic.

Best search engines for research

Now that we’ve understood the importance of using reliable search engines for academic research, let's explore some of the best academic literature search engines available:

1. SciSpace

SciSpace

SciSpace is considered the best academic search engine that hosts and provides free access to a comprehensive index of 300 million+ scholarly articles from various fields. It utilizes advanced algorithms to provide users with highly relevant search results. Its intuitive and user-friendly interface makes it ideal for both novice and experienced researchers to navigate millions of research papers with no mess around.

One of the standout features of SciSpace is its “ Trace feature ” which allows users to find relevant research papers based on the preferred criteria including citation counts, related publications, references, authors, and more. It helps you land on the right research paper based on your preferences or research needs.

SciSpace is the only search engine that not only helps you discover relevant scholarly scientific literature but also allows you to read a research paper using its AI research assistant, conduct a literature review, and generate accurate citations for your research publications. It is an all-in-one platform that accelerates your research workflow with its AI-powered tools. You can explore all of them here

2. Google Scholar

Google-Scholar

Google Scholar is undoubtedly one of the popular search engines. With its vast database of scholarly literature, Google Scholar allows users to search for articles, theses, books, and conference papers across multiple academic disciplines. Google Scholar helps users save their search queries and set up email alerts for new publications in their field of interest. This ensures that researchers stay up-to-date with the latest developments in their respective fields.

PubMed

PubMed is a go-to academic search engine for those in the field of medicine and life sciences. Developed by the National Center for Biotechnology Information (NCBI), PubMed provides access to a vast collection of medicine, biomedical, health sciences, or literature, including journals, clinical trials, and scientific articles. Its meticulously curated articles makes it a trusted resource for medical professionals, scientists, researchers, and students alike.

Scopus

Scopus is a comprehensive database of science that covers a wide range of scholarly literature across multiple disciplines. It offers a vast collection of peer reviewed articles, including publications, conference papers, and patents. With its extensive coverage and powerful search capabilities, Scopus is a valuable tool for researchers looking to explore the latest developments in their respective fields.

JSTOR

JSTOR is a repository that provides access to a vast collection of academic journals, books, and primary sources. Its interdisciplinary approach makes it a valuable resource for researchers across various fields of study.

6. IEEE Xplore

IEEE Xplore

IEEE Xplore is a premier academic search engine for those in the fields of engineering, computer science, and technology. It provides access to a vast collection of technical articles, conference papers, and standards published by the Institute of Electrical and Electronics Engineers (IEEE).

IEEE Xplore is a treasure trove of knowledge for researchers and engineers looking to stay at the forefront of technological advancements.

Criteria for choosing the best academic search engine

With so many free academic search engines to choose from, it can be challenging to determine which one is the best fit for your research needs. Here are some criteria to consider when selecting an academic search engine:

  • Relevance: The search engine should provide highly relevant search results that are specific to your area of study.
  • Database size: A larger database gives you access to a broader range of scientific literature.
  • Advanced search capabilities: Look for search engines that offer advanced search filters, allowing you to refine your search based on specific criteria.
  • User-friendly interface: A user-friendly interface makes it easier for researchers to navigate and retrieve the information they need efficiently.
  • Accessibility: Consider the availability of full-text or PDF access to articles and the ease of obtaining the necessary permissions to cite or use the content.

In conclusion, academic search engines play a vital role in scholarly communication, facilitating efficient and reliable academic research. They provide scholars, researchers, and students with access to a vast array of scholarly articles, research papers, and other academic resources. By using the best academic search engines, researchers can ensure that their research is backed by evidence (accurate and trustworthy information).

While each search engine has its own unique features and strengths, the key is to choose the one that best aligns with your research needs and preferences. Remember to utilize advanced search filters, explore related articles and citations, and keep your research well-organized for maximum efficiency. As technology continues to advance, we can expect academic search engines to evolve and provide even more innovative solutions to the challenges faced in academic research.

So, embrace these powerful tools, explore the above-featured academic search engines, and let us know which tool you are clinging to!

Frequently Asked Questions

Google Scholar, SciSpace, PubMed, and JSTOR are widely used tools for academic research.

Academic search engineinvolves an in-depth examination of scholarly sources with a rigorous approach, while a Google search engine explores a wider range of web content, including non-academic sources, with varying levels of reliability.

They provide a comprehensive overview of existing research on diverse topics aiding researchers in conducting an efficient literature review without investing more time.

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In the biggest mass-market AI launch yet, Google is rolling out Gemini , its family of large language models, across almost all its products, from Android to the iOS Google app to Gmail to Docs and more. You can also now get your hands on Gemini Ultra, the most powerful version of the model, for the first time.  

With this launch, Google is sunsetting Bard , the company's answer to ChatGPT. Bard, which has been powered by a version of Gemini since December, will now be known as Gemini too.  

ChatGPT , released by Microsoft-backed OpenAI just 14 months ago, changed people’s expectations of what computers could do. Google, which has been racing to catch up ever since, unveiled its Gemini family of models in December. They are multimodal large language models that can interact with you via voice, image, and text. Google claimed that its own benchmarking showed that Gemini could outperform OpenAI's multimodal model, GPT-4, on a range of standard tests. But the margins were slim. 

By baking Gemini into its ubiquitous products, Google is hoping to make up lost ground. “Every launch is big, but this one is the biggest yet,” Sissie Hsiao, Google vice president and general manager of Google Assistant and Bard (now Gemini), said in a press conference yesterday. “We think this is one of the most profound ways that we’re going to advance our company’s mission.”

But some will have to wait longer than others to play with Google’s new toys. The company has announced rollouts in the US and East Asia but said nothing about when the Android and iOS apps will come to the UK or the rest of Europe. This may be because the company is waiting for the EU’s new AI Act to be set in stone, says Dragoș Tudorache, a Romanian politician and member of the European Parliament, who was a key negotiator on the law.

“We’re working with local regulators to make sure that we’re abiding by local regime requirements before we can expand,” Hsiao said. “Rest assured, we are absolutely working on it and I hope we’ll be able to announce expansion very, very soon.”

How can you get it? Gemini Pro, Google’s middle-tier model that has been available via Bard since December, will continue to be available for free on the web at gemini.google.com (rather than bard.google.com). But now there is a mobile app as well.

If you have an Android device, you can either download the Gemini app or opt in to an upgrade in Google Assistant. This will let you call up Gemini in the same way that you use Google Assistant: by pressing the power button, swiping from the corner of the screen, or saying “Hey, Google!” iOS users can download the Google app, which will now include Gemini.

Gemini will pop up as an overlay on your screen, where you can ask it questions or give it instructions about whatever’s on your phone at the time, such as summarizing an article or generating a caption for a photo.  

Finally, Google is launching a paid-for service called Gemini Advanced. This comes bundled in a subscription costing $19.99 a month that the company is calling the Google One Premium AI Plan. It combines the perks of the existing Google One Premium Plan, such as 2TB of extra storage, with access to Google's most powerful model, Gemini Ultra, for the first time. This will compete with OpenAI’s paid-for service, ChatGPT Plus, which buys you access to the more powerful GPT-4 (rather than the default GPT-3.5) for $20 a month.

At some point soon (Google didn't say exactly when) this subscription will also unlock Gemini across Google’s Workspace apps like Docs, Sheets, and Slides, where it works as a smart assistant similar to the GPT-4-powered Copilot that Microsoft is trialing in Office 365.

When can you get it? The free Gemini app (powered by Gemini Pro) is available from today in English in the US. Starting next week, you’ll be able to access it across the Asia Pacific region in English and in Japanese and Korean. But there is no word on when the app will come to the UK, countries in the EU, or Switzerland.

Gemini Advanced (the paid-for service that gives access to Gemini Ultra) is available in English in more than 150 countries, including the UK and EU (but not France). Google says it is analyzing local requirements and fine-tuning Gemini for cultural nuance in different countries. But the company promises that more languages and regions are coming.

What can you do with it? Google says it has developed its Gemini products with the help of more than 100 testers and power users. At the press conference yesterday, Google execs outlined a handful of use cases, such as getting Gemini to help write a cover letter for a job application. “This can help you come across as more professional and increase your relevance to recruiters,” said Google’s vice president for product management, Kristina Behr.

Or you could take a picture of your flat tire and ask Gemini how to fix it. A more elaborate example involved Gemini managing a snack rota for the parents of kids on a soccer team. Gemini would come up with a schedule for who should bring snacks and when, help you email other parents, and then field their replies. In future versions, Gemini will be able to draw on data in your Google Drive that could help manage carpooling around game schedules, Behr said.   

But we should expect people to come up with a lot more uses themselves. “I’m really excited to see how people around the world are going to push the envelope on this AI,” Hsaio said.

Is it safe? Google has been working hard to make sure its products are safe to use. But no amount of testing can anticipate all the ways that tech will get used and misused once it is released. In the last few months, Meta saw people use its image-making app to produce pictures of Mickey Mouse with guns and SpongeBob SquarePants flying a jet into two towers. Others used Microsoft’s image-making software to create fake pornographic images of Taylor Swift .

The AI Act aims to mitigate some—but not all—of these problems. For example, it requires the makers of powerful AI like Gemini to build in safeguards, such as watermarking for generated images and steps to avoid reproducing copyrighted material. Google says that all images generated by its products will include its SynthID watermarks. 

Like most companies, Google was knocked onto the back foot when ChatGPT arrived. Microsoft’s partnership with OpenAI has given it a boost over its old rival. But with Gemini, Google has come back strong: this is the slickest packaging of this generation’s tech yet. 

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Our next-generation model: Gemini 1.5

Feb 15, 2024

The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.

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A note from Google and Alphabet CEO Sundar Pichai:

Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced . Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI .

Our teams continue pushing the frontiers of our latest models with safety at the core. They are making rapid progress. In fact, we’re ready to introduce the next generation: Gemini 1.5. It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.

This new generation also delivers a breakthrough in long-context understanding. We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet.

Longer context windows show us the promise of what is possible. They will enable entirely new capabilities and help developers build much more useful models and applications. We’re excited to offer a limited preview of this experimental feature to developers and enterprise customers. Demis shares more on capabilities, safety and availability below.

Introducing Gemini 1.5

By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team

This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0 , we’ve been testing, refining and enhancing its capabilities.

Today, we’re announcing our next-generation model: Gemini 1.5.

Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.

The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra , our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.

Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.

As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.

These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI.

Context lengths of leading foundation models

Highly efficient architecture

Gemini 1.5 is built upon our leading research on Transformer and MoE architecture. While a traditional Transformer functions as one large neural network, MoE models are divided into smaller "expert” neural networks.

Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialization massively enhances the model’s efficiency. Google has been an early adopter and pioneer of the MoE technique for deep learning through research such as Sparsely-Gated MoE , GShard-Transformer , Switch-Transformer, M4 and more.

Our latest innovations in model architecture allow Gemini 1.5 to learn complex tasks more quickly and maintain quality, while being more efficient to train and serve. These efficiencies are helping our teams iterate, train and deliver more advanced versions of Gemini faster than ever before, and we’re working on further optimizations.

Greater context, more helpful capabilities

An AI model’s “context window” is made up of tokens, which are the building blocks used for processing information. Tokens can be entire parts or subsections of words, images, videos, audio or code. The bigger a model’s context window, the more information it can take in and process in a given prompt — making its output more consistent, relevant and useful.

Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.

This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.

Complex reasoning about vast amounts of information

1.5 Pro can seamlessly analyze, classify and summarize large amounts of content within a given prompt. For example, when given the 402-page transcripts from Apollo 11’s mission to the moon, it can reason about conversations, events and details found across the document.

Reasoning across a 402-page transcript: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can understand, reason about and identify curious details in the 402-page transcripts from Apollo 11’s mission to the moon.

Better understanding and reasoning across modalities

1.5 Pro can perform highly-sophisticated understanding and reasoning tasks for different modalities, including video. For instance, when given a 44-minute silent Buster Keaton movie , the model can accurately analyze various plot points and events, and even reason about small details in the movie that could easily be missed.

Multimodal prompting with a 44-minute movie: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can identify a scene in a 44-minute silent Buster Keaton movie when given a simple line drawing as reference material for a real-life object.

Relevant problem-solving with longer blocks of code

1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.

Problem solving across 100,633 lines of code | Gemini 1.5 Pro Demo

Gemini 1.5 Pro can reason across 100,000 lines of code giving helpful solutions, modifications and explanations.

Enhanced performance

When tested on a comprehensive panel of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing our large language models (LLMs). And when compared to 1.0 Ultra on the same benchmarks, it performs at a broadly similar level.

Gemini 1.5 Pro maintains high levels of performance even as its context window increases. In the Needle In A Haystack (NIAH) evaluation, where a small piece of text containing a particular fact or statement is purposely placed within a long block of text, 1.5 Pro found the embedded text 99% of the time, in blocks of data as long as 1 million tokens.

Gemini 1.5 Pro also shows impressive “in-context learning” skills, meaning that it can learn a new skill from information given in a long prompt, without needing additional fine-tuning. We tested this skill on the Machine Translation from One Book (MTOB) benchmark, which shows how well the model learns from information it’s never seen before. When given a grammar manual for Kalamang , a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.

As 1.5 Pro’s long context window is the first of its kind among large-scale models, we’re continuously developing new evaluations and benchmarks for testing its novel capabilities.

For more details, see our Gemini 1.5 Pro technical report .

Extensive ethics and safety testing

In line with our AI Principles and robust safety policies, we’re ensuring our models undergo extensive ethics and safety tests. We then integrate these research learnings into our governance processes and model development and evaluations to continuously improve our AI systems.

Since introducing 1.0 Ultra in December, our teams have continued refining the model, making it safer for a wider release. We’ve also conducted novel research on safety risks and developed red-teaming techniques to test for a range of potential harms.

In advance of releasing 1.5 Pro, we've taken the same approach to responsible deployment as we did for our Gemini 1.0 models, conducting extensive evaluations across areas including content safety and representational harms, and will continue to expand this testing. Beyond this, we’re developing further tests that account for the novel long-context capabilities of 1.5 Pro.

Build and experiment with Gemini models

We’re committed to bringing each new generation of Gemini models to billions of people, developers and enterprises around the world responsibly.

Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI . Read more about this on our Google for Developers blog and Google Cloud blog .

We’ll introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.

Early testers can try the 1 million token context window at no cost during the testing period, though they should expect longer latency times with this experimental feature. Significant improvements in speed are also on the horizon.

Developers interested in testing 1.5 Pro can sign up now in AI Studio, while enterprise customers can reach out to their Vertex AI account team.

Learn more about Gemini’s capabilities and see how it works .

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Sponsored Content | 5 Best Sites to Buy Research Papers Online

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Buying research papers online is a legitimate way to advance in your academic career without burning out. However, it’s crucial to use well-regarded services to ensure plagiarism-free, high-quality writing.

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Is it safe to buy research paper online?

Students often worry about safety issues when using the services of custom writing companies—and with good reason! Most institutions view such practices as cheating, making it particularly risky if professors discover that your assignment was completed by someone else.

The negative consequences can damage your reputation in the academic world and disrupt your career prospects. However, your fears should not deter you from seeking help. In fact, established and reliable companies are absolutely safe for users. They protect your private data and do not share any information with third parties. Your identity will remain confidential, ensuring that no one, including your instructors, will ever know if your assignment was commissioned.

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How much does it cost to buy a research paper?

Price is one of the most important aspects to consider when choosing a custom writing company. If you are a student on a budget, you are probably limited in the amount of money to spend on your studies.  Hence, you must be wondering if buying a research paper online is expensive. It’s difficult to give an exact answer to this question because the prices vary dramatically from one service to another. What’s more, they depend on your academic level and some other criteria.

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Who will write my research paper?

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We recommend hiring expert writers from reputable websites only, despite the potentially low cost of those advertising their services on Reddit.

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Article paid for by: Ocasio Media The news and editorial staffs of the Bay Area News Group had no role in this post’s preparation.

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Gemini 1.5: Our next-generation model, now available for Private Preview in Google AI Studio

February 15, 2024

google search for research papers

Last week, we released Gemini 1.0 Ultra in Gemini Advanced. You can try it out now by signing up for a Gemini Advanced subscription . The 1.0 Ultra model, accessible via the Gemini API, has seen a lot of interest and continues to roll out to select developers and partners in Google AI Studio .

Today, we’re also excited to introduce our next-generation Gemini 1.5 model , which uses a new Mixture-of-Experts (MoE) approach to improve efficiency. It routes your request to a group of smaller "expert” neural networks so responses are faster and higher quality.

Developers can sign up for our Private Preview of Gemini 1.5 Pro , our mid-sized multimodal model optimized for scaling across a wide-range of tasks. The model features a new, experimental 1 million token context window, and will be available to try out in  Google AI Studio . Google AI Studio is the fastest way to build with Gemini models and enables developers to easily integrate the Gemini API in their applications. It’s available in 38 languages across 180+ countries and territories .

1,000,000 tokens: Unlocking new use cases for developers

Before today, the largest context window in the world for a publicly available large language model was 200,000 tokens. We’ve been able to significantly increase this — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model. Gemini 1.5 Pro will come with a 128,000 token context window by default, but today’s Private Preview will have access to the experimental 1 million token context window.

We’re excited about the new possibilities that larger context windows enable. You can directly upload large PDFs, code repositories, or even lengthy videos as prompts in Google AI Studio. Gemini 1.5 Pro will then reason across modalities and output text.

Upload multiple files and ask questions We’ve added the ability for developers to upload multiple files, like PDFs, and ask questions in Google AI Studio. The larger context window allows the model to take in more information — making the output more consistent, relevant and useful. With this 1 million token context window, we’ve been able to load in over 700,000 words of text in one go. Gemini 1.5 Pro can find and reason from particular quotes across the Apollo 11 PDF transcript. 
[Video sped up for demo purposes]
Query an entire code repository The large context window also enables a deep analysis of an entire codebase, helping Gemini models grasp complex relationships, patterns, and understanding of code. A developer could upload a new codebase directly from their computer or via Google Drive, and use the model to onboard quickly and gain an understanding of the code. Gemini 1.5 Pro can help developers boost productivity when learning a new codebase.  
Add a full length video Gemini 1.5 Pro can also reason across up to 1 hour of video. When you attach a video, Google AI Studio breaks it down into thousands of frames (without audio), and then you can perform highly sophisticated reasoning and problem-solving tasks since the Gemini models are multimodal. Gemini 1.5 Pro can perform reasoning and problem-solving tasks across video and other visual inputs.  

More ways for developers to build with Gemini models

In addition to bringing you the latest model innovations, we’re also making it easier for you to build with Gemini:

Easy tuning. Provide a set of examples, and you can customize Gemini for your specific needs in minutes from inside Google AI Studio. This feature rolls out in the next few days. 
New developer surfaces . Integrate the Gemini API to build new AI-powered features today with new Firebase Extensions , across your development workspace in Project IDX , or with our newly released Google AI Dart SDK . 
Lower pricing for Gemini 1.0 Pro . We’re also updating the 1.0 Pro model, which offers a good balance of cost and performance for many AI tasks. Today’s stable version is priced 50% less for text inputs and 25% less for outputs than previously announced. The upcoming pay-as-you-go plans for AI Studio are coming soon.

Since December, developers of all sizes have been building with Gemini models, and we’re excited to turn cutting edge research into early developer products in Google AI Studio . Expect some latency in this preview version due to the experimental nature of the large context window feature, but we’re excited to start a phased rollout as we continue to fine-tune the model and get your feedback. We hope you enjoy experimenting with it early on, like we have.

To revisit this article, visit My Profile, then View saved stories .

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By Steven Levy

OpenAI’s Sora Turns AI Prompts Into Photorealistic Videos

We already know that OpenAI’s chatbots can pass the bar exam without going to law school. Now, just in time for the Oscars, a new OpenAI app called Sora hopes to master cinema without going to film school. For now a research product, Sora is going out to a few select creators and a number of security experts who will red-team it for safety vulnerabilities. OpenAI plans to make it available to all wannabe auteurs at some unspecified date, but it decided to preview it in advance.

Other companies, from giants like Google to startups like Runway , have already revealed text-to-video AI projects . But OpenAI says that Sora is distinguished by its striking photorealism—something I haven’t seen in its competitors—and its ability to produce longer clips than the brief snippets other models typically do, up to one minute. The researchers I spoke to won’t say how long it takes to render all that video, but when pressed, they described it as more in the “going out for a burrito” ballpark than “taking a few days off.” If the hand-picked examples I saw are to be believed, the effort is worth it.

OpenAI didn’t let me enter my own prompts, but it shared four instances of Sora’s power. (None approached the purported one-minute limit; the longest was 17 seconds.) The first came from a detailed prompt that sounded like an obsessive screenwriter’s setup: “Beautiful, snowy Tokyo city is bustling. The camera moves through the bustling city street, following several people enjoying the beautiful snowy weather and shopping at nearby stalls. Gorgeous sakura petals are flying through the wind along with snowflakes.”

AI-generated video made with OpenAI's Sora.

The result is a convincing view of what is unmistakably Tokyo, in that magic moment when snowflakes and cherry blossoms coexist. The virtual camera, as if affixed to a drone, follows a couple as they slowly stroll through a streetscape. One of the passersby is wearing a mask. Cars rumble by on a riverside roadway to their left, and to the right shoppers flit in and out of a row of tiny shops.

It’s not perfect. Only when you watch the clip a few times do you realize that the main characters—a couple strolling down the snow-covered sidewalk—would have faced a dilemma had the virtual camera kept running. The sidewalk they occupy seems to dead-end; they would have had to step over a small guardrail to a weird parallel walkway on their right. Despite this mild glitch, the Tokyo example is a mind-blowing exercise in world-building. Down the road, production designers will debate whether it’s a powerful collaborator or a job killer. Also, the people in this video—who are entirely generated by a digital neural network—aren’t shown in close-up, and they don’t do any emoting. But the Sora team says that in other instances they’ve had fake actors showing real emotions.

The other clips are also impressive, notably one asking for “an animated scene of a short fluffy monster kneeling beside a red candle,” along with some detailed stage directions (“wide eyes and open mouth”) and a description of the desired vibe of the clip. Sora produces a Pixar-esque creature that seems to have DNA from a Furby, a Gremlin, and Sully in Monsters, Inc . I remember when that latter film came out, Pixar made a huge deal of how difficult it was to create the ultra-complex texture of a monster’s fur as the creature moved around. It took all of Pixar’s wizards months to get it right. OpenAI’s new text-to-video machine … just did it.

“It learns about 3D geometry and consistency,” says Tim Brooks, a research scientist on the project, of that accomplishment. “We didn’t bake that in—it just entirely emerged from seeing a lot of data.”

AI-generated video made with the prompt, “animated scene features a close-up of a short fluffy monster kneeling beside a melting red candle. the art style is 3d and realistic, with a focus on lighting and texture. the mood of the painting is one of wonder and curiosity, as the monster gazes at the flame with wide eyes and open mouth. its pose and expression convey a sense of innocence and playfulness, as if it is exploring the world around it for the first time. the use of warm colors and dramatic lighting further enhances the cozy atmosphere of the image.”

While the scenes are certainly impressive, the most startling of Sora’s capabilities are those that it has not been trained for. Powered by a version of the diffusion model used by OpenAI’s Dalle-3 image generator as well as the transformer-based engine of GPT-4, Sora does not merely churn out videos that fulfill the demands of the prompts, but does so in a way that shows an emergent grasp of cinematic grammar.

That translates into a flair for storytelling. In another video that was created off of a prompt for “a gorgeously rendered papercraft world of a coral reef, rife with colorful fish and sea creatures.” Bill Peebles, another researcher on the project, notes that Sora created a narrative thrust by its camera angles and timing. “There's actually multiple shot changes—these are not stitched together, but generated by the model in one go,” he says. “We didn’t tell it to do that, it just automatically did it.”

In another example I didn’t view, Sora was prompted to give a tour of a zoo. “It started off with the name of the zoo on a big sign, gradually panned down, and then had a number of shot changes to show the different animals that live at the zoo,” says Peebles, “It did it in a nice and cinematic way that it hadn't been explicitly instructed to do.”

One feature in Sora that the OpenAI team didn’t show, and may not release for quite a while, is the ability to generate videos from a single image or a sequence of frames. “This is going to be another really cool way to improve storytelling capabilities,” says Brooks. “You can draw exactly what you have on your mind and then animate it to life.” OpenAI is aware that this feature also has the potential to produce deepfakes and misinformation. “We’re going to be very careful about all the safety implications for this,” Peebles adds.

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Expect Sora to have the same restrictions on content as Dall-E 3 : no violence, no porn, no appropriating real people or the style of named artists. Also as with Dall-E 3, OpenAI will provide a way for viewers to identify the output as AI-created. Even so, OpenAI says that safety and veracity is an ongoing problem that's bigger than one company. “The solution to misinformation will involve some level of mitigations on our part, but it will also need understanding from society and for social media networks to adapt as well,” says Aditya Ramesh, lead researcher and head of the Dall-E team.

Another potential issue is whether the content of the video Sora produces will infringe on the copyrighted work of others. “The training data is from content we’ve licensed and also publicly available content,” says Peebles. Of course, the nub of a number of lawsuits against OpenAI hinges on the question whether “publicly available” copyrighted content is fair game for AI training.

It will be a very long time, if ever, before text-to-video threatens actual filmmaking. No, you can’t make coherent movies by stitching together 120 of the minute-long Sora clips, since the model won’t respond to prompts in the exact same way—continuity isn’t possible. But the time limit is no barrier for Sora and programs like it to transform TikTok, Reels, and other social platforms. “In order to make a professional movie, you need so much expensive equipment,” says Peebles. “This model is going to empower the average person making videos on social media to make very high-quality content.”

As for now, OpenAI is faced with the huge task of making sure that Sora isn’t a misinformation train wreck. But after that, the long countdown begins until the next Christopher Nolan or Celine Song gets a statuette for wizardry in prompting an AI model. The envelope, please!

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