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The top list of academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, 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

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

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

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)

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

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

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

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

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!

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How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications. If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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Apple targets Google staff to build artificial intelligence team

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Michael Acton in London

Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.

Apple has poached dozens of artificial intelligence experts from Google and has created a secretive European laboratory in Zurich, as the tech giant builds a team to battle rivals in developing new AI models and products.

According to a Financial Times analysis of hundreds of LinkedIn profiles as well as public job postings and research papers, the $2.7tn company has undertaken a hiring spree over recent years to expand its global AI and machine learning team.

The iPhone maker has particularly targeted workers from Google , attracting at least 36 specialists from its rival since it poached John Giannandrea to be its top AI executive in 2018.

While the majority of Apple ’s AI team work from offices in California and Seattle, the tech group has also expanded a significant outpost in Zurich.

Professor Luc Van Gool from Swiss university ETH Zurich said Apple’s acquisitions of two local AI start-ups — virtual reality group FaceShift and image recognition company Fashwell — led Apple to build a research laboratory, known as its “Vision Lab”, in the city.

Zurich-based employees have been involved in Apple’s research into the underlying technology that powers products such as OpenAI’s ChatGPT chatbot. Their papers have focused on ever more advanced AI models that incorporate text and visual inputs to produce responses to queries.

The company has been advertising jobs in generative AI across two locations in Zurich, one of which has a particularly low profile. A neighbour told the FT they were not even aware of the office’s existence. Apple did not respond to requests to comment.

Apple has been typically tight-lipped about its AI plans even as big tech rivals Microsoft, Google and Amazon tout multibillion-dollar investments in the cutting-edge technology.

Its shares have slipped since the start of the year, while rivals’ stocks have soared, adding pressure on the tech giant to announce game-changing AI features that could boost device sales.

Industry insiders suggest Apple is focused on deploying generative AI on its mobile devices, a breakthrough that would allow AI chatbots and apps to run on the phone’s own hardware and software rather than be powered by cloud services in data centres.

Chief executive Tim Cook has told analysts Apple “has been doing research across a wide range of AI technologies” and investing and innovating “responsibly” around the new technology.

However, the tech group has developed AI products for more than a decade, such as its voice assistant Siri. The company has long been aware of the potential of “ neural networks ” — a form of AI inspired by the way neurons interact in the human brain and a technology that underpins breakthrough products such as ChatGPT.

Chuck Wooters, an expert in conversational AI and large language models who joined Apple in December 2013 and worked on Siri for almost two years, said: “During the time that I was there, one of the pushes that was happening in the Siri group was to move to a neural architecture for speech recognition. Even back then, before large language models took off, they were huge advocates of neural networks.”

That interest appears to have led Apple to researchers who were the driving force behind neural networks which power AI models.

In 2016, Apple acquired Perceptual Machines, a company founded by Ruslan Salakhutdinov and two of his students at Carnegie Mellon University, which worked on generative AI-powered image detection.

“Around that time they were hunting quite a few researchers and trying to build the infrastructure for training these models,” Salakhutdinov told the FT.

Salakhutdinov is a key figure in the history of neural networks, and studied at the University of Toronto under the “godfather” of the technology, Geoffrey Hinton, who left Google last year citing concerns about the dangers of generative AI. Salakhutdinov worked as director of AI research at Apple until 2020, when he returned to academia at Carnegie Mellon.

Apple’s top AI team is now made up of former key figures from Google, including Giannandrea, who previously oversaw Google Brain, the search company’s AI lab which has since been merged with DeepMind.

Samy Bengio, senior director of AI and ML research, was formerly one of Google’s top AI scientists. Ruoming Pang, who leads Apple’s “Foundation Models” team working on LLMs, previously led Google’s AI speech recognition research.

The company also once hired Ian Goodfellow, another deep learning pioneer, but he returned to Google in 2022, protesting against Apple’s return to work policy.

Six former Google employees hired over the past two years were listed among the authors of a significant research paper published in March, in which Apple revealed it had developed a family of AI models known as “MM1” that use text and visual inputs to generate responses.

Apple has also bought about two dozen AI start-ups in the past 10 years, focused on the application of AI reasoning to image and video recognition, data processing, search capabilities and music content curation.

Of these, founders from Musicmetric, Emotient, Silk Labs, PullString, CamerAI, Fashwell, Spectral Edge, Inductiv Inc, Vilynx, AI Music and WaveOne all still work at Apple, according to their LinkedIn profiles.

Salakhutdinov said Apple had been focused on doing “as much as you can on the device”, which will bring the need for more powerful chips with so-called dynamic random access memory (Dram) that can handle the vast amount of data required to power AI models.

“The next big thing is going to be ‘AI smartphones’ — and these will require a lot more Dram,” said Sumit Sadana, executive vice-president and chief business officer of Micron Technology, one of Apple’s chip suppliers.

Sadana added that the average smartphone memory chip today has less than the minimum needed to run an LLM on-device.

Salakhutdinov said another reason for Apple’s slow AI rollout was the tendency of language models to provide incorrect or problematic answers. “I think they are just being a little bit more cautious because they can’t release something they can’t fully control,” he added.

Apple’s foray into generative AI features may first be glimpsed at the company’s Worldwide Developers Conference in June.

Erik Woodring, an analyst at Morgan Stanley, said the next iPhone “could become much more of a voice-activated, smart personal assistant, led by an upgraded Siri that could for example interact with all the apps on your phone through voice control”.

He added: “What we’ll be looking for at WWDC are previews of one or two AI features that can become game changers for the average consumer.”

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