Objective To investigate the replication validity of biomedical association studies covered by newspapers. Methods We used a database of 4723 primary studies included in 306 meta-analysis articles. These studies associated a risk factor with a disease in three biomedical domains, psychiatry, neurology and four somatic diseases. They were classified into a lifestyle category (e.g. smoking) and a non-lifestyle category (e.g. genetic risk). Using the database Dow Jones Factiva, we investigated the newspaper coverage of each study. Their replication validity was assessed using a comparison with their corresponding meta-analyses. Results Among the 5029 articles of our database, 156 primary studies (of which 63 were lifestyle studies) and 5 meta-analysis articles were reported in 1561 newspaper articles. The percentage of covered studies and the number of newspaper articles per study strongly increased with the impact factor of the journal that published each scientific study. Newspapers almost equally covered initial (5/39 12.8%) and subsequent (58/600 9.7%) lifestyle studies. In contrast, initial non-lifestyle studies were covered more often (48/366 13.1%) than subsequent ones (45/3718 1.2%). Newspapers never covered initial studies reporting null findings and rarely reported subsequent null observations. Only 48.7% of the 156 studies reported by newspapers were confirmed by the corresponding meta-analyses. Initial non-lifestyle studies were less often confirmed (16/48) than subsequent ones (29/45) and than lifestyle studies (31/63). Psychiatric studies covered by newspapers were less often confirmed (10/38) than the neurological (26/41) or somatic (40/77) ones. This is correlated to an even larger coverage of initial studies in psychiatry. Whereas 234 newspaper articles covered the 35 initial studies that were later disconfirmed, only four press articles covered a subsequent null finding and mentioned the refutation of an initial claim. Conclusion Journalists preferentially cover initial findings although they are often contradicted by meta-analyses and rarely inform the public when they are disconfirmed.

This collection uses primary sources to explore the Populist Movement. Digital Public Library of America Primary Source Sets are designed to help students develop their critical thinking skills and draw diverse material from libraries, archives, and museums across the United States. Each set includes an overview, ten to fifteen primary sources, links to related resources, and a teaching guide. These sets were created and reviewed by the teachers on the DPLA's Education Advisory Committee.

This collection uses primary sources to explore the postwar growth of the American suburbs. Digital Public Library of America Primary Source Sets are designed to help students develop their critical thinking skills and draw diverse material from libraries, archives, and museums across the United States. Each set includes an overview, ten to fifteen primary sources, links to related resources, and a teaching guide. These sets were created and reviewed by the teachers on the DPLA's Education Advisory Committee.

6.334 examines the application of electronics to energy conversion and control. Topics covered include: modeling, analysis, and control techniques; design of power circuits including inverters, rectifiers, and DC-DC converters; analysis and design of magnetic components and filters; and characteristics of power semiconductor devices. Numerous application examples will be presented such as motion control systems, power supplies, and radio-frequency power amplifiers. The course is worth 6 engineering design points.

This activity can be used after reading Chapter 5 of The Great Gatsby. Listening and watching the video for Lana Del Rey's song, and title track for the film, students will dig deep into the lyrics of the song identifying figurative language, draw connections between the lyrics of the song and direct quotes from the text, and have meaningful discussion about point-of-view and symbolism in the video. Guaranteed to engage students and make valuable text to text connections!

The evidence-based community has championed the public registration of pre-analysis plans (PAPs) as a solution to the problem of research credibility, but without any evidence that PAPs actually bolster the credibility of research. We analyze a representative sample of 195 pre-analysis plans (PAPs) from the American Economic Association (AEA) and Evidence in Governance and Politics (EGAP) registration platforms to assess whether PAPs are sufficiently clear, precise and comprehensive to be able to achieve their objectives of preventing “fishing” and reducing the scope for post-hoc adjustment of research hypotheses. We also analyze a subset of 93 PAPs from projects that have resulted in publicly available papers to ascertain how faithfully they adhere to their pre-registered specifications and hypotheses. We find significant variation in the extent to which PAPs are accomplishing the goals they were designed to achieve

In this webinar Professor Brian Nosek, Executive Director of the Center for Open Science (https://cos.io), outlines the practice of Preregistration and how it can aid in increasing the rigor and reproducibility of research. The webinar is co-hosted by the Health Research Alliance, a collaborative member organization of nonprofit research funders. Slides available at: https://osf.io/9m6tx/

In recent years, open science practices have become increasingly popular in psychology and related sciences. These practices aim to increase rigour and transparency in science as a potential response to the challenges posed by the replication crisis. Many of these reforms -- including the highly influential preregistration -- have been designed for experimental work that tests simple hypotheses with standard statistical analyses, such as assessing whether an experimental manipulation has an effect on a variable of interest. However, psychology is a diverse field of research, and the somewhat narrow focus of the prevalent discussions surrounding and templates for preregistration has led to debates on how appropriate these reforms are for areas of research with more diverse hypotheses and more complex methods of analysis, such as cognitive modelling research within mathematical psychology. Our article attempts to bridge the gap between open science and mathematical psychology, focusing on the type of cognitive modelling that Crüwell, Stefan, & Evans (2019) labelled model application, where researchers apply a cognitive model as a measurement tool to test hypotheses about parameters of the cognitive model. Specifically, we (1) discuss several potential researcher degrees of freedom within model application, (2) provide the first preregistration template for model application, and (3) provide an example of a preregistered model application using our preregistration template. More broadly, we hope that our discussions and proposals constructively advance the debate surrounding preregistration in cognitive modelling, and provide a guide for how preregistration templates may be developed in other diverse or complex research contexts.

This course is an introduction to the design, analysis, and fundamental limits of wireless transmission systems. Topics to be covered include: wireless channel and system models; fading and diversity; resource management and power control; multiple-antenna and MIMO systems; space-time codes and decoding algorithms; multiple-access techniques and multiuser detection; broadcast codes and precoding; cellular and ad-hoc network topologies; OFDM and ultrawideband systems; and architectural issues.

Students investigate the life cycles of engineered products and how they impact the environment. They use a basic life cycle assessment method that assigns fictional numerical values for different steps in the life cycle. Then they use their analyses to compare the impacts of their products to other products, and suggest ways to reduce environmental impact based on their analyses.

Don’t take your data at face value. That is the key message of this tutorial which focuses on how scholars can diagnose and act upon the accuracy of data. In this lesson, you will learn the principles and practice of data cleaning, as well as how OpenRefine can be used to perform four essential tasks that will help you to clean your data:
1. Remove duplicate records
2. Separate multiple values contained in the same field
3. Analyse the distribution of values throughout a data set
4. Group together different representations of the same reality

These steps are illustrated with the help of a series of exercises based on a collection of metadata from the Powerhouse museum, demonstrating how (semi-)automated methods can help you correct the errors in your data.

Computer programs can become long, unwieldy and confusing without special mechanisms for managing complexity. This lesson will show you how to reuse parts of your code by writing Functions and break your programs into Modules, in order to keep everything concise and easier to debug. Being able to remove a single dysfunctional module can save time and effort.

Your list is now clean enough that you can begin analyzing its contents in meaningful ways. Counting the frequency of specific words in the list can provide illustrative data. Python has an easy way to count frequencies, but it requires the use of a new type of variable: the dictionary. Before you begin working with a dictionary, consider the processes used to calculate frequencies in a list.

This lesson uses Python to create and view an HTML file. If you write programs that output HTML, you can use any browser to look at your results. This is especially convenient if your program is automatically creating hyperlinks or graphic entities like charts and diagrams.

Here you will learn how to create HTML files with Python scripts, and how to use Python to automatically open an HTML file in Firefox.

In this two-part lesson, we will build on what you’ve learned about Working with Webpages, learning how to remove the HTML markup from the webpage of Benjamin Bowsey’s 1780 criminal trial transcript. We will achieve this by using a variety of string operators, string methods and close reading skills. We introduce looping and branching so that programs can repeat tasks and test for certain conditions, making it possible to separate the content from the HTML tags. Finally, we convert content from a long string to a list of words that can later be sorted, indexed, and counted.

In this lesson, you will learn the Python commands needed to implement the second part of the algorithm begun in the From HTML to a List of Words (part 1). The first half of the algorithm gets the content of an HTML page and saves only the content that follows the tags.

In this lesson you will first learn what topic modeling is and why you might want to employ it in your research. You will then learn how to install and work with the MALLET natural language processing toolkit to do so. MALLET involves modifying an environment variable (essentially, setting up a short-cut so that your computer always knows where to find the MALLET program) and working with the command line (ie, by typing in commands manually, rather than clicking on icons or menus). We will run the topic modeller on some example files, and look at the kinds of outputs that MALLET installed. This will give us a good idea of how it can be used on a corpus of texts to identify topics found in the documents without reading them individually.

This tutorial assumes basic knowledge of HTML, CSS, and the Document Object Model. It also assumes some knowledge of Python. For a more basic introduction to Python, see Working with Text Files.

Like in Output Data as HTML File, this lesson takes the frequency pairs collected in Counting Frequencies and outputs them in HTML. This time the focus is on keywords in context (KWIC) which creates n-grams from the original document content – in this case a trial transcript from the Old Bailey Online. You can use your program to select a keyword and the computer will output all instances of that keyword, along with the words to the left and right of it, making it easy to see at a glance how the keyword is used.

Once the KWICs have been created, they are then wrapped in HTML and sent to the browser where they can be viewed. This reinforces what was learned in Output Data as HTML File, opting for a slightly different output.

At the end of this lesson, you will be able to extract all possible n-grams from the text. In the next lesson, you will be learn how to output all of the n-grams of a given keyword in a document downloaded from the Internet, and display them clearly in your browser window.

The list that we created in the From HTML to a List of Words (2) needs some normalizing before it can be used further. We are going to do this by applying additional string methods, as well as by using regular expressions. Once normalized, we will be able to more easily analyze our data.