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Improving research transparency in the social sciences through pre-analysis plans

Posted: Mar, 23, 2021

By: Jordan Weiss

Openness, transparency, and reproducibility in research are critical to scientific progress.

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AI and Archives: finding the optimal path from analog to digital

Computational Text Analysis Working Group (CTAWG)

Title: AI and Archives: finding the optimal path from analog to digital

Presenter: Adam Anderson, Mellon Postdoctoral Fellow in the Digital Humanities, UC Berkeley

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Manuscript Workflow with R Markdown and Git

Posted: Mar, 16, 2021

By: Lawrence Tello

As part of my Masters of Public Health program I needed to complete a capstone. Working on a manuscript is a lot of back and forth: You need to make edits, fix your words and figures, and sometimes re-work entire sections. If you are like me, the thought of doing this process over a long period of time in Word makes me nauseous. Two main issues that cause this nausea for me are:

  1. I frequently forget to make a record of my writing and often overwrite work 

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Covidence: A Game Changer For Systematic Reviews

Posted: Mar, 09, 2021

By: Lawrence Tello

If you are planning on conducting a systematic review consider using Covidence for your next project! Your UC Berkeley account gives you access to an unlimited number of reviews and citations per review, as well enhanced support. 

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Enjoy the Layover

Posted: Mar, 09, 2021

By: Frank Hidalgo

Intercontinental trips require multiple days of traveling, layovers, and more often than not, rerouting due to bad weather. While some may consider a storm to be a roadblock, others may take it as an opportunity to explore a new city and its culture. A year ago, a heavy storm hit the entire world; COVID-19 sent us into lockdown. Most of our activities were cancelled. We could not go to work, eat at restaurants, or gather in-person with our friends anymore. That forced us to take a step back and adjust to the new situation.

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R Introduction to Deep Learning: Parts 1-2

This workshop introduces the basic concepts of Deep Learning - the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels.

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Python Introduction to Machine Learning: Parts 1-2

Overview

This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets.

Prior knowledge: We will assume a basic knowledge of Python and a basic understanding of machine learning techniques. No theory instruction will be provided.

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Python Visualization

For this workshop, we'll provide an introduction to visualization with Python. We'll cover visualization theory and plotting with Matplotlib and Seaborn, working through examples in a Jupyter (formerly IPython) notebook. The following plot types will be covered:

  • line

  • bar

  • scatter

  • boxplot

We'll also learn about styles and customizing plots.

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R Introduction to Machine Learning tidymodels: Parts 1-2

Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.

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