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Category: Data Science

Literature Mining for Meta-Analysis Using the scopusmining Package

Literature Mining for Meta-Analysis Using the scopusmining Package

Meta-analysis is a quantitative method that synthesizes results from multiple independent studies to identify overall patterns, effect sizes, and sources of variability for specific treatments or research questions. It is particularly powerful for summarizing existing evidence and placing new findings within the context of current scientific trends through transparent and reproducible statistical integration of prior studies. A rigorous meta-analysis begins with a comprehensive and systematic literature search using predefined search terms and Boolean combinations across multiple databases. Study selection and…

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How to Set Up RStudio Server on a Linux-Based Virtual Private Server (VPS)

How to Set Up RStudio Server on a Linux-Based Virtual Private Server (VPS)

A Virtual Private Server (VPS) is a virtualized computer within a larger physical server. It acts like an independent server, offering dedicated resources and control at a lower cost than a full physical machine. VPS hosting uses this setup to give users private, customizable environments for web hosting or applications. Some reputable VPS providers include DigitalOcean, Linode, and Vultr — I currently use DigitalOcean. A Droplet in DigitalOcean is basically a virtual private server (VPS) — a self-contained Linux machine…

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[Data article] How to Import Data from MySQL Server to R?

[Data article] How to Import Data from MySQL Server to R?

In my previous post, I introduced how to import data into a Cloud MySQL database using Python from the Command Prompt. By typing the code below in your Command Prompt, you can automatically import data into your MySQL server. This is the next step. After importing data into the MySQL server, what if I want to bring that data into R? Is that possible? Yes—it is. 1) install and load the package and library. 2) Create a connection to the…

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[Data article] Predicting Intermediate Data Points with Linear Interpolation in Excel and R

[Data article] Predicting Intermediate Data Points with Linear Interpolation in Excel and R

Today, I’ll explain the interpolation technique used to predict in-between data points. For example, when collecting field data, we might not be able to gather information every day, so we establish our own interval (e.g., weekly or bi-weekly). However, when presenting the data, it might be necessary to show it on a daily basis. As another example, consider investigating yield differences in response to varying continuous variables, such as nitrogen at levels of 0, 30, 60, 120. What if we…

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[Meta-Analysis] Mining Academic Papers from SCOPUS with Pybliometrics in Python

[Meta-Analysis] Mining Academic Papers from SCOPUS with Pybliometrics in Python

SCOPUS is one of the largest abstract and citation databases, providing access to a wide range of peer-reviewed literature across various disciplines. It ensures researchers have access to high-quality, up-to-date academic papers, conference proceedings, and other scholarly materials. Pybliometrics is a Python library that streamlines the retrieval of bibliometric data from SCOPUS. It simplifies accessing and manipulating large datasets, saving researchers time and effort compared to manual data collection. Using Pybliometrics to mine academic papers from SCOPUS enables efficient data…

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[Data article] How many times do we need to compare each other according to group numbers ?

[Data article] How many times do we need to compare each other according to group numbers ?

All of a sudden, I became curious about this question, How many time do we need to compare each other according to group numbers?” and searched for the answer on a website, but I couldn’t find a clear answer. Therefore, I calculated it myself. For example, when there are two groups, we will compare them only once. When there are three groups, we need to compare each group with every other group, resulting in three comparisons. With four groups, we…

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Simplifying Data Manipulation: Transposing Columns into Rows with Ease

Simplifying Data Manipulation: Transposing Columns into Rows with Ease

Sometimes, I see many people organizing their data in columns, as shown in the example below. This format may seem convenient because it allows us to view all the data at once. However, it poses problems for data analysis, which fundamentally relies on variables — namely, independent and dependent variables. Download data file (.csv) https://github.com/agronomy4future/raw_data_practice/blob/main/yield_per_location.csv In the given data format, each level of the independent variable (i.e., location) is not combined into a single column, so we need to rearrange…

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[데이터 칼럼] 선형 보간법 (Linear Interpolation) 을 사용하여 중간 데이터를 예측해 보자

[데이터 칼럼] 선형 보간법 (Linear Interpolation) 을 사용하여 중간 데이터를 예측해 보자

오늘은 데이터 사이에 있는 값을 예측하기 위한 선형 보간법 (Linear Interpolation) 에 대해 설명하겠습니다. 예를 들어, 현장에서 데이터를 수집할 때 매일 데이터를 수집할 수는 없을 것입니다. 그래서 우리는 일정한 간격 (매주, 격주, etc.,) 으로 데이터를 수집합니다. 그러나 데이터를 제시할 때는 일별로 표시해야 할 경우가 발생 합니다. 예를 들어, 질소 비료 시비량이 0kg/ha, 30kg/ha, 60kg/ha, 120kg/ha 일 때 반응하는 작물의 수확량 차이를 조사한다고 가정해 보겠습니다. 0부터 120까지의 각 질소 비료량에서 수확량 차이를 나타내야 한다면 어떻게 데이터를 추정할 수 있을까요? 이런 상황에서…

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