Merge pull request #84 from fhdsl/updates

Updates

03-Data_Security.Rmd

@@ -436,7 +436,7 @@ In summary, we covered issues related to data security in this chapter. We prese
 - Strong passwords should be used, preferably in the form of sentences with symbols and numbers. Password managers like Keychain, Dashlane, or other services can help securely store passwords.
 - Computers use caching to store recent data for faster access. Clearing caches regularly is essential to avoid security risks and potential exposure of sensitive data.
 - USB drives can pose security risks due to portability, malware, and memory issues. It's best to use reputable drives, enable encryption, disable AutoRun software, and remove the drive when not in use.
--Data masking obscures sensitive data to protect privacy. De-identification and encryption are common methods. De-identification removes identifiers, usually using the Safe Harbor or Expert Determination method. Encryption encodes data to prevent unauthorized access.
+- Data masking obscures sensitive data to protect privacy. De-identification and encryption are common methods. De-identification removes identifiers, usually using the Safe Harbor or Expert Determination method. Encryption encodes data to prevent unauthorized access.
 - Encryption involves encoding data with keys to protect it from unauthorized access. Asymmetric encryption uses two keys (public and private), while symmetric encryption uses one key for both encryption and decryption.
 - SSL (Secure Socket Layer) and SSH (Secure Shell) are protocols that establish secure connections and encrypt data. SSL is commonly used for websites, while SSH is used to connect to remote computers.
 - When files are deleted, the data remains on storage hardware, posing a security risk. Software-based data erasure methods overwrite the data, ensuring it cannot be recovered, allowing hardware to be reused securely.

06-Current_Data_Concerns.Rmd

@@ -172,7 +172,7 @@ This ultimately suggests that where researchers have two compatible types of dat
 
 ### Data falsification
 
-Although the previous example ultimately led to some discoveries of falsification, the majority of the discovery started with findings of simple mistakes. However, there are many other reported cases of researchers falsifying or modifying their data to improve their results. See [here](https://ori.hhs.gov/education/products/RIandImages/misconduct_cases/findings_of_misconduct.pdf) for examples of misconduct cases identified by the [Office of Research Integrity at the US department of Health and Human Services](https://ori.hhs.gov/).  
+Although the previous example ultimately led to some discoveries of falsification, the majority of the discovery started with findings of simple mistakes. However, there are many other reported cases of researchers falsifying or modifying their data to improve their results. See [here](https://ori.hhs.gov/content/case_summary) for examples of misconduct cases identified by the [Office of Research Integrity at the US department of Health and Human Services](https://ori.hhs.gov/).  
 
 
 ### Improper Data Reuse for Research

index.Rmd

@@ -1,5 +1,5 @@
 --- 
-title: "Ethical Data Handling for Cancer Research"
+title: "Ethical Data Handling"
 date: "`r Sys.Date()`"
 site: bookdown::bookdown_site
 documentclass: book