text: import requests
  from bs4 import BeautifulSoup
  import pandas as pd
  import unicodedata
  
  # def parseFaculty():
  # use this if you want to directly parse from the website
  # pageContent = requests.get('http://informationsystems.umbc.edu/home/faculty-and-staff/faculty/')
  # soup = BeautifulSoup(pageContent.content)
  
  # offline parsing code
  with open('raw-data/faculty.html', encoding='utf-8') as f: facultyHtml = f.read()
  soup = BeautifulSoup(facultyHtml)
  # print(soup.prettify())
  article = soup.select('body#home div#container section.page-container.layout-default section.page-content section.main-content article')[0]
  # names = []
  # for item in [item.string for item in article.select('h6 a')]:
  #     if not '@umbc.edu' in item: names.append(item)
  
  qualifiedNames, researchDescriptions = [], []
  for item in article.select('h6'):
      if item.text:
          qualifiedNames.append(unicodedata.normalize("NFKD", item.text))    
          researchDescriptions.append(unicodedata.normalize("NFKD", item.next_sibling.next_sibling.text))
  data = dict(zip(qualifiedNames, researchDescriptions))
  df = pd.DataFrame([qualifiedNames, researchDescriptions]).T
  df.columns = ['qualifiedNames', 'researchDescriptions']
  df.to_csv('crawled-data/faculty.csv')
  # END parseFaculty()
      
  # def parseProjects():
  with open('raw-data/current-extramural-funded-projects.html', encoding='utf-8') as f: projectsHtml = f.read()
  soup = BeautifulSoup(projectsHtml)
  article = soup.select('body#home div#container section.page-container.layout-default section.page-content section.main-content article')[0]
  data = []
  for item in article.select('h4'):
      current = item
      infos = []
      while True:
          try:
              if current.text:
                  infos.append(unicodedata.normalize("NFKD", current.text))
                  if len(infos) == 5: break
          except AttributeError: pass
          current = current.next_sibling
      data.append(infos)
  df = pd.DataFrame(data)
  df.columns = ['title', 'soruce', 'principalInvestigator', 'awarded', 'description']
  df.to_csv('crawled-data/projects.csv')
  # END parseProjects()