```python
import pandas as pd
import numpy as np
import re
# Identify columns and match them so combining into 'records.csv' is seamless.
pm_cols = {'PMID': 'id',
'DOI': 'doi',
'Authors': 'authors',
'Title': 'title',
'Publication Year': 'year',
'Journal/Book': 'journal'}
em_cols = {'Embase Link': 'id',
'DOI':'doi',
'Author Names': 'authors',
'Title': 'title',
'Publication Year': 'year',
'Source': 'journal'}
wos_cols = {'UT (Unique WOS ID)':'id',
'DOI':'doi',
'Authors': 'authors',
'Article Title': 'title',
'Publication Year': 'year',
'Source Title': 'journal'}
pm_bptb = pd.read_csv('./rct/pubmed/pm_bptb.csv', encoding = 'utf-8', usecols = ['PMID', 'DOI', 'Authors', 'Title', 'Publication Year', 'Journal/Book']).rename(columns = pm_cols)
pm_ht = pd.read_csv('./rct/pubmed/pm_ht.csv', encoding = 'utf-8', usecols = ['PMID', 'DOI', 'Authors', 'Title', 'Publication Year', 'Journal/Book']).rename(columns = pm_cols)
pm_qt = pd.read_csv('./rct/pubmed/pm_qt.csv', encoding = 'utf-8', usecols = ['PMID', 'DOI', 'Authors', 'Title', 'Publication Year', 'Journal/Book']).rename(columns = pm_cols)
pm_plt = pd.read_csv('./rct/pubmed/pm_plt.csv', encoding = 'utf-8', usecols = ['PMID', 'DOI', 'Authors', 'Title', 'Publication Year', 'Journal/Book']).rename(columns = pm_cols)
pm_at = pd.read_csv('./rct/pubmed/pm_at.csv', encoding = 'utf-8', usecols = ['PMID', 'DOI', 'Authors', 'Title', 'Publication Year', 'Journal/Book']).rename(columns = pm_cols)
pm_ta = pd.read_csv('./rct/pubmed/pm_ta.csv', encoding = 'utf-8', usecols = ['PMID', 'DOI', 'Authors', 'Title', 'Publication Year', 'Journal/Book']).rename(columns = pm_cols)
pubmed = pd.concat([pm_bptb, pm_ht, pm_qt, pm_plt, pm_at, pm_ta])
pubmed.to_csv('./rct/pubmed/pubmed.csv', encoding = 'utf-8')
em_bptb = pd.read_csv('./rct/embase/em_bptb.csv', encoding = 'utf-8', usecols =['Embase Link', 'DOI', 'Author Names', 'Title', 'Publication Year', 'Source']).rename(columns = em_cols)
em_ht = pd.read_csv('./rct/embase/em_ht.csv', encoding = 'utf-8', usecols = ['Embase Link', 'DOI', 'Author Names', 'Title', 'Publication Year', 'Source']).rename(columns = em_cols)
em_qt = pd.read_csv('./rct/embase/em_qt.csv', encoding = 'utf-8', usecols = ['Embase Link', 'DOI', 'Author Names', 'Title', 'Publication Year', 'Source']).rename(columns = em_cols)
em_plt = pd.read_csv('./rct/embase/em_plt.csv', encoding = 'utf-8', usecols = ['Embase Link', 'DOI', 'Author Names', 'Title', 'Publication Year', 'Source']).rename(columns = em_cols)
em_at = pd.read_csv('./rct/embase/em_at.csv', encoding = 'utf-8', usecols = ['Embase Link', 'DOI', 'Author Names', 'Title', 'Publication Year', 'Source']).rename(columns = em_cols)
em_ta = pd.read_csv('./rct/embase/em_ta.csv', encoding = 'utf-8', usecols = ['Embase Link', 'DOI', 'Author Names', 'Title', 'Publication Year', 'Source']).rename(columns = em_cols)
embase = pd.concat([em_bptb, em_ht, em_qt, em_plt, em_at, em_ta])
embase.to_csv('./rct/embase/embase.csv', encoding = 'utf-8')
wos_bptb = pd.read_csv('./rct/wos/wos_bptb.csv', encoding = 'latin-1', usecols = ['UT (Unique WOS ID)', 'DOI', 'Authors', 'Article Title', 'Publication Year', 'Source Title']).rename(columns = wos_cols)
wos_ht = pd.read_csv('./rct/wos/wos_ht.csv', encoding = 'latin-1', usecols = ['UT (Unique WOS ID)', 'DOI', 'Authors', 'Article Title', 'Publication Year', 'Source Title']).rename(columns = wos_cols)
wos_qt = pd.read_csv('./rct/wos/wos_qt.csv', encoding = 'latin-1', usecols = ['UT (Unique WOS ID)', 'DOI', 'Authors', 'Article Title', 'Publication Year', 'Source Title']).rename(columns = wos_cols)
wos_plt = pd.read_csv('./rct/wos/wos_plt.csv', encoding = 'latin-1', usecols = ['UT (Unique WOS ID)', 'DOI', 'Authors', 'Article Title', 'Publication Year', 'Source Title']).rename(columns = wos_cols)
wos = pd.concat([wos_bptb, wos_ht, wos_qt, wos_plt])
wos.to_csv('./rct/embase/wos.csv', encoding = 'latin-1')
```