diff --git a/scripts/lantern.py b/scripts/lantern.py
index bf12ca9..a4d973d 100644
--- a/scripts/lantern.py
+++ b/scripts/lantern.py
@@ -635,7 +635,6 @@ Maintenance:
                                 print("[+] Removing the participant's duplicate entries... ")
                                 # REMOVE DUPLICATES !!! do not accept any duplicate from remote participants
                                 csvdf = csvdf.drop_duplicates(subset=['URL'], keep="first", inplace=False)
-                                csvdf = csvdf.drop_duplicates(subset=['Name'], keep="first", inplace=False)
                                 csvdf.to_csv(csvfilepath, index=False)
 
                                 csvdf = pd.read_csv(csvfilepath, on_bad_lines='skip')
@@ -650,7 +649,6 @@ Maintenance:
 
 
 
-
                                     ################################ SANITY CHECKS ####################################
     							    ### SANITY CHECK 0: make sure that ✔️ and x are replaced with YES/NO, as it changed since v1.0.1 ###
                                     if  csvdf.at[i, 'Status'] == "✔️" or csvdf.at[i, 'Status'] == "YES" :
@@ -691,16 +689,13 @@ Maintenance:
                                                 filterterm=csvdf.at[i, 'URL']
                                                 #print('1)',filterterm)
                                                 filter_vdf= vdf[vdf.URL.str.contains(filterterm,na=False)]
-                                                filter_vdf2= vdf[vdf.Name.str.contains(filterterm,na=False)] # do not accept the new link if the name already exists in verified.csv
                                                 #print('2)',filter_vdf)
                                                 #print('3)',uvdf[uvdf.URL.str.contains(filterterm,na=False)] )
                                                 uvdf = pd.read_csv(unverifiedcsvfile, on_bad_lines='skip')
                                                 # TODO DELETE ALL DUPLICATES OF UVDF !
                                                 uvdf = uvdf.drop_duplicates(subset=['URL'], keep="first", inplace=False)
-                                                uvdf = uvdf.drop_duplicates(subset=['Name'], keep="first", inplace=False)
                                                 filter_uvdf= uvdf[uvdf.URL.str.contains(filterterm,na=False)]
-                                                filter_uvdf2= uvdf[uvdf.Name.str.contains(filterterm,na=False)] # do not accept the new link if the name already exists in unverified.csv
-                                                if len(filter_uvdf.index) == 0 and len(filter_vdf.index) == 0 and len(filter_uvdf2.index) == 0 and len(filter_vdf2.index) == 0 :
+                                                if len(filter_uvdf.index) == 0 and len(filter_vdf.index) == 0:
                                                     newrow=row
                                                     uvdf.loc[-1] = newrow  # adding a row
                                                     uvdf.index = uvdf.index + 1  # shifting index
@@ -728,66 +723,12 @@ Maintenance:
 
                                     #print_colors(f'[-] Rows to delete: {rows2delete}', is_error=True)
                                 # only delete rows after you've gone through all the unverified.csv OR verified.csv rows'
-                                # check for NAME duplicates and mark them for deletion:
-                                # remove name duplicates that are in unverifie.csv yet exist in verified.csv (as verified.csv takes the priority)
-                                if w == 'unverified.csv':
-                                    try:
-                                        # check if the given row Name already exists in verified.csv
-                                        filterterm=csvdf.at[i, 'Name']
-                                        filter_vdf= vdf[vdf.Name.str.contains(filterterm,na=False)]
-                                        print('[+] CHECKING FOR DUPLIATES: ',filterterm)
-                                        if len(filter_vdf.index) != 0:
-                                            # drop the unverified.csv row if its name already exists in verified.csv
-                                            print('[+] DUPLICATE FOUND, MARKING ROW FOR DELETION: ',row)
-                                            rows2delete.append(i) #mark the row for deletion if not already done
-                                    except:
-                                        pass
-
-
                                 for i in rows2delete:
                                     row=csvdf.loc[i,:].values.tolist()
                                     print_colors(f'[+] REMOVING ROW: {i}{row}')
                                     csvdf.drop(i, inplace= True)
                                     csvdf.to_csv(csvfilepath, index=False)
                                     rows2delete= [] # it is an empty list at first
-
-                                # fill missing description in our unverified.csv that other participants verified.csv have filled
-                                if w == 'verified.csv':
-                                    uvdf = pd.read_csv(unverifiedcsvfile, on_bad_lines='skip')
-                                    # merge participant's verified.csv on our unverified.csv on URL
-                                    merged_df = uvdf.merge(csvdf[['URL', 'Description']], 
-                                        on='URL', 
-                                        how='left', 
-                                        suffixes=('', '_participant'))
-                                    # filter empty description that has participant's description
-                                    no_descr_filter = ((merged_df['Description'].isna()) | (merged_df['Description'].str.strip() == '')) & \
-                                        (~merged_df['Description_participant'].isna()) & (merged_df['Description_participant'].str.strip() != '')
-                                    no_descr_filter_count = no_descr_filter.sum()
-                                    # update our empty description if the participant has any filled description
-                                    if no_descr_filter_count > 0:
-                                        merged_df.loc[no_descr_filter, 'Description'] = merged_df.loc[no_descr_filter, 'Description_participant']
-                                        # keep only original columns
-                                        uvdf_updated = merged_df[uvdf.columns]
-                                        uvdf_updated.to_csv(unverifiedcsvfile, index=False)
-                                        print(f'[+] Updated {no_descr_filter_count} empty description(s) in your unverified.csv found on partipant\'s {w}')
-                # remove all name duplicates from your own unverified.csv file:  
-                for i,j in uvdf.iterrows():
-                    row=uvdf.loc[i,:].values.tolist()
-                    # check if the given row Name already exists in verified.csv
-                    filterterm=uvdf.at[i, 'Name']
-                    filter_vdf= vdf[vdf.Name.str.contains(filterterm,na=False)]
-                    print('[+] CHECKING FOR DUPLIATES: ',filterterm)
-                    if len(filter_vdf.index) != 0:
-                        # drop the unverified.csv row if its name already exists in verified.csv
-                        print('[+] DUPLICATE FOUND, MARKING ROW FOR DELETION: ',row)
-                        rows2delete.append(i) #mark the row for deletion if not already done
-                for i in rows2delete:
-                    row=uvdf.loc[i,:].values.tolist()
-                    print_colors(f'[+] REMOVING ROW: {i}{row}')
-                    uvdf.drop(i, inplace= True)
-                    uvdf.to_csv(unverifiedcsvfile, index=False)
-                    rows2delete= [] # it is an empty list at first
-
                 break