You are going to have a challenge here because the tolerance indicated in your example (~1 minute) is much larger than the smallest step size in you data. This means that using +/- 1 min everywhere might result in combining duplicate rows.
In addition, you have a couple places where your time goes backwards, and then once sorted, several times where the adjacent rows have the same time, but different values. There is also one instance where you just have a date and no time. You will need to work out a solution for these situations.
There are two possible approaches I would start with: Convert your cells to timetables (cell2table and table2timetable), then use outerjoin or use synchronize to combine the two tables, horizontally concatenating the two data sets.
load TVKdata.mat
% Conver cells to timetables. I choose to sort
rawTt = cell2table(rawT(2:end,:),'VariableNames', rawT(1,:));
rawTt.tijd = datetime(rawTt.tijd,'InputFormat','dd-MM-yyyy HH:mm:ss');
rawTT = table2timetable(rawTt);
rawTT = sortrows(rawTT,"tijd");
rawVt = cell2table(rawV(2:end,:),'VariableNames', rawV(1,:));
rawVt.tijd = datetime(rawVt.tijd,'InputFormat','dd-MM-yyyy HH:mm:ss');
rawVV = table2timetable(rawVt);
rawVV = sortrows(rawVV,"tijd");
% Option 1: Join tables
joinedData = outerjoin(rawTT,rawVV)
% Option 2: synchronize
rawTT(ismissing(rawTT.tijd),:) = [];
finalTT = synchronize(rawTT,rawVV)
Note that the number of rows in both options is less than the sum of rows from rawT and rawV (210583), meaning it has combined rows that have the same exact time.
