def METHOD_NAME(verbosity, import_emplids=None):
    """
    Get all timeline data for programs we care about.
    """
    prog_map = build_program_map()
    import_units = Unit.objects.filter(slug__in=IMPORT_UNIT_SLUGS)
    acad_progs = [acad_prog for acad_prog, program in prog_map.items() if program.unit in import_units]
    check_environment()
    # always do these: safe and rest of the import gets weird without them.
    manual_cleanups(verbosity=verbosity, dry_run=False)
    # Get the basic program data we need to generate a Timeline object (JSONable so we can throw it in celery later)
    # each entry is a tuple of ('ClassName', *init_args)
    timeline_data = defaultdict(list)
    if import_emplids:
        import_emplids = set(import_emplids)
    for acad_prog in acad_progs:
        prog_changes = grad_program_changes(acad_prog)
        for p in prog_changes:
            emplid = p[1]
            if import_emplids and emplid not in import_emplids:
                continue
            d = timeline_data[emplid]
            d.append(p)
        # temporarily disable while we settle out everything else
        appl_changes = grad_appl_program_changes(acad_prog)
        for a in appl_changes:
            emplid = a[1]
            if import_emplids and emplid not in import_emplids:
                continue
            d = timeline_data[emplid]
            d.append(a)
    timeline_data = dict(timeline_data)
    if import_emplids:
        emplids = list(import_emplids)
    else:
        emplids = sorted(timeline_data.keys())
    # fetch all of the grad/committee data now that we know who to fetch for
    for r in itertools.chain(*_batch_call(grad_semesters, emplids)):
        emplid = r[1]
        timeline_data[emplid].append(r)
    for r in itertools.chain(*_batch_call(committee_members, emplids)):
        emplid = r[1]
        timeline_data[emplid].append(r)
    for r in itertools.chain(*_batch_call(grad_metadata, emplids, batchsize=50)):
        emplid = r[1]
        timeline_data[emplid].append(r)
    for r in itertools.chain(*_batch_call(research_areas, emplids)):
        emplid = r[1]
        timeline_data[emplid].append(r)
    return timeline_data