DOI: 10.5281/zenodo.17977459
title: A Dataset of 735,000 Migration Records (Bremen, 1830-1939)

description: Documentation

This dataset provides structured, enriched, and normalised historical passenger list data from the German North Sea port of Bremen. It comprises 735,545 individual records spanning the years 1830 to 1939.

The dataset is derived from the transcription efforts of the Bremen Society for Family Research (MAUS) and the Bremen Chamber of Commerce [1]. This version has been post-processed to enhance its analytical value for historical migration research, social history, and demography.

Key enhancements



Normalization: Standardised versions of primary dataset values.

Cleaning: Correction of orthographic variants and structural inconsistencies.

Enrichment: Georeferencing (Lat/Lon) for ports of departure and arrival.

Classification: Occupations mapped to HISCO codes, KldB2010 sectors, and training levels.


Technical Specifications



Filename: CHUP_Passengerlists.csv

File Format: CSV (Delimiter: "@")

File Size: ≈ 259.6 MB

Encoding: UTF-8

Dimensions: 735,545 Rows × 62 Columns

Language: Mostly German


Methodology

The processing workflow utilised string matching algorithms, regular expressions, and manualverification:



Extraction: Unique values were extracted from the primary MAUS dataset.

Normalization: Orthographic errors and variants were consolidated (e.g., 145 travel classvariants reduced to 16 standardised classes).

Enrichment: 



Geography: Ports were geocoded using the OpenStreetMap Nominatim API.

Occupations: Most frequent job titles were manually classified into sectors and training levels [2] and mapped to HISCO codes [3] where possible.




Data Dictionary

The dataset includes both the original transcription fields (derived from the primary MAUS database) and processed fields (indicated by the suffix _cleaned).

 

A. Metadata




Column Name
Data Type
Description


id
Integer
Unique identifier for the data entry.


archive_id
String
Archival identifier linking to the physical list.


notes
String
Remarks or notes on duplicate passengers.


sort_da
String
Sorting helper column for dates.


pfd
String
Holds dates of departure for certain voyages.




 

B. Passenger Data




Column Name
Data Type
Description


last_name
String
Last name of passenger.


first_name
String
First name of passenger.


gender
String
Gender (Original).


gender_cleaned
String
Gender (Standardised: Male 53.6%, Female 46.4%).


age
String
Age of Passenger (Original).


age_cleaned
Integer
Transformed birth dates to age in years (Mean age: 31.1).


marital_status
String
Marital status of passenger (Original).


marital_status_cleaned
String
Marital status (Standardised).


nationality
String
Citizenship (Original).


nationality_cleaned
String
Citizenship (Standardised).


previous_residence
String
City/Town of previous residence (Original).


state_or_province
String
Region of previous residence (Original).


state_or_province_cleaned
String
Region of previous residence (Standardised).


occupation
String
Occupational title (Original).


occupation_cleaned
String
Occupational title (Standardised).


occupation_hisco_nr
Float
HISCO classification code.


occupation_sector
Integer
KldB2010 Sector (0–9) classification.


occupation_training_level
Integer
KldB2010 Training Level (1–4) classification.


religion
String
Religion of passenger (Original).


religion_cleaned
String
Religion of passenger (Standardised).


ethnicity
String
Ethnicity of passenger (Original).


ethnicity_cleaned
String
Ethnicity of passenger (Standardised).


nr
String
Passenger number on the list.


emigrant
String
Passenger emigrant status.


amount_of_money
String
Cash carried by passenger.


literacy
String
Literacy status of passenger.


relative
String
Relatives of passenger.


ticket
String
Ticket details.


who_paid
String
Payer information.


previous_US_stay
String
Details on previous US residency.


duration_of_stay
String
Intended duration of stay (Original).


duration_of_stay_cleaned
String
Intended duration of stay (Standardized).


place_of_stay
String
Place of residence.


reference_person
String
Contact person.




 

C. Voyage Data




Column Name
Data Type
Description


date_of_departure
String
Departure date (Original).


date_of_departure_cleaned
String
Standardised departure date (ISO-8601: YYYY-MM-DD).


ship
String
Name of ship (Original).


ship_cleaned
String
Name of ship (Standardised).


agent
String
Shipping company (Original).


agent_cleaned
String
Shipping company (Standardised).


travel_class
String
Travel class (Original).


travel_class_cleaned
String
Travel class (S


captain
String
Captain of ship.


port_of_departure
String
Port of departure (Original).


port_of_departure_cleaned
String
Port of departure (Standardised).


port_of_departure_country
String
Country of port of departure.


port_of_departure_LAT/_LON
Float
Geocoordinates (WGS84) of departure port city.


port_of_arrival
String
Arrival port (Original).


port_of_arrival_cleaned
String
Arrival port (Standardised).


port_of_arrival_country
String
Country of arrival port.


port_of_arrival_LAT/_LON
Float
Geocoordinates (WGS84) of arrival port city.


port_of_arrival_US_state
String
US-state of arrival port.


emigration_destination
String
General destination stated by passenger.


US_state
String
State of destination in the US (Original).


US_state_cleaned
String
State of destination in the US (Standardised).




 

Limitations



Missing Years: Passenger lists from 1875 to 1907 were destroyed due to historical storagepolicies and are not included in this dataset. In total, the dataset holds just a few entriesfrom the 19th century.

Interwar Gaps: Records for the years 1924 and 1929 are notably incomplete due toarchival loss during WWII.

Data Sparsity: While core fields are nearly 100% complete, socio-economic variables likeethnicity, religion, or amount_of_money have higher rates of missing data.

Unprocessed Data: Some columns were not standardised, either because their cleaningwould not have enhanced the analytical value of the column, or because the amount ofunique values would have made an attempt of normalising unfeasible.