Batch Import Format - ccnmtl/footprints GitHub Wiki

Preparing data for import

Footprints can ingest data in a .csv format, encoded as UTF-8. The fields are detailed below. (Stay tuned: we are working on a Google Sheets template to help with constructing data sets.)

There are a few options to ensure the data is properly encoded. Using the default Microsoft Excel export to .csv will not work as Excel exports with the ANSI character set.

  1. Prepare your data in Google Sheets and export to .csv. Google Sheets handles special characters properly. Detailed Directions

  2. Prepare your data in Excel, save as .xls, import the .xls into Google Sheets, then export to .csv. Detailed Directions

Fields

bold fields are required

  • Catalog Link
    • Validation: url
  • BHB number
    • Unique identifier for the Imprint.
    • Validation: numeric
  • Imprint Title
    • Validation: text
  • Literary Work Title
    • Standardized English title, ideally from Library of Congress
    • Validation: text
  • Literary Work Author
    • Validation: name
  • Literary Work Author VIAF ID
    • Validation: numeric
  • Literary Work Author Birth Date
    • Validation: date
  • Literary Work Author Death Date
    • Validation: date
  • Publisher
    • Validation: name
  • Publisher VIAF ID
    • Validation: numeric
  • Publication Location
    • Validation: numeric, geonameId
  • Publication Date
    • Validation: date
  • Book Copy Call Number
    • Unique book copy identifier
    • Validation: text
  • Evidence Type
    • Validation: evidence type
  • Evidence Description
    • Validation: text
  • Evidence Location Description
    • Validate: text
  • Evidence Citation
    • Validation: text
  • Footprint Actor (Former Owner/Seller/Other)
    • Validation: name
  • Footprint Actor VIAF ID
    • Validation: numeric
  • Footprint Actor Role
    • Validation: role
  • Footprint Actor Begin Date
    • Validation: date
  • Footprint Actor End Date
    • Validation: date
  • Footprint Notes
    • Validation: text
  • Footprint Location
    • Validation: numeric, geonameId
  • Footprint Date
    • Validation: date
  • Footprint Narrative
    • Validation: text

Validators

Date

The python-edtf library supports a set of natural language date formats. Here are example of valid dates and date ranges that can be specified in the data to import. Additional date formats may/can be supported.

Basic Examples

  • century: 16th century
  • century: 1800s
  • decade: 1860s
  • year: 1860
  • month & year: January 2008
  • month, day, year: January 12, 1940

Uncertain/approximate

  • adding a ? indicates uncertainty
    • 1860?
  • prepending a ~ or c. prefix or approximately indicates approximate
    • c. 1860
    • ~1860
    • approximately 1860
  • Both modifiers can be used
    • ~1860?

Date Ranges

Any valid date separated by a hyphen. NO SPACES. Approximate, uncertain and unknown can be used to denote ambiguity. "before" is used to indicate an unknown start date, "after" is used to indicate an unknown end date

  • 1841?-~1879? - interval whose beginning is uncertain but thought to be 1841, and whose end is uncertain and approximate but thought to be 1879
  • January 12, 1940-December 1941
  • before 1992
  • after 1992

Evidence Type (Medium)

text must match exactly.

  • Approbation in imprint
  • Booklist/estate inventory
  • Bookseller/auction catalog (pre-1850)
  • Bookseller/auction catalog (1850-present)
  • Bookseller marking in extant copy
  • Censor signature in extant copy
  • Dedication in imprint
  • Library catalog/union catalog
  • Owner signature/bookplate in extant copy
  • Reference in another text
  • Subscription list in imprint

Name

  • Valid text string
  • Match on VIAF id, if exists
  • Match on name, birth date, death date

Numeric

  • Value must be a string of numbers, 0-9.

Numeric, Imprint & Footprint Location

Roles

text must match exactly.

  • Anthologizer
  • Bookdealer
  • Estate Agent
  • Expurgator
  • Giver
  • Librarian
  • Owner
  • Seller
  • Subscriber
  • Viewer

Url