6.4. Data dictionary (DD)

The data dictionary is a TSV file with a single header row, and columns as defined below. (The DD columns can be in any order as long as the header row matches the data, and the column heading names are exactly as follows.)

Once you have edited your anonymiser config file to point to your source database, you can generate a draft data dictionary like this:

crate_anonymise --draftdd > mydd.tsv

Now edit the data dictionary as required. Then make your config file point to the data dictionary you want to use.

6.4.1. src_db

This column specifies the source database, using a name that matches one from the source_databases list in the config file.

6.4.2. src_table

This column specifies the table name in the source database.

6.4.3. src_field

This column specifies the field (column) name in the source database.

6.4.4. src_datatype

This column gives the source column’s SQL data type (e.g. INT, VARCHAR(50)).

6.4.5. src_flags

This field can be blank or can contain a string made up of one or more characters. The characters have the following meanings:




This field is the primary key (PK) for the table it’s in.


Add source hash of the record, for incremental updates?
  • This flag may only be set for source PK (K) fields (which cannot then be omitted in the destination, and which require the index=U setting, so that a unique index is created for this field).

  • If set, a field is added to the destination table, with field name as set by the config’s source_hash_fieldname variable, containing a hash of the contents of the source record – all fields that are not omitted, OR contain scrubbing information (scrub_src). The field is of type VARCHAR and its length is determined by the hash_method parameter (see below).

  • This table is then capable of incremental updates.


Record contents are constant (will not change) for a given PK.
  • An alternative to H. Can’t be used with it.

  • The flag can be set only on src_pk fields, which can’t be omitted in the destination, and which have the same index requirements as the H flag.

  • If set, no hash is added to the destination, but the destination contents are assumed to exist and not to have changed.

  • Be CAUTIOUS with this flag, i.e. certain that the contents will not change.

  • Intended for very data-intensive fields, such as BLOB fields containing binary documents, where hashing would be quite slow over many gigabytes of data.

  • Does not imply that the whole table cannot change!


Marks an addition-only table. It is assumed that records can only be added to this table, not deleted.
  • The field is permitted only for PK (K) fields.


Primary patient ID field. If set,
  1. This field will be used to link records for the same patient across all tables. It must therefore be present, and marked in the data dictionary, for ALL tables that contain patient-identifiable information.

  2. If the field is not omitted: the field will be hashed as the primary ID (database patient primary key) in the destination, and a transient research ID (TRID) also added.


This field defines primary PIDs. If set, this row will be used to search for all patient IDs, and will define them for this database. Only those patients will be processed (for all tables containing patient info). Typically, this flag is applied to a SINGLE field in a SINGLE table, usually the principal patient registration/demographics table.


Master ID (e.g. NHS number).
The field will be hashed with the master PID hasher.


This field is used to mark that the patient wishes to opt out entirely. It must be in a table that also has a primary patient ID field (because that’s the ID that will be omitted). If the opt-out field contains a value that’s defined in the optout_col_values setting (see config file), that patient will be opted out entirely from the anonymised database.


If this field is a scrub_src field (see below), and this flag is set, then at least one non-NULL value for this field must be present for each patient, or no information will be processed for this patient. (Typical use: where you have a master patient index separate from the patient name table, and data might have been brought across partially, so there are some missing names. In this situation, text might go unscrubbed because the names are missing. Setting this flag for the name field will prevent this.)

6.4.6. scrub_src

One of the following values, or blank:




Contains patient-identifiable information that must be removed from scrub_in fields.


Contains identifiable information about a carer, family member, or other third party, which must be removed from scrub_in fields.


This field is a patient identifier for ANOTHER patient (such as a relative). The scrubber should recursively include THAT patient’s identifying information as third-party information for THIS patient.

6.4.7. scrub_method

Applicable to scrub_src fields, this column determines the manner in which this field should be treated for scrubbing. It must be one of the following values (or blank):




Treat as a set of textual words. This is the default for all textual fields (e.g. CHAR, VARCHAR, TEXT). Typically used for names. Also OK for e-mail addresses.


Treat as a textual phrase (a sequence of words to be replaced only when they occur in sequence). Typically used for address components.


Treat as a number. This is the default for all numeric fields (e.g. INTEGER, FLOAT). If you have a phone number in a text field, use this method; it will be scrubbed regardless of spacing/punctuation.


Teat as an alphanumeric code. Suited to postcodes. Very like the numeric method, but permits non-digits.


Treat as a date. This is the default for all DATE/DATETIME fields.

6.4.8. decision

One of the following two values:




Omit the field from the output entirely.


Include it.

This is case sensitive, for safety.

6.4.9. inclusion_values

Either blank, or an expression that evaluates to a Python iterable (e.g. list or tuple) with Python’s ast.literal_eval() function (see https://docs.python.org/3.4/library/ast.html).

  • If this is not blank/None, then it serves as a ROW INCLUSION LIST – the source row will only be processed if the field’s value is one of the inclusion values.

  • It applies to the raw value from the database (before any transformation via alter_method).

  • This is not applied to scrub_src fields (which contribute to the scrubber regardless).

  • Note that [None] is a list with one member, None, whereas None is equivalent to leaving the field blank.


  • [None, 0]

  • [True, 1, 'yes', 'true', 'Yes', 'True']

6.4.10. exclusion_values

As for inclusion_values, but the row is excluded if the field’s value is in the exclusion_values list.

6.4.11. alter_method

Manner in which to alter the data. Blank, or a comma-separated list of one or more of:




Scrub in. Applies to text fields only. The field will have its contents anonymised (using information from other fields). Use this for any text field that end users might store free-text comments in.


Truncate this date to the first of the month. Applicable to text or date-as-text fields.


Convert a binary field (e.g. `VARBINARY`, `BLOB`) to text (e.g. `LONGTEXT`). The binary data is taken to be the representation of a document. The field EXTFIELDNAME, which must be in the same source table, must contain the file extension (e.g. 'pdf', '.pdf') or a filename with that extension (e.g. '/some/path/mything.pdf'), so that the anonymiser knows how to treat the binary data to extract text from it.


A more powerful way of specifying a filename that can be created using data from this table. The FMT parameter is an unquoted Python str.format() string; see https://docs.python.org/3.4/library/stdtypes.html#str.format. The dictionary passed to format() is created from all fields in the row.

Using an example from RiO: if your ClientDocuments table contains a ClientID column (e.g. 999999) and a Path column (e.g. 'appointment_letter.pdf'), and you know that the actual file will then be found at C:\some\path\999999\docs\appointment_letter.pdf, then you can specify this with


You probably want to apply this alter_method to the Path column in this example, though that’s not mandatory.


As for the binary-to-text option, but the field contains a filename (the contents of which is converted to text), rather than containing binary data directly.


If one of the text extraction methods is specified, and this flag is also specified, then the data row will be skipped if text extraction fails (rather than inserted with a NULL value for the text). This is helpful, for example, if your text-processing pipeline breaks; the option prevents rows being created erroneously with NULL text values, so that a subsequent incremental update will fix the problems once you’ve fixed your text extraction tools.


HTML encoding is removed, e.g. convert &amp; to & and &lt; to <


HTML tags are removed, e.g. from <a href="http://somewhere">see link</a> to see link


Hash this field,

You can specify multiple options separated by commas.

Not all are compatible (e.g. scrubbing is for text; date truncation is for dates).

If there’s more than one, text extraction from BLOBs/files is performed first. After that, they are executed in sequence. (The position of the skip-if-text-extraction-fails flag is immaterial.)

A typical combination might be:




6.4.12. dest_table

Table name in the destination database.

6.4.13. dest_field

Field (column) name in the destination database.

6.4.14. dest_datatype

SQL data type in the destination database.

If omitted, the source SQL data type is translated appropriately.

6.4.15. index

One of:




No index.


Create a normal index on the destination field.


Create a unique index on the destination field.


Create a FULLTEXT index, for rapid searching within long text fields. Only applicable to one field per table.

6.4.16. indexlen

Integer. Can be blank. If not, sets the prefix length of the index. This is mandatory in MySQL if you apply a normal (+/- unique) index to a TEXT or BLOB field. It is not required for FULLTEXT indexes.

6.4.17. comment

Field (column) comment, stored in the destination database.

6.4.18. Minimal data dictionary example

This illustrates a data dictionary for a fictional database.

Some more specialist columns (inclusion_values, exclusion_values) are not shown for clarity. Blank lines and comment lines (lines beginning with #) are ignored.

src_db  src_table  src_field    src_datatype  src_flags  scrub_src  scrub_method  decision  alter_method            dest_table  dest_field  dest_datatype  index  indexlen  comment
------- ---------- ------------ ------------- ---------- ---------- ------------- --------- ----------------------- ----------- ----------- -------------- ------ --------- ----------------------------------------------------

# The source table "patients" defines our patients.
# This is also a primary source of information that is used to build our scrubbers.
# Most information shouldn't come through to the destination database, but some (e.g. DOB) is helpful in a truncated form.
# This table also includes our master opt-out switch.

mydb    patients   patientnum   INTEGER(11)   K*H        patient    number        OMIT                                                                                      Local patient ID (PID); will be replaced by RID+TRID
mydb    patients   nhsnum       INTEGER(11)   M          patient    number        OMIT                                                                                      NHS number (MPID); will be replaced by MRID
mydb    patients   dob          DATE                     patient    date          include   truncate_date           patients    dob         DATE                            Date of birth (truncated to first of month)
mydb    patients   dod          DATE                                              include                           patients    dod         DATE                            Date of death, or NULL if alive
mydb    patients   forename     VARCHAR(255)             patient    words         OMIT
mydb    patients   surname      VARCHAR(255)             patient    words         OMIT
mydb    patients   telephone    VARCHAR(255)             patient    number        OMIT                                                                                      A phone number.
mydb    patients   opt_out_anon BIT           !

# The "addresses" table gives (potentially several) addresses per patient.

mydb    addresses  pk           INTEGER(11)   KH                                  include                           addresses   pk          INTEGER(11)     U               Arbitrary address PK.
mydb    addresses  patientnum   INTEGER(11)   P                                   OMIT
mydb    addresses  from_date    DATE                                              include                           addresses   from_date                   I
mydb    addresses  to_date      DATE                                              include                           addresses   to_date                     I
mydb    addresses  line1        VARCHAR(255)             patient    phrase        OMIT
mydb    addresses  line2        VARCHAR(255)             patient    phrase        OMIT
mydb    addresses  line3        VARCHAR(255)             patient    phrase        OMIT
mydb    addresses  line4        VARCHAR(255)             patient    phrase        OMIT
mydb    addresses  line5        VARCHAR(255)             patient    phrase        OMIT
mydb    addresses  postcode     VARCHAR(10)              patient    code          OMIT                                                                                      UK postcode.
mydb    addresses  lsoa         VARCHAR(10)                                       include                           addresses   lsoa                                        Lower Super Output Area, added by CRATE preprocessor (calculated from postcode).
mydb    addresses  imd          INTEGER                                           include                           addresses   imd                                         UK Index of Multiple Deprivation, added by CRATE preprocessor.

# The "relatives" table gives us some third-party information to add to our scrubbers.

mydb    relatives  pk           INTEGER(11)   KH                                  OMIT
mydb    relatives  patientnum   INTEGER(11)   P                                   OMIT
mydb    relatives  relationship VARCHAR(255)                                      OMIT
mydb    relatives  forename     VARCHAR(255)             thirdparty words         OMIT
mydb    relatives  surname      VARCHAR(255)             thirdparty words         OMIT

# The "notes" table contains simple text that needs scrubbing.

mydb    notes      pk           INTEGER(11)   KH                                  include                           notes       pk          INTEGER(11)      U
mydb    notes      patientnum   INTEGER(11)   P                                   OMIT                                                                                      Patient ID will be replaced by RID+TRID
mydb    notes      when         DATETIME                                          include                           notes       when        DATETIME         I
mydb    notes      note         VARCHAR(MAX)                                      include   scrub                   notes       note        LONGTEXT                        Gives the scrubbed note.

# The "documents" table uses filenames to refer to binary documents on disk, which need scrubbing.
# (If binary documents won't change once added, you might want to set the "C" flag on "doc_id", instead of "H", for efficiency.)

mydb    documents  doc_id       INTEGER(11)   KH                                  include                           documents   doc_id      INTEGER(11)      U              Document PK
mydb    documents  patientnum   INTEGER(11)   P                                   OMIT                                                                                      Patient ID will be replaced by RID+TRID
mydb    documents  when_added   DATETIME                                          include                           documents   when_added  DATETIME         I
mydb    documents  filename     VARCHAR(255)                                      include   filename_to_text,scrub  documents   contents    LONGTEXT         F              Becomes scrubbed document contents with FULLTEXT index.


Check minimal data dictionary example works.