#UKArcDiscovery16

Andrew Janes

Senior Archivist – Future Catalogues

UKAD forum, 17 March 2016

Twitter: @cartivist #UKArcDiscovery16

State of the Catalogue

2

C Madsen and M Hurst, Resource Discovery @ The University of Oxford 2015, p 24

http://discovery.bodleian.ox.ac.uk/wp-content/uploads/sites/5/2016/02/OxfordResourceDiscoveryReport_Final_public.pdf

“To state the obvious, collections cannot be

discovered using electronic search tools

unless they have some sort of

representative electronic description.”

3

S van Hooland and R Verborgh, Linked Data for Libraries, Archives and Museums (2014), p 71

“All metadata is dirty,

but you can do something about it.”

4

The next 15 minutes

• Data-(in)adequacy: the good, the bad and the ugly

• How did we end up like this?

• The State of the Catalogue approach

• “Data-centric” or “neo-Jenkinsonian”?

5

TNA’s catalogue within Discovery

6

Discovery

Finding Archives

data

ePRO and DRI

data

TNA Catalogue

data

Inadequate catalogue data

7

“It does exactly what it says on the tin”

Reference: OS 33/1812

Description: Map showing the boundaries of the

following places: Somerset: Clapton-in-

Gordano (parish), Portbury (parish)

Date: 1964

Held by: The National Archives, Kew

Legal status: Public Record

Closure status: Open Document, Open Description

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The bare minimum?

Essential elements

according to ISAD(G)2*

• Reference code

• Title

• Date(s)

• Extent

• Level of description

• Name of creator(s)

TNA measures of basic

data adequacy

• Description

• Date

• Unique reference code

Non-measurable data

adequacy

• Hierarchical structure

• Browsing order*ISAD(G), 2nd edition (2000), para I.12

www.ica.org/10207/standards/isadg-general-international-

standard-archival-description-second-edition.html

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The extent of the problem challenge

File- and item-level catalogue entries that fail the

Ronseal test as of January 2016:

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Unreferenced items: 477,723

Files and items with no descriptions: 305,429

Files and items with blank dates: 454,345

Total: 1,237,497

Uneven distribution of data problems

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30% in worst 10 series

39% in worst 20 series

56% in worst 50 series

69% in worst 100 series

81% in worst 200 series

92% in worst 500 series

97% in worst 1000 series

PRO finding aids system in the mid 1990s

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Creating PROCAT,

the original PRO online

catalogue

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• Series level and

above: curated entries

• Subseries level and

below: bulk conversion

of entries

Residual issues after

retrospective conversion

14

OS 33/1812

[blank description]

[blank date]

OS 33/1812

Clapton-in-Gordano

1964

OS 33/1812

Portbury

1964

Subsequent developments

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Data enhancement

• More conversion of

paper finding aids

• More cataloguing

from original

records

• Tidying up for data-

adequacy

• Correcting errors

• Accessions: more

records, more data

Changes in the context

of presenting the data

• Users expect to search

bottom-up not top-down

• Growth of digital and

digitised content

• Discovery: a combined

search tool

Where are we now with our catalogue data?

16

Grows and improves

every day

Multifaceted approach

to enhancement

Very uneven richness

and granularity

1 million + major

inadequacies

State of the Catalogue: purpose

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• Assess the scope and extent of bad data

• Reduce bad data proactively and efficiently

• Working series by series

• Making records more accessible

• Faciliating research

• Influence wider cataloguing priorities and decisions

DO

Understand context,

structure and content

Consult relevant

colleagues

Check and fix errors

and anomalies

Edit data in bulk

Check the records

systematically

Expand descriptions

from paper finding aids

Enforce finer detail of

descriptive standards

Proofread every line

State of the Catalogue: in and out of scope

DON’T

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State of the Catalogue: typical outline process

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1. Download data to Excel

2. Fix errors and anomalies

3. Make bulk changes

4. Fix errors and anomalies

5. Upload data to PROCAT

Editorial

6. Manual editing (e.g. of

subseries)

7. Release into Discovery

8. Delete any redundant

entries

9. Check and mop up

Some simple and useful data tools in Excel

• Sorting

• Filtering

• Conditional formatting

• Text to columns

• Functions

Functions include:

• AND, OR, NOT

• IF

• CONCATENATE

• LEFT, MID, RIGHT

• VLOOKUP

• LEN

• TRIM

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Progress in eliminating bad data

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• January 2013:

2.85 million data problems

• January 2016:

1.23 million data problems

Is State of the Catalogue “data-centric”?

• A radically-traditional approach

• Moral defence of the record requires adequate data

• Helps redress huge variation in depth and richness of

catalogue data (see MAD’s rule against bias*)

• Prioritises adequate breadth over extra depth

• A record-centric approach

• A data-centric approach?

22

*M Proctor and M Cook, A Manual of Archival Description, 3rd edition (2000), para 8.6E