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On Your MARC, Get
Set, Code!
Hosted by Core: Leadership, Infrastructure, Futures
March 23, 2022
Presenters
Paul Daybell
Archival Cataloging Librarian
paul.daybell@usu.edu
Andrea Payant
Metadata Librarian
andrea.payant@usu.edu
Liz Woolcott
Cataloging and Metadata Services Unit Head
liz.woolcott@usu.edu
Project Team Leadership
Anna-Maria Arnljots
Metadata Assistant
anna-maria.arnljots@usu.edu
Paul Daybell
Archival Cataloging Librarian
paul.daybell@usu.edu
Kurt Meyer
Government Information and E-
Resource Cataloger
kurt.meyer@usu.edu
Andrea Payant
Metadata Librarian
andrea.payant@usu.edu
Becky Skeen
Special Collection Cataloging Librarian
becky.skeen@usu.edu
Liz Woolcott
Cataloging and Metadata Services Unit Head
liz.woolcott@usu.edu
Full Research Team
• Anna-Maria Arnljots
• Josee Butler
• Ryan Bushman (Stats)
• Paul Daybell
• Barbara Fleming
• Maddie Gardner
• Alisha Grant
• Bryn Larsen
• Sabrina Leatham
• Rachel Olsen
• Andrea Payant
• Kurt Meyer
• Jessica Mills
• Abby Rodabough
• MaKayla Roundy
• Melanie Shaw
• Becky Skeen
• Sara Skindelien
• Seth Westenburg
• Liz Woolcott

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Background
• Multi-year research into user search behavior for all metadata
standards employed by the unit
 First phase: MARC
 Second phase: EAD
 Current phase: Dublin Core
• Project started just as the library moved everyone to work from
home
• Whole unit was able to participate in the coding project
Problem Statement
How do well do MARC records perform in a typical
user search process?
Research Questions
• What is the frequency and placement of MARC
records in search results lists?
• Where are search terms located in Marc
records?
Table of Contents
Log Analysis (Liz)
Methodology (Andrea)
Results and Analysis (Paul)
Programs and Resources (Liz)

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MARC-based catalog records 5264 3299 4749 13312
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Cataloging and Metadata Services 5,066 38.06% 239 57.18%
Distance Campus Libraries 410 3.08% 5 1.20%
Record unavailable at time of coding 52 0.39% 2 0.48%
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Acquisitions 16 0.12% 0 0.00%
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Percentage of Times Available Whole Object Appeared in Search Results by Position Number
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Results
6-10
Results
11-15
Results
16-20
Results
21-25
Total # 125 107 61 49 37 104 67 56 35
% in results 18.7% 16.0% 9.1% 7.3% 5.5% 15.6% 10.0% 8.4% 5.2%
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Where are search terms located in
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Poll 2
Besides the title (245) field,
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search terms?
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What fields are used most in retrieving records?
9100
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245 505 650 520 600
Number
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MARC Fields Where Search Terms Were
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For locally created records and vendor-supplied records, is
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Percentage of fields used in record retrieval (top 5 most frequent)
Field Field Description CMS Records Vendor Records
245 Title Statement 43.80% 51.64%
505 Formatted Contents Note 28.13% 69.65%
650 Subject Added Entry - Topical 40.89% 56.58%
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Author (both 1xx and 7xx) 0.75% 99.25% 1.18% 98.82%
Subject (any authorized) 4.46% 95.54% 6.73% 93.27%
505 Formatted Contents Note 63.96% 36.04% 45.54% 54.46%
520 Summary Note 75.60% 24.40% 50.45% 49.55%
All Categories Present 14.86% 33.26%
Analysis 2.5:
Which fields would make the greatest impact if not included in the record?
• The top four fields with the greatest impact on retrieval, if not found in a record:
505, 245, 520, and 650
• Without the 505 or 520, 16.86% of all records appearing in results would not
have shown up
• In contrast, without 650 and 600 fields, only 0.66% of records would not have
appeared in the search results
MARC Fields
Analysis
Results and Analysis
MARC Fields Findings
1
WORD COUNT
IMPROVES
DISCOVERY
2
TABLE OF CONTENTS
AIDS DISCOVERY
3
ABSTRACTS AID
DISCOVERY
4
NAME AUTHORITY
FILES ARE USED
5
SUBJECT FIELDS NOT
AS FREQUENTLY USED

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Analysis
• Non-MARC records have
advantage over MARC
• MARC vendor records appear
more often than locally
created MARC records
80% Of all records in search
results are Non-MARC
25% Of MARC records place in the
top 5 search results
505/520
Occur more frequently in
vendor records
1xx/6xx/7xx
Occur at the same rate in
Vendor and Locally
created records
Analysis
Title fields are most important overall, but…
505 =
• Ranked higher than 245
for records where search
terms matched only one
field
• Consistently in the top
4 fields that retrieved
a record (along with
520)
• If missing, 12% of all
MARC results would
not have been
displayed
Analysis
3rd Most important
field for matching
search terms
2nd Most important
field for records
viewed by patrons
1xx fields were much more likely to be “clicked on”
.66%
Would not have been
displayed if field
were missing
1
Instance of subject
fields being “clicked
on”
Subject fields
are important
BUT…
MARC Take-Aways
• Cataloger will retain ability to make best judgment for each
record, but will be asked to consider the following
guidelines:
 More emphasis on creating 505 and 520 notes in
local records
 Less emphasis on 6xx fields as an entry point
 More emphasis on 1xx fields as an entry point

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Poll 3
I have used the following
programs:
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• Google Analytics
Pro
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 Customizable reports
 Good export options (PDF, Google
Sheets, CSV, Excel)
 Runs constantly –good for historical
data
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 Privacy issues
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• Octoparse
Pros
 Free option (under 10, trial)
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 Free version is limited in projects
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Pros and Cons: Airtable
Pros
 Linking
 Flexible
 Dynamic dashboards
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 Subscription
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Web log
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 Dublin Core Discoverability
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Upcoming
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Coding Group
• Anna-Maria Arnljots
• Josee Butler
• Ryan Bushman (Stats)
• Paul Daybell
• Barbara Fleming
• Maddie Gardner
• Alisha Grant
• Bryn Larsen
• Sabrina Leatham
• Rachel Olsen
• Andrea Payant
• Kurt Meyer
• Jessica Mills
• Abby Rodabough
• MaKayla Roundy
• Melanie Shaw
• Becky Skeen
• Sara Skindelien
• Seth Westenburg
• Liz Woolcott
Questions?
Anna-Maria Arnljots
Metadata Assistant
anna-maria.arnljots@usu.edu
Paul Daybell
Archival Cataloging Librarian
paul.daybell@usu.edu
Kurt Meyer
Government Information and E-
Resource Cataloger
kurt.meyer@usu.edu
Andrea Payant
Metadata Librarian
andrea.payant@usu.edu
Becky Skeen
Special Collection Cataloging Librarian
becky.skeen@usu.edu
Liz Woolcott
Cataloging and Metadata Services Unit Head
liz.woolcott@usu.edu
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On Your MARC, Get Set, Code!

  • 1. On Your MARC, Get Set, Code! Hosted by Core: Leadership, Infrastructure, Futures March 23, 2022
  • 2. Presenters Paul Daybell Archival Cataloging Librarian paul.daybell@usu.edu Andrea Payant Metadata Librarian andrea.payant@usu.edu Liz Woolcott Cataloging and Metadata Services Unit Head liz.woolcott@usu.edu
  • 3. Project Team Leadership Anna-Maria Arnljots Metadata Assistant anna-maria.arnljots@usu.edu Paul Daybell Archival Cataloging Librarian paul.daybell@usu.edu Kurt Meyer Government Information and E- Resource Cataloger kurt.meyer@usu.edu Andrea Payant Metadata Librarian andrea.payant@usu.edu Becky Skeen Special Collection Cataloging Librarian becky.skeen@usu.edu Liz Woolcott Cataloging and Metadata Services Unit Head liz.woolcott@usu.edu
  • 4. Full Research Team • Anna-Maria Arnljots • Josee Butler • Ryan Bushman (Stats) • Paul Daybell • Barbara Fleming • Maddie Gardner • Alisha Grant • Bryn Larsen • Sabrina Leatham • Rachel Olsen • Andrea Payant • Kurt Meyer • Jessica Mills • Abby Rodabough • MaKayla Roundy • Melanie Shaw • Becky Skeen • Sara Skindelien • Seth Westenburg • Liz Woolcott
  • 5. Background • Multi-year research into user search behavior for all metadata standards employed by the unit  First phase: MARC  Second phase: EAD  Current phase: Dublin Core • Project started just as the library moved everyone to work from home • Whole unit was able to participate in the coding project
  • 6. Problem Statement How do well do MARC records perform in a typical user search process?
  • 7. Research Questions • What is the frequency and placement of MARC records in search results lists? • Where are search terms located in Marc records?
  • 8. Table of Contents Log Analysis (Liz) Methodology (Andrea) Results and Analysis (Paul) Programs and Resources (Liz)
  • 9. Log Analysis What is log analysis? What kind of data can we get from it?
  • 10. • Rezarta Islamaj Dogan, G. Craig Murray, Aurélie Névéol, Zhiyong Lu, Understanding PubMed® user search behavior through log analysis, Database, 2009, https://doi.org/10.1093/database/bap018 “Web logs can capture a number of informative aspects of a user’s interaction, including timing, query term selection and paths taken through a Web site.”
  • 11. ENCORE Single search box presented on the library homepage
  • 12. Web Logs Example of time- stamped web logs from Google Analytics
  • 13. Breaking down the URL http://discover.lib.usu.edu/iii/encore/plus/C__ Senvironmental sociology__P1__O- date__X0__T__Ks@2000e@2020?lang=eng& suite=cobalt
  • 14. ENCORE Example of search results page http://discover.lib.usu.edu/iii/encore/p lus/C__Senvironmental sociology__P1__O- date__X0__T__Ks@2000e@2020?l ang=eng&suite=cobalt
  • 15. ENCORE Example of record page (This is exclusively from Sierra.) http://discover.lib.usu.edu/iii/encore/record/C_ _Rb4067331__Senvironmental sociology__P1__O- date__X0__T__Ks@2000e@2020?lang=eng&su ite=cobalt http://discover.lib.usu.edu/iii/encore/p lus/C__Senvironmental sociology__P1__O- date__X0__T__Ks@2000e@2020?l ang=eng&suite=cobalt
  • 16. ENCORE Example of advanced search page http://discover.lib.usu.edu/iii/encore/plus/ C__S(environmental sociology) a:(Gustavo Medina) f:a y:[2000- 2020]__U__X0?lang=eng&suite=cobalt
  • 20. WEB LOGS Exported list of all URLs accessed the previous day, sorted by time
  • 21. • Uploaded into Airtable • Assigned ID • Sorted for search vs. record page Web Logs Search results page URLs fed into Octoparse
  • 24. WEB SCRAPE Each item on a search results page is numbered, uploaded into Airtable, and linked with the URL that generated the item.
  • 27. Poll 1 What types of data do you have experience coding (if any)?
  • 28. CODING • Extract search terms • Coded for:  Page Type  Advanced Search fields used  Facets Used  Page # URL Content
  • 29. CODING • URLS grouped into search sessions • Assigned a search ID • Put in order of occurrence • Search re-run for QC • Coded for:  Search term construction  Search Categories (known item, topical, etc.)  User Path  Known Item Titles Search Queries
  • 30. CODING • Extracted from URL/Search Query coding • Coded for:  Format/Genre type  Availability  Physical/Electronic  Location  Steps to access (e-resources)  Listed by (in Encore)  Final content provider  Check-outs  Discoverability in Google Scholar and Microsoft Academic o Step to access (e-resources) Known Items
  • 31. CODING • Filtered for just Sierra records • BIB # extracted from URL • MARC record copy/pasted from WebPac • MARC record coded for:  Creator  Material Type  MARC field where search term is found  Fields not present  Word Count MARC Records
  • 32. 1,040 13,312 609 Search Sessions Coded MARC Records Coded Known Items identified and coded
  • 35. Research Question #1 What is the frequency and placement of MARC records in search results lists?
  • 36. Batch 1 Batch 2 Batch 3 Combined MARC-based catalog records 5264 3299 4749 13312 Records from other platforms 20326 17560 16811 54697 Total Records 25603 20859 21560 68022 Percent MARC records 20.56% 15.82% 22.03% 19.57% Analysis 1.1: How frequently are MARC records showing up in search results?
  • 37. Analysis 1.2: Is there a difference between locally created records and vendor supplied records in the frequency of listing in search results? Record Creator # Records in results list % Total records in results list # Records accessed % Total records accessed Vendor 7,727 58.05% 163 39.00% Cataloging and Metadata Services 5,066 38.06% 239 57.18% Distance Campus Libraries 410 3.08% 5 1.20% Record unavailable at time of coding 52 0.39% 2 0.48% Patron Services, Library Media Collections, or Resource Sharing and Document Delivery 33 0.25% 8 1.91% Acquisitions 16 0.12% 0 0.00% Unknown 5 0.04% 1 0.24% Natural History Library 3 0.02% 0 0.00% Total 13,312 418
  • 38. Analysis 1.3: How are MARC records ranked in the search results list? • Most common position for MARC records in a search result set of 25 items, is position 4 • MARC records appear in the top five search results 25.35% of the time
  • 39. Analysis 1.4: Where do MARC records for known items rank in the search results list? Percentage of Times Available Whole Object Appeared in Search Results by Position Number Result 1 Result 2 Result 3 Result 4 Result 5 Results 6-10 Results 11-15 Results 16-20 Results 21-25 Total # 125 107 61 49 37 104 67 56 35 % in results 18.7% 16.0% 9.1% 7.3% 5.5% 15.6% 10.0% 8.4% 5.2%
  • 40. Research Question #2 Where are search terms located in MARC records?
  • 41. Poll 2 Besides the title (245) field, what field do you think most frequently contained user search terms?
  • 42. Analysis 2.1: What fields are used most in retrieving records? 9100 4998 4806 3700 1328 245 505 650 520 600 Number of Records MARC Fields MARC Fields Where Search Terms Were Located (Top 5)
  • 43. Analysis 2.2: For records accessed by the patron, is there a difference in where search terms are located? • The 245 Title statement remained highest, appearing 64% more often than the next most utilized field • Instead of the 505 Formatted Contents Note being in second place, the 650 Subject Added Entry is the next most used field • The 505 Formatted Contents Note and 520 Summary fields retained a spot in the top four fields
  • 44. Analysis 2.3: For locally created records and vendor-supplied records, is there a difference in where search terms are located? Percentage of fields used in record retrieval (top 5 most frequent) Field Field Description CMS Records Vendor Records 245 Title Statement 43.80% 51.64% 505 Formatted Contents Note 28.13% 69.65% 650 Subject Added Entry - Topical 40.89% 56.58% 520 Summary, etc. 23.41% 76.03% 600 Subject Added Entry – Personal Name 59.94% 32.68%
  • 45. Analysis 2.4: What fields are not present in the records? CMS Vendor Not Present Present Not Present Present Author (both 1xx and 7xx) 0.75% 99.25% 1.18% 98.82% Subject (any authorized) 4.46% 95.54% 6.73% 93.27% 505 Formatted Contents Note 63.96% 36.04% 45.54% 54.46% 520 Summary Note 75.60% 24.40% 50.45% 49.55% All Categories Present 14.86% 33.26%
  • 46. Analysis 2.5: Which fields would make the greatest impact if not included in the record? • The top four fields with the greatest impact on retrieval, if not found in a record: 505, 245, 520, and 650 • Without the 505 or 520, 16.86% of all records appearing in results would not have shown up • In contrast, without 650 and 600 fields, only 0.66% of records would not have appeared in the search results
  • 48. MARC Fields Findings 1 WORD COUNT IMPROVES DISCOVERY 2 TABLE OF CONTENTS AIDS DISCOVERY 3 ABSTRACTS AID DISCOVERY 4 NAME AUTHORITY FILES ARE USED 5 SUBJECT FIELDS NOT AS FREQUENTLY USED
  • 49. Analysis • Non-MARC records have advantage over MARC • MARC vendor records appear more often than locally created MARC records 80% Of all records in search results are Non-MARC 25% Of MARC records place in the top 5 search results 505/520 Occur more frequently in vendor records 1xx/6xx/7xx Occur at the same rate in Vendor and Locally created records
  • 50. Analysis Title fields are most important overall, but… 505 = • Ranked higher than 245 for records where search terms matched only one field • Consistently in the top 4 fields that retrieved a record (along with 520) • If missing, 12% of all MARC results would not have been displayed
  • 51. Analysis 3rd Most important field for matching search terms 2nd Most important field for records viewed by patrons 1xx fields were much more likely to be “clicked on” .66% Would not have been displayed if field were missing 1 Instance of subject fields being “clicked on” Subject fields are important BUT…
  • 52. MARC Take-Aways • Cataloger will retain ability to make best judgment for each record, but will be asked to consider the following guidelines:  More emphasis on creating 505 and 520 notes in local records  Less emphasis on 6xx fields as an entry point  More emphasis on 1xx fields as an entry point
  • 54. Poll 3 I have used the following programs:
  • 55. Pros and Cons: Google Analytics • Google Analytics Pro  Lots of data  Customizable reports  Good export options (PDF, Google Sheets, CSV, Excel)  Runs constantly –good for historical data Cons  Privacy issues  Only downloads 5,000 at a time  Institution chosen
  • 56. Pros and Cons: Octoparse • Octoparse Pros  Free option (under 10, trial)  Speeds up the data collection process  Can be simple – autodetect  Fast  Export into Excel, CSV, HTML, JSON Cons  Free version is limited in projects  Sometimes skips records, need to keep track  Slight learning curve
  • 57. Pros and Cons: Airtable Pros  Linking  Flexible  Dynamic dashboards  Multi-user + Versioning  Communication (commenting, tagging)  Color Coding  Views  Codebooks Cons  Subscription  Structuring can be complex  Simplistic dashboard
  • 58. Alternative Programs Web log generation  Matomo  Open Web Analytics Web Scraping  ScrapingBot  ParseHub  Data Scraper (Chrome browser extension)  Web Scraper (Chrome and cloud extension)  Scraper (Chrome browser extension) Data Coding  Excel  Dedoose  QDA Miner Lite  Google Sheets
  • 59. Next Steps PROJECTS In process  Dublin Core Discoverability  Encore vs Google Scholar Upcoming  Search query construction  Controlled field analysis Completed MARC Discoverability EAD Discoverability User search habits in Encore
  • 60. Resources Full Procedures: https://usulibrary.atlassian.net/l/c/8H7jgU98 Article with final results: Liz Woolcott, Andrea Payant, Becky Skeen & Paul Daybell (2021) Missing the MARC: Utilization of MARC Fields in the Search Process, Cataloging & Classification Quarterly, 59:1, 28-52, DOI: 10.1080/01639374.2021.1881010 Related articles Robert Heaton & Liz Woolcott. Unraveling the (Search) String: Assessing Library Discovery Layers Using Patron Queries. Library Assessment Conference, January 2021, https://www.libraryassessment.org/wp-content/uploads/2021/06/261-Heaton- Unraveling-the-Search-String.pdf
  • 61. Coding Group • Anna-Maria Arnljots • Josee Butler • Ryan Bushman (Stats) • Paul Daybell • Barbara Fleming • Maddie Gardner • Alisha Grant • Bryn Larsen • Sabrina Leatham • Rachel Olsen • Andrea Payant • Kurt Meyer • Jessica Mills • Abby Rodabough • MaKayla Roundy • Melanie Shaw • Becky Skeen • Sara Skindelien • Seth Westenburg • Liz Woolcott
  • 62. Questions? Anna-Maria Arnljots Metadata Assistant anna-maria.arnljots@usu.edu Paul Daybell Archival Cataloging Librarian paul.daybell@usu.edu Kurt Meyer Government Information and E- Resource Cataloger kurt.meyer@usu.edu Andrea Payant Metadata Librarian andrea.payant@usu.edu Becky Skeen Special Collection Cataloging Librarian becky.skeen@usu.edu Liz Woolcott Cataloging and Metadata Services Unit Head liz.woolcott@usu.edu Thank You!