O slideshow foi denunciado.
Seu SlideShare está sendo baixado. ×

Capturing and Analyzing Publication, Citation and Usage Data for Contextual Collection Development

Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio

Confira estes a seguir

1 de 51 Anúncio

Capturing and Analyzing Publication, Citation and Usage Data for Contextual Collection Development

Baixar para ler offline

Libraries have long sought to demonstrate the value of their collections through a variety of usage statistics. Traditionally, a strong emphasis is placed on high usage statistics when evaluating journals in collection development discussions. However, as budget pressures persist, administrators are increasingly concerned with looking beyond traditional usage metrics to determine the real impact of library services and collections. By examining journal usage in the context of scholarly communication, we hope to gain a more holistic understanding of the use and impact of our library’s resources. In this session, we begin by outlining our methodology for gathering comprehensive publication and citation data for authors affiliated with Northwestern University’s Feinberg School of Medicine, utilizing Web of Science as our primary data source and leveraging a custom Python script to manage the data. Using this data we discuss various potential metrics that could be employed to measure and evaluate journals in institutional and field-specific contexts, including but not limited to: number of publications and references per journal, co-citation networks, percentage of references per journal, and increases or decreases of references over time per title. We then consider the development of normalized benchmarks and criteria for creating field-specific core journal lists. We also discuss a process for establishing usage thresholds to evaluate existing journal subscriptions and to highlight potential gaps in the collection. Finally, we apply and compare these metrics to traditional collection development tools like COUNTER usage reports, cost-per-use analysis, Inter-Library Loan statistics and turnaway reports, to determine what correlations or discrepancies might exist. We finish by highlighting some use-cases which demonstrate the value of considering publication and citation metrics, and provide suggestions for incorporating these metrics into library collection development practices.

Speakers: Joelen Pastva and Jonathan Shank, Northwestern University
Project GitHub page: https://goo.gl/2C2Pcy

Libraries have long sought to demonstrate the value of their collections through a variety of usage statistics. Traditionally, a strong emphasis is placed on high usage statistics when evaluating journals in collection development discussions. However, as budget pressures persist, administrators are increasingly concerned with looking beyond traditional usage metrics to determine the real impact of library services and collections. By examining journal usage in the context of scholarly communication, we hope to gain a more holistic understanding of the use and impact of our library’s resources. In this session, we begin by outlining our methodology for gathering comprehensive publication and citation data for authors affiliated with Northwestern University’s Feinberg School of Medicine, utilizing Web of Science as our primary data source and leveraging a custom Python script to manage the data. Using this data we discuss various potential metrics that could be employed to measure and evaluate journals in institutional and field-specific contexts, including but not limited to: number of publications and references per journal, co-citation networks, percentage of references per journal, and increases or decreases of references over time per title. We then consider the development of normalized benchmarks and criteria for creating field-specific core journal lists. We also discuss a process for establishing usage thresholds to evaluate existing journal subscriptions and to highlight potential gaps in the collection. Finally, we apply and compare these metrics to traditional collection development tools like COUNTER usage reports, cost-per-use analysis, Inter-Library Loan statistics and turnaway reports, to determine what correlations or discrepancies might exist. We finish by highlighting some use-cases which demonstrate the value of considering publication and citation metrics, and provide suggestions for incorporating these metrics into library collection development practices.

Speakers: Joelen Pastva and Jonathan Shank, Northwestern University
Project GitHub page: https://goo.gl/2C2Pcy

Anúncio
Anúncio

Mais Conteúdo rRelacionado

Diapositivos para si (20)

Semelhante a Capturing and Analyzing Publication, Citation and Usage Data for Contextual Collection Development (20)

Anúncio

Mais de NASIG (20)

Mais recentes (20)

Anúncio

Capturing and Analyzing Publication, Citation and Usage Data for Contextual Collection Development

  1. 1. Capturing and Analyzing Publication, Citation and Usage Data for Contextual Collection Development Presenters: Joelen Pastva, Metadata Librarian Jonathan Shank, Acquisitions & E-Resources Librarian Project Team: Ramune Kubilius, Collection Development, Special Projects Librarian Karen Gutzman, Impact and Evaluation Librarian Madhuri Kaul, Ph.D., Data Consultant NASIG 2017, Indianapolis, IN
  2. 2. About us: Galter Health Sciences Library Northwestern University Feinberg School of Medicine Chicago, Illinois
  3. 3. Galter Health Sciences Library • Serves Northwestern University’s Feinberg School of Medicine (FSM) in Chicago, Ill. • Approx. 3,349 students, residents, and fellows • Approx. 4,000 in the medical school’s faculty roster • Staff (professional, research, support, etc.) • Administratively separate from Northwestern University Library in Evanston • Cost sharing with Evanston on big deal agreements and other large packages • NU enterprise-wide system– Alma; custom front-end – Primo • Separate standalone subscriptions and a medical-specific collection • Centralized budget and selection model • Cooperate with affiliated hospital libraries on some clinical medical resources • Currently in transitional phase for handling of COUNTER • No ERMS or usage client, efforts currently focused on JR1 stats • Usage functionality coming to Alma in Summer of 2017 3
  4. 4. Project background • COUNTER Overview • Collection Development Motivations
  5. 5. COUNTER overview • Standard format and “consistency” across vendors (Wical and Vandenbark 2014) • Ease of utilizing for critical CPU analysis (Rathemacher 2010; Bordeaux, Kramer, and Sullenger 2005) • Increasing compliance among vendors • Growing interoperability • Iterative improvements with each new release • Active and engaged community of librarians, publishers and vendors • Previous studies show COUNTER correlates significantly with other usage data metrics like proxy logs, link resolver stats, web analytics, etc (De Groote, Blecic, and Martin 2013; Gao 2016) Whatworks well 5
  6. 6. COUNTER limitations • Merging multiple providers and platforms without a client (Luther 2002) • Manual retrieval of reports and management of login credentials (Rathemacher 2010) • Issues with accuracy and consistency with title changes, splits and merges • Not all vendors are compliant or consistent with reports (Noonan 2007; Welker 2012) • Interface & platform design can inflate stats (Davis and Price 2006) • Usage is a relatively poor indicator of impact and value (Conger 2007; Noonan 2007) • Conflicting studies on correlations with citation metrics, research activity & JIF (Bollen and Van de Sompel 2008; De Groote, Blecic, and Martin 2013; Duy and Vaugh 2006; Gao 2016; Ralston et al. 2008) • Incorrect IP information can distort figures - 58% of IPs held by publishers to authenticate libraries are wrong (according to audit by PSI Ltd) • Lack of distinction by location, school, campus, or department Whatdoesn’t worksowell 6
  7. 7. COUNTER limitations GHSL Licenses EMBASE ClinicalKey Accesses NUL Licenses ScienceDirect Scopus Cell Press Accesses NMH Accesses LCH Licenses ClinicalKey Nursing Accesses Overview ofNU’sElsevier landscape 7
  8. 8. Project background • COUNTER Overview • Collection Development Motivations
  9. 9. Challenges for collection development What resources best meet the needs of our users/institution? Budget strain due to rising journal costs: • What are essential titles, and what can we cut? • How to demonstrate the value of library collections? • How to demonstrate the impact of library collections? Traditional usagedata 9 Idea Preparation ResearchWriting DisseminationResource
  10. 10. Challenges for collection development Traditional usagedata +citation analysis What resources best meet the needs of our users/institution? What are essential titles, and what can we cut?  Most commonly cited journals  Least commonly cited journals  Number of citations per article How to demonstrate the value of library collections? How to demonstrate the impact of library collections?  Citations show role journal title plays in generating and validating new scholarship 10 Idea Preparation ResearchWriting DisseminationResource
  11. 11. Citation analysis: background • Precedent – Citation studies date back to 1927, (Gross and Gross 1927) and have long been recognized as a way to provide more context to supplement traditional usage data - Methodology best practices (Hoffmann and Doucette 2012) • Flexibility in choice of data source, scope, and tools • General to local – citation patterns differ by field and institution, offering a more localized view of the value of a resource (Belter and Kaske 2016, 420; Cusker 2012; Davis 2002, 157) - Some studies contradict usage data (Gao 2016, 124; Ke and Bronicki 2015, 174) - Some studies reinforce usage data (De Groote, Blecic, and Martin 2013, 117; Tsay 1998, 39) 11
  12. 12. Citation analysis: limitations • Citing patterns potentially shaped by what library provides access to (Wilson and Tenopir 2008, 1395) • Citations do not reflect overall use, such as what is used for instruction (De Groote, Blecic, and Martin 2013, 111) • Journals publishing more frequently tend to be cited more frequently (Blecic 1999, 21; Tsay, 35) • Accuracy depends on source data for publications • Time consuming process 12
  13. 13. Citation analysis methodology
  14. 14. Citation analysis: data collection • Web of Science (WoS) - Northwestern University author affiliation - Full publication record, including cited references - 5 datasets • 2007-2016, clinical, pre-clinical, and health (FSM) • 2007-2016, dermatology • 2016, clinical, pre-clinical, and health (FSM) • 2016, dermatology • 2016, all Northwestern University - Subjects limited by WoS category • Clinical, pre-clinical, and health represented by 45 total categories from Global Institutional Profiles Project (GIPP) schema 14 Overall view COUNTER comparison
  15. 15. Citation analysis: data wrangling • WoS UI limits exports to 500 records at a time - need to combine for analysis • Parse cited references and year of publication columns • Larger file sizes are more challenging • Messy data Translates to tedious (and possibly error-prone) work 15
  16. 16. Citation analysis: data wrangling Solution - Python Why Python? • Simple, easy to learn • Libraries developed for data munging and analysis - NumPy, pandas, matplotlib, etc. • Work can easily be replicated • Eliminates potential for user error • Faster (after initial time investment) Drawbacks • Larger files are slow to run • Data inconsistencies require manual cleanup 16
  17. 17. Citation analysis: data wrangling Steps for working with Python script: 1. Run script to clean and concatenate files output from WoS - Cleans data for reading into pandas library - Concatenates multiple files into one file for analysis 2. Run concatenated file through main script - Creates dataframe from WoS data - Parses cited references data • Counts most cited journal titles - Extracts original article’s publication year - Generates figures based on data extracted and basic counting/comparison - Option for additional views of data after processing 17
  18. 18. Citation analysis: data wrangling Python script output: • .csv file listing journal titles ordered by citation counts • .csv files with processed data • Figures • Number of articles published per year • Year of publication of cited articles • Age of cited articles • Number of citations per year • Average number of citations per article, per year Project GitHub page: https://goo.gl/2C2Pcy 18
  19. 19. Citation analysis findings
  20. 20. 20 Cited Reference Analysis of Feinberg School of Medicine’s publications from 2007 - 2016 Please note: Publication data (including cited references) exported from Web of Science in 5/2017. Analysis of cited references was completed using custom Python script. Data visualized using Microsoft Excel. All publication types from journals included in the Pre-Clinical, Clinical and Health GIPP schema were included in the analysis. 1,741 3,194 - 500 1,000 1,500 2,000 2,500 3,000 3,500 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 NumberofDocuments Year Document Published Number of Documents Published Per Year Northwestern University Feinberg School of Medicine 2007 - 2016
  21. 21. 21 Please note: Publication data (including cited references) exported from Web of Science in 5/2017. Analysis of cited references was completed using custom Python script. Data visualized using Microsoft Excel. All publication types from journals included in the Pre- Clinical, Clinical and Health GIPP schema were included in the analysis. of Feinberg School of Medicine’s publications from 2007 - 2016 Cited Reference Analysis continued… 48,576 journals were cited from 2007-2016 Journal Name Number of Cited References from Journal NEW ENGL J MED 21227 CIRCULATION 14224 J CLIN ONCOL 13988 JAMA-J AM MED ASSOC 12978 BLOOD 9774 LANCET 9004 P NATL ACAD SCI USA 8938 J AM COLL CARDIOL 8663 J BIOL CHEM 7287 CANCER 6489 NATURE 6462 SCIENCE 5661 CANCER RES 5532 PEDIATRICS 5424 J ALLERGY CLIN IMMUN 5029 J CLIN ENDOCR METAB 4906 ANN INTERN MED 4726 NEUROLOGY 4660 GASTROENTEROLOGY 4397 ARCH INTERN MED 4337 Top 20 Most Cited Journals 80% of citations were to top 2.69% of journals
  22. 22. 22 of Feinberg School of Medicine’s publications from 2007 - 2016 Cited Reference Analysis continued… 0 200 400 600 800 1000 1200 1400 1600 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Number of Citations for Top 20 titles, Per Journal Per Year Feinberg School of Medicine, 2007-2016 NEW ENGL J MED CIRCULATION J CLIN ONCOL JAMA-J AM MED ASSOC BLOOD LANCET P NATL ACAD SCI USA J AM COLL CARDIOL J BIOL CHEM CANCER NATURE SCIENCE CANCER RES PEDIATRICS J ALLERGY CLIN IMMUN J CLIN ENDOCR METAB ANN INTERN MED NEUROLOGY GASTROENTEROLOGY ARCH INTERN MED
  23. 23. 23 of Feinberg School of Medicine’s publications from 2007 - 2016 Cited Reference Analysis continued… 2007, 35.78 2014, 41.37 35.00 36.00 37.00 38.00 39.00 40.00 41.00 42.00 2006 2008 2010 2012 2014 2016 Average#ofCitedReferencesPerDocument Year of Document Publication Average Number of Cited References, Per Document Per Year Northwestern University Feinberg School of Medicine 2007 - 2016 Please note: Publication data (including cited references) exported from Web of Science in 5/2017. Analysis of cited references was completed using custom Python script. Data visualized using Microsoft Excel. All publication types from journals included in the Pre-Clinical, Clinical and Health GIPP schema were included in the analysis.
  24. 24. Cited Reference Analysis continued… of Feinberg School of Medicine’s publications from 2007 - 2016 24 Please note: Publication data (including cited references) exported from Web of Science in 5/2017. Analysis of cited references was completed using custom Python script. Data visualized using Microsoft Excel. All publication types from journals included in the Pre-Clinical, Clinical and Health GIPP schema were included in the analysis. 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 174 190 216 247 331 NumberofCitedReferences Age in Years of Cited Reference Age of Cited Reference Compared to Age of Citing Document Northwestern University Feinberg School of Medicine 2007-2016 Number of Cited References 2 years, 91,687
  25. 25. 25 Cited Reference Analysis continued… of Feinberg School of Medicine’s publications from 2007 - 2016 Please note: Publication data (including cited references) exported from Web of Science in 5/2017. Analysis of cited references was completed using custom Python script. Data visualized using VOSviewer. All publication types from journals included in the Pre-Clinical, Clinical and Health GIPP schema were included in the analysis. Cited References Journal Co-Citation Network Northwestern University Feinberg School of Medicine 2007 - 2016 Circles Size indicates number of cited references Color and proximity indicates topical similarity Lines Thickness indicates number of times cited together in same reference list Color indicates topical similarity
  26. 26. 26 Cited Reference Analysis continued… of Feinberg School of Medicine’s Dermatology publications from 2007 - 2016 Please note: Publication data (including cited references) exported from Web of Science in 5/2017. Analysis of cited references was completed using custom Python script. Data visualized using Microsoft Excel. All publication types from journals included in Dermatology research area of the Web of Science schema were included in the analysis. 3,346 journals were cited in Dermatology from 2007 - 2016 Top 20 Most Cited Journals in Dermatology, 2007-2016 Journal Name J AM ACAD DERMATOL Number of Cited References from Journal 1699 BRIT J DERMATOL 1134 ARCH DERMATOL 1020 DERMATOL SURG 781 J INVEST DERMATOL 687 NEW ENGL J MED 311 INT J DERMATOL 296 J ALLERGY CLIN IMMUN 288 J EUR ACAD DERMATOL 277 J DRUGS DERMATOL 263 PEDIATR DERMATOL 252 PLAST RECONSTR SURG 219 CLIN EXP DERMATOL 205 ACTA DERM-VENEREOL 199 CANCER 196 DERMATOLOGY 190 LANCET 190 AM J SURG PATHOL 190 BLOOD 184 J CUTAN PATHOL 183 80% of citations were to top 13.87% of journals
  27. 27. 27 0 20 40 60 80 100 120 140 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Number of Citations for Top 20 Titles, Per Journal Per Year Dermatology, 2007-2016 J AM ACAD DERMATOL BRIT J DERMATOL ARCH DERMATOL DERMATOL SURG J INVEST DERMATOL NEW ENGL J MED INT J DERMATOL J ALLERGY CLIN IMMUN J EUR ACAD DERMATOL J DRUGS DERMATOL PEDIATR DERMATOL PLAST RECONSTR SURG CLIN EXP DERMATOL ACTA DERM-VENEREOL CANCER DERMATOLOGY LANCET AM J SURG PATHOL BLOOD J CUTAN PATHOL Cited Reference Analysis continued… of Feinberg School of Medicine’s Dermatology publications from 2007 - 2016
  28. 28. 28 Cited Reference Analysis of Feinberg School of Medicine’s Dermatology publications in 2016 Please note: Publication data (including cited references) exported from Web of Science in 5/2017. Analysis of cited references was completed using custom Python script. Data visualized using Microsoft Excel. All publication types from journals included in Dermatology research area of the Web of Science schema were included in the analysis. 1,079 journals were cited in Dermatology in 2016 Top 20 Most Cited Journals in Dermatology, 2016 Journal Name Number of Cited References from Journal J AM ACAD DERMATOL 275 BRIT J DERMATOL 160 ARCH DERMATOL 135 DERMATOL SURG 119 J INVEST DERMATOL 93 J ALLERGY CLIN IMMUN 63 JAMA DERMATOL 63 J EUR ACAD DERMATOL 59 INT J DERMATOL 55 J DRUGS DERMATOL 55 NEW ENGL J MED 43 ACTA DERM-VENEREOL 38 CLIN EXP DERMATOL 35 DERMATOLOGY 35 PEDIATR DERMATOL 34 LASER SURG MED 32 CUTIS 31 J CUTAN PATHOL 30 LANCET 27 J DERMATOL 27 80% of citations were to top 33.73% of journals
  29. 29. 29 Cited Reference Analysis of Feinberg School of Medicine’s Dermatology publications in 2016 Please note: Publication data (including cited references) exported from Web of Science in 4/2017. Analysis of cited references was completed using custom Python script. Data visualized using VOSviewer. All publication types from journals included in Dermatology research area of the Web of Science schema were included in the analysis. Cited References Journal Co-Citation Network Northwestern University Feinberg School of Medicine Dermatology, 2016 Circles Size indicates number of cited references Color and proximity indicates topical similarity Lines Thickness indicates number of times cited together in same reference list Color indicates topical similarity
  30. 30. Comparisons with COUNTER usage data
  31. 31. COUNTER Methodology: Data Gathering • JR1 Reports retrieved manually from publishers and shared library dashboard - Reports retrieved for both HSL subscriptions and Main Library package deals • Reports were merged into single Excel file with over 30,000 lines (OCFTRTA) - Note: Process would have been much easier with COUNTER client • Titles from cited reference reports then manually matched to COUNTER stats - Issues with journal abbreviations from WoS, would need to overcome before automating and/or looking at data in aggregate - Titles with multiple providers were accounted for, collated and totaled, although this was less of an issue than anticipated 31
  32. 32. COUNTER Methodology: Data Gathering 32
  33. 33. COUNTER Methodology: Data Comparisons • Top 30 cited journals for all of NU in 2016 vs COUNTER usage • Top 50 cited medical* journals in 2016 vs COUNTER usage • Top 50 cited dermatology journals in 2016 vs COUNTER usage *clinical, pre-clinical, and health 33
  34. 34. Top 30 Journals Cited by NU vs COUNTER, 2016 34 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 FulltextRetrievals CitedReferences Cited reference count COUNTER JR1 Total
  35. 35. Top 30 Journals Cited by NU vs COUNTER, 2016 35 • Pearson correlation coefficient, r = 0.46 • Spearman’s rho, ρ = 0.25 • Why so low? What’s going on here? • Citation and usage patterns vary widely across disciplines
  36. 36. Top 30 20 Journals Cited by NU vs COUNTER, 2016 36 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 FullTextRetrievals CitedReferences (Excludes Physics titles) Cited reference count Aggregate COUNTER JR1 Spearman ρ = 0.62 Pearson r = 0.81
  37. 37. Top 50 Cited Medical Journals vs COUNTER, 2016 37 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 0 500 1000 1500 2000 2500 3000 NEWENGLJMED CIRCULATION JAMA-JAMMEDASSOC JCLINONCOL LANCET JAMCOLLCARDIOL PNATLACADSCIUSA BLOOD PEDIATRICS NATURE JALLERGYCLINIMMUN JBIOLCHEM CANCERRES SCIENCE CANCER JAMAInternMed GASTROENTEROLOGY NEUROLOGY OBSTETGYNECOL SPINE ANNINTERNMED STROKE AMJOBSTETGYNECOL CLINCANCERRES DIABETESCARE AMJTRANSPLANT ARCHPHYSMEDREHAB RADIOLOGY JCLININVEST* AMJRESPCRITCARE CLININFECTDIS CELL AMJEPIDEMIOL COCHRANEDBSYSTREV AMJGASTROENTEROL JAMACADDERMATOL ANNSURG ANNTHORACSURG EURHEARTJ AMJCARDIOL NATGENET CHEST JNEUROSCI LARYNGOSCOPE AMJPUBLICHEALTH BRITMEDJ JUROLOGY JIMMUNOL FullTextretrievals CitedReferences Cited reference count Aggregate COUNTER JR1
  38. 38. Top 50 Cited Medical Journals vs COUNTER, 2016 • Obvious outliers with inflated COUNTER stats for multi-disciplinary titles (Nature, Science, Cell, etc.) • No “low” use titles in top 50 - Lowest journal still had 1906 full text retrievals (excl. Cochrane) • No gaps (titles without current access) in top 50 • Slight statistical correlation, lower than other studies Brief Analysis 38 Spearman ρ = 0.54 Pearson r = 0.52
  39. 39. Top 50 Cited Dermatology Journals vs COUNTER, 2016 39 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 0 50 100 150 200 250 300 FullTextRetrievals CitedReferences (excludes OA, titles with missing data, and titles w/o current access) Cited reference count Aggregate COUNTER JR1
  40. 40. Top 50 Cited Dermatology Journals vs COUNTER, 2016 • Obvious outliers with large COUNTER stats for multi-disciplinary titles (NEJM, Lancet, JAMA, etc.) • 3 “low” use titles with less than 100 full text retrievals make an appearance: - Photodermatol Photo (74), Acta Derm-Venereol (57), & J Cutan Med Surg (25) • 5 gap titles (journals w/o current full text) make an appearance: - Dermatology, J Dermatol Treat, Eur J Dermatol, Am J Clin Dermatol, & Dermatologica • No overall statistical correlation, which is to be expected Brief Analysis 40 Spearman ρ = 0.13 Pearson r = -.08
  41. 41. Just for “Fun”: Cost Per Use vs Cost Per Cited Reference WhereCPUisgreater than$5 41 $0.00 $50.00 $100.00 $150.00 $200.00 $250.00 BMJ Quality & Safety Journal of Clinical Pathology Allergy and Asthma Proceedings British Journal of Sports Medicine British Journal of Ophthalmology Journal of Medical Genetics Thorax Antioxidants & Redox Signaling Teaching and Learning in Medicine Diabetes Gut Journal of Neurology Neurosurgery & Psychiatry American Journal of Rhinology & Allergy Archives of Disease in Childhood Heart Journal of Neurotrauma AIDS Research and Human Retroviruses Diabetes Care Cost Per Cited Reference Cost Per Use
  42. 42. Collection Development Applications
  43. 43. Collection Development Applications • Prevent undervaluing of a title when other stats are questionable - Cited reference count can easily be consulted before making decision - Especially useful in instances of low usage, or high CPU based on reporting issues • Also useful for evaluating OA titles, or titles without COUNTER Before making a painful cut, all possible data points should be consulted and documented in order to back up or defend the decision. Contextualizing Usage Statistics withCited Reference Counts Full Title COUNTER JR1 Total Med Cited Reference Count The journal of clinical endocrinology & metabolism 0 556 43
  44. 44. Collection Development Applications • Use cited reference counts to identify and rank high impact titles outside of collection - Check against other metrics like turnaways, ILL’s etc., to inform CD • With more automation, this could be done in aggregate • Another data point to use in evaluating “wish list” or bubble titles Identifying Gaps 44
  45. 45. Collection Development Applications • Cited reference counts are perhaps more compelling than ILL requests - Illustrate need for highly requested titles, or - Demonstrate low research impact of highly requested titles to defend not subscribing Top 4 Most Requested Titles Through ILL Contextualizing orSupplementing ILLData withCited Reference Counts Title ILL Requests Cited References Brain Inj (Brain Injury) 21 54 Disabil Rehabil (Disability and rehabilitation) 20 11 Curr Pharm Des (Current pharmaceutical design) 17 20 Psychol Med (Psychological medicine) 16 83 45
  46. 46. Collection Development Applications GapAnalysis: Users arefinding access outside oftraditional library services 46 0 5 10 15 20 25 30 35 40 Dermatology J Dermatol Treat Eur J Dermatol Am J Clin Dermatol Dermatlogica Highly Cited Dermatology titles without current subscriptions Cited References Cited references outside of access entitlements ILL Requests
  47. 47. Collection Development Applications • Librarians often need to evaluate usage and impact for a specific context • Most standard metrics (web analytics, link resolver stats, COUNTER, JIF) are at much higher levels - Impact factor has limited utility for school or library specific evaluation - No significant correlation found between impact factor and # cited references for FSM publications • Citation analysis by school or research area, layered on top of broader usage statistics, can provide a more holistic and contextualized understanding of usage and impact within specific environments • Outliers from any metric can be checked against other data points and evaluated with more context Contextualizing usagebyschool ordiscipline 47
  48. 48. Final Thoughts • Outside of some commercial services, no automated solution for scaling up • Cited reference data on it’s own is not that useful for collection development - However, when used in conjunction with other metrics, meaningful information surfaces quite easily • Citation figures also useful for troubleshooting, sanity checks or substitutes, when other stats are unavailable • With more automation, might be possible to use citation data for broader collection development and assessment activities without making as many comparisons, we’re almost there but not quite yet • Overall it’s a worthwhile tool to have for collection development, yay! Collection Development Implications 48
  49. 49. References • Belter, Christopher W., and Neal K. Kaske. "Using Bibliometrics to Demonstrate the Value of Library Journal Collections." College and Research Libraries 77, no. 4 (2016): 410-22. • Blecic, D. D. "Measurements of Journal Use: An Analysis of the Correlations between Three Methods." Bulletin of the Medical Library Association 87, no. 1 (1999): 20-25. • Bollen, Johan, and Herbert Van de Sompel. "Usage impact factor: the effects of sample characteristics on usage‐based impact metrics." Journal of the American Society for Information Science and technology 59, no. 1 (2008): 136-149. • Bordeaux, Abigail, Alfred B. Kraemer, and Paula Sullenger. "Making the most of your usage statistics." The Serials Librarian 48, no. 3-4 (2005): 295-299. • Cusker, Jeremy. "Using Isi Web of Science to Compare Top-Ranked Journals to the Citation Habits of a "Real World" Academic Department." Issues in Science and Technology Librarianship, Summer (2012). • Davis, Philip M. "Where to Spend Our E-Journal Money? Defining a University Library's Core Collection through Citation Analysis." portal: Libraries and the Academy 2, no. 1 (2002): 155-66. • De Groote, Sandra L., Deborah D. Blecic, and Kristin Martin. "Measures of Health Sciences Journal Use: A Comparison of Vendor, Link-Resolver, and Local Citation Statistics." Journal of the Medical Library Association : JMLA 101, no. 2 (2013): 110-19. • Gao, Wenli. "Beyond Journal Impact and Usage Statistics: Using Citation Analysis for Collection Development." The Serials Librarian 70, no. 1/4 (2016): 121-27. • Gross, P.L.K., and E.M. Gross. "College Libraries and Chemical Education." Science N.S. 66, no. 1713 (1927): 385-89. • Hoffmann, Kristin, and Lise Doucette. "A Review of Citation Analysis Methodologies for Collection Management." 2012 73, no. 4 (2012): 15. • Ke, Irene, and Jackie Bronicki. "Using Scopus to Study Researchers’ Citing Behavior for Local Collection Decisions: A Focus on Psychology." Journal of Library Administration 55, no. 3 (2015): 165-78. • Kraemer, Alfred. "Ensuring consistent usage statistics, part 2: working with use data for electronic journals." The Serials Librarian 50, no. 1-2 (2006): 163-172. • Luther, Judy. "White paper on electronic journal usage statistics." The Serials Librarian 41, no. 2 (2002): 119-148. • Noonan, Christine F., and Melissa K. McBurney. "Application of electronic serial usage statistics in a national laboratory." In Usage statistics of e-serials, ed. David C. Fowler, 151-60. Binghamton, NY: Haworth Information Press, 2007. • Ralston, Rick, Carole Gall, and Frances A. Brahmi. "Do local citation patterns support use of the impact factor for collection development?." Journal of the Medical Library Association: JMLA 96, no. 4 (2008): 374. • Rathemacher, Andrée J. “E-Journal Usage Statistics in Collection Management Decisions: A Literature Review.” In Library Data: Empowering Practice and Persuasion, ed. Darby Orcutt, 71-89. Santa Barbara, Calif.: Libraries Unlimited, 2010. • Tsay, M. Y. "The Relationship between Journal Use in a Medical Library and Citation Use." Bulletin of the Medical Library Association 86, no. 1 (1998): 31-39. • Wical, Stephanie H., and R. Todd Vandenbark. "Notes on Operations: Combining Citation Studies and Usage Statistics to Build a Stronger Collection." Library Resources & Technical Services 59, no. 1 (2015): 33-42. • Welker, Josh. "Counting on COUNTER: The Current State of E-Resource Usage Data in Libraries." Computers in Libraries 32, no. 9 (2012): 6-11. • Wilson, Concepción S., and Carol Tenopir. "Local Citation Analysis, Publishing and Reading Patterns: Using Multiple Methods to Evaluate Faculty Use of an Academic Library's Research Collection." Journal of the American Society for Information Science and Technology 59, no. 9 (2008): 1393-408.
  50. 50. Questions? joelen.pastva@northwestern.edu @jolophon j-shank@northwestern.edu @ShankLib
  51. 51. Thank you!

×