How many UCSF people have ORCID IDs?

ORCID provides a globally unique identifier for researchers. These identifiers help disambiguate researchers as they become more widely accepted across the research ecosystem. While UCSF does not automatically issue ORCID identifiers to researchers, we know, anecdotally, that many members of the UCSF community have either manually signed up an ORCID ID, or been issued an identifier by another institution.

Every year, the ORCID team releases a public data file of information about every researcher issued an ORCID identifier. I’ve been digging into the October 2018 dataset to look at explore people from UCSF.

How many UCSF people have an ORCID?

Researchers can include their education and employment history in their ORCID data. I looked at people who are appear to be current UCSF employees, in that they:

  • listed employment history
  • had at least once employment entry at UCSF (or one of the common spellings thereof, as this was a freetext field)
  • and the UCSF employment entry did not not have an end date, suggesting it might be ongoing

I found 762 people who listed what could be current employment at UCSF.

How many UCSF Profiles users have an ORCID ID?

Who are the UCSF people with an ORCID? I tried disambiguating the 762 names using UCSF Profiles and UCSF Directory data. I could disambiguate ~570 of those names with some confidence. ~500 of them were in UCSF Profiles, of whom ~350 were faculty members.


Image: “Preparations underway for Orchid Symphony exhibit at U.S. Botanic Garden,” Architect of the Capitol, 2014, U.S. government work

UCSF’s most extreme cross-campus collaborators

UCSF is spread all across San Francisco, with faculty members’ primary addresses spanning over a dozen zip codes. We know that geographical proximity helps collaboration, but some UCSF researchers are comfortably working with collaborators all across the university, regardless of campus.

I used UCSF Profiles data to look at researchers who have co-authored publications since 2017 with other people currently at UCSF who have primary addresses in a different zip code. I skipped publications with more than 6 total co-authors, since it’s less likely that any two co-authors collaborated directly. (See the example at the end of this post.)

I expected most of the top cross-campus collaborators to be from Epidemiology & Biostatistics, UCSF’s most internally collaborative department—but the list wasn’t as lopsided as I expected.

The list

  • Isabel Elaine Allen (Epidemiology & Biostatistics) has, since 2017, co-authored 6 qualifying papers with UCSF people spread across 8 other ZIP codes
  • Patricia O’Sullivan (Medicine) has, since 2017, co-authored 14 qualifying papers with UCSF people spread across 8 other ZIP codes
  • Martha Shumway (Psychiatry) has, since 2017, co-authored 7 qualifying papers with UCSF people spread across 6 other ZIP codes
  • Christine Ritchie (Medicine) has, since 2017, co-authored 8 qualifying papers with UCSF people spread across 6 other ZIP codes
  • Emily Finlayson (Surgery) has, since 2017, co-authored 4 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Patrick Yuan (Epidemiology & Biostatistics) has, since 2017, co-authored 3 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Jason Satterfield (Medicine) has, since 2017, co-authored 4 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Youngho Seo (Radiology) has, since 2017, co-authored 5 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Rebecca Sudore (Medicine) has, since 2017, co-authored 4 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Christy Boscardin (Medicine) has, since 2017, co-authored 7 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Gabriela Schmajuk (Medicine) has, since 2017, co-authored 7 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Jinoos Yazdany (Medicine) has, since 2017, co-authored 7 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Bridget O’Brien (Medicine) has, since 2017, co-authored 6 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Mary Whooley (Medicine) has, since 2017, co-authored 5 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Amber Bahorik (Psychiatry) has, since 2017, co-authored 10 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Derek Satre (Psychiatry) has, since 2017, co-authored 11 qualifying papers with UCSF people spread across 5 other ZIP codes
  • John Boscardin (Medicine) has, since 2017, co-authored 9 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Kirsten Bibbins-Domingo (Epidemiology & Biostatistics) has, since 2017, co-authored 10 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Michael Matthay (Medicine) has, since 2017, co-authored 11 qualifying papers with UCSF people spread across 5 other ZIP codes
  • Eric Vittinghoff (Epidemiology & Biostatistics) has, since 2017, co-authored 21 qualifying papers with UCSF people spread across 5 other ZIP codes

Extended example

  • A (in 94158) co-authors a paper with UCSF colleague B (in 94117) and non-UCSF person C
  • A co-authors another paper with UCSF colleagues D (in 94110) and E (in 94158, the same as A)
  • A co-authors one more paper with ten co-authors — which doesn’t qualify, because we only care about papers with 2-6 total co-authors
  • A has therefore co-authored 2 qualifying papers with UCSF people spread across 2 other ZIP codes.

Photo: Philip Leara, public domain


UCSF’s top 15 departments for clinical trials

Photo: Thomas Hawk

UCSF is a top institution for clinical trials. Here are UCSF’s departments, sorted by the number of open clinical trials from principal investigators who have a primary association to that department. (This list is based on open trials listed in UCSF’s new Clinical Trials website, in cases where trials are associated with one or more principal investigators’ UCSF Profiles pages. If a trial’s PIs span multiple departments, the trial will be counted once for each department.)

  1. Medicine is running 370 trials from 133 PIs/co-PIs
  2. Pediatrics is running 97 trials from 35 PIs/co-PIs
  3. Neurology is running 96 trials from 29 PIs/co-PIs
  4. Surgery is running 41 trials from 28 PIs/co-PIs
  5. Psychiatry is running 40 trials from 28 PIs/co-PIs
  6. Ob/Gyn, Reproductive Sciences is running 35 trials from 13 PIs/co-PIs
  7. Neurological Surgery is running 26 trials from 9 PIs/co-PIs
  8. Radiology is running 24 trials from 14 PIs/co-PIs
  9. Radiation Oncology is running 23 trials from 4 PIs/co-PIs
  10. Anesthesia is running 19 trials from 15 PIs/co-PIs
  11. Dermatology is running 13 trials from 8 PIs/co-PIs
  12. Ophthalmology is running 13 trials from 7 PIs/co-PIs
  13. Proctor Foundation is running 12 trials from 4 PIs/co-PIs
  14. Orthopaedic Surgery is running 11 trials from 8 PIs/co-PIs
  15. Dean’s Office is running 11 trials from 11 PIs/co-PIs
  16. Urology is running 9 trials from 6 PIs/co-PIs

Photo: Thomas Hawk, CC-BY-NC 2.0

Are UCSF’s Twitter users just like everyone else?

Are faculty who tweet different from the non-tweeters? My first guess would be that UCSF’s Twitter community would be more likely to be earlier in their career, and that the practice of tweeting would affect the way they write bios. Turns out I was wrong on both counts.

I used UCSF Profiles data to look at faculty (people with “professor,” “dean,” or “chancellor” anywhere in their primary title) who have either listed or not listed a Twitter account on their UCSF Profiles page.

Do Twitter users write more readable bios? Nope.

  • When Twitter users have have bios, the bios have a median Flesch-Kincaid grade level of 18.3
  • When non-Twitter users have have bios, the bios have a median Flesch-Kincaid grade level of 18.1

Do Twitter users write shorter bios? Nope.

In fact, Twitter users have somewhat longer bios!

  • When Twitter users have have bios, the bios have a median length of 1602 characters.
  • When non-Twitter users have have bios, the bios have a median length of 1465 characters.

Are Twitter users earlier in their career? Nope.

In fact, Twitter users have about two years more experience in their publishing career.

  • Twitter users have a median span of 16.6 years between their earliest and latest publications
  • Non-Twitter users have a median span of 14.7 years between their earliest and latest publications
  • Twitter users have a median rank of “Associate Professor”
  • Non-Twitter users have a median rank of “Associate Professor”

Are Twitter users more awarded? Maybe.

When Twitter users list awards, they list more of them. But are they more awarded, or just more completionist in what they list?

  • Twitter users with awards listed have a median 8.4 awards listed
  • Non-Twitter users with awards listed have a median 6.3 awards listed

Are Twitter users more in the media? Maybe.

When Twitter users list media hits, they list more of them. But are they more in the media, or just more completionist in what they list?

  • Twitter users with media mentions listed have a median 5.3 mentions listed
  • Non-Twitter users with media mentions listed have a median 3.2 mentions listed

Are Twitter users in more videos? Maybe.

When Twitter users list videos that they’re in, they list more of them. But are they in more videos, or just more completionist in what they list?

  • Twitter users with videos listed have a median 2.9 videos listed
  • Non-Twitter users with videos listed have a median 1.8 videos listed

Image: Coffee Bean Works, Twitter

Top 20 UCSF departments for junior/senior faculty collaboration

Some UCSF departments do a better job of fostering collaboration between junior and senior faculty members. Using UCSF Profiles data, I looked at co-authorship patterns among current faculty at departments across UCSF, to see which departments have the highest rate of junior-senior collaborations. (Caveat: Departments can have different sizes, faculty experience mixes, and field-specific publishing patterns, so comparisons are always imperfect.)

Method

  1. I used UCSF Profiles to identity current UCSF faculty (title includes the words “Professor,” “Dean,” or “Chancellor”) with at least 5 publications, and at least 3 years of publishing experience (i.e. time between the earliest and latest publications). I assigned faculty to departments using their current primary departmental affiliation, and considered only those departments with 20 such faculty members.
  2. In each department, I sorted the faculty by seniority using, in order, title (e.g. “Professor” outranks “Assistant Professor”), number of publications, and length of publishing experience. I then selected the 25% most junior and 25% most senior faculty from each department, and considered every possible junior-senior pair. (So for a department of 40 people, I’d pick out 10 junior, and 10 senior faculty, for a total of 100 junior-senior combinations).
  3. For each of these combinations, I checked if there exists at least one publication where both the junior and senior faculty members are listed as co-authors. For example, if there was a department of 12 faculty members, I’d pick the 3 most junior (A, B, C) and 3 most senior (X, Y, Z); if A and X have been co-authors on 1 publication, and B and Y on 3 publications, then there have been 2 unique junior/senior co-authorship pairs, of 9 possible.

The top 20 departments

  1. Urology • 43%
    the 10 most junior and 10 most senior faculty have 43 unique junior/senior co-authorship pairs, of 100 possible
  2. Physiological Nursing • 35%
    the 7 most junior and 7 most senior faculty have 17 unique junior/senior co-authorship pairs, of 49 possible
  3. Radiation Oncology • 34%
    the 8 most junior and 8 most senior faculty have 22 unique junior/senior co-authorship pairs, of 64 possible
  4. Neurological Surgery • 30%
    the 15 most junior and 15 most senior faculty have 67 unique junior/senior co-authorship pairs, of 225 possible
  5. Orofacial Sciences • 22%
    the 8 most junior and 8 most senior faculty have 14 unique junior/senior co-authorship pairs, of 64 possible
  6. Preventive & Restorative Dental Sciences • 20%
    the 12 most junior and 12 most senior faculty have 29 unique junior/senior co-authorship pairs, of 144 possible
  7. Cellular Molecular Pharmacology • 19%
    the 6 most junior and 6 most senior faculty have 7 unique junior/senior co-authorship pairs, of 36 possible
  8. Orthopaedic Surgery • 18%
    the 16 most junior and 16 most senior faculty have 46 unique junior/senior co-authorship pairs, of 256 possible
  9. Family Community Medicine • 16%
    the 10 most junior and 10 most senior faculty have 16 unique junior/senior co-authorship pairs, of 100 possible
  10. Pathology • 15%
    the 16 most junior and 16 most senior faculty have 39 unique junior/senior co-authorship pairs, of 256 possible
  11. Family Health Care Nursing • 14%
    the 7 most junior and 7 most senior faculty have 7 unique junior/senior co-authorship pairs, of 49 possible
  12. Laboratory Medicine • 14%
    the 13 most junior and 13 most senior faculty have 24 unique junior/senior co-authorship pairs, of 169 possible
  13. Cardiovascular Research Institute • 14%
    the 6 most junior and 6 most senior faculty have 5 unique junior/senior co-authorship pairs, of 36 possible
  14. Radiology • 13%
    the 35 most junior and 35 most senior faculty have 157 unique junior/senior co-authorship pairs, of 1225 possible
  15. Bioengineering • 12%
    the 8 most junior and 8 most senior faculty have 8 unique junior/senior co-authorship pairs, of 64 possible
  16. Institute for Health Aging • 12%
    the 8 most junior and 8 most senior faculty have 8 unique junior/senior co-authorship pairs, of 64 possible
  17. Otolaryngology • 12%
    the 11 most junior and 11 most senior faculty have 15 unique junior/senior co-authorship pairs, of 121 possible
  18. Pharmaceutical Chemistry • 11%
    the 8 most junior and 8 most senior faculty have 7 unique junior/senior co-authorship pairs, of 64 possible
  19. Neurology • 10%
    the 42 most junior and 42 most senior faculty have 173 unique junior/senior co-authorship pairs, of 1764 possible
  20. Dermatology • 10%
    the 12 most junior and 12 most senior faculty have 14 unique junior/senior co-authorship pairs, of 144 possible

Photo: CTSI at UCSF

Clinical trial? Research study? Medical trial? Medical study? Research trial? Paid study?

It helps to use the same language as your customers.

As part of the UCSF Clinical Trials website, we worked hard to make the language as accessible as possible, particularly in text that will be seen by search engine users. The most important phrase on the site is, obviously, clinical trials. But is that the wording our users actually use? Google Trends to the rescue!

I used Google Trends to see what language the general public uses to look for trials. And as it turns out, it’s complicated.

While clinical trial is most searched-for term among Google users across the U.S., it’s closely trailed by research study, followed by a bevy of lesser-used terms. (Not all research studies are clinical trials, but patients sometimes use these and other terms interchangeably.)

UCSF has locations both in the San Francisco Bay Area and in Fresno. And in Fresno, research studies is actually more popular than clinical trials.

via Google Trends search for clinical trials vs. research studies

But it’s not just a matter of those two terms. I tried using Google Trends to explore the relative number of searches for a wide variety of popular synonyms for clinical trials. Every one of the terms are used.

So while we will continue to use clinical trials across our clinical trials website, we’ve been making increasing use of alternate terms in places where it makes sense, to reflect the language that our users prefer.

UCSF’s most internally collaborative departments, 2017

Some UCSF departments consistently reach out out to collaborate with other members of the UCSF community. Here are the top UCSF departments whose researchers have the highest proportion of publications co-authored with people at other UCSF departments.

This ranking is based on data is drawn from UCSF Profiles, and only includes departments whose researchers had 100+ publications in 2017. We counted departmental affiliations based on someone’s primary department in UCSF Profiles in April 2018. So if someone subsequently left UCSF or changed primary departments, we wouldn’t count them as part of the primary department they were affiliated with at the time of publication. Or if they published something while at another institution in 2017, but subsequently moved to UCSF by April 2018, that paper would be chalked up their current UCSF department.

  1. Epidemiology & Biostatistics: 54.2%
    260 of 480 publications were co-authored with other UCSF departments
  2. Pathology: 49.5%
    93 of 188 publications were co-authored with other UCSF departments
  3. Radiology: 38.1%
    159 of 417 publications were co-authored with other UCSF departments
  4. Ob/Gyn, Reproductive Sciences: 37.1%
    101 of 272 publications were co-authored with other UCSF departments
  5. Pediatrics: 35.2%
    153 of 435 publications were co-authored with other UCSF departments
  6. Neurological Surgery: 34.9%
    112 of 321 publications were co-authored with other UCSF departments
  7. Bioengineering: 33.1%
    47 of 142 publications were co-authored with other UCSF departments
  8. Pharmaceutical Chemistry: 32.1%
    42 of 131 publications were co-authored with other UCSF departments
  9. Laboratory Medicine: 31.9%
    52 of 163 publications were co-authored with other UCSF departments
  10. Neurology: 30.7%
    149 of 485 publications were co-authored with other UCSF departments
  11. Anesthesia: 29.7%
    43 of 145 publications were co-authored with other UCSF departments
  12. Medicine: 29.0%
    584 of 2011 publications were co-authored with other UCSF departments
  13. Psychiatry: 28.1%
    125 of 445 publications were co-authored with other UCSF departments
  14. Surgery: 27.9%
    99 of 355 publications were co-authored with other UCSF departments
  15. Radiation Oncology: 27.6%
    34 of 123 publications were co-authored with other UCSF departments
  16. Dermatology: 25.6%
    41 of 160 publications were co-authored with other UCSF departments
  17. Orthopaedic Surgery: 24.8%
    39 of 157 publications were co-authored with other UCSF departments
  18. Urology: 24.0%
    31 of 129 publications were co-authored with other UCSF departments
  19. Ophthalmology: 19.5%
    26 of 133 publications were co-authored with other UCSF departments

How to get started with Git for biomedical researchers

Learning basic Git is a pretty essential skill for biomedical researchers sharing code—and increasingly, data—with others.

Thankfully, there’s a wealth of helpful guides out there on how to get started. Here are 4 resources we recommend:

  1. Why use Git? Skim “Git can facilitate greater reproducibility and increased transparency in science” by Karthik Ram
  2. Are you a visual learner? Watch Data School’s Git and GitHub videos for beginners
  3. Want to learn by doing, and already have Git on your command line? Begin with Software Carpentry’s self-guided lesson on how to get started with Git
  4. Want to work with others? You’ll probably want to set up a free account on Github (the most commonly used Git hosting/collaboration site) — and if you want to upgrade, you can take advantage of their education discounts

 

RNS SEO 2016: How 90 research networking sites perform on Google — and what that tells us

RNS SEO 2015 header

Research networking systems (RNS) like Vivo, Profiles, and Pure are often sometimes undiscoverable by real users because of poor search engine optimization (SEO).

Last year, we released RNS SEO 2015, the first-ever report describing how RNS performs in terms of real-world discoverability on Google.

We re-ran our analysis for 2016, to see which of 90 different research networking sites has the highest proportion of their people pages among the top 3 search results on Google.

1. Methodology

  • Pick 90 different VIVO, Profiles, Pure, and custom RNS websites
  • Retrieve a large number of people page URLs (via sitemaps, crawling)
  • Grab 100 random people names and URLs from each site
  • For each name, search Google for PersonName InstitutionName
    • e.g. “Jane Doe Harvard”
  • Count what % have pages come up in the top 3

2. Results

  1. Brown 93% [VIVO] [under official domain]
  2. University of California, San Francisco 90% [Profiles] [under official domain]
  3. University of Colorado Profiles 87% [Profiles] [under official domain]
  4. Stephenson Cancer Center 87% [Pure]
  5. Mayo Clinic 85% [Pure]
  6. University of Bristol 84% [Pure] [under official domain]
  7. Royal Holloway, University of London 83% [Pure] [under official domain]
  8. University of Stirling 83% [Custom] [under official domain]
  9. King’s College London 80% [Pure] [under official domain]
  10. Oregon Health & Science University 78% [Pure]
  11. University of the Highlands and Islands 76% [Pure] [under official domain]
  12. University of Melbourne 76% [Custom] [under official domain]
  13. Lancaster University 73% [Pure] [under official domain]
  14. University of New Mexico 72% [VIVO] [under official domain]
  15. Queen’s University Belfast 71% [Pure] [under official domain]
  16. University of Strathclyde 70% [Pure] [under official domain]
  17. University of St. Andrews 70% [Pure] [under official domain]
  18. Northern Arizona University 69% [Pure]
  19. Duke 69% [VIVO] [under official domain]
  20. MD Anderson Cancer Center 68% [Pure]
  21. University of Michigan 64% [Pure] [under official domain]
  22. UT Health Science Center at San Antonio 60% [Pure] [under official domain]
  23. University of Texas at Tyler 59% [Pure] [under official domain]
  24. University of York 59% [Pure] [under official domain]
  25. The University of Texas at Austin 56.% [Pure] [under official domain]
  26. Medical College of Wisconsin 56.% [Custom] [under official domain]
  27. Boston University 55.% [Profiles] [under official domain]
  28. Northwestern University 55.% [Pure] [under official domain]
  29. University of Texas at San Antonio 51% [Pure] [under official domain]
  30. University of Dundee 50% [Pure] [under official domain]
  31. University of Minnesota 50% [Pure] [under official domain]
  32. Heriot-Watt University, Edinburgh 49% [Pure] [under official domain]
  33. UT Southwestern Medical Center 48% [Pure] [under official domain]
  34. Johns Hopkins University 48% [Pure]
  35. University of Miami 46% [Pure]
  36. University of Arizona 46% [Pure]
  37. University of Nebraska 45% [Pure]
  38. University of Utah 44% [Pure]
  39. Michigan State University 44% [Pure] [under official domain]
  40. University of Texas of the Permian Basin 44% [Pure] [under official domain]
  41. Wake Forest Baptist Medical Center 42% [Profiles] [under official domain]
  42. University of Massachusetts 41% [Profiles] [under official domain]
  43. The University of Texas at Dallas 40% [Pure] [under official domain]
  44. UT Health Northeast 40% [Pure] [under official domain]
  45. Scripps 39% [VIVO] [under official domain]
  46. Case Western Reserve University 39% [Pure]
  47. Augusta University 38% [Pure]
  48. Western Michigan University 38% [Pure]
  49. University of Texas Medical Branch at Galveston 38% [Pure] [under official domain]
  50. University of Illinois at Chicago 36% [Pure]
  51. Houston Methodist 35% [Pure] [under official domain]
  52. Albert Einstein College of Medicine 33% [Pure]
  53. University of Edinburgh 33% [Pure] [under official domain]
  54. University of Florida 32% [VIVO] [under official domain]
  55. Arizona State University 31% [Pure]
  56. University of Texas Arlington 30% [Pure] [under official domain]
  57. Stanford University 30% [Custom] [under official domain]
  58. Thomas Jefferson University 29.% [Profiles] [under official domain]
  59. The University of Texas at El Paso 28.% [Pure] [under official domain]
  60. Cornell 28.% [VIVO] [under official domain]
  61. University of Rochester 26% [Profiles] [under official domain]
  62. New York University 23% [Pure]
  63. University of Iowa 23% [Custom] [under official domain]
  64. Clemson University College 22% [Pure]
  65. Baylor College of Medicine 20% [Profiles]
  66. Indiana University School of Medicine 18% [Pure]
  67. Wayne State University 18% [Pure] [under official domain]
  68. University of Texas Health Science Center at Houston 17% [Pure] [under official domain]
  69. University of South Africa 12% [Pure]
  70. University of Idaho 10% [VIVO] [under official domain]
  71. Dartmouth 9% [VIVO] [under official domain]
  72. Griffith 8% [Custom] [under official domain]
  73. George Washington University 4% [VIVO] [under official domain]
  74. Tufts 4% [Profiles] [under official domain]
  75. US Department of Agriculture 3% [VIVO] [under official domain]
  76. University of Montana 2% [VIVO]
  77. East Carolina University 1% [VIVO] [under official domain]
  78. Texas A&M 0% [VIVO] [under official domain]
  79. Boise State 0% [VIVO]
  80. University of Hawai‘i 0% [VIVO]
  81. Idaho State 0% [VIVO]
  82. Montana State University 0% [VIVO]
  83. New Mexico State 0% [VIVO]
  84. University of Alaska Anchorage 0% [VIVO]
  85. UCLA School of Medicine 0% [Custom] [under official domain]
  86. University of Nevada, Las Vegas 0% [VIVO]
  87. University of Nevada, Reno 0% [VIVO]
  88. University of Pennsylvania 0% [VIVO] [under official domain]
  89. University of Wyoming 0% [VIVO]
  90. Virginia Commonwealth University 0% [VIVO] [under official domain]

3. Conclusions

Which software has the best real-world SEO performance?

Average scores by platform

  • Pure = 50%
  • Profiles RNS = 44%
  • Custom = 39%
  • VIVO = 15%

Average scores, by use of official vs. other domain

  • Official domain? (e.g. vivo.cornell.edu)
    average score = 44%
  • Other domain? (e.g. stephenson.pure.elsevier.com)
    average score = 30%

Average scores by platform, taking domain names into account (where n >= 5)

  • Pure + Institutional Domain = 53%
  • Profiles + Institutional Domain = 47%
  • Pure + other domain = 45%
  • Profiles + other domain = 35%*
  • Custom + Institutional Domain = 39%
  • VIVO + Institutional Domain = 26%
  • VIVO + other domain = 18%*

* includes some data from 2015 survey

Does getting lots of incoming links help?

It appears to. The top 10 sites have a median 560 linking root domains — one of several metrics related to incoming link diversity mentioned in the Moz Search Engine Ranking Factors 2015.

The correlation between linking root domains and search rankings holds true across our dataset:

RNS SEO 2016 root linking domains

4. How do you increase your site’s search rankings?

Read our helpful guides: