UCSF dentistry co-authorships, internal vs. external (by institutions)

What does a typical UCSF publication look like, in terms of the number of internal co-authors vs. the number of external co-authoring institutions? Here’s a breakdown among dentistry-related publications by UCSF researchers published in 2013. (This is the same analysis as yesterday, but looking at the number of external institutions, vs. the number of external people.)

Again, I was surprised to see so many co-authorships between a single UCSF researcher and one or researchers from one or more external institutions (the very top row of results), which accounts for 52% of the papers we looked at.

UCSF vs External Co-Authoring InstitutionsView as PDF

Method: I searched Web of Science for dentistry-related articles published between January 1-December 5, 2013. I began by running a search for any articles published in 2013 matching a number of dentistry-related keywords (dental, dentistry, electrogalvanism, endodontics, jaw relation record, mouth rehabilitation, odontometry, oral, orthodontics, periodontics, prosthodontics, teeth, tooth), then filtered only those that matched the “DENTISTRY ORAL SURGERY MEDICINE” Web of Science category. Web of Science automatically attempts to normalize institution names; in addition, I lumped all institution labels with names that start with “UCSF”, “UC San Francisco”, or “UCSF Sch ” to “Univ Calif San Francisco” as part of the main UCSF grouping.

UCSF dentistry co-authorships, internal vs. external

What does a typical UCSF publication look like, in terms of internal vs. external co-authors? Here’s a breakdown of each type of co-author, among dentistry-related publications by UCSF researchers published in 2013.

Three immediate take-aways:

  • I was surprised to see so many co-authorships between a single UCSF researcher and one or more external researchers — the very top row of results. By volume, this accounts for 52% of the papers we looked at.
  • When every author is internal to UCSF, there’s an average of 3.5 UCSF co-authors
  • When there’s an external collaboration, there’s an average of 2.0 UCSF co-authors

UCSF vs External Co-AuthorsView as PDF

Method: I searched Web of Science for dentistry-related articles published between January 1-December 5, 2013. I began by running a search for any articles published in 2013 matching a number of dentistry-related keywords (dental, dentistry, electrogalvanism, endodontics, jaw relation record, mouth rehabilitation, odontometry, oral, orthodontics, periodontics, prosthodontics, teeth, tooth), then filtered only those that matched the “DENTISTRY ORAL SURGERY MEDICINE” Web of Science category. Web of Science automatically attempts to normalize institution names; in addition, I lumped all institution labels with names that start with “UCSF”, “UC San Francisco”, or “UCSF Sch ” to “Univ Calif San Francisco” as part of the main UCSF grouping.

UCSF dentistry collaborations, mapped

Social network graphs are pretty, but they’re not the only way we can try to visualized cross-institutional research collaborations. Here’s a geographic view of some of the institutions that UCSF dentistry researchers have co-authored with over the course of 2013.

UCSF Dentistry Collaborations, Jan 1 - Dec 5, 2013 (World)

UCSF Dentistry Collaborations, Jan 1 - Dec 5, 2013 (USA)

UCSF Dentistry Collaborations, Jan 1 - Dec 5, 2013 (Europe)

Method: I searched Web of Science for dentistry-related articles published in 2013 (i.e. from January 1-December 5, 2013). I began by running a search for any articles published in 2013 matching a number of dentistry-related keywords (dental, dentistry, electrogalvanism, endodontics, jaw relation record, mouth rehabilitation, odontometry, oral, orthodontics, periodontics, prosthodontics, teeth, tooth), then filtered only those that matched the “DENTISTRY ORAL SURGERY MEDICINE” Web of Science category. I pulled out institution names, tried to geolocate them using the OpenStreetMap Nominatim service, and used Google Fusion Maps to put them on a map. This map is by no means authoritative, given that a significant number of institutions could not be trivially geolocated.

UCSF dentistry collaborations, visualized

Looking at cross-institutional co-authorship networks is a useful way of seeing not only who we work with, but also where there may be gaps of interest.

I first looked at dentistry-related publications by UCSF researchers published in 2013, breaking out the institutions we co-authored with. And there we are, sitting pretty in the center of our universe, collaborating with major institutions in the US, Korea, Australia, Italy, Denmark, and more.

(Details: Institution node sizes indicate the total volume of dentistry-related articles published. Connecting line widths indicate the number of articles co-authored between two institutions. Distance between nodes indicates the tightness of co-authorship networks, and different sets of node colors help distinguish groups of institutions whose researchers frequently co-author together. Of 462 institutions that collaborated with UCSF researchers, we’re showing only 91 that had 10 or more cross-institutional articles in that time.)

View full-size visualization (PDF)

UCSF dentistry research co-authorships, Jan 1 - Dec 5 2013

Then I looked at the total universe of dentistry-related publications published in 2013 (see below). Notice a difference? I have to admit that it took me a while to find UCSF in the mess of dots. (If you look at the full-size view, we’re in the medium blue section, next to the pinks.) Of course this says more about the sheer volume of research being published by universities all over the world, than about any lack of cross-institutionally collaborative spirit on our part; in fact I hid over 80% of the institutions in the first image to keep it readable, which accounts for a a good chunk of the difference. But the sheer weight of institutions from Europe, East Asia, and Latin America in this second image that aren’t there in the first is intriguing, and something I’m going to try digging into.

(Details: Institution node sizes indicate the total volume of dentistry-related articles published. Connecting line widths indicate the number of articles co-authored between two institutions. Distance between nodes indicates the tightness of co-authorship networks, and different sets of node colors help distinguish groups of institutions whose researchers frequently co-author together. Of 2,575 institutions that we found, we’re showing only 374 that had 10 or more cross-institutional articles in that time.)

View full-size visualization (PDF)

Dentistry research co-authorships, Jan 1-Dec 5 2013

(And yes, I realize fully well that I’m probably looking at the wrong things here, privileging increasing the count of cross-institutional collaborations as an end in itself, avoiding any consideration of research quality, and giving greater visual weight to institutions that publish more, regardless of the size of the institution or the quality of work. Pretty pictures lie can hide lots of flaws. I hope you’ll bear with me as I publicly iterate through these topics, step by step, hopefully getting just a little bit less dumb every time.)

Additional uninteresting details: I searched Web of Science for dentistry-related articles published in 2013 (i.e. from January 1-December 5, 2013). I began by running a search for any articles published in 2013 matching a number of dentistry-related keywords (dental, dentistry, electrogalvanism, endodontics, jaw relation record, mouth rehabilitation, odontometry, oral, orthodontics, periodontics, prosthodontics, teeth, tooth), then filtered only those that matched the “DENTISTRY ORAL SURGERY MEDICINE” Web of Science category.

The 100 top researcher keywords at UCSF

I was looking to dig into some examples of collaboration patterns in different research areas, when I realized I didn’t even know the basics — what do UCSF researchers actually research?

UCSF Profiles uses PubMed data to extract MeSH keywords for every publication by every UCSF researcher in the system. We can use this to look at the most commonly used MeSH keywords across every researcher’s body of work. There are lots of caveats here (looking at all publications emphasizes past research interests over current ones; we’re not grouping related obscure MeSH terms with more popular ones; MeSH term assignment practices change over time; and this analysis ignores someone’s role as a first, middle, or last author). But this is certainly a start.

Here’s what I found, using the latest UCSF Profiles data:

  1. 98 researchers have HIV Infections in their top 5 MeSH keywords
  2. 53 researchers have Breast Neoplasms in their top 5 MeSH keywords
  3. 42 researchers have Magnetic Resonance Imaging in their top 5 MeSH keywords
  4. 39 researchers have Tomography, X-Ray Computed in their top 5 MeSH keywords
  5. 39 researchers have Brain Neoplasms in their top 5 MeSH keywords
  6. 37 researchers have Internship and Residency in their top 5 MeSH keywords
  7. 37 researchers have HIV-1 in their top 5 MeSH keywords
  8. 34 researchers have Alzheimer Disease in their top 5 MeSH keywords
  9. 33 researchers have Prostatic Neoplasms in their top 5 MeSH keywords
  10. 32 researchers have Saccharomyces cerevisiae in their top 5 MeSH keywords
  11. 31 researchers have Brain in their top 5 MeSH keywords
  12. 31 researchers have Anti-HIV Agents in their top 5 MeSH keywords
  13. 30 researchers have Neoplasms in their top 5 MeSH keywords
  14. 30 researchers have Smoking in their top 5 MeSH keywords
  15. 29 researchers have Diabetes Mellitus, Type 2 in their top 5 MeSH keywords
  16. 29 researchers have Asthma in their top 5 MeSH keywords
  17. 28 researchers have Stroke in their top 5 MeSH keywords
  18. 28 researchers have Sexual Behavior in their top 5 MeSH keywords
  19. 27 researchers have Myocardial Infarction in their top 5 MeSH keywords
  20. 27 researchers have Proteins in their top 5 MeSH keywords
  21. 26 researchers have Neurons in their top 5 MeSH keywords
  22. 26 researchers have Skin Neoplasms in their top 5 MeSH keywords
  23. 26 researchers have Antineoplastic Combined Chemotherapy Protocols in their top 5 MeSH keywords
  24. 25 researchers have Cognition Disorders in their top 5 MeSH keywords
  25. 25 researchers have Homosexuality, Male in their top 5 MeSH keywords
  26. 25 researchers have Emergency Service, Hospital in their top 5 MeSH keywords
  27. 25 researchers have Students, Medical in their top 5 MeSH keywords
  28. 24 researchers have Obesity in their top 5 MeSH keywords
  29. 24 researchers have Glioblastoma in their top 5 MeSH keywords
  30. 23 researchers have Epilepsy in their top 5 MeSH keywords
  31. 23 researchers have Pancreatic Neoplasms in their top 5 MeSH keywords
  32. 23 researchers have Dementia in their top 5 MeSH keywords
  33. 23 researchers have Liver Transplantation in their top 5 MeSH keywords
  34. 23 researchers have Hispanic Americans in their top 5 MeSH keywords
  35. 23 researchers have Education, Medical, Undergraduate in their top 5 MeSH keywords
  36. 22 researchers have Lung in their top 5 MeSH keywords
  37. 22 researchers have Genetic Predisposition to Disease in their top 5 MeSH keywords
  38. 22 researchers have Saccharomyces cerevisiae Proteins in their top 5 MeSH keywords
  39. 22 researchers have Lung Neoplasms in their top 5 MeSH keywords
  40. 22 researchers have Glioma in their top 5 MeSH keywords
  41. 21 researchers have Drosophila in their top 5 MeSH keywords
  42. 21 researchers have Mass Screening in their top 5 MeSH keywords
  43. 21 researchers have Heart Defects, Congenital in their top 5 MeSH keywords
  44. 21 researchers have Anti-Bacterial Agents in their top 5 MeSH keywords
  45. 21 researchers have Liver in their top 5 MeSH keywords
  46. 21 researchers have Polymorphism, Single Nucleotide in their top 5 MeSH keywords
  47. 21 researchers have Physician-Patient Relations in their top 5 MeSH keywords
  48. 21 researchers have Signal Transduction in their top 5 MeSH keywords
  49. 21 researchers have Primary Health Care in their top 5 MeSH keywords
  50. 21 researchers have Nerve Tissue Proteins in their top 5 MeSH keywords
  51. 21 researchers have Stem Cells in their top 5 MeSH keywords
  52. 21 researchers have Drosophila melanogaster in their top 5 MeSH keywords
  53. 20 researchers have Colorectal Neoplasms in their top 5 MeSH keywords
  54. 20 researchers have Stress Disorders, Post-Traumatic in their top 5 MeSH keywords
  55. 20 researchers have Calcium in their top 5 MeSH keywords
  56. 20 researchers have Health Services Accessibility in their top 5 MeSH keywords
  57. 20 researchers have Smoking Cessation in their top 5 MeSH keywords
  58. 20 researchers have Epithelial Cells in their top 5 MeSH keywords
  59. 20 researchers have Wounds and Injuries in their top 5 MeSH keywords
  60. 20 researchers have Drosophila Proteins in their top 5 MeSH keywords
  61. 20 researchers have Models, Molecular in their top 5 MeSH keywords
  62. 19 researchers have Magnetic Resonance Spectroscopy in their top 5 MeSH keywords
  63. 19 researchers have MicroRNAs in their top 5 MeSH keywords
  64. 19 researchers have Respiratory Distress Syndrome, Adult in their top 5 MeSH keywords
  65. 19 researchers have Curriculum in their top 5 MeSH keywords
  66. 19 researchers have Aging in their top 5 MeSH keywords
  67. 19 researchers have Embryonic Stem Cells in their top 5 MeSH keywords
  68. 19 researchers have Caenorhabditis elegans in their top 5 MeSH keywords
  69. 19 researchers have Kidney Transplantation in their top 5 MeSH keywords
  70. 18 researchers have Heart Failure in their top 5 MeSH keywords
  71. 18 researchers have Membrane Proteins in their top 5 MeSH keywords
  72. 18 researchers have Asian Americans in their top 5 MeSH keywords
  73. 18 researchers have DNA in their top 5 MeSH keywords
  74. 18 researchers have Tuberculosis in their top 5 MeSH keywords
  75. 18 researchers have Mental Disorders in their top 5 MeSH keywords
  76. 18 researchers have Transcription Factors in their top 5 MeSH keywords
  77. 18 researchers have Coronary Disease in their top 5 MeSH keywords
  78. 18 researchers have Gene Expression Profiling in their top 5 MeSH keywords
  79. 17 researchers have DNA-Binding Proteins in their top 5 MeSH keywords
  80. 17 researchers have CD8-Positive T-Lymphocytes in their top 5 MeSH keywords
  81. 17 researchers have Skin Diseases in their top 5 MeSH keywords
  82. 17 researchers have Bacterial Proteins in their top 5 MeSH keywords
  83. 17 researchers have Apoptosis in their top 5 MeSH keywords
  84. 17 researchers have Protein-Serine-Threonine Kinases in their top 5 MeSH keywords
  85. 17 researchers have Homeodomain Proteins in their top 5 MeSH keywords
  86. 17 researchers have Hypertension in their top 5 MeSH keywords
  87. 17 researchers have Stress, Psychological in their top 5 MeSH keywords
  88. 17 researchers have T-Lymphocytes in their top 5 MeSH keywords
  89. 17 researchers have Abortion, Induced in their top 5 MeSH keywords
  90. 17 researchers have Schizophrenia in their top 5 MeSH keywords
  91. 17 researchers have Antineoplastic Agents in their top 5 MeSH keywords
  92. 17 researchers have Proteomics in their top 5 MeSH keywords
  93. 17 researchers have Multiple Sclerosis in their top 5 MeSH keywords
  94. 17 researchers have Teaching in their top 5 MeSH keywords
  95. 17 researchers have Acquired Immunodeficiency Syndrome in their top 5 MeSH keywords
  96. 17 researchers have Hepatitis C in their top 5 MeSH keywords
  97. 17 researchers have Laparoscopy in their top 5 MeSH keywords
  98. 16 researchers have Muscle, Skeletal in their top 5 MeSH keywords
  99. 16 researchers have Amyloid beta-Peptides in their top 5 MeSH keywords
  100. 16 researchers have Ovarian Neoplasms in their top 5 MeSH keywords

Every researcher at UCSF — by department

Co-authorship networks can help us understand internal research collaboration patterns at UCSF. I used data from UCSF Profiles to create a visualization of (almost) every researcher currently at UCSF, and how their intra-UCSF co-authorship networks break out by department.

This visualization by department bears some more investigation than the previous one by school. Department of Medicine researchers are all over, collaborating with a wide variety of external departments. But an initial visual inspection suggests that almost all major departments have co-authorship relationships with members of other departments; some, like neurology, appear to form large standalone clusters, while others, like radiology, are more enmeshed in the work of others. This visualization flattens complex relationships into two dimensions, but it’s a starting point as we work to understand how UCSF collaborates.

View full-size visualization (PDF)

Every Researcher at UCSF, by department

Every researcher at UCSF — by school

Co-authorship networks can help us understand internal research collaboration patterns at UCSF. I used data from UCSF Profiles to create a visualization of (almost) every researcher currently at UCSF, and how their intra-UCSF co-authorship networks break out by school.

Unsurprisingly, the School of Medicine takes up most of the space, and the visualization is probably most interesting in terms of what it might suggest about the smaller schools. Researchers from the Schools of Nursing and Dentistry form their own visible clusters, who often work with each other, but also have co-authorship relationships with researchers at the School of Medicine. But I was surprised by the School of Pharmacy, whose researchers form a main clusters in the bottom right, as well as additional clusters in the middle and top left, due to strong collaborative relationships with School of Medicine researchers.

View full-size visualization (PDF)

Every Researcher at UCSF, by school

Departmental BFFs: Which UCSF departments publish the most often together?

Batman and Robin smoking

Some UCSF departments work more closely together than others. I looked at co-authorship patterns in papers published between January 2012 and November 2013, based on data in UCSF Profiles, and pulled out the UCSF departments that collaborate the most frequently. The results aren’t necessarily surprising. The Department of Medicine is huge, and their cross-departmental collaborations make up 8 of the top 10 collaborations, measured by volume. On the flip side, smaller groups with research areas similar to others make up many of the most common collaborations, by percentage; for example, one-third of papers by researchers primarily affiliated with the Proctor Foundation for Research in Opthalmology are co-authored with researchers from the Department of Opthalmology. I wouldn’t have necessarily guessed, however, connections like that between nursing and psychiatry.

Top UCSF cross-departmental collaborations, by volume

  1. Epidemiology & Biostatistics + Medicine: 365 collaborative papers
  2. Medicine + Pediatrics: 139 collaborative papers
  3. Medicine + Psychiatry: 127 collaborative papers
  4. Neurological Surgery + Neurology: 115 collaborative papers
  5. Medicine + Pathology: 105 collaborative papers
  6. Laboratory Medicine + Medicine: 104 collaborative papers
  7. Medicine + Surgery: 99 collaborative papers
  8. Neurology + Radiology and Biomedical Imaging: 92 collaborative papers
  9. Medicine + Radiology and Biomedical Imaging: 90 collaborative papers
  10. Medicine + Neurology: 86 collaborative papers

Top UCSF cross-departmental collaborations, by percentage

  1. 64.8% of School of Nursing Dean’s Office papers are co-authored with Physiological Nursing
  2. 36.8% of Proctor Foundation papers are co-authored with Ophthalmology
  3. 33.8% of School of Nursing Dean’s Office papers are co-authored with Medicine
  4. 33.3% of Physiological Nursing papers are co-authored with School of Nursing Dean’s Office
  5. 33.3% of Institute for Neurodegenerative Disease papers are co-authored with Neurology
  6. 32.4% of School of Nursing Dean’s Office papers are co-authored with Psychiatry
  7. 29.8% of Physical Therapy & Rehab Sciences papers are co-authored with Radiology and Biomedical Imaging
  8. 27.5% of Physiological Nursing papers are co-authored with Medicine
  9. 25.0% of Epidemiology & Biostatistics papers are co-authored with Medicine
  10. 22.8% of Family & Community Medicine papers are co-authored with Medicine

Details: Data is drawn from UCSF Profiles, and is based on a list of all publications listed on PubMed published between Jan 2012–Nov 2013, focusing on those whose authors include groups of researchers that have primary affiliations to more than one UCSF department. We counted only those publications from researchers with a listed department, and for the purposes of counting top cross-departmental collaborations by percentage, only those collaborations that generated 10 or more papers during the time period. No attempt was made to account for the widely varying sizes and scopes of different departments, the fact that researchers may have multiple departmental affiliations, or the fact that some publications may have been authored before the researchers were affiliated with their current primary departments at UCSF.

Photo: “Glasgow’s own superheroes having a smoke outside the Counting House” by Stephen Fyfe/Flickr, under CC-BY-NC-ND

UCSF’s top 20 most diverse internally-collaborative departments

When UCSF researchers collaborate between departments, how diverse are the collaborations? Here are the top 20 UCSF departments, ranked by the average numbers of UCSF departments their multi-departmental papers include as co-authors (from among the 39 departments whose researchers had a total of 25+ multi-departmental publications published between January 2012 and November 2013).

Details: Data is drawn from UCSF Profiles, and is based on a list of all publications listed on PubMed published between Jan 2012–Nov 2013 whose authors include groups of researchers with primary affiliations to more than one UCSF department. We counted only those publications from researchers with a listed department, and only those departments whose current associated researchers published 25+ publications in conjunction with current members of other UCSF departments between Jan 2012–Nov 2013. No attempt was made to account for the widely varying sizes and scopes of different departments, the fact that researchers may have multiple departmental affiliations, or the fact that some publications may have been authored before the researchers were affiliated with their current primary departments at UCSF. These are the top 20 departments, out of a total of 39 that match our criteria.

  1. Physiological Nursing: co-authors from avg. 2.57 other UCSF departments, among 116 multi-department papers
  2. School of Nursing Dean’s Office: co-authors from avg. 2.44 other UCSF departments, among 52 multi-department papers
  3. Anesthesia/Perioperative Care: co-authors from avg. 1.84 other UCSF departments, among 69 multi-department papers
  4. Physiology: co-authors from avg. 1.83 other UCSF departments, among 29 multi-department papers
  5. Family Health Care Nursing: co-authors from avg. 1.64 other UCSF departments, among 47 multi-department papers
  6. Laboratory Medicine: co-authors from avg. 1.63 other UCSF departments, among 104 multi-department papers
  7. Pharmaceutical Chemistry: co-authors from avg. 1.63 other UCSF departments, among 120 multi-department papers
  8. Pathology: co-authors from avg. 1.62 other UCSF departments, among 234 multi-department papers
  9. Radiation Oncology: co-authors from avg. 1.60 other UCSF departments, among 53 multi-department papers
  10. Microbiology and Immunology: co-authors from avg. 1.57 other UCSF departments, among 49 multi-department papers
  11. Cellular & Molecular Pharmacology: co-authors from avg. 1.57 other UCSF departments, among 74 multi-department papers
  12. Orofacial Sciences: co-authors from avg. 1.57 other UCSF departments, among 53 multi-department papers
  13. HDF Comprehensive Cancer Center: co-authors from avg. 1.55 other UCSF departments, among 31 multi-department papers
  14. Anatomy: co-authors from avg. 1.55 other UCSF departments, among 55 multi-department papers
  15. Pediatrics: co-authors from avg. 1.53 other UCSF departments, among 321 multi-department papers
  16. School of Nursing Community Health Systems: co-authors from avg. 1.52 other UCSF departments, among 31 multi-department papers
  17. Surgery: co-authors from avg. 1.50 other UCSF departments, among 227 multi-department papers
  18. Biochemistry & Biophysics: co-authors from avg. 1.49 other UCSF departments, among 75 multi-department papers
  19. Neurological Surgery: co-authors from avg. 1.47 other UCSF departments, among 393 multi-department papers
  20. Cardiovascular Research Institute: co-authors from avg. 1.45 other UCSF departments, among 53 multi-department papers

UCSF’s top 20 internally collaborative departments

Some UCSF departments consistently reach out out to collaborate with other members of the UCSF community. Here are the top 20 UCSF departments whose researchers have the highest proportion of publications co-authored with members of other UCSF departments from among departments whose researchers had a total of 100+ publications published between January 2012 and November 2013.

Details: Data is drawn from UCSF Profiles, and is based on a list of all publications listed on PubMed published between Jan 2012–Nov 2013 whose authors include groups of researchers with primary affiliations to more than one UCSF department. We counted only publications from researchers with a listed department, and departments with 100+ publications by current associated researchers between Jan 2012–Nov 2013. No attempt was made to account for the widely varying sizes and scopes of different departments, the fact that researchers may have multiple departmental affiliations, or the fact that some publications may have been authored before the researchers were affiliated with their current primary departments at UCSF. These are the top 20 departments, out of a total of 42 that match our criteria.

  1. Epidemiology & Biostatistics: 51.1%
    424 of 829 publications co-authored with other UCSF departments
  2. Proctor Foundation: 50.3%
    82 of 163 publications co-authored with other UCSF departments
  3. Pathology: 49.2%
    234 of 476 publications co-authored with other UCSF departments
  4. Physiological Nursing: 45.5%
    116 of 255 publications co-authored with other UCSF departments
  5. Neurological Surgery: 43.9%
    393 of 896 publications co-authored with other UCSF departments
  6. Orofacial Sciences: 42.7%
    53 of 124 publications co-authored with other UCSF departments
  7. Family Health Care Nursing: 37.0%
    47 of 127 publications co-authored with other UCSF departments
  8. Clinical Pharmacy: 36.9%
    58 of 157 publications co-authored with other UCSF departments
  9. Family & Community Medicine: 36.2%
    54 of 149 publications co-authored with other UCSF departments
  10. Radiology and Biomedical Imaging: 35.0%
    449 of 1284 publications co-authored with other UCSF departments
  11. Psychiatry: 33.5%
    252 of 753 publications co-authored with other UCSF departments
  12. Pharmaceutical Chemistry: 33.3%
    120 of 360 publications co-authored with other UCSF departments
  13. Pediatrics: 32.5%
    321 of 989 publications co-authored with other UCSF departments
  14. Anatomy: 31.8%
    55 of 173 publications co-authored with other UCSF departments
  15. Ob/Gyn & Reproductive Sciences: 30.7%
    185 of 602 publications co-authored with other UCSF departments
  16. Cell & Tissue Biology: 30.5%
    32 of 105 publications co-authored with other UCSF departments
  17. Dermatology: 30.1%
    129 of 429 publications co-authored with other UCSF departments
  18. Medicine: 27.7%
    1257 of 4545 publications co-authored with other UCSF departments
  19. Biochemistry & Biophysics: 26.8%
    75 of 280 publications co-authored with other UCSF departments
  20. Neurology: 26.8%
    400 of 1495 publications co-authored with other UCSF departments

HIV/AIDS research collaborations, visualized

Co-authorship networks give us a sense of the strength of research collaborations. We used co-authorship data to visualize how top HIV/AIDS research institutions worked with one another, based on publications from June 2012 to September 2013. UCSF collaborations are indicated via red lines.

Visualization details: Data includes all known publications related to HIV/AIDS between June 2012 and September 2013 that includes co-authors from two or more institutions. We map each author to their institution, and the size of each institution corresponds with the number of HIV/AIDS publications its members co-authored in that time; only the most prolific institutions are shown to ensure readability of the image. The width of the lines connecting institutions corresponds to the number of publications that include co-authors from both of these institutions. Collaborations with UCSF researchers are indicated with red lines. Colors indicate clusters of institutions that often publish collaboratively, based on network modularity.

Click to view full-size image

HIV-AIDS Collaborations, June 2012 - Sep 2013

How Mailchimp uses network analysis

An example of Mailchimp mailing list clustering by readership overlap — “Fantasy sports! Guns! And flowers, for what I can only assume are apologies for doing something stupid with the first two.”

I sometimes forget what a powerful tool network analysis is. Mailchimp, a popular email newsletter provider, used several standard network analysis tools to look at subscribers to mailing lists using their platform, to calculate similarities between both subscribers and lists.

Read more:

Enhance your research networking platform, the UCSF way

Golden Gate Bridge

CTSI at UCSF has invested in increasing the usage and usability of UCSF Profiles, our research networking system. Based on our presentation at the 2012 IKFC meeting, here are our top 5 technical tips on how to increase the impact of your institution’s investment in research networking platforms, based on our past three years of work.

1. Measure

You can’t understand how you’re doing without measuring usage.

  • Install Google Analytics, then learn how to use this incredibly powerful tool (make sure to segment on-campus vs. off-campus traffic by setting up advanced segments based on service provider)
  • Register your site on Google Webmaster Tools to understand how search engines see your data

2. Optimize for search engines

UCSF Profiles gets over 50,000 visits a month. 72% of that traffic comes from search engines, primarily Google. Here’s how to increase traffic from search engines:

  • Implement a sitemap containing links to all your people profile pages, and make sure Google sees it using Google Webmaster Tools
  • Add a readable meta description (e.g. “Jane Doe’s profile, publications, research topics, and co-authors”) to your profile pages so they look better in search engine results
  • Add Schema.org data about your people on people profile pages
  • Advanced: use rel=canonical to prevent different versions of the same content from being indexed

3. Build inbound links

Linking is a critical way to both increase site traffic, and to signal importance to search engines.

  • Get websites large and small at your institution to link to your site (two years after launch, there are over 100 websites at UCSF that link to one or more pages on Profiles)
  • Encourage heavy linking to individual profile pages, e.g. from the campus directory, news articles, departmental profiles

4. Reuse data

Your research profiling system comes with APIs. Encouraging campus-wide reuse of this data can increase the impact of your investment. See opendata.profiles.ucsf.edu to see how UCSF is marketing this data.

  • Learn how to use your system’s APIs, so you can share that experience with others
  • Publicly document how the APIs work, and include sample source code
  • Reach out to campus technologists and webmasters to demonstrate how easy it is for them to reuse your data (e.g. the inclusion of Profiles data in UCSF’s mobile app was the result of technologist outreach)
  • Reach out to campus leaders to show them what kind of efficiencies they can gain by reusing your data (e.g. the inclusion of links to researcher profiles on the UCSF Directory was the result of a strategic partnership)

5. Extend with ORNG (advanced)

ORNG (OpenSocial Research Networking Gadgets) is a plugin system that allows you to add new apps into instances of Profiles or VIVO. Apps are written in HTML, CSS, and JavaScript, and are easy to share and reuse.

  • Install ORNG (OpenSocial) into your copy of Profiles or VIVO
  • Add new apps from the ORNG library of free apps
  • Write your own apps — most JavaScript programmers can get started in hours

Good luck! Feel free to leave comments and questions on this post—we’re happy to share what we know.

P.S. Thinking about how to make your campus equipment/services more discoverable? Try UCSF’s Plumage, the open source platform behind UCSF Cores Search.

Photo credit: digitonin via photopin cc

Leveraging the Social Web for Research Networking

Five Questions with CTSI Technical Architect Eric Meeks About the Benefits of OpenSocial 

This article highlights…

  • …what OpenSocial is and how it can help advance research networking, 
  • …what institutions interested in using OpenSocial should keep in mind,
  • …what the Clinical and Translational Science Institute (CTSI) at UCSF has done to promote OpenSocial in academia and to build a community of developers and supporters,
  • …and how the business sector can tap into this emerging market.

Eric Meeks has worked for numerous startups in Silicon Valley including Ning, one of the first social network systems to support OpenSocial. Since 2009, he has been the lead technical architect for the Clinical & Translational Science Institute (CTSI) at the University of California, San Francisco (UCSF), the first academic biomedical institution using OpenSocial in an open source research networking product. He is also a founder of the Open Research Network Gadgets (ORNG).

Q: OpenSocial is built upon the idea that ‘the web is better when it’s social’. It encourages developers to build standardized web applications that can be shared across different social networking platforms. How can academia and biomedical research benefit from OpenSocial?

If you look at the topography of all the different research institutions, many of them run different back-end systems, from Windows to Linux, Oracle to MySQL to SQL Server, and all with custom data models. Despite differences in our underlying systems, however, we’re becoming very common in that we deploy research networking systems to help investigators find and connect with one another. Some systems are based on Profiles, some on VIVO, some are custom like LOKICAP, or Digital Vita. As much as we want to share new features and applications to extend these systems, it’s really difficult to do that because the applications are hard-coded and tailored to our specific institutional databases. As a consequence, many applications are rebuilt for the various systems. OpenSocial allows us to change that.

By agreeing upon a solution that is standardized by a large community, we can agree upon a way for sharing these things. We can build one version, share it amongst everybody, and be more cost-effective. In other words, implementing OpenSocial makes applications interoperable with any social network system that supports them. Academic research institutions can use OpenSocial to open up their websites so that multiple people can add new features and applications and they can all do it at the same time, and independently of one another. ‘A platform beats an application every time,’ as O’Reilly Media put it. I see that as extremely powerful. It’s the only way I see to solve the problem that we have with different institutions deploying different research networking tools.

Q: What are some of the ways that the social web and OpenSocial are relevant to academia?

In research as in in other areas of life, communication and collaboration are supported by relationships. At their core, research networking systems are similar to social networking systems like Facebook, LinkedIn or Ning. They are showing a researcher’s expertise and how researchers are connected to each other.

The difference is that the social ‘friend’ in an academic network is embellished with different attributes such as co-authorship, mentorship, and shared areas of research. Research networking systems were created to leverage and enhance these academic relationships.

Q: You lead the OpenSocial efforts at UCSF’s Clinical and Translational Science Institute. How would you describe your work to improve research networking?

We’re working with researchers and administrators to identify needs for applications that are appropriate for research networking tools. The gadgets that we have developed are intended to fill some of the gaps that exist. They allow individuals to update their research profile by adding presentations hosted at SlideShare, data that indicates an interest in mentoring, and relevant web links. Gadgets also make it easy to export publications in different formats from any profile, and help users build lists of people based on common attributes like research interests. And finally, a more generic Google Search gadget broadens the existing Profiles search to include free-text fields like the profile narrative and awards. All of these applications are available to any institution that wants to utilize them. (See the full gadget library.) It is our hope that our library of free gadgets will grow as more institutions join the OpenSocial community.

Right now, we’re in the process of building the community, which also means that the first members of this community don’t really get to benefit from it, but that’s changing. Wake Forest University, for example, has adopted OpenSocial and is using one of our applications. But they also built their own applications and made them available for free. Andy Bowline, Programmer at Wake Forest University, created a gadget that matches NIH reporter grant data with researchers’ profiles to identify grants that may be appropriate for a researcher to look into. Andy reached out to the NIH OER Grant Search team and got permission to scrape their website on a daily basis. The gadget grabs the data and throws them into a search engine. It also talks to the web service that Andy built on top of that to find matching grants. Andy not only shared the gadget that does this, but they also allow us to use their web services. That’s exactly what we want to see. We’re not competing. On the contrary, we’re trying to do the same thing. By using OpenSocial we can do it together.

Most excitingly, our OpenSocial code is becoming an official part of both the Profiles and the VIVO products. We have been working with both developer teams of both products supporting OpenSocial in our development environment. It’s great to see the same gadgets running in both of these two different systems, especially when you consider that the technology stacks between the two products couldn’t be more different.  Profiles runs on Microsoft technologies while VIVO runs on Java.  However, since they are both supporting OpenSocial, those differences don’t matter; the gadgets run in either environment without alteration.

Q: What tips do you have for academic institutions interested in adopting OpenSocial? 

There are a few things. I think it is helpful to have internal discussions with your development team and web strategy leaders to discuss how existing applications could be repurposed in a research networking site. Not all applications are suited for this type of deployment of course, but for some it may be the best way to make sure that these applications are seen and used.

OpenSocial is a huge API. I recommend integrating with Apache Shindig, which is the reference standard for OpenSocial. It shows you in ‘living code’ what an OpenSocial website should do and it also serves as a library to make your website OpenSocial compliant. LinkedIn, Google, Nature Networks and others are using it.

I also recommend taking advantage of the applications we already developed to help save time and money. We’re giving out both the applications and the code we use to convert our website to be OpenSocial-enabled, which lowers the technical bar quite a bit. It’s easy to apply the code in a few days.

Another important lesson that we learned is the need to prepare for managing expectations and overcoming political hurdles. OpenSocial is extremely powerful, but as with any technology, it doesn’t do everything and it does require some amount of technical investment.

It is also helpful for interested groups to know that we have combined OpenSocial with the Resource Definition Framework (RDF) standard that is core to VIVO and can now also be found in Profiles and LOKI. RDF is a component of the semantic web and Linked Open Data. When applications support RDF it is much easier for them to share data. As a matter of fact, the CTSA network recommends that institutions use VIVO compatible RDF within their research networking tools so that all of our data can be accessed more easily. With OpenSocial, we are able to use VIVO RDF to expose much richer data to our gadgets than the OpenSocial specification originally allowed. This is a great win and allows us to build gadgets that are very specific to our biomedical researcher needs without having to sacrifice interoperability.

If your institution uses a product like VIVO or Profiles that you think would benefit from being OpenSocial, we definitely want to hear from you, because we want to make sure that we have the same flavor of OpenSocial across our products that are truly interoperable. And, consider joining our new initiative called Open Research Networking Gadgets (ORNG), pronounced “orange.”

Q: Originally, OpenSocial was designed by corporations such as Google and MySpace Google for social network applications. While OpenSocial is seeing wider adoption in enterprise companies, that adoptions has been slower in the academic biomedical arena. What would you like to see from the business sector?

I would like for industry to recognize that there is an emerging market here that they can tap into. It’s a market that has a lot of value, a lot of social benefit, and a lot of wonderful brands behind it such as UCSF, Harvard, and Cornell. This is the kind of work that industry should be proud to be a part of, and they can convert that into a marketing message. I also want industry to know that we would like to work with them.

What we don’t want is for OpenSocial to drift off into some area where it’s dominated by the entertainment or finance industry and no longer viable to science and academia. The OpenSocial Foundation is a main driver in this respect, and they are eager for adoption by people and institutions working in the health sciences. The Open Social Foundation is much more targeted at collaboration and productivity as opposed to entertainment.

Q: What’s your vision for OpenSocial at UCSF and for academia in general? 

A standard only has value when it has adoption across multiple platforms, so we want to promote it and build a community. We also want to be a part of that community to be able to share the benefits of the networking effect. Right now research networking systems only give us a hint of what they’re capable of doing. People today are using these platforms to find out about one another, and even this is happening in a limited sense. People should be using these platforms not just to find out about one another, but to interact and get things done. That’s what people are doing with LinkedIn, Facebook, etc. With a strong OpenSocial community we can advance and extend current research networking systems much faster and cheaper to give researchers and administrators the opportunity to be hyper-connected and hopefully more productive.

This Q&A is part of “Digital Media & Science: A Perspectives Series from CTSI at UCSF” moderated by Katja Reuter, PhD, associate director of communications for CTSI. This series explores how digital media and communications can be used to advance science and support academia. 

Original post on CTSI at UCSF

Social Networks for Academics Proliferate, Despite Some Scholars Doubts

Here’s an article with an overview of online products out there for research social networking;  the big gap in the article is that no institutional products are included such as Profiles, VIVO, etc. This is noted in one of the comments at the end, by Titus Schleyer.

That aside, there are interesting opinions in this piece, a few clipped below, and perhaps pointing to the current status of the space,  where the sweet spot has not yet been found.  

“After six years of running Zotero, it’s not clear that there is a whole lot of social value to academic social networks,” says Sean Takats, the site’s director, who is an assistant professor of history at George Mason University. “Everyone uses Twitter, which is an easy way to pop up on other people’s radar screens without having to formally join a network.” 

Scholars aren’t interested in sharing original ideas on such sites, [Christopher Blanchard, an adjunct professor of community and regional planning at Boise State University] now believes, “because they’re afraid they’ll be ripped off” and because they simply don’t have the time.

“We have thousands of new discussions taking place every day—scientists helping scientists without getting anything for it,” [Dr. Madisch, of ResearchGate] says. “Three years ago, people were smiling at me and saying that scientists aren’t social. They won’t share information. They were wrong.”

Social Networks for Academics Proliferate, Despite Some Scholars Doubts – Technology – The Chronicle of Higher Education.

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