Measures of Interdisciplinarity Summary

Interesting email from SCISIP mailing list: Re: [scisip] Looking for statistics about state of interdisciplinary research
Tue 11/22/2016 8:10 AM

Hi, all:

Thank you to those who helped me pull some numbers on the status interdisciplinary research. Here is what I found just from these two listservs. It’s simply a summary of the replies I received, not a comprehensive literature review. Hope it’s helpful. Full references are below my signature.

Interdisciplinary Research (IDR) Statistics
Funding:
• Bethany Laursen (22 November 2016) did a keyword search for “interdisciplinary” of the active, forecasted, closed, and archived grants posted at http://www.grants.gov. This search returned $15.6 trillion earmarked for IDR and programs from 2007-2019 by all US agencies. Active and forecasted grants from this search total nearly $1.3 trillion.

Publications:
Authorship
• Porter and Rafols (2009) reported an approximately 75% increase in the number of co-authors across 6 topic domains from 1975-2005
• The National Research Council publication Enhancing the Effectiveness of Team Science (2015, p.1) reported that 90% of all publications are now authored by two or more people.

Topics/Subject Matter
• Van Noorden (2015) illustrated a slight increase in the % of papers citing other disciplines over recent decades, and a slight decrease in the % of papers citing the same discipline
• Porter and Rafols (2009) report an approximately 50% increase in the # of cited disciplines per article across 6 topic domains from 1975-2005.
• Porter and Rafols (2009) also report that the disciplines that are cited tend to be near each other (i.e., narrow IDR). However, this might be due to the dominance of the quantity of articles coming from the USA in 1975-2005. A report from Elsevier (2015) showed that USA articles tend to be narrowly interdisciplinary. Interestingly, as of 2013, China now produces as many (perhaps more) IDR articles as the USA, and their articles are much more broadly interdisciplinary.

Citation Impact
• Van Noorden (2015) showed that in the short term (3 years post-publication) the more interdisciplinary an article is, the fewer citations it gets compared to other articles. However, that trend reverses over the long term (13 years post-publication).

Titles of Articles
• Van Noorden (2015) showed there has been a dramatic increase in the number of social science and humanities articles that include the word “interdisciplin*” in their titles over recent decades. This increase has been less dramatic for the natural sciences and engineering.

Dissertations:
• Bowman et al (2014) demonstrate that 2.3 million dissertations in ProQuest are better classified by topic than by subject category, indicating an emerging “post-disciplinary” research space.

How to Measure IDR:
• Wagner et al (2010) discuss principles for choosing measures of IDR
• Sugimoto and Weingart (2015) clearly and comprehensively review how the word “discipline” developed across the world and how we now operationalize it in terms of journals, founding “great men,” and other social phenomena.
• Porter and Rafols (2009) describe and apply the Rao-Stirling diversity index
• Guevara et al (2016) demonstrate a new method for mapping the “research space” in terms of fields in which the same author publishes.

Thanks, all! Hope to see many of you at SciTS next year.

Bethany

______________
Bethany Laursen
Ph.D. student, Community Sustainability
M.A. student, Philosophy
Michigan State University

References
Bowman, T. D., Tsou, A., Ni, C., & Sugimoto, C. R. (2014). Post-interdisciplinary frames of reference: exploring permeability and perceptions of disciplinarity in the social sciences. Scientometrics, 101(3), 1695–1714. http://doi.org/10.1007/s11192-014-1338-z

Elsevier. (2015). A Review of the UK’s Interdisciplinary Research Using a Citation-Based Approach (pp. 1–102).

Guevara, M. R., Hartmann, D., Aristarán, M., Mendoza, M., & Hidalgo, C. A. (2016). The research space: using career paths to predict the evolution of the research output of individuals, institutions, and nations. Scientometrics, 1–22. http://doi.org/10.1007/s11192-016-2125-9

National Research Council. (2015). Enhancing the Effectiveness of Team Science. (N. J. Cooke & M. L. Hilton, Eds.) (pp. 1–256). Washington, D.C.: National Academies Press. http://doi.org/10.17226/19007

Porter, A. L., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745. http://doi.org/10.1007/s11192-008-2197-2

Sugimoto, C. R., & Weingart, S. (2015). The kaleidoscope of disciplinarity. Journal of Documentation, 71(4), 775–794. http://doi.org/10.1108/JD-06-2014-0082

Van Noorden, R. (2015). Interdisciplinary research by the numbers. Nature, 525(7569), 306–307. http://doi.org/10.1038/525306a

Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5(1), 14–26. http://doi.org/10.1016/j.joi.2010.06.004

On Nov 11, 2016, at 10:38 AM, Bethany Laursen wrote:

Hi, everyone:

My work in this field tends to be qualitative and philosophical. Does anyone have some recent statistics that might summarize the state of interdisciplinary research, in the USA or beyond? For example,

• $$ spent on interdisciplinary research annually by NSF, NIH
• Trending average # of fields per publication per the Web of Science taxonomy
• Trend in # of interdisciplinary journals
• etc.

Thanks in advance for filling in a gap in my knowledge!

Bethany

______________
Bethany Laursen
Ph.D. student, Community Sustainability
M.A. student, Philosophy
Michigan State University

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Perfect Timing

This may be just what I needed:

Dear Abraham,

I am pleased to announce that Foundations and Trends in Machine Learning (www.nowpublishers.com/mal) has published the following issue:

Volume 9, Issue 2-3
Patterns of Scalable Bayesian Inference
By Elaine Angelino (University of California, Berkeley, USA), Matthew James Johnson (Harvard University, USA) and
Ryan P. Adams (Harvard University and Twitter, USA)
http://dx.doi.org/10.1561/2200000052

The link will take you to the article abstract. If your library has a subscription, you will be able to download the PDF of the article.
If you do not have access, download the free preview here: http://www.nowpublishers.com/article/DownloadSummary/MAL-052

To purchase the book version of this issue, go to the secure Order Form:
http://www.nowpublishers.com/Order/BuyBook?isbn=978-1-68083-218-1
You will receive the alert member discount price of $40 (includes shipping) by quoting the Promotion Code: 318306

This issue is also available for purchase at this year’s NIPS conference. Visit our booth to view all of the latest FnT ML titles.

Best Regards,

Tanya Capawana
now publishers

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New words of wisdom from S Few

The Visual Perception of Variation in Data Displays

Click to access the_visual_perception_of_variation.pdf

(well, it was new when I started this post)

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People of ACM interview with Margaret Burnett

http://www.acm.org/articles/people-of-acm/2016/margaret-burnett

a software inspection process called GenderMag. You can try it for yourself. The process is freely available, and major technology companies are looking at the possibility of adopting it.

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How someone uses GitHub and Zenodo to archive scholarly code

You can make GitHub repositories archival by using Zenodo or Figshare!
Wed 16 November 2016
By C. Titus Brown
In science.
tags: githubzenododoidatalib
Bioinformatics researchers are increasingly pointing reviewers and readers at their GitHub repositories in the Methods sections of their papers. Great! Making the scripts and source code for methods available via a public version control system is a vast improvement over the methods of yore (“e-mail me for the scripts” or “here’s a tarball that will go away in 6 months”).

http://ivory.idyll.org/blog/2016-using-zenodo-to-archive-github.html

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Now available: Big Data and Analytics for Infectious Disease Research, Operations, and Policy: Proceedings of a Workshop

Hay spoke of the difficulty of conveying the uncertainty that goes along with
these predictions. For example, his team spends half of its time developing the
correct uncertainty envelopes for the maps, and he does not have a good idea
on how to communicate this uncertainty to the many constituencies that would
find the maps useful. One aspect of these maps that he finds particularly vexing
is the tendency for people to just look at the map and ignore all of the richer
detail about uncertainty that his team provides with the maps.

https://www.nap.edu/download/23654

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Stunning Python Visuals

Found this from Software Carpentry: https://software-carpentry.org/blog/2016/12/art-with-python.html
https://github.com/TabletopWhale/AnimatedPythonPatterns

Led me here: http://tabletopwhale.com/index.html

All amazing!

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Deep Learning Frameworks

I was nearly convinced that Google’s TensorFlow would take over the world, but now I’ll need to also consider MXNet: http://mxnet.io/

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Mapping from SmartVA-Analyze output to ICD-10 causes

There has got to be a quick way to format this better:

Cause list for SmartVA against ICD-10 codes

ADULT CAUSES
Code to ICD-10 WHO ICD definition and comments
GBD Cause Group A: Communicable, maternal, neonatal and nutritional disorders
AIDS B24 Unspecified human immunodeficiency virus [HIV] disease
Diarrhea/Dysentery A09 Other gastroenteritis and colitis of infectious and unspecified origin
Malaria B54 Unspecified malaria
Maternal O95 Obstetric death of unspecified cause: Maternal death from unspecified cause occurring during pregnancy, labour and delivery, or the puerperium
Other Infectious Diseases B99 Other and unspecified infectious diseases
Pneumonia J22 Unspecified acute lower respiratory infection
TB A16 Respiratory tuberculosis, not confirmed bacteriologically or histologically
GBD Cause Group B: Non-communicable diseases
Acute Myocardial Infarction I24 Other acute ischaemic heart diseases (as for WHO 2014)
Asthma J45 Asthma
Breast Cancer C50 Malignant neoplasm of breast
COPD J44 Other chronic obstructive pulmonary disease
Cervical Cancers C53 Malignant neoplasm of cervix uteri (WHO VA has C55 for all female reproductive neoplasms)
Cirrhosis K74 Fibrosis and cirrhosis of liver
Colorectal Cancer C18 Malignant neoplasm of colon
Diabetes E14 Unspecified diabetes mellitus
Epilepsy G40 Epilepsy
Esophageal Cancer C15 Malignant neoplasm of oesophagus
Leukemia/Lymphomas C96 Other and unspecified malignant neoplasms of lymphoid, haematopoietic and related tissue
Lung Cancer C34 Malignant neoplasm of bronchus and lung
Other Cardiovascular Diseases I99 Other and unspecified disorders of circulatory system
Other Non-communicable Diseases R99 Other ill-defined and unspecified causes of mortality
Prostate Cancer C61 Malignant neoplasm of prostate
Renal Failure (due to renal disease) N19 Unspecified kidney failure
Stomach Cancer C16 Malignant neoplasm of stomach
Stroke I64 Stroke, not specified as haemorrhage or infarction
Other Cancers C76 Malignant neoplasm of other and ill-defined sites
GBD Cause Group C: Injuries
Bite of Venomous Animal X27 Contact with other specified venomous animals
Drowning W74 Unspecified drowning and submersion
Falls W19 Unspecified fall
Fires X09 Exposure to unspecified smoke, fire and flames
Homicide (assault) Y09 Assault by unspecified means
Other Injuries X58 Exposure to other specified factors
Poisonings (accidental) X49 Accidental poisoning by and exposure to other and unspecified chemicals and noxious substances
Road Traffic V89 Motor- or nonmotor-vehicle accident, type of vehicle unspecified
Suicide (intentional self-harm) X84 Intentional self-harm by unspecified means
CHILD CAUSES
GBD Cause Group A: Communicable, maternal, neonatal and nutritional disorders
AIDS B24 Unspecified human immunodeficiency virus [HIV] disease
Diarrhea/Dysentery A09 Other gastroenteritis and colitis of infectious and unspecified origin
Encephalitis G04 Encephalitis, myelitis and encephalomyelitis
Hemorrhagic fever A99 Unspecified viral haemorrhagic fever
Malaria B54 Unspecified malaria
Measles B05 Measles
Meningitis G03 Meningitis due to other and unspecified causes
Other Infectious Diseases B99 Other and unspecified infectious diseases
Pneumonia J22 Unspecified acute lower respiratory infection
Sepsis A41 Other sepsis
GBD Cause Group B: Non-communicable diseases
Other Cancers C76 Malignant neoplasm of other and ill-defined sites
Other Cardiovascular Diseases I99 Other and unspecified disorders of circulatory system
Other Defined Causes of Child Deaths R99 Other ill-defined and unspecified causes of mortality
Other Digestive Diseases K92 Other diseases of digestive system
GBD Cause Group C: Injuries
Bite of Venomous Animal X27 Contact with other specified venomous animals
Drowning W74 Unspecified drowning and submersion
Falls W19 Unspecified fall
Fires X09 Exposure to unspecified smoke, fire and flames
Poisonings X49 Accidental poisoning by and exposure to other and unspecified chemicals and noxious substances
Road Traffic V89 Motor- or nonmotor-vehicle accident, type of vehicle unspecified
Violent Death Y09 Assault by unspecified means
NEONATE CAUSES
Birth asphyxia P21 Birth asphyxia
Congenital malformation Q89 Other congenital malformations, not elsewhere classified
Meningitis/Sepsis P36 Bacterial sepsis of newborn
Pneumonia P23/J22 Congenital pneumonia/Unspecified acute lower respiratory infection
Preterm Delivery P07 Disorders related to short gestation and low birth weight, not elsewhere classified
Stillbirth P95 Fetal death of unspecified cause

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dfply package

Potentially of interest, although I’ve done enough d3js to think that .select .head is fine notation:

dfply Version: 0.2.4

GitHub – kieferk from November 28, 2016
“The dfply package makes it possible to do R’s dplyr-style data manipulation with pipes in python on pandas DataFrames.”
https://github.com/kieferk/dfply

from dfply import *

diamonds >> select(X.carat, X.cut) >> head(3)

   carat      cut
0   0.23    Ideal
1   0.21  Premium
2   0.23     Good

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