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NEH funding histories for AAU Institutions

Introduction

The first pillar in the strategic plan of my home institution, the University of Oklahoma is "Become a Top-Tier Public Research University." Strategy #1 in that pillar is "Meet Association of American Universities-quality benchmarks to position OU among the top public universities in the U.S." OU's Dodge Family College of Arts and Sciences supports this strategy in its (draft) strategic plan: "The DFCAS must build, maintain, and continually advance a culture of excellence to ensure we are at the leading edge of OU’s research ambitions and AAU aspirations."

Accordingly, to do my part to support these efforts, I aim to use my background in the humanities and my data skills (such as they are) to shed some light on research metrics in humanities departments at AAU institutions. What follows is my first effort: analyzing the history of research funding from the National Endowment for the Humanities (NEH) to public AAU institutions.

Sources

I searched the NEH's database, entering the names of AAU institutions into the "Organization Name" field. I downloaded the results in CSV form, then I used the Python Pandas library to analyze the data.

The data and the Jupyter notebook are available on my GitHub page at https://github.com/sjhuskey/aau.

Exploring the Data

After creating a big dataframe out of the 35 CSV files that I downloaded from the NEH's database, I explored the data.

First, I used the info() method to see what was available in the dataframe.

big_frame.info()


<class 'pandas.core.frame.DataFrame'>
Int64Index: 9590 entries, 0 to 154
Data columns (total 45 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ApplicationID 9590 non-null int64
1 ApplicationNumber 9590 non-null object
2 ApplicantType 9590 non-null int64
3 SuppCount 9590 non-null int64
4 ProductCount 9590 non-null int64
5 CoverageCount 9590 non-null int64
6 PrizeCount 9590 non-null int64
7 ParticipatingInstitutionCount 9590 non-null int64
8 WhitePaper 9590 non-null int64
9 WhitePaperPath 167 non-null object
10 PDFirstname 9590 non-null object
11 PDMiddlename 6406 non-null object
12 PDLastname 9590 non-null object
13 PDNameSuffix 210 non-null object
14 CoPDFirstname 162 non-null object
15 CoPDMiddlename 76 non-null object
16 CoPDLastname 162 non-null object
17 CoPDNameSuffix 3 non-null object
18 Participants 9590 non-null object
19 GranteeDisplay 9590 non-null object
20 Institution 9590 non-null object
21 InstCity 9590 non-null object
22 InstState 9590 non-null object
23 InstPostalCode 9589 non-null object
24 InstCountry 9590 non-null object
25 CouncilDate 9483 non-null object
26 YearAwarded 9590 non-null int64
27 ProjectTitle 9590 non-null object
28 PrimaryDiscipline 9569 non-null object
29 AllDisciplines 9144 non-null object
30 ProgramID 9590 non-null int64
31 ProgramName 9590 non-null object
32 DivisionID 9590 non-null int64
33 DivisionName 9590 non-null object
34 OfferedOutright 9590 non-null float64
35 OfferedMatching 9590 non-null float64
36 ApprovedOutright 9590 non-null float64
37 ApprovedMatching 9590 non-null float64
38 AwardOutright 9590 non-null float64
39 AwardMatching 9590 non-null float64
40 OriginalAmount 9590 non-null float64
41 SupplementAmount 9590 non-null float64
42 GrantPeriod 9590 non-null object
43 ProjectDesc 9590 non-null object
44 ToSupport 0 non-null float64
dtypes: float64(9), int64(11), object(25)
memory usage: 3.4+ MB

I used unique() to zoom in on the 'Institution' column to see what was there:

big_frame['Institution'].unique()

array(['University of Wisconsin System',
'University of Wisconsin Extension',
'University of Wisconsin Colleges',
'University of Wisconsin Foundation', 'University of Minnesota',
'University of Minnesota Press',
'University of Minnesota, University Gallery',
'University of Maryland, College Park',
'President and Fellows of Harvard College', 'MLA',
'Regents of the University of California, Santa Barbara',
'Regents of the University of California, San Diego',

'Indiana University of Pennsylvania',
'Indiana University Southeast', 'Indiana University at South Bend',
'Indiana University, Kokomo',
'Indiana University Alumni Association', 'Indiana University East',
'Indiana University Press', 'University Of Houston',
'University of Kansas, Lawrence',
'University of Kansas Center for Research, Inc.',
'Kansas Humanities Council', 'University of Washington',
'University of Washington, Bothell',
'Regents of the University of California, Irvine'], dtype=object)

It's clear that many of the institutions in the data are not AAU institutions, so I took steps to filter the data.

I made a list of just the AAU institutions:

aau_list = ['University of Kansas, Lawrence',
'Georgia Tech',
'University of Maryland, College Park',
'Mizzou',
'Board of Trustees of the University of Illinois',
'UCLA; Regents of the University of California, Los Angeles',
'Penn State',
'University of Florida',
'Purdue University',
'Rutgers, The State University of New Jersey',
'Michigan State University',
'University of Wisconsin System',
'Trustees of Indiana University',
'Texas A & M University, College Station',
'Regents of the University of California, Irvine',
'University of North Carolina at Chapel Hill',
'University of Virginia',
'University of Minnesota',
'University of California, Berkeley',
'Regents of the University of Colorado, Boulder',
'University of Texas, Austin',
'Regents of the University of California, San Diego',
'University of Washington',
'Regents of the University of California, Davis',
'University of Utah',
'Regents of the University of California, Santa Barbara',
'SUNY Research Foundation, Stony Brook',
'Regents of the University of Michigan',
'Regents of the University of California, Santa Cruz',
'University of Iowa',
'SUNY Research Foundation, Buffalo State College',
'Arizona Board of Regents',
'Ohio State University',
'University of Pittsburgh',
'University of Oregon',
]
# How many institutions are in the aau_list? There should be 35.
len(aau_list)
35

Then I used isin() to filter the dataframe to include only the institutions in my list of public AAU institutions.

aau_frame = big_frame[big_frame['Institution'].isin(aau_list)]

I surveyed the columns in the dataframe to see how I might further filter it.

aau_frame.columns

Index(['ApplicationID', 'ApplicationNumber', 'ApplicantType', 'SuppCount',
'ProductCount', 'CoverageCount', 'PrizeCount',
'ParticipatingInstitutionCount', 'WhitePaper', 'WhitePaperPath',
'PDFirstname', 'PDMiddlename', 'PDLastname', 'PDNameSuffix',
'CoPDFirstname', 'CoPDMiddlename', 'CoPDLastname', 'CoPDNameSuffix',
'Participants', 'GranteeDisplay', 'Institution', 'InstCity',
'InstState', 'InstPostalCode', 'InstCountry', 'CouncilDate',
'YearAwarded', 'ProjectTitle', 'PrimaryDiscipline', 'AllDisciplines',
'ProgramID', 'ProgramName', 'DivisionID', 'DivisionName',
'OfferedOutright', 'OfferedMatching', 'ApprovedOutright',
'ApprovedMatching', 'AwardOutright', 'AwardMatching', 'OriginalAmount',
'SupplementAmount', 'GrantPeriod', 'ProjectDesc', 'ToSupport'],
dtype='object')

We'll probably want at least Institution, YearAwarded, ProjectTitle, PrimaryDiscipline, ProgramName, DivisionName, and AwardOutright, so I made a frame with those columns.

data = aau_frame[['Institution','ProjectTitle','PrimaryDiscipline','ProgramName','DivisionName','AwardOutright','YearAwarded']]
Institution ProjectTitle PrimaryDiscipline ProgramName DivisionName AwardOutright YearAwarded
0 University of Wisconsin System Accessing the History of Health, Pharmacy, and... History, General Humanities Collections and Reference Resources Preservation and Access 326326.0 2022
1 University of Wisconsin System "Alternative Modernities" and the Modernizatio... Theater History and Criticism Fellowships Research Programs 60000.0 2020
2 University of Wisconsin System The Ford Foundation, Social Science, and the P... Latin American History Fellowships Research Programs 60000.0 2020
3 University of Wisconsin System The History of Cartography Project Interdisciplinary Studies, General Humanities Collections and Reference Resources Preservation and Access 185000.0 2021
4 University of Wisconsin System Open Access Edition of "Citizen Countess: Sofi... Russian History Fellowships Open Book Program Digital Humanities 5500.0 2020
... ... ... ... ... ... ... ...
150 Mizzou Utopian Societies Founded in the Soviet Union ... History, General Summer Stipends Research Programs 1500.0 1970
151 Mizzou The Process of Government During the Presidenc... History, General Fellowships for University Teachers Research Programs 15500.0 1970
152 Mizzou Dr. Johnson and the New Philosophy Philosophy, General Summer Stipends Research Programs 1500.0 1970
153 Mizzou Title not available History, General Fellowships for Younger Scholars Fellowships and Seminars 8387.0 1967
154 Mizzou Title not available Philosophy, General Summer Stipends Research Programs 1500.0 1967

8929 rows × 7 columns

Since the NEH has numerous funding programs, it's important to include only those having to do with research. I took a closer look at the ProgramName column:

data['ProgramName'].unique().tolist()

['Humanities Collections and Reference Resources',
'Fellowships',
'Fellowships Open Book Program',
'Summer Stipends',
'Scholarly Editions and Translations',
'Preservation Assistance Grants',
'Humanities Connections',
'Digital Humanities Advancement Grants',
'Fellowships for University Teachers',
'Challenge Grants',
'Fellowships for Advanced Social Science Research on Japan',
'Research and Development',
'Documenting Endangered Languages - Preservation',
'Reference Materials',
'Preservation/Access Projects',
'Humanities Projects in Media',
'Collaborative Research',
'Education Development and Demonstration',
'Institutes for Higher Education Faculty',
'Regional Center Implementation Grants',
'Institutes for K-12 Educators',
'Humanities Projects in Libraries and Archives',
'Regional Center Planning Grants',
'Basic Research',
'Seminars for K-12 Educators',
'Seminars for Higher Education Faculty',
'Humanities Projects in Museums and Historical Organizations',
'Editions',
'Reference Materials - Tools',
'Study Grants for College Teachers',
'Translations',
'Scholarly Publications',
'Reference Materials - Guides',
'Humanities, Science, and Technology',
'Archaeology Projects',
'Travel to Collections, 11/85 - 2/95',
'Preservation and Access Projects Pre-1996',
'Conferences',
'Younger Scholars, 2/86 - 2/95',
'Education Development & Demonstration (OLD)',
'Travel to Collections, 11/83 - 5/85',
'Humanities Projects for Youth',
'Reference Materials - Access',
'State and Local and Regional Studies',
'Fellowships for College Teachers and Independent Scholars',
'Fellowships and Stipends for Professionals',
'Residential College Teacher Fellowships, 1976-1981',
'Younger Scholars, 2/76 - 2/85',
'Institutional Development',
'Special Projects',
'State Humanities Councils General Operating Support Grants',
'Program Development/Planning Grants',
'Special Fellowships Programs',
'Bicentennial Histories',
'Institutional Planning and Development',
'Institutes for Advanced Topics in the Digital Humanities',
'Public Scholars',
'Sustaining Cultural Heritage Collections',
'Enduring Questions: Pilot Course Grants',
'Humanities in the Public Square',
'Digital Humanities Implementation Grants',
'Digging into Data',
'Small Grants to Libraries: King James Bible',
'Bridging Cultures Forums and Workshops',
"Interpreting America's Historic Places: Planning Grants",
'Dynamic Language Infrastructure-Documenting Endangered Languages - Fellowships',
'Special Opportunity in Foreign Language Education',
'Dissertation Grants',
'Humanities Programs for Nontraditional Learners',
'Pilot Grants - Education',
'Research Challenge Grants',
'Education Consultant Grants',
'Cooperative Agreements and Special Projects (Public Programs)',
'Common Heritage',
'Dialogues on the Experience of War',
'NEH/DFG Bilateral Digital Humanities Program',
'Digital Humanities Start-Up Grants',
'National Digital Newspaper Program',
'Cooperative Agreements and Special Projects (Digital Humanities)',
'Stabilization Grants',
'Teaching and Learning Resources and Curriculum Development',
'Faculty Humanities Workshops',
'Residential College Teacher Fellowships, 1980',
'Education Challenge Grants',
'Fellowships for Younger Scholars',
'Humanities Initiatives at Colleges and Universities',
'Next Generation Humanities PhD (Planning)',
'Digital Projects for the Public: Discovery Grants',
"Small Grants to Libraries: America's Music",
'Digital Humanities Fellowships',
'Challenge Grants for Universities',
'Humanities Institutes Program',
'NEH on the Road',
'International Research',
'Planning and Assessment Studies',
'Public Programs Special Projects (GD)',
'Humanities Connections Planning Grants',
'ARP-Organizations (Preservation-related)',
'Exhibitions: Implementation',
"America's Historical and Cultural Organizations: Planning Grants",
"Save America's Treasures",
'Preservation and Access Education and Training',
'Digital Humanities Workshops',
'Preservation - Assistance Grants (ER Title Ib)',
'Consultation Grants (ER Title Ib)',
'National Heritage Preservation Projects',
'ARP-Organizations (Research-related)',
'Small Grants to Libraries: Lincoln, Constitution and Civil War',
'Grants to Preserve and Create Access to Humanities Collections',
'Challenge Grants for Museums',
'NEH/AHRC New Directions for Digital Scholarship in Cultural Institutions',
'Humanities Open Book Program',
'Humanities Access Grants',
"Interpreting America's Historic Places Consultation",
'Landmarks of American History and Culture',
'Small Grants to Libraries: Pride and Passion',
'Special Initiatives',
'Libraries Implementation',
'Humanities Connections Implementation Grants',
'Bridging Cultures at Community Colleges',
'Iraqi Cultural Heritage Initiative',
'Small Grants to Libraries: Louisa May Alcott',
'Media Challenge Grants',
'Short Documentaries',
'Awards for Faculty',
'Small Grants to Libraries: John Adams Unbound',
'Conferences and Congresses',
'Archaeological and Ethnographic Field Research',
'Exhibitions: Planning',
'Advancing Knowledge: The IMLS/NEH Digital Partnership',
'Infrastructure and Capacity Building Challenge Grants',
'Enterprise Awards Pre-2001',
'ARP-Organizations (Public-related)',
"America's Historical and Cultural Organizations: Implementation Grants",
'Bicentennial Projects',
'Media Projects Production',
'Digital Projects for the Public: Production Grants',
"America's Media Makers: Production Grants",
'NEH/DOE Humanities High Performance Computing Program',
'Fellowships at Digital Humanities Centers',
'NEH/DFG Symposia and Workshops Program',
'Distinguished Teaching Professorships (Challenge)',
'Museums Implementation',
'History Project',
'NEH Teacher-Scholar Program',
'ARP-Organizations (Digital humanities-related)',
'Cooperative Agreements and Special Projects (P&A)',
'Cooperative Agreements and Special Projects (Research)',
'Humanities Initiatives at Hispanic-Serving Institutions',
'Cooperative Agreements and Special Projects (Education)',
'ARP-Organizations (Education-related)',
'Faculty Research Awards',
'Fostering Coherence Through Instruction',
'Picturing America',
'Special Project Challenge Grants',
'Improving Introductory Courses',
'Picturing America School Collaboration Projects',
'Museums Planning',
'Public Challenge Grants']

Many of those awards don't look relevant to AAU considerations. It's possible that the DivisionName column will give us a better picture:

data['DivisionName'].unique().tolist()

['Preservation and Access',
'Research Programs',
'Digital Humanities',
'Education Programs',
'Challenge Programs',
'Public Programs',
'Agency-wide Projects',
'Fellowships and Seminars',
'Federal/State Partnership']

Using `value_counts() will show us how many awards have been given by those divisions.

# Let's get counts of the grants from those programs.
data['DivisionName'].value_counts()


Research Programs 5287
Education Programs 1360
Preservation and Access 852
Fellowships and Seminars 701
Public Programs 427
Digital Humanities 173
Challenge Programs 84
Agency-wide Projects 33
Federal/State Partnership 12
Name: DivisionName, dtype: int64

Let's see that as a bar graph:

data['DivisionName'].value_counts().plot.barh()

png

It would be helpful to have a dataframe with grants from Research Programs, Digital Humanities and Fellowship and Seminars.

programs = ['Research Programs', 'Digital Humanities','Fellowships and Seminars']
aau_research = data[data['DivisionName'].isin(programs)]

Let's see how many awards each institution has received over the history of NEH's funding:

# Use value_counts() to get the number of awards per institution.
total_number = aau_research['Institution'].value_counts()
total_number


Regents of the University of Michigan 422
University of California, Berkeley 413
Trustees of Indiana University 361
University of Virginia 336
UCLA; Regents of the University of California, Los Angeles 315
University of Texas, Austin 279
Board of Trustees of the University of Illinois 255
University of Wisconsin System 252
University of Maryland, College Park 230
University of North Carolina at Chapel Hill 214
Rutgers, The State University of New Jersey 199
University of Minnesota 194
University of Washington 178
Regents of the University of California, Santa Barbara 176
University of Iowa 163
Ohio State University 146
University of Kansas, Lawrence 144
Penn State 142
Regents of the University of California, Santa Cruz 135
Arizona Board of Regents 132
University of Oregon 132
Regents of the University of California, Davis 130
Regents of the University of Colorado, Boulder 126
Regents of the University of California, San Diego 119
University of Florida 118
Mizzou 115
University of Pittsburgh 111
Michigan State University 106
SUNY Research Foundation, Stony Brook 102
Regents of the University of California, Irvine 102
SUNY Research Foundation, Buffalo State College 82
Texas A & M University, College Station 76
Purdue University 72
University of Utah 54
Georgia Tech 30
Name: Institution, dtype: int64

Here's the same information as a bar graph.

png

How about the total amount the institutions have received?

# How about total amount?
total_amount = aau_research.groupby('Institution')['AwardOutright'].sum().sort_values().plot.barh(figsize=(10,10))

png

I used value_counts() and nlargest() to get the top ten institution by number of grants received.

# Use nlargest() to get the top ten institutions by number of grants received.
top_ten = aau_research['Institution'].value_counts().nlargest(10).to_frame()

png

I wanted to make sure that the institutions had equivalent funding histories, so I checked on the earliest and most recent dates of awards for each one.

# Make dataframes of the earliest and most recent years per institution.
maximum = aau_research.groupby(['Institution'])['YearAwarded'].max().reset_index()
minimum = aau_research.groupby(['Institution'])['YearAwarded'].min().reset_index()

# Merge the minimum and maximum frames.
merged = pd.merge(minimum,maximum, on='Institution').rename(columns={'YearAwarded_x':'Earliest','YearAwarded_y':'Latest'})
merged
Institution Earliest Latest
0 Arizona Board of Regents 1967 2021
1 Board of Trustees of the University of Illinois 1967 2022
2 Georgia Tech 1970 2017
3 Michigan State University 1967 2021
4 Mizzou 1967 2021
5 Ohio State University 1967 2021
6 Penn State 1967 2021
7 Purdue University 1967 2021
8 Regents of the University of California, Davis 1967 2021
9 Regents of the University of California, Irvine 1967 2021
10 Regents of the University of California, San D... 1967 2020
11 Regents of the University of California, Santa... 1967 2021
12 Regents of the University of California, Santa... 1967 2020
13 Regents of the University of Colorado, Boulder 1967 2022
14 Regents of the University of Michigan 1967 2022
15 Rutgers, The State University of New Jersey 1967 2021
16 SUNY Research Foundation, Buffalo State College 1966 2015
17 SUNY Research Foundation, Stony Brook 1967 2016
18 Texas A & M University, College Station 1971 2021
19 Trustees of Indiana University 1967 2021
20 UCLA; Regents of the University of California,... 1967 2021
21 University of California, Berkeley 1967 2022
22 University of Florida 1967 2021
23 University of Iowa 1967 2022
24 University of Kansas, Lawrence 1967 2022
25 University of Maryland, College Park 1967 2021
26 University of Minnesota 1967 2022
27 University of North Carolina at Chapel Hill 1967 2021
28 University of Oregon 1970 2022
29 University of Pittsburgh 1967 2022
30 University of Texas, Austin 1967 2022
31 University of Utah 1967 2022
32 University of Virginia 1967 2021
33 University of Washington 1967 2022
34 University of Wisconsin System 1967 2021

There's some variation in there, but not much.

Next, I wanted to know more about the disciplines represented in the dataset.

# How many disciplines are represented in this dataset?
aau_research['PrimaryDiscipline'].nunique()
150
# What are the top twenty disciplines in number of grants?
aau_research['PrimaryDiscipline'].value_counts().nlargest(20).to_frame().reset_index()
index PrimaryDiscipline
0 U.S. History 481
1 Interdisciplinary Studies, General 370
2 History, General 274
3 British Literature 255
4 European History 235
5 Art History and Criticism 217
6 Music History and Criticism 215
7 American Literature 210
8 Archaeology 210
9 Literature, General 205
10 Anthropology 157
11 Philosophy, General 156
12 Linguistics 138
13 History and Philosophy of Science, Technology,... 114
14 East Asian History 111
15 Latin American History 100
16 Comparative Literature 98
17 Medieval Studies 96
18 British History 87
19 French Literature 83

Here's that information as a pie chart:

png

Looking at OU

Now that I had some data on the NEH funding history of AAU institutions, it was time to start looking at OU. First, I repeated the steps that I took above: downloading the data, narrowing it down to just OU ('Board of Regents of the University of Oklahoma'), and further narrowing it down to just the NEH awards having to do with research.

Here, I used groupby() and sum() to get a picture of OU's funding history over time:

# Show the history of NEH funding at OU
ou_years = ou_research.groupby('YearAwarded')['AwardOutright'].sum().to_frame()
ou_years
AwardOutright
YearAwarded
1967 25407.0
1970 9000.0
1972 34750.0
1973 68795.0
1974 4000.0
1975 2000.0
1977 30000.0
1978 101770.0
1979 102065.0
1980 2500.0
1981 4500.0
1983 15900.0
1984 88652.0
1985 58990.0
1986 29750.0
1987 7000.0
1988 10750.0
1989 30500.0
1990 2950.0
1996 30000.0
1997 34000.0
1998 34000.0
1999 34000.0
2000 50000.0
2001 4500.0
2002 85000.0
2003 24000.0
2004 24000.0
2005 40000.0
2008 6000.0
2009 50400.0
2013 50400.0
2014 12000.0
2015 100800.0
2016 6000.0
2017 50400.0
2019 6000.0
2022 6000.0

Here's a dataframe with the most relevant information:

# Here's a dataframe with just the pertinent information about OU.
ou_neh = ou[['Institution','YearAwarded','PDLastname','PDFirstname','ProjectTitle','AwardOutright','PrimaryDiscipline']]
ou_neh
Institution YearAwarded PDLastname PDFirstname ProjectTitle AwardOutright PrimaryDiscipline
3 Board of Regents of the University of Oklahoma 2021 Marshall Kimberly New Stories of the West, for the West 500000.0 U.S. History
7 Board of Regents of the University of Oklahoma 2022 Heaton Raina Creating Online Access for the Native American... 345494.0 Linguistic Anthropology
8 Board of Regents of the University of Oklahoma 2022 Mansky Joseph Plays, Libels, and the Public Sphere in Shakes... 6000.0 British Literature
15 Board of Regents of the University of Oklahoma 2020 Heaton Raina Collaboration and development for digital acce... 49495.0 Native American Studies
27 Board of Regents of the University of Oklahoma 2019 Marshall Kimberly Re-Membering the Boise Valley People: Rethinki... 6000.0 Cultural Anthropology
... ... ... ... ... ... ... ...
460 Board of Regents of the University of Oklahoma 1970 Davis Robert Title not available 1500.0 English
463 Board of Regents of the University of Oklahoma 1970 Calhoun Dougald Correspondence of French Revolutionary Deputie... 7500.0 History, General
468 Board of Regents of the University of Oklahoma 1967 Opler Morris Title not available 15520.0 Anthropology
471 Board of Regents of the University of Oklahoma 1967 Davis Gwenn Title not available 1500.0 English
472 Board of Regents of the University of Oklahoma 1967 Miller David Title not available 8387.0 Hispanic American Studies

114 rows × 7 columns

Which disciplines at OU have been the most successful at getting NEH grants?

ou_neh['PrimaryDiscipline'].value_counts().nlargest(10).plot.pie(label='',figsize=(10,20),fontsize=14)

png

Comparing OU to AAU institutions

For a very basic overview, I compared OU's NEH funding per year to each AAU institution's NEH funding per year.

'''
Make a frame of OU's NEH funding history to compare to AAU institutions.
'''

OU = ou_neh.groupby('YearAwarded')['AwardOutright'].sum().to_frame()

Getting individual AAU institutions was tricky for me. I'm sure there are much better ways of doing this, but I settled on this method.

First, I made a Python dictionary of AAU institutions, with the AAU name as key and the name as it appears in the NEH data as the value. I used that to make individual dataframes to compare with OU.

aau_dict = {'University of Kansas':'University of Kansas, Lawrence',
'Georgia Tech':'Georgia Tech',
'University of Maryland':'University of Maryland, College Park',
'University of Missouri':'Mizzou',
'University of Illinois':'Board of Trustees of the University of Illinois',
'UCLA':'UCLA; Regents of the University of California, Los Angeles',
'Penn State':'Penn State',
'University of Florida':'University of Florida',
'Purdue':'Purdue University',
'Rutgers':'Rutgers, The State University of New Jersey',
'Michigan State University':'Michigan State University',
'University of Wisconsin':'University of Wisconsin System',
'Indiana University':'Trustees of Indiana University',
'Texas A & M':'Texas A & M University, College Station',
'UC Irvine':'Regents of the University of California, Irvine',
'UNC':'University of North Carolina at Chapel Hill',
'UVa':'University of Virginia',
'University of Minnesota':'University of Minnesota',
'Berkeley':'University of California, Berkeley',
'University of Colorado':'Regents of the University of Colorado, Boulder',
'University of Texas':'University of Texas, Austin',
'UCSD':'Regents of the University of California, San Diego',
'University of Washington':'University of Washington',
'UC Davis':'Regents of the University of California, Davis',
'University of Utah':'University of Utah',
'UC Santa Barbara':'Regents of the University of California, Santa Barbara',
'SUNY Stony Brook':'SUNY Research Foundation, Stony Brook',
'University of Michigan':'Regents of the University of Michigan',
'UC Santa Cruz':'Regents of the University of California, Santa Cruz',
'University of Iowa':'University of Iowa',
'SUNY Buffalo':'SUNY Research Foundation, Buffalo State College',
'University of Arizona':'Arizona Board of Regents',
'The Ohio State University':'Ohio State University',
'University of Pittsburgh':'University of Pittsburgh',
'University of Oregon':'University of Oregon',}

'''
Loop through the AAU dictionary to get individual histories.
'''

aau_frames = {}
for key,value in aau_dict.items():
institution = key
df = aau_research[aau_research['Institution'] == value]
history = df.groupby('YearAwarded')['AwardOutright'].sum().to_frame()
aau_frames.update({institution:history})

'''
Loop through the aau_frames and make lines graphs
comparing OU to individual AAU institutions.
'''

for key,value in aau_frames.items():
merged = pd.merge(OU,value,on='YearAwarded')
plot = merged.rename(columns={'AwardOutright_x':'OU','AwardOutright_y':key}).plot.line(color={'OU':'#841617',key:'#168483'},figsize=(10,5),title=('OU vs. ' + str(key)),xlabel='Year Awarded',ylabel='Amount ($)')
fig = plot.get_figure()
fig.savefig('../plots/OU-' + str(key) + '.png')
print(plot)

Here are the line graphs showing the NEH funding history of OU as compared to that of each public AAU institution.

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More to Come

I have also done some Natural Language Processing work on the ProjectTitle field, just to see if I could identify any trends in projects that receive funding. I plan to write up that work in a future post.

Do you have questions you want to ask of this data? Please feel free to contact me at huskey@ou.edu.

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