Overview

Dataset statistics

Number of variables6
Number of observations9667
Missing cells99
Missing cells (%)0.2%
Duplicate rows871
Duplicate rows (%)9.0%
Total size in memory462.7 KiB
Average record size in memory49.0 B

Variable types

Text4
Numeric1
Categorical1

Dataset

Description2021년9월23일기준 국가과학기술표준분류에 대한 정보입니다. 국가과학기술표준분류: 과학기술 관련 정보, 인력, 연구개발사업 등의 효율적 관리하고, 국가연구개발사업의 연구기획·평가 및 관리, 과학기술예측 및 기술수준평가 수행, 과학기술 정보의 관리·유통 등을 위한 과학기술 표준분류틀 해당 데이터가 보유한 컬럼은 다음과 같습니다. 컬럼명:상위과학기술분류코드, 상위과학기술분류코드한글명, 과학기술분류코드, 분류명, 분류순서, 비고
Author한국과학기술기획평가원(KISTEP)
URLhttps://www.data.go.kr/data/15065871/fileData.do

Alerts

Dataset has 871 (9.0%) duplicate rowsDuplicates
비고 is highly imbalanced (81.3%)Imbalance

Reproduction

Analysis started2023-12-12 23:44:04.492064
Analysis finished2023-12-12 23:44:05.277188
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상위과학기술분류코드
Text

Distinct70
Distinct (%)0.7%
Missing88
Missing (%)0.9%
Memory size75.7 KiB
2023-12-13T08:44:05.406093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.3786408
Min length2

Characters and Unicode

Total characters22785
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowFSC
2nd rowCL001
3rd rowRCSARE
4th rowNAARE
5th rowNAARE
ValueCountFrequency (%)
sc 544
 
5.7%
he 512
 
5.3%
sb 416
 
4.3%
hd 416
 
4.3%
hc 416
 
4.3%
hb 352
 
3.7%
sd 320
 
3.3%
inare 320
 
3.3%
sh 288
 
3.0%
ha 256
 
2.7%
Other values (60) 5739
59.9%
2023-12-13T08:44:05.670925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3059
13.4%
E 2960
13.0%
H 2576
11.3%
A 2320
10.2%
T 1823
8.0%
C 1796
7.9%
B 1264
 
5.5%
R 1183
 
5.2%
D 1072
 
4.7%
I 800
 
3.5%
Other values (14) 3932
17.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22642
99.4%
Decimal Number 143
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 3059
13.5%
E 2960
13.1%
H 2576
11.4%
A 2320
10.2%
T 1823
8.1%
C 1796
7.9%
B 1264
 
5.6%
R 1183
 
5.2%
D 1072
 
4.7%
I 800
 
3.5%
Other values (10) 3789
16.7%
Decimal Number
ValueCountFrequency (%)
0 76
53.1%
1 52
36.4%
3 8
 
5.6%
2 7
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 22642
99.4%
Common 143
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 3059
13.5%
E 2960
13.1%
H 2576
11.4%
A 2320
10.2%
T 1823
8.1%
C 1796
7.9%
B 1264
 
5.6%
R 1183
 
5.2%
D 1072
 
4.7%
I 800
 
3.5%
Other values (10) 3789
16.7%
Common
ValueCountFrequency (%)
0 76
53.1%
1 52
36.4%
3 8
 
5.6%
2 7
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 3059
13.4%
E 2960
13.0%
H 2576
11.3%
A 2320
10.2%
T 1823
8.0%
C 1796
7.9%
B 1264
 
5.5%
R 1183
 
5.2%
D 1072
 
4.7%
I 800
 
3.5%
Other values (14) 3932
17.3%

상위과학기술분류코드한글명
Text

Distinct63
Distinct (%)0.7%
Missing3
Missing (%)< 0.1%
Memory size75.7 KiB
2023-12-13T08:44:05.909289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length46
Mean length24.189466
Min length2

Characters and Unicode

Total characters233767
Distinct characters133
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row분류체계
2nd row연구분야
3rd row자연
4th row수학(Mathematics)
5th row수학(Mathematics)
ValueCountFrequency (%)
736
 
4.5%
and 696
 
4.3%
경제/경영,경제/경영(economics/management 544
 
3.3%
문화/예술/체육,문화/예술/체육(culture/arts/sports 512
 
3.2%
문학,문학(literature 416
 
2.6%
administration 416
 
2.6%
언어,언어(linguistics 416
 
2.6%
public 416
 
2.6%
of 416
 
2.6%
정치/행정,정치/행정(politics/science 416
 
2.6%
Other values (79) 11269
69.3%
2023-12-13T08:44:06.576340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 16146
 
6.9%
i 13056
 
5.6%
o 12736
 
5.4%
n 10272
 
4.4%
e 9648
 
4.1%
t 8600
 
3.7%
c 8248
 
3.5%
r 7664
 
3.3%
a 7288
 
3.1%
( 7224
 
3.1%
Other values (123) 132885
56.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 113728
48.7%
Other Letter 62392
26.7%
Other Punctuation 21786
 
9.3%
Uppercase Letter 14736
 
6.3%
Open Punctuation 7224
 
3.1%
Close Punctuation 7224
 
3.1%
Space Separator 6677
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 3907
 
6.3%
문 2961
 
4.7%
지 2457
 
3.9%
경 2432
 
3.9%
정 2416
 
3.9%
과 1750
 
2.8%
사 1649
 
2.6%
육 1600
 
2.6%
교 1489
 
2.4%
인 1471
 
2.4%
Other values (80) 40260
64.5%
Lowercase Letter
ValueCountFrequency (%)
i 13056
11.5%
o 12736
11.2%
n 10272
9.0%
e 9648
 
8.5%
t 8600
 
7.6%
c 8248
 
7.3%
r 7664
 
6.7%
a 7288
 
6.4%
s 6080
 
5.3%
l 5016
 
4.4%
Other values (11) 25120
22.1%
Uppercase Letter
ValueCountFrequency (%)
S 2296
15.6%
A 1912
13.0%
E 1872
12.7%
P 1608
10.9%
L 1440
9.8%
C 1272
8.6%
R 672
 
4.6%
M 664
 
4.5%
W 640
 
4.3%
H 608
 
4.1%
Other values (6) 1752
11.9%
Other Punctuation
ValueCountFrequency (%)
/ 16146
74.1%
, 5544
 
25.4%
& 96
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 7224
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7224
100.0%
Space Separator
ValueCountFrequency (%)
6677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 128464
55.0%
Hangul 62392
26.7%
Common 42911
 
18.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 3907
 
6.3%
문 2961
 
4.7%
지 2457
 
3.9%
경 2432
 
3.9%
정 2416
 
3.9%
과 1750
 
2.8%
사 1649
 
2.6%
육 1600
 
2.6%
교 1489
 
2.4%
인 1471
 
2.4%
Other values (80) 40260
64.5%
Latin
ValueCountFrequency (%)
i 13056
 
10.2%
o 12736
 
9.9%
n 10272
 
8.0%
e 9648
 
7.5%
t 8600
 
6.7%
c 8248
 
6.4%
r 7664
 
6.0%
a 7288
 
5.7%
s 6080
 
4.7%
l 5016
 
3.9%
Other values (27) 39856
31.0%
Common
ValueCountFrequency (%)
/ 16146
37.6%
( 7224
16.8%
) 7224
16.8%
6677
15.6%
, 5544
 
12.9%
& 96
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171375
73.3%
Hangul 62392
 
26.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 16146
 
9.4%
i 13056
 
7.6%
o 12736
 
7.4%
n 10272
 
6.0%
e 9648
 
5.6%
t 8600
 
5.0%
c 8248
 
4.8%
r 7664
 
4.5%
a 7288
 
4.3%
( 7224
 
4.2%
Other values (33) 70493
41.1%
Hangul
ValueCountFrequency (%)
학 3907
 
6.3%
문 2961
 
4.7%
지 2457
 
3.9%
경 2432
 
3.9%
정 2416
 
3.9%
과 1750
 
2.8%
사 1649
 
2.6%
육 1600
 
2.6%
교 1489
 
2.4%
인 1471
 
2.4%
Other values (80) 40260
64.5%

과학기술분류코드
Text

Distinct695
Distinct (%)7.2%
Missing8
Missing (%)0.1%
Memory size75.7 KiB
2023-12-13T08:44:06.898020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length4
Mean length3.8700694
Min length2

Characters and Unicode

Total characters37381
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.3%

Sample

1st rowCL001
2nd rowRCSARE
3rd rowNAARE
4th rowNA01
5th rowNA02
ValueCountFrequency (%)
sf06 32
 
0.3%
se04 32
 
0.3%
sd01 32
 
0.3%
hd08 32
 
0.3%
sc99 32
 
0.3%
sc16 32
 
0.3%
sc15 32
 
0.3%
sc14 32
 
0.3%
sc13 32
 
0.3%
sc12 32
 
0.3%
Other values (685) 9339
96.7%
2023-12-13T08:44:07.334441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6992
18.7%
S 2995
 
8.0%
1 2976
 
8.0%
H 2616
 
7.0%
9 2176
 
5.8%
E 2040
 
5.5%
T 1816
 
4.9%
C 1631
 
4.4%
B 1344
 
3.6%
A 1236
 
3.3%
Other values (22) 11559
30.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 19128
51.2%
Decimal Number 18253
48.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 2995
15.7%
H 2616
13.7%
E 2040
10.7%
T 1816
9.5%
C 1631
8.5%
B 1344
7.0%
A 1236
 
6.5%
D 1128
 
5.9%
O 624
 
3.3%
L 578
 
3.0%
Other values (12) 3120
16.3%
Decimal Number
ValueCountFrequency (%)
0 6992
38.3%
1 2976
16.3%
9 2176
 
11.9%
2 1156
 
6.3%
3 987
 
5.4%
4 978
 
5.4%
5 898
 
4.9%
6 801
 
4.4%
7 729
 
4.0%
8 560
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 19128
51.2%
Common 18253
48.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 2995
15.7%
H 2616
13.7%
E 2040
10.7%
T 1816
9.5%
C 1631
8.5%
B 1344
7.0%
A 1236
 
6.5%
D 1128
 
5.9%
O 624
 
3.3%
L 578
 
3.0%
Other values (12) 3120
16.3%
Common
ValueCountFrequency (%)
0 6992
38.3%
1 2976
16.3%
9 2176
 
11.9%
2 1156
 
6.3%
3 987
 
5.4%
4 978
 
5.4%
5 898
 
4.9%
6 801
 
4.4%
7 729
 
4.0%
8 560
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6992
18.7%
S 2995
 
8.0%
1 2976
 
8.0%
H 2616
 
7.0%
9 2176
 
5.8%
E 2040
 
5.5%
T 1816
 
4.9%
C 1631
 
4.4%
B 1344
 
3.6%
A 1236
 
3.3%
Other values (22) 11559
30.9%

분류명
Text

Distinct878
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size75.7 KiB
2023-12-13T08:44:07.586555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length57
Mean length17.487225
Min length1

Characters and Unicode

Total characters169049
Distinct characters308
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row분류체계
2nd row연구분야
3rd row자연
4th row수학(Mathematics)
5th row대수학(Algebra)
ValueCountFrequency (%)
기타 801
 
4.2%
및 539
 
2.8%
and 368
 
1.9%
science 248
 
1.3%
management 216
 
1.1%
literature 192
 
1.0%
general 192
 
1.0%
linguistics 176
 
0.9%
technology 160
 
0.8%
of 136
 
0.7%
Other values (1176) 16270
84.3%
2023-12-13T08:44:07.990534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9687
 
5.7%
e 9568
 
5.7%
i 8696
 
5.1%
n 7744
 
4.6%
o 7120
 
4.2%
a 6768
 
4.0%
t 6424
 
3.8%
r 5856
 
3.5%
c 5048
 
3.0%
s 4768
 
2.8%
Other values (298) 97370
57.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82536
48.8%
Other Letter 51157
30.3%
Uppercase Letter 10984
 
6.5%
Space Separator 9687
 
5.7%
Other Punctuation 5061
 
3.0%
Close Punctuation 4736
 
2.8%
Open Punctuation 4736
 
2.8%
Dash Punctuation 152
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 2615
 
5.1%
기 2526
 
4.9%
정 1321
 
2.6%
리 1233
 
2.4%
사 1026
 
2.0%
과 1024
 
2.0%
자 1018
 
2.0%
지 1011
 
2.0%
타 938
 
1.8%
교 905
 
1.8%
Other values (243) 37540
73.4%
Lowercase Letter
ValueCountFrequency (%)
e 9568
11.6%
i 8696
10.5%
n 7744
9.4%
o 7120
8.6%
a 6768
8.2%
t 6424
 
7.8%
r 5856
 
7.1%
c 5048
 
6.1%
s 4768
 
5.8%
l 4312
 
5.2%
Other values (15) 16232
19.7%
Uppercase Letter
ValueCountFrequency (%)
S 1424
13.0%
P 1088
 
9.9%
E 896
 
8.2%
C 872
 
7.9%
M 848
 
7.7%
A 736
 
6.7%
L 672
 
6.1%
T 552
 
5.0%
R 544
 
5.0%
F 480
 
4.4%
Other values (13) 2872
26.1%
Other Punctuation
ValueCountFrequency (%)
/ 4413
87.2%
, 624
 
12.3%
& 24
 
0.5%
Space Separator
ValueCountFrequency (%)
9687
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4736
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4736
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93520
55.3%
Hangul 51157
30.3%
Common 24372
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 2615
 
5.1%
기 2526
 
4.9%
정 1321
 
2.6%
리 1233
 
2.4%
사 1026
 
2.0%
과 1024
 
2.0%
자 1018
 
2.0%
지 1011
 
2.0%
타 938
 
1.8%
교 905
 
1.8%
Other values (243) 37540
73.4%
Latin
ValueCountFrequency (%)
e 9568
 
10.2%
i 8696
 
9.3%
n 7744
 
8.3%
o 7120
 
7.6%
a 6768
 
7.2%
t 6424
 
6.9%
r 5856
 
6.3%
c 5048
 
5.4%
s 4768
 
5.1%
l 4312
 
4.6%
Other values (38) 27216
29.1%
Common
ValueCountFrequency (%)
9687
39.7%
) 4736
19.4%
( 4736
19.4%
/ 4413
18.1%
, 624
 
2.6%
- 152
 
0.6%
& 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117892
69.7%
Hangul 51157
30.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9687
 
8.2%
e 9568
 
8.1%
i 8696
 
7.4%
n 7744
 
6.6%
o 7120
 
6.0%
a 6768
 
5.7%
t 6424
 
5.4%
r 5856
 
5.0%
c 5048
 
4.3%
s 4768
 
4.0%
Other values (45) 46213
39.2%
Hangul
ValueCountFrequency (%)
학 2615
 
5.1%
기 2526
 
4.9%
정 1321
 
2.6%
리 1233
 
2.4%
사 1026
 
2.0%
과 1024
 
2.0%
자 1018
 
2.0%
지 1011
 
2.0%
타 938
 
1.8%
교 905
 
1.8%
Other values (243) 37540
73.4%

분류순서
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4162615
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.1 KiB
2023-12-13T08:44:08.098519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile14
Maximum21
Range20
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1768152
Coefficient of variation (CV)0.65097335
Kurtosis0.067880506
Mean6.4162615
Median Absolute Deviation (MAD)3
Skewness0.75166007
Sum62026
Variance17.445786
MonotonicityNot monotonic
2023-12-13T08:44:08.223037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 979
10.1%
2 970
10.0%
3 956
9.9%
4 874
9.0%
5 858
8.9%
6 793
8.2%
7 777
8.0%
8 732
7.6%
9 560
 
5.8%
10 480
 
5.0%
Other values (11) 1688
17.5%
ValueCountFrequency (%)
1 979
10.1%
2 970
10.0%
3 956
9.9%
4 874
9.0%
5 858
8.9%
6 793
8.2%
7 777
8.0%
8 732
7.6%
9 560
5.8%
10 480
5.0%
ValueCountFrequency (%)
21 16
 
0.2%
20 32
 
0.3%
19 32
 
0.3%
18 32
 
0.3%
17 64
 
0.7%
16 128
 
1.3%
15 160
1.7%
14 160
1.7%
13 288
3.0%
12 344
3.6%

비고
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.7 KiB
국가과학기술표준분류체계_중분류
8520 
국가과학기술표준분류체계_대분류
1048 
국가과학기술표준분류체계_분야
 
64
국가과학기술표준분류체계(임시분류)_중분류
 
15
국가과학기술표준분류체계_분류
 
8
Other values (4)
 
12

Length

Max length22
Median length16
Mean length16.006414
Min length14

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row분류정보,임시분류 구분코드
2nd row국가과학기술표준분류체계_분류
3rd row국가과학기술표준분류체계_분야
4th row국가과학기술표준분류체계_대분류
5th row국가과학기술표준분류체계_중분류

Common Values

ValueCountFrequency (%)
국가과학기술표준분류체계_중분류 8520
88.1%
국가과학기술표준분류체계_대분류 1048
 
10.8%
국가과학기술표준분류체계_분야 64
 
0.7%
국가과학기술표준분류체계(임시분류)_중분류 15
 
0.2%
국가과학기술표준분류체계_분류 8
 
0.1%
국가과학기술표준분류체계(임시분류)_대분류 5
 
0.1%
분류정보,임시분류 구분코드 3
 
< 0.1%
국가과학기술표준분류체계(임시분류)_분야 3
 
< 0.1%
국가과학기술표준분류체계(임시분류)_분류 1
 
< 0.1%

Length

2023-12-13T08:44:08.366748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:44:08.473744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국가과학기술표준분류체계_중분류 8520
88.1%
국가과학기술표준분류체계_대분류 1048
 
10.8%
국가과학기술표준분류체계_분야 64
 
0.7%
국가과학기술표준분류체계(임시분류)_중분류 15
 
0.2%
국가과학기술표준분류체계_분류 8
 
0.1%
국가과학기술표준분류체계(임시분류)_대분류 5
 
0.1%
분류정보,임시분류 3
 
< 0.1%
구분코드 3
 
< 0.1%
국가과학기술표준분류체계(임시분류)_분야 3
 
< 0.1%
국가과학기술표준분류체계(임시분류)_분류 1
 
< 0.1%

Interactions

2023-12-13T08:44:04.968521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:44:08.565788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상위과학기술분류코드상위과학기술분류코드한글명분류순서비고
상위과학기술분류코드1.0001.0000.5991.000
상위과학기술분류코드한글명1.0001.0000.5780.967
분류순서0.5990.5781.0000.112
비고1.0000.9670.1121.000
2023-12-13T08:44:08.658070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류순서비고
분류순서1.0000.050
비고0.0501.000

Missing values

2023-12-13T08:44:05.070091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:44:05.151558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T08:44:05.229170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

상위과학기술분류코드상위과학기술분류코드한글명과학기술분류코드분류명분류순서비고
0FSC<NA>CL001분류체계1분류정보,임시분류 구분코드
1CL001분류체계RCSARE연구분야1국가과학기술표준분류체계_분류
2RCSARE연구분야NAARE자연2국가과학기술표준분류체계_분야
3NAARE자연<NA>수학(Mathematics)1국가과학기술표준분류체계_대분류
4<NA>수학(Mathematics)NA01대수학(Algebra)1국가과학기술표준분류체계_중분류
5<NA>수학(Mathematics)NA02해석학(Analysis)2국가과학기술표준분류체계_중분류
6<NA>수학(Mathematics)NA03위상수학(Topology)3국가과학기술표준분류체계_중분류
7<NA>수학(Mathematics)NA04기하학(Geometry)4국가과학기술표준분류체계_중분류
8<NA>수학(Mathematics)NA05응용수학(Applied Mathematics)5국가과학기술표준분류체계_중분류
9<NA>수학(Mathematics)NA06이산 및 정보수학(Discrete/Information Mathematics)6국가과학기술표준분류체계_중분류
상위과학기술분류코드상위과학기술분류코드한글명과학기술분류코드분류명분류순서비고
9657TMPR01L02M02건설/교통TMPR01L02M02S01지능형 수자원 관리1국가과학기술표준분류체계(임시분류)_중분류
9658TMPR01연구분야TMPR01L03생명3국가과학기술표준분류체계(임시분류)_분야
9659TMPR01L03생명TMPR01L03M01농림수산식품1국가과학기술표준분류체계(임시분류)_대분류
9660TMPR01L03M01농림수산식품TMPR01L03M01S01원예특용작물과학1국가과학기술표준분류체계(임시분류)_중분류
9661TMPR01L03M01농림수산식품TMPR01L03M01S02농생물학2국가과학기술표준분류체계(임시분류)_중분류
9662TMPR01L03M01농림수산식품TMPR01L03M01S03농업환경생태3국가과학기술표준분류체계(임시분류)_중분류
9663TMPR01L03M01농림수산식품TMPR01L03M01S04농업기계/설비4국가과학기술표준분류체계(임시분류)_중분류
9664TMPR01L03M01농림수산식품TMPR01L03M01S05농업인프라공학5국가과학기술표준분류체계(임시분류)_중분류
9665TMPR01L03M01농림수산식품TMPR01L03M01S06농수축산물 안전6국가과학기술표준분류체계(임시분류)_중분류
9666TMPR01L03M01농림수산식품TMPR01L03M01S07농림수산식품사회과학7국가과학기술표준분류체계(임시분류)_중분류

Duplicate rows

Most frequently occurring

상위과학기술분류코드상위과학기술분류코드한글명과학기술분류코드분류명분류순서비고# duplicates
27CMARE공공X03국방3국가과학기술표준분류체계_대분류16
28CMARE공공X04사회구조 및 관계4국가과학기술표준분류체계_대분류16
31CMARE공공X06우주개발 및 탐사6국가과학기술표준분류체계_대분류16
32CMARE공공X07지구개발 및 탐사7국가과학기술표준분류체계_대분류16
37CMARE공공X10사회질서 및 안전10국가과학기술표준분류체계_대분류16
156HA역사/고고학,역사/고고학(History/Archeology)HA01역사일반1국가과학기술표준분류체계_중분류16
157HA역사/고고학,역사/고고학(History/Archeology)HA01역사일반(History, General)1국가과학기술표준분류체계_중분류16
158HA역사/고고학,역사/고고학(History/Archeology)HA02한국사2국가과학기술표준분류체계_중분류16
159HA역사/고고학,역사/고고학(History/Archeology)HA02한국사(Korean History)2국가과학기술표준분류체계_중분류16
160HA역사/고고학,역사/고고학(History/Archeology)HA03동양사3국가과학기술표준분류체계_중분류16