Overview

Dataset statistics

Number of variables9
Number of observations354
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.7 KiB
Average record size in memory74.4 B

Variable types

Categorical6
Text3

Dataset

Description한국연구재단이 보유하고있는 인재매칭플랫폼 시스템의 '연도별 지역별 연구분야별 연구실 회원가입 현황' 입니다. 가입년도, 지역(대), 지역(중), 연구분야(대), 연구분야(중), 연구실명에 대한 정보가 있습니다
URLhttps://www.data.go.kr/data/15117654/fileData.do

Alerts

지역(대) is highly overall correlated with 지역(중)High correlation
지역(중) is highly overall correlated with 지역(대)High correlation
기관유형 is highly imbalanced (72.2%)Imbalance
회원가입자수 is highly imbalanced (77.3%)Imbalance

Reproduction

Analysis started2023-12-12 09:29:24.615967
Analysis finished2023-12-12 09:29:25.401646
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가입년도
Categorical

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2020
148 
2021
141 
2022
60 
2023
 
5

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 148
41.8%
2021 141
39.8%
2022 60
16.9%
2023 5
 
1.4%

Length

2023-12-12T18:29:25.469171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:29:25.577971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 148
41.8%
2021 141
39.8%
2022 60
16.9%
2023 5
 
1.4%

지역(대)
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Seoul
103 
Gyeonggi-do
43 
Daejeon
36 
Gyeongsangbuk-do
25 
Gyeongsangnam-do
23 
Other values (12)
124 

Length

Max length18
Median length17
Mean length9.6271186
Min length6

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row Global
2nd row Gyeonggi-do
3rd row Gyeonggi-do
4th row Chungcheongnam-do
5th row Gyeongsangnam-do

Common Values

ValueCountFrequency (%)
Seoul 103
29.1%
Gyeonggi-do 43
12.1%
Daejeon 36
 
10.2%
Gyeongsangbuk-do 25
 
7.1%
Gyeongsangnam-do 23
 
6.5%
Gwangju 23
 
6.5%
Daegu 20
 
5.6%
Busan 14
 
4.0%
Ulsan 13
 
3.7%
Gangwon-do 11
 
3.1%
Other values (7) 43
12.1%

Length

2023-12-12T18:29:25.724717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seoul 103
29.1%
gyeonggi-do 43
12.1%
daejeon 36
 
10.2%
gyeongsangbuk-do 25
 
7.1%
gyeongsangnam-do 23
 
6.5%
gwangju 23
 
6.5%
daegu 20
 
5.6%
busan 14
 
4.0%
ulsan 13
 
3.7%
gangwon-do 11
 
3.1%
Other values (7) 43
12.1%

지역(중)
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Total
216 
Suwon-si
25 
Jinju-si
 
16
Yong in-si
 
12
Pohang-si
 
10
Other values (24)
75 

Length

Max length14
Median length6
Mean length7.6440678
Min length5

Unique

Unique10 ?
Unique (%)2.8%

Sample

1st rowChina
2nd rowAnsan-si
3rd rowAnsan-si
4th rowAsan-si
5th rowChangwon-si

Common Values

ValueCountFrequency (%)
Total 216
61.0%
Suwon-si 25
 
7.1%
Jinju-si 16
 
4.5%
Yong in-si 12
 
3.4%
Pohang-si 10
 
2.8%
Sangju-si 9
 
2.5%
Jeonju-si 8
 
2.3%
Cheonan-si 8
 
2.3%
Chuncheon-si 7
 
2.0%
Cheongju-si 6
 
1.7%
Other values (19) 37
 
10.5%

Length

2023-12-12T18:29:25.863358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
total 216
59.0%
suwon-si 25
 
6.8%
jinju-si 16
 
4.4%
yong 12
 
3.3%
in-si 12
 
3.3%
pohang-si 10
 
2.7%
sangju-si 9
 
2.5%
jeonju-si 8
 
2.2%
cheonan-si 8
 
2.2%
chuncheon-si 7
 
1.9%
Other values (20) 43
 
11.7%

연구분야(대)
Categorical

Distinct19
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
생명과학
51 
정보/ 통신
38 
화학
33 
물리학
26 
재료
26 
Other values (14)
180 

Length

Max length21
Median length11
Mean length5.3700565
Min length3

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row정보/ 통신
2nd row정보/ 통신
3rd row화학
4th row보건의료
5th row생명과학

Common Values

ValueCountFrequency (%)
생명과학 51
14.4%
정보/ 통신 38
10.7%
화학 33
9.3%
물리학 26
 
7.3%
재료 26
 
7.3%
보건의료 25
 
7.1%
기계 24
 
6.8%
전기/ 전자 21
 
5.9%
환경 19
 
5.4%
화공 19
 
5.4%
Other values (9) 72
20.3%

Length

2023-12-12T18:29:25.992548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
생명과학 51
 
10.7%
정보 38
 
8.0%
통신 38
 
8.0%
화학 33
 
6.9%
물리학 26
 
5.5%
재료 26
 
5.5%
보건의료 25
 
5.2%
기계 24
 
5.0%
전기 21
 
4.4%
전자 21
 
4.4%
Other values (17) 174
36.5%

연구분야(중)
Text

Distinct109
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-12T18:29:26.229812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length8.3361582
Min length3

Characters and Unicode

Total characters2951
Distinct characters160
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)10.7%

Sample

1st row RFID/ USN
2nd row 이동 통신
3rd row 전기 화학
4th row 보건학
5th row 분자 세포 생물학
ValueCountFrequency (%)
화학 47
 
5.5%
기타 32
 
3.8%
생물학 32
 
3.8%
기술 29
 
3.4%
재료 29
 
3.4%
과학 26
 
3.1%
물리 22
 
2.6%
시스템 21
 
2.5%
세포 20
 
2.4%
공정 20
 
2.4%
Other values (153) 572
67.3%
2023-12-12T18:29:26.706547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
850
28.8%
학 158
 
5.4%
기 125
 
4.2%
물 107
 
3.6%
/ 99
 
3.4%
생 89
 
3.0%
화 66
 
2.2%
자 62
 
2.1%
공 44
 
1.5%
리 44
 
1.5%
Other values (150) 1307
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1991
67.5%
Space Separator 850
28.8%
Other Punctuation 99
 
3.4%
Uppercase Letter 9
 
0.3%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 158
 
7.9%
기 125
 
6.3%
물 107
 
5.4%
생 89
 
4.5%
화 66
 
3.3%
자 62
 
3.1%
공 44
 
2.2%
리 44
 
2.2%
분 43
 
2.2%
재 43
 
2.2%
Other values (140) 1210
60.8%
Uppercase Letter
ValueCountFrequency (%)
U 3
33.3%
N 1
 
11.1%
R 1
 
11.1%
S 1
 
11.1%
F 1
 
11.1%
I 1
 
11.1%
D 1
 
11.1%
Space Separator
ValueCountFrequency (%)
850
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1991
67.5%
Common 951
32.2%
Latin 9
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 158
 
7.9%
기 125
 
6.3%
물 107
 
5.4%
생 89
 
4.5%
화 66
 
3.3%
자 62
 
3.1%
공 44
 
2.2%
리 44
 
2.2%
분 43
 
2.2%
재 43
 
2.2%
Other values (140) 1210
60.8%
Latin
ValueCountFrequency (%)
U 3
33.3%
N 1
 
11.1%
R 1
 
11.1%
S 1
 
11.1%
F 1
 
11.1%
I 1
 
11.1%
D 1
 
11.1%
Common
ValueCountFrequency (%)
850
89.4%
/ 99
 
10.4%
- 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1991
67.5%
ASCII 960
32.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
850
88.5%
/ 99
 
10.3%
U 3
 
0.3%
- 2
 
0.2%
N 1
 
0.1%
R 1
 
0.1%
S 1
 
0.1%
F 1
 
0.1%
I 1
 
0.1%
D 1
 
0.1%
Hangul
ValueCountFrequency (%)
학 158
 
7.9%
기 125
 
6.3%
물 107
 
5.4%
생 89
 
4.5%
화 66
 
3.3%
자 62
 
3.1%
공 44
 
2.2%
리 44
 
2.2%
분 43
 
2.2%
재 43
 
2.2%
Other values (140) 1210
60.8%

기관유형
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
대학교
337 
출연연
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대학교
2nd row대학교
3rd row대학교
4th row대학교
5th row출연연

Common Values

ValueCountFrequency (%)
대학교 337
95.2%
출연연 17
 
4.8%

Length

2023-12-12T18:29:26.864195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:29:26.993220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대학교 337
95.2%
출연연 17
 
4.8%

연구기관명
Text

Distinct65
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-12T18:29:27.296028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length43
Mean length28.912429
Min length14

Characters and Unicode

Total characters10235
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)5.4%

Sample

1st rowPukyong National University
2nd rowHanyang University
3rd rowHanyang University
4th rowHoseo University
5th rowMasan National Tuberculosis Hospital
ValueCountFrequency (%)
university 284
22.3%
national 162
12.7%
technology 85
 
6.7%
of 85
 
6.7%
science 82
 
6.5%
institute 66
 
5.2%
and 64
 
5.0%
korea 44
 
3.5%
seoul 33
 
2.6%
kyungpook 24
 
1.9%
Other values (71) 342
26.9%
2023-12-12T18:29:27.741504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1134
 
11.1%
917
 
9.0%
i 917
 
9.0%
e 793
 
7.7%
o 709
 
6.9%
t 655
 
6.4%
a 632
 
6.2%
y 473
 
4.6%
s 423
 
4.1%
r 344
 
3.4%
Other values (38) 3238
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8188
80.0%
Uppercase Letter 1099
 
10.7%
Space Separator 917
 
9.0%
Other Punctuation 25
 
0.2%
Dash Punctuation 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1134
13.8%
i 917
11.2%
e 793
9.7%
o 709
 
8.7%
t 655
 
8.0%
a 632
 
7.7%
y 473
 
5.8%
s 423
 
5.2%
r 344
 
4.2%
l 306
 
3.7%
Other values (13) 1802
22.0%
Uppercase Letter
ValueCountFrequency (%)
U 297
27.0%
N 162
14.7%
S 145
13.2%
K 107
 
9.7%
T 88
 
8.0%
I 74
 
6.7%
C 45
 
4.1%
G 33
 
3.0%
H 33
 
3.0%
A 25
 
2.3%
Other values (11) 90
 
8.2%
Other Punctuation
ValueCountFrequency (%)
& 20
80.0%
' 5
 
20.0%
Space Separator
ValueCountFrequency (%)
917
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9287
90.7%
Common 948
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1134
 
12.2%
i 917
 
9.9%
e 793
 
8.5%
o 709
 
7.6%
t 655
 
7.1%
a 632
 
6.8%
y 473
 
5.1%
s 423
 
4.6%
r 344
 
3.7%
l 306
 
3.3%
Other values (34) 2901
31.2%
Common
ValueCountFrequency (%)
917
96.7%
& 20
 
2.1%
- 6
 
0.6%
' 5
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1134
 
11.1%
917
 
9.0%
i 917
 
9.0%
e 793
 
7.7%
o 709
 
6.9%
t 655
 
6.4%
a 632
 
6.2%
y 473
 
4.6%
s 423
 
4.1%
r 344
 
3.4%
Other values (38) 3238
31.6%

연구실명
Text

Distinct345
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-12T18:29:28.155404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length49
Mean length31.793785
Min length2

Characters and Unicode

Total characters11255
Distinct characters102
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique336 ?
Unique (%)94.9%

Sample

1st rowAIoT laboratory
2nd rowWireless Communication Systems Lab
3rd rowNanostructured Energy Materials Laboratory
4th rowEssential oil and skin science
5th rowClinical Research Center
ValueCountFrequency (%)
lab 133
 
9.6%
laboratory 106
 
7.7%
and 76
 
5.5%
materials 35
 
2.5%
32
 
2.3%
energy 29
 
2.1%
research 27
 
2.0%
engineering 27
 
2.0%
advanced 16
 
1.2%
of 16
 
1.2%
Other values (478) 886
64.1%
2023-12-12T18:29:28.811864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1039
 
9.2%
a 989
 
8.8%
e 870
 
7.7%
o 799
 
7.1%
r 766
 
6.8%
n 708
 
6.3%
t 644
 
5.7%
i 643
 
5.7%
l 426
 
3.8%
s 400
 
3.6%
Other values (92) 3971
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8564
76.1%
Uppercase Letter 1433
 
12.7%
Space Separator 1039
 
9.2%
Other Punctuation 90
 
0.8%
Other Letter 56
 
0.5%
Close Punctuation 24
 
0.2%
Open Punctuation 24
 
0.2%
Dash Punctuation 19
 
0.2%
Decimal Number 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 5
 
8.9%
연 5
 
8.9%
소 4
 
7.1%
의 3
 
5.4%
양 2
 
3.6%
실 2
 
3.6%
재 2
 
3.6%
자 2
 
3.6%
류 2
 
3.6%
지 2
 
3.6%
Other values (27) 27
48.2%
Lowercase Letter
ValueCountFrequency (%)
a 989
11.5%
e 870
10.2%
o 799
9.3%
r 766
 
8.9%
n 708
 
8.3%
t 644
 
7.5%
i 643
 
7.5%
l 426
 
5.0%
s 400
 
4.7%
c 399
 
4.7%
Other values (15) 1920
22.4%
Uppercase Letter
ValueCountFrequency (%)
L 250
17.4%
E 146
10.2%
C 133
9.3%
S 129
9.0%
M 110
 
7.7%
N 82
 
5.7%
I 73
 
5.1%
A 71
 
5.0%
P 65
 
4.5%
R 58
 
4.0%
Other values (15) 316
22.1%
Other Punctuation
ValueCountFrequency (%)
. 41
45.6%
& 36
40.0%
, 5
 
5.6%
/ 4
 
4.4%
? 1
 
1.1%
· 1
 
1.1%
@ 1
 
1.1%
' 1
 
1.1%
Decimal Number
ValueCountFrequency (%)
2 4
66.7%
4 1
 
16.7%
3 1
 
16.7%
Space Separator
ValueCountFrequency (%)
1039
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9997
88.8%
Common 1202
 
10.7%
Hangul 56
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 989
 
9.9%
e 870
 
8.7%
o 799
 
8.0%
r 766
 
7.7%
n 708
 
7.1%
t 644
 
6.4%
i 643
 
6.4%
l 426
 
4.3%
s 400
 
4.0%
c 399
 
4.0%
Other values (40) 3353
33.5%
Hangul
ValueCountFrequency (%)
구 5
 
8.9%
연 5
 
8.9%
소 4
 
7.1%
의 3
 
5.4%
양 2
 
3.6%
실 2
 
3.6%
재 2
 
3.6%
자 2
 
3.6%
류 2
 
3.6%
지 2
 
3.6%
Other values (27) 27
48.2%
Common
ValueCountFrequency (%)
1039
86.4%
. 41
 
3.4%
& 36
 
3.0%
) 24
 
2.0%
( 24
 
2.0%
- 19
 
1.6%
, 5
 
0.4%
2 4
 
0.3%
/ 4
 
0.3%
4 1
 
0.1%
Other values (5) 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11198
99.5%
Hangul 56
 
0.5%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1039
 
9.3%
a 989
 
8.8%
e 870
 
7.8%
o 799
 
7.1%
r 766
 
6.8%
n 708
 
6.3%
t 644
 
5.8%
i 643
 
5.7%
l 426
 
3.8%
s 400
 
3.6%
Other values (54) 3914
35.0%
Hangul
ValueCountFrequency (%)
구 5
 
8.9%
연 5
 
8.9%
소 4
 
7.1%
의 3
 
5.4%
양 2
 
3.6%
실 2
 
3.6%
재 2
 
3.6%
자 2
 
3.6%
류 2
 
3.6%
지 2
 
3.6%
Other values (27) 27
48.2%
None
ValueCountFrequency (%)
· 1
100.0%

회원가입자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
1
341 
2
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 341
96.3%
2 13
 
3.7%

Length

2023-12-12T18:29:29.004673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:29:29.130162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 341
96.3%
2 13
 
3.7%

Correlations

2023-12-12T18:29:29.566036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가입년도지역(대)지역(중)연구분야(대)기관유형연구기관명회원가입자수
가입년도1.0000.3770.4320.0610.0000.6810.071
지역(대)0.3771.0000.9720.3970.0000.9920.000
지역(중)0.4320.9721.0000.4350.5680.9870.268
연구분야(대)0.0610.3970.4351.0000.0000.5490.124
기관유형0.0000.0000.5680.0001.0000.8960.000
연구기관명0.6810.9920.9870.5490.8961.0000.000
회원가입자수0.0710.0000.2680.1240.0000.0001.000
2023-12-12T18:29:29.713426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역(대)가입년도지역(중)연구분야(대)기관유형회원가입자수
지역(대)1.0000.2140.7480.1320.0000.000
가입년도0.2141.0000.2290.0310.0000.046
지역(중)0.7480.2291.0000.1280.4700.219
연구분야(대)0.1320.0310.1281.0000.0000.107
기관유형0.0000.0000.4700.0001.0000.000
회원가입자수0.0000.0460.2190.1070.0001.000
2023-12-12T18:29:29.857405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가입년도지역(대)지역(중)연구분야(대)기관유형회원가입자수
가입년도1.0000.2140.2290.0310.0000.046
지역(대)0.2141.0000.7480.1320.0000.000
지역(중)0.2290.7481.0000.1280.4700.219
연구분야(대)0.0310.1320.1281.0000.0000.107
기관유형0.0000.0000.4700.0001.0000.000
회원가입자수0.0460.0000.2190.1070.0001.000

Missing values

2023-12-12T18:29:25.179412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:29:25.337198image/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.

Sample

가입년도지역(대)지역(중)연구분야(대)연구분야(중)기관유형연구기관명연구실명회원가입자수
02020GlobalChina정보/ 통신RFID/ USN대학교Pukyong National UniversityAIoT laboratory1
12020Gyeonggi-doAnsan-si정보/ 통신이동 통신대학교Hanyang UniversityWireless Communication Systems Lab1
22020Gyeonggi-doAnsan-si화학전기 화학대학교Hanyang UniversityNanostructured Energy Materials Laboratory1
32020Chungcheongnam-doAsan-si보건의료보건학대학교Hoseo UniversityEssential oil and skin science1
42020Gyeongsangnam-doChangwon-si생명과학분자 세포 생물학출연연Masan National Tuberculosis HospitalClinical Research Center1
52020Chungcheongnam-doCheonan-si생명과학분자 세포 생물학대학교Soon Chun Hyang UniversityCancer Stem Cell Laboratory1
62020Chungcheongnam-doCheonan-si생명과학분자 세포 생물학대학교Soon Chun Hyang UniversityStem cells and biomaterials engineering lab1
72020Chungcheongnam-doCheonan-si에너지/ 자원신재생 에너지대학교Sangmyung UniversityMultiscale Energy Materials and Systems Laboratory1
82020Chungcheongbuk-doCheongju-si생명과학분류/ 생태/ 환경 생물학대학교Chungbuk National UniversityDepartment of Parasitology, School of Medicine1
92020Chungcheongbuk-doCheongju-si화공생물 화학 공정 기술대학교Chungbuk National UniversityBiochemical Engineering Laboratory1
가입년도지역(대)지역(중)연구분야(대)연구분야(중)기관유형연구기관명연구실명회원가입자수
3442022SeoulTotal화학무기 화학대학교Hanyang UniversityTEST LABORATORY1
3452022SeoulTotal화학전기 화학대학교Dongguk UniversityDIS&AE Lab1
3462022SeoulTotal화학전기 화학대학교Sangmyung UniversityLiquids materials for energy devices1
3472022SeoulTotal환경물관리대학교Konkuk UniversityAdvanced Clean Environmental Engineering LAB1
3482022Gyeonggi-doYong in-si정보/ 통신기타 정보/ 통신대학교Dankook UniversityEmbedded & Parallel Systems Lab1
3492023Jeju-doJeju-si물리학응집 물질 물리대학교Jeju National UniversityCondensed Matter Physics Laboratory1
3502023DaejeonTotal정보/ 통신이동 통신대학교Hanbat National UniversityArtificial Intelligence and Radio Technology Laboratory1
3512023SeoulTotal생명과학생화학/ 구조 생물학대학교Sookmyung Women's UniversityOligonucleotide therapeutics Lab1
3522023SeoulTotal정보/ 통신이동 통신대학교Kookmin UniversitySpecial Communication & Convergence Service Research Center1
3532023UlsanTotal정보/ 통신소프트 웨어대학교Ulsan National Institute of Science and TechnologyPLaSE (Programming Languages and Software Engineering Lab)1