Overview

Dataset statistics

Number of variables6
Number of observations566
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.7 KiB
Average record size in memory48.2 B

Variable types

Categorical3
Text3

Dataset

Description한국연구재단이 보유하고 있는 온라인논문투고심사시스템에 있는학술대회운영조직구성원 데이터 입니다. 대표 데이터는 기관명, 학술대회 아이디 등이 있습니다.
Author한국연구재단
URLhttps://www.data.go.kr/data/15092932/fileData.do

Alerts

기관 아이디 is highly overall correlated with 기관명 and 1 other fieldsHigh correlation
기관명 is highly overall correlated with 기관 아이디 and 1 other fieldsHigh correlation
학술대회 아이디 is highly overall correlated with 기관명 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 10:49:20.277799
Analysis finished2023-12-12 10:49:20.798775
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
대한통합의학회
96 
(사)한국스마트미디어학회
77 
대한방사선과학회
68 
대한수학교육학회
57 
한국지구물리？물리탐사학회
36 
Other values (33)
232 

Length

Max length27
Median length17
Mean length9.295053
Min length5

Unique

Unique6 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
대한통합의학회 96
17.0%
(사)한국스마트미디어학회 77
13.6%
대한방사선과학회 68
12.0%
대한수학교육학회 57
10.1%
한국지구물리？물리탐사학회 36
 
6.4%
한국중국언어학회 25
 
4.4%
한국산업정보학회 22
 
3.9%
한국음성학회 21
 
3.7%
한국정보교육학회 17
 
3.0%
한국융합신호처리학회 15
 
2.7%
Other values (28) 132
23.3%

Length

2023-12-12T19:49:20.889185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대한통합의학회 96
16.3%
사)한국스마트미디어학회 77
13.1%
대한방사선과학회 68
11.6%
대한수학교육학회 57
 
9.7%
한국지구물리？물리탐사학회 36
 
6.1%
한국중국언어학회 25
 
4.3%
한국산업정보학회 22
 
3.7%
한국음성학회 21
 
3.6%
한국정보교육학회 17
 
2.9%
한국융합신호처리학회 15
 
2.6%
Other values (38) 154
26.2%

기관 아이디
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
INS000011210
96 
INS000010113
77 
INS000001040
68 
INS000000952
57 
INS000002518
36 
Other values (33)
232 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique6 ?
Unique (%)1.1%

Sample

1st rowINS000006678
2nd rowINS000006678
3rd rowINS000006678
4th rowINS000006678
5th rowINS000006678

Common Values

ValueCountFrequency (%)
INS000011210 96
17.0%
INS000010113 77
13.6%
INS000001040 68
12.0%
INS000000952 57
10.1%
INS000002518 36
 
6.4%
INS000001701 25
 
4.4%
INS000001286 22
 
3.9%
INS000006678 21
 
3.7%
INS000001650 17
 
3.0%
INS000001823 15
 
2.7%
Other values (28) 132
23.3%

Length

2023-12-12T19:49:21.068750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ins000011210 96
17.0%
ins000010113 77
13.6%
ins000001040 68
12.0%
ins000000952 57
10.1%
ins000002518 36
 
6.4%
ins000001701 25
 
4.4%
ins000001286 22
 
3.9%
ins000006678 21
 
3.7%
ins000001650 17
 
3.0%
ins000001823 15
 
2.7%
Other values (28) 132
23.3%

학술대회 아이디
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
AC0000000001
107 
AC0000000006
90 
AC0000000007
89 
AC0000000004
54 
AC0000000013
43 
Other values (13)
183 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowAC0000000001
2nd rowAC0000000002
3rd rowAC0000000002
4th rowAC0000000002
5th rowAC0000000001

Common Values

ValueCountFrequency (%)
AC0000000001 107
18.9%
AC0000000006 90
15.9%
AC0000000007 89
15.7%
AC0000000004 54
9.5%
AC0000000013 43
7.6%
AC0000000003 40
 
7.1%
AC0000000002 33
 
5.8%
AC0000000005 27
 
4.8%
AC0000000020 18
 
3.2%
AC0000000011 13
 
2.3%
Other values (8) 52
9.2%

Length

2023-12-12T19:49:21.360264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ac0000000001 107
18.9%
ac0000000006 90
15.9%
ac0000000007 89
15.7%
ac0000000004 54
9.5%
ac0000000013 43
7.6%
ac0000000003 40
 
7.1%
ac0000000002 33
 
5.8%
ac0000000005 27
 
4.8%
ac0000000020 18
 
3.2%
ac0000000016 13
 
2.3%
Other values (8) 52
9.2%

운영조직 아이디
Text

Distinct116
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2023-12-12T19:49:21.809578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6792
Distinct characters12
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

Unique51 ?
Unique (%)9.0%

Sample

1st rowOOR000000049
2nd rowOOR000000046
3rd rowOOR000000046
4th rowOOR000000046
5th rowOOR000000049
ValueCountFrequency (%)
oor000000309 42
 
7.4%
oor000000245 33
 
5.8%
oor000000313 30
 
5.3%
oor000000297 21
 
3.7%
oor000000308 18
 
3.2%
oor000000271 17
 
3.0%
oor000000301 15
 
2.7%
oor000000277 15
 
2.7%
oor000000274 13
 
2.3%
oor000000298 12
 
2.1%
Other values (106) 350
61.8%
2023-12-12T19:49:22.419624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3589
52.8%
O 1132
 
16.7%
R 566
 
8.3%
3 305
 
4.5%
2 252
 
3.7%
1 219
 
3.2%
9 187
 
2.8%
7 173
 
2.5%
4 104
 
1.5%
5 102
 
1.5%
Other values (2) 163
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5094
75.0%
Uppercase Letter 1698
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3589
70.5%
3 305
 
6.0%
2 252
 
4.9%
1 219
 
4.3%
9 187
 
3.7%
7 173
 
3.4%
4 104
 
2.0%
5 102
 
2.0%
6 87
 
1.7%
8 76
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
O 1132
66.7%
R 566
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5094
75.0%
Latin 1698
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3589
70.5%
3 305
 
6.0%
2 252
 
4.9%
1 219
 
4.3%
9 187
 
3.7%
7 173
 
3.4%
4 104
 
2.0%
5 102
 
2.0%
6 87
 
1.7%
8 76
 
1.5%
Latin
ValueCountFrequency (%)
O 1132
66.7%
R 566
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3589
52.8%
O 1132
 
16.7%
R 566
 
8.3%
3 305
 
4.5%
2 252
 
3.7%
1 219
 
3.2%
9 187
 
2.8%
7 173
 
2.5%
4 104
 
1.5%
5 102
 
1.5%
Other values (2) 163
 
2.4%

회원 아이디
Text

Distinct441
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2023-12-12T19:49:22.894432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.1590106
Min length2

Characters and Unicode

Total characters4052
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique352 ?
Unique (%)62.2%

Sample

1st rowysyun
2nd rowtaogi
3rd rowshgrace
4th rowcjseong
5th rowtaogi
ValueCountFrequency (%)
f452000 5
 
0.9%
realotr 5
 
0.9%
jecclub 5
 
0.9%
math1207 4
 
0.7%
hanqy 4
 
0.7%
sjjh0314 4
 
0.7%
pourpeda 4
 
0.7%
1008kmc 4
 
0.7%
kyungbaekkim 4
 
0.7%
khs3378 4
 
0.7%
Other values (431) 523
92.4%
2023-12-12T19:49:23.538549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 281
 
6.9%
n 271
 
6.7%
a 236
 
5.8%
h 231
 
5.7%
e 213
 
5.3%
s 212
 
5.2%
k 191
 
4.7%
i 185
 
4.6%
y 157
 
3.9%
0 153
 
3.8%
Other values (28) 1922
47.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3309
81.7%
Decimal Number 736
 
18.2%
Uppercase Letter 7
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 281
 
8.5%
n 271
 
8.2%
a 236
 
7.1%
h 231
 
7.0%
e 213
 
6.4%
s 212
 
6.4%
k 191
 
5.8%
i 185
 
5.6%
y 157
 
4.7%
m 147
 
4.4%
Other values (16) 1185
35.8%
Decimal Number
ValueCountFrequency (%)
0 153
20.8%
1 114
15.5%
2 89
12.1%
7 76
10.3%
4 69
9.4%
3 58
 
7.9%
5 46
 
6.2%
9 45
 
6.1%
8 44
 
6.0%
6 42
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
T 6
85.7%
D 1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3316
81.8%
Common 736
 
18.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 281
 
8.5%
n 271
 
8.2%
a 236
 
7.1%
h 231
 
7.0%
e 213
 
6.4%
s 212
 
6.4%
k 191
 
5.8%
i 185
 
5.6%
y 157
 
4.7%
m 147
 
4.4%
Other values (18) 1192
35.9%
Common
ValueCountFrequency (%)
0 153
20.8%
1 114
15.5%
2 89
12.1%
7 76
10.3%
4 69
9.4%
3 58
 
7.9%
5 46
 
6.2%
9 45
 
6.1%
8 44
 
6.0%
6 42
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 281
 
6.9%
n 271
 
6.7%
a 236
 
5.8%
h 231
 
5.7%
e 213
 
5.3%
s 212
 
5.2%
k 191
 
4.7%
i 185
 
4.6%
y 157
 
3.9%
0 153
 
3.8%
Other values (28) 1922
47.4%

운영직위 아이디
Text

Distinct192
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2023-12-12T19:49:23.827846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6792
Distinct characters12
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

Unique117 ?
Unique (%)20.7%

Sample

1st rowPO0000000068
2nd rowPO0000000064
3rd rowPO0000000064
4th rowPO0000000064
5th rowPO0000000068
ValueCountFrequency (%)
po0000000420 30
 
5.3%
po0000000306 18
 
3.2%
po0000000382 17
 
3.0%
po0000000342 17
 
3.0%
po0000000418 15
 
2.7%
po0000000387 15
 
2.7%
po0000000417 14
 
2.5%
po0000000378 12
 
2.1%
po0000000346 12
 
2.1%
po0000000354 11
 
1.9%
Other values (182) 405
71.6%
2023-12-12T19:49:24.454755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4159
61.2%
P 566
 
8.3%
O 566
 
8.3%
4 315
 
4.6%
3 287
 
4.2%
2 236
 
3.5%
1 169
 
2.5%
8 131
 
1.9%
6 124
 
1.8%
7 93
 
1.4%
Other values (2) 146
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5660
83.3%
Uppercase Letter 1132
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4159
73.5%
4 315
 
5.6%
3 287
 
5.1%
2 236
 
4.2%
1 169
 
3.0%
8 131
 
2.3%
6 124
 
2.2%
7 93
 
1.6%
5 86
 
1.5%
9 60
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
P 566
50.0%
O 566
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5660
83.3%
Latin 1132
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4159
73.5%
4 315
 
5.6%
3 287
 
5.1%
2 236
 
4.2%
1 169
 
3.0%
8 131
 
2.3%
6 124
 
2.2%
7 93
 
1.6%
5 86
 
1.5%
9 60
 
1.1%
Latin
ValueCountFrequency (%)
P 566
50.0%
O 566
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4159
61.2%
P 566
 
8.3%
O 566
 
8.3%
4 315
 
4.6%
3 287
 
4.2%
2 236
 
3.5%
1 169
 
2.5%
8 131
 
1.9%
6 124
 
1.8%
7 93
 
1.4%
Other values (2) 146
 
2.1%

Correlations

2023-12-12T19:49:24.632686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명기관 아이디학술대회 아이디
기관명1.0001.0000.936
기관 아이디1.0001.0000.936
학술대회 아이디0.9360.9361.000
2023-12-12T19:49:24.775362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관 아이디학술대회 아이디기관명
기관 아이디1.0000.5681.000
학술대회 아이디0.5681.0000.568
기관명1.0000.5681.000
2023-12-12T19:49:24.892230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명기관 아이디학술대회 아이디
기관명1.0001.0000.568
기관 아이디1.0001.0000.568
학술대회 아이디0.5680.5681.000

Missing values

2023-12-12T19:49:20.584237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:49:20.733057image/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

기관명기관 아이디학술대회 아이디운영조직 아이디회원 아이디운영직위 아이디
0한국음성학회INS000006678AC0000000001OOR000000049ysyunPO0000000068
1한국음성학회INS000006678AC0000000002OOR000000046taogiPO0000000064
2한국음성학회INS000006678AC0000000002OOR000000046shgracePO0000000064
3한국음성학회INS000006678AC0000000002OOR000000046cjseongPO0000000064
4한국음성학회INS000006678AC0000000001OOR000000049taogiPO0000000068
5한국음성학회INS000006678AC0000000001OOR000000049shgracePO0000000068
6한국음성학회INS000006678AC0000000001OOR000000049jeongimhanPO0000000068
7한국음성학회INS000006678AC0000000001OOR000000049owkwonPO0000000068
8한국음성학회INS000006678AC0000000002OOR000000046ysyunPO0000000064
9한국음성학회INS000006678AC0000000002OOR000000046jeongimhanPO0000000064
기관명기관 아이디학술대회 아이디운영조직 아이디회원 아이디운영직위 아이디
556부경대학교 인문사회과학연구소INS000002983AC0000000051OOR000000332minminchoiPO0000000433
557부경대학교 인문사회과학연구소INS000002983AC0000000051OOR000000332djsonPO0000000433
558부경대학교 인문사회과학연구소INS000002983AC0000000051OOR000000332tuxyo100PO0000000433
559대한공간정보학회INS000001280AC0000000011OOR000000077drlee3PO0000000089
560대한공간정보학회INS000001280AC0000000011OOR000000076leesukbaePO0000000088
561대한공간정보학회INS000001280AC0000000011OOR000000075leebgprofPO0000000087
562대한범죄학회INS000002207AC0000000019OOR000000338ngokr1025PO0000000443
563대한범죄학회INS000002207AC0000000019OOR000000338hsjang32PO0000000442
564대한범죄학회INS000002207AC0000000019OOR000000338kwakdaehoonPO0000000441
565디그니타스교양교육연구소INS000061622AC0000000001OOR000000314dasanPO0000000421