Overview

Dataset statistics

Number of variables15
Number of observations3595
Missing cells331
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory446.0 KiB
Average record size in memory127.0 B

Variable types

Numeric4
Text5
Categorical6

Dataset

Description통일연구원에서 발간하는 연구보고서(발간자료, 정기간행물, 온라인시리즈 등)의 목록 및 원문URL 정보를 제공
Author통일연구원
URLhttps://www.data.go.kr/data/15069795/fileData.do

Alerts

지원레벨(SUPPORT_LEVEL) is highly overall correlated with 순번 and 8 other fieldsHigh correlation
간략한 설명(SHORT_DESCRIPTION) is highly overall correlated with 순번 and 8 other fieldsHigh correlation
설명(DESCRIPTION) is highly overall correlated with 순번 and 8 other fieldsHigh correlation
MIMETYPE(MIMETYPE) is highly overall correlated with 순번 and 8 other fieldsHigh correlation
내부 사용 여부(INTERNAL) is highly overall correlated with 순번 and 8 other fieldsHigh correlation
비트스트림 포맷 ID(BITSTREAM_FORMAT_ID) is highly overall correlated with 순번 and 8 other fieldsHigh correlation
순번 is highly overall correlated with 비트스트림 포맷 ID(BITSTREAM_FORMAT_ID) and 5 other fieldsHigh correlation
아이템ID(ITEM_ID) is highly overall correlated with 발행일(ISSUED_NEW) and 7 other fieldsHigh correlation
발행일(ISSUED_NEW) is highly overall correlated with 아이템ID(ITEM_ID) and 7 other fieldsHigh correlation
식별자(IDENTIFIER) is highly overall correlated with 아이템ID(ITEM_ID) and 7 other fieldsHigh correlation
비트스트림 포맷 ID(BITSTREAM_FORMAT_ID) is highly imbalanced (57.0%)Imbalance
MIMETYPE(MIMETYPE) is highly imbalanced (57.0%)Imbalance
간략한 설명(SHORT_DESCRIPTION) is highly imbalanced (57.0%)Imbalance
설명(DESCRIPTION) is highly imbalanced (57.0%)Imbalance
지원레벨(SUPPORT_LEVEL) is highly imbalanced (57.0%)Imbalance
내부 사용 여부(INTERNAL) is highly imbalanced (57.0%)Imbalance
비트스트림 URL(BITSTREAM_URL) has 317 (8.8%) missing valuesMissing
순번 has unique valuesUnique
아이템ID(ITEM_ID) has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:16:04.076189
Analysis finished2023-12-12 14:16:07.749995
Duration3.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3595
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1798
Minimum1
Maximum3595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T23:16:07.828362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile180.7
Q1899.5
median1798
Q32696.5
95-th percentile3415.3
Maximum3595
Range3594
Interquartile range (IQR)1797

Descriptive statistics

Standard deviation1037.9314
Coefficient of variation (CV)0.57726999
Kurtosis-1.2
Mean1798
Median Absolute Deviation (MAD)899
Skewness0
Sum6463810
Variance1077301.7
MonotonicityStrictly increasing
2023-12-12T23:16:07.986700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2473 1
 
< 0.1%
2391 1
 
< 0.1%
2392 1
 
< 0.1%
2393 1
 
< 0.1%
2394 1
 
< 0.1%
2395 1
 
< 0.1%
2396 1
 
< 0.1%
2397 1
 
< 0.1%
2398 1
 
< 0.1%
Other values (3585) 3585
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3595 1
< 0.1%
3594 1
< 0.1%
3593 1
< 0.1%
3592 1
< 0.1%
3591 1
< 0.1%
3590 1
< 0.1%
3589 1
< 0.1%
3588 1
< 0.1%
3587 1
< 0.1%
3586 1
< 0.1%

아이템ID(ITEM_ID)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3595
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3756.501
Minimum3
Maximum11844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T23:16:08.171586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile182.7
Q1901.5
median1800
Q38056.5
95-th percentile10029.8
Maximum11844
Range11841
Interquartile range (IQR)7155

Descriptive statistics

Standard deviation3683.5483
Coefficient of variation (CV)0.98057963
Kurtosis-1.2275437
Mean3756.501
Median Absolute Deviation (MAD)1215
Skewness0.71066512
Sum13504621
Variance13568528
MonotonicityNot monotonic
2023-12-12T23:16:08.303264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1
 
< 0.1%
1809 1
 
< 0.1%
1195 1
 
< 0.1%
1196 1
 
< 0.1%
1216 1
 
< 0.1%
1229 1
 
< 0.1%
1236 1
 
< 0.1%
1238 1
 
< 0.1%
1243 1
 
< 0.1%
1245 1
 
< 0.1%
Other values (3585) 3585
99.7%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
ValueCountFrequency (%)
11844 1
< 0.1%
11843 1
< 0.1%
11842 1
< 0.1%
11841 1
< 0.1%
11821 1
< 0.1%
11801 1
< 0.1%
11784 1
< 0.1%
11783 1
< 0.1%
11782 1
< 0.1%
11781 1
< 0.1%
Distinct3520
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
2023-12-12T23:16:08.640802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length172
Median length135
Mean length34.016968
Min length4

Characters and Unicode

Total characters122291
Distinct characters1023
Distinct categories15 ?
Distinct scripts6 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3476 ?
Unique (%)96.7%

Sample

1st row韓·蘇, 日·蘇 頂上會談 結果 分析 : 韓半島 周邊情勢 및 南北韓關係에 미칠 영향을 중심으로
2nd row李鵬 中國總理의 訪北結果 分析 : 韓半島 周邊情勢 및 南北韓關係에 미칠 영향을 중심으로
3rd row美·蘇의 對 東北亞政策과 東北亞 軍事秩序 再編 可能性
4th row東西獨 事例를 통해 본 南北韓關係 改善方案 : 정상회담과 기본조약체결사례 중심
5th row美國의 對韓半島政策
ValueCountFrequency (%)
the 482
 
2.3%
of 458
 
2.2%
and 374
 
1.8%
주간통일정세 352
 
1.7%
north 273
 
1.3%
korean 252
 
1.2%
북한의 243
 
1.2%
북한 228
 
1.1%
226
 
1.1%
전망 212
 
1.0%
Other values (6267) 17595
85.0%
2023-12-12T23:16:09.240715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17328
 
14.2%
o 3926
 
3.2%
0 3909
 
3.2%
n 3867
 
3.2%
e 3758
 
3.1%
1 3503
 
2.9%
2 3479
 
2.8%
a 3232
 
2.6%
t 3061
 
2.5%
i 2989
 
2.4%
Other values (1013) 73239
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41734
34.1%
Lowercase Letter 34811
28.5%
Space Separator 17330
14.2%
Decimal Number 15356
 
12.6%
Uppercase Letter 5743
 
4.7%
Other Punctuation 3941
 
3.2%
Dash Punctuation 840
 
0.7%
Open Punctuation 764
 
0.6%
Close Punctuation 764
 
0.6%
Math Symbol 606
 
0.5%
Other values (5) 402
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1709
 
4.1%
1631
 
3.9%
1605
 
3.8%
1499
 
3.6%
1123
 
2.7%
1073
 
2.6%
866
 
2.1%
862
 
2.1%
716
 
1.7%
667
 
1.6%
Other values (912) 29983
71.8%
Lowercase Letter
ValueCountFrequency (%)
o 3926
11.3%
n 3867
11.1%
e 3758
10.8%
a 3232
9.3%
t 3061
8.8%
i 2989
8.6%
r 2488
 
7.1%
s 2047
 
5.9%
l 1332
 
3.8%
h 1292
 
3.7%
Other values (16) 6819
19.6%
Uppercase Letter
ValueCountFrequency (%)
K 728
12.7%
S 521
 
9.1%
N 521
 
9.1%
P 493
 
8.6%
I 430
 
7.5%
R 362
 
6.3%
C 342
 
6.0%
U 308
 
5.4%
T 273
 
4.8%
A 259
 
4.5%
Other values (16) 1506
26.2%
Other Punctuation
ValueCountFrequency (%)
. 2508
63.6%
: 521
 
13.2%
· 420
 
10.7%
, 288
 
7.3%
' 103
 
2.6%
? 63
 
1.6%
11
 
0.3%
" 8
 
0.2%
/ 7
 
0.2%
& 7
 
0.2%
Other values (3) 5
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 3909
25.5%
1 3503
22.8%
2 3479
22.7%
3 781
 
5.1%
9 729
 
4.7%
4 709
 
4.6%
7 586
 
3.8%
5 582
 
3.8%
6 542
 
3.5%
8 536
 
3.5%
Math Symbol
ValueCountFrequency (%)
~ 503
83.0%
86
 
14.2%
= 5
 
0.8%
< 4
 
0.7%
> 4
 
0.7%
| 4
 
0.7%
Letter Number
ValueCountFrequency (%)
9
32.1%
9
32.1%
6
21.4%
4
14.3%
Open Punctuation
ValueCountFrequency (%)
( 741
97.0%
19
 
2.5%
[ 4
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 741
97.0%
19
 
2.5%
] 4
 
0.5%
Space Separator
ValueCountFrequency (%)
17328
> 99.9%
  2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
247
91.8%
22
 
8.2%
Initial Punctuation
ValueCountFrequency (%)
60
73.2%
22
 
26.8%
Modifier Symbol
ValueCountFrequency (%)
´ 21
95.5%
` 1
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 840
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40582
33.2%
Common 39975
32.7%
Hangul 38313
31.3%
Han 3415
 
2.8%
Katakana 3
 
< 0.1%
Hiragana 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1709
 
4.5%
1631
 
4.3%
1605
 
4.2%
1499
 
3.9%
1123
 
2.9%
1073
 
2.8%
866
 
2.3%
862
 
2.2%
716
 
1.9%
667
 
1.7%
Other values (500) 26562
69.3%
Han
ValueCountFrequency (%)
153
 
4.5%
151
 
4.4%
72
 
2.1%
69
 
2.0%
68
 
2.0%
68
 
2.0%
63
 
1.8%
63
 
1.8%
60
 
1.8%
58
 
1.7%
Other values (397) 2590
75.8%
Latin
ValueCountFrequency (%)
o 3926
 
9.7%
n 3867
 
9.5%
e 3758
 
9.3%
a 3232
 
8.0%
t 3061
 
7.5%
i 2989
 
7.4%
r 2488
 
6.1%
s 2047
 
5.0%
l 1332
 
3.3%
h 1292
 
3.2%
Other values (46) 12590
31.0%
Common
ValueCountFrequency (%)
17328
43.3%
0 3909
 
9.8%
1 3503
 
8.8%
2 3479
 
8.7%
. 2508
 
6.3%
- 840
 
2.1%
3 781
 
2.0%
( 741
 
1.9%
) 741
 
1.9%
9 729
 
1.8%
Other values (35) 5416
 
13.5%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Katakana
ValueCountFrequency (%)
2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79599
65.1%
Hangul 38313
31.3%
CJK 3361
 
2.7%
None 482
 
0.4%
Punctuation 362
 
0.3%
Math Operators 86
 
0.1%
CJK Compat Ideographs 54
 
< 0.1%
Number Forms 28
 
< 0.1%
Katakana 3
 
< 0.1%
Hiragana 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17328
21.8%
o 3926
 
4.9%
0 3909
 
4.9%
n 3867
 
4.9%
e 3758
 
4.7%
1 3503
 
4.4%
2 3479
 
4.4%
a 3232
 
4.1%
t 3061
 
3.8%
i 2989
 
3.8%
Other values (75) 30547
38.4%
Hangul
ValueCountFrequency (%)
1709
 
4.5%
1631
 
4.3%
1605
 
4.2%
1499
 
3.9%
1123
 
2.9%
1073
 
2.8%
866
 
2.3%
862
 
2.2%
716
 
1.9%
667
 
1.7%
Other values (500) 26562
69.3%
None
ValueCountFrequency (%)
· 420
87.1%
´ 21
 
4.4%
19
 
3.9%
19
 
3.9%
  2
 
0.4%
1
 
0.2%
Punctuation
ValueCountFrequency (%)
247
68.2%
60
 
16.6%
22
 
6.1%
22
 
6.1%
11
 
3.0%
CJK
ValueCountFrequency (%)
153
 
4.6%
151
 
4.5%
72
 
2.1%
69
 
2.1%
68
 
2.0%
68
 
2.0%
63
 
1.9%
63
 
1.9%
60
 
1.8%
58
 
1.7%
Other values (384) 2536
75.5%
Math Operators
ValueCountFrequency (%)
86
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
15
27.8%
11
20.4%
5
 
9.3%
5
 
9.3%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
Other values (3) 4
 
7.4%
Number Forms
ValueCountFrequency (%)
9
32.1%
9
32.1%
6
21.4%
4
14.3%
Katakana
ValueCountFrequency (%)
2
66.7%
1
33.3%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Distinct1260
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
2023-12-12T23:16:09.557180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length354
Median length217
Mean length17.853408
Min length2

Characters and Unicode

Total characters64183
Distinct characters354
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique903 ?
Unique (%)25.1%

Sample

1st row강원식||박영규||강원식 공저
2nd row정규섭 저
3rd row김성진||박영규||김성진 공저
4th row民族統一硏究院 編
5th row이삼성 저
ValueCountFrequency (%)
668
 
7.2%
293
 
3.2%
통일연구원 262
 
2.8%
국제관계연구센터 211
 
2.3%
국제관계연구센터||북한연구센터 208
 
2.2%
편||북한연구센터 208
 
2.2%
북한연구실 167
 
1.8%
166
 
1.8%
통일연구원||통일연구원 148
 
1.6%
for 141
 
1.5%
Other values (1731) 6829
73.4%
2023-12-12T23:16:10.082828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
| 6766
 
10.5%
5733
 
8.9%
2150
 
3.3%
2146
 
3.3%
n 2006
 
3.1%
o 1997
 
3.1%
, 1927
 
3.0%
e 1289
 
2.0%
i 1285
 
2.0%
a 1278
 
2.0%
Other values (344) 37606
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31147
48.5%
Lowercase Letter 14132
22.0%
Math Symbol 6766
 
10.5%
Space Separator 5734
 
8.9%
Uppercase Letter 3644
 
5.7%
Other Punctuation 2211
 
3.4%
Dash Punctuation 478
 
0.7%
Close Punctuation 31
 
< 0.1%
Open Punctuation 31
 
< 0.1%
Decimal Number 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2150
 
6.9%
2146
 
6.9%
1102
 
3.5%
1082
 
3.5%
987
 
3.2%
987
 
3.2%
906
 
2.9%
805
 
2.6%
759
 
2.4%
722
 
2.3%
Other values (275) 19501
62.6%
Lowercase Letter
ValueCountFrequency (%)
n 2006
14.2%
o 1997
14.1%
e 1289
9.1%
i 1285
9.1%
a 1278
9.0%
u 955
 
6.8%
t 851
 
6.0%
g 734
 
5.2%
r 629
 
4.5%
h 607
 
4.3%
Other values (16) 2501
17.7%
Uppercase Letter
ValueCountFrequency (%)
K 540
14.8%
H 398
10.9%
J 397
10.9%
C 344
9.4%
S 326
8.9%
I 180
 
4.9%
N 179
 
4.9%
P 170
 
4.7%
L 161
 
4.4%
U 150
 
4.1%
Other values (15) 799
21.9%
Other Punctuation
ValueCountFrequency (%)
, 1927
87.2%
. 241
 
10.9%
; 37
 
1.7%
? 2
 
0.1%
/ 1
 
< 0.1%
· 1
 
< 0.1%
1
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 4
44.4%
1 3
33.3%
6 1
 
11.1%
5 1
 
11.1%
Space Separator
ValueCountFrequency (%)
5733
> 99.9%
  1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
| 6766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 478
100.0%
Close Punctuation
ValueCountFrequency (%)
] 31
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30327
47.3%
Latin 17776
27.7%
Common 15260
23.8%
Han 820
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2150
 
7.1%
2146
 
7.1%
1102
 
3.6%
1082
 
3.6%
987
 
3.3%
987
 
3.3%
906
 
3.0%
805
 
2.7%
759
 
2.5%
722
 
2.4%
Other values (257) 18681
61.6%
Latin
ValueCountFrequency (%)
n 2006
 
11.3%
o 1997
 
11.2%
e 1289
 
7.3%
i 1285
 
7.2%
a 1278
 
7.2%
u 955
 
5.4%
t 851
 
4.8%
g 734
 
4.1%
r 629
 
3.5%
h 607
 
3.4%
Other values (41) 6145
34.6%
Common
ValueCountFrequency (%)
| 6766
44.3%
5733
37.6%
, 1927
 
12.6%
- 478
 
3.1%
. 241
 
1.6%
; 37
 
0.2%
] 31
 
0.2%
[ 31
 
0.2%
0 4
 
< 0.1%
1 3
 
< 0.1%
Other values (8) 9
 
0.1%
Han
ValueCountFrequency (%)
101
12.3%
100
12.2%
100
12.2%
100
12.2%
100
12.2%
100
12.2%
100
12.2%
100
12.2%
4
 
0.5%
4
 
0.5%
Other values (8) 11
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33033
51.5%
Hangul 30327
47.3%
CJK 819
 
1.3%
None 3
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
| 6766
20.5%
5733
17.4%
n 2006
 
6.1%
o 1997
 
6.0%
, 1927
 
5.8%
e 1289
 
3.9%
i 1285
 
3.9%
a 1278
 
3.9%
u 955
 
2.9%
t 851
 
2.6%
Other values (56) 8946
27.1%
Hangul
ValueCountFrequency (%)
2150
 
7.1%
2146
 
7.1%
1102
 
3.6%
1082
 
3.6%
987
 
3.3%
987
 
3.3%
906
 
3.0%
805
 
2.7%
759
 
2.5%
722
 
2.4%
Other values (257) 18681
61.6%
CJK
ValueCountFrequency (%)
101
12.3%
100
12.2%
100
12.2%
100
12.2%
100
12.2%
100
12.2%
100
12.2%
100
12.2%
4
 
0.5%
4
 
0.5%
Other values (7) 10
 
1.2%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
· 1
33.3%
  1
33.3%
1
33.3%
Distinct692
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
2023-12-12T23:16:10.456189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length5.2369958
Min length4

Characters and Unicode

Total characters18827
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique515 ?
Unique (%)14.3%

Sample

1st row1991
2nd row1991
3rd row1991
4th row1991
5th row1992
ValueCountFrequency (%)
2012 239
 
6.6%
2010 214
 
6.0%
2011 206
 
5.7%
2008 201
 
5.6%
2009 185
 
5.1%
2007 143
 
4.0%
2013 126
 
3.5%
2006 119
 
3.3%
2005 109
 
3.0%
2004 88
 
2.4%
Other values (654) 1965
54.7%
2023-12-12T23:16:10.948043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5487
29.1%
2 3969
21.1%
1 3376
17.9%
9 1619
 
8.6%
. 1037
 
5.5%
8 583
 
3.1%
3 536
 
2.8%
6 523
 
2.8%
7 502
 
2.7%
5 485
 
2.6%
Other values (3) 710
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17544
93.2%
Other Punctuation 1037
 
5.5%
Dash Punctuation 240
 
1.3%
Lowercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5487
31.3%
2 3969
22.6%
1 3376
19.2%
9 1619
 
9.2%
8 583
 
3.3%
3 536
 
3.1%
6 523
 
3.0%
7 502
 
2.9%
5 485
 
2.8%
4 464
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 1037
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18821
> 99.9%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5487
29.2%
2 3969
21.1%
1 3376
17.9%
9 1619
 
8.6%
. 1037
 
5.5%
8 583
 
3.1%
3 536
 
2.8%
6 523
 
2.8%
7 502
 
2.7%
5 485
 
2.6%
Other values (2) 704
 
3.7%
Latin
ValueCountFrequency (%)
c 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5487
29.1%
2 3969
21.1%
1 3376
17.9%
9 1619
 
8.6%
. 1037
 
5.5%
8 583
 
3.1%
3 536
 
2.8%
6 523
 
2.8%
7 502
 
2.7%
5 485
 
2.6%
Other values (3) 710
 
3.8%

발행일(ISSUED_NEW)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.5691
Minimum1991
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T23:16:11.095186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile1994
Q12004
median2010
Q32014
95-th percentile2019
Maximum2020
Range29
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.4981647
Coefficient of variation (CV)0.0037330877
Kurtosis-0.47188979
Mean2008.5691
Median Absolute Deviation (MAD)5
Skewness-0.63149151
Sum7220806
Variance56.222474
MonotonicityNot monotonic
2023-12-12T23:16:11.232388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2012 246
 
6.8%
2010 219
 
6.1%
2011 210
 
5.8%
2008 205
 
5.7%
2009 189
 
5.3%
2015 186
 
5.2%
2013 181
 
5.0%
2017 169
 
4.7%
2016 168
 
4.7%
2018 167
 
4.6%
Other values (20) 1655
46.0%
ValueCountFrequency (%)
1991 38
1.1%
1992 64
1.8%
1993 66
1.8%
1994 85
2.4%
1995 54
1.5%
1996 64
1.8%
1997 51
1.4%
1998 75
2.1%
1999 57
1.6%
2000 72
2.0%
ValueCountFrequency (%)
2020 71
 
2.0%
2019 121
3.4%
2018 167
4.6%
2017 169
4.7%
2016 168
4.7%
2015 186
5.2%
2014 158
4.4%
2013 181
5.0%
2012 246
6.8%
2011 210
5.8%
Distinct460
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
2023-12-12T23:16:11.602999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.1630042
Min length2

Characters and Unicode

Total characters11371
Distinct characters11
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

Unique122 ?
Unique (%)3.4%

Sample

1st row16p
2nd row10p
3rd row26p
4th row48p
5th row167p
ValueCountFrequency (%)
5p 145
 
4.0%
2p 143
 
4.0%
4p 133
 
3.7%
6p 128
 
3.6%
3p 116
 
3.2%
7p 77
 
2.1%
8p 51
 
1.4%
9p 43
 
1.2%
26p 39
 
1.1%
10p 39
 
1.1%
Other values (450) 2681
74.6%
2023-12-12T23:16:12.118156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 3595
31.6%
1 1367
 
12.0%
2 1153
 
10.1%
3 951
 
8.4%
4 848
 
7.5%
5 791
 
7.0%
6 677
 
6.0%
7 544
 
4.8%
9 484
 
4.3%
8 481
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7776
68.4%
Lowercase Letter 3595
31.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1367
17.6%
2 1153
14.8%
3 951
12.2%
4 848
10.9%
5 791
10.2%
6 677
8.7%
7 544
 
7.0%
9 484
 
6.2%
8 481
 
6.2%
0 480
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
p 3595
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7776
68.4%
Latin 3595
31.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1367
17.6%
2 1153
14.8%
3 951
12.2%
4 848
10.9%
5 791
10.2%
6 677
8.7%
7 544
 
7.0%
9 484
 
6.2%
8 481
 
6.2%
0 480
 
6.2%
Latin
ValueCountFrequency (%)
p 3595
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 3595
31.6%
1 1367
 
12.0%
2 1153
 
10.1%
3 951
 
8.4%
4 848
 
7.5%
5 791
 
7.0%
6 677
 
6.0%
7 544
 
4.8%
9 484
 
4.3%
8 481
 
4.2%

식별자(IDENTIFIER)
Real number (ℝ)

HIGH CORRELATION 

Distinct3581
Distinct (%)100.0%
Missing14
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1158219.4
Minimum29105
Maximum1538419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T23:16:12.253727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29105
5-th percentile578526
Q1602435
median1447356
Q31476754
95-th percentile1512246
Maximum1538419
Range1509314
Interquartile range (IQR)874319

Descriptive statistics

Standard deviation430430.18
Coefficient of variation (CV)0.37163096
Kurtosis-0.72570819
Mean1158219.4
Median Absolute Deviation (MAD)37615
Skewness-0.87720243
Sum4.1475835 × 109
Variance1.8527014 × 1011
MonotonicityNot monotonic
2023-12-12T23:16:12.423419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1447237 1
 
< 0.1%
1200281 1
 
< 0.1%
1206479 1
 
< 0.1%
1206504 1
 
< 0.1%
1389702 1
 
< 0.1%
1389982 1
 
< 0.1%
1447192 1
 
< 0.1%
1447220 1
 
< 0.1%
1447227 1
 
< 0.1%
1447229 1
 
< 0.1%
Other values (3571) 3571
99.3%
(Missing) 14
 
0.4%
ValueCountFrequency (%)
29105 1
< 0.1%
29106 1
< 0.1%
29107 1
< 0.1%
29108 1
< 0.1%
29109 1
< 0.1%
29110 1
< 0.1%
29111 1
< 0.1%
29112 1
< 0.1%
29113 1
< 0.1%
29114 1
< 0.1%
ValueCountFrequency (%)
1538419 1
< 0.1%
1538418 1
< 0.1%
1538417 1
< 0.1%
1538416 1
< 0.1%
1538407 1
< 0.1%
1538402 1
< 0.1%
1538394 1
< 0.1%
1538393 1
< 0.1%
1538392 1
< 0.1%
1538391 1
< 0.1%
Distinct3278
Distinct (%)100.0%
Missing317
Missing (%)8.8%
Memory size28.2 KiB
2023-12-12T23:16:12.639063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length36
Mean length35.707138
Min length33

Characters and Unicode

Total characters117048
Distinct characters24
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

Unique3278 ?
Unique (%)100.0%

Sample

1st rowhttp://repo.kinu.or.kr/retrieve/1
2nd rowhttp://repo.kinu.or.kr/retrieve/3
3rd rowhttp://repo.kinu.or.kr/retrieve/12
4th rowhttp://repo.kinu.or.kr/retrieve/19
5th rowhttp://repo.kinu.or.kr/retrieve/38
ValueCountFrequency (%)
http://repo.kinu.or.kr/retrieve/72 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/1041 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/1105 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/1053 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/969 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/971 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/984 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/1004 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/1016 1
 
< 0.1%
http://repo.kinu.or.kr/retrieve/1021 1
 
< 0.1%
Other values (3268) 3268
99.7%
2023-12-12T23:16:12.977046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 16390
14.0%
/ 13112
11.2%
e 13112
11.2%
t 9834
 
8.4%
. 9834
 
8.4%
p 6556
 
5.6%
o 6556
 
5.6%
k 6556
 
5.6%
i 6556
 
5.6%
h 3278
 
2.8%
Other values (14) 25264
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 78672
67.2%
Other Punctuation 26224
 
22.4%
Decimal Number 12152
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 16390
20.8%
e 13112
16.7%
t 9834
12.5%
p 6556
 
8.3%
o 6556
 
8.3%
k 6556
 
8.3%
i 6556
 
8.3%
h 3278
 
4.2%
v 3278
 
4.2%
u 3278
 
4.2%
Decimal Number
ValueCountFrequency (%)
1 2372
19.5%
4 1353
11.1%
5 1246
10.3%
2 1189
9.8%
6 1085
8.9%
7 1048
8.6%
8 1026
8.4%
0 963
7.9%
3 953
7.8%
9 917
 
7.5%
Other Punctuation
ValueCountFrequency (%)
/ 13112
50.0%
. 9834
37.5%
: 3278
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 78672
67.2%
Common 38376
32.8%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 13112
34.2%
. 9834
25.6%
: 3278
 
8.5%
1 2372
 
6.2%
4 1353
 
3.5%
5 1246
 
3.2%
2 1189
 
3.1%
6 1085
 
2.8%
7 1048
 
2.7%
8 1026
 
2.7%
Other values (3) 2833
 
7.4%
Latin
ValueCountFrequency (%)
r 16390
20.8%
e 13112
16.7%
t 9834
12.5%
p 6556
 
8.3%
o 6556
 
8.3%
k 6556
 
8.3%
i 6556
 
8.3%
h 3278
 
4.2%
v 3278
 
4.2%
u 3278
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 16390
14.0%
/ 13112
11.2%
e 13112
11.2%
t 9834
 
8.4%
. 9834
 
8.4%
p 6556
 
5.6%
o 6556
 
5.6%
k 6556
 
5.6%
i 6556
 
5.6%
h 3278
 
2.8%
Other values (14) 25264
21.6%

비트스트림 포맷 ID(BITSTREAM_FORMAT_ID)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
4
3278 
<NA>
 
317

Length

Max length4
Median length1
Mean length1.2645341
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 3278
91.2%
<NA> 317
 
8.8%

Length

2023-12-12T23:16:13.143805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:16:13.244885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 3278
91.2%
na 317
 
8.8%

MIMETYPE(MIMETYPE)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
application/pdf
3278 
<NA>
 
317

Length

Max length15
Median length15
Mean length14.030042
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapplication/pdf
2nd rowapplication/pdf
3rd rowapplication/pdf
4th rowapplication/pdf
5th rowapplication/pdf

Common Values

ValueCountFrequency (%)
application/pdf 3278
91.2%
<NA> 317
 
8.8%

Length

2023-12-12T23:16:13.341730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:16:13.473280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
application/pdf 3278
91.2%
na 317
 
8.8%

간략한 설명(SHORT_DESCRIPTION)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
Adobe PDF
3278 
<NA>
 
317

Length

Max length9
Median length9
Mean length8.5591099
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdobe PDF
2nd rowAdobe PDF
3rd rowAdobe PDF
4th rowAdobe PDF
5th rowAdobe PDF

Common Values

ValueCountFrequency (%)
Adobe PDF 3278
91.2%
<NA> 317
 
8.8%

Length

2023-12-12T23:16:13.577005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:16:13.683643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
adobe 3278
47.7%
pdf 3278
47.7%
na 317
 
4.6%

설명(DESCRIPTION)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
Adobe Portable Document Format
3278 
<NA>
 
317

Length

Max length30
Median length30
Mean length27.707371
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdobe Portable Document Format
2nd rowAdobe Portable Document Format
3rd rowAdobe Portable Document Format
4th rowAdobe Portable Document Format
5th rowAdobe Portable Document Format

Common Values

ValueCountFrequency (%)
Adobe Portable Document Format 3278
91.2%
<NA> 317
 
8.8%

Length

2023-12-12T23:16:13.782101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:16:13.876664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
adobe 3278
24.4%
portable 3278
24.4%
document 3278
24.4%
format 3278
24.4%
na 317
 
2.4%

지원레벨(SUPPORT_LEVEL)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
1
3278 
<NA>
 
317

Length

Max length4
Median length1
Mean length1.2645341
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 3278
91.2%
<NA> 317
 
8.8%

Length

2023-12-12T23:16:13.985461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:16:14.089359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3278
91.2%
na 317
 
8.8%

내부 사용 여부(INTERNAL)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
0
3278 
<NA>
 
317

Length

Max length4
Median length1
Mean length1.2645341
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3278
91.2%
<NA> 317
 
8.8%

Length

2023-12-12T23:16:14.188596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:16:14.292905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3278
91.2%
na 317
 
8.8%

Interactions

2023-12-12T23:16:06.392031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:05.304362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:05.712883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:06.044773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:06.482313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:05.403218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:05.796194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:06.138726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:06.871793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:05.514378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:05.872497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:06.220372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:07.002464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:05.615069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:05.954098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:16:06.297247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:16:14.368468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번아이템ID(ITEM_ID)발행일(ISSUED_NEW)식별자(IDENTIFIER)
순번1.0000.2710.3850.192
아이템ID(ITEM_ID)0.2711.0000.7970.620
발행일(ISSUED_NEW)0.3850.7971.0000.724
식별자(IDENTIFIER)0.1920.6200.7241.000
2023-12-12T23:16:14.470162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지원레벨(SUPPORT_LEVEL)간략한 설명(SHORT_DESCRIPTION)설명(DESCRIPTION)MIMETYPE(MIMETYPE)내부 사용 여부(INTERNAL)비트스트림 포맷 ID(BITSTREAM_FORMAT_ID)
지원레벨(SUPPORT_LEVEL)1.0001.0001.0001.0001.0001.000
간략한 설명(SHORT_DESCRIPTION)1.0001.0001.0001.0001.0001.000
설명(DESCRIPTION)1.0001.0001.0001.0001.0001.000
MIMETYPE(MIMETYPE)1.0001.0001.0001.0001.0001.000
내부 사용 여부(INTERNAL)1.0001.0001.0001.0001.0001.000
비트스트림 포맷 ID(BITSTREAM_FORMAT_ID)1.0001.0001.0001.0001.0001.000
2023-12-12T23:16:14.864232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번아이템ID(ITEM_ID)발행일(ISSUED_NEW)식별자(IDENTIFIER)비트스트림 포맷 ID(BITSTREAM_FORMAT_ID)MIMETYPE(MIMETYPE)간략한 설명(SHORT_DESCRIPTION)설명(DESCRIPTION)지원레벨(SUPPORT_LEVEL)내부 사용 여부(INTERNAL)
순번1.0000.1400.1210.0951.0001.0001.0001.0001.0001.000
아이템ID(ITEM_ID)0.1401.0000.8590.7731.0001.0001.0001.0001.0001.000
발행일(ISSUED_NEW)0.1210.8591.0000.9081.0001.0001.0001.0001.0001.000
식별자(IDENTIFIER)0.0950.7730.9081.0001.0001.0001.0001.0001.0001.000
비트스트림 포맷 ID(BITSTREAM_FORMAT_ID)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
MIMETYPE(MIMETYPE)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
간략한 설명(SHORT_DESCRIPTION)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설명(DESCRIPTION)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지원레벨(SUPPORT_LEVEL)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
내부 사용 여부(INTERNAL)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T23:16:07.206086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:16:07.425422image/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.
2023-12-12T23:16:07.610112image/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

순번아이템ID(ITEM_ID)제목(TITLE)저자(AUTHOR)발행일(ISSUED)발행일(ISSUED_NEW)페이지(PAGE)식별자(IDENTIFIER)비트스트림 URL(BITSTREAM_URL)비트스트림 포맷 ID(BITSTREAM_FORMAT_ID)MIMETYPE(MIMETYPE)간략한 설명(SHORT_DESCRIPTION)설명(DESCRIPTION)지원레벨(SUPPORT_LEVEL)내부 사용 여부(INTERNAL)
013韓·蘇, 日·蘇 頂上會談 結果 分析 : 韓半島 周邊情勢 및 南北韓關係에 미칠 영향을 중심으로강원식||박영규||강원식 공저1991199116p602223http://repo.kinu.or.kr/retrieve/14application/pdfAdobe PDFAdobe Portable Document Format10
125李鵬 中國總理의 訪北結果 分析 : 韓半島 周邊情勢 및 南北韓關係에 미칠 영향을 중심으로정규섭 저1991199110p602225http://repo.kinu.or.kr/retrieve/34application/pdfAdobe PDFAdobe Portable Document Format10
2314美·蘇의 對 東北亞政策과 東北亞 軍事秩序 再編 可能性김성진||박영규||김성진 공저1991199126p602234http://repo.kinu.or.kr/retrieve/124application/pdfAdobe PDFAdobe Portable Document Format10
3421東西獨 事例를 통해 본 南北韓關係 改善方案 : 정상회담과 기본조약체결사례 중심民族統一硏究院 編1991199148p602251http://repo.kinu.or.kr/retrieve/194application/pdfAdobe PDFAdobe Portable Document Format10
4541美國의 對韓半島政策이삼성 저19921992167p602259http://repo.kinu.or.kr/retrieve/384application/pdfAdobe PDFAdobe Portable Document Format10
5642日本의 國際的 役割增大와 東北亞秩序길정우 외 저||김영춘||여인곤||전동진||최춘흠19921992429p602260http://repo.kinu.or.kr/retrieve/394application/pdfAdobe PDFAdobe Portable Document Format10
6743軍備統制 檢證 硏究: 理論 및 歷史와 事例를 中心으로전성훈 저19921992231p602261http://repo.kinu.or.kr/retrieve/404application/pdfAdobe PDFAdobe Portable Document Format10
7844北韓住民의 人性硏究김태일||서재진||김태일 공저19921992113p602262http://repo.kinu.or.kr/retrieve/414application/pdfAdobe PDFAdobe Portable Document Format10
8954南北韓 經濟共同體 形成方案김국신||이유진||이유진 공저19921992143p602272http://repo.kinu.or.kr/retrieve/504application/pdfAdobe PDFAdobe Portable Document Format10
910641992年度 統一問題 國民與論調査 結課김규륜||박종철 외 공저||박종철||이우영19921992260p1450110<NA><NA><NA><NA><NA><NA><NA>
순번아이템ID(ITEM_ID)제목(TITLE)저자(AUTHOR)발행일(ISSUED)발행일(ISSUED_NEW)페이지(PAGE)식별자(IDENTIFIER)비트스트림 URL(BITSTREAM_URL)비트스트림 포맷 ID(BITSTREAM_FORMAT_ID)MIMETYPE(MIMETYPE)간략한 설명(SHORT_DESCRIPTION)설명(DESCRIPTION)지원레벨(SUPPORT_LEVEL)내부 사용 여부(INTERNAL)
3585358611082국민과 함께하는 통일‧대북 정책이상신,20192019572p1532018http://repo.kinu.or.kr/retrieve/79654application/pdfAdobe PDFAdobe Portable Document Format10
3586358711241김정은 시대 서부 주요 도시의 기업현황 및 가동률 결정요인 분석정은이,20192019290p1532188http://repo.kinu.or.kr/retrieve/81214application/pdfAdobe PDFAdobe Portable Document Format10
3587358811301KINU한반도동향 2020년 4월이무철,20200508202042p1532329http://repo.kinu.or.kr/retrieve/130034application/pdfAdobe PDFAdobe Portable Document Format10
3588358911441한반도 비핵·평화 추진환경에 대한 전문가 인식조사정성윤,2020061920208p1532549http://repo.kinu.or.kr/retrieve/131414application/pdfAdobe PDFAdobe Portable Document Format10
3589359011563International Journal of Korean Unification Studies 2020 Vol.29 No.1통일연구원2020-06-302020296p62298http://repo.kinu.or.kr/retrieve/132934application/pdfAdobe PDFAdobe Portable Document Format10
3590359111621한‧미 방위비분담금 협상에 대한 한국 여론이상신2020073020207p1538262http://repo.kinu.or.kr/retrieve/133814application/pdfAdobe PDFAdobe Portable Document Format10
3591359211743Toward a Genuine Liberation: The Need for a Discussion on the Korean CommonwealthLee, Moo Chul20200824202010p1538331http://repo.kinu.or.kr/retrieve/134834application/pdfAdobe PDFAdobe Portable Document Format10
3592359311781코로나 19 전후의 평양: ‘숫자’와의 전쟁강채연2020091520206p1538391http://repo.kinu.or.kr/retrieve/135214application/pdfAdobe PDFAdobe Portable Document Format10
3593359411801Pyongyang before and after COVID-19: A War on 'Numbers'Kang, Chae Yeon2020092220209p1538402http://repo.kinu.or.kr/retrieve/135414application/pdfAdobe PDFAdobe Portable Document Format10
3594359511841The Peace Agreement on the Korean Peninsula: Legal Issues and ChallengesKyung-ok Do,2020202068p1538416http://repo.kinu.or.kr/retrieve/135814application/pdfAdobe PDFAdobe Portable Document Format10