Overview

Dataset statistics

Number of variables4
Number of observations1574
Missing cells0
Missing cells (%)0.0%
Duplicate rows10
Duplicate rows (%)0.6%
Total size in memory50.9 KiB
Average record size in memory33.1 B

Variable types

Numeric1
Text3

Dataset

Description농림수산식품 자원·환경·생태기반R&D 논문 정보
Author농림식품기술기획평가원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220211000000001847

Alerts

Dataset has 10 (0.6%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 03:46:31.264013
Analysis finished2023-12-11 03:46:32.059753
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PRESNATN_YM
Real number (ℝ)

Distinct51
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201124.16
Minimum200903
Maximum201312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T12:46:32.134312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200903
5-th percentile200912
Q1201012
median201112
Q3201212
95-th percentile201212
Maximum201312
Range409
Interquartile range (IQR)200

Descriptive statistics

Standard deviation100.65473
Coefficient of variation (CV)0.00050046065
Kurtosis-0.67384689
Mean201124.16
Median Absolute Deviation (MAD)100
Skewness-0.54996015
Sum3.1656943 × 108
Variance10131.374
MonotonicityNot monotonic
2023-12-11T12:46:32.322294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201212 536
34.1%
201112 315
20.0%
201012 244
15.5%
200912 108
 
6.9%
201106 24
 
1.5%
201209 24
 
1.5%
201204 20
 
1.3%
201206 18
 
1.1%
201110 16
 
1.0%
201109 16
 
1.0%
Other values (41) 253
16.1%
ValueCountFrequency (%)
200903 1
 
0.1%
200906 3
 
0.2%
200908 1
 
0.1%
200911 3
 
0.2%
200912 108
6.9%
201001 4
 
0.3%
201002 6
 
0.4%
201003 5
 
0.3%
201004 6
 
0.4%
201005 2
 
0.1%
ValueCountFrequency (%)
201312 10
0.6%
201311 4
 
0.3%
201310 2
 
0.1%
201309 1
 
0.1%
201307 1
 
0.1%
201306 5
0.3%
201305 6
0.4%
201304 7
0.4%
201303 1
 
0.1%
201301 5
0.3%

THESIS_NM
Text

Distinct1543
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2023-12-11T12:46:32.599949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length221
Median length150
Mean length71.962516
Min length9

Characters and Unicode

Total characters113269
Distinct characters678
Distinct categories14 ?
Distinct scripts5 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1515 ?
Unique (%)96.3%

Sample

1st rowThe minor spliceosomal protein U11/U12-31K is an RNA chaperone crucial for U12 intron splicing and the development of dicot and monocot plants
2nd rowPyrosequencing 을 이용한 장기 연용지 벼 뿌리 내생세균의 군집 분석
3rd rowAnalysis of Agricultural Characteristics to Establish the Evaluation Protocol and Environmental Risk Assessment for Genetically Modified Hot Pepper Crops
4th rowOne-step identification of B and Q biotypes of Bemisia tabaci based on intron variation of carboxylesterase 2
5th row기상자료 분석을 통한 대관령 지역의 작물 최저한계온도일 추정
ValueCountFrequency (%)
of 882
 
5.0%
in 454
 
2.6%
and 445
 
2.5%
the 239
 
1.3%
및 220
 
1.2%
a 183
 
1.0%
korea 152
 
0.9%
on 145
 
0.8%
from 134
 
0.8%
미치는 119
 
0.7%
Other values (6719) 14810
83.3%
2023-12-11T12:46:33.140893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16295
 
14.4%
e 7363
 
6.5%
i 6934
 
6.1%
a 6312
 
5.6%
o 6115
 
5.4%
n 5421
 
4.8%
t 5258
 
4.6%
r 4639
 
4.1%
s 4361
 
3.9%
l 3128
 
2.8%
Other values (668) 47443
41.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69427
61.3%
Other Letter 19194
 
16.9%
Space Separator 16296
 
14.4%
Uppercase Letter 6147
 
5.4%
Decimal Number 624
 
0.6%
Other Punctuation 616
 
0.5%
Dash Punctuation 345
 
0.3%
Open Punctuation 277
 
0.2%
Close Punctuation 277
 
0.2%
Math Symbol 34
 
< 0.1%
Other values (4) 32
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 693
 
3.6%
에 560
 
2.9%
한 365
 
1.9%
성 362
 
1.9%
생 360
 
1.9%
과 317
 
1.7%
이 298
 
1.6%
지 258
 
1.3%
수 255
 
1.3%
가 251
 
1.3%
Other values (570) 15475
80.6%
Lowercase Letter
ValueCountFrequency (%)
e 7363
10.6%
i 6934
10.0%
a 6312
 
9.1%
o 6115
 
8.8%
n 5421
 
7.8%
t 5258
 
7.6%
r 4639
 
6.7%
s 4361
 
6.3%
l 3128
 
4.5%
c 3077
 
4.4%
Other values (20) 16819
24.2%
Uppercase Letter
ValueCountFrequency (%)
A 600
 
9.8%
C 591
 
9.6%
S 568
 
9.2%
P 523
 
8.5%
R 353
 
5.7%
E 330
 
5.4%
I 300
 
4.9%
M 282
 
4.6%
F 265
 
4.3%
D 262
 
4.3%
Other values (16) 2073
33.7%
Other Punctuation
ValueCountFrequency (%)
, 323
52.4%
. 125
 
20.3%
: 103
 
16.7%
' 26
 
4.2%
/ 22
 
3.6%
· 5
 
0.8%
？ 2
 
0.3%
? 2
 
0.3%
; 2
 
0.3%
& 2
 
0.3%
Other values (3) 4
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 137
22.0%
2 134
21.5%
1 127
20.4%
9 41
 
6.6%
3 40
 
6.4%
4 36
 
5.8%
5 31
 
5.0%
6 29
 
4.6%
8 27
 
4.3%
7 22
 
3.5%
Math Symbol
ValueCountFrequency (%)
+ 10
29.4%
> 6
17.6%
< 6
17.6%
| 5
14.7%
~ 3
 
8.8%
～ 2
 
5.9%
× 2
 
5.9%
Space Separator
ValueCountFrequency (%)
16295
> 99.9%
　 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 272
98.2%
[ 5
 
1.8%
Close Punctuation
ValueCountFrequency (%)
) 272
98.2%
] 5
 
1.8%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 345
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 16
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 13
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75571
66.7%
Hangul 19190
 
16.9%
Common 18499
 
16.3%
Greek 5
 
< 0.1%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 693
 
3.6%
에 560
 
2.9%
한 365
 
1.9%
성 362
 
1.9%
생 360
 
1.9%
과 317
 
1.7%
이 298
 
1.6%
지 258
 
1.3%
수 255
 
1.3%
가 251
 
1.3%
Other values (566) 15471
80.6%
Latin
ValueCountFrequency (%)
e 7363
 
9.7%
i 6934
 
9.2%
a 6312
 
8.4%
o 6115
 
8.1%
n 5421
 
7.2%
t 5258
 
7.0%
r 4639
 
6.1%
s 4361
 
5.8%
l 3128
 
4.1%
c 3077
 
4.1%
Other values (44) 22963
30.4%
Common
ValueCountFrequency (%)
16295
88.1%
- 345
 
1.9%
, 323
 
1.7%
( 272
 
1.5%
) 272
 
1.5%
0 137
 
0.7%
2 134
 
0.7%
1 127
 
0.7%
. 125
 
0.7%
: 103
 
0.6%
Other values (30) 366
 
2.0%
Greek
ValueCountFrequency (%)
γ 2
40.0%
β 1
20.0%
ν 1
20.0%
α 1
20.0%
Han
ValueCountFrequency (%)
齡 1
25.0%
葉 1
25.0%
舍 1
25.0%
鷄 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94026
83.0%
Hangul 19190
 
16.9%
Punctuation 30
 
< 0.1%
None 17
 
< 0.1%
CJK 4
 
< 0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16295
17.3%
e 7363
 
7.8%
i 6934
 
7.4%
a 6312
 
6.7%
o 6115
 
6.5%
n 5421
 
5.8%
t 5258
 
5.6%
r 4639
 
4.9%
s 4361
 
4.6%
l 3128
 
3.3%
Other values (74) 28200
30.0%
Hangul
ValueCountFrequency (%)
의 693
 
3.6%
에 560
 
2.9%
한 365
 
1.9%
성 362
 
1.9%
생 360
 
1.9%
과 317
 
1.7%
이 298
 
1.6%
지 258
 
1.3%
수 255
 
1.3%
가 251
 
1.3%
Other values (566) 15471
80.6%
Punctuation
ValueCountFrequency (%)
’ 16
53.3%
‘ 13
43.3%
′ 1
 
3.3%
None
ValueCountFrequency (%)
· 5
29.4%
？ 2
 
11.8%
～ 2
 
11.8%
× 2
 
11.8%
γ 2
 
11.8%
β 1
 
5.9%
ν 1
 
5.9%
　 1
 
5.9%
α 1
 
5.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
CJK
ValueCountFrequency (%)
齡 1
25.0%
葉 1
25.0%
舍 1
25.0%
鷄 1
25.0%

AUTHR
Text

Distinct975
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2023-12-11T12:46:33.599340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length3
Mean length5.772554
Min length2

Characters and Unicode

Total characters9086
Distinct characters280
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique680 ?
Unique (%)43.2%

Sample

1st row강훈승
2nd row김병용
3rd row정규환
4th rowSoyoungKang
5th row류종수
ValueCountFrequency (%)
kim 48
 
2.4%
lee 36
 
1.8%
park 19
 
0.9%
s 18
 
0.9%
최현석 18
 
0.9%
김재옥 16
 
0.8%
kang 16
 
0.8%
이영한 16
 
0.8%
권진혁 16
 
0.8%
h 16
 
0.8%
Other values (1067) 1805
89.2%
2023-12-11T12:46:34.180507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 566
 
6.2%
451
 
5.0%
o 438
 
4.8%
e 334
 
3.7%
, 316
 
3.5%
g 300
 
3.3%
a 291
 
3.2%
u 275
 
3.0%
i 242
 
2.7%
김 225
 
2.5%
Other values (270) 5648
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3488
38.4%
Lowercase Letter 3167
34.9%
Uppercase Letter 1288
 
14.2%
Other Punctuation 514
 
5.7%
Space Separator 451
 
5.0%
Dash Punctuation 177
 
1.9%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 225
 
6.5%
이 166
 
4.8%
정 127
 
3.6%
영 109
 
3.1%
박 83
 
2.4%
현 79
 
2.3%
최 71
 
2.0%
호 62
 
1.8%
석 61
 
1.7%
진 59
 
1.7%
Other values (216) 2446
70.1%
Uppercase Letter
ValueCountFrequency (%)
K 171
13.3%
J 159
12.3%
S 152
11.8%
H 152
11.8%
Y 108
8.4%
M 77
 
6.0%
L 73
 
5.7%
C 50
 
3.9%
P 43
 
3.3%
W 42
 
3.3%
Other values (15) 261
20.3%
Lowercase Letter
ValueCountFrequency (%)
n 566
17.9%
o 438
13.8%
e 334
10.5%
g 300
9.5%
a 291
9.2%
u 275
8.7%
i 242
7.6%
h 137
 
4.3%
y 131
 
4.1%
m 125
 
3.9%
Other values (11) 328
10.4%
Other Punctuation
ValueCountFrequency (%)
, 316
61.5%
. 159
30.9%
; 37
 
7.2%
¡ 1
 
0.2%
: 1
 
0.2%
Space Separator
ValueCountFrequency (%)
451
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 177
100.0%
Other Symbol
ValueCountFrequency (%)
ⓒ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4455
49.0%
Hangul 3488
38.4%
Common 1143
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 225
 
6.5%
이 166
 
4.8%
정 127
 
3.6%
영 109
 
3.1%
박 83
 
2.4%
현 79
 
2.3%
최 71
 
2.0%
호 62
 
1.8%
석 61
 
1.7%
진 59
 
1.7%
Other values (216) 2446
70.1%
Latin
ValueCountFrequency (%)
n 566
 
12.7%
o 438
 
9.8%
e 334
 
7.5%
g 300
 
6.7%
a 291
 
6.5%
u 275
 
6.2%
i 242
 
5.4%
K 171
 
3.8%
J 159
 
3.6%
S 152
 
3.4%
Other values (36) 1527
34.3%
Common
ValueCountFrequency (%)
451
39.5%
, 316
27.6%
- 177
 
15.5%
. 159
 
13.9%
; 37
 
3.2%
ⓒ 1
 
0.1%
¡ 1
 
0.1%
: 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5596
61.6%
Hangul 3488
38.4%
Enclosed Alphanum 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 566
 
10.1%
451
 
8.1%
o 438
 
7.8%
e 334
 
6.0%
, 316
 
5.6%
g 300
 
5.4%
a 291
 
5.2%
u 275
 
4.9%
i 242
 
4.3%
- 177
 
3.2%
Other values (42) 2206
39.4%
Hangul
ValueCountFrequency (%)
김 225
 
6.5%
이 166
 
4.8%
정 127
 
3.6%
영 109
 
3.1%
박 83
 
2.4%
현 79
 
2.3%
최 71
 
2.0%
호 62
 
1.8%
석 61
 
1.7%
진 59
 
1.7%
Other values (216) 2446
70.1%
Enclosed Alphanum
ValueCountFrequency (%)
ⓒ 1
100.0%
None
ValueCountFrequency (%)
¡ 1
100.0%

VOUCHER_COPY_NM
Text

Distinct527
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2023-12-11T12:46:34.453613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length124
Median length79
Mean length17.817027
Min length3

Characters and Unicode

Total characters28044
Distinct characters250
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique343 ?
Unique (%)21.8%

Sample

1st rowPLOS ONE
2nd row한국토양비료학회지
3rd rowHORTICULTURE ENVIRONMENT AND BIOTECHNOLOGY
4th rowJournal of Asia-Pacific Entomology
5th row한국토양비료학회지
ValueCountFrequency (%)
journal 336
 
9.2%
of 333
 
9.1%
한국토양비료학회지 228
 
6.3%
and 115
 
3.2%
science 83
 
2.3%
plant 80
 
2.2%
korean 79
 
2.2%
the 68
 
1.9%
veterinary 56
 
1.5%
한국응용곤충학회지 49
 
1.3%
Other values (532) 2217
60.8%
2023-12-11T12:46:34.879797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2071
 
7.4%
o 1232
 
4.4%
O 939
 
3.3%
e 867
 
3.1%
학 863
 
3.1%
a 846
 
3.0%
n 845
 
3.0%
회 832
 
3.0%
A 824
 
2.9%
한 778
 
2.8%
Other values (240) 17947
64.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9235
32.9%
Uppercase Letter 8830
31.5%
Other Letter 7708
27.5%
Space Separator 2071
 
7.4%
Other Punctuation 64
 
0.2%
Dash Punctuation 51
 
0.2%
Decimal Number 29
 
0.1%
Open Punctuation 26
 
0.1%
Close Punctuation 26
 
0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 863
 
11.2%
회 832
 
10.8%
한 778
 
10.1%
국 775
 
10.1%
지 761
 
9.9%
양 263
 
3.4%
료 239
 
3.1%
비 233
 
3.0%
토 232
 
3.0%
농 185
 
2.4%
Other values (171) 2547
33.0%
Lowercase Letter
ValueCountFrequency (%)
o 1232
13.3%
e 867
9.4%
a 846
9.2%
n 845
9.1%
l 727
 
7.9%
i 706
 
7.6%
r 674
 
7.3%
t 523
 
5.7%
c 481
 
5.2%
u 354
 
3.8%
Other values (16) 1980
21.4%
Uppercase Letter
ValueCountFrequency (%)
O 939
 
10.6%
A 824
 
9.3%
E 702
 
8.0%
I 685
 
7.8%
N 634
 
7.2%
R 561
 
6.4%
L 549
 
6.2%
C 506
 
5.7%
S 439
 
5.0%
T 430
 
4.9%
Other values (16) 2561
29.0%
Other Punctuation
ValueCountFrequency (%)
& 36
56.2%
. 11
 
17.2%
, 10
 
15.6%
: 4
 
6.2%
？ 2
 
3.1%
/ 1
 
1.6%
Decimal Number
ValueCountFrequency (%)
0 9
31.0%
2 7
24.1%
1 5
17.2%
6 4
13.8%
5 3
 
10.3%
3 1
 
3.4%
Space Separator
ValueCountFrequency (%)
2071
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Math Symbol
ValueCountFrequency (%)
= 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18065
64.4%
Hangul 7684
27.4%
Common 2271
 
8.1%
Han 24
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 863
 
11.2%
회 832
 
10.8%
한 778
 
10.1%
국 775
 
10.1%
지 761
 
9.9%
양 263
 
3.4%
료 239
 
3.1%
비 233
 
3.0%
토 232
 
3.0%
농 185
 
2.4%
Other values (158) 2523
32.8%
Latin
ValueCountFrequency (%)
o 1232
 
6.8%
O 939
 
5.2%
e 867
 
4.8%
a 846
 
4.7%
n 845
 
4.7%
A 824
 
4.6%
l 727
 
4.0%
i 706
 
3.9%
E 702
 
3.9%
I 685
 
3.8%
Other values (42) 9692
53.7%
Common
ValueCountFrequency (%)
2071
91.2%
- 51
 
2.2%
& 36
 
1.6%
( 26
 
1.1%
) 26
 
1.1%
. 11
 
0.5%
, 10
 
0.4%
0 9
 
0.4%
2 7
 
0.3%
1 5
 
0.2%
Other values (7) 19
 
0.8%
Han
ValueCountFrequency (%)
會 3
12.5%
國 3
12.5%
韓 3
12.5%
誌 3
12.5%
學 2
8.3%
壤 2
8.3%
土 2
8.3%
動 1
 
4.2%
物 1
 
4.2%
究 1
 
4.2%
Other values (3) 3
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20334
72.5%
Hangul 7684
 
27.4%
CJK 24
 
0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2071
 
10.2%
o 1232
 
6.1%
O 939
 
4.6%
e 867
 
4.3%
a 846
 
4.2%
n 845
 
4.2%
A 824
 
4.1%
l 727
 
3.6%
i 706
 
3.5%
E 702
 
3.5%
Other values (58) 10575
52.0%
Hangul
ValueCountFrequency (%)
학 863
 
11.2%
회 832
 
10.8%
한 778
 
10.1%
국 775
 
10.1%
지 761
 
9.9%
양 263
 
3.4%
료 239
 
3.1%
비 233
 
3.0%
토 232
 
3.0%
농 185
 
2.4%
Other values (158) 2523
32.8%
CJK
ValueCountFrequency (%)
會 3
12.5%
國 3
12.5%
韓 3
12.5%
誌 3
12.5%
學 2
8.3%
壤 2
8.3%
土 2
8.3%
動 1
 
4.2%
物 1
 
4.2%
究 1
 
4.2%
Other values (3) 3
12.5%
None
ValueCountFrequency (%)
？ 2
100.0%

Interactions

2023-12-11T12:46:31.814001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-11T12:46:31.944507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:46:32.024481image/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

PRESNATN_YMTHESIS_NMAUTHRVOUCHER_COPY_NM
0201212The minor spliceosomal protein U11/U12-31K is an RNA chaperone crucial for U12 intron splicing and the development of dicot and monocot plants강훈승PLOS ONE
1201212Pyrosequencing 을 이용한 장기 연용지 벼 뿌리 내생세균의 군집 분석김병용한국토양비료학회지
2201212Analysis of Agricultural Characteristics to Establish the Evaluation Protocol and Environmental Risk Assessment for Genetically Modified Hot Pepper Crops정규환HORTICULTURE ENVIRONMENT AND BIOTECHNOLOGY
3201212One-step identification of B and Q biotypes of Bemisia tabaci based on intron variation of carboxylesterase 2SoyoungKangJournal of Asia-Pacific Entomology
4201212기상자료 분석을 통한 대관령 지역의 작물 최저한계온도일 추정류종수한국토양비료학회지
5201212Assessing irrigation water capacity of land use change in a data-scarce watershed of KoreaTaeilJangJournal of Irrigation and Drainage Engineering
6201212Intake and Potential Health Risk of Butyltin Compounds from Seafood Consumption in Korea최민규ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY
7201212분광계를 이용한 한국토양 내 유기물 예측전현정한국토양비료학회지
8201212Hexokinase-mediated sugar signaling controls expression of the calcineurin B-like interacting protein kinase 15 gene and is perturbed by oxidative phosphorylation inhibitionYim, Hui-kyeongJOURNAL OF PLANT PHYSIOLOGY
9201212Brucellosis among ruminants in some districts of Bangladesh using four conventional serological assaysM.S.RahmanAfr J Microbiol Res
PRESNATN_YMTHESIS_NMAUTHRVOUCHER_COPY_NM
1564201301Which acetylcholinesterase functions as the main catalytic enzyme in the Class Insecta?YoungHoKimInsect Biochemistry and Molecular Biology
1565201301Induction of protective immune responses against challenge of Actinobacillus pleuropneumoniae by oral administration with Saccharomyces cerevisiae expressing Apx toxins in pigsMin-KyoungShinVeterinary Immunology and Immunopathology
1566201301Remodeling of Deteriorated Irrigation Aqueducts Using Precast Polymer Concrete FlumeKyu-SeokYeonAdvanced Materials Research
1567201212Insight into genes involved in the production of extracellular chitinase in a biocontrol bacterium Lysobacter enzymogenes C-3김현정The Plant Pathology Journal
1568201212Loop-mediated Isothermal Amplification(LAMP) 법을 이용한 Kakugo Virus의 검출법 개발Joong-Goo Lee경기대학교 기초과학논문집
1569201211SWAT-QUALKO2 연계 모형을 이용한 관개기 순별 관개수질 모의김지혜한국농공학회논문집
1570201211Molecular and kinetic properties of two acetylcholinesterases from the Western honey bee, Apis melliferaYoung Ho Kimplos one
1571201209Proteomics-based identification and characterization of biotype-specific carboxylesterase 2 putatively associated with insecticide resistance in Bemisia tabaciSoyoung KangJournal of Asia-Pacific Entomology
1572201209Proteomics-based identification and characterization of biotype-specific carboxylesterase 2 putatively associated with insecticide resistance in Bemisia tabaciSoyoung KangJournal of Asia-Pacific Entomology
1573201209One-step identification of B and Q biotypes of Bemisia tabaci based on intron variation of carboxylesterase 2Soyoung KangJournal of Asia-Pacific Entomology

Duplicate rows

Most frequently occurring

PRESNATN_YMTHESIS_NMAUTHRVOUCHER_COPY_NM# duplicates
7201209Proteomics-based identification and characterization of biotype-specific carboxylesterase 2 putatively associated with insecticide resistance in Bemisia tabaciSoyoung KangJournal of Asia-Pacific Entomology3
02010121990년부터 2008년까지 우리나라 경종분야 온실가스(메탄) 배출량 평가정현철한국토양비료학회지2
1201012고구마의 생산과정에서 발생하는 탄소배출량 산정 및 전과정평가소규호한국토양비료학회지2
2201012고추의 생산과정에서 발생하는 탄소배출량 산정 및 전과정평가소규호한국토양비료학회지2
3201012우리나라 강우량 변화 시나리오에 따른 밭토양의 토양 유실량 변화 예측김민경한국토양비료학회지2
4201012우리나라 토양의 크롬 분포특성에 관한 고찰김록영한국토양비료학회지2
5201012콩의 생산과정에서 발생하는 탄소배출량 산정 및 전과정평가소규호한국토양비료학회지2
6201112The anti-wrinkle and whitening effect of extract of Castanea crenata inner shell장민정한국생명과학회지2
8201212Generation of a Monoclonal Antibody against Mycoplasma spp. following Accidental Contamination during Production of a Monoclonal Antibody against Lawsonia intracellularisHwang, Jeong-MinAPPLIED AND ENVIRONMENTAL MICROBIOLOGY2
9201212Loop-mediated Isothermal Amplification(LAMP) 법을 이용한 Kakugo Virus의 검출법 개발Joong-Goo Lee경기대학교 기초과학논문집2