TY  - CONF
AU  - Liu, Vinci
AU  - Curran, James R.
A2  - 
T1  - Web Text Corpus for Natural Language Processing.
T2  - EACL
PB  - The Association for Computer Linguistics
CY  - 
PY  - 2006/
M2  - 
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://dblp.uni-trier.de/db/conf/eacl/eacl2006.html#LiuC06
M3  - 
KW  - corpus
KW  - dataset
KW  - web
KW  - synonym_detection
KW  - nlp
L1  - 
SN  - 1-932432-59-0
N1  - dblp
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - La Rowe, Gavin
AU  - Ambre, Sumeet
AU  - Burgoon, John
AU  - Ke, Weimao
AU  - Börner, Katy
T1  - The Scholarly Database and its utility for scientometrics research
JO  - Scientometrics
PY  - 2009/
VL  - 79
IS  - 2
SP  - 219
EP  - 234
UR  - http://dx.doi.org/10.1007/s11192-009-0414-2
M3  - 10.1007/s11192-009-0414-2
KW  - analysis
KW  - database
KW  - dataset
KW  - gaw
KW  - science
KW  - scientometrics
KW  - sdb
KW  - sota
L1  - 
SN  - 
N1  - 
N1  - 
AB  - The Scholarly Database aims to serve researchers and practitioners interested in the analysis, modelling, and visualization of large-scale data sets. A specific focus of this database is to support macro-evolutionary studies of science and to communicate findings via knowledge-domain visualizations. Currently, the database provides access to about 18 million publications, patents, and grants. About 90% of the publications are available in full text. Except for some datasets with restricted access conditions, the data can be retrieved in raw or pre-processed formats using either a web-based or a relational database client. This paper motivates the need for the database from the perspective of bibliometric/scientometric research. It explains the database design, setup, etc., and reports the temporal, geographical, and topic coverage of data sets currently served via the database. Planned work and the potential for this database to become a global testbed for information science research are discussed at the end of the paper.
ER  -

TY  - JOUR
AU  - Adomavicius, Gediminas
AU  - Zhang, Jingjing
T1  - Impact of Data Characteristics on Recommender Systems Performance
JO  - ACM Trans. Manage. Inf. Syst.
PY  - 2012/04
VL  - 3
IS  - 1
SP  - 3:1
EP  - 3:17
UR  - http://doi.acm.org/10.1145/2151163.2151166
M3  - 10.1145/2151163.2151166
KW  - characteristics
KW  - dataset
KW  - dependence
KW  - evaluation
KW  - model
KW  - recommender
L1  - 
SN  - 
N1  - Impact of data characteristics on recommender systems performance
N1  - 
AB  - This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups of data characteristics: rating space, rating frequency distribution, and rating value distribution. A sampling procedure was employed to obtain different rating data subsamples with varying characteristics; recommendation algorithms were used to estimate the predictive accuracy for each sample; and linear regression-based models were used to uncover the relationships between data characteristics and recommendation accuracy. Experimental results on multiple rating datasets show the consistent and significant effects of several data characteristics on recommendation accuracy.
ER  -

TY  - JOUR
AU  - Zubiaga, Arkaitz
AU  - Fresno, Victor
AU  - Martinez, Raquel
AU  - Garcia-Plaza, Alberto P.
T1  - Harnessing Folksonomies to Produce a Social Classification of Resources
JO  - IEEE Transactions on Knowledge and Data Engineering
PY  - 2012/
VL  - 99
IS  - PrePrints
SP  - 
EP  - 
UR  - 
M3  - http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115
KW  - classification
KW  - delicious
KW  - folksonomy
KW  - tagging
KW  - toread
KW  - dataset
L1  - 
SN  - 
N1  - 
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Sinha, Arnab
AU  - Shen, Zhihong
AU  - Song, Yang
AU  - Ma, Hao
AU  - Eide, Darrin
AU  - Hsu, Bo-June Paul
AU  - Wang, Kuansan
A2  - Gangemi, Aldo
A2  - Leonardi, Stefano
A2  - Panconesi, Alessandro
T1  - An Overview of Microsoft Academic Service (MAS) and Applications.
T2  - WWW (Companion Volume)
PB  - ACM
CY  - 
PY  - 2015/
M2  - 
VL  - 
IS  - 
SP  - 243
EP  - 246
UR  - http://dblp.uni-trier.de/db/conf/www/www2015c.html#SinhaSSMEHW15
M3  - 
KW  - MSAC
KW  - dataset
KW  - toread
L1  - 
SN  - 978-1-4503-3473-0
N1  - 
N1  - 
AB  - 
ER  -