NDLI: Quality Measurement beyond Bibliometry

Content Provider	IEEE Xplore Digital Library
Author	Severiens, T.
Copyright Year	2015
Description	Author affiliation: Inst. for Sci. Networking Oldenburg GmbH, Carl von Ossietzky Univ. Oldenburg, Oldenburg, Germany (Severiens, T.)
Abstract	Research Administration has always been an important part of research organization, as it has the authority to boost research activities in selected fields. One of the challenges is, to identify those directions of research and those spontaneously popping up findings as early as possible, which will become of high importance for economy and research in the nearer or more distant future. Without good and well tested technical aids, this task may be impossible to meet. For many years, bibliometry was the method of first choice, to identify persons and groups of high impact (=quality?) research. The problem with using bibliometry is, that publishing is a slow process, resulting in measuring the quality and impact of those activities years back. It also results in support for those groups and individuals, who already had been supported for a long time, moving those researchers out of focus, who may come up with new, innovative findings. This paper gives an overview of research activities for measuring research quality beyond the bibliometric paradigm, but making use of big data, research data and computer based learning in Germany. It also gives some short but generally understandable introduction into the used technologies to enable the audience to discuss on the shown methodologies and techniques.
Sponsorship	IIAI
Starting Page	483
Ending Page	486
File Size	256082
Page Count	4
File Format	PDF
ISBN	9781479999576
e-ISBN	9781479999583
DOI	10.1109/IIAI-AAI.2015.219
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Voltage measurement Bibliometry Bibliometrics Training data Europe Big data Frequency measurement Computer based learning Research selection criteria
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

Panel on big data to enhance grid planning and operations

Overview of data quality challenges in the context of Big Data

Categorical Document Frequency Based Feature Selection for Text Categorization

Meta Data: Big Data Research Evolving across Disciplines, Players, and Topics

A measurement-based study of big-data movement

Balance learning to rank in big data

Frequency Principle in Deep Learning Beyond Gradient-descent-based Training

High-quality training data selection using latent topics for graph-based semi-supervised learning.

Quality Measurement beyond Bibliometry

Similar Documents

Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection

Panel on big data to enhance grid planning and operations

Overview of data quality challenges in the context of Big Data

Categorical Document Frequency Based Feature Selection for Text Categorization

Meta Data: Big Data Research Evolving across Disciplines, Players, and Topics

A measurement-based study of big-data movement

Balance learning to rank in big data

Frequency Principle in Deep Learning Beyond Gradient-descent-based Training

High-quality training data selection using latent topics for graph-based semi-supervised learning.

Quality Measurement beyond Bibliometry