This document provides information on publishing and metrics for PhD students. It discusses topics like motives for publishing, types of publications, peer review, choosing journals, open access, rejection/acceptance rates, journal circulation, coverage in databases, making publications known through networking and cooperation, author metrics like the h-index, journal metrics from sources like Journal Citation Reports, and analyzing research group metrics and quality over time. Exercises are provided to help students learn how to analyze citation data, journal rankings, and perform bibliometric analyses.
Presentation slides on Open Science and research reproducibility. Presented by Gareth Knight (LSHTM Research Data Manager) on 18th September 2018, as part of an Open Science event for LSHTM Week 2018.
Presentation slides on Open Science and research reproducibility. Presented by Gareth Knight (LSHTM Research Data Manager) on 18th September 2018, as part of an Open Science event for LSHTM Week 2018.
IR Strangelove or: How I Learned to Stop Worrying and Love the Institutional ...OCLC Research
A view of the research support landscape and RLG partnership activities to help academic librarians provide better services. Given at the Spring CNI briefing in Minneapolis April 6, 2009.
By Ricky Erway, OCLC Research
The Global Open Access Debate & Institutional Repositories for ResearchersGaz Johnson
Talk delivered to the Dermatology research unit at the University of Nottingham Mar 2007; focussing on open access, scholarly communication and repositories
Managing Ireland's Research Data - 3 Research MethodsRebecca Grant
Slides providing an overview of the research methods used in the author's thesis, "Managing Ireland's Research Data: Recognising Roles for Recordkeepers". The methods discussed are online surveys, comparative case studies, and autoethnography.
Licensed as CC-BY.
Spring 2014 Data Management Lab: Session 2 Slides (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
Enabling Precise Identification and Citability of Dynamic Data: Recommendatio...LEARN Project
Enabling Precise Identification and Citability of Dynamic Data: Recommendations of the RDA Working Group, by Andreas Rauber – 2nd LEARN Workshop, Vienna, 6th April 2016
This presentation was provided by Clara Llebot of Oregon State University, during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
Data Management for Research (New Faculty Orientation)aaroncollie
Situates research data management as a contingency that should be addressed and provisioned for during planning and research design. Draws out fundamental practices for file management, data description, and enumerates storage decision points.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Keynote Address: Data Management Plan Requirements at the US Department of Energy
Laura J. Biven, Ph.D., Senior Science and Technology Advisor, Office of the Deputy Director for Science Programs, Office of Science, US Department of Energy
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Using data management plans as a research tool: an introduction to the DART Project
Amanda L. Whitmire, Ph.D., Assistant Professor, Data Management Specialist, Oregon State University Libraries & Press
The Kaleidoscope of Impact: same data, different perspectives, constantly cha...Kudos
Scholars, scientists, academic institutions, publishers and funders are all interested in impact. We have different roles and goals, and therefore different reasons for needing to understand impact; we are therefore asking different questions about impact, and those questions continue to evolve, much as the concept of impact itself is evolving. To answer our different questions, do we need different data, in separate silos, or are we looking at the same data, from different angles? This session gathered researcher, library, publisher and metrics provider perspectives to consider who has an interest in impact, what data they are interested in, how they use it, and how the situation is evolving as e.g. business models and technical infrastructures shift.
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
IR Strangelove or: How I Learned to Stop Worrying and Love the Institutional ...OCLC Research
A view of the research support landscape and RLG partnership activities to help academic librarians provide better services. Given at the Spring CNI briefing in Minneapolis April 6, 2009.
By Ricky Erway, OCLC Research
The Global Open Access Debate & Institutional Repositories for ResearchersGaz Johnson
Talk delivered to the Dermatology research unit at the University of Nottingham Mar 2007; focussing on open access, scholarly communication and repositories
Managing Ireland's Research Data - 3 Research MethodsRebecca Grant
Slides providing an overview of the research methods used in the author's thesis, "Managing Ireland's Research Data: Recognising Roles for Recordkeepers". The methods discussed are online surveys, comparative case studies, and autoethnography.
Licensed as CC-BY.
Spring 2014 Data Management Lab: Session 2 Slides (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
This slideshow was used in a Preparing Your Research Data for the Future course taught in the Medical Sciences Division, University of Oxford, on 2015-06-08. It provides an overview of some key issues, focusing on long-term data management, sharing, and curation.
Enabling Precise Identification and Citability of Dynamic Data: Recommendatio...LEARN Project
Enabling Precise Identification and Citability of Dynamic Data: Recommendations of the RDA Working Group, by Andreas Rauber – 2nd LEARN Workshop, Vienna, 6th April 2016
This presentation was provided by Clara Llebot of Oregon State University, during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
Data Management for Research (New Faculty Orientation)aaroncollie
Situates research data management as a contingency that should be addressed and provisioned for during planning and research design. Draws out fundamental practices for file management, data description, and enumerates storage decision points.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Keynote Address: Data Management Plan Requirements at the US Department of Energy
Laura J. Biven, Ph.D., Senior Science and Technology Advisor, Office of the Deputy Director for Science Programs, Office of Science, US Department of Energy
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Using data management plans as a research tool: an introduction to the DART Project
Amanda L. Whitmire, Ph.D., Assistant Professor, Data Management Specialist, Oregon State University Libraries & Press
The Kaleidoscope of Impact: same data, different perspectives, constantly cha...Kudos
Scholars, scientists, academic institutions, publishers and funders are all interested in impact. We have different roles and goals, and therefore different reasons for needing to understand impact; we are therefore asking different questions about impact, and those questions continue to evolve, much as the concept of impact itself is evolving. To answer our different questions, do we need different data, in separate silos, or are we looking at the same data, from different angles? This session gathered researcher, library, publisher and metrics provider perspectives to consider who has an interest in impact, what data they are interested in, how they use it, and how the situation is evolving as e.g. business models and technical infrastructures shift.
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
Bibliometrics and research impact workshop in the scienes and engineering fieldsDiane Clark
This presentation gives an introduction to researchers in the sciences and engineering about bibliometrics. It also recommends ways to increase impact of published and non-published works.
Showcasing your Research Impact using BibliometricsCiarán Quinn
Seminar to make academics aware of the bibliometric resources available to them and how to use them to improve their research impact. The session looked at
• What are Bibliometrics and Altmetrics
• Why they are important for you
• How to identify your research impact
and research profile
• How to improve your citations
• How to identify potential research collaborations
Making an Impact: The Impact Factor's Intent, Benefits, Limitations, and Comp...Erin Owens
The Impact Factor is popularly viewed as a representation of a scholarly journal's quality and desirability for publication. But this metric is frequently misused, while other metrics more suitable to a goal may be overlooked. This presentation will help researchers understand the purpose of the Impact Factor, analyze its benefits and limitations, and evaluate available alternatives.
“Support Programs to Increase the Number of Scientific Publications Using Bib...Yasar Tonta
Yaşar Tonta, “Support Programs to Increase the Number of Scientific Publications Using Bibliometric Measures: The Turkish Case”. In Salah, A.A. et al. (eds.) Proceedings of ISSI 2015 Istanbul: 15th International Society of Scientometrics and Informetrics Conference, Istanbul, Turkey, 29 June to 4 July, 2015 (pp. 767-777). İstanbul: Boğaziçi University.
A combination of powerpoint presentations on bibliometrics in higher education, originally presented at (CONCERT) Council on Core Electronic Resources in Taiwan, November 2008 and modified for a paper on bibliometrics and university rankings.
http://ir.library.smu.edu.sg/record=d1010558
Research proposal and assessment of outputs jan 2021. prof.s.p.singhSaurashtra University
This is about the preparation of research proposals for PhD research and research projects. Further, it also includes the matrix and Indexes to evaluate research outputs.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
2. Programme
Publishing
Metrics
● Article metrics
● Author metrics
● Journal metrics
● Research group metrics
3. Programme
Publishing
Metrics
● Article metrics
● Author metrics
● Journal metrics
● Research group metrics
4. Motives for publishing
Edge, P., Martin, F., Fao, S. R., & Manning, N. (2011).
Researcher Attitudes and Behaviour Towards the “ Openness ” of Research Outputs in
Agriculture and Related Fields.
6. Types of publications
Reports
Conference proceedings
Books/book chapters
Journals
● Professional journals
● Scholarly journals
7. (Scientific journals) Peer review
Peer review is the corner stone of scholarly quality
control
● Publications
● Research proposals/grants
● Research institutions/universities
More info
● http://www.rin.ac.uk/peer-review-guide
● Course on Peer Review organized by WGS
8. Choosing the right journal to publish
Many factors influence journal selection
● Journal scope/Intended audience
● Editorial board/standing
● Open Access
● The speed of reviewing and publication
● Acceptance/Rejection rate
● Journal circulation
● Coverage in A&I databases (bibliographies)
● Journal performance
10. Open Access
OA publishing e.g. PLoS, BMC and Sage Open
Self-archiving in repositories e.g. Wageningen Yield
(WaY)
SHERPA/RoMEO: Publisher copyright policies & self-archiving
http://www.sherpa.ac.uk/romeo/
Directory of open access journals DOAJ (currently ca.
10,000 journals)
Be aware of predatory OA publishers
14. Rejection / acceptance rates
Sugimoto, C. R., Larivière, V., Ni, C., & Cronin, B. (2013). Journal acceptance rates: A cross-disciplinary analysis of
variability and relationships with journal measures. Journal of Informetrics, 7(4), 897–906. doi:10.1016/j.joi.2013.08.007
20. Making your publications known:
cooperation
WTI2 report 2011
UNIV.
Single Author
address
National
copublication
International
copublication
EUR 1.16 1.23 1.92
RUG 1.15 1.19 1.62
RUN 1.14 1.18 1.81
TUD 1.27 1.12 1.36
TUE 1.27 1.30 1.49
LEI 1.18 1.26 1.72
MAA 0.91 1.19 1.51
TUT 1.20 1.32 1.42
UU 1.83 1.28 1.74
UVA 0.98 1.20 1.67
TIU 1.09 0.98 1.19
VU 1.21 1.26 1.66
WUR 1.19 1.43 1.49
Avg 1.20 1.23 1.58
21. Cooperation
Teams increasingly dominate solo authors in the
production of knowledge. Research is increasingly done
in teams across nearly all fields.
Teams typically produce more frequently cited research
than individuals do, and this advantage has been
increasing over time.
Teams now also produce the exceptionally high-impact
research, even where that distinction was once the
domain of solo authors.
Wuchty, S., B. F. Jones, et al. (2007). The increasing dominance of teams in
production of knowledge. Science 316(5827): 1036-1039.
http://dx.doi.org/10.1126/science.1136099
22. Additional information
http://wageningenur.nl/library
● Write & Cite a.o.
● Publishing and impact
● Copyright
● Open Access
● PhD theses submission
23. Advertise yourself
Cite your previous articles!
Be active at conferences
Cooperate with other people/research groups
Write, or expand, articles in the Wikipedia, refer to your
thesis.
Blog or tweet about your research and thesis research
Make use of social networking tools (LinkedIn,
Researchgate.net, Mendeley etc.)
Create author’s identifiers (ScopusID, Researcher ID,
ORCID)
24. What's in a name
On the cover:
● Arina Schrier
First first title page:
● A.P. Schrier-Uyl
Second title page:
● Adriana Pia Uyl
In here own publication list
● A. Uyl
● A. Uijl
● A.P. Schrier Uyl
25. This also applies to the names of groups
Environmental Policy Group, Department of Social Sciences, Wageningen University
Environmental Policy Group, Wageningen University
Environmental Policy Group, Wageningen University and Research Centre
Environmental Policy Group, Wageningen UR
26. Get your affiliation right
For the university:
Chair group + Wageningen University
Plant Production Systems Group, Wageningen University,
P.O. box ..., 6700 HA Wageningen, The Netherlands
For the institutes:
Institute + Wageningen University & Research Centre
Alterra, Wageningen University & Research Centre, P.O.
box ..., 6700 HA Wageningen, The Netherlands
28. Programme
Publishing
Metrics
● Article metrics
● Author metrics
● Journal metrics
● Research group metrics
29. Web of Science
Search:
● Articles are found based on Authors, Addresses,
etc.
● For each article Times cited is presented
Cited reference search:
● Searches in the reference lists of records
● Not all of your articles are found. Non-cited articles
are missing
31. How do we compare numbers
Scientist Z. Math has a publication from 2003 with 17
citations
Scientist M. Biology has a publication from 2009 with 24
citations
33. Baselines for Molecular Biology
400
300
200
100
0
0 2 4 6 8 10 12
Years after publication
Cumulative no. citations
Baseline
top 10%
top 1%
34. Citation enhanced A&I databases
Web of Science
● Based on ± 12000 journals
● Metrics: Impact factor
● Baselines per ‘discipline’ (ESI)
● Analysis tools (Insight)
Scopus
● Based on ± 19000 journals + other
publication types
● Metrics: SNIP and SJR
● Baselines + analysis tool (Scival)
Google Scholar (http://scholar.google.com)
● Based on unknown journals + many
other things
● No baselines
There are other citation
enhanced databases:
PsychInfo,
SciFinder (Chemical abstracts)
ArXiv (Physics)
Spires (high energy physics)
Citeseer (ICT)
35. Programme
Publishing
Metrics
● Article metrics
● Author metrics
● Research group metrics
36. Bibliometric indicators: An example
Kroes-Nijboer, A; Venema, P; Bouman, J; van der Linden, E
(2009) The Critical Aggregation Concentration of beta-
Lactoglobulin-Based Fibril Formation. Food Biophysics 4(2):59-
63.
● Citations from WoS: 11
Journal: Food Biophysics
● Categorised by ESI in Agricultural Sciences
Baseline data for Agricultural Sciences.
● Article from 2009 in Agricultural Sciences:
● On average: 5.47 citations; top 10%: 14 citations; top
1%: 34 citations
Relative Impact: 11/5.47 = 2.01 Values June 2013
37. Essential Science Indicators (ESI)
Analytical database, covering 10 years + current year
building
Comparisons between Countries, Institutes, Scientists
and Journals
Hot papers / Highly cited papers
Research fronts
Baselines
41. Steps in a citation analysis
1. Look up the citation data (Web of Science)
2. Matching Journal(s) with appropriate research fields
(Essential Science Indicators)
3. Collect baseline data (Essential Science Indicators)
4. Calculate the relative impact
42. Exercises
Manual Chapter 9.8
● Exercise 2: Number of publications and times cited
● Exercise 2.1
● Exercise 2.2 is optional
44. Programme
Publishing
Metrics
● Article metrics
● Author metrics
● Journal metrics
● Research group metrics
45. H-index
Balance between productivity
and citedness
To rule out the effect of one
or two highly cited papers
Applicable to authors,
journals, research groups,
compounds, subjects etc.
But there are some serious
doubts about robustness
Waltman, L. & N. J. van Eck (2011). The inconsistency of
the h-index. Journal of the American Society for
Information Science and Technology 63(2):406-415
http://dx.doi.org/10.1002/asi.21678
48. Programme
Publishing
Metrics
● Article metrics
● Author metrics
● Journal metrics
● Research group metrics
49. Journal Performance Indicators
Journal performance indicators are based on citations to
articles
Journal Citation Reports (JCR)
● a.o. standard Journal Impact Factors and 5-year
Impact Factors
Scopus Journal Analyzer (SJA)
● a.o. SCImago Journal Rank (SJR) and Source
Normalized Impact per Paper (SNIP)
● Also available on http://journalmetrics.com/
50. Journal Citation Reports (JCR)
Reports three measures
Impact factor
Immediacy Index
Cited half life
Adapted from: Amin, M and Mabe, M. (2000) Impact factors: use
and abuse. Perspectives in Publishing, No. 1, 6 pp.
http://www.elsevier.com/framework_editors/pdfs/Perspectives1.pdf
52. Selecting journals on the basis of IF
Word of warning
● Our opinion: Be careful when using Journal Impact
factors to judge the performance of a group or
individual scientist
● Used for NWO grant applications and Tenure track
at Wageningen UR
Opthof, T. (1997) Sense and nonsense about he impact factor. Cardiovascular Research,
33(1): 1-7 http://dx.doi.org/10.1016/S0008-6363(96)00215-5
61. Interpretation of RI for small groups
With 10-50 publications per year
RI ≤ 0.8 : below world average impact
0.8 < RI ≤ 1.2 : world average impact
1.2 < RI ≤ 2.0 : above world average impact
2.0 < RI ≤ 3.0 : very good average impact
RI > 3.0 : excellent average impact