Example of Information Visualization format
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Example of Information Visualization format Example of Information Visualization format Example of Information Visualization format Example of Information Visualization format Example of Information Visualization format
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Example of Information Visualization format Example of Information Visualization format Example of Information Visualization format Example of Information Visualization format Example of Information Visualization format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Information Visualization — Template for authors

Publisher: SAGE
Categories Rank Trend in last 3 yrs
Computer Vision and Pattern Recognition #40 of 85 down down by 5 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 80 Published Papers | 270 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 20/07/2020
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Related Journals

open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 8.6
SJR: 0.53
SNIP: 2.363
open access Open Access

Springer

Quality:  
High
CiteRatio: 8.8
SJR: 0.612
SNIP: 1.787
open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 11.4
SJR: 1.005
SNIP: 2.547
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 8.6
SJR: 0.86
SNIP: 1.676

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.325

15% from 2018

Impact factor for Information Visualization from 2016 - 2019
Year Value
2019 1.325
2018 1.15
2017 0.923
2016 0.923
graph view Graph view
table view Table view

3.4

6% from 2019

CiteRatio for Information Visualization from 2016 - 2020
Year Value
2020 3.4
2019 3.2
2018 2.8
2017 2.5
2016 3.5
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 15% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 6% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.179

51% from 2019

SJR for Information Visualization from 2016 - 2020
Year Value
2020 0.179
2019 0.362
2018 0.435
2017 0.277
2016 0.486
graph view Graph view
table view Table view

0.536

55% from 2019

SNIP for Information Visualization from 2016 - 2020
Year Value
2020 0.536
2019 1.198
2018 1.057
2017 0.646
2016 1.328
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 51% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 55% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Information Visualization

Guideline source: View

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SAGE

Information Visualization

Information Visualization is essential reading for researchers and practitioners of information visualization and is of interest to computer scientists and data analysts working on related specialisms. This journal is an international, peer-reviewed journal publishing articles...... Read More

Computer Vision and Pattern Recognition

Computer Science

i
Last updated on
20 Jul 2020
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ISSN
1473-8716
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Impact Factor
High - 1.054
i
Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
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Sherpa RoMEO Archiving Policy
Green faq
i
Endnote Style
Download Available
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Bibliography Name
SageV
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Citation Type
Numbered (Superscripted)
25
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Bibliography Example
Blonder GE, Tinkham M and Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 1982; 25(7): 4515–4532. URL 10.1103/PhysRevB.25.4515.

Top papers written in this journal

open accessOpen access Book Chapter DOI: 10.1007/978-3-540-70956-5_7
Visual Analytics: Definition, Process, and Challenges
Daniel A. Keim1, Gennady Andrienko2, Jean-Daniel Fekete3, Carsten Görg4, Jörn Kohlhammer2, Guy Melançon5

Abstract:

We are living in a world which faces a rapidly increasing amount of data to be dealt with on a daily basis. In the last decade, the steady improvement of data storage devices and means to create and collect data along the way influenced our way of dealing with information: Most of the time, data is stored without filtering an... We are living in a world which faces a rapidly increasing amount of data to be dealt with on a daily basis. In the last decade, the steady improvement of data storage devices and means to create and collect data along the way influenced our way of dealing with information: Most of the time, data is stored without filtering and refinement for later use. Virtually every branch of industry or business, and any political or personal activity nowadays generate vast amounts of data. Making matters worse, the possibilities to collect and store data increase at a faster rate than our ability to use it for making decisions. However, in most applications, raw data has no value in itself; instead we want to extract the information contained in it. read more read less
View PDF
1,288 Citations
Proceedings Article DOI: 10.1109/IVS.2002.1188024
Real time obstacle detection in stereovision on non flat road geometry through "v-disparity" representation

Abstract:

Presents a road obstacle detection method able to cope with uphill and downhill gradients and dynamic pitching of the vehicle. Our approach is based on the construction and investigation of the "v-disparity" image which provides a good representation of the geometric content of the road scene. The advantage of this image is t... Presents a road obstacle detection method able to cope with uphill and downhill gradients and dynamic pitching of the vehicle. Our approach is based on the construction and investigation of the "v-disparity" image which provides a good representation of the geometric content of the road scene. The advantage of this image is that it provides semi-global matching and is able to perform robust obstacle detection even in the case of partial occlusion or errors committed during the matching process. Furthermore, this detection is performed without any explicit extraction of coherent structures. This paper explains the construction of the "v-disparity" image, its main properties, and the obstacle detection method. The longitudinal profile of the road is estimated and the objects located above the road surface are then extracted as potential obstacles; subsequently, the accurate detection of road obstacles, in particular the position of tyre-road contact points is computed in a precise manner. The whole process is performed at frame rate with a current-day PC. Our experimental findings and comparisons with the results obtained using a flat geometry hypothesis show the benefits of our approach. read more read less
View PDF
800 Citations
Journal Article DOI: 10.1177/1473871611416549
Visual comparison for information visualization
Michael Gleicher1, Danielle Albers1, Rick Walker2, Ilir Jusufi3, Charles Hansen4, Jonathan C. Roberts2

Abstract:

Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. IncreasingLy, information visuaLization tools support such comparisons explicitLy, beyond simply aLLowing a viewer to examine each obje... Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. IncreasingLy, information visuaLization tools support such comparisons explicitLy, beyond simply aLLowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a generaL understanding of comparative visualization and faciLitating the development of more comparative visualization tools. read more read less
646 Citations
open accessOpen access Journal Article DOI: 10.1057/PALGRAVE.IVS.9500013
Cognitive measurements of graph aesthetics
Colin Ware1, Helen C. Purchase2, Linda Colpoys2, Matthew McGill2

Abstract:

A large class of diagrams can be informally characterized as node-link diagrams. Typically nodes represent entities, and links represent relationships between them. The discipline of graph drawing is concerned with methods for drawing abstract versions of such diagrams. At the foundation of the discipline are a set of graph a... A large class of diagrams can be informally characterized as node-link diagrams. Typically nodes represent entities, and links represent relationships between them. The discipline of graph drawing is concerned with methods for drawing abstract versions of such diagrams. At the foundation of the discipline are a set of graph aesthetics (rules for graph layout) that, it is assumed, will produce graphs that can be clearly understood. Examples of aesthetics include minimizing edge crossings and minimizing the sum of the lengths of the edges. However, with a few notable exceptions, these aesthetics are taken as axiomatic, and have not been empirically tested. We argue that human pattern perception can tell us much that is relevant to the study of graph aesthetics including providing a more detailed understanding of aesthetics and suggesting new ones. In particular, we find the importance of good continuity (ie keeping multi-edge paths as straight as possible) has been neglected. We introduce a methodology for evaluating the cognitive cost of graph aesthetics and we apply it to the task of finding the shortest paths in spring layout graphs. The results suggest that after the length of the path the two most important factors are continuity and edge crossings, and we provide cognitive cost estimates for these parameters. Another important factor is the number of branches emanating from nodes on the path. read more read less
View PDF
447 Citations
Book Chapter DOI: 10.1007/978-3-540-70956-5_2
Evaluating Information Visualizations
Sheelagh Carpendale1

Abstract:

Information visualization research is becoming more established, and as a result, it is becoming increasingly important that research in this field is validated. With the general increase in information visualization research there has also been an increase, albeit disproportionately small, in the amount of empirical work dir... Information visualization research is becoming more established, and as a result, it is becoming increasingly important that research in this field is validated. With the general increase in information visualization research there has also been an increase, albeit disproportionately small, in the amount of empirical work directly focused on information visualization. The purpose of this chapter is to increase awareness of empirical research in general, of its relationship to information visualization in particular; to emphasize its importance; and to encourage thoughtful application of a greater variety of evaluative research methodologies in information visualization. read more read less
View PDF
431 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

Get MS-Word and LaTeX output to any Journal within seconds
1
Choose a template
Select a template from a library of 40,000+ templates
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Import a MS-Word file or start fresh
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SciSpace will automatically format your output to meet journal guidelines
clock Less than 3 minutes

What to expect from SciSpace?

Speed and accuracy over MS Word

''

With SciSpace, you do not need a word template for Information Visualization.

It automatically formats your research paper to SAGE formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

Publisher Logos

Freedom from formatting guidelines

One editor, 100K journal formats – world's largest collection of journal templates

With such a huge verified library, what you need is already there.

publisher-logos

Easy support from all your favorite tools

Information Visualization format uses SageV citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Information Visualization in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Information Visualization guidelines and auto format it.

2. Do you follow the Information Visualization guidelines?

Yes, the template is compliant with the Information Visualization guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Information Visualization?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Information Visualization citation style.

4. Can I use the Information Visualization templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Information Visualization.

5. Can I use a manuscript in Information Visualization that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Information Visualization that you can download at the end.

6. How long does it usually take you to format my papers in Information Visualization?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Information Visualization.

7. Where can I find the template for the Information Visualization?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Information Visualization's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Information Visualization's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Information Visualization an online tool or is there a desktop version?

SciSpace's Information Visualization is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Information Visualization?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Information Visualization?”

11. What is the output that I would get after using Information Visualization?

After writing your paper autoformatting in Information Visualization, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Information Visualization's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Information Visualization?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Information Visualization. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Information Visualization?

The 5 most common citation types in order of usage for Information Visualization are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Information Visualization?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Information Visualization's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Information Visualization in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Information Visualization Endnote style according to Elsevier guidelines.

Fast and reliable,
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Instant formatting to 100% publisher guidelines on - SciSpace.

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No word template required

Typset automatically formats your research paper to Information Visualization formatting guidelines and citation style.

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One editor, 100K journal formats.
With the largest collection of verified journal formats, what you need is already there.

Trusted by academicians

I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

Andreas Frutiger
Researcher & Ex MS Word user
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