Example of Annals of Data Science format
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Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format Example of Annals of Data Science format
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open access Open Access

Annals of Data Science — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Statistics, Probability and Uncertainty #55 of 152 down down by None rank
Business, Management and Accounting (miscellaneous) #46 of 101 down down by None rank
Computer Science Applications #385 of 693 down down by None rank
Artificial Intelligence #149 of 227 down down by None rank
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 140 Published Papers | 299 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 17/06/2020
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Related Journals

open access Open Access

Hindawi

Quality:  
High
CiteRatio: 5.0
SJR: 0.371
SNIP: 1.169
open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 19.8
SJR: 2.882
SNIP: 3.86
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recommended Recommended

American Association for the Advancement of Science

Quality:  
High
CiteRatio: 25.7
SJR: 5.619
SNIP: 5.246

Journal Performance & Insights

CiteRatio

Source Normalized Impact per Paper (SNIP)

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

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

2.1

CiteRatio for Annals of Data Science from 2016 - 2020
Year Value
2020 2.1
graph view Graph view
table view Table view

1.402

Year Value
2020 1.402
graph view Graph view
table view Table view

insights Insights

  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • This journal’s SNIP is in the top 10 percentile category.

Annals of Data Science

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Springer

Annals of Data Science

Approved by publishing and review experts on SciSpace, this template is built as per for Annals of Data Science formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 929 authors to write and format their manuscripts to this journal.

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Last updated on
17 Jun 2020
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ISSN
2198-5812
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Open Access
Hybrid
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Author Year
(Blonder et al, 1982)
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Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1007/S40745-015-0040-1
A Comprehensive Survey of Clustering Algorithms
Dongkuan Xu1, Yingjie Tian1
12 Aug 2015 - Annals of Data Science

Abstract:

Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase ... Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22. read more read less

Topics:

Cluster analysis (74%)74% related to the paper, Fuzzy clustering (71%)71% related to the paper, Correlation clustering (70%)70% related to the paper, Canopy clustering algorithm (69%)69% related to the paper, CURE data clustering algorithm (68%)68% related to the paper
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1,234 Citations
Journal Article DOI: 10.1007/S40745-017-0112-5
Internet of Things, Real-Time Decision Making, and Artificial Intelligence
James M. Tien1
01 Jun 2017 - Annals of Data Science

Abstract:

In several earlier papers, the author defined and detailed the concept of a servgood, which can be thought of as a physical good or product enveloped by a services-oriented layer that makes the good smarter or more adaptable and customizable for a particular use Adding another layer of physical sensors could then enhance its ... In several earlier papers, the author defined and detailed the concept of a servgood, which can be thought of as a physical good or product enveloped by a services-oriented layer that makes the good smarter or more adaptable and customizable for a particular use Adding another layer of physical sensors could then enhance its smartness and intelligence, especially if it were to be connected with other servgoods—thus, constituting an Internet of Things (IoT) or servgoods More importantly, real-time decision making is central to the Internet of Things; it is about decision informatics and embraces the advanced technologies of sensing (ie, Big Data), processing (ie, real-time analytics), reacting (ie, real-time decision-making), and learning (ie, deep learning) Indeed, real-time decision making (RTDM) is becoming an integral aspect of IoT and artificial intelligence (AI), including its improving abilities at voice and video recognition, speech and predictive synthesis, and language and social-media understanding These three key and mutually supportive technologies—IoT, RTDM, and AI—are considered herein, including their progress to date read more read less

Topics:

Marketing and artificial intelligence (59%)59% related to the paper, Big data (55%)55% related to the paper, Analytics (53%)53% related to the paper
187 Citations
Journal Article DOI: 10.1007/S40745-020-00253-5
A Comprehensive Survey of Loss Functions in Machine Learning
Qi Wang1, Yue Ma1, Kun Zhao2, Yingjie Tian1
12 Apr 2020 - Annals of Data Science

Abstract:

As one of the important research topics in machine learning, loss function plays an important role in the construction of machine learning algorithms and the improvement of their performance, which has been concerned and explored by many researchers. But it still has a big gap to summarize, analyze and compare the classical l... As one of the important research topics in machine learning, loss function plays an important role in the construction of machine learning algorithms and the improvement of their performance, which has been concerned and explored by many researchers. But it still has a big gap to summarize, analyze and compare the classical loss functions. Therefore, this paper summarizes and analyzes 31 classical loss functions in machine learning. Specifically, we describe the loss functions from the aspects of traditional machine learning and deep learning respectively. The former is divided into classification problem, regression problem and unsupervised learning according to the task type. The latter is subdivided according to the application scenario, and here we mainly select object detection and face recognition to introduces their loss functions. In each task or application, in addition to analyzing each loss function from formula, meaning, image and algorithm, the loss functions under the same task or application are also summarized and compared to deepen the understanding and provide help for the selection and improvement of loss function. read more read less

Topics:

Unsupervised learning (64%)64% related to the paper, Deep learning (55%)55% related to the paper, Function (engineering) (52%)52% related to the paper
160 Citations
open accessOpen access Journal Article DOI: 10.1007/S40745-015-0029-9
Forecasting with Big Data: A Review
Hossein Hassani1, Emmanuel Sirimal Silva1
10 Apr 2015 - Annals of Data Science

Abstract:

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most... Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data. read more read less

Topics:

Big data (57%)57% related to the paper, Population (52%)52% related to the paper
View PDF
111 Citations
open accessOpen access Journal Article DOI: 10.1007/S40745-014-0026-4
How Many Judges Should There Be in a Group
Thomas L. Saaty1, Müjgan Sagir Özdemir2
01 Dec 2014 - Annals of Data Science

Abstract:

This paper briefly examines the question of how many judges are needed to obtain valid and consistent judgments when using the analytic hierarchy process. It turns out that if a judge is experienced and well versed in an area, he can be sufficient to provide the judgments instead of diluting his accuracy with the participatio... This paper briefly examines the question of how many judges are needed to obtain valid and consistent judgments when using the analytic hierarchy process. It turns out that if a judge is experienced and well versed in an area, he can be sufficient to provide the judgments instead of diluting his accuracy with the participation of others who may not be as good. How to discover such a person requires criteria used to judge his adequacy and that of others. read more read less

Topics:

Analytic hierarchy process (52%)52% related to the paper
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87 Citations
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2. Do you follow the Annals of Data Science guidelines?

Yes, the template is compliant with the Annals of Data Science 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 Annals of Data Science?

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 Annals of Data Science citation style.

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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 Annals of Data Science.

5. Can I use a manuscript in Annals of Data Science 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 Annals of Data Science that you can download at the end.

6. How long does it usually take you to format my papers in Annals of Data Science?

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

7. Where can I find the template for the Annals of Data Science?

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 Annals of Data Science'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 Annals of Data Science'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. Annals of Data Science an online tool or is there a desktop version?

SciSpace's Annals of Data Science 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 Annals of Data Science?

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 Annals of Data Science?”

11. What is the output that I would get after using Annals of Data Science?

After writing your paper autoformatting in Annals of Data Science, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Annals of Data Science'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 Annals of Data Science?

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 Annals of Data Science. 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 Annals of Data Science?

The 5 most common citation types in order of usage for Annals of Data Science 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 Annals of Data Science?

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 Annals of Data Science's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Annals of Data Science 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 Annals of Data Science Endnote style according to Elsevier guidelines.

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