Example of Data Science and Engineering format
Recent searches

Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
Look Inside
Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering format Example of Data Science and Engineering 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

Data Science and Engineering — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Computational Mechanics #11 of 79 down down by None rank
Computer Science Applications #181 of 693 down down by None rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 100 Published Papers | 490 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 20/06/2020
Related journals
Insights
General info
Top papers
Popular templates
Get started guide
Why choose from SciSpace
FAQ

Related Journals

open access Open Access

Hindawi

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

Elsevier

Quality:  
High
CiteRatio: 9.9
SJR: 2.53
SNIP: 2.275
open access Open Access

Wiley

Quality:  
High
CiteRatio: 4.1
SJR: 0.938
SNIP: 1.105
open access Open Access

IEEE

Quality:  
High
CiteRatio: 6.4
SJR: 0.786
SNIP: 2.027

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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

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.

4.9

11% from 2019

CiteRatio for Data Science and Engineering from 2016 - 2020
Year Value
2020 4.9
2019 4.4
2018 0.2
graph view Graph view
table view Table view

0.497

22% from 2019

SJR for Data Science and Engineering from 2019 - 2020
Year Value
2020 0.497
2019 0.635
graph view Graph view
table view Table view

1.938

2% from 2019

SNIP for Data Science and Engineering from 2018 - 2020
Year Value
2020 1.938
2019 1.973
2018 1.913
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 11% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 22% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Data Science and Engineering

Guideline source: View

All company, product and service names used in this website are for identification purposes only. All product names, trademarks and registered trademarks are property of their respective owners.

Use of these names, trademarks and brands does not imply endorsement or affiliation. Disclaimer Notice

Springer

Data Science and Engineering

Approved by publishing and review experts on SciSpace, this template is built as per for Data Science and Engineering formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 933 authors to write and format their manuscripts to this journal.

Knowledge extraction

i
Last updated on
20 Jun 2020
i
ISSN
1606-8610
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Citation Type
Author Year
(Blonder et al, 1982)
i
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/S41019-016-0022-0
Big Data Reduction Methods: A Survey

Abstract:

Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently... Research on big data analytics is entering in the new phase called fast data where multiple gigabytes of data arrive in the big data systems every second. Modern big data systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity in the acquired data and consequently give rise to the 6Vs of big data. The reduced and relevant data streams are perceived to be more useful than collecting raw, redundant, inconsistent, and noisy data. Another perspective for big data reduction is that the million variables big datasets cause the curse of dimensionality which requires unbounded computational resources to uncover actionable knowledge patterns. This article presents a review of methods that are used for big data reduction. It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine learning methods. In addition, the open research issues pertinent to the big data reduction are also highlighted. read more read less

Topics:

Big data (62%)62% related to the paper, Data stream mining (57%)57% related to the paper
View PDF
138 Citations
open accessOpen access Journal Article DOI: 10.1007/S41019-016-0011-3
Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis
Siuly Siuly1, Yanchun Zhang2, Yanchun Zhang1

Abstract:

Diagnosis of neurological diseases is a growing concern and one of the most difficult challenges for modern medicine. According to the World Health Organisation’s recent report, neurological disorders, such as epilepsy, Alzheimer’s disease and stroke to headache, affect up to one billion people worldwide. An estimated 6.8 mil... Diagnosis of neurological diseases is a growing concern and one of the most difficult challenges for modern medicine. According to the World Health Organisation’s recent report, neurological disorders, such as epilepsy, Alzheimer’s disease and stroke to headache, affect up to one billion people worldwide. An estimated 6.8 million people die every year as a result of neurological disorders. Current diagnosis technologies (e.g. magnetic resonance imaging, electroencephalogram) produce huge quantity data (in size and dimension) for detection, monitoring and treatment of neurological diseases. In general, analysis of those medical big data is performed manually by experts to identify and understand the abnormalities. It is really difficult task for a person to accumulate, manage, analyse and assimilate such large volumes of data by visual inspection. As a result, the experts have been demanding computerised diagnosis systems, called “computer-aided diagnosis (CAD)” that can automatically detect the neurological abnormalities using the medical big data. This system improves consistency of diagnosis and increases the success of treatment, save lives and reduce cost and time. Recently, there are some research works performed in the development of the CAD systems for management of medical big data for diagnosis assessment. This paper explores the challenges of medical big data handing and also introduces the concept of the CAD system how it works. This paper also provides a survey of developed CAD methods in the area of neurological diseases diagnosis. This study will help the experts to have some idea and understanding how the CAD system can assist them in this point. read more read less

Topics:

Modern medicine (51%)51% related to the paper
View PDF
122 Citations
open accessOpen access Journal Article DOI: 10.1007/S41019-018-0074-4
Approximate Query Processing: What is New and Where to Go?: A Survey on Approximate Query Processing
Kaiyu Li1, Guoliang Li1

Abstract:

Online analytical processing (OLAP) is a core functionality in database systems. The performance of OLAP is crucial to make online decisions in many applications. However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance req... Online analytical processing (OLAP) is a core functionality in database systems. The performance of OLAP is crucial to make online decisions in many applications. However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance requirement. To alleviate this problem, approximate query processing (AQP) has been proposed, which aims to find an approximate answer as close as to the exact answer efficiently. Existing AQP techniques can be broadly categorized into two categories. (1) Online aggregation: select samples online and use these samples to answer OLAP queries. (2) Offline synopses generation: generate synopses offline based on a-priori knowledge (e.g., data statistics or query workload) and use these synopses to answer OLAP queries. We discuss the research challenges in AQP and summarize existing techniques to address these challenges. In addition, we review how to use AQP to support other complex data types, e.g., spatial data and trajectory data, and support other applications, e.g., data visualization and data cleaning. We also introduce existing AQP systems and summarize their advantages and limitations. Lastly, we provide research challenges and opportunities of AQP. We believe that the survey can help the partitioners to understand existing AQP techniques and select appropriate methods in their applications. read more read less

Topics:

Online analytical processing (61%)61% related to the paper, Online aggregation (58%)58% related to the paper
View PDF
99 Citations
open accessOpen access Journal Article DOI: 10.1007/S41019-015-0001-X
Big Data Privacy: Challenges to Privacy Principles and Models

Abstract:

This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure. Anonymiz... This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure. Anonymization appears as the best tool to mitigate such conflicts, and it is best implemented by adhering to a privacy model with precise privacy guarantees. For this reason, we evaluate how well the two main privacy models used in anonymization (k-anonymity and \(\varepsilon \)-differential privacy) meet the requirements of big data, namely composability, low computational cost and linkability. read more read less

Topics:

Information privacy (70%)70% related to the paper, Privacy by Design (66%)66% related to the paper, Privacy software (64%)64% related to the paper, Big data (56%)56% related to the paper, k-anonymity (52%)52% related to the paper
View PDF
89 Citations
open accessOpen access Journal Article DOI: 10.1007/S41019-020-00151-Z
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation
Haitao Yuan1, Guoliang Li1

Abstract:

Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic pred... Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications. read more read less

Topics:

Intelligent transportation system (61%)61% related to the paper, Data pre-processing (54%)54% related to the paper, Data access layer (53%)53% related to the paper
View PDF
87 Citations
Author Pic

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
2
Import a MS-Word file or start fresh
It takes only few seconds to import
3
View and edit your final output
SciSpace will automatically format your output to meet journal guidelines
4
Submit directly or Download
Submit to journal directly or Download in PDF, MS Word or LaTeX

(Before submission check for plagiarism via Turnitin)

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 Data Science and Engineering.

It automatically formats your research paper to Springer 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

Plagiarism Reports via Turnitin

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

Turnitin Stats
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

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 Data Science and Engineering in LaTeX?

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

2. Do you follow the Data Science and Engineering guidelines?

Yes, the template is compliant with the Data Science and Engineering 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 Data Science and Engineering?

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 Data Science and Engineering citation style.

4. Can I use the Data Science and Engineering 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 Data Science and Engineering.

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

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

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

7. Where can I find the template for the Data Science and Engineering?

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 Data Science and Engineering'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 Data Science and Engineering'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. Data Science and Engineering an online tool or is there a desktop version?

SciSpace's Data Science and Engineering 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 Data Science and Engineering?

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 Data Science and Engineering?”

11. What is the output that I would get after using Data Science and Engineering?

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

12. Is Data Science and Engineering'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 Data Science and Engineering?

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 Data Science and Engineering. 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 Data Science and Engineering?

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

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

16. Can I download Data Science and Engineering 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 Data Science and Engineering Endnote style according to Elsevier guidelines.

Fast and reliable,
built for complaince.

Instant formatting to 100% publisher guidelines on - SciSpace.

Available only on desktops 🖥

No word template required

Typset automatically formats your research paper to Data Science and Engineering formatting guidelines and citation style.

Verifed journal formats

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
Use this template