Example of Digital Scholarship in the Humanities format
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Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format Example of Digital Scholarship in the Humanities format
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open access Open Access

Digital Scholarship in the Humanities — Template for authors

Categories Rank Trend in last 3 yrs
Language and Linguistics #118 of 879 up up by 59 ranks
Linguistics and Language #132 of 935 up up by 64 ranks
Computer Science Applications #396 of 693 down down by 2 ranks
Information Systems #192 of 329 down down by 4 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 252 Published Papers | 513 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 22/07/2020
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Related Journals

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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.

2.0

43% from 2019

CiteRatio for Digital Scholarship in the Humanities from 2016 - 2020
Year Value
2020 2.0
2019 1.4
2018 1.6
2017 1.1
2016 0.8
graph view Graph view
table view Table view

0.4

32% from 2019

SJR for Digital Scholarship in the Humanities from 2016 - 2020
Year Value
2020 0.4
2019 0.304
2018 0.305
2017 0.259
2016 0.175
graph view Graph view
table view Table view

1.353

45% from 2019

SNIP for Digital Scholarship in the Humanities from 2016 - 2020
Year Value
2020 1.353
2019 0.935
2018 1.01
2017 1.686
2016 1.838
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 43% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 32% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 45% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Digital Scholarship in the Humanities

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Oxford University Press

Digital Scholarship in the Humanities

DSH or Digital Scholarship in the Humanities is an international, peer reviewed journal which publishes original contributions on all aspects of digital scholarship in the Humanities including, but not limited to, the field of what is currently called the Digital Humanities. L...... Read More

Language and Linguistics

Linguistics and Language

Computer Science Applications

Information Systems

Arts and Humanities

i
Last updated on
21 Jul 2020
i
ISSN
2055-7671
i
Impact Factor
High - 1.452
i
Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
unsrt
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Citation Type
Numbered
[25]
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Bibliography Example
C. W. J. Beenakker. Specular andreev reflection in graphene. Phys. Rev. Lett., 97(6):067007, 2006.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1093/LLC/FQT031
On the features of translationese
Vered Volansky1, Noam Ordan2, Shuly Wintner1

Abstract:

Much research in translation studies indicates that translated texts are ontologically different from original non-translated ones. Translated texts, in any language, can be considered a dialect of that language, known as ‘translationese’. Several characteristics of translationese have been proposed as universal in a series o... Much research in translation studies indicates that translated texts are ontologically different from original non-translated ones. Translated texts, in any language, can be considered a dialect of that language, known as ‘translationese’. Several characteristics of translationese have been proposed as universal in a series of hypotheses. In this work, we test these hypotheses using a computational methodology that is based on supervised machine learning. We define several classifiers that implement various linguistically informed features, and assess the degree to which different sets of features can distinguish between translated and original texts. We demonstrate that some feature sets are indeed good indicators of translationese, thereby corroborating some hypotheses, whereas others perform much worse (sometimes at chance level), indicating that some ‘universal’ assumptions have to be reconsidered. In memoriam: Miriam Shlesinger, 1947–2012 read more read less
View PDF
141 Citations
Journal Article DOI: 10.1093/LLC/FQU057
ANNIS3: A new architecture for generic corpus query and visualization
Thomas Krause1, Amir Zeldes2

Abstract:

This article is concerned with the data structures, properties of query languages, and visualization facilities required for the generic representation of richly annotated, heterogeneous linguistic corpora. We propose that above and beyond a general graph-based data model, which is becoming increasingly popular in many comple... This article is concerned with the data structures, properties of query languages, and visualization facilities required for the generic representation of richly annotated, heterogeneous linguistic corpora. We propose that above and beyond a general graph-based data model, which is becoming increasingly popular in many complex annotation formats, a well-defined concept of multiple, potentially conflicting segmentation layers must be introduced to deal with different sources and applications of corpus data flexibly. We also propose a generic solution for specialized corpus visualizations in a Web interface using annotation-triggered style sheets, which leverage the power of modern browsers and CSS for multiple and highly customizable views of primary data. We offer an implementation and evaluation of our architecture in ANNIS3, an open-source browser-based architecture for corpus search and visualization. We present three case studies to test the coverage of the system, encompassing core linguistic and digital humanities use-cases including richly annotated newspaper treebanks, multilingual diplomatic and normalized manuscript materials edited in TEI, and analysis of multimodal recordings of spoken language. read more read less

Topics:

Query language (55%)55% related to the paper, Style sheet (53%)53% related to the paper, Visualization (53%)53% related to the paper
View PDF
127 Citations
Journal Article DOI: 10.1093/LLC/FQT066
Does Size Matter? Authorship Attribution, Small Samples, Big Problem
Maciej Eder1

Abstract:

The aim of this study is to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise. A few controlled tests for different sample lengths, languages, and genres are discussed and compared. Depending on the corpus used, the minimal sample length varied fr... The aim of this study is to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise. A few controlled tests for different sample lengths, languages, and genres are discussed and compared. Depending on the corpus used, the minimal sample length varied from 2,500 words (Latin prose) to 5,000 or so words (in most cases, including English, German, Polish, and Hungarian novels). Another observation is connected with the method of sampling: contrary to common sense, randomly excerpted ‘bags of words’ turned out to be much more effective than the classical solution, i.e. using original sequences of words (‘passages’) of desired size. Although the tests have been performed using the Delta method ( Burrows, J.F . (2002). ‘Delta’: a measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing , 17 (3): 267–87) applied to the most frequent words, some additional experiments have been conducted for support vector machines and k -NN applied to most frequent words, character 3-grams, character 4-grams, and parts-of-speech-tag 3-grams. Despite significant differences in overall attributive success rate between particular methods and/or style markers, the minimal amount of textual data needed for reliable authorship attribution turned out to be method-independent. read more read less
114 Citations
open accessOpen access Journal Article DOI: 10.1093/LLC/FQX023
Understanding and explaining Delta measures for authorship attribution

Abstract:

This article builds on a mathematical explanation of one the most prominent stylometric measures, Burrows’s Delta (and its variants), to understand and explain its working. Starting with the conceptual separation between feature selection, feature scaling, and distance measures, we have designed a series of controlled experim... This article builds on a mathematical explanation of one the most prominent stylometric measures, Burrows’s Delta (and its variants), to understand and explain its working. Starting with the conceptual separation between feature selection, feature scaling, and distance measures, we have designed a series of controlled experiments in which we used the kind of feature scaling (various types of standardization and normalization) and the type of distance measures (notably Manhattan, Euclidean, and Cosine) as independent variables and the correct authorship attributions as the dependent variable indicative of the performance of each of the methods proposed. In this way, we are able to describe in some detail how each of these two variables interact with each other and how they influence the results. Thus we can show that feature vector normalization, that is, the transformation of the feature vectors to a uniform length of 1 (implicit in the cosine measure), is the decisive factor for the improvement of Delta proposed recently. We are also able to show that the information particularly relevant to the identification of the author of a text lies in the profile of deviation across the most frequent words rather than in the extent of the deviation or in the deviation of specific words only. ................................................................................................................................................................................. read more read less
View PDF
102 Citations
open accessOpen access Journal Article DOI: 10.1093/LLC/FQU064
Significance testing of word frequencies in corpora

Abstract:

Finding out whether a word occurs significantly more often in one text or corpus than in another is an important question in analysing corpora. As noted by Kilgarriff (Language is never, ever, ever, random, Corpus Linguistics and Linguistic Theory , 2005; 1(2): 263–76.), the use of the χ2 and log-likelihood ratio tests is pro... Finding out whether a word occurs significantly more often in one text or corpus than in another is an important question in analysing corpora. As noted by Kilgarriff (Language is never, ever, ever, random, Corpus Linguistics and Linguistic Theory , 2005; 1(2): 263–76.), the use of the χ2 and log-likelihood ratio tests is problematic in this context, as they are based on the assumption that all samples are statistically independent of each other. However, words within a text are not independent. As pointed out in Kilgarriff (Comparing corpora, International Journal of Corpus Linguistics , 2001; 6(1): 1–37) and Paquot and Bestgen (Distinctive words in academic writing: a comparison of three statistical tests for keyword extraction. In Jucker, A., Schreier, D., and Hundt, M. (eds), Corpora: Pragmatics and Discourse . Amsterdam: Rodopi, 2009, pp. 247–69), it is possible to represent the data differently and employ other tests, such that we assume independence at the level of texts rather than individual words. This allows us to account for the distribution of words within a corpus. In this article we compare the significance estimates of various statistical tests in a controlled resampling experiment and in a practical setting, studying differences between texts produced by male and female fiction writers in the British National Corpus. We find that the choice of the test, and hence data representation, matters. We conclude that significance testing can be used to find consequential differences between corpora, but that assuming independence between all words may lead to overestimating the significance of the observed differences, especially for poorly dispersed words. We recommend the use of the t-test, Wilcoxon rank-sum test, or bootstrap test for comparing word frequencies across corpora. read more read less

Topics:

Corpus linguistics (63%)63% related to the paper, Word lists by frequency (60%)60% related to the paper, Text corpus (60%)60% related to the paper, British National Corpus (58%)58% related to the paper
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86 Citations
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Digital Scholarship in the Humanities format uses unsrt citation style.

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Frequently asked questions

1. Can I write Digital Scholarship in the Humanities in LaTeX?

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

2. Do you follow the Digital Scholarship in the Humanities guidelines?

Yes, the template is compliant with the Digital Scholarship in the Humanities 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 Digital Scholarship in the Humanities?

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 Digital Scholarship in the Humanities citation style.

4. Can I use the Digital Scholarship in the Humanities 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 Digital Scholarship in the Humanities.

5. Can I use a manuscript in Digital Scholarship in the Humanities 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 Digital Scholarship in the Humanities that you can download at the end.

6. How long does it usually take you to format my papers in Digital Scholarship in the Humanities?

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

7. Where can I find the template for the Digital Scholarship in the Humanities?

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 Digital Scholarship in the Humanities'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 Digital Scholarship in the Humanities'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. Digital Scholarship in the Humanities an online tool or is there a desktop version?

SciSpace's Digital Scholarship in the Humanities 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 Digital Scholarship in the Humanities?

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 Digital Scholarship in the Humanities?”

11. What is the output that I would get after using Digital Scholarship in the Humanities?

After writing your paper autoformatting in Digital Scholarship in the Humanities, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Digital Scholarship in the Humanities'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 Digital Scholarship in the Humanities?

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 Digital Scholarship in the Humanities. 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 Digital Scholarship in the Humanities?

The 5 most common citation types in order of usage for Digital Scholarship in the Humanities 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 Digital Scholarship in the Humanities?

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 Digital Scholarship in the Humanities's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Digital Scholarship in the Humanities 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 Digital Scholarship in the Humanities Endnote style according to Elsevier guidelines.

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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.

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