discoverableresearch

Checks Title, Abstract and Keywords to Optimise Discoverability

CRAN Package

A suite of tools are provided here to support authors in making their research more discoverable. check_keywords() - this function checks the keywords to assess whether they are already represented in the title and abstract. check_fields() - this function compares terminology used across the title, abstract and keywords to assess where terminological diversity (i.e. the use of synonyms) could increase the likelihood of the record being identified in a search. The function looks for terms in the title and abstract that also exist in other fields and highlights these as needing attention. suggest_keywords() - this function takes a full text document and produces a list of unigrams, bigrams and trigrams (1-, 2- or 2-word phrases) present in the full text after removing stop words (words with a low utility in natural language processing) that do not occur in the title or abstract that may be suitable candidates for keywords. suggest_title() - this function takes a full text document and produces a list of the most frequently used unigrams, bigrams and trigrams after removing stop words that do not occur in the abstract or keywords that may be suitable candidates for title words. check_title() - this function carries out a number of sub tasks: 1) it compares the length (number of words) of the title with the mean length of titles in major bibliographic databases to assess whether the title is likely to be too short; 2) it assesses the proportion of stop words in the title to highlight titles with low utility in search engines that strip out stop words; 3) it compares the title with a given sample of record titles from an .ris import and calculates a similarity score based on phrase overlap. This highlights the level of uniqueness of the title. This version of the package also contains functions currently in a non-CRAN package called 'litsearchr' https://github.com/elizagrames/litsearchr.

  • Version0.0.1
  • R versionunknown
  • LicenseGPL-3
  • Needs compilation?No
  • Last release10/10/2020

Documentation


Team


Insights

Last 30 days

This package has been downloaded 161 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.

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0 downloadsFeb 23, 2025
0 downloadsFeb 24, 2025
8 downloadsFeb 25, 2025
2 downloadsFeb 26, 2025
6 downloadsFeb 27, 2025
3 downloadsFeb 28, 2025
4 downloadsMar 1, 2025
8 downloadsMar 2, 2025
12 downloadsMar 3, 2025
10 downloadsMar 4, 2025
4 downloadsMar 5, 2025
4 downloadsMar 6, 2025
5 downloadsMar 7, 2025
3 downloadsMar 8, 2025
0 downloadsMar 9, 2025
4 downloadsMar 10, 2025
10 downloadsMar 11, 2025
3 downloadsMar 12, 2025
4 downloadsMar 13, 2025
3 downloadsMar 14, 2025
6 downloadsMar 15, 2025
4 downloadsMar 16, 2025
4 downloadsMar 17, 2025
2 downloadsMar 18, 2025
7 downloadsMar 19, 2025
12 downloadsMar 20, 2025
9 downloadsMar 21, 2025
4 downloadsMar 22, 2025
7 downloadsMar 23, 2025
9 downloadsMar 24, 2025
1 downloadsMar 25, 2025
3 downloadsMar 26, 2025
0 downloadsMar 27, 2025
0 downloadsMar 28, 2025
0 downloadsMar 29, 2025
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The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 1,925 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 21 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Data provided by CRAN


Binaries


Dependencies

  • Imports9 packages
  • Suggests2 packages