(Note: This README file describes the unreleased Git version, not the last release, which may be different. Select the appropriate release tag at the top left to see the README for the version you're using. This application was previously known as RLetters.)
Sciveyor is an application designed to let users perform complex searches as well as digital-humanities and text-mining analysis tasks on a corpus of journal articles.
Sciveyor allows users to save the results of a given search as a "dataset." This produces a saved record that users can return to later in order to perform text analysis tasks.
While text analysis tasks are a current area of active development in Sciveyor, currently the following are available:
- Compute term frequency information (for single words or multiple-word phrases)
- Compare word usage in two different datasets
- Graph dataset by publication date
- Determine statistically significant pairs of words (collocations) or associations between words at distance (cooccurrences)
- Compute network of words used around a focal word
- Extract references to proper names (locations, people, organizations)
- Export dataset as citations in a variety of formats
The Solr backend on which Sciveyor is based allows for a number of complicated searching operations:
- Searching on the basis of particular fields ("title:hominid", or "title:fish")
- Boolean operators ("darwin OR huxley")
- Wildcard search ("*fish" or "wom?n")
- Text stemming ("evolution" matching "evolutionary" or "evolutionist")
- Fuzzy matching (matching words similar to the requested term)
- Proximity searching (two terms within N words of one another)
Support for web and library standards
Sciveyor features a JSON API to return search results to other services around the internet. We also provide support for the following web and library standards:
- unAPI for automatic bibliographic data retrieval from individual documents
- WorldCat OpenURL Registry for detection of the OpenURL resolver of the user's local library
And you can export bibliographic data in the following standard formats:
- MARC 21 transmission format
- MARC-JSON (draft)
- RDF/XML (using Dublin Core Grammar)
- RDF/N3 (using Dublin Core Grammar)
- EndNote (ENW format)
- Reference Manager (RIS format)
Cutting-edge development and maintenance tools
Sciveyor doesn't leave your developers out in the cold, either. We've got speedy deployments with Ansible and Capistrano, exception monitoring with Sentry, and our code has a thorough test suite and adheres to a probably unnecessary level of linting and style-guide fanaticism.
Contributors / Support
Special thanks to all contributors to the code. In addition to the list of contributors on Codeberg, thanks as well to rrrene and etahnsr who contributed over on GitHub.
We also have received the help of a great community of translators at Transifex. Thanks especially to Alejandro León Aznar.
Also, several features of Sciveyor wouldn't be possible without the excellent
work of other Ruby programmers. Thanks in particular to those behind
bibtex-ruby. The stop lists found in
app/lib/sciveyor/analysis/stop_list are released under the BSD license by the
Apache Solr project. The colors in
are released by the ColorBrewer project under the
Charles Pence and Grant Ramsey were supported in the development of Sciveyor by the National Science Foundation, #1456573, and the National Evolutionary Synthesis Center (NESCent), NSF #EF-0905606.
Sciveyor © 2011–2021 Charles Pence. Sciveyor is licensed under the MIT license. Please see the COPYING document for more information.