Metadata-Version: 2.1
Name: asreview
Version: 0.17.1
Summary: Active learning for Systematic Reviews
Home-page: https://github.com/asreview/asreview
Author: ASReview Core Development Team
Author-email: asreview@uu.nl
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/asreview/asreview/issues
Project-URL: Source, https://github.com/asreview/asreview/
Description: <p align="center">
          <a href="https://github.com/asreview/asreview">
            <img width="60%" height="60%" src="https://raw.githubusercontent.com/asreview/asreview/master/images/RepoCardGithub-1280x640px.png">
          </a>
        </p>
        
        ## ASReview: Active learning for Systematic Reviews
        
        [![Build Status](https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fasreview%2Fasreview%2Fbadge%3Fref%3Dmaster&style=flat)](https://actions-badge.atrox.dev/asreview/asreview/goto?ref=master) [![Documentation Status](https://readthedocs.org/projects/asreview/badge/?version=latest)](https://asreview.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3345592.svg)](https://doi.org/10.5281/zenodo.3345592) [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/4755/badge)](https://bestpractices.coreinfrastructure.org/projects/4755)
        
        Systematic Reviews are “top of the bill” in research. The number of scientific
        studies are increasing exponentially in many scholarly fields. Performing a
        sound systematic review is a time-consuming and sometimes boring task. The ASReview
        software is designed to accelerate the step of screening abstracts and titles
        with a minimum of papers to be read by a human with no or very few false
        negatives.
        
        The Active learning for Systematic Reviews (ASReview) project, publised in
        [*Nature Machine Intelligence*](https://doi.org/10.1038/s42256-020-00287-7),
        implements machine learning algorithms that interactively query the
        researcher. This way of interactive machine learning is known as [Active
        Learning](https://asreview.readthedocs.io/en/latest/guides/activelearning.html).
        ASReview offers support for classical learning algorithms and state-of-the-art
        learning algorithms like neural networks.
        
        ASReview software implements two different modes:
        
        - **ASReview LAB**  This modus is used to perform a systematic review with
          interaction by the reviewer (the 'oracle' in literature on active learning).
          The software presents papers to the reviewer, whereafter the reviewer classifies them. See [ASReview LAB](https://github.com/asreview/asreview#asreview-lab).
        - **Simulate**  The simulation modus is used to measure
          the performance of the active learning software on the results of fully labeled systematic
          reviews. To use the simulation mode, knowledge on programming and bash/Command Prompt
          is highly recommended.
        
        ## Installation
        
        The ASReview software requires Python 3.6+. Detailed step-by-step instructions
        to install Python and ASReview are available for
        [Windows](https://asreview.nl/installation-guide-windows/) and
        [macOS](https://asreview.nl/installation-guide-mac/) users. The project is
        available on [Pypi](https://pypi.org/project/asreview/). Install the project
        with (Windows users might have to use the prefix `python -m`):
        
        ```bash
        pip install asreview
        ```
        
        Upgrade ASReview with the following command:
        
        ```bash
        pip install --upgrade asreview
        ```
        
        ## ASReview LAB
        
        ASReview LAB is a user-friendly interface for screening documents and
        experimentation with AI-aided systematic reviews. Read more about using the
        software in the [Quick
        Tour](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html).
        
        [![ASReview LAB](https://github.com/asreview/asreview/blob/master/images/ASReviewWebApp.png?raw=true)](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html "ASReview LAB Quick Tour")
        
        ## Covid-19 plugin
        
        [![Covid-19 Plugin](https://github.com/asreview/asreview/blob/master/images/intro-covid19-small.png?raw=true)](https://github.com/asreview/asreview-covid19 "ASReview against COVID-19")
        
        The ASReview team developed a plugin for researchers and doctors to facilitate
        the reading of literature on the Coronavirus. The
        [plugin](https://github.com/asreview/asreview-covid19) makes the
        [CORD-19](https://pages.semanticscholar.org/coronavirus-research) dataset
        available in the ASReview software. A second database with studies published
        after December 1st 2019 is available as well (this dataset is more specific
        for publications on COVID-19).
        
        Install the plugin with the command below.
        
        ```
        pip install asreview-covid19
        ```
        
        ## Citation
        
        The following publication in [Nature Machine
        Intelligence](https://doi.org/10.1038/s42256-020-00287-7) can be used to cite
        the project.
        
        > van de Schoot, R., de Bruin, J., Schram, R. et al. An open source machine
          learning framework for efficient and transparent systematic reviews.
          Nat Mach Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7
        
        For citing the software, please refer to the specific release of
        the ASReview software on Zenodo https://doi.org/10.5281/zenodo.3345592. The menu on the
        right can be used to find the citation format of prevalence. 
        
        For more scientific publications on the ASReview software, go to 
        [asreview.nl/papers](https://asreview.nl/papers/).
        
        ## Contact
        
        ASReview is a research project coordinated by [Rens van de
        Schoot](www.rensvandeschoot.com) (full professor at the Department of
        Methodology & Statistics at [Utrecht University](https://www.uu.nl), The
        Netherlands), together with ASReview lead engineer 
        [Jonathan de Bruin](https://github.com/J535D165). For an overview of the team working on
        ASReview, see [ASReview Research Team](https://asreview.readthedocs.io/en/latest/intro/about.html#research-team). 
        
        The best resources to find an answer to your question or ways to get in 
        contact with the team are:
        
        - Documentation - [asreview.readthedocs.io](https://asreview.readthedocs.io/)
        - Quick tour - [ASReview LAB quick tour](https://asreview.readthedocs.io/en/latest/lab/overview_lab.html)
        - Issues or feature requests - [ASReview issue tracker](https://github.com/asreview/asreview/issues)
        - FAQ - [ASReview Discussions](https://github.com/asreview/asreview/discussions?discussions_q=sort%3Atop)
        - Donation - [asreview.nl/donate](https://asreview.nl/donate)
        - Contact - [asreview@uu.nl](mailto:asreview@uu.nl)
        
        ## License
        
        The ASReview software has an Apache 2.0 [LICENSE](LICENSE). The ASReview team
        accepts no responsibility or liability for the use of the ASReview tool or any
        direct or indirect damages arising out of the application of the tool.
        
Keywords: systematic review machine-learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Framework :: Flask
Requires-Python: ~=3.6
Description-Content-Type: text/markdown
Provides-Extra: sbert
Provides-Extra: doc2vec
Provides-Extra: tensorflow
Provides-Extra: dev
Provides-Extra: test
Provides-Extra: all
