Metadata-Version: 2.1
Name: asreview
Version: 0.8rc3
Summary: Automated Systematic Review
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: [![ASReview bot](images/RepoCardGithub-1280x640px.png)](https://github.com/asreview/asreview)
        
        ## 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)
        
         *This project is work in progress and not production ready.*
        
        ---
        
         [Check out our new tutorial "10 minutes into ASReview"](https://asreview.readthedocs.io/en/latest/10minutes_asreview.html) 
        
        Systematic Reviews are “top of the bill” in research. The number of systematic
        reviews published by researchers increases year after year. But performing a
        sound systematic review is a time-consuming and sometimes boring task. Our
        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 Automated Systematic Review (ASReview) project implements learning algorithms that interactively query the
        researcher. This way of interactive training is known as
        [Active Learning](https://en.wikipedia.org/wiki/Active_learning_(machine_learning)).
        ASReview offers support for classical learning algorithms and
        state-of-the-art learning algorithms like neural networks. The following image
        gives an overview of the process.
        
        [![Active Learning for reviewing papers](https://github.com/asreview/asreview/blob/master/images/deepreview.png?raw=true)](https://github.com/asreview/asreview)
        
        Our ASReview software implements two different modes:
        
        - **Oracle**  The oracle 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.
        - **Simulate**  The simulation modus is used to measure the performance of our
          software on existing systematic reviews. The software shows how many
          papers you could have potentially skipped during the systematic review.
        
        
        ## Installation
        
        The ASReview software requires Python 3.6+. The project is available on [Pypi](https://pypi.org/project/asreview/). Install the
        project with:
        
        ```bash
        pip install asreview
        ```
        
        Or, install the development version of the Automated Systematic Review project directly
        from this Github page.
        
        ``` bash
        pip install git+https://github.com/asreview/asreview.git
        ```
        
        ## Quick start
        
        The quickest way to start using the Automated Systematic Review (ASR) software is
        the Command Line Interface (CLI).
        Start an interactive systematic review (Oracle mode) with the following line in CMD or shell:
        
        ``` sh
        asreview oracle YOUR_DATA.csv --log_file results.json
        ```
        
        ![ASReview Command Line Interface](https://github.com/asreview/asreview/raw/master/docs/gifs/asreview-intro.gif)
        
        This command (`asreview oracle`) runs the software in oracle mode on the
        `YOUR_DATA.csv` dataset.
        
        The higher the number of papers that you manually include in ASReview,
        the quicker the ASReview software will understand your choices for inclusion.
        The IDs are the identifiers of papers, starting from
        0 for the first paper found in the dataset.
        
        To benchmark an already executed review, use the simulation modus (`asreview simulation`).
        The dataset then needs an additional column ("label_included") to signify their inclusion
        in the final review. The command for the simulation modus is similar to the oracle
        mode:
        
        ``` sh
        asreview simulate YOUR_DATA.csv --n_prior_included 5 --n_prior_excluded 5 --log_file results.h5
        ```
        
        ## Resources
        
        - The full documentation is available at [asreview.rtfd.io](https://asreview.rtfd.io)
        - [10 Minutes into ASReview](https://asreview.readthedocs.io/en/latest/10minutes_asreview.html) An introduction into ASReview for new users. 
        - [automated-systematic-review-datasets](https://github.com/asreview/systematic-review-datasets) A project with systematic review datasets optimized and processed for use with ASReview or other systematic review software. The project describes the preferred format to store systematic review datasets.
        - [automated-systematic-review-simulations](https://github.com/asreview/automated-systematic-review-simulations) A repository with scripts for a simulation study and scripts for the aggregation and visualisation of the results.
        
        
        ## Contributing
        Got ideas for improvement? We would love to hear about your suggestions! Get started [here ](contributing.md)
        
        See who have contributed to ASReview [here](contributors.md)
        
        ## Contact
        This project is coordinated by by [Rens van de Schoot](https://www.rensvandeschoot.com) ([@Rensvandeschoot](https://github.com/Rensvandeschoot)) and [Daniel Oberski](https://www.uu.nl/staff/DLOberski) ([@daob](https://github.com/daob)) and is part of the research work conducted by the [Department of
        Methodology & Statistics](https://www.uu.nl/en/organisation/faculty-of-social-and-behavioural-sciences/about-the-faculty/departments/methodology-statistics), Faculty of Social and Behavioral Sciences, Utrecht
        University, The Netherlands. Maintainers are [Jonathan de Bruin](https://www.uu.nl/staff/JdeBruin1) (Lead engineer, [@J535D165](https://github.com/J535D165)) and Raoul Schram ([@qubixes](https://github.com/qubixes)).
        
        For any questions or remarks, please send an email to asreview@uu.nl.
        
        
        ## License
        [LICENSE](LICENSE)
        
        
        ## Publications
        
        - Dutch newspaper NRC on this project ["Software vist de beste artikelen uit een bibliotheek van duizenden."](https://www.nrc.nl/nieuws/2019/01/14/software-vist-de-beste-artikelen-eruit-a3628952)
        - News site of Utrecht University: [ASReview: Smart source screening software for academia and beyond](https://www.uu.nl/en/news/asreview-smart-source-screening-software-for-academia-and-beyond)
        - News site of Utrecht University: ["A digital tracker dog for datasets"
        ](https://www.dub.uu.nl/en/depth/digital-tracker-dog-datasets)
        
        
        ## Citation
        
        A research paper is coming up for this project. In the mean time, it can be cited with (fill in x and y for the version number):
        
        ```
        ASReview Core Development Team (2019). ASReview: Software for automated systematic reviews [version 0.x.y]. Utrecht University, Utrecht, The Netherlands. Available at https://github.com/asreview/asreview.
        ```
        
        BibTeX:
        
        ```bibtex
        @Manual{,
            title = {ASReview: Active learning for systematic reviews},
            author = {{ASReview Core Development Team}},
            organization = {Utrecht University},
            address = {Utrecht, The Netherlands},
            year = 2019,
            doi = {10.5281/zenodo.3345592},
            url = {https://doi.org/10.5281/zenodo.3345592}
        }
        ```
        
Keywords: systematic review
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: ~=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: all
Provides-Extra: doc2vec
Provides-Extra: tensorflow
Provides-Extra: sbert
Provides-Extra: test
