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A Jupyter Notebook implementing a simple parser used to transform the Multilingual Data Stewardship terminology and Metadata, created in the Task 3.1 of the SSHOC project, into SKOS resources. The parser transforms the content in SKOS data following a set of mapping rules, the result is stored in two Turtle files.

phonemizer-3.0.1 This version is linked to the phonemizer JOSS paper. Please see ChangeLog for details on that release.

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This repository contains the scripts for a simulation study performed on data from the systematic review by Brouwer et al. (2019). The goal was to obtain the Time to Discovery (TD) for each relevant paper. The simulation study has the following characteristics: The number of runs is equal to the number of inclusions in the dataset; Every run starts with 1 prior inclusion and 10 prior exclusions; The prior inclusion is different for every run, e.g. all inclusions in the data are used as a prior inclusion once; The 10 prior exclusions are the same for every run, and they are randomly sampled from the dataset. The output is a file with all inclusions ordered by their Time to Discovery.

text id an R-package for analyzing and visualizing human language using natural language processing and deep learning. The language that individuals use contains a wealth of psychological information interesting for research.

ThermoScan is a tool for semi-automatic identification of protein stability data from PubMed.

The repository is part of the so-called, Mega-Meta study on reviewing factors contributing to substance use, anxiety, and depressive disorders. The study protocol has been pre-registered at Prospero. The procedure for obtaining the search terms, the exact search query, and selecting key papers by expert consensus can be found on the Open Science Framework. The three datasets, one for each disorder, used for screening in ASReview and the partly labeled output datasets can be found on DANS[NEEDS LINK].

This repository contains reusable Machine Translation (MT) by the MasakhaneNLP Community. MASAKHANE is an research effort for NLP for African languages that is OPEN SOURCE, CONTINENT-WIDE, DISTRIBUTED and ONLINE. This repository houses the models for Machine Translation for African languages. See masakhane-mt-current-models.csv for current model information. This repository was created by the Masakhane Web Translate Team. You can see some of the models in action on http://translate.masakhane.io/

A Python script used to convert introductions and historical-critical notes, prepared by researchers and curators, into PDF files. The `stylesheet.css` file is used to define the style of the PDF output. You will either need a `config.json` file containing some variables in order to make it work (such as your local path and the name of the directory containing the HTML essays), or write them directly into the code as variables.