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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.

JavaScript code used in the geospatial analysis searching for Paititi. The code, coupled with the provided data, allows studying the distribution of morphometric characteristics of terrain for specified point samples by making a chart and exporting the derived terrain products as GeoTiffs. In theory, it can bring insights on where to look for ancient settlements of Inca and other Andean cultures. You must run the code inside the Google Earth Engine, a free-to-use cloud computing platform for processing satellite imagery and other Earth observation data. It requires you to have a Google account and a related Earth Engine account. To successfully apply this code, you also need to have basic experience with coding and GIS. Please, read the README.md file for more details. Links Paititi Research Google Earth Engine GitHub Repository

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.

Current version: v1.0.0 Jupyter notebooks to work with data from Te Papa's collections API. For more information see the Te Papa section of the GLAM Workbench. Notebook topics Exploring the Te Papa collection API — some preliminary poking around, focusing in particular on using facets and nested records Mapping Te Papa's collections — create some simple maps using the production.spatial facet of the Te Papa API to identify places where collection objects were created Cite as See the GLAM Workbench or Zenodo for up-to-date citation details. This repository is part of the GLAM Workbench. If you think this project is worthwhile, you can become a GitHub sponsor.

Current version: v1.0.0 Jupyter notebooks to work with data from DigitalNZ's API. For more information see the DigitalNZ section of the GLAM Workbench. Notebook topics Exploring the API Build a DigitalNZ API search query – experiment with the DigitalNZ search API Getting some top-level data from the DigitalNZ API – poke around at the top-level of DigitalNZ, mainly using facets to generate some collection overviews and summaries Harvesting data Harvest facet data from DigitalNZ – explores what facets are available from the DigitalNZ API and demonstrates how to harvest data from them Harvest data from Papers Past – harvest large amounts of data from Papers Past (via DigitalNZ) for further analysis Tips and tricks Select a random(ish) record from DigitalNZ – examines the available facets, then uses them to reduce the size of the results set until it's possible to select a random record Find results by country in DigitalNZ – find records relating to particular countries by searching for geocoded locations using bounding boxes Visualising collections QueryPic DigitalNZ – a web app to visualise searches in Papers Past over time Visualise a search in Papers Past – create a simple visualisation showing the distribution of a search over time and by newspaper Papers Past newspapers in DigitalNZ – displays details of the Papers Past newspapers available through DigitalNZ Visualising open collections in DigitalNZ – assemble data relating to the usage status of collections and visualise the results in a suitably colourful burst of fireworks! Datasets Summary of facets Individual facets: collection.csv creator.csv subject.csv format.csv placename.csv decade.csv content_partner.csv language.csv century.csv usage.csv rights.csv year.csv copyright.csv dc_type.csv category.csv primary_collection.csv collections_by_partner.csv usage_by_collection_and_partner.csv See the GLAM Workbench for more details. Cite as See the GLAM Workbench or Zenodo for up-to-date citation details. This repository is part of the GLAM Workbench. If you think this project is worthwhile, you can become a GitHub sponsor.

Code for the paper: Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective