Reproducibility and Replicability of Web Measurement Studies

Last update: Dec 31, 2022

Overview

Reproducibility and Replicability of Web Measurement Studies

This repository holds additional material to the paper "Reproducibility and Replicability of Web Measurement Studies" submitted to the ACM Web Conference 2022.

The repository is organized as follows:

In 01_Plots, one finds all codes and the underlying data used to produce the plots in the paper
In 02_Data, we provide a link to the raw measurement results.
In 03_Framework, one can find the framework we used for our measurement study.
In 04_Paper_Survey, we provide a listing of all analyzed papers.
Finally, in 05_misc, we provide further data (e.g., the used EasyList, list of visited pages).

Owner

GitHub Repository

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Reproducibility and Replicability of Web Measurement Studies

Related tags

Overview

Reproducibility and Replicability of Web Measurement Studies

Owner

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

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Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)"

Apache (Py)Spark type annotations (stub files).

database for artificial intelligence/machine learning data

Auto updating website that tracks closed & open issues/PRs on scikit-learn/scikit-learn.