Datasheets for Datasets - https://arxiv.org/abs/1803.09010
"Document [the dataset] motivation, composition, collection process, recommended uses, and so on. [They] have the potential to increase transparency and accountability within the machine learning community, mitigate unwanted biases in machine learning systems, facilitate greater reproducibility of machine learning results, and help researchers and practitioners select more appropriate datasets for their chosen tasks.''
The motivation behind the proposal was the electronics industry, where every component has a datasheet that describes its operating characteristics and recommended uses. In machine learning, data is the input for model training. Using the wrong dataset, or using a dataset outside of its original intent, or even not understanding well enough the limitations of a dataset, has dire consequences for the model. However, ``[d]espite the importance of data to machine learning, there is no standardized process for documenting machine learning datasets. To address this gap, we propose datasheets for datasets.''
Adaptive CV allows to compile different variants of a CV (e.g., a résumé and an extended CV) from a single LaTeX source. It is particularly suitable for academic CVs but flexible enough to be used with any CVs.
Sleek Template is a minimal collection of LaTeX packages and settings that ease the writing of beautiful documents. While originally meant for theses, it is perfectly suitable for project reports, articles, syntheses, etc. – with a few adjustments, like margins.
It is composed of four separate packages – sleek, sleek-title, sleek-theorems and sleek-listings – each of which can be used individually.