NLP resources are unevenly distributed across languages, with English being overrepresented. However, creating one's own annotated resources can be advantageous, allowing for full control over data selection and annotation. The Haystack framework offers a range of tools to create NLP resources, including preprocessing tools, an annotation tool, and a question generation node. Other smart people have devised crafty solutions for dataset creation in low-resource settings, such as translating existing datasets or using data augmentation techniques. Joining the Haystack NLP community can help collect more resources for underrepresented languages and connect with other users and the engineering team. Monolingual models perform better than multilingual models on certain tasks, suggesting a focus on creating monolingual NLP resources is beneficial. The creation of SQuAD-like datasets for German and French has shown improved model performance compared to multilingual models. A range of tools and techniques are available to create and preprocess NLP resources, making it more accessible to developers.