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__Type of tool:__ e.g. web application or desktop application

__Required skills: __

- Process/material knowledge: e.g. knowledge of material properties

- Digitalization knowledge: e.g. no programming

__Short description of the tool: __

- Detailed description: Link to the guideline: 

__Use case/problem:__ Selecting material (recyclate) for specific
product requirements

__Description of the problem the tools solves:__ [[General] + [[Tool-specific]

__Disclaimer:__

__How to use/download/access it:__ e.g. got the the gitup [[link], copy
the code into [[XY] and start using\\


__Contact person of the tool: __
Stefan Bloemheuvel

__Related tools:__

- Set up a working data infrastucture

- Find the right sensor to survey your process 

- Validate your process data

- 



__Data Validation:__

Having high quality data has an enormous potential in improving the data flows within the processes of your company, and is vital in analyzing your data and interpret the results. Therefore, the data quality needs to be checked. For instance, the data may contain errors, missing values, wrongfully labeled data or low sampling rates from sensors. The data validation tool guides you through the important steps when assessing the quality of the data, and gives you tips and tricks how and what to adjust for improving the overall quality of your data. In addition, the Data Validation tool functions as a stepping stone towards analyzing your data using the Data Analytics tool.

The tool can be accessed throughout the following link: [https://share.streamlit.io/cslab-hub/data_validation_diplast/main/main.py]