Collecting, Processing, and Analyzing Data

The collection stage encompasses the methods and processes necessary for acquiring and creating datasets. Next comes the processing stage, an intermediate phase that involves a series of activities aimed at verifying, sorting, filtering, and preparing the data for further analysis. The analysis stage consists of exploring, summarizing, and interpreting the data in order to extract relevant information. Although these activities correspond to distinct stages of the data lifecycle, we will treat them here as a cohesive whole, since the recommendations associated with them are essentially the same.

To ensure the reproducibility of the research and to facilitate the reuse of data by other researchers after the project is completed, it is strongly recommended to carefully document, at each of these stages, the choices made by the team, the methods and tools used, as well as the different types of data collected. This documentation helps maintain a record of the collection, processing, and analysis processes, while facilitating the understanding and reuse of the data. Furthermore, adherence to best practices and discipline-specific standardized protocols is essential to ensure data interoperability and facilitate its use by other researchers.

Data Types and Acquisition Methods

In certain projects, one may feel that no data is being produced. In such cases, it is important to examine the preparatory materials for publications and identify those whose loss would constitute irreparable damage to the ongoing research. It is these materials that can constitute the data for this type of project.

Reproducibility: Open Formats, Disciplinary Standards, and Documentation

The issue of reproducibility is central to modern research. Research data must be collected in such a way that the acquisition process can be replicated, thereby ensuring the integrity of the datasets.

It is therefore essential to share the protocols and scripts associated with data collection, analysis, and visualization processes. It is also important to document these various steps using a laboratory notebook, whether paper or electronic.

For best practices regarding metadata, documentation, and data organization, please visit the dedicated page.

Note that the steps involved in data collection and analysis are highly specific to each research field. Research data management support units, such as LabRADoR, may therefore face difficulties in adhering to all best practices and standardized methods in order to provide appropriate support. For these discipline-specific aspects, scientific platforms are often the best points of contact. You can also find information through national research infrastructures, particularly the thematic reference centers of Recherche Data Gouv.