We thank the reviewers for carefully reading our manuscript and giving us pertinent insights on how to improve it. Reviewer #1 Yes, our paper is a preliminary feasibility study on how to assess data FAIRness. Because of the special issue deadline, we had just a few months to frame the problem and implement a solution. Currently, work is currently ongoing for a follow up paper. In the manuscript, we now clearly state that our work is a preliminary feasibility study in the abstract, in the last paragraph of the introduction, and in the first paragraph of the discussion (in bold). Reviewer #2 - Figures 1 and 2 not visible in html doc We contacted the journal and the figures should now work. - In several places missing periods in R1.2, A 1.2 etc., instead often reads A12 R12 We corrected throughout the text. - Consider changing the title to include the main achievement of the authors, the semi automated workflow Thank you for this suggestion. We changed our title from “Two real use cases of FAIR maturity indicators in the life sciences” to “A semi-automated workflow for FAIR maturity indicators in the life sciences” - Adding additional, less similar, use cases would greatly increase the impact of the presented work. This could also help solve questions of the scalability of the Approach when working accross different repositories. We agree with this comment. Currently there is ongoing work in our group to extend the approach we presented in this paper. With this work, we wanted to contribute to the special issue of the journal, and thus we present preliminary results about the feasibility of our new approach. We added information about the preliminary nature of our work in the abstract, the last paragraph of the introduction, and the first paragraph of the discussion (in bold). Reviewer #3 QA1) What is the main component of the workflow implemented in the pipeline presented in this paper? Our paper provides a semi-automated workflow to calculate FAIR maturity indicators in the life sciences. The workflow strictly follows the recommendations provided by the Maturity Indicator Authoring Group and outputs a visualization that summarizes and compares indicator across various datasets. QA2) How a research question (e.g., “What are the differentially expressed genes between normal subjects and subjects with Parkinson’s diseases in the brain frontal lobe?”) is interpreted? A research questions is the reason why a researcher looks for data into a database. By analyzing the data s/he finds, the researcher will answer to the research question. QA3) Why state-of-the-art Name Entity Recognition (NER) tools are not used to support this task? We decided to use an approach that researchers with limited computational skills could easily implement and reproduce for their own datasets. Ongoing work in our group is considering more sophisticated approaches. QA4) Why only two use cases were selected? Two use cases are not enough to show the features of a given approach. We agree that two cases represent a limited number of use cases. However, the aim of this paper was to propose a method and test its feasibility. We added text that better specify our intention in abstract, last paragraph of introduction, and first paragraph of discussion (in bold). QA5) Why did only two users evaluate the pipeline? Which criteria were followed to select these two evaluators? Under which conditions were the pipeline was evaluated? So far, we have not asked users to evaluate the pipeline. However, we think that this is an excellent suggestion, and we will consider it for our next paper. QA6) Why controlled vocabularies and semantic enrichment techniques are not utilized to describe the metadata of the datasets? As we mentioned in QA3, we decided to use a simple approach so that researchers from diverse discipline could reuse it for their own data. QA7) What will be the behavior of the proposed workflow if several data collections are relevant for answering a research question? How the reported measures will be computed? In case of several dataset, the workflow will be separately run for each dataset. The comparison among the outputs will be performed using the balloon plot we proposed, where each line corresponds to a dataset, and each column to a FAIR maturity indicator.