The paper by Schmehl et al. presents multiple data sets on post-fire recovery in temperate pine forests (Brandenburg, Germany), collected within the PYROPHOB project.  PYROPHOB investigates relevant ecosystem components and their interactions on these burned sites under different post-fire management scenarios (Heinken et al., 2024).   As the authors correctly point out, post-fire recovery data is sparse in temperate regions, because these ecosystems are usually not subject to high frequency fire regimes.  Combined with its interdiscipinary nature, this makes the presented data set unique and valuable to a broader scientific community.  I recommend publishing the paper after some minor revisions.

The study site and research design is sufficiently presented, with a link to Heinken et al. (2024) that discuss these in more detail.  The methodology is described sufficiently.  I have two minor comments:

1. On P.1, L.20: ``Among the above-mentioned hazards...''

      It would be good mentioning that all these drivers of forest stress (listed on L.15) are more or less interlinked.  For example, climate change increases intensity and frequency of wildfires.

      Also, in the list on L.15, natural disasters are explicitly listed.  Isn't a wildfire also a natural disaster?

2. On P.22, L.505: The authors mention 47 student theses that have been written within this project.  Are these publicly available?  Would it be worthwhile to compile them and upload them as supplementary data with a unique DOI?  Otherwise, I would omit mentioning them in this data paper.

There are three data sets associated with this paper.  The data in general seems of high quality.  I have commented some minor issues I had when I reviewed them.

1. Main repository

   1. readme [good]

   2. Precipitation data [good]

   3. Deadwood data [good]

   4. Geospatial data [good]

   5. Photomonitoring instruments [ok, more information on individual pictures might be helpful]

   6. Photomonitoring thumbnails [site pictures are good, same comment applies for pictures of instruments]

   7. Photomonitoring plots [good]

   8. Soil condition data [good, dates are not in order]

   9. Soil solution data [good]

   10. Soil N mineralisation data [see below]

       What does "Date_0" and "Date_Exp" mean?  It's neither explained in the json file, nor in the article.  Seems to be start and end dates?

       What does "L" mean in the variable "depth"?  Only S and M are explained.

   11. Soil moisture data [see below]

       This might be me, but I don't understand exactly what (0-5|A-F) means.  Could you please explain?

       Column names in epsilon_series are not explained.

   12. Soil litter decomposition data [good]

   13. Soil moisture campaigns data [ok, columns in voltage_epsilon_theta_FDR.txt could be explained a bit more]

   14. Soil humus data [good]

   15. Stand structure data [good]

   16. Canopy cover data [good]

   17. Vegetation rejunevation data [good]

   18. Vegetation species grid [good]

   19. Synthesis data [good]

2. Embargoed repository (no review possible)

3. Imagery data

   1. Burn class data [good]

   2. dNBR data [good]

   3. UAV data [good]

Thank you for letting me review the manuscript by Schmehl et al. The authors present data from the PYROPHOB project, focusing on the monitoring of abiotic and biotic parameters of forest regrowth under different management regimes on former wildfire sites in northeastern Germany.

I have reviewed the ms. carefully and fully agree with the authors' notion that such post-fire datasets are scarce and highly useful for others studying fire-related aspects. The data is well prepared, documented and explained. I only have a few minor comments, but otherwise support publication of the dataset.

General comments

In the Abstract and introduction, the authors poste the word “holistic monitoring” several times. It would be interesting, if it could be clarified at somewhere what holistic what mean in this context. From a reader and researchers’ perspective, it would be extremely good to know for which aspects/analysis the dataset has the highest potential and where it has its weaknesses or where additional data would be needed. (edit: I see that this appears somewhat under 1.3 “Structure” – I think it would fit better to the introduction).

2.2.1 on question coming to my mind is: How was the forest structure before burning? Was it homogeneous?

Specific comments

1. 6-7: UAV-based remote sensing, and photo monitoring: please provide details as for the other groups of data (which parameters/type of data?)
2. 47-48: “PYROPHOB explicitly considers a wider selection of management options to assess their impact on natural regeneration and other related environmental variables.” à which ones? Would make sense to list them here imo.

l.101-102: the criteria for the pre-screening is unclear (e.g., similar soil type /geology for all plots, aspect, etc.?)

Fig. 1:

• the no removal and unburnt colors are difficult to distinguish

3.1: For all temporal data, it would be useful to include the temporal resolution in the table

l.267: in table 3.1 it states soil moisture is recorded in 30cm, here it is spatially distinct until 60cm

Technical comments

• not sure if it is a BE vs. AE thing, but usually there is a comma after (i.e.,) and (e.g.,)