I have read the manuscript by Dr Böhmländer et al. and checked the associated dataset. In my opinion, this dataset is extremely valuable for the atmospheric scientific community, considered the scarcity and relevance of INP data. Additionally, INP concentration data are provided at high time resolution which is certainly an added value for investigating INP properties, sources and impact. Nevertheless, before publication some issues should be addressed in the manuscript.

My main concern regards the measurement uncertainty. This is a fundamental aspect characterising the dataset, yet it is not discussed at all in the manuscript, nor uncertainty ranges are presented in the data files. I would invite the authors to address this aspect of the data quality in the text and, if possible, to associate uncertainty “bars” to the provided data values.

One of the strong points of the dataset is the high temporal resolution, which provides valuable information on the short-term variability of INP concentration at the study site. I would invite the authors to provide some further quantitative information about this in describing the dataset. I understand that the main topic of a data journal is not the scientific exploitation of the data, but some further information can be provided. For instance, which is the extent of the short-term variability in INP concentration? How does it compare with the day to day and seasonal variability? Is the short-term variability constant trough the study periods or does it vary with season?

Specific comments

L12. It is not clear what the authors mean with “arriving aerosol particles”; are they referring to long range transport, maybe? Please reformulate this sentence for major clarity.

L104. cINP = 108.495 L⁻¹: I am wondering if all the figures are significant in this concentration.

I have noticed some discrepancies between the data files and the manuscript. (1) the “flag” data column is labelled “INP_qc” in the data file, while it is “INP_cn_qc” in the text and in the data info file. (2) The column “INP_cn_flush” is “INP_flush” in the data info file. (3) There is not information on the meaning of the “INP_flush” data column in the data info file.

This paper describes a unique dataset of INP concentrations taken using a cloud chamber-style INP measurer, the Portable Ice Nucleation Experiment. The unusually high temporal resolution of INP measurements offers rare opportunities for future users to investigate correlations between INP concentrations and other high-temporal resolution data.

The paper is mostly well-written and the data is well-presented and highly accessible. Following best practice, the authors have not removed data that should be excluded based on quality control flags (which are provided), and I was able to replicate the basic plots in Figs. 1 and 2 within a few minutes of downloading the dataset using basic plotting tools in Python, which is commendable.

However, I have some minor concerns regarding the measurement uncertainty, and one undocumented piece of data contained in the files. I also think this paper occasionally relies on the reader having experience of INP measurements, which is not necessarily the case for some readers of this journal given its interdisciplinary nature.

If these minor concerns are addressed, I believe this paper will be an excellent contribution to ESSD.

Specific comments on the paper:

1. Throughout: The authors point to other papers which discuss uncertainties associated with PINE, but the review criteria in ESSD requires that “error estimates and sources of errors [are] given (and discussed in the article)”. There is a brief statement that concentrations may be biased low (line 86) but there is no indication of how low. It would be helpful for the authors to add a short discussion of the uncertainty here in order that users of this data can easily consider this.
2. Throughout: The authors largely attribute the importance of this dataset to the high temporal resolution and highlight on line 114 that “rapid changes are visible in the dataset”. Could the authors:
   1. demonstrate that the dataset is able to capture “short-term fluctuations in INP concentration” (line 105)? It may be helpful to do this by showing a subset of a small time-period of data alongside the whole campaign’s data depicted in Figure 1.
   2. briefly describe the current dearth of high-temporal resolution measurements in the introduction (see also comment 8)?
3. Lines 24-28: I found this section to be a little unclear at times. To make the link between INP and cloud phase more explicit, when discussing the Vergara-Temprado (2018) paper, could the authors point out that INP were shown to control cloud reflectivity?
4. Line 31 and Section 2: If a particular INP species is known to dominate at Pallas, could the authors consider specifying which INP types are targeted by these measurements?
5. Lines 48-49: Table 2 of the Asmi paper cited says that between Nov and Feb, the Arctic and Southern origin airmasses have frequencies of 31 and 28% respectively, suggesting that the Arctic air is not “dominant”. Additionally, the Asmi definition of the Arctic is a few degrees above the Arctic Circle. Could the authors describe the airmass origins with more precision?
6. Line 65: In this setup, what is the “certain size” below which aerosols are not measured?
7. Lines 67-68: Could the authors explicitly describe how the INP concentration is counted; i.e. the number of ice crystals is determined to be the number of immersion-mode INP (Möhler, et al. 2021)?
8. Line 115: In the vein of comment (2.2), while readers familiar with INP measurements will be aware of the challenges of measuring INP at high temporal resolution, consider pointing readers less familiar to papers which use filter-based methods and describing the difference in time resolution between PINE and these methods.

Specific comments about the dataset:

1. The dataset contains a variable “INP_cn_flush”. There is no metadata for this variable and it is not mentioned in the paper. If these are also INP concentrations, they are sometimes 2 orders of magnitude below those in “INP_cn” which was used to generate the figures in this paper, a significant difference. Please could the authors add documentation and then describe this data in the paper.
2. The data is currently in a number of separate files all of different lengths. Sometimes, these clearly represent a single day, but on rare occasions this does not. Is there a particular reason for this?
3. L1_data is provided but not raw_data. For clarity, why is the raw data not provided?

Technical corrections:

1. Lines 1-2 – It may be useful to rephrase the end of this sentence so the time period is clearer immediately to the reader, as December is typically considered outside autumn. For instance, “…(PaCE) at the Sammaltunturi station between late September 2022 and late December 2022.” However, I understand if the authors would rather keep it as is/include the word autumn to make it easier for people to find the measurements using search engines.
2. In my opinion, Section 3 follows a slightly illogical order. I think it would make more sense to first describe the chamber and its general principles of operation, then describe the specifics of the operation in this case.
3. Line 74 and corresponding reference – As alluded to later (line 129), v2.0.2 of the PIA software is currently available online. As such, I think this citation should not be “in preparation” but link directly to this version of the code, preferably via permalink to a permanent archive such as Zenodo if possible. P.S. The code is very well-documented, which is excellent!

References:

Möhler, O., Adams, M., Lacher, L., Vogel, F., Nadolny, J., Ullrich, R., Boffo, C., Pfeuffer, T., Hobl, A., Weiß, M., Vepuri, H. S. K., Hiranuma, N., and Murray, B. J.: The Portable Ice Nucleation Experiment (PINE): a new online instrument for laboratory studies and automated long-term field observations of ice-nucleating particles, Atmos. Meas. Tech., 14, 1143–1166, https://doi.org/10.5194/amt-14-1143-2021, 2021.

Vergara-Temprado, J., Miltenberger, A. K., Furtado, K., Grosvenor, D. P., Shipway, B. J., Hill, A. A., Wilkinson, J. M., Field, P. R., Murray, B. J., and Carslaw, K. S.: Strong control of Southern Ocean cloud reflectivity by ice-nucleating particles, Proc. Natl. Acad. Sci., 115, 2687–2692, https://doi.org/10.1073/pnas.1721627115, 2018