https://doi.org/10.5061/dryad.v15dv4253

The Sedimentation and Particle Image Velocimetry (PIV) data of marine snow aggregates were acquired using a vertical tracking microscope, called Gravity Machine (GM) [for details on this instrument see Krishnamurthy et al. Nat Methods 17, 1040–1051 (2020). https://doi.org/10.1038/s41592-020-0924-7 ]. The aggregates were collected using sediment trap in the Gulf of Maine [for details of the net trap design see Peterson et al. (2005) Limnol. Oceanogr. Methods, 3, doi:10.4319/lom.2005.3.520 ], with subsequent measurement of these aggregates on the GM made immediately upon retrieval of net trap, on board a research vessel R/V Endeavor. The microscope itself was mounted on a mechanical 2-axis gimbal to minimize the low-frequency background noise coming from the rock-and-roll dynamics of the ship. Further details of the experimental setup and conditions is provided in the resulting manuscript.

GM directly gives the virtual vertical distance traversed as a function of time z(t). The effective vertical position as a function of time, shows fluctuation in vertical velocity due to the ship motion, but this disturbance only adds a residual error in a linear fit in z Vs t plane, thanks to the gimbal. This gives us the vertical settling speed. To correlate it with the visible particle size we threshold the images after conversion to 8-bit and measure the projected area and from this get the effective radius of a circle with an equivalent area [for further details see Supplementary material of the preprint R. Chajwa et al. 2023 https://arxiv.org/abs/2310.01982 ]

The collected images (with resolution 0.828 μm/ pixel) and tracks were initially processed in a customized ImageJ macros. The vibrations of the ship resulted in misalignment of the aggregates in adjacent frames. To remove this high-frequency noise in PIV data we automated the image registration process using a Matlab script. A command-line based PIV was conducted on the resulting images, on Matlab PIVLab. In the datasets we have included the registered image frames for every aggregate and the same folder contains the PIV data. The GM tracking data and PIV dataset were then simultaneously analyzed for mucus and particulate matter quantification using a matlab code automating_mucus_analysis.m, provided herewith. The invisible length-scale coming from the mucus is measured using the PIV data using a thresholding method given in this code.

Description of the data and file structure

Marine_Snow_data directory contains two folders and one xlsx datafile: 1) Raw_Sedimentation_PIV_data 2) Analyzed_data 3) Size_Sinking_data.xlsx

The contents of the two folders is described below:

1) Raw_Sedimentation_PIV_data directory contains various folders with folder name indicating dates of data collection. Each of these folder further consists of subfolders with GM data for individual aggregate tracks. These subfolders contains the following: 

>> input_raw contains raw images from the GM acquisition. 

>> output_processed contains frames after basic contrast enhancement through ImageJ. 

>> registered_output contains frames after image registration step to remove high frequency background noise. This folder also contains the PIV analysis .mat file.

 >> track.csv contains the tracking data from the GM

Size of mucus halo is acquired by thresholding the vertical flow field data and fitting an ellipse shown as dotted red curve.

Note that this dataset also contains the tracks for which we could not acquire clean flow-field due to background noise. In the mucus analysis code automating_mucus_analysis.m we ask a user prompt for checking if the PIV based flow-field are clean or not with the identifier '0' and '1' meaning No and Yes respectively. We study trends only for the particles for which we could acquire clean flow fields, since these flow-field captures the hitherto missing length scale set by the mucus degrees of freedom.

2) Analyzed_Data -- for every folder in *Raw_Sedimentation_PIV_data * there is a corresponding 1x1 structure element in Dataset >> .mat with the following field:

• Stru.stokes -- consists of the touple [velocity (cm/s), equivalent spherical radius (cm), semi-major exis of mucus ellipse (cm), semi-minor axis of the mucus ellipse (cm), ellipse orientation (degree) ]
• Stru.flow -- an image of the flow field, with an elliple fitted around the mucus
• Stru.clean -- '1' and '0' indicating clean and not-clean flow-field 
• Stru.imraw -- a raw dark-field GM image

The code: plot_mucus_data.m analyzes these set of structures and compiles a single dataset.

3) Size_Sinking_data.xlsx -- for the ease of reproducing the plots in the manuscript we are providing an excel sheet of the processed and analyzed dataset. with the following columns [for their precise definitions refer to the supplementary material in the preprint]

[Visible size (m), Invisible size (m), Undistorted size b0 (m), Sinking Speed U (m/day), Non-dimensional U, Elastic modulus E (Pascal) ] 

To get other non-dimensional parameters of interest see plot_mucus_data.m

Code/Software

We are providing following two MATLAB scripts, whose implementation is described above:

1) automating_mucus_analysis.m* --* this script simultaneously analyzes the GM track given in track.csv and their PIV data, particle-by-particle. 

While running this code include the PATH to the folder Raw_Sedimentation_PIV_data. The code sequentially analyzes the data contained in each subfolder. For example: if you run the code in the subfolder 20210621-2 and this code with sequentially go through each of the 38 folder within it. For each of these 38 dataset the code will display the mucus halo around the visible particle and prompt you to enter '0' or '1'. Once it has analyzed all 38 files it will generate a structure 'Stru' which contains 38 entries with the physical parameters of individual marine snow aggregate.

It uses the Perceptually Uniform Colormap: https://www.mathworks.com/matlabcentral/fileexchange/51986-perceptually-uniform-colormaps. 

The code takes as an input, images in folder input_raw, registered_output, track.csv, and the PIV .mat file in registered_output. As an output it generates the images flow_field.png of the PIV and mucus_halo.png of the visible particle embedded in the mucus halo defined by the yellow region in the flow field, and the structure data Stru which you can save as the name of the folder that you analyze, for example 20210621-2.mat. The .mat files in the Analyzed_Data folder are the outputs of this code.

2) plot_mucus_data.m -- using the conceptual framework provided in R. Chajwa et al. 2023 https://arxiv.org/abs/2310.01982 , this scripts extracts various quantities from the structure data files resulting from the automating_mucus_analysis.m.

The code takes as an input .mat files in Analyzed_Data and generates 1xN dimensional variables corresponding to the visible size, invisible size, sinking speed, non-dimensional sinking speed, buoyancy, relative mucus volume, elastic modulus, aggregate's specific gravity for the total of N tracks with identifier '1'. The resulting variables (commented in the code) are subsequently used to create the various plots in the manuscript. For ease of making plots, we have also included the excel spreadsheet Size_Sinking.xlsx where each columns are the various variables resulting from the output of plot_mucus_data.m.