19:I[42426,["/_next/static/immutable/chunks/1xctgzqre-wsy.js","/_next/static/immutable/chunks/06_-wz6pdtp2b.js","/_next/static/immutable/chunks/30e52g49hg40p.js","/_next/static/immutable/chunks/02yq6tul2z-oh.js","/_next/static/immutable/chunks/10bx_ec2vj7n_.js","/_next/static/immutable/chunks/33bfn03fo8xnt.js","/_next/static/immutable/chunks/0tfjf78scu61b.js","/_next/static/immutable/chunks/31xnvej4n1bi9.js"],"default"] 1a:T1272,

Culturing:

Wild-type Bacillus subtilis bacteria (strain OI1084) were taken from −80°C frozen stock and streaked onto 1.5% agar plates prepared with Terrific Broth (TB, Sigma). Plates were incubated at 25°C for 24 hours, after which time a single colony from the plate was used to inoculate an overnight liquid TB culture at 30°C with shaking (200 rpm). The bacterial suspension was then subcultured (1.5 ml of cell culture into 60 ml of pre-warmed TB) and grown at 35°C and 200 rpm for 6 hours to mid-log phase (OD600 ≈ 0.2). Immediately prior to experiments, dense cell suspensions (∼ 1010 cells.ml−1) were prepared by centrifugation at 5000 g for 5 minutes, and the pellet was resuspended with 2 μl of fresh TB media.

Microfluidics and image analysis

Polydimethlysiloxane (PDMS) microfluidic channels were fabricated through soft lithography and plasma bonded to No. 1 thickness glass coverslips. The PDMS channels were thinly cast to ensure ample diffusion of oxygen to the bacterial suspension and prolonged cell activity. Dense cell suspensions were gently loaded into the microfluidic devices via pipette, and the channel inlet and outlet were sealed with wax to prevent residual flows. For all experiments, bacterial suspensions were imaged with brightfield illumination on an inverted microscope (Nikon Ti-E) using a sCMOS camera (Zyla 5.5, Andor Technology). Time-resolved velocity fields, u(x, t), of the bacterial suspensions were measured by performing Particle Image Velocimetry (PIV) using PIVLab implemented in MATLAB. The subsequent velocity fields were then lightly smoothed using a Guassian kernel with a standard deviation of one PIV pixel (1.73 μm) in space and one frame (9.5 ms) in time. Vorticity fields, ω(x, t), were computed from measured velocity fields using central differencing (MATLAB).

Bulk suspensions with decaying activity (quasi-2D)

To capture the bulk dynamics of dense bacterial suspensions in the absence of lateral wall effects, large microfluidic chambers (2 mm ×2 mm ×26 μm) were prepared, where the side length corresponds to ≈80 correlation lengths of the turbulent bacterial suspension. Imaging was performed with a 20 × objective (0.45 NA) at 105.5 fps. Bacterial activity was varied using a previously established approach. Briefly, the cell suspensions were imaged periodically over the course of 30 minutes, during which time the cell swimming speed naturally decayed due to oxygen depletion, where the decay rate of cell activity was controlled via the thickness of the PDMS device. Without the need to manipulate the cell suspension, this approach ensured that the bacterial concentration was constant across varying activity levels. Seven data sets were captured in total, which consisted of 6, 300 frames each (≈ 1 min per video) and a 2 − 3 min delay between acquisitions. Data analysis was restricted to the central portion of the chamber, ≈ 30 correlation lengths from the lateral boundaries.

Cell suspensions under varying confinement

To quantify POD modes for bacterial suspensions under varying degrees of confinement experiments from Wioland et al (2016). were replicated with minor modifications. Microfluidic devices (19 µm deep) were fabricated, which comprised a series of racetrack geometries connected by narrow inlets (30 µm wide). Nine racetrack widths were chosen between 30 µm ≤ W ≤ 171 µm in fractional increments of our measured characteristic vortex size (38 µm) taken as the minimum point in the spatial ACF in quasi-2D conditions (Fig. 1C, dotted red line). Cell suspensions were imaged at 40× (0.6 NA) magnification and 100 fps for 10 s per channel width. All confined microfluidic geometries were simultaneously loaded with the same cell suspension and imaged within the first 10 min of loading to ensure consistent bacterial activity. Experiments were 8 repeated across multiple days with freshly cultured bacteria to verify repeatability. The net flow ψ was calculated following the definition in [18]: ψ = |(Σu · eˆx) / (Σ||u||)|, where eˆx is the unit vector along the principal channel direction and the sum is over all PIV sub-windows over 5 s (500 frames) of video. ψ = 1 indicates uni-directional circulation flow around the racetrack, and ψ = 0 corresponds to a globally stationary suspension. The stream-wise flow profiles, F(y), were generated from the velocity field by averaging over time and space as F(y) = hu(x, y, t) · eˆxi(x,t) , where eˆx was chosen as defined in [18], such that F(y) is on average positive.

1b:T4a7,Dense suspensions of swimming bacteria exhibit chaotic flow patterns that promote the mixing and transport of resources and signalling chemicals within cell colonies. While the importance of active turbulence is widely recognized, the structure and dynamics of the resulting collective flows are the subject of intense investigation. Here, we combine microfluidic experiments with proper orthogonal decomposition (POD) analysis to quantify the dynamical flow structure of this model active matter system under a variety of conditions. In isotropic bulk turbulence, the modal representation shows that the most energetic flow structures dictate the spatio-temporal dynamics across a range of suspension activity levels. In confined geometries, POD analysis illustrates the role of boundary interactions for the transition to bacterial turbulence, and it quantifies the evolution of coherent active structures in externally applied flows. Beyond establishing the physical flow structures underpinning the complex dynamics of bacterial turbulence, the low-dimensional representation afforded by this modal analysis offers a potential path toward data-driven modelling of active turbulence 1c:T5b4,An inverted microscope is a microscope with its light source and condenser on the top, above the stage pointing down, while the objectives and turret are below the stage pointing up. It was invented in 1850 by J. Lawrence Smith, a faculty member of Tulane University (then named the Medical College of Louisiana). Inverted microscopes are useful for observing living cells or organisms at the bottom of a large container (e.g. a tissue culture flask) under more natural conditions than on a glass slide, as is the case with a conventional microscope. Inverted microscopes are also used in micromanipulation applications where space above the specimen is required for manipulator mechanisms and the microtools they hold, and in metallurgical applications where polished samples can be placed on top of the stage and viewed from underneath using reflecting objectives. The stage on an inverted microscope is usually fixed, and focus is adjusted by moving the objective lens along a vertical axis to bring it closer to or further from the specimen. The focus mechanism typically has a dual concentric knob for coarse and fine adjustment. Depending on the size of the microscope, four to six objective lenses of different magnifications may be fitted to a rotating turret known as a nosepiece. These microscopes may also be fitted with accessories for fitting still and video cameras, fluorescence illumination, confocal scanning and many other applications.1d:T96c,NSF Award Abstract: Drifting photosynthetic microbes in surface ocean waters carry out nearly half of global carbon (C) fixation, both supporting the marine food web and reducing atmospheric carbon dioxide (CO2) levels. The fate of C in ocean ecosystems is controlled by myriad individual interactions within a highly interconnected planktonic food web, the sheer complexity of which has hindered predictive understanding of global C cycling. Chemical cues govern microbial interactions, and during infection, marine viruses manipulate the metabolism of phytoplankton and bacteria, facilitating the release of dissolved organic matter from infected cells. This research aims to determine how viral metabolic reprogramming of and organic matter release from intact, infected phytoplankton influences microbial interactions and C cycling. The interdisciplinary, collaborative nature of the project will enable direct training of two postdoctoral researchers, one graduate student, and undergraduate students in viral ecology, microfluidics, and metabolomics. An educational outreach program that engages middle school students in hands-on, high speed imaging of microbes will be expanded, and the project will culminate in a three-day workshop to advance the application of microfluidic devices and mass spectrometry analyses in microbial ecology. The overarching hypothesis behind this research is that viral infection alters the chemical landscape of intact, infected picophytoplankton cells, attracting neighboring chemotactic bacteria and protistan zooplankton, and altering C flux pathways. To test this idea, a series of linked multi-scale laboratory-based experiments will be run to 1) Characterize the response of diverse model marine microbes to dissolved organic matter (DOM) released from intact, virus-infected picophytoplankton using microfluidics-based chemotaxis assays, 2) Identify key viral-derived DOM compounds eliciting chemotactic responses using stable isotope labeling, metabolomics analyses, and chemotaxis assays, and 3) Quantify micron-scale cross-trophic encounter dynamics and evaluate their impact on bulk-scale C cycling using liter-scale measurements of C dynamics linked to high spatiotemporal resolution live imaging of microbial food webs. The ultimate goal of the project is to develop a mechanistic understanding of the role of intact, virus-infected cells in oceanic C cycling.6:["$","$L19",null,{"dataset":{"_id":"885923","description":"","problem":"","acquisition":"$1a","processing":"

Flow fields were calculated using PIV (PIVLab, MATLAB), publicly available here. https://www.mathworks.com/matlabcentral/fileexchange/27659-pivlab-particle-image-velocimetry-piv-tool-with-gui . Box sizes of 32 pixel then 16 pixel (50%) overlap were used.

The subsequent velocity fields were then lightly smoothed using a Guassian kernel with a standard deviation of one PIV pixel (1.73 μm) in space and one frame (9.5 ms) in time.

An example script of the POD analysis is included in the submitted data files - this makes uses of the in-built \"svd\" function in MATLAB, and should be compatible with all versions.

\n","bcodmoProcessing":"Primary data file set to a csv representation of POD Flow Field Averages from each of the seven experiments.\r\n\r\nSpaces removed from parameter names in primary data file (csv).\r\n","title":"Dynamic Mode Structure of Active Turbulence Modeling Results from 2019-2022 (VIC project)","nickname":"Dynamic mode structure of active turbulence","dataUrl":"","currentState":"Final no updates expected","currentStateId":"7","validated":true,"restricted":false,"version":"1","version_date":"2022-12-28T05:00:00.000Z","abstract":"$1b","citation":"Richard J. Henshaw (2025) Dynamic Mode Structure of Active Turbulence Modeling Results from 2019-2022 (VIC project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-12-28 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.885923.1 [access date]","citationParts":{"identifier":"doi:10.26008/1912/bco-dmo.885923.1","versionDate":"2022-12-28","versionId":"1","year":"2025","title":"Dynamic Mode Structure of Active Turbulence Modeling Results from 2019-2022 (VIC project)","authors":"Richard J. Henshaw","publisher":"Biological and Chemical Oceanography Data Management Office (BCO-DMO)."},"doi":"10.26008/1912/bco-dmo.885923.1","release_date":null,"field_dataset_location":"","temporal_extent":{"start":"2019-09-30T04:00:00.000Z","end":"2022-05-01T04:00:00.000Z"},"temporal":{"resolution":"time:unitDay","type":"time:Interval","start":{"value":"1569816000","label":"2019-09-30"},"end":{"value":"1651377600","label":"2022-05-01"}},"spatial_extent":{"shape":"","center":{"latitude":0,"longitude":0},"wkt":"","north":0,"east":0,"south":0,"west":0},"dataset_type":["model results","experimental"],"keywords":["oceans"],"data_files":[{"id":"910969","title":"8855923_v1_PODFlowFieldAverages.csv","url":"https://datadocs.bco-dmo.org/dataset/885923/file/OJqvNRDHZnkAgV/8855923_v1_PODFlowFieldAverages.csv","showTableUrl":"https://www.bco-dmo.org/dataset/885923/data/view/910969","description":"File processed with laminar pipeline \"8855923_v1_PODFlowFieldAverages\" at path 8855923/1/data/8855923_v1_PODFlowFieldAverages.csv","filename":"8855923_v1_PODFlowFieldAverages.csv","bytesize":313,"checksum":{"value":"232d3912229b3bd8dc6dcc50a39ccac5","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"text/csv","fileType":{"_id":"192","name":"Comma Separated Values (.csv)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":true,"showTable":true,"restricted":false}],"supplemental_files":[{"id":"908957","title":"Example script for POD analysis","url":"https://datadocs.bco-dmo.org/dataset/885923/file/ZZpZBvZuDYj6XL/Example_POD_Analysis.m","showTableUrl":"","description":"Sample MATLAB script to conduct POD analysis on an input flow field. Uses in-built \"svd\" function for the decomposition","filename":"Example_POD_Analysis.m","bytesize":1234,"checksum":{"value":"ea781c05d5bf2d34a263875d4e09f0c7","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"text/x-matlab","fileType":{"_id":"425","name":"MATLAB Programming Script (.m)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908958","title":"Exp01_PODFiles","url":"https://datadocs.bco-dmo.org/dataset/885923/file/EKGKM16ugR6LNy/Exp01_PODFiles.zip","showTableUrl":"","description":"POD files for decomposition of both the velocity and vorticity fields of Exp01 (corresponds to Exp01 of the FlowFields data)","filename":"Exp01_PODFiles.zip","bytesize":29030222905,"checksum":{"value":"b7a2468408159922473fe74e693067e8","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908959","title":"Exp02_PODFiles","url":"https://datadocs.bco-dmo.org/dataset/885923/file/P6r6og5Hv4PEvz/Exp02_PODFiles.zip","showTableUrl":"","description":"POD files from the decomposition of both velocity and vorticity fields. Corresponds to Exp02 of the flow fields data.","filename":"Exp02_PODFiles.zip","bytesize":35336938064,"checksum":{"value":"ce32fa8c9e511724bacf511e1083eb66","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908960","title":"Exp03_PODFiles","url":"https://datadocs.bco-dmo.org/dataset/885923/file/nG8Gq78txOoJLL/Exp03_PODFiles.zip","showTableUrl":"","description":"POD files from the decomposition of both velocity and vorticity fields. Corresponds to Exp03 of the flow fields data.","filename":"Exp03_PODFiles.zip","bytesize":35334696018,"checksum":{"value":"6c936fb2a0f5958353b9f00b25e83abe","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908961","title":"Exp04_PODFiles","url":"https://datadocs.bco-dmo.org/dataset/885923/file/vmDmBRDu7ABX36/Exp04_PODFiles.zip","showTableUrl":"","description":"POD files from the decomposition of both velocity and vorticity fields. Corresponds to Exp04 of the flow fields data.","filename":"Exp04_PODFiles.zip","bytesize":35332348027,"checksum":{"value":"6bb9adaedd59ef8f041077faf07b87cb","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908962","title":"Exp05_PODFiles","url":"https://datadocs.bco-dmo.org/dataset/885923/file/QAvApjvH32Z47P/Exp05_PODFiles.zip","showTableUrl":"","description":"POD files from the decomposition of both velocity and vorticity fields. Corresponds to Exp05 of the flow fields data.","filename":"Exp05_PODFiles.zip","bytesize":35328718900,"checksum":{"value":"a7effa92c515644bacf754571da484e8","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908963","title":"Exp06_PODFiles","url":"https://datadocs.bco-dmo.org/dataset/885923/file/OJqJnLLUwXv7Nw/Exp06_PODFiles.zip","showTableUrl":"","description":"POD files from the decomposition of both velocity and vorticity fields. Corresponds to Exp06 of the flow field data.","filename":"Exp06_PODFiles.zip","bytesize":35323615697,"checksum":{"value":"2345816b9c63d0e20f87f1dd58fc7020","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908964","title":"Exp07_PODFiles","url":"https://datadocs.bco-dmo.org/dataset/885923/file/1VQVlEEfBqR1wN/Exp07_PODFiles.zip","showTableUrl":"","description":"POD files from the decomposition of both velocity and vorticity fields. Corresponds to Exp07 of the flow field data.","filename":"Exp07_PODFiles.zip","bytesize":35338600008,"checksum":{"value":"563b94bd2137996a9e2617639e658228","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908965","title":"Racetrack PIV flow fields","url":"https://datadocs.bco-dmo.org/dataset/885923/file/vmDmBRLFA5o0Kv/FlowFields_Racetracks.zip","showTableUrl":"","description":"PIV fields of the flow fields measurement in racetracks of widths:\n30, 38, 45, 57, 66, 76, 95, 114, 171um.\n\nAll flow fields were measured from the same suspension and within the first <10 mins (see methods for full details)\n","filename":"FlowFields_Racetracks.zip","bytesize":902053619,"checksum":{"value":"14edbd8616c22c52cc4f23cd1508726f","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908956","title":"Figure data and plotting scripts","url":"https://datadocs.bco-dmo.org/dataset/885923/file/zq9qJQqiMyz2wW/PaperFigures_PlottingScripts_RefinedData.zip","showTableUrl":"","description":"Associated plotting scripts and refined panel data to reproduce all plots in results publication:\nHenshaw et al - Dynamic mode structure of active turbulence.","filename":"PaperFigures_PlottingScripts_RefinedData.zip","bytesize":440909418,"checksum":{"value":"bba55aefef255d8713f2dd1e7842eafd","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908966","title":"POD Data: Racetracks","url":"https://datadocs.bco-dmo.org/dataset/885923/file/A8A8GWKhENrWXE/PODData_Racetracks.zip","showTableUrl":"","description":"POD files from the decomposition of the flow fields measured from a turbulent suspension under confinement (of various widths). Corresponds to the flow fields in FlowFields_Racetracks.zip","filename":"PODData_Racetracks.zip","bytesize":3663346931,"checksum":{"value":"8250825ff35a398916572ef3e4924737","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908955","title":"PODFlowFields_AverageSpeeds.xls","url":"https://datadocs.bco-dmo.org/dataset/885923/file/ypMpG76S6lElvB/PODFlowFields_AverageSpeeds.xls","showTableUrl":"","description":"Average flow speeds (um/s) of each of the flow fields in POD_FlowFields.zip","filename":"PODFlowFields_AverageSpeeds.xls","bytesize":30720,"checksum":{"value":"cb607d43989458e8191e86c764175831","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/vnd.ms-excel","fileType":{"_id":"564","name":"Microsoft Excel"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false},{"id":"908954","title":"Flow fields for dense suspensions of bulk bacterial turbulence.","url":"https://datadocs.bco-dmo.org/dataset/885923/file/l8D8oXpI1N4zO6/POD_FlowFields.zip","showTableUrl":"","description":"","filename":"POD_FlowFields.zip","bytesize":44253667819,"checksum":{"value":"12a8c54f8ce5ade614d354c9b0dd5d6c","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"application/zip","fileType":{"_id":"196","name":"ZIP Archive (ZIP)"},"isErddap":"","date":"","isPublicDuringEmbargo":false,"primaryDataFile":false,"showTable":false,"restricted":false}],"roles":[{"role":{"_id":"138","name":"Principal Investigator"},"performedBy":{"_id":"783786","name":"Jeffrey Guasto"},"performedAtOrganization":{"_id":"535503","name":"Tufts University"}},{"role":{"_id":"130","name":"Scientist"},"performedBy":{"_id":"885696","name":"Richard J. Henshaw"},"performedAtOrganization":{"_id":"535503","name":"Tufts University"}},{"role":{"_id":"133","name":"Contact"},"performedBy":{"_id":"885696","name":"Richard J. Henshaw"},"performedAtOrganization":{"_id":"535503","name":"Tufts University"}},{"role":{"_id":"145","name":"BCO-DMO Data Manager"},"performedBy":{"_id":"852000","name":"Sawyer Newman"},"performedAtOrganization":{"_id":"191","name":"Woods Hole Oceanographic Institution"}}],"deployments":[],"funding":[{"_id":"783784","name":"OCE-1829827","fundingSource":{"_id":"355","name":"NSF Division of Ocean Sciences"},"url":"https://www.nsf.gov/awardsearch/show-award?AWD_ID=1829827"}],"instruments":[{"_id":"885926","name":"Nikon Ti-E Inverted Microscope","description":"For all experiments, bacterial suspensions were imaged with brightfield illumination on an inverted microscope (Nikon Ti-E) using a sCMOS camera (Zyla 5.5, Andor Technology). ","type_id":"675","type_name":"Inverted Microscope","type_desc":"$1c","type_acronym":""}],"parameters":[{"_id":"910919","name":"Experiment_Number","format":"","frictionlessType":"string","description":"

ID representing a unique experiment. These IDs correspond with the POD data files available for download in the Supplemental Files section of the BCO-DMO metadata page for this representative dataset (https://www.bco-dmo.org/dataset/885923).

","units":{"_id":"","name":"unitless"},"type":{"_id":"1759","name":"exp_id","description":"

Experiment Id

"}},{"_id":"910920","name":"Average_speed","format":"","frictionlessType":"number","description":"

Average flow speeds in um/s of each of the flow fields in each of the experimental PODFiles zip files.

","units":{"_id":"","name":"um/s"},"type":{"_id":"671108","name":"flow_rate","description":"

Volume of liquid or gas moving through an area over time. Includes volume moving through a tube, pipe, or channel.

"}},{"_id":"910921","name":"Dataset","format":"","frictionlessType":"string","description":"

Name of corresponding experimental POD zip file

","units":{"_id":"","name":"unites"},"type":{"_id":"659543","name":"file_name","description":"

File name

"}}],"related_publications":[{"id":"885933","citation":"Henshaw, R. J., Martin, O. G., & Guasto, J. S. (2022). Dynamic mode structure of active turbulence. https://doi.org/10.1101/2022.04.15.488501\r\n","relation_type":["Results"],"relation_desc":""},{"id":"885934","citation":"Wioland, H., Lushi, E., & Goldstein, R. E. (2016). Directed collective motion of bacteria under channel confinement. New Journal of Physics, 18(7), 075002. https://doi.org/10.1088/1367-2630/18/7/075002\r\n","relation_type":["Methods"],"relation_desc":""}],"related_datasets":[],"services":[{"grouping":"","items":[{"name":"View Data","description":"An in-browser view of the primary tabular data file","url":"https://www.bco-dmo.org/dataset/885923/data/view","contentType":"text/html","actionType":"view"}]},{"grouping":"Metadata","items":[{"name":"HTML","description":"A simple HTML page of the dataset metadata","url":"https://www.bco-dmo.org/dataset/885923/description","contentType":"text/html","actionType":"view"},{"name":"PDF","description":"A PDF of the HTML dataset metadata","url":"https://www.bco-dmo.org/dataset/885923/Dataset_description.pdf","contentType":"application/pdf","actionType":"download"},{"name":"ISO 19115-2 (NOAA Profile)","description":"","url":"https://www.bco-dmo.org/dataset/885923/iso","contentType":"application/xml","actionType":"download"}]}],"programs":[],"projects":[{"_id":"783777","name":"Collaborative Research: Viral induced chemotaxis mediating cross-trophic microbial interactions and carbon flux","acronym":"VIC","description":"$1d"}],"archives":[{"id_value":"10.26008/1912/bco-dmo.885923.1","url":"https://doi.org/10.26008/1912/bco-dmo.885923.1","date_assigned":"2025-12-01","version_date":"2022-12-28","archiver":"Woods Hole Oceanographic Institution (WHOI BCO-DMO)","archiver_id":"191"}],"license":{"name":"Creative Commons Attribution 4.0","id":"CC-BY-4.0","uri":"https://creativecommons.org/licenses/by/4.0/","spdxUri":"https://spdx.org/licenses/CC-BY-4.0"},"metrics":{"views":{"count":1226},"downloads":{"count":1350}}}}]