FlowRepository is a database of flow cytometry experiments where you can query and download data collected and annotated according to the MIFlowCyt standard. It is primarily used as a data deposition place for experimental findings published in peer-reviewed journals in the flow cytometry field.

A database of flow cytometry experiments where users can query and download data collected and annotated according to the MIFlowCyt data standard.

This data repository contains original files (fcs) of flow cytometry experiments. The data was used to demonstrate the use of stochastic regression to quantify subpopulations of cells that have distinctly different genome copies per cell within a heterogenous population of Escherichia coli (E. coli) cells. This new approach gives estimates of signal and noise, the former of which is used for analysis, and the latter is used to quantify uncertainty. By separating these two components, the signal and noise can be compared independently to evaluate measurement quality across different experimental conditions. The files contain experiments from a single stock of Escherichia coli cells that was diluted to different concentrations, stained with Hoechst33342, and acquired on a CytoFLEX LX under the same acquisition conditions. ?Control_Hoechst? is a biologic control sample stained only with Hoechst. ?RainbowBeads? is a control of hard-dyed fluorescent beads with 8 distinct peaks of known fluorescent intensities per manufacturer documentation. ?Test_double? indicates test samples with double fluorescent probe staining, the fractional number (e.g. 0.7) indicates the dilution factor from the stock, and the integer at the end represents the technical replicate.The downloaded Exp_20230921_1_Cyto-A-journal.zip file contains 14 files in .fcs format, which requires suitable software to read/analyze data (i.e., FCS Express).

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods.Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

Flow virometry (FVM) can support advanced water treatment and reuse by delivering near real-time information about viral water quality. But maximizing the potential of FVM in water treatment and reuse applications requires protocols to facilitate data validation and interlaboratory comparison—as well as approaches to protocol design to extend the suite of viruses that FVM can feasibly and efficiently monitor. In the npj Clean Water article "Flow virometry for water-quality assessment: Protocol optimization for a model virus and automation of data analysis," we address these needs by first optimizing a sample-preparation protocol for a model virus (T4 bacteriophage) using a fractional factorial experimental design. We then compare manual and algorithmic methods of analyzing complex FCM data collected by applying the optimized protocol to (i) a clean solution spiked with a variety of biological and non-biological viral surrogates [mixed-target experiment], and (ii) tertiary treated wastewater effluent spiked with T4 bacteriophage and two sizes of fluorescent polystyrene beads [environmental spike experiment]. This repository contains the FCM data used to develop the optimized protocol and to test the two analytical methods.

This file contains the experimental details and full characterisation data (NMR, IR, HRMS) of all compounds produced in this publication.

We have added 6 new cruises to the SeaFlow data repository. Clic here for details regarding these cruises.

The linked bookdown contains the notes and most exercises for a course on data analysis techniques in hydrology using the programming language R. The material will be updated each time the course is taught. If new topics are added, the topics they replace will remain, in case they are useful to others.

I hope these materials can be a resource to those teaching themselves R for hydrologic analysis and/or for instructors who may want to use a lesson or two or the entire course. At the top of each chapter there is a link to a github repository. In each repository is the code that produces each chapter and a version where the code chunks within it are blank. These repositories are all template repositories, so you can easily copy them to your own github space by clicking Use This Template on the repo page.

In my class, I work through the each document, live coding with students following along.Typically I ask students to watch as I code and explain the chunk and then replicate it on their computer. Depending on the lesson, I will ask students to try some of the chunks before I show them the code as an in-class activity. Some chunks are explicitly designed for this purpose and are typically labeled a “challenge.”

Chapters called ACTIVITY are either homework or class-period-long in-class activities. The code chunks in these are therefore blank. If you would like a key for any of these, please just send me an email.

If you have questions, suggestions, or would like activity answer keys, etc. please email me at jpgannon at vt.edu

Finally, if you use this resource, please fill out the survey on the first page of the bookdown (https://forms.gle/6Zcntzvr1wZZUh6S7). This will help me get an idea of how people are using this resource, how I might improve it, and whether or not I should continue to update it.

This dataset is designed to test Machine-Learning techniques on Computational Fluid Dynamics (CFD) data.

It contains two-dimensional RANS simulations of the turbulent flow around NACA 4-digits airfoils, at fixed angle of attack (10 degrees) and at a fixed Reynolds number (3x10^6). The whole NACA family is spawned.

The present dataset contains 425 geometries, 2600 further geometries are published in accompanying repository (10.5281/zenodo.4106752).

For further information refer to: Schillaci, A., Quadrio, M., Pipolo, C., Restelli, M., Boracchi, G. "Inferring Functional Properties from Fluid Dynamics Features" 2020 25th International Conference on Pattern Recognition (ICPR) Milan, Italy, Jan 10-15, 2021

Graph and download economic data for Revolving Consumer Credit Owned by Depository Institutions, Flow (FLREVOLNDI) from Feb 1968 to Jan 2025 about owned, revolving, consumer credit, flow, loans, consumer, depository institutions, and USA.

Additional file 10: Table S5. Flow repository codes for flow cytometry data used in this study

A progressive loss of functional nephrons defines chronic kidney disease (CKD). Complications related to cardiovascular disease (CVD) are the principal causes of mortality in CKD; however, the acceleration of CVD in CKD remains unresolved. Our study used a complementary proteomic approach to assess mild and advanced CKD patients with different atherosclerosis stages and two groups of patients with different classical CVD progression but without renal dysfunction. We utilized a label-free approach based on LC-MS/MS and functional bioinformatic analyses to profile CKD and CVD leukocyte proteins. We revealed dysregulation of proteins involved in different phases of leukocytes’ diapedesis process that is very pronounced in CKD’s advanced stage. We also showed an upregulation of apoptosis-related proteins in CKD as compared to CVD. The differential abundance of selected proteins was validated by multiple reaction monitoring, ELISA, Western blotting, and at the mRNA level by ddPCR. An increased rate of apoptosis was then functionally confirmed on the cellular level. Hence, we suggest that the disturbances in leukocyte extravasation proteins may alter cell integrity and trigger cell death, as demonstrated by flow cytometry and microscopy analyses. Our proteomics data set has been deposited to the ProteomeXchange Consortium via the PRIDE repository with the data set identifier PXD018596.

This data archive contains datasets developed for the purpose of training and applying random forest models to the Mississippi Embayment Regional Aquifer. The random forest models are designed to predict total stream flow and baseflow as a function of a combination of watershed characteristics and monthly weather data. These datasets are associated with a report (SIR 2022-xxxx) and code contained in a USGS GitLab repository. The GitLab repository (https://code.usgs.gov/map/maprandomforest/) contains much more information about how these data may be used to supply predictions of stream flow and baseflow.

Two photon microscopy time series and lightsheet microscopy z-stack and time series of early Drosophila embryogenesis during posterior midgut invagination.

United States Liabilities: Flow: FC: Loans: Depository Institution nec data was reported at 0.000 USD bn in Mar 2018. This stayed constant from the previous number of 0.000 USD bn for Dec 2017. United States Liabilities: Flow: FC: Loans: Depository Institution nec data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 354.856 USD bn in Dec 2008 and a record low of -139.910 USD bn in Jun 2009. United States Liabilities: Flow: FC: Loans: Depository Institution nec data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB032: Funds by Sector: Flows and Outstanding: Funding Corporations.

This repository is associated with the manuscript for Water Resource Research (WRR): Flow and Entrainment Mechanisms around a Freshwater Mussel Aligned with the Incoming Flow. The repository contains the data files for the manuscript.

Hydrology data for the UK area. Includes peak flow data, data from the National Hydrological Monitoring Programme, and other general data. The National River Flow Archive (NRFA) is the UK's focal point for river flow data. The NRFA collates, quality controls, and archives hydrometric data from gauging station networks across the UK including the extensive networks operated by the Environment Agency (England), Natural Resources Wales, the Scottish Environment Protection Agency and the Rivers Agency (Northern Ireland).

Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, Geophysical Research Letters, https://doi.org/10.1002/2018GL077914

The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: https://doi.org/10.5281/zenodo.1219784

Abstract

Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.

Description of repository datasets:

1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:

"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758

"UP_CELLS" : number of upstream cells (pixels)

"AREA" : upstream drainage area (km2)

"DISCHARGE" : discharge (m3/s)

"WIDTH" : mean bankfull river width (m)

"WIDTH5" : 5th percentile confidence interval bankfull river width (m)

"WIDTH95" : 95th percentile confidence interval bankfull river width (m)

"DEPTH" : mean bankfull river depth (m)

"DEPTH5" : 5th percentile bankfull river depth (m)

"DEPTH95" : 95th percentile confidence bankfull river depth (m)

"LENGTH_KM" : segment length (km)

"ORIG_FID" : original ID of segment

"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg

"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)

"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)

"SLOPE" : average slope of segment (m/m)

"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/

"CITY_POP_M" : population of joined city (max N inhabitants)

"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html

"DAM_AREA_S" : surface area of joined dam (m2)

"DAM_CAP_MC" : volumetric capacity of joined dam (m3)

"CELER_MPS" : modeled river flow wave celerity (m/s)

"PROPTIME_D" : travel time of flow wave along segment (days)

"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins

"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0

"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution

"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.

"UPSTR_DIST" : upstream distance to the basin outlet (km)

"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)

"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)

"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)

"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)

"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)

"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)

"MC_LENGTH" : mean of Monte Carlo simulated river length (m)

"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)

"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)

"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))

"CONTINENT" : integer indicating the HydroSHEDS region of shapefile

1. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:

Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles

Col2: Downstream UID

Col3: Number of upstream UIDs

Col4 – Col12: Upstream UIDs

1. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:

FID : unique identifier of each polygon

CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)

CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)

COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)

1. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table.

2. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:

GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv

GAUGE_SITE: USGS gauge site number of joined gauge

GAUGE_HUC8: which hydrological unit code the gauge is located in

OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R.

OBS_CEL_MPS: gauge-based celerity estimate (m/s).

1. tab1_latencies.csv contains data shown in Table 1 of the manuscript.

2. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.

3. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:

tabdTT_b.csv : basin outlet travel times for all rivers

tabdTT_b_swot.csv : basin outlet travel times for SWOT

tabdTT_c.csv : next downstream city travel times for all rivers

tabdTT_c_swot.csv : next downstream city travel times for SWOT

tabdTT_d.csv : next downstream dam travel times for all rivers

tabdTT_d_swot.csv : next downstream dam travel times for SWOT

This repository contains the dataset and analysis scripts associated with the upcoming publication titled Growing malaria parasites at a critical shaking speed that mimics physiological flow conditions reveals new phenotypes for EBA and RH invasion ligands. The repository includes a comprehensive collection of data and scripts used to generate all the plots, along with videos showing how the red blood cells behave in culture media for different shaking speeds in the different shaking vessels. The growth assay data used for this experiment was collected in four batches, labelled GA1 to 4:

• GA1: compares the different knockout lines.
• GA2: compares different hematocrits.
• GA3: compares different growth vessels.
• GA4: contains more repeats of lines from GA1.

This repository offers all necessary resources to replicate the findings, including the complete codebase, raw data, and graphical representations of results. Researchers are encouraged to explore the included notebooks and datasets for detailed insights.

A series of nozzle chevrons were designed to create a parametric family with varying length, penetration and width, with the objective of demonstrating noise reduction for supersonic nozzles. Eight sets of chevrons were fabricated and tested on the High-Flow Jet Exit Rig in the Aero-Acoustic Propulsion Lab at the NASA Glenn Research Center in 2009. Details of the test are given in the paper "An MDOE Investigation of Chevrons for Supersonic Jet Noise Reduction" by Henderson and Bridges (DOI: 10.2514/6.2010-3926). This data repository contains a test requirements document with configuration and flow definitions, a spreadsheet with measured jet flow conditions from the test, chevron geometry files in CAD format, and a set of files containing spectral directivity measurements of the acoustic far-field at the ~350 test points. Details about the data files are contained in a README document.