DF = datafile/filename Can load file from Import Dataset dropdown in RStudio. Make sure to set number columns to numerical. Setting one such columns sets all the other number columns Exported data will be in Finder->Jeana’s MacBook Pro->Macintosh HD->Users->Jeana Drake If receive “incomplete final line on Filename(.fasta), go to final line of fasta file and hit Enter and re-save.
getwd()
max.print=# #can set at 100000 to be safe
Control + C
my_dataframe <- data.frame(Name = c(“Name1”, “Name2”), Age = c(1,2)) #Creating a data table uses the same commands except must open library(data.table) first and use <- data.table command
library(data.table) SubsetDF <- setDT(LargeDF) [ColumnName %chin% SmallDF$ColumnName) library(xlsx) write.xlsx(SubsetDF, file = “SubsetDF.xlsx”)
library(seqinr) #make sure SequenceFile.fasta is in wd #load DF with list of sequences IDs in subset. Make sure the sequence ID does NOT have > NewFastaName <- read.fasta(file = “SequenceFile.fasta”, seqtype = c(“AA”), as.string = FALSE, seqonly = FALSE, strip.desc = FALSE) NewFastaName2 <- NewFastaName[names(NewFastaName) %in% DF$ColumnName] write.fasta(sequences = NewFastaName2, names = names(NewFastaName2), file.out = “NewFastaName2.fasta”, open = “w”, nbchar = 60, as.string = FALSE)
library(Biostrings) library(strings) #Redirect console output to file in home directory (see commands below) NewFastaName <- readAAStringSet(“Filename.fasta”) CharacterVectorName <- as.character(NewFastaName) Fasta_Matches <- str_extract_all(CharacterVectorName, “[AApattern]{MinimumCount,}”) #Pattern is any amino acids of interest (e.g., [DE]) #Count for pattern is range contained in curly brackets, {#,} gives lower bounds, {#1,#2} gives lower and upper bounds, {,#} gives upper bounds names(Fasta_Matches) <- names(NewFastaNames) print(Fasta_Matches) #stop redirecting output to file
sink(file = “Filename.txt”) #Run R commands, end with print command print(OutputOfLastCommand) #Check output file sink() #Stops redirecting output and returns to the console
merge(DF1, DF2, by.x = c(“ColumnNameDF1”), by.y = c(“ColumnNameDF2”), all = TRUE) #works even if there are different numbers of rows MergedDF <- merge(DF1, DF2, by.x = c(“ColumnNameDF1”), by.y = c(“ColumnNameDF2”), all = TRUE) #Makes a new table that can be exported. The first command just prints the output in the terminal.
library(xlsx) library(“CHNOSZ”) SubsetDF <- read.fasta(“filename.fasta”, iseq = NULL, ret = “count”, lines = NULL, ihead = NULL, start=NULL, stop=NULL, type=”protein”, id = NULL) #OR SubsetDF <- read.fasta(“filename.fasta”, seqtype = c(“AA”), as.string = FALSE, seqonly = FALSE, strip.desc = FALSE) write.xlsx(SubsetDF, file = “SubsetDF.xlsx”) #If R gives “line # did not have # elements” error, use BioStrings to accurately read FASTA file library(Biostrings) DF_fasta_data <- readAAStringSet(“filename.fasta”) #CHNOSZ does not seem to work with Biostrings-imported FASTA files DF_aa_counts <- letterFrequency(DF_fasta_data, letters = AA_ALPHABET) write.xlsx(DF_aa_counts, file = “DF_aa_counts.xlsx”) #If “WARNING: An illegal reflective access operation has occurred” error is shown, ignore it and check to see if the file has been exported to the Home Directory anyway
library(seqinr) library(Biostrings) library(xlsx) library(Peptides) DF_fasta_proteins <- read.fasta(file = “filename.fasta”, seqtype = “AA”, as.string = TRUE) #requires an empty row at the bottom of the fasta file DF_seq <- unlist(DF_fasta_proteins) DF_pi_values <- sapply(DF_seq, pI) DF_pI_export <- data.frame(Proteins = names(DF_pi_values), pI = DF_pi_values) write.xlsx(DF_pI_export, file = “DF_pI.xlsx”)
library(seqinr) translate(SEQ, frame = 0, sens = “F”, numcode = 1, NAstring = “X”, ambiguous = FALSE) #frames are 0, 1, or 2; sens F is forward, R would be reverse; numcode 1 is standard genetic code; NAstring X translates aa’s for ambiguous bases as X; ambiguous FALSE doesn’t take ambiguous bases into account #stop codons are printed as ‘*’ #list of numcodes (for mitochondrial, etc) are at the translate can page #the above translates one sequence, see below to translate from fasta file in home drive FASTA <- read.fasta(file = “SequenceFile.fasta”, seqtype = c(“DNA”), as.string = FALSE, seqonly = FALSE, strip.desc = FALSE) TRANSLATED_FASTA <- translate(seq = FASTA, etc) #Might require a carriage return after last line
write.table(DF, file = “DF”, sep = “\t”, rownames = FALSE)
write.table(DF, file = “DF.txt”)
boxplot(NumericalColumn~FactorColumn, data = DF, cex.axis = #, cex.lab = #, ylim = c(#,#), ylab = “Text”, horizontal = TRUE, col = #) #cex.axis, cex.lab, cex.main is an expansion factor; >1 maxes larger, <1 makes smaller #ylim (or xlim) changes axis limit; can be reversed by switching values (xlim = c(##, #)) where values are related to axis size, not necessary to values in the spreadsheet (I.e., depths of 10 and 30 m might have c(##, #) values of c(2.5, 0.5)) #ylab (or xlab) changes axis label #horizontal rotates axes; must then rename axis labels #col sets the color according to the codes below in Mean Centered Cluster Plots #For multiple colors, col = c(“#”, “#”, “#”, etc) above #or boxplot(DF$NumericalColumn, DF$FactorColumn) boxplot(DF$Numerical ~ DF$Factor)
boxplot(DF$NumericalColumn, outcol = “red”, cex=#) #outcol sets the color of outlier circles #cex sets the outlier circle size
o = ordered(DF$FactorColumn, levels = c(“Factor1”, “Factor2”, etc)) boxplot(NumeralColumn~o, data = DF)
var(DF$NumeralColumn)
var(DF$NumeralColumn[DF$FactorColumn==“factorterm”])
fligner.test(DF$NumeralColumn~DF$FactorColumn) #test for homogeneity of variance
shapiro.test(DF$NumeralColumn[DF$FactorColumn==“factorterm”]) #test for normal distribution #can add [1:5000] immediately after FactorColumn and continue in groups of 5000 if >5000 rows
oneway.test(DF$NumeralColumn~DF$FactorColumn) #independent samples, normal distribution, same variance
data.lm <- lm(DF$DependentVariable~DF$IndependentVariable, data = DF) data.av <- aov(data.lm) summary(data.av) data.test <- TukeyHSD(data.av) data.test
kruskal.test(DF$NumeralColumn~DF$FactorColumn) #for non-normal distribution or inhomogeneous variance
library(dunn.test) dunn.test(DF$DependentVariable, DF$IndependentVariable, method=“bonferroni”)
Alternatively:
library(FSA) PT = dunnTest(DependentVariable ~ IndependentVariable, data=DF, method=“bonferroni”)
t.test(NumericalColumn~FactorColumn, data = DF, mv = 0, alt = “two.sided”, conf = 0.95, var.eq = F, paired = F) #mv = 0 is null hypothesis that mean=0.
wilcox.test(DF$NumeralColumn~DF$FactorColumn) #non-parametric t-test
pairwise.wilcox.test(DF$NumeralColumn, DF$FactorColumn, paired = FALSE, p.adjust.method=“BH”) #can use default p.adjust method
library(readr) library(outliers) library(EnvStats) #See box plot for outliers above rosnerTest(DF$NumericalColumn, k=#, warn=TRUE #k=# is number of red outlier circles plus one. Can ballpark if you can’t tell bc there are so many #If k=# value is too low, the same number of outliers as the # will be returned #$all.stats information will list the outliers first and will give the row number on which they fall
#all pheatmap library except for pheatmap
DF_clean = DF %>% select(“ColumnName”, starts_with(“Column1”, “Column2”)%>% as.data.frame() #ignore select if DF only has the information to be graphed #as.data.frame converts from tibble to dataframe DF_clean2 <- DF_clean[, -1] #removes row name column rownames(DF_clean2) <- DF_clean[, 1] #moves row name column over to dataframe’s rownames pValues <- apply(DF_clean2, 1, function(x) t.test(x[Column#:Column#],x[Column#:Column#])$p.value) #first range of column numbers is 1st group, second range is second group library(xlsx) write.xlsx(as.data.frame(pValues), file = “filename.xlsx”)
MatrixName = rbind(c(x,x,x), c(x,x,x), c(x,x,x), etc); MatrixName fisher.test(MatrixName, workspace = 2000000))
hist(df$NumeralColumn)
Category1 <- c(“Name1”, “Name2”) Category2 <- c(1,2) DataName <- data.frame(Category1, Category2) #Can see data frame with the following command, but not necessary DataName mycolors <- c(“red”, “yellow”) pie(DataName$Category2, labels = Category1, cex = #, main = “Title”, col = mycolors) #cex changes the label font size #base pie command keeps order of Category1 pie(DataName$Category2, labels = NULL, cex = #, main = “Title”, col = mycolors) #Removes label names, replaces them with numbers that can be removed in Illustrator legend(“right”, legend = Category1, fill = mycolors, cex = #) #Add legend. Can use bottomright, bottom, bottomleft, left, or combos with top. #Legend overwrites part of pie chart. Generate pie chart w/ and w/o legend and crop if needed
library(ggplot2) #make data frame as above for base pie chart p <- ggplot(DataName, aes(x = “”, y = Category2/value, fill = Category1/category)) + geom_bar(width = 1, stat = “identity”) #ggplot doesn’t have a pie chart so start as a bar chart and the x=“” creates a fake x-axis #fill fills in the slices to assign colors later pie_chart <- p + coord_polar(theta = “y”) #converts the bar chart (Cartesian coordinates) to polar coordinates as a circle pie_chart_enhanced <- pie_chart + theme_void() + labs(title = “Distribution of Categories”) + geom_text(aes(label = category), position = position_stack(vjust = 0.5)) #Not required but enhances the appearance pie_chart_enhanced #Displays pie chart #ggplot2 pie command reorder Category1 alphabetically
plot(df$x-axis, df$y-axis, col = “color”, pch = ##, xlim = range(c(#,#)), xlab = “Label”) #pch gives scatter as points and value changes the size, with smaller values yielding larger size, max value of 25 points(df$x-axis, df$y-axis, col = color2”, pch = ##) #Adds second set of data on same axes legend(“toplight”, legend = c(“Label1”, “Label2”), col = c(“color1”, “color2”), pch = 3#
library(tidyverse) library(magrittr) library(pheatmap) library(RColorBrewer) library(rio) library(dplyr) DF_clean = DF %>% mutate(newcolumnname = paste0(column1, “:”, column2) %>% select(“newcolumnname”, starts_with(“Numbercolumn1”, etc) %>% as.data.frame() #ignore mutate command if gene ID and annotation are already in the same column or if that information isn’t needed #ignore select if DF only has the information to be graphed #as.data.frame converts from tibble to dataframe DF_clean2 <- DF_clean[, -1] #removes row name column rownames(DF_clean2) <- DF_clean[, 1] #moves row name column over to dataframe’s rownames boxplot(DF_clean2, las = 2) #checks distribution of data #las = 2 makes sample label perpendicular DF_clean2_log10 = log10(DF_clean2) boxplot(DF_clean2_log10, las = 2) #minimizes variance pheatmap(DF_clean2 or DF_clean2_log10), border_color = NA, fontsize_row = #, fontsize_col = #, cluster_rows = F, show_rownames = F) #cluster_rows = F orders rows according to how they are in the dataframe, like if making a heat map to match a ranking graph #show_rownames = F removes rownames if making a giant heatmap of tons of genes #to set the order of the columns but keep their dendrogram, do the following: library(dendextend) phtmap <- pheatmap::pheatmap(DF_clean2) col_dend <- phtmap[[2]] col_dend <- dendextend::rotate(cold_dend, order = c(“Column1”, “Column2”, etc)) pheatmap(DF_clean2, cluster_cols=as.hclust(col_dend), all the other pheatmap stuff) #assigned column order must be possible given the dendrogram or it will give an error or just not change the order.
library(tidyverse) library(magrittr) library(RColorBrewer) library(rio) library(dplyr) #prcomp is standard in R #Load file #If file is small, can load with samples in rows and genes/proteins in columns. Remove sample name column as all data must be numerical and R won’t move row names in 1st column to row names using pheatmap commands #If file is large (like normalized and transformed deseq output) and can’t be transposed first in excel bc it exceeds the column limit, load as a normal file with samples as columns and genes as rows. Remove first row with sample names before loading DF = as.martrix(DF) #if file has samples as columns and genes as rows, transpose first using the following command. If file loaded already transposed, skip it DF_trans <- t(DF) DF_PCA <- prcomp(DF, center = TRUE, scale = TRUE #center shifts the variables to be zero centered #scale shifts the unit variance before the analysis takes place. Scaling is advisable by the rdocumentation. #if transpose command was used, make sure to do prcomp on DF_trans rather than DF summary(DF_PCA) #Gives SD, % of variance, and cumulative % of variance explained by each PC str(DF_PCA) #gives information about the PCA object itself rownames(DF) <- c(“Sample1”, “Sample2”, etc) #makes position of each sample in PCA plot show up as sample name library(devtools) library(ggbiplot) ggbiplot(DF_PCA, var.axes = FALSE, labels=rownames(DF), labels.size=#) #4 is a good size for row name font #var.axes removes the arrows for each factor placing the samples in the PCA. Each factor is a gene or protein #if xlim or ylim need to be adjusted or to add label or change background color, make the ggbiplot its own object DF_PCA_obj <- ggbiplot(DF_PCA, var.axes = FALSE, labels=rownames(DF), labels.size=#) DF_PCA_obj + coord_cartesian(xlim = c(#,#), ylim = (c(#,#) + theme_bw(), ggtitle(“Title”) + theme(plot.title = element_text(hjust = 0.5)) #theme_bw leaves gray lines from tick points but makes background white and puts solid black lines around the plot #theme(plot.title…) centers the title. The default is to have it left-justified for some stupid reason
library(vegan) m_DF = as.matrix(DF) #just values, no row names or other columns DF_nmds = metaMDS(m_DF, distance = “dissimilarityindex”, maxit = 200, sfgrmin = 1e-7) #bray-curtis is default #maxit increases the # of iteration #sfgrmin decreases scale factor of the gradient, add this if getting the error ‘scale factor of the gradient <sfgrmin’ #if nmds doesn’t converge, try the following: DF_nmds2 = metaDMS(m_DF, distance = “dissimilarityindex”, previous.best = DF_nmds, maxit = 200) #if nmds still doesn’t converge, give up and do PCA
maxplot(DF, type = “1”, pch = 1, col = #, ylab = “labelname”, xlay = “labelname”, xaxis = c(#,#), main = “titlename”) #type is line type assignment, 1=solid line #col is color assignment, 1=black, 2=pink, 3=green, 4=blue, 5=cyan, 6=magenta, 7=yellow, 8=gray, 9=black, 10=dark pink #can also use the HEX color code in “” “” #to make lines different colors, have each group as a separate tab #after maxplot, do the following: maxlines(DF, type = “1”, pch = 1, col = different#thaninmaxplot, lay = #forlinetype, lwd = #forlinewidth)
Transposedf <- t(df)
Listname=list(title1=as.matrix(df1),title2=as.matrix(df2))
library(tidyverse) Sep_DF <- DF %>% tidyr::separate_rows(“Column name”, sep = “,”) library(xlsx) write.csv(Sep_DF, file = “Filename.csv”) #Replace , with whatever separator is used. #Open in excel and save as tab separated text file #For Blast2GO, change suffix to .annot
install.packages(“signalHsmm”) library(signalHsmm) #Read in fasta file per above, making sure that stop codon ‘*’ is removed from all sequences FASTA_signalP <- run_signalHsmm(FASTA[1:n]) #n = highest number of sequences desired library(xlsx) FASTA_signalP_table <- pred2df(FASTA_signalP) write.xlsx(FASTA_signalP_table, file = “FASTA_signal_table.xlsx”)
#This is more for text/BBEdit #For eDNA sequencing core output for SNPs #Open in Excel
=(cell with “>”)&(cell with sequence) #Drop to all sequences #Control-c #Open in BBEdit #Right-click #Paste-values #Control-f Replace > Replace with >control-return #Replace all
library(ape) DendroName <- read.tree(text = “NEWICK FILE TEXT PASTED IN”) #Ensure that the tree string ends in a semi-colon. #Make sure that brackets are matched. Some text editors, such as RStudio or Notepad++, contain built-in syntax highlighting that will highlight the counterpart to each parenthesis as you type. #Look out for ((double brackets)) or brackets enclosing a single node: the resultant ‘invisible nodes’ can cause R to crash. >plot(DendroName) #write.dendrogram from ape package doesn’t seem to work (could be RStudio version on my very old laptop) plot(DendroName, show.node.label = TRUE) #shows bootstrap values at nodes nodelabels() #labels the nodes with their number tiplabels() #labels the tips with their number #If the values beyond the decimal point should be shortened or changed, must be done manually in the Newick file; the node values to be changed are before the : in the format #.########(node value):#.########(node length) rt.DendroName# <- rotate(DendroName,node#) plot(rt.DendroName# #rotates at a node of Node# #Node #s seem to start at the top of the dendrogram rr.DendroName# <- root(DendroName,node=#) plot(rr.Dendroname#) #reroot at a node of Node#
boxplot(NC~FC, data =DF, ylab = expression(paste(“Text“^”Text”*” Text”))) #^ raises the next alphanumeric, * lowers the next alphanumeric back to normal #[] around alphanumeric makes the contents subscripted, * lowers the next alphanumeric back to normal
#RStudio allows copy/paste of symbols and emojis
library(taxonomizr) prepareDatabase(‘accessionTaxa.sql’) SQLite database accessionTaxa.sql already exists. Delete to regenerate #Do NOT delete the database and associated files [1] “accessionTaxa.sql” taxaYOURSAMPLE<-getId(c(“sps 1”, “sps 1”, “sps 3”, “etc”), ‘accessionTaxa.sql’) #Give whatever name you want for taxaYOURSAMPLE #Each Latin name must be in double quotations and separated by a comma #There can be a space between the names and the commas #Just copying the vertical list of Latin names from the Organism column of the Blast output doesn’t work #RStudio can only handle 4091 characters per line. To get around this for long lists of Latin names, highlight names in the alphabetized list (see Megablast .csv instructions above) up to 4000 characters inclusive of quotes and commas (BBEdit shows character count in the lower right side of the file border) starting with a double quote before a species name and the comma after a species name. Then hit enter. This yields a ‘+’ and starts a new line of the same command. You can then copy in the next batch of species names up to ~4000 characters and continue with species name batches until you run through your entire non-redundant list. On the last line of the command, include the “), ‘accessionTaxa.sql’)” to close out the command. DF<-getTaxonomy(taxaYOURSAMPLE, ‘accessionTaxa.sql’) write.table(DF, file = “DF.txt”) #This command writes a txt file to the home directory containing the taxonomy assignments for all species across all lines of the command above that are separated by ‘+’
if (!require(“BiocManager”, quietly = TRUE))
- install.packages(“BiocManager”) BiocManager::install(“msa”) library(msa) #Make sure .fasta file is in Users directory msa(“Filename.fasta”, method = c(“ClustalOmega”), type = “protein” #Output to cluster format is not yet resolved.
]]>library(protti) library(devtools) library(tidyverse) library(dplyr) library(magrittr) #Open .xlsx file of Accession# + Peptide #Open .xlsx file of Accession# + Protein Merged_ProtPep <- merge(DF1, DF2, by.x = c(“DF1AccessColumn”), by.y = c(“DF2AccessColumn”), all = TRUE) write.table(Merged_ProtPep, file = “Merged_ProtPep.txt”) #Open Merged_ProtPep ProtPepDF <- as.data.frame(Merged_ProtPep) PepMap <- find_peptide(data = ProtPepDF,protein_sequence = ProteinColumn,peptide_sequence = PeptideColumn) write.table(PepMap, file = “MappedPeptides.txt”)
Based on “Preparation of Artificial Seawater” by Kester
Check if these are already made first
The steps below allow you to prepare 7L of artificial seawater. You will also have leftovers of each of the solutions 2: volumetric salts to use the next time you prepare the artificial seawater.
First, prepare each of the volumetric salt solutions as they need extra time on the stir plates to dissolve.
A. To prepare the magnesium chloride solution, measure 1000 ml of D.I. water weigh out 203.31 grams of magnesium chloride, and add both to a 1500 ml flask with a stir bar, put on a stir plate until dissolved
B. To prepare the calcium chloride solution, measure 200 ml of D.I. water weigh out 29.4 g grams of calcium chloride, and add both to a 500 ml flask with a stir bar, put on a stir plate until dissolved
C. To prepare the strontium chloride solution, measure 60 ml of D.I. water weigh out 1.6 g grams of strontium chloride, and add both to a 100 ml flask with a stir bar, put on a stir plate until dissolved
Now prepare solution 1, in contrast to the previous steps these salts will be directly added to the 10 L carboy
A. First, add 5L of D.I. water to the 10 L carboy
B. Then weigh out the following salts and add directly to the 10 L carboy with the 5L of D.I. water (Note: Some of the salts may be crumpled together it is better to pour these salts out into their respective container caps and break them apart using the spatula before weighing, remember to clean the spatula with D.I. water between salts)
1. 167.48g Sodium chloride (NaCl)
2. 28.06 g Sodium sulfate (Na<sub>2</sub>SO<sub>4</sub>)
3. 4.74 g Potassium chloride (KCl)
4. 1.37 g Sodium bicarbonate (NaHCO<sub>3</sub>)
5. 0.69 g Potassium bromide (KBr)
6. 0.18 g Boric acid (H<sub>3</sub>BO<sub>3</sub>)
7. 0.02 g Sodium fluoride (NaF)
8. Measure and add 373 ml of magnesium chloride, 72.3 ml of calcium chloride, and 6.30 ml of strontium chloride to the 10 L carboy.
C. Top off the 10 L Carboy with D.I. water up to the 7L mark.
D. Add a stir bar and place it on a stirrer for 24 hours.
pH Check: Plug in and calibrate the pH meter
A. Materials
1. Kim wipes
2. Sample
3. pH meter
4. pH buffer solutions
5. DI water in a squirt bottle
6. Gloves
B. Make sure the pH meter is plugged in, and move the rubber stopper part so that the hole is uncovered allowing air to move in and out of the pH meter.
C. Take the meter out of the standing solution, gently wash it with DI water, and gently pat dry with a Kim wipe.
D. Dip in the first buffer solution and press calibrate, wait until the meter stabilizes should be within .1 of what the pH of the buffer is.
E. Gently wash it with DI water, and gently pat it dry with a Kim wipe and repeat with other buffer solutions.
F. Before taking a reading of your sample place it back in one of the buffers and press read to determine if the meter is correctly reading the buffer.
G. Again, wash and dry the meter, place the meter in the sample press read, wait for the meter to stabilize, and then record the pH measurement.
H. Clean and dry the meter, ALWAYS MOVE THE RUBBER STOPPER BACK TO COVER THE HOLE WHEN YOU ARE FINISHED USING THE METER.
I. Press and hold the power button to shut the meter off.
All steps carried out in a laminar flow biological hood with all preparation tools and surfaces bleached to avoid contamination. Use head net. Beakers, flasks and bottles need to be acid washed and autoclaved before use.
Archimedes’ principle states that the upward buoyant force on an object submerged in a fluid is equal to the weight of the fluid it displaces. The buoyant weight technique utilizes this principle to make observations about the mechanical properties of solid materials like shells and skeletons. Measures of weight using the buoyant weight technique can be used to calculate skeletal parameters, including micro density, bulk density, and porosity. This protocol is adapted from “Skeletal micro-density, porosity and bulk density of acroporid corals” (Bucher et al. 1998).
Make sure water is not leaking into the filtering flask. This can damage the pump if water enters the pump.
16.When all samples are saturated (aka the samples are completely submerged in water), turn the carboy nozzle (c) to the “off” mode, and let the rest of the water in the tubing drain into the desiccator.
Utilize the Mechanical Properties Spreadsheet to input the dry, buoyant, and saturated weights. This spreadsheet will then automatically calculate averages and standard deviations for each type of weight. Automatic calculations of mechanical properties will be performed, which will be used to calculate average microdesnity, average porosity, and average bulk density.
Dry Weight (DW) = Sample’s weight in air Buoyant Weight (BW) = Sample weight when partly supported by buoyancy BW = DW – (mass of water displaced) Mass of water displaced = DW - BW
Based on Archimedes’ principle Saturated Weight (SW) = Sample’s weight in air when all pores are saturated with water
Matrix Volume (cm3) = DW - BW
Pore Volume (cm3) = SW – DW
Total Volume (cm3) = Matrix Volume (cm3) + Pore Volume (cm3)
Microdensity g/cm3 = mg/mm = DW/Volume of the Matrix Porosity % = (Pore Volume/ Total Volume) * 100 Bulk Density (g/cm3 = mg/mm3) = DW - Total Volume
]]>Training by William Biggs
*NOTE- THIS DRAG OVER THE CELLS SIZE- look up cell size if you dont know *
Maxim Gorbunov
Gorbunov, Maxim Y., and Paul G. Falkowski. “Using chlorophyll fluorescence kinetics to determine photosynthesis in aquatic ecosystems.” Limnology and Oceanography 66.1 (2021): 1-13.
Gorbunov, Maxim Y., and Paul G. Falkowski. “Using chlorophyll fluorescence to determine the fate of photons absorbed by phytoplankton in the World’s oceans.” Annual Review of Marine Science 14 (2022): 213-238.
Phytoplankton physiology and chlorophyll variable fluorescence: how it works and how we can do better
Omega HOBO Data Loggers Manual
Calibration is essential before each deployment to ensure accurate pH measurements.
Note: Calibration should be performed using standard pH buffers. Custom buffers are not supported.
Note: The logger is waterproof up to 40 meters and suitable for both freshwater and saltwater environments.
Note: The pH electrode has a typical lifespan of 6 months and should be replaced as needed.
MX2500 Replacement Electrode Cheaper to do a purchase order on LI-COR INC (approved vendor) than VWR
MX2500 Anti-Biofouling Copper Guard Cheaper to do a purchase order on LI-COR INC (approved vendor) than VWR
Training Received by Travis Miles, Becca Horwitz, and Kayla Cayemitte on November 6-8th, 2024 by Michael Rock.
Customer Portal Access to training videos, training manual, download docker simulation Password: Pod_of_Bots
Github to Install the JAIA Bot Simulator in a Docker Container Note: There is a laptop with the simulator already installed in the seawater lab Do not attempt to install on Mac.
]]>If you need to restart the SEM press Ctrl Alt F4, then wait a few seconds and on next black screen press Ctrl Alt delete.
]]>