Science Score: 18.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic links in README
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (0.5%) to scientific vocabulary
Last synced: 10 months ago
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JSON representation
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Repository
Code for qPCR analysis with Nina Wale
Basic Info
- Host: GitHub
- Owner: kmanlove
- Language: R
- Default Branch: master
- Size: 105 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 4
- Releases: 0
Created over 11 years ago
· Last pushed over 11 years ago
Metadata Files
Citation
Owner
- Name: Kezia Manlove
- Login: kmanlove
- Kind: user
- Location: Bozeman
- Repositories: 27
- Profile: https://github.com/kmanlove
Citation (CitationStripCode_10Dec2014.R)
# 1) store a couple functions that will be used to clean things up later
trim.leading <- function (x) sub("^\\s+", "", x)
# 2) read in data.frame (which is csv of citations as exported from Web of Science, but you could modify that)
data.frame <- read.csv(file.choose())
data.frame$CitedRefs <- data.frame$CR # renames Web of Science export column "CR" in data.frame "CitedRefs"
data.frame$Authors <- data.frame$AU # renames Web of Science export column "AU" to "Authors"
data.frame$PubYear <- data.frame$PY # renames Web of Science export column "PY" to "PubYear"
# 3) build some storage objects to stick the citations in
citation.list <- citation.frame <- citation.frame.small <- vector("list", dim(data.frame)[1])
first.author <- pub.year <- rep(NA, dim(data.frame)[1])
# 4) strip out any papers that don't have references (for me, those were the 1st, 484th, 591st and 843rd entried in my data.frame)
papers.with.cites <- c(1:dim(data.frame)[1])[-c(1, 484, 591, 843)]
# 5) loop over all papers in the papers.with.cites object
for(i in papers.with.cites){
citation.list[[i]] <- strsplit(x = as.character(data.frame$CitedRefs)[i], split = ";")[[1]]
citation.frame[[i]] <- matrix(NA, nrow = length(citation.list[[i]]), ncol = 15)
citation.frame.small[[i]] <- matrix(NA, nrow = length(citation.list[[i]]), ncol = 12)
first.author[i] <- strsplit(as.character(data.frame$Authors[i]), split = ";")[[1]][1]
pub.year[i] <- data.frame$PubYear[i]
for(j in 1:length(citation.list[[i]])){
citation.frame.small[[i]][j, ] <- trim.leading(c(strsplit(citation.list[[i]][j], split = ",")[[1]], rep(NA, 12 - length(strsplit(citation.list[[i]][j], split = ",")[[1]]))))
if(is.na(as.numeric(citation.frame.small[[i]][j, 1])) == F){
citation.frame.small[[i]][j, ] <- c(NA, citation.frame.small[[i]][j, -12])
}
citation.frame[[i]][j, ] <- c(as.character(data.frame$DOI)[i], as.character(first.author[i]), pub.year[i], citation.frame.small[[i]][j, ])
citation.frame[[i]][j, 1] <- paste("DOI ", citation.frame[[i]][j, 1], sep = "")
citation.frame[[i]][j, 2] <- tolower(trim.leading(citation.frame[[i]][j, 2]))
citation.frame[[i]][j, 4] <- tolower(trim.leading(citation.frame[[i]][j, 4]))
}
}
full.citation.frame <- do.call("rbind", citation.frame) # builds data frame that contains ALL cited refs
table(full.citation.frame$SO) # tables source journals for all papers cited in original search