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This dataset contains the content of the subset of all files with a correct publication date from the 2017 release of files related to the JFK case (retrieved from https://www.archives.gov/research/jfk/2017-release). This content was extracted from the source PDF files using the R OCR libraries tesseract and pdftools. The code to derive the dataset is given as follows: ### BEGIN R DATA PROCESSING SCRIPT library(tesseract) library(pdftools) pdfs <- list.files("[path to your output directory containing all PDF files]") meta <- read.csv2("[path to your input directory]/jfkrelease-2017-dce65d0ec70a54d5744de17d280f3ad2.csv",header = T,sep = ',') #the meta file containing all metadata for the PDF files (e.g. publication date) meta$Doc.Date <- as.character(meta$Doc.Date) meta.clean <- meta[-which(meta$Doc.Date=="" | grepl("/0000",meta$Doc.Date)),] for(i in 1:nrow(meta.clean)){ meta.clean$Doc.Date[i] <- gsub("00","01",meta.clean$Doc.Date[i]) if(nchar(meta.clean$Doc.Date[i])<10){ meta.clean$Doc.Date[i]<-format(strptime(meta.clean$Doc.Date[i],format = "%d/%m/%y"),"%m/%d/%Y") } } meta.clean$Doc.Date <- strptime(meta.clean$Doc.Date,format = "%m/%d/%Y") meta.clean <- meta.clean[order(meta.clean$Doc.Date),] docs <- data.frame(content=character(0),dpub=character(0),stringsAsFactors = F) for(i in 1:nrow(meta.clean)){ #for(i in 1:3){ pdf_prop <- pdftools::pdf_info(paste0("[path to your output directory]/",tolower(meta.clean$File.Name[i]))) tmp_files <- c() for(k in 1:pdf_prop$pages){ tmp_files <- c(tmp_files,paste0("/home/STAFF/luczakma/RProjects/JFK/data/tmp/",k)) } img_file <- pdftools::pdf_convert(paste0("[path to your output directory]/",tolower(meta.clean$File.Name[i])), format = 'tiff', pages = NULL, dpi = 700,filenames = tmp_files) txt <- "" for(j in 1:length(img_file)){ extract <- ocr(img_file[j], engine = tesseract("eng")) #unlink(img_file) txt <- paste(txt,extract,collapse = " ") } docs <- rbind(docs,data.frame(content=iconv(tolower(gsub("\\s+"," ",gsub("[[:punct:]]|[\n]"," ",txt))),to="UTF-8"),dpub=format(meta.clean$Doc.Date[i],"%Y/%m/%d"),stringsAsFactors = F),stringsAsFactors = F) } write.table(docs,"[path to your output directory]/documents.csv", row.names = F) ### END R DATA PROCESSING SCRIPT
OCR, JFK, content analysis, temporal data mining
OCR, JFK, content analysis, temporal data mining
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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