Online Trick

If you're interested in analysing your blog's statistics this can easily be done with a web-service like StatCounter (free, only registration needed, quite extensive service) and with R.
After implementing the StatCounter script in the html code of a webpage or blog one can download and inspect log-files with R with some short lines of code (like below) and then inspect visitor activity..

url <- "http://statcounter.com/p7447608/csv/download_log_file?form_user=MYUSERNAME&form_pass=MYPASSWORD"
file <- paste(tempdir(), "\\log", ".CSV", sep = "")
download.file(url, dest = file)
log <- read.csv(file, as.is = T, header = T)

str(log)

'data.frame':   500 obs. of  19 variables:
 $ Date.and.Time   : chr  "2011-12-19 23:32:30" "2011-12-19 23:20:04" "2011-12-19 23:16:24" "2011-12-19 23:14:40" ...
 $ IP.Address      : chr  "93.129.245.130" "128.227.27.189" "207.63.124.250" "140.247.40.121" ...
 $ IP.Address.Label: logi  NA NA NA NA NA NA ...
 $ Browser         : chr  "Chrome" "Firefox" "Chrome" "Firefox" ...
 $ Version         : chr  "16.0" "8.0" "15.0" "6.0" ...
 $ OS              : chr  "MacOSX" "WinXP" "Win7" "MacOSX" ...
 $ Resolution      : chr  "1280x800" "1680x1050" "1280x1024" "1280x800" ...
 $ Country         : Factor w/ 44 levels "Argentina","Australia",..: 17 44 44 44 44 44 44 44 44 44 ...
 $ Region          : chr  "Nordrhein-Westfalen" "Florida" "Illinois" "Massachusetts" ...
 $ City            : chr  "Köln" "Gainesville" "Chicago" "Cambridge" ...
 $ Postal.Code     : int  NA 32611 NA 2138 2138 NA 10003 2138 2138 2138 ...
 $ ISP             : chr  "Telefonica Deutschland GmBH" "UNIVERSITY OF FLORIDA" "Illinois Century Network" "Harvard University" ...
 $ Returning.Count : int  2 0 4 2 2 0 0 2 2 2 ...
 $ Page.URL        : chr  "http://onlinetrickpdf.blogspot.com/2015/09/r-function-google-scholar-webscraper.html" "http://onlinetrickpdf.blogspot.com/2015/09/if-then-vba-script-usage-in-arcgis.html" "http://onlinetrickpdf.blogspot.com/2015/09/how-to-link-to-google-docs-for-download.html" "http://onlinetrickpdf.blogspot.com/2015/09/two-way-permanova-adonis-with-custom.html" ...
 $ Page.Title      : Factor w/ 53 levels "","onlinetrickpdf*",..: 36 50 23 46 10 20 13 9 10 46 ...
 $ Came.From       : chr  "http://stackoverflow.com/questions/5005989/how-to-download-search-results-on-google-scholar-using-r" "http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CCwQFjAC&url=http%3A%2F%2Fonlinetrickpdf.blogspot.com%2F2011%"| __truncated__ "" "" ...
 $ SE.Name         : chr  "" "" "" "" ...
 $ SE.Host         : chr  "" "" "" "" ...
 $ SE.Term         : chr  "" "" "" "" ...

In QGIS:

- from menu: Raster / Extraction / Contour

- define output name path/to/output.shp

- alter GDAL call for single contour line at 900 m asl:
gdal_contour -fl 900 "path/to/dem_raster.asc" "path/to/output.shp"


- for removing small poplygons or lines add area or length field (attr table / field calc or vector / geometry / add geometry)

- query by length or area to deselect unwanted iso-lines


Finally, you can export the contours as KML and check it in Google Earth:


I downloaded my DEM-rasters (Nord-Tirol, Österreich) HERE

# Example 1:
setwd(tempdir())
download.file("http://docs.google.com/uc?export=download&id=0B2wAunwURQNsMDViYzllMTMtNjllZS00ZTc4LTgzMzEtNDFjMWQ3MTUzYTRk",
              destfile = "test_google_docs.txt", mode = "wb")
# the file contains: x <- sample(100); plot(x)
source(paste(tempdir(), "/test_google_docs.txt", sep = ""))
# remove files from tempdir:
unlink(dir())

# Example 2:
setwd(tempdir())
download.file("http://docs.google.com/uc?export=download&id=0B2wAunwURQNsY2MwMzNhNGMtYmU4Yi00N2FlLWEwYTctYWU3MDhjNTkzOTdi",
              destfile = "google_docs_script.txt", mode = "wb")
# the downloaded script is the GScholarScraper-Function,
# read it and run an example:
source(paste(tempdir(), "/google_docs_script.txt", sep = ""))
# remove files from tempdir:
unlink(dir())

EDIT, MARCH 2013:
Method is outdated, use Tony's from below!

A quick geo-tip:
With the osmar and maptools package you can easily pull an OpenStreetMap object and convert it to KML, like below (thanks to adibender helping out on SO). I found the relation ID by googling for it (www.google.at/search?q=openstreetmap+relation+innsbruck).

# get OSM data
library(osmar)
library(maptools)
 
innsbruck <- get_osm(relation(113642), full = T)
sp_innsbruck <- as_sp(innsbruck, what = "lines")
 
# convert to KML
for( i in seq_along(sp_innsbruck) ) {
      kmlLine(sp_innsbruck@lines[[i]], kmlfile = "innsbruck.kml",
               lwd = 3, col = "blue", name = "Innsbruck") 
      }
 
shell.exec("innsbruck.kml")

..The dismo package is awesome: with some short lines of code you can read & map species distribution data from GBIF (the global biodiversity information facility) easily:

library(dismo)

# get GBIF data with function:
myrger <- gbif("Myricaria", "germanica", geo = T)

# check:
str(myrger)

# plot occurrences:
library(maptools)
data(wrld_simpl)
plot(wrld_simpl, col = "light yellow", axes = T)
points(myrger$lon, myrger$lat, col = "red", cex = 0.5)
text(-140, -50, "MYRICARIA\nGERMANICA")

First off, I'd really like to give a shout-out to the brave people who have created and maintain this great package - the fame is yours!

So, while exploring the capabilities of the package some issues with the ITIS-Server arose and with large datasets things weren't working out quite well for me.
I then switched to the NCBI API and saw that the result is much better here (way quicker, on first glance also a higher coverage).
At the time there is no taxize-function that will pull taxonomic details from a classification returned by NCBI, that's why I plugged together a little wrapper - see here:

# some species data:
spec <- data.frame("Species" = I(c("Bryum schleicheri", "Bryum capillare", "Bryum argentum", "Escherichia coli", "Glis glis")))
spl <- strsplit(spec$Species, " ")
spec$Genus <- as.character(sapply(spl, "[[", 1))

# for pulling taxonomic details we'd best submit higher rank taxons
# in this case Genera. Then we'll submit Genus Bryum only once and 
# save some computation time (might be an issue if you deal 
# with large datasets..)

gen_uniq <- unique(spec$Genus)

# function for pulling classification details ("phylum" in this case)
get_sys_level <- function(x){ require(taxize)
                              a <- classification(get_uid(x))
                              y <- data.frame(a[[1]])                                        # if there are multiple results, take the first..
                              z <- tryCatch(as.character(y[which(y[,2] == "phylum"), 1]),    # in case of any other errors put NA
                                            error = function(e) NA)
                              z <- ifelse(length(z) != 0, z, NA)                             # if the taxonomic detail is not covered return NA
                              return(data.frame(Taxon = x, Syslevel = z))
                             }

# call function and rbind the returned values 
result <- do.call(rbind, lapply(gen_uniq, get_sys_level))
print(result)
#         Taxon       Syslevel
# 1       Bryum   Streptophyta
# 2 Escherichia Proteobacteria
# 3        Glis       Chordata

# now merge back to the original data frame
spec_new <- merge(spec, result, by.x = "Genus", by.y = "Taxon")
print(spec_new)
#         Genus           Species       Syslevel
# 1       Bryum Bryum schleicheri   Streptophyta
# 2       Bryum   Bryum capillare   Streptophyta
# 3       Bryum    Bryum argentum   Streptophyta
# 4 Escherichia  Escherichia coli Proteobacteria
# 5        Glis         Glis glis       Chordata
#

A commentator on my blog recently asked if it is possible to retrieve all direct links to your Google Documents. And indeed it can be very easily done with R, just like so:

# you'll need RGoogleDocs (with RCurl dependency..)
install.packages("RGoogleDocs", repos = "http://www.omegahat.org/R", type="source")
library(RGoogleDocs)



gpasswd = "mysecretpassword"
auth = getGoogleAuth("kay.cichini@gmail.com", gpasswd)
con = getGoogleDocsConnection(auth)

CAINFO = paste(system.file(package="RCurl"), "/CurlSSL/ca-bundle.crt", sep = "")
docs <- getDocs(con, cainfo = CAINFO)

# get file references
hrefs <- lapply(docs, function(x) return(x@access["href"]))
keys <- sub(".*/full/.*%3A(.*)", "\\1", hrefs)
types <- sub(".*/full/(.*)%3A.*", "\\1", hrefs)

# make urls (for url-scheme see: http://techathlon.com/download-shared-files-google-drive/)
# put format parameter for other output formats!
pdf_urls <- paste0("https://docs.google.com/uc?export=download&id=", keys)
doc_urls <- paste0("https://docs.google.com/document/d/", keys, "/export?format=", "txt")

# download documents with your browser
gdoc_ids <- grep("document", types)
lapply(gdoc_ids, function(x) shell.exec(doc_urls[x]))

pdf_ids <- grep("pdf", types, ignore.case = T)
lapply(pdf_ids, function(x) shell.exec(pdf_urls[x]))