open-science | Thought splinters

Memento Time Travel

Tue, 25 May 2021 00:00:00 +0000

Research with archived web data

In a previous article, I wrote about the possibilities of the Wayback Machine for scientific writing. I argued that archiving web pages are essential for references as they prevent link rots when cited web resources are not available anymore. With this blog entry, I am looking into the reverse option: Finding and retrieving archived web pages for research reasons.

Archived web pages as permanently stored data are indispensable for reproducibility issues. But they are also valuable research resources as they are data for historical and comparative research.

This article rewrites my blog entry from 2019 on Scraping Archived Data with the Wayback Machine in three aspects:

I am going into more detail to explain the Memento Protocol that stands behind the archiving procedures of the Internet Archive with the Wayback Machine.
I will demonstrate the research significance of archived data with another — more simple — example. Instead, to analyze yearly differences in the ranking of the popularity of static site generators, I will focus on the number of statistical packages developed by the R community over time. The website structure for static site generators is complex, has changed several times, and contradicts the demonstration purpose.¹
To argue the importance of archived data convincingly, it is necessary to extract the URLs of archived data and use these data for research issues. Therefore, I will show how R can scrape web data and display the data for further analysis.

How does the Memento API work?

Mementos are prior versions of web pages cached by web crawlers and stored in web archives. The HTTP-based Memento framework is a description for Time-Based Access to Resource States.

With Mementos, you can access a version of a Web resource as it existed at some date in the past. The complete information about the Memento Project is specified in RFC 7089 as HTTP Framework for Time-Based Access to Resource States – Memento. I will explain the essential idea with the gentle non-technical introduction on the Memento website.

In the Memento protocol there are four important components:

Original Resource (URI-R): A Web resource that exists or used to exist on the live Web for which we want to find a prior version.

Memento (URI-M): A Web resource that is a prior version of the Original Resource. Prior versions are Web resources encapsulated in what the Original Resource was like at some time in the past.

TimeGate (URI-G): A Web resource that “decides” on the basis of a given datetime, which Memento best matches what the Original Resource was like around that given datetime.

TimeMap (URI-T): A list of URIs of Mementos of the Original Resource that is archived, e.g., available online.

The central component is the TimeMap Resource. It is a machine-readable document that lists the Original Resource itself, its TimeGate, and its Mementos, as well as associated metadata such as archival DateTime for Mementos.

The HTTP-based Memento framework bridges the present and past Web. It facilitates obtaining representations of prior states of a given resource by introducing datetime negotiation and TimeMaps. Datetime negotiation is a variation on content negotiation that leverages the given resource’s URI and a user agent’s preferred datetime. TimeMaps are lists that enumerate URIs of resources that encapsulate prior states of the given resource. (Quoted from RFC 7089)

Architectural overview of how the Memento framework supports batch discovery of prior/archived versions of a resource (Mementos).

Where to find the desired information?

To demonstrate the interplay of the Memento protocol with the Wayback Machine, I will look into the history of the R project web page. I want to know the number of available packages over time.

There are three web pages where I can find out the number of available packages:

Contributed Packages

Contributed Packages: On this page is the number of available packages written directly under the subheading “Available Packages”.

Page ‘Contributed Packages’ mentions the number of available packages.

Packages By Name

Available CRAN Packages By Name: This page list all packages alphabetically. One could count the lines with the package’s name to get the desired number.

Screenshot of the web page ‘Available CRAN Packages By Name’.

Packages By Date of Publication

Available CRAN Packages By Date of Publication: This page list all packages by date of publication. Here one could also count the lines with the package’s dates to get the desired number.

Screenshot of the web page ‘Available CRAN Packages By Date of Publication’.

Which page to scrape?

The question now arises if these pages are also available in the past. And if so: Have they the same structure to use the identical CSS selector in all archived instances?

The question now arises if this page is available also in the past. And if so: Has it the same structure to use the identical CSS selector in all prior archived instances?

Contributed Packages

It turns out that the first line after the subheading could be scraped with the CSS selector #pkgs + p.

Context menue via right clicke opened to select the code inspector.

Code inspector detects CSS selector ‘h3#pgks’ and displays this result with a magnifier glass.

To check if the website structure for the paragraph after the header with id = “pkgs” remains constant, we can use the browser plugin for the Wayback Machine (available for Google Chrome and Firefox.).

Browser Plugin Wayback Machine

If you click on the Wayback Machine plugin, you can either go directly to the first or last (recent) snapshot of this web page. I choose “Overview” to display the calendar to inspect different instances of the archived page.

The calender displays time span and distribution of the archived page.

The calendar shows that the page was between May 14, 2008, and April 13, 2021, 145 times archived. From this overview page, one can select different instances and content and design of the ‘Contributed Packages’ page. To get an idea of possible changes in the structure, I start with the first instance of the archived page.

The number of times the page was crawled by the Wayback Machine (in my example: 145) has nothing to do with how often the page was updated.

Screenshot of the ‘Contributed Package’ page from May 14, 2008.

The archived page displays a different layout. The number of packages is not immediately after the first heading but after several paragraphs later (see number 2 in the image). Also, the name of the heading has changed from “Available Packages” to “Available Bundles and Packages”. But more importantly: The paragraph following immediately of this header mentions the amounts of different kinds of packages and bundles. It would be challenging to detect the desired number (in this case, 1425 packages) programmatically.

A further inspection shows that the id still remains “pkgs”. But because of the different text of the first paragraph, I want to look at another page to grab my desired information easier.

Packages By Name

Here I will demonstrate the use of a helpful tool. The Web scraping 101 vignettes of the rvest package references SelectgorGadget, a JavaScript bookmarklet to find out the required CSS selector interactively.

SelectorGadget selects the first name.

After selecting the first name, the SelectorGadget shows the CSS selector a, referring to all hyperlinks of this page. At the bottom, you can see that there are 17674 hyperlinks on this page. The problem is that the headline consists of the letters of the alphabet, which also are hyperlinks.

SelectorGadget selects the first letter of the alphabet.

To select another position on the page with the SelectorGadget, subtracts these selected items. The bottom line shows the CSS selector with ‘td a’ (table data followed by a hyperlink) and removes the 26 characters to get 17648 selected objects. So this would be an easy way to get the number of available R packages.

The first archived page starts with September 24, 2011, and reduces, therefore, our survey period. But more important: From the 193 archived pages (more than the 145 instances of the ‘Contributed Package’ page!) are many redirects.

Calender view with many green cicled dates (=redirects).

The size of the circles displays the number of pages crawled. Blue is directly archived, green is an archived page after a redirect. I could not find a solution to distinguish the difference between direct archiving and after a redirect programmatically. The problem with a redirect is that the same state of a page is archived several times but counts as different pages in time. This pollutes the resulting data set.

Packages by Date of Publication

Finally, I decided to scrape the page “Packages by Date of Publication” with the simple CSS selector ‘a’. It has the same period as the “Package By Name” page (the first instance is from September 26, 2011). It was archived only 81 times but with very few redirects.

Calendar view of the ‘Packages by Date of Publication’ page.

Memento protocol in action

Package installation

At first, we have to install and load the wayback package, a wonderful and very practical library of Memento API wrappers, written by Bob Rudis.²

In the meanwhile there exists another package with the same name on CRAN (Comprehensive R Archive Network). It is dedicated to the installation for legacy R versions and has nothing to do with the wayback package on GitHub for the Wayback Machine of the Internet Archive.

if (!require("wayback"))
        {remotes::install_github("hrbrmstr/wayback", build_vignettes = TRUE)
        library(wayback)}

## Loading required package: wayback

Mementos Link Types

The next step is to use the get_mementos() function. With get_mementos(url, timestamp = format(Sys.Date(), "%Y")) we will receive a shortlist of relevant links to the archived content. Only the first parameter, url, is mandatory. If no timestamp is provided, then the actual year is taken, and the most recent archived page will be the endpoint, which in our case is ok. The function will return the 4 link relation types as in the Request for Comment for the Memento framework described and outlined above.

Link Relation Type “original.”
Link Relation Type “timemap.”
Link Relation Type “timegate.”
Link Relation Type “memento.”

url = "https://cran.r-project.org/web/packages/available_packages_by_date.html"
cran_link_types <- wayback::get_mementos(url)
saveRDS(cran_link_types, "data/cran_link_types.rds")

cran_link_types <- readRDS("data/cran_link_types.rds")
knitr::kable(cran_link_types, 
             caption = "Memento Link Types", 
             label="memento-link-types", 
             format = "html")

Table 1: Memento Link Types
link	rel	ts
https://cran.r-project.org/web/packages/available_packages_by_date.html	original	NA
http://web.archive.org/web/timemap/link/https://cran.r-project.org/web/packages/available_packages_by_date.html	timemap	NA
http://web.archive.org/web/https://cran.r-project.org/web/packages/available_packages_by_date.html	timegate	NA
http://web.archive.org/web/20110926172444/http://cran.r-project.org:80/web/packages/available_packages_by_date.html	first memento	2011-09-26 17:24:44
http://web.archive.org/web/20210128201734/https://cran.r-project.org/web/packages/available_packages_by_date.html	prev memento	2021-01-28 20:17:34
http://web.archive.org/web/20210130222751/https://cran.r-project.org/web/packages/available_packages_by_date.html	memento	2021-01-30 22:27:51
http://web.archive.org/web/20210412212953/http://cran.r-project.org/web/packages/available_packages_by_date.html	next memento	2021-04-12 21:29:53
http://web.archive.org/web/20210412212953/http://cran.r-project.org/web/packages/available_packages_by_date.html	last memento	2021-04-12 21:29:53

Besides these 4 main types of link relations, the function also provides the first, previous, next, and last available memento. When no particular date is given, then the last memento is identical with the next (= nearest) memento. In addition to the two columns, link and rel, there is a third one, ts, containing the timestamps (empty for the first 3 link relation types). The return value in total is a tibble with eight observations (rows) and three columns.³

Memento Craw List

Providing an URL in the search field of the Wayback Machine results in the interactive browser version to the calendar view as shown by Figure 12 and Figure 13. The dates with archived content are blue or green (= redirected URL) circles. The bigger the circles, the more snapshots were archived on these dates.

We get these dated crawl lists with the get_timemap() function using the second observation of the result of the get_mementos function. This is in our case cran_link_types$link[2].

The execution of the following code chunk can take some time, depending on how many pages of the URL are archived. Be aware that the Wayback server is strained by this query, so do not repeat this operation. I store the result on my hard disk and will use the saved data for further processing.

cran_link_types <- readRDS("data/cran_link_types.rds")
cran_crawl_list <- wayback::get_timemap(cran_link_types$link[2])
saveRDS(cran_crawl_list, "data/cran_crawl_list.rds")

cran_crawl_list <- readRDS("data/cran_crawl_list.rds")
reactable::reactable(
    cran_crawl_list,
    pagination = FALSE, 
    highlight = TRUE, 
    height = 500,
    compact = TRUE,
    bordered = TRUE,
    striped = TRUE,
    wrap = FALSE,
    resizable = TRUE
    )

We get a table with 85 rows, where the first three rows are not relevant for our purpose, and the last row is empty. So we get 85 - 3 - 1 = 81 mementos, which conforms to the number of Figure 13. The URLs to the mementos are pretty long. You can widen/narrow the columns to inspect the structure of the URLs. Typically they start with “http://web.archive.org/web/” followed by the date-time string of the memento and the original URL.

Harvesting Web Pages

We have now collected all the URLs for the mementos. The next task is now getting our data from these pages and display them in a graphic. For this last step, we will use the packages rvest and ggplot2. It is a “standard” task of manipulating HTML and has only insofar with the memento framework to do, as we are using the URLs of the archived web pages.

Tidy data

We only need the memento links, date, and new column for our number of available packages.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.3     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ dplyr   1.0.6
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

cran_tidy_data <- readRDS("data/cran_crawl_list.rds") %>% 
  filter(stringr::str_detect(rel, "memento")) %>% 
  mutate(date = anytime::anydate(datetime)) %>% 
  add_column(pkgs = 0) %>% 
  select(link, date, pkgs)
saveRDS(cran_tidy_data, "data/cran_tidy_data.rds")

glimpse(cran_tidy_data)

## Rows: 81
## Columns: 3
## $ link <chr> "http://web.archive.org/web/20110926172444/http://cran.r-project.…
## $ date <date> 2011-09-26, 2011-10-11, 2011-10-29, 2011-11-29, 2011-12-29, 2012…
## $ pkgs <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…

For the purpose of this demonstration, I do not want to scrape every one of the 81 links but only one link per year. As the archiving dates do not have a systematic regularity, we cannot provide time series, say January 1st every year. But this does not matter for this demo.

cran_yearly_links <- readRDS("data/cran_tidy_data.rds") %>% 
  dplyr::mutate(year = lubridate::year(date), .after = "date")  

cran_yearly_links <- cran_yearly_links[!duplicated(cran_yearly_links[ , "year"]), ]
saveRDS(cran_yearly_links, "data/cran_yearly_links.rds")
cran_yearly_links

## # A tibble: 11 x 4
##    link                                                   date        year  pkgs
##    <chr>                                                  <date>     <dbl> <dbl>
##  1 http://web.archive.org/web/20110926172444/http://cran… 2011-09-26  2011     0
##  2 http://web.archive.org/web/20120127155954/http://cran… 2012-01-27  2012     0
##  3 http://web.archive.org/web/20130121072548/http://cran… 2013-01-21  2013     0
##  4 http://web.archive.org/web/20140402061423/http://cran… 2014-04-02  2014     0
##  5 http://web.archive.org/web/20150124092856/http://cran… 2015-01-24  2015     0
##  6 http://web.archive.org/web/20160104062049/https://cra… 2016-01-04  2016     0
##  7 http://web.archive.org/web/20170502002538/http://cran… 2017-05-02  2017     0
##  8 http://web.archive.org/web/20180822083144/https://cra… 2018-08-22  2018     0
##  9 http://web.archive.org/web/20190103070122/http://cran… 2019-01-03  2019     0
## 10 http://web.archive.org/web/20200103222850/https://cra… 2020-01-03  2020     0
## 11 http://web.archive.org/web/20210128201734/https://cra… 2021-01-28  2021     0

Get number of available packages

Although there are only 10 web pages to scrape, this will still take some time. Therefore I provide a progress indicator to monitor how much time the procedure will still last.

library(rvest)

cran_pkgs <- readRDS("data/cran_yearly_links.rds")
max = nrow(cran_pkgs)

pb <- txtProgressBar(min = 0, max = max, style = 3)
  for(i in 1:max) {
      html <- read_html(cran_pkgs$link[i])
      links <- html_nodes(html, "a")
      cran_pkgs$pkgs[i] <- length(links)
    setTxtProgressBar(pb, i)
    }
close(pb)
saveRDS(cran_pkgs, "data/cran_pkgs.rds")

(avail_pkgs <- readRDS("data/cran_pkgs.rds"))

## # A tibble: 11 x 4
##    link                                                   date        year  pkgs
##    <chr>                                                  <date>     <dbl> <dbl>
##  1 http://web.archive.org/web/20110926172444/http://cran… 2011-09-26  2011  3307
##  2 http://web.archive.org/web/20120127155954/http://cran… 2012-01-27  2012  3563
##  3 http://web.archive.org/web/20130121072548/http://cran… 2013-01-21  2013  4262
##  4 http://web.archive.org/web/20140402061423/http://cran… 2014-04-02  2014  5374
##  5 http://web.archive.org/web/20150124092856/http://cran… 2015-01-24  2015  6221
##  6 http://web.archive.org/web/20160104062049/https://cra… 2016-01-04  2016  7722
##  7 http://web.archive.org/web/20170502002538/http://cran… 2017-05-02  2017 10513
##  8 http://web.archive.org/web/20180822083144/https://cra… 2018-08-22  2018 12938
##  9 http://web.archive.org/web/20190103070122/http://cran… 2019-01-03  2019 13645
## 10 http://web.archive.org/web/20200103222850/https://cra… 2020-01-03  2020 15348
## 11 http://web.archive.org/web/20210128201734/https://cra… 2021-01-28  2021 17038

Visualizing the results

I am not going to produce a sophisticated graphic. A simple line graph to see how the number of packages is increasing has to be enough.

p <- readRDS("data/cran_pkgs.rds") %>% 
  ggplot(aes(year, pkgs, group = 1)) +
  geom_line()
p

Summary

In this post, I have presented the memento protocol, a framework to generate and retrieve prior versions of web pages cached by web crawlers and stored in web archives. I have explained how to Memento API for the Wayback Machine of the Internet Archive is working. With the wayback packages by Bob Rudis, I have demonstrated the practical handling to use the framework for retrieving, collecting, and displaying historical data.

Recently, even the URL has changed from https://www.staticgen.com/ to https://jamstack.org/generators/.↩︎
I have forked his GitHub repo and looked into his R scripts. I have to say that I became somewhat depressed as I noticed how much knowledge I am still lacking. Despite working now for four years with R, there are so many think I still have to learn!↩︎
In many cases, the last memento is identical with the memento link relation type. Then the tibble has only seven rows.↩︎

DORA and Wakelet: Two Badges!

Wed, 25 Dec 2019 00:00:00 +0000

Wakelet Community Badge

Last week – just before Christmas – I received two badges: DORA (San Francisco Declaration on Research Assessment) sent me a newsletter about their advances for fostering Open Science in 2019. On this occasion, DORA also issued badges for individuals and organizations that have signed the declaration.

The other badge came from Wakelet. As I had applied to be part of the Wakelet community, they welcomed me with an email with some tips and hints and issued the Community Badge to me.

DORA Signatory Badge

So just in time for Christmas Eve, I am honored with these two gratifications. I thought about a little thank-you gift. What I came up with was a Wakelet collection of DORA’s newsletter, which essentially is a commented list link. I turned these links into a ‘Wake’ with some added value: Besides the DORA list, there is additional information in every linked section generated automatically by Wakelet. Additionally, the link list is now more visually appealing. So I killed two birds with one stone: A Wakelet about DORA’s advances of the year 2019! Enjoy! (There is more on my Wakelet Homepage.)

Archiving Quoted Web Resources

Mon, 22 Jul 2019 00:00:00 +0000

Quoting web resources is a hassle for several reasons:

Web pages are not available anymore.
Web pages have moved to another URL.
Web pages change their content so that the cited reference is not correct anymore.

Humanities, where detailed content analysis of websites is a popular research method. Referring to exact quotes is a question of reproducibility and therefore crucial in science generally. This article presents some strategies and tools to bypass the challenges mentioned above.

Quote websites with the Wayback Machine

With WebCite, there used to be a web service to circumvent link rot and changed the content. WebCite allowed to archive online resources and returned an URL where these filed pages could be accessed. Besides that this service was often down and therefore notoriously unreliable, as of July 14, 2019, it does not accept any new archive requests anymore.

Figure 1: Start page of the WebCite service, proclaiming that new archiving request are currently not feasible anymore.

Luckily with Wayback Machine¹, operated by the Internet Archive, there is recently a new and reliable web service available. Although there is a sophisticated how-to use of this service in the Wikipedia context, I have prepared my own How-to use Wayback Machine for the general public.

Visit my slides on How-to use Wayback Machine for the general public.

Figure 2: Start page of the Wayback Machine, a service by the Internet Archive

How to cite archived resources?

Internet Archive asked the Modern Language Association (MLA) how to cite resources archived with the Wayback Machine. MLA Style is a prevalent system for documenting sources in scholarly writing.

MLA answered

that there is no established format for resources like the Wayback Machine, but it’s best to err on the side of more information. You should cite the webpage as you would normally, and then give the Wayback Machine information.

MLA also provided an example:

McDonald, R. C. “Basic Canary Care.” Robirda Online. 12 Sept. 2004. 18 Dec. 2006 $$<http://www.robirda.com/cancare.html>$$. Internet Archive. $$ http://web.archive.org/web/20041009202820/http://www.robirda.com/cancare.html$$.

Note there are several additions to a standard bibliography:

Two dates: The first is the date of the archive, then comes the date when the page is retrieved.
Two URLs: The first is the original URL (not available anymore), then comes the archived URL from the Internet Archive.
Web service: Between the two URLs comes the ‘second’ author, the name of the internet service which archived the resource and generated its URL.

According to MLA, both URLs shouldn’t be underlined in the bibliography.

Let’s try another example. The archiving service Peeep.Us is not available anymore. The Wayback Machine gives us as archived URL https://web.archive.org/web/20180813205348/http://peeep.us:80/. If we are are going to compose this bibliography in the usual way, we would get:

Nikolaev, Cyril. “Peeep.Us.” Save Snapshot of a Web Page Forever!, 13 Aug. 2018, https://web.archive.org/web/20180813205348/http://peeep.us:80/.

Using a name for web sites may questionable, but I use it whenever there is a reasonable possibility (e.g., from the Copyright or from the name of the institution, which produces the web site).

Now we have to add the retrieval date, the original URL and the name of the archiving service:

Nikolaev, Cyril. “Peeep.Us.” Save Snapshot of a Web Page Forever!, 13 Aug. 2018, 22 Jul. 2019 $$<http://peeep.us>$$. Internet Archive. $$<https://web.archive.org/web/20180813205348/http://peeep.us:80/>$$.

Wakelet

In addition to the following Wakelet, there is also a community edition on my Wakelet homepage where you can add relevant links.

To access this page you must be registered by archive.org. ↩︎

Open Citations - TOS

Tue, 25 Jun 2019 00:00:00 +0000

Introduction

This is the second post of a series of ten contributions about a better understanding of the different aspects of Open Science. In this post, I will outline the rationale and significance behind the Open Citation movement, to collect material for the development of a taxonomy of Open Science (TOS).

The following graphics summarizes my proposal for the first level of a taxonomy of Open Science (TOS). Branches with red pointers are active links connecting to the corresponding posts I have written so far.

Figure 1: The first level of a suggested Taxonomy of Open Science (TOS)

Citations: An essential activity during the research process

In contrast to Open Access, the movement on Open Citation is not so well known. However, to build a full-fledged ecosphere for Open Science, it is essential that citations are freely available, downloadable, machine-readable, and reusable.

The following two quotes explain the significance of Open Citations:

While the act of citation by the author may be the work of a moment, the citation itself, once the citing work is published, becomes an enduring component of the academic ecosystem. Open Citation Definition

Citations are the links that knit together our scientific and cultural knowledge. They are primary data that provide both provenance and an explanation for how we know facts. They allow us to attribute and credit scientific contributions, and they enable the evaluation of research and its impacts. In sum, citations are the most important vehicle for the discovery, dissemination, and evaluation of all scholarly knowledge. I4OC: Initiative for Open Citations

Even if an article is published as Open Access, its citations are not automatically Open Citations. To qualify as Open Citations, the publisher must fulfill some conditions:

Three obligatory requirements for Open Citations

Citations must be structured in a way that they can be accessed programmatically.
Citations must be accessed separable from their sources, such as journals articles or books.
Citations must not only be free accessible but also reusable.

The origins of this movement can be traced back to OpenCitaton, a project funded by JISC, a UK based organization, which provides digital solutions for the education and research. In 2016 the Initiative for Open Citation was launched, which today is the driving force behind the movement. It aims for free availability and usage of all metadata from publications with a digital object identifier (DOI) registered by Crossref. Freely available citation data are accessible through the Crossref program interface or the Open Citation Corpus. Open Citations can be used to find publications, but also for the analysis of the citation corpus as well (e.g., “how do different fields of knowledge fit together?”).

Figure 2: How many citations referenced by Crossref are Open Citations? (see: https://web.archive.org/web/20190623212728/https://i4oc.org/, accessed 2019-06-23.)

Keep in mind that the figures above refer only to those citations referenced by Crossref. The relation of all scientific publication to Open Citations is much worse. The biggest problem is that Open Citations are not in the business interests of two key players: Elsevier (Scopus) and Clarivate Analytics (Web of Science).

Web of Science (WoS):

WoS (previously known as Web of Knowledge) is a commercial online citation indexing service owned by Clarivate Analytics.

Clarivate Analytics was formerly the Intellectual Property and Science division of Thomson Reuters. In 2016 Thomson Reuters struck a 3.55 billion dollar deal in which they spun it off into an independent company and sold it. Wikipedia. See more in detail the The Scientist, a magazine for life science professionals.

Figure 3: Clarivate Analytics Web of Science product portfolio

WoS is not only one product but platform with many different indexing services and several scientific literature search databases. The central product in the portfolio of Clarivate Analytics is the Web of Science Core Collection (see Figure 3). It includes:

Science Citation Index (SCI),
Social Sciences Citation Index (SSCI) and
Arts & Humanities Citation Index (A&HCI).

From the WoS Core Collection data set derives the Journal Impact Factor (JIF), published in the yearly Journal Citation Reports (JCR). Although the JIF is seriously flawed, almost all academic institution e require their researcher to play by the rules of the JIF. Only in the last few years, the critiques gather speed. So signed to date (2019-06-24) already 1,415 organizations and 14,467 individual researchers the San Francisco Declaration On Research Assessment (DORA) against the Journal Impact Factor. But not only critique but also the development of bibliometric alternatives (altmetrics) gain importance. I will cover bibliometric measures and these recent developments in other posts more in detail.

WoS has a tremendous impact on the behavior of researchers and their career development. To date (2019-06-24) Clarivate Analytics covers the following numbers of academic publictions¹

Category	WoS Core	WoS Platform
# of journals	> 20,900	> 34,200
# of records	> 73 million	> 155 million
Cited references	1.4 billion	1.6 billion

To put these figures into perspectives: They cover “only” between 35% (Natural Sciences) to 12% (Arts and humanities) of all journals (> 62,500) as listed in UlrichsWeb (Ulrich’s Global Serials Directory) [@mongeon_journal_2016],

Clarivate also possesses other vital tools and services for scholarly research:

Endnote, a popular reference management software, formerly the property of Thomson Reuters.
Publons: is a service for academics to honor respectively showcase their scientific work which does not lead to a “standard” publication in one of the JIF journals. The name of the enterprise is an homage to the moniker publon, signifying the smallest publishable unit. This concept is a cynical reference to the phenomenon that for the academic career the number of publications is often more important than their individual quality, resulting in “salami slicing” of papers.
Kopernio is a technology startup, which developed a web-browser extension that simplifies the process of finding and legally downloading scholarly publications.

Clarivate Analytics acquired Publons in 2017 and Kopernio in April 2018. These purchases in recent years demonstrate that Clarivate Analytics knows how to secure its leading market position: Both services are (currently) free and are doubtless useful for the individual researcher. Besides generating revenues from publishers, Clarivate binds academics to their main product as both free services are closely related and linked to WoS.

This kind of effective use of the academic community is a well-known strategy, illustrated by two more examples.

First example:

In September 2008 Thomson Reuter – at that time still the owner of Endnote – started a lawsuit with the argument of copyright infringement for US$10 million against the developer of Zotero, the Center for History and New Media at the George Mason University’s. Back then, I wrote about this lawsuit in my German blog (Endnote klagt Zotero auf 10 Mio $ and Zotero sieht der Klage gelassen entgegen).

Thomson Reuters criticized that Zotero has reverse engineered their Endnote bibliographic citation styles where each style addresses the particular requirement of a journal. Reuter saw it as a violation of the site license agreement, especially as Zotero transformed these bibliographic styles into the XML-based open Citation Style Language.

However, the crux of the matter is that members of the community developed all these Endnote styles hosted on the Endnote website. Imagine a situation where Microsoft Word claims to be the owner of all MS Word templates, designed by us, the users! It seems that this is the way for many endeavors in academia: We scientist do the whole work free (e.g., peer review) and the commercial enterprises sell it (e.g., as quality assurance for their journals). – BTW: Thomson Reuters lost the case against Zotero (Wikipedia)

Second example:

As helpful the services of Pablons for the scientists are, we have to keep in mind, that academic work by the research community on the Clarivate website are freebies in exchange to just higher visibility of their research. Researcher track their publications, citation metrics, peer reviews, and journal editing work not only for free but it is hardly any surprise, that their writings are imported from Clarivates WoS, their Endnote bibliographic reference manager (bought with 250 US$ from Clarivate) and their citation metrics come from the Web of Science Core Collection, owned again by Clarivate.

Scopus (Elsevier)

Elsevier has even more market power than Clarivate Analytics. It owns

Scopus, a database of abstracts and citations with a coverage from about 47% (Biomedical journals) to 18% (Arts & Humanities)
Science Direct, a database for scientific publications and ebooks (inclusive medical journals), which sells via subscriptions
Mendeley, a desktop and web program for bibliographic management but also a social networking website for academics with to date (2019-06-24) more than 6 million users

In contrast to WoS and Google (with Google Scholar another big player in the citation reference business): Elsevier does not only sell the usage of their database but is also the owner of a vast list of journals themselves. From the perspective of this double ownership, Elsevier’s business model is a closed circle: It includes the paid use of their database so that academics can find and cite scientific literature. The result of these citations is an increase of reputation of Elsevier’s journals through a higher Journal Impact Factor.

Elsevier is infamous for his incredibly high-profit margin, which is about 35-40%. In contrast, financial institutes and banks work with 10-15%, and the much-criticized Walmart has only about 3% profit. These figures come from the excellent and free accessible documentary “Paywall: The Business of Scholarship” [@schmitt_paywall_2018]. In other posts, I will dwell more about the business model and the nasty role of Elsevier in the Open Science movement.

However, back to the Open Citations issue regarding Elsevier: It turns out that Elsevier is the biggest obstacle for a better proportion of Open Citations:

… of all 956,050,193 references from journal articles stored at Crossref, 305,956,704 (32.00%) are from journal articles published by Elsevier, none of which are in the Crossref “Open” category, freely available for others to use.
Put another way, of the 470,008,522 references from journal articles stored at Crossref that are not open, 305,956,704 (65.10%) are from journals published by Elsevier (Open Citations Blog).

Summary

In this post, I have outlined the rationale and significance of the Open Citation movement. Citations reflect the structure and relationship of our scientific and cultural knowledge and deserve research in its own right. As “dwarfs standing on the shoulders of giants” (Isaac Newton and nowadays also the motto of Google), we generate our knowledge from previous discoveries. Citations are the expression of a social network of interconnected links which itself are due to scientific research. Much can be learned of this interplay of different researchers, subject areas, and language communities through different times and regions.

I have also argued that the economic interests of two key players in the research business are obstacles to overcome for a higher rate of Open Citations. Clarivate Analytics form together with Elsevier a duopoly and maybe with Google even a tripoly [@schoolworkhelper_business_2018]: Because of the competition between two or three sellers they cannot work like a monopoly and dictate without consideration their market condition. However, they can work in a kind competition-cooperation relationship; an economic framework called the coopetition paradox [@raza-ullah_coopetition_2014].

Add relevant links to the subject of Open Citation on my Wakelet page.

References

The category “journals” includes books, conference proceedings, and data sets. ↩︎

Toward a Taxonomy of Open Science (TOS)

Mon, 24 Jun 2019 00:00:00 +0000

This post starts a series of ten contributions about a better understanding of the different aspects of Open Science. I want to collect material to develop a taxonomy of Open Science (TOS). The primary goal of this undertaking is not only to build a hierarchical system where every notion is unambiguous but to develop a heuristic tool useful for further research.

The many faces of Open Science

With a superficial approach to the topic, it almost looks as if Open Science is identical to Open Access. Indeed, Open Access represents within Open Science the strongest and far-reaching movement with the highest financial and political consequences. However, Open Access is only one (important) part of the Open Science movement, which consists of a wide variety of different viewpoints, each with distinct socio-political conditions and effects. The planned series of post on Open Science will show how extensive and diverse this area already is. A first impression about the complexity of the subject communicates the following graphic [@pontika_fostering_2015].

Although I consider this compilation of the numerous fields of Open Science to be valuable, I see – sensitized by my own work on taxonomies [@baumgartner_feedback-arten_2016; @baumgartner_taxonomie_2014-5; @baumgartner_potential_2009] – in Figure 1 some inconsistency and shortcomings:

I miss some critical areas such as Open Content, Open Educational Resources, and Open Licenses.
There are theoretical discrepancies and overlaps, e.g., Open Science Workflows is under Open Reproducible Research. In my opinion we need different workflows not only in the area of reproducible research but in all Open Science activities. Another example: Irreproducible Research, is classified as a sub-item of Open Reproducible Research. However, how is it possible that the contraction of a thing is a sub-category of the very same thing? Irreproducible Research is not a part of Open Reproducible Research; it is plain and simple a misnomer.

Figure 1: Open Science Taxonomy: Originally published in (Pontika et al. 2015, 3). See also the interactive graphic, where all terms are linked to additional material on the Foster website (FOSTER consortium 2015).

Maybe the above figure was not really intended to be inherently consistent? As there is another version with live links to different subject worked out by the members of the project team, the diagram maybe just functions as a starting point to explore in a more systematic way the various relevant topics? Anyway: I believe that a self-consistent taxonomy would a helpful for a better understanding and holistic perspective of Open Science.

I can’t here already provide a comprehensive alternative proposal for a taxonomy. For a logically consistent counterdraft of a taxonomy I would need to look more detailed into all the different aspects of Open Science. At them moment my understanding and knowledge on Open Science is still elementary.

But what I will do here, is to suggest a different first level for a taxonomy of Open Science (TOS). Hopefully this will work out as a more precise and coherent starting point for further research. My recommendation corresponds to the nine elements of the first level of the FOSTER taxonomy: Open Access, Open Data, Open Reproducible Research, Open Science Definition, Open Science Evaluation, Open Science Guidelines, Open Science Policies, Open Science Projects, and Open Science Tools.

My proposal contains also nine elements for the first level, but they differ essentially form the FOSTER taxonomy as the following graph shows:

Figure 02: Toward a taxonomy for Open Science (TOS)

I believe that the essence of Open Science is a particular (new) set of activities during the research process. These nine categories designate a particular set of activities.To describe these activities in detail is the keypart of a better understanding about Open Science. I have already stated in another post, that the “Open” in Open Science refers to the participatory way of knowledge creation and the shared usage of its products. Therefore we are going to look into the different phases of the research process and its exploitation.

In the next couple of weeks, I will therefore look into these activities of the research process. Hopefully this will provide me nd a deeper understaning what kinds of processes Open Science constitute. This should provide me with the material for the third tier of a taxonomy for Open Science.

Every time I will publish one of these articles, I will repeat my graph with a slight change: You will see a new red arrow to the left of the relevant first level category. Behind these marked categories you will find active links to the corresponding post.

References

FOSTER consortium. 2015. “Resources | FOSTER.” The Future of Science Is Open. https://www.fosteropenscience.eu/resources.

Pontika, Nancy, Petr Knoth, Matteo Cancellieri, and Samuel Pearce. 2015. “Fostering Open Science to Research Using a Taxonomy and an eLearning Portal.” In Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business, 11:1–8. I-KNOW ’15. New York, NY, USA: ACM. https://doi.org/10.1145/2809563.2809571.

Bibliographic Metadata for your web page

Wed, 19 Jun 2019 00:00:00 +0000

With Web 2.0, we see a radical change in scholarly communication. On the one hand, there is a growing need for researchers to be present on different web channels (blog, twitter, youtube, and much more). On the other hand, the more traditional ways of publications in high ranked peer review channels are still prevalent. It is difficult for researchers to meet this twofold challenge at the same time: After all the day has only 24 hours!

Double bind for ECRs

Figure 1: EURAXESS - Researchers in Motion is a unique pan-European initiative delivering information and support services to professional researchers.

Especially for young researchers (so-called Early Career Researchers, or ECRs¹ is this situation alarming and even existence-threatening. The find themselves in a double-bind: Should they more invest in traditional ways of career planning, or is it more promising to expand and foster their internet presence? I do not have a clear solution for myself. I think a transition period, the most secure strategy is to follow both paths.

ECRs have both the most to gain and the most to lose from being at the forefront of changes to scholarly communications [@eve_open_2014; @st._louis_open_2015].

COinS and Zotero

Figure 2: Zotero is a free, easy-to-use tool to help you collect, organize, cite, and share research.

The only way I can think of to meet both challenges is to maximize the efficiency of all career moves. One of my aims with this blog on Open Science Education is to talk about technology supported new ways for research and publication to fulfill both requirements.

With the following lines, I will show you how you can help your blog readers or website visitors to cite your posts and pages correctly and in way that they could count as a kind of publication.

My suggestion is

to use the free research tool Zotero
to produce a specific HTML code (COinS = Context Objects in Spans) and
inject this code into your web page.

COinS is a microformat standard to embed bibliographic metadata for Embedding in HTML. COinS include as HTML code all the information necessary to cite a publication correctly. It works for every type of publication (books, papers, web pages).

Figure 3: Zotero is designed to store, manage, and cite bibliographic references, such as books and articles. It is available for Mac, Windows, and Linux.

There are several possibilities to generate COinS, but I will focus on Zotero. I am not going into further details here, but I have prepared a tutorial with images from all the necessary steps to produce, embed, and download COinS.

References

There are different definitions for ECRs around, varying how many years after a Ph.D. are included. The period starts from 4 years and goes to in some institutions to 10 years. A more detailed description of different research profile descriptors has the European Research Commission (EURAXESS) ↩︎

What is Open Science About?

Wed, 12 Jun 2019 00:00:00 +0000

The double meaning of ‘Open Science’

To understand the challenges Open Science poses to civil society, scientists and the public alike have to focus on the double meaning of this term. On the one hand, it refers to a movement to make scientific knowledge publicly accessible for everybody, and on the other hand, it strives for open procedures in the knowledge creation itself.

This second aspect (“to strive for open procedures in the knowledge creation”) is often not included in the definition of Open Science, or at least the significance of this goal is not appropriately valued. The following quotes illustrate our claim that the process facet of Open Science is frequently underdeveloped.

Open science is the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of an inquiring society, amateur or professional. Wikipedia

Open data and content can be freely used, modified, and shared by anyone for any purpose. The Open Definition

The first example is from Wikipedia, the most popular website featuring not only free content but also supporting community building for Open Knowledge. The immediately following quote is the short version of the famous Open Definition by Open Knowledge Foundation, a global non-profit organization, dedicated to help civil society groups to access and use data to solve social problems.

Accountable for not mentioning of the process components of Open Science is not the compact form of these two definitions, as can be seen by a more exhaustive wording in the next citation. The following third example uses the term Open Knowledge instead of Open Science, but as explained further below we will not only show the equivalence of these two notions but argue that Open Knowledge is even the better concept.

‘Open knowledge’ is any content, information or data that people are free to use, re-use and redistribute — without any legal, technological or social restriction. We detail exactly what openness entails in the Open Knowledge Definition. The main principles are:

Free and open access to the material

Freedom to redistribute the material

Freedom to reuse the material

No restriction of the above based on who someone is (such as their job) or where they are (such as their country of residence) or their field of endeavour (including whether they are working on a commercial or non-commercial project)

Open knowledge is what open data becomes when it’s useful, usable and used - not just that some data is open and can be freely used, but that it is useful – accessible, understandable, meaningful, and able to help someone solve a real problem.

So open knowledge is empowering – it helps us effect change and improve the world.

Clearly, this an applaudable quote! It targets to foster social responsibility and civil society. But look at it in more detail: The focus is on ‘material’ (= product), there are no references to the generation processes of material. Taken verbatim it does not necessarily include Open Research Workflow, Open Methodology, Open (Peer) Review or Open Scholarship, to name just a few notions relevant in the making of knowledge. Knowledge does not sit around and wait to be picked up but emerges in a (controversial) construction process. (Latour, 2007)^[Latour, B. (2007). Reassembling the Social: An Introduction to Actor-Network-Theory (New Ed). Oxford University Press.]

Openness as conditia qua non for transparency

A better approach is, in our opinion, the definition by Foster, an EU funded project:

Open Science is the practice of science in such a way that others can collaborate and contribute, where research data, lab notes and other research processes are freely available, under terms that enable reuse, redistribution and reproduction of the research and its underlying data and methods. Foster

To embrace collaboration and contribution in the definition signifies that only access to the products is not enough and automatically includes the requirement that the whole research process has to be open to qualify as transparent.

What are the consequences of our insistence in transparent processes in all phases of the research endeavor? Transparency in its own right is crucial for reproducibility, a topic we will have to say a lot more later on. If one takes the double meaning of Open Science seriously, then it includes technology means (e.g., machine processing techniques) as well as behavior adaptation of institutions and scientists (e.g., social changes). If not only the product (the scientific findings) but also the process of knowledge production (the scientific workflow) has to take place without (social, technical, legal, etc.,) barriers, then research data and their interpretation must be transparent in every aspect. It covers the epistemological interest as the starting point, followed up by all kinds of manipulation and reprocessing until the findings get finally published.

Open Science versus Free or Libre Science

The openness in Open Science is not restricted to public access. Therefore, we cannot translate the term as ‘Public Science’ without losing some of its meaning. Maybe “open” is the wrong qualifier in the first place? Richard Stallman argues extensively that Open Source is not the same as Free Software (see for instance What is free software? or Why Open Source misses the point of Free Software). He explains that ‘free’ is not a matter of price like in “free beer” but of freedom like in “free speech.” Looking for a better word, he suggests to use Libre; a word borrowed from the French or Spanish language and where there is no confusion between ‘free’ and ‘freedom.’ Taken this line of reasoning into account: Perhaps we should also prefer to talk about “Free Science” or even better of “Libre Science” instead of “Open Science”?

Assuming a holistic perspective and taking all mentioned three elements together – access to the research products, access to the research process and the right (=freedom) to participate or collaborate – has significant consequences: It calls for a cultural transition with a modified research practice (Open Research Practice) and thus a new self-concept as a researcher (Open Scholarship). And last not least, we will not only focus on some technical improvements but also on the need to change the power relations in our society.

Open Science versus Open Knowledge

The English terminology of ‘Open Science’ has the disadvantage that with ‘Science’ is meant the natural sciences predominantly. And indeed, the Open Science movement is far more prevalent and entrenched in the STEM subjects (Science, Technology, Engineering and Mathematics than in HASS (Humanities, Arts and Social Sciences) disciplines. As a result of this observation, Michelle Sidler suggests:

The movement should thus consider changing its moniker to open knowledge in order to include academic disciplines that do not self-identify as science (2014, 77).

Although we doubt that the meager involvement of HASS in Open Science can be attributed solely to the chosen naming, Sidler’s suggestion is appealing for us. Even under the caveat that ‘Knowledge’ also has a double meaning in English: ‘Knowledge’ in the general understanding that some knowledge is already acquired and knowledge in the more philosophical sense of insight or discovery. In the first denotation, ‘Knowledge’ refers to the ownership of a product, in the second to the process of acquisition. However, from our perspective, this is just a desired ambiguity!

Open Science versus eScience

Another misunderstanding arises from the notions of ‘Cyberscience,’ ‘Science 2.0’ and ‘eScience.’

Although there are overlaps between Open Science and Science 2.0, we want to emphasize that these two terms are not congruent: Science 2.0 refers to collaborative processes using the so-called Web 2.0 and stresses, therefore, the elements of participation and sharing. It is legitimate to use the notion of ‘eScience’ when, for instance, researchers work together with appropriate cloud-based software on a scientific contribution. But if the resulting publication is not freely available, then we cannot speak of ‘Open Science.’

For Open Science, it is not decisive whether the research process takes place individually or collectively, but whether all activities are transparent and freely available and changeable. Yes, web-based technologies facilitate free availability, access, and transparency, but we should not confuse the possibilities and properties of tools with their final product. (See similar reasoning in Bartling and Friesike 2014, 8^[Bartling, S., & Friesike, S. (2014). Opening science: The evolving guide on how the internet is changing research, collaboration and scholarly publishing. Springer.]; Leibniz Research Alliance Science 2.0 2016^[Leibniz Research Alliance Science 2.0. (2016, May 27). Aim and scope | Leibniz Research Alliance Science 2.0. Retrieved October 8, 2018, from Leibniz-Forschungsverbund Science 2.0 website: http://www.leibniz-science20.de/ueber-uns/aufgaben-und-ziele/]).

Wrapping up

We have discussed several elements and approaches to defining Open Science. It turns out that this notion suffers from some inherent and implicit shortcoming. It would be better to use the term ‘Libre Knowledge’ to avoid possible misunderstandings. But as the concept of ‘Libre Knowledge’ is not widespread and in common use, for the sake of simplicity we will apply ‘Open Knowledge’ or even ‘Open Science,’ but in the broader meaning as we have outlined above.

ORCID - Researchers uniquely identified and connected

Sun, 26 Aug 2018 00:00:00 +0000

Do you know ORCID? I applied for my ORCID-ID already several years ago, but only yesterday I understood the advantage of yet another web presence: ORCID is a non-profit organization and aims to identify researchers uniquely and to connect their contributions and affiliations across disciplines, borders, and time. With this post, I tell you some advantages of ORCID to convince you to join.

What is an ORCID ID?

The last two week I worked hard to finish the ‘official’ nitty-gritty this web presence: CV, downloads for publications, disclaimer and privacy policy. By this occasion, I cleaned up my .bib files and subsequently my Zotero and ORCID presence.

The goal of ORCID is to provide an identifying infrastructure for individual research activities. The main idea is to connect research, funding, and publishing institutions with the individual researcher via a unique ID. It is similar to an electronic portfolio where you can establish so-called ‘trusted’ connections, e.g., where institutions are allowed to look into your scientific record and to add information to it. These institutional connections are — by the way — also the funding structure of ORCID: Membership organizations have to pay a pretty high subscription fee (starting with 5.150 US $) to connect and use their infrastructure with ORCID.

However, this is not very important to me. At the time of this writing (2018-08-26) there are solely 910 member organization, only four of them from Austria:

The Austrian Science Fund (FWF)
Technical University Graz (TU Graz)
University of Natural Resources and Applied Life Sciences Vienna (BOKU) and
University of Vienna

Only TU Graz has already established integration with ORCID. The other three universities are still in the process of planning the technical infrastructure for this bonding.

A personal, digital, international and persistent name identifier

However, ORCID is already very advantageous for the individual researcher: The ORCID ID gives you a personal, digital, international, and persistent name identifier. This ID outlives the change of working positions with their associated web presence and email address. More important: The ID identifies you unmistakable, e.g., independently name changes with marriage or a pool of people with precisely the same name.

QR Code for my ORCID ID

Especially the last case is crucial for me: I know from a funny event (1st International Meeting of people with the family name of ‘Baumgartner’) that there are about 4.700 (!) Baumgartner’s in Austria alone and perhaps several thousand more in the world. As my given name ‘Peter’ is also trendy, there are always a bunch of Peter Baumgartner’s in every database. Even when I want to redeem a voucher in a small shop, I have to provide my address as there are two or three names with “Peter Baumgartner” in their database.

This coincidence of names is very cumbersome when somebody tries to find my publications: There are way too many publications under my name! And not in all cases, you can decide and separate them with adding the research field.

There is especially another Peter Baumgartner in Computer Science situated for many years in Koblenz (Germany). As I am working on E-Learning issues, you can imagine that there arose many times confusion: Sometimes we got even E-Mails for the other person. This Peter Baumgartner has now changed to Australia, but this has not helped much to clear up the situation as the confusion between Australia and Austria is legendary.

If you Google my name, you will find several others Peter Baumgartner, especially a top-rated speaker and coach: Peter Baumgartner, der Mutmacher (Peter Baumgartner ‘the bolsterer’ from ‘to bolster somebody up’). ORCID helps to prevent precisely this mistaking identity!

Other similar initiatives

There are other similar initiatives like Scopus Author ID and ResearcherID.

ResearcherID (developed by Thomas Reuters and used in Web of Science) and Scopus Author ID (generated by Elsevier and utilized in Scopus) are two examples of these efforts. Whereas ORCID is “a platform-agnostic identifier,” ResearcherID and Scopus Author ID connect to proprietary, subscription-based systems. (Quoted from the Library of University of Chicago)

Remains to mention: ORCID is free, platform independent and Open Source under the MIT License.

Screenshot of my the upper part of my ORCID page

Conclusion

ORCID aims to be a part of a broader social and technical infrastructure for uniquely identifying researchers with their work, affiliations, funding, and publishing institutions. Almost 5.2 Million researchers have already applied for their free ORCID ID. In the long run, many institutions engaged with research (universities, funding agencies, publishers) should be connected and form part of this global infrastructure. Even if this is not the case at the moment, applying has already some advantages: It gives you another place to present your work, e.g., a free web presence and provides you furthermore with a personal, digital, international and persistent name identifier.