From f0489db71406fa557dcfd3b3b0c68310ba604bb0 Mon Sep 17 00:00:00 2001 From: Najko Jahn Date: Thu, 27 Feb 2020 09:06:35 +0100 Subject: [PATCH] add PIDs #9 --- _posts/datacite_graph/datacite_graph.Rmd | 3 +- _posts/datacite_graph/datacite_graph.html | 404 +++++++++--------- _posts/datacite_graph/distill-preview.png | Bin 498766 -> 497813 bytes _posts/elsevier_invoice/blog_post.Rmd | 1 + _posts/elsevier_invoice/blog_post.html | 34 +- docs/posts/datacite_graph/distill-preview.png | Bin 498766 -> 497813 bytes docs/posts/datacite_graph/index.html | 57 +-- docs/posts/elsevier_invoice/index.html | 24 +- docs/posts/posts.json | 4 +- docs/sitemap.xml | 4 +- 10 files changed, 279 insertions(+), 252 deletions(-) diff --git a/_posts/datacite_graph/datacite_graph.Rmd b/_posts/datacite_graph/datacite_graph.Rmd index 67e0f2e..911a70b 100644 --- a/_posts/datacite_graph/datacite_graph.Rmd +++ b/_posts/datacite_graph/datacite_graph.Rmd @@ -7,8 +7,9 @@ author: url: https://twitter.com/najkoja affiliation: State and University Library Göttingen affiliation_url: https://www.sub.uni-goettingen.de/ -date: "`r Sys.Date()`" +date: Oct 24, 2019 output: distill::distill_article +doi: "21.11101/0000-0007-DFAD-C" bibliography: pubs.bib --- diff --git a/_posts/datacite_graph/datacite_graph.html b/_posts/datacite_graph/datacite_graph.html index 28b914e..09db8ff 100644 --- a/_posts/datacite_graph/datacite_graph.html +++ b/_posts/datacite_graph/datacite_graph.html @@ -20,26 +20,26 @@ Interfacing the PID Graph with R - + - - + + - + - + - + @@ -48,9 +48,9 @@ - + @@ -60,41 +60,41 @@ - + - + - + @@ -1949,25 +1949,25 @@

This article is in review.

Interfacing the PID Graph with R

-

The PID Graph from DataCite interlinks persistent identifiers (PID) in research. In this blog post, I will present how to interface this graph using the DataCite GraphQL API with R. To illustrate it, I will visualise the research information network of a person.

+

The PID Graph from DataCite interlinks persistent identifiers (PID) in research. In this blog post, I will present how to interface this graph using the DataCite GraphQL API with R. To illustrate it, I will visualise the research information network of a person.

Najko Jahn https://twitter.com/najkoja (State and University Library Göttingen)https://www.sub.uni-goettingen.de/ -
2019-10-24 +
Oct 24, 2019
-

In 1965, Derek J. de Solla Price proposed to study the relationships between research articles using bibliographic references(Solla Price 1965). Ever since, scholars and librarians have been working on interrelating research activities and making such links discoverable.

+

In 1965, Derek J. de Solla Price proposed to study the relationships between research articles using bibliographic references(Solla Price 1965). Ever since, scholars and librarians have been working on interrelating research activities and making such links discoverable.

In this context, the FREYA project, funded by the European Commission, connects and interlinks persistent identifier (PID) schemes. FREYA focuses, among others, on PIDs for persons (ORCID), organisations (ROR), publications, research data, and software (DOI). The project has created a PID Graph, which connects various resources using persistent identifiers. A GraphQL interface allows accessing these data.

-

Upon invitation of Martin Fenner, Technical Director of DataCite and FREYA team member, I attended the half-day workshop Project FREYA: connecting knowledge in the European Open Science Cloud, co-located at 14th Plenary Meeting of the Research Data Alliance in Helsinki. Using data analytics as an outreach strategy, Martin prepared a large collection of Juypter notebooks showcasing how the PID Graph can be interfaced using R and Python(Fenner 2019c). During the workshop, we presented two interactive notebooks deployed on mybinder.org, and invited workshop participants to re-run them in the web browser. The first notebook(Fenner 2019b) presents the overall indexing coverage of the PID Graph, while the second notebook demonstrated how to obtain data about a personal researcher network(Fenner 2019a).

+

Upon invitation of Martin Fenner, Technical Director of DataCite and FREYA team member, I attended the half-day workshop Project FREYA: connecting knowledge in the European Open Science Cloud, co-located at 14th Plenary Meeting of the Research Data Alliance in Helsinki. Using data analytics as an outreach strategy, Martin prepared a large collection of Juypter notebooks showcasing how the PID Graph can be interfaced using R and Python(Fenner 2019c). During the workshop, we presented two interactive notebooks deployed on mybinder.org, and invited workshop participants to re-run them in the web browser. The first notebook(Fenner 2019b) presents the overall indexing coverage of the PID Graph, while the second notebook demonstrated how to obtain data about a personal researcher network(Fenner 2019a).

In this blog post, I want to expand on what I have learned during the FREYA workshop. Although most participants were able to run the interactive Jupyter notebooks, some articulated problems along the data transformation path. In the following, I will therefore present an complementary approach of how to transform and visualise data from the PID graph with R by using tools from the popular tidyverse package collection.

Accessing the PID Graph using GraphQL

A first version of the PID graph is accessible via the DataCite GraphQL API. GraphQL is a query language designed to request multiple connections across resources at once. As an example, a query for accessing publications, research data and software by a particular researcher using the DataCite GraphQL API looks like this:


-graphql_query <- '{
+graphql_query <- '{
   person(id: "https://orcid.org/0000-0003-1444-9135") {
     id
     type
@@ -2003,7 +2003,7 @@ 

Accessing the PID Graph using Gra } } } -}'

+}'

Here, I query for publications, research data and software code authored by Scott Chamberlain, who is represented by his ORCID. I also retrieve relations between his research activities that are represented in the relatedIdentifier node. The query is stored in the R object graphql_query that will be used to interface the DataCite GraphQL API in the following.

To make GraphQL requests with R, Scott developed the R package ghql, which is maintained by rOpenSci. The package is not on CRAN, but can be installed from GitHub.

@@ -2059,12 +2059,12 @@

Data Transformation

# A tibble: 6 x 4 id type relatedIdentifier to <chr> <chr> <chr> <chr> -1 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/… -2 https://doi.org/10… Scholarl… <NA> <NA> +1 https://doi.org/10… Scholarl… <NA> <NA> +2 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/… 3 https://doi.org/10… Scholarl… <NA> <NA> -4 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/… -5 https://doi.org/10… Scholarl… <NA> <NA> -6 https://doi.org/10… Scholarl… <NA> <NA> +4 https://doi.org/10… Scholarl… <NA> <NA> +5 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/… +6 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/…

A network consists of nodes (vertices) and links (edges). Nodes represent an output, while links describes relationships between them (relatedIdentifier).

Let’s create node data.frame

@@ -2080,12 +2080,12 @@

Data Transformation

# A tibble: 6 x 2 name type <chr> <chr> -1 https://doi.org/10.6084/m9.figshare.97215.v1 ScholarlyArticle -2 https://doi.org/10.6084/m9.figshare.94090 ScholarlyArticle +1 https://doi.org/10.6084/m9.figshare.97222 ScholarlyArticle +2 https://doi.org/10.6084/m9.figshare.94217.v2 ScholarlyArticle 3 https://doi.org/10.6084/m9.figshare.94296 ScholarlyArticle -4 https://doi.org/10.6084/m9.figshare.94295.v2 ScholarlyArticle -5 https://doi.org/10.6084/m9.figshare.94089 ScholarlyArticle -6 https://doi.org/10.6084/m9.figshare.94217 ScholarlyArticle +4 https://doi.org/10.6084/m9.figshare.94090 ScholarlyArticle +5 https://doi.org/10.6084/m9.figshare.94295.v2 ScholarlyArticle +6 https://doi.org/10.6084/m9.figshare.97215.v1 ScholarlyArticle

and a data.frame with the relationships between these nodes, i.e. edges:

@@ -2094,7 +2094,7 @@

Data Transformation

select(source = id, target = to) %>% # we only observe links between them filter(target %in% my_nodes$name) -#' lets ad relationsships between person and outputs +#' lets ad relationsships between person and outputs my_edges <- tibble(source = my_data$data$person$id, target = my_nodes$name) %>% # no self loop @@ -2121,17 +2121,17 @@

Network visualisation

graph_from_data_frame(d = my_edges, vertices = my_nodes, directed = FALSE) -#' remove potential loops +#' remove potential loops g <- igraph::simplify(g)

Next, some visualisation parameter are defined including node colours and labels. Here, node colours represent the person and the three different publication types.


-#' define node colours
+#' define node colours
 my_palette <-
   c("#6da7de", "#9e0059", "#dee000", "#d82222")
 my_color <- my_palette[as.numeric(as.factor(V(g)$type))]
-#' don't display label
+#' don't display label
 V(g)$label = NA

Finally, let’s visualise Scott’s publication network according to DataCite metadata.

@@ -2151,15 +2151,15 @@

Network visualisation

horiz = FALSE, inset = c(0.1,-0.1) ) -

+

Discussion and Outlook

Using data analytics is a great outreach activity to promote the PID Graph. During the workshop, participants were able to run the interactive notebooks with analytical code. This enabled a hands-on experience about how to interface the graph with GraphQL. It also led to great discussions about the PID Graph’s indexing coverage and potential use-cases. In particular, participants raised the issue of yet-incomplete PID metadata coverage. In our example, for instance, we likely miss a considerable amount of Scott’s software projects linked with a DOI, because the underlying metadata records lack his ORCID.

Besides the fruitful discussion about PID coverage in the metadata, I had the feeling that many participants struggled with following the steps for data transformation. Therefore, I decided to dry out the code from the initial notebook using the packages tidyr and dplyr from the tidyverse. I hope that such an approach will make the examples clearer.

-

In the future, the FREYA team will continuously extend the indexing coverage of the PID Graph in collaboration with related research graph activities from OpenAIRE(Manghi et al. 2019), and the Wikibase community. On 25 October, there will be a joint meeting of large data providers for Open Science Graphs at the RDA 14th Plenary. Together with the Software Sustainability Institute, FREYA will hold a day-long hackathon on 4 December at the British Library so as to further improve data analytics using the PID graph.

-

Acknowledgments

+

In the future, the FREYA team will continuously extend the indexing coverage of the PID Graph in collaboration with related research graph activities from OpenAIRE(Manghi et al. 2019), and the Wikibase community. On 25 October, there will be a joint meeting of large data providers for Open Science Graphs at the RDA 14th Plenary. Together with the Software Sustainability Institute, FREYA will hold a day-long hackathon on 4 December at the British Library so as to further improve data analytics using the PID graph.

+

Acknowledgments

I would like to thank Martin Fenner, Kristian Garza, Slava Tykhonov, and Maaike de Jong for having me at the workshop, and their valuable help with the analysis and the use of the PID Graph with Jupyter Notebooks.

-
+

Fenner, Martin. 2019a. “FREYA Pid Graph for a Specific Researcher.” DataCite. https://doi.org/10.14454/628M-3882.

@@ -2167,10 +2167,10 @@

Acknowledgments

———. 2019b. “FREYA Pid Graph Key Performance Indicators (Kpis).” DataCite. https://doi.org/10.14454/3BPW-W381.

-

———. 2019c. “Using Jupyter Notebooks with Graphql and the Pid Graph.” DataCite. https://doi.org/10.5438/HWAW-XE52.

+

———. 2019c. “Using Jupyter Notebooks with Graphql and the Pid Graph.” https://doi.org/10.5438/HWAW-XE52.

-

Manghi, Paolo, Alessia Bardi, Claudio Atzori, Miriam Baglioni, Natalia Manola, Jochen Schirrwagen, and Pedro Principe. 2019. “The Openaire Research Graph Data Model.” Zenodo. https://doi.org/10.5281/ZENODO.2643199.

+

Manghi, Paolo, Alessia Bardi, Claudio Atzori, Miriam Baglioni, Natalia Manola, Jochen Schirrwagen, and Pedro Principe. 2019. “The Openaire Research Graph Data Model.” https://doi.org/10.5281/ZENODO.2643199.

Solla Price, D. J. de. 1965. “Networks of Scientific Papers.” Science 149 (3683): 510–15. https://doi.org/10.1126/science.149.3683.510.

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zfBADif6pLz#~_)v7O-IYZ8!P#8&_o4 zozTL(bolkZ{{AobhmRG|d41mx9{#hZ{`WuPVGU<=@>c0T{a6o=>rRA!^zHnoXA})R zL?7t?UFadu(_sFs1@QY`xaM)AVTfn|M#B)X0E~tqqG=YbfYt<>KU)e7J!t6pTR97H zB{cN>`9Q7t3L1LQ&_gV96D?Oapj8hs+o7RnO`!SnJsu4`Xy_q!e9+KC^!WVwK&|-- z8hVJ@4h=nM=s`ozPj@P`a&}kRZh>A|Th(=U&+C{X5g@zt9^!)F`&3|1G(9na1 z9%9Fb_&pL0J;ZE>Ry}CdLo{=vp@-PGG9rW$|lvjTU z45)Qb>kv((KgM{}cYe`z{J|*S?S-B!^kktY>-%v)UZ5GBSo;;V4r(3ZJ?3908uVll zOEjo;Q0x5NG>rNV>N}|K{G5}}iw&_;fm#Q(&fkq0sPCY@gZd6qb$#!$p_k(S*y6iU zzP~`NgIZ_JVfpSCzw-k19n^PF-&rdhgO*=t`GuBWX!%9#6rk2Yt%IVQ{<3aBQz)_H zfm#Q(4q9NKDU{eLK&^vX2Q4tr0%L6x{CC=U-@XSeFo*>Y)HN3)HaOjPc z*ug-pgIeeBh7HtrP~Sm)=g%1jEyakP3)DKOb^dPDKz#@G9n^RJH?Xir)*6K@a*Z_# zSwwGz9=YysZEot${59GC&x!9>UpY&8`mAso)4sp^*#7l9(YHga6^L2~wGPqKOyu;T tzJvM>>N`Ii5Hx2I%lfEwh+Ah%%rqtc!(2r{kR@FwEiNyXdQSJ&{{vQ2EWZE% diff --git a/_posts/elsevier_invoice/blog_post.Rmd b/_posts/elsevier_invoice/blog_post.Rmd index 1eec85c..ed36a52 100644 --- a/_posts/elsevier_invoice/blog_post.Rmd +++ b/_posts/elsevier_invoice/blog_post.Rmd @@ -7,6 +7,7 @@ author: url: https://twitter.com/najkoja affiliation: State and University Library Göttingen affiliation_url: https://www.sub.uni-goettingen.de/ +doi: "21.11101/0000-0007-DFAC-D" output: distill::distill_article bibliography: literature.bib #always_allow_html: true diff --git a/_posts/elsevier_invoice/blog_post.html b/_posts/elsevier_invoice/blog_post.html index 57fa7f0..f3ab543 100644 --- a/_posts/elsevier_invoice/blog_post.html +++ b/_posts/elsevier_invoice/blog_post.html @@ -40,6 +40,14 @@ + + + + + + + + @@ -64,7 +72,7 @@ @@ -16520,7 +16528,7 @@

This article is in review.

@@ -16698,8 +16706,8 @@

3), created with the echarts4r package(Coene 2019), lets you browse the invoicing data. I recommend using a recent Chrome browser ot interact with the visualisation.

-
- +
+

Figure 3: Breakdown of Elsevier hybrid open access journal articles by invoice recipient. Each rectangle represents an invoicing type and can be broken down by recipient. Data Source: Elsevier B.V.

@@ -16723,8 +16731,8 @@

Who published hyb

In the following figure, a hierarchical, interactive treemap visualises the distribution of the email domains (see Figure 5). It appears that this distribution roughly represents the overall national research landscapes measured by publication output. However, the dominance of domains from commercial organisations, mostly email providers like “gmail.com” or the Chinese “163.com” and “126.com”, highlights the limitations of this approach to infer eligible funding institutions with author email addresses.

-
- +
+

Figure 5: Email domain analysis of first corresponding authors publishing open access in Elsevier hybrid journals. Each top-level domain can be subdivided further into domain names representing academic institutions or companies. Data Source: Elsevier B.V.

@@ -16841,7 +16849,19 @@

Acknowledgments

-
+
+

Citation

+

For attribution, please cite this work as

+
Jahn, "Mining and analysing invoice data from Elsevier relative to hybrid open access", Scholarly Communication Analytics Blog, 2019
+

BibTeX citation

+
@article{jahn2019mining,
+  author = {Jahn, Najko},
+  title = {Mining and analysing invoice data from Elsevier relative to hybrid open access},
+  journal = {Scholarly Communication Analytics Blog},
+  year = {2019},
+  doi = {21.11101/0000-0007-DFAC-D}
+}
+
@@ -2323,7 +2324,7 @@

This article is in review.

@@ -2349,25 +2350,25 @@

This article is in review.

Interfacing the PID Graph with R

-

The PID Graph from DataCite interlinks persistent identifiers (PID) in research. In this blog post, I will present how to interface this graph using the DataCite GraphQL API with R. To illustrate it, I will visualise the research information network of a person.

+

The PID Graph from DataCite interlinks persistent identifiers (PID) in research. In this blog post, I will present how to interface this graph using the DataCite GraphQL API with R. To illustrate it, I will visualise the research information network of a person.

-

In 1965, Derek J. de Solla Price proposed to study the relationships between research articles using bibliographic references(Solla Price 1965). Ever since, scholars and librarians have been working on interrelating research activities and making such links discoverable.

+

In 1965, Derek J. de Solla Price proposed to study the relationships between research articles using bibliographic references(Solla Price 1965). Ever since, scholars and librarians have been working on interrelating research activities and making such links discoverable.

In this context, the FREYA project, funded by the European Commission, connects and interlinks persistent identifier (PID) schemes. FREYA focuses, among others, on PIDs for persons (ORCID), organisations (ROR), publications, research data, and software (DOI). The project has created a PID Graph, which connects various resources using persistent identifiers. A GraphQL interface allows accessing these data.

-

Upon invitation of Martin Fenner, Technical Director of DataCite and FREYA team member, I attended the half-day workshop Project FREYA: connecting knowledge in the European Open Science Cloud, co-located at 14th Plenary Meeting of the Research Data Alliance in Helsinki. Using data analytics as an outreach strategy, Martin prepared a large collection of Juypter notebooks showcasing how the PID Graph can be interfaced using R and Python(Fenner 2019c). During the workshop, we presented two interactive notebooks deployed on mybinder.org, and invited workshop participants to re-run them in the web browser. The first notebook(Fenner 2019b) presents the overall indexing coverage of the PID Graph, while the second notebook demonstrated how to obtain data about a personal researcher network(Fenner 2019a).

+

Upon invitation of Martin Fenner, Technical Director of DataCite and FREYA team member, I attended the half-day workshop Project FREYA: connecting knowledge in the European Open Science Cloud, co-located at 14th Plenary Meeting of the Research Data Alliance in Helsinki. Using data analytics as an outreach strategy, Martin prepared a large collection of Juypter notebooks showcasing how the PID Graph can be interfaced using R and Python(Fenner 2019c). During the workshop, we presented two interactive notebooks deployed on mybinder.org, and invited workshop participants to re-run them in the web browser. The first notebook(Fenner 2019b) presents the overall indexing coverage of the PID Graph, while the second notebook demonstrated how to obtain data about a personal researcher network(Fenner 2019a).

In this blog post, I want to expand on what I have learned during the FREYA workshop. Although most participants were able to run the interactive Jupyter notebooks, some articulated problems along the data transformation path. In the following, I will therefore present an complementary approach of how to transform and visualise data from the PID graph with R by using tools from the popular tidyverse package collection.

Accessing the PID Graph using GraphQL

A first version of the PID graph is accessible via the DataCite GraphQL API. GraphQL is a query language designed to request multiple connections across resources at once. As an example, a query for accessing publications, research data and software by a particular researcher using the DataCite GraphQL API looks like this:


-graphql_query <- '{
+graphql_query <- '{
   person(id: "https://orcid.org/0000-0003-1444-9135") {
     id
     type
@@ -2403,7 +2404,7 @@ 

Accessing the PID Graph using Gra } } } -}'

+}'

Here, I query for publications, research data and software code authored by Scott Chamberlain, who is represented by his ORCID. I also retrieve relations between his research activities that are represented in the relatedIdentifier node. The query is stored in the R object graphql_query that will be used to interface the DataCite GraphQL API in the following.

To make GraphQL requests with R, Scott developed the R package ghql, which is maintained by rOpenSci. The package is not on CRAN, but can be installed from GitHub.

@@ -2459,12 +2460,12 @@

Data Transformation

# A tibble: 6 x 4 id type relatedIdentifier to <chr> <chr> <chr> <chr> -1 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/… -2 https://doi.org/10… Scholarl… <NA> <NA> +1 https://doi.org/10… Scholarl… <NA> <NA> +2 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/… 3 https://doi.org/10… Scholarl… <NA> <NA> -4 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/… -5 https://doi.org/10… Scholarl… <NA> <NA> -6 https://doi.org/10… Scholarl… <NA> <NA> +4 https://doi.org/10… Scholarl… <NA> <NA> +5 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/… +6 https://doi.org/10… Scholarl… 10.6084/m9.figshare… https://doi.org/…

A network consists of nodes (vertices) and links (edges). Nodes represent an output, while links describes relationships between them (relatedIdentifier).

Let’s create node data.frame

@@ -2480,12 +2481,12 @@

Data Transformation

# A tibble: 6 x 2 name type <chr> <chr> -1 https://doi.org/10.6084/m9.figshare.97215.v1 ScholarlyArticle -2 https://doi.org/10.6084/m9.figshare.94090 ScholarlyArticle +1 https://doi.org/10.6084/m9.figshare.97222 ScholarlyArticle +2 https://doi.org/10.6084/m9.figshare.94217.v2 ScholarlyArticle 3 https://doi.org/10.6084/m9.figshare.94296 ScholarlyArticle -4 https://doi.org/10.6084/m9.figshare.94295.v2 ScholarlyArticle -5 https://doi.org/10.6084/m9.figshare.94089 ScholarlyArticle -6 https://doi.org/10.6084/m9.figshare.94217 ScholarlyArticle +4 https://doi.org/10.6084/m9.figshare.94090 ScholarlyArticle +5 https://doi.org/10.6084/m9.figshare.94295.v2 ScholarlyArticle +6 https://doi.org/10.6084/m9.figshare.97215.v1 ScholarlyArticle

and a data.frame with the relationships between these nodes, i.e. edges:

@@ -2494,7 +2495,7 @@

Data Transformation

select(source = id, target = to) %>% # we only observe links between them filter(target %in% my_nodes$name) -#' lets ad relationsships between person and outputs +#' lets ad relationsships between person and outputs my_edges <- tibble(source = my_data$data$person$id, target = my_nodes$name) %>% # no self loop @@ -2521,17 +2522,17 @@

Network visualisation

graph_from_data_frame(d = my_edges, vertices = my_nodes, directed = FALSE) -#' remove potential loops +#' remove potential loops g <- igraph::simplify(g)

Next, some visualisation parameter are defined including node colours and labels. Here, node colours represent the person and the three different publication types.


-#' define node colours
+#' define node colours
 my_palette <-
   c("#6da7de", "#9e0059", "#dee000", "#d82222")
 my_color <- my_palette[as.numeric(as.factor(V(g)$type))]
-#' don't display label
+#' don't display label
 V(g)$label = NA

Finally, let’s visualise Scott’s publication network according to DataCite metadata.

@@ -2551,15 +2552,15 @@

Network visualisation

horiz = FALSE, inset = c(0.1,-0.1) ) -

+

Discussion and Outlook

Using data analytics is a great outreach activity to promote the PID Graph. During the workshop, participants were able to run the interactive notebooks with analytical code. This enabled a hands-on experience about how to interface the graph with GraphQL. It also led to great discussions about the PID Graph’s indexing coverage and potential use-cases. In particular, participants raised the issue of yet-incomplete PID metadata coverage. In our example, for instance, we likely miss a considerable amount of Scott’s software projects linked with a DOI, because the underlying metadata records lack his ORCID.

Besides the fruitful discussion about PID coverage in the metadata, I had the feeling that many participants struggled with following the steps for data transformation. Therefore, I decided to dry out the code from the initial notebook using the packages tidyr and dplyr from the tidyverse. I hope that such an approach will make the examples clearer.

-

In the future, the FREYA team will continuously extend the indexing coverage of the PID Graph in collaboration with related research graph activities from OpenAIRE(Manghi et al. 2019), and the Wikibase community. On 25 October, there will be a joint meeting of large data providers for Open Science Graphs at the RDA 14th Plenary. Together with the Software Sustainability Institute, FREYA will hold a day-long hackathon on 4 December at the British Library so as to further improve data analytics using the PID graph.

-

Acknowledgments

+

In the future, the FREYA team will continuously extend the indexing coverage of the PID Graph in collaboration with related research graph activities from OpenAIRE(Manghi et al. 2019), and the Wikibase community. On 25 October, there will be a joint meeting of large data providers for Open Science Graphs at the RDA 14th Plenary. Together with the Software Sustainability Institute, FREYA will hold a day-long hackathon on 4 December at the British Library so as to further improve data analytics using the PID graph.

+

Acknowledgments

I would like to thank Martin Fenner, Kristian Garza, Slava Tykhonov, and Maaike de Jong for having me at the workshop, and their valuable help with the analysis and the use of the PID Graph with Jupyter Notebooks.

-
+

Fenner, Martin. 2019a. “FREYA Pid Graph for a Specific Researcher.” DataCite. https://doi.org/10.14454/628M-3882.

@@ -2567,10 +2568,10 @@

Acknowledgments

———. 2019b. “FREYA Pid Graph Key Performance Indicators (Kpis).” DataCite. https://doi.org/10.14454/3BPW-W381.

-

———. 2019c. “Using Jupyter Notebooks with Graphql and the Pid Graph.” DataCite. https://doi.org/10.5438/HWAW-XE52.

+

———. 2019c. “Using Jupyter Notebooks with Graphql and the Pid Graph.” https://doi.org/10.5438/HWAW-XE52.

-

Manghi, Paolo, Alessia Bardi, Claudio Atzori, Miriam Baglioni, Natalia Manola, Jochen Schirrwagen, and Pedro Principe. 2019. “The Openaire Research Graph Data Model.” Zenodo. https://doi.org/10.5281/ZENODO.2643199.

+

Manghi, Paolo, Alessia Bardi, Claudio Atzori, Miriam Baglioni, Natalia Manola, Jochen Schirrwagen, and Pedro Principe. 2019. “The Openaire Research Graph Data Model.” https://doi.org/10.5281/ZENODO.2643199.

Solla Price, D. J. de. 1965. “Networks of Scientific Papers.” Science 149 (3683): 510–15. https://doi.org/10.1126/science.149.3683.510.

diff --git a/docs/posts/elsevier_invoice/index.html b/docs/posts/elsevier_invoice/index.html index e0007b3..efa8c6e 100644 --- a/docs/posts/elsevier_invoice/index.html +++ b/docs/posts/elsevier_invoice/index.html @@ -54,6 +54,8 @@ + + @@ -83,7 +85,7 @@ @@ -16905,7 +16907,7 @@

This article is in review.

@@ -17098,8 +17100,8 @@

3), created with the echarts4r package(Coene 2019), lets you browse the invoicing data. I recommend using a recent Chrome browser ot interact with the visualisation.

-
- +
+

Figure 3: Breakdown of Elsevier hybrid open access journal articles by invoice recipient. Each rectangle represents an invoicing type and can be broken down by recipient. Data Source: Elsevier B.V.

@@ -17123,8 +17125,8 @@

Who published hyb

In the following figure, a hierarchical, interactive treemap visualises the distribution of the email domains (see Figure 5). It appears that this distribution roughly represents the overall national research landscapes measured by publication output. However, the dominance of domains from commercial organisations, mostly email providers like “gmail.com” or the Chinese “163.com” and “126.com”, highlights the limitations of this approach to infer eligible funding institutions with author email addresses.

-
- +
+

Figure 5: Email domain analysis of first corresponding authors publishing open access in Elsevier hybrid journals. Each top-level domain can be subdivided further into domain names representing academic institutions or companies. Data Source: Elsevier B.V.

@@ -17248,13 +17250,15 @@

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/subugoe/scholcomm_analytics, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

-
Jahn (2019, Nov. 25). Scholarly Communication Analytics: Mining and analysing invoice data from Elsevier relative to hybrid open access. Retrieved from https://subugoe.github.io/scholcomm_analytics/posts/elsevier_invoice/
+
Jahn, "Scholarly Communication Analytics: Mining and analysing invoice data from Elsevier relative to hybrid open access", Scholarly Communication Analytics Blog, 2019

BibTeX citation

-
@misc{jahn2019mining,
+  
@article{jahn2019mining,
   author = {Jahn, Najko},
   title = {Scholarly Communication Analytics: Mining and analysing invoice data from Elsevier relative to hybrid open access},
-  url = {https://subugoe.github.io/scholcomm_analytics/posts/elsevier_invoice/},
-  year = {2019}
+  journal = {Scholarly Communication Analytics Blog},
+  year = {2019},
+  note = {https://subugoe.github.io/scholcomm_analytics/posts/elsevier_invoice/},
+  doi = {21.11101/0000-0007-DFAC-D}
 }