The 2018 Altmetric Top 100 Outputs with ‘Comics’ as Keyword

As it’s that time of the year and Altmetric has released its 2018 Top 100, in this post I share the 2018 Top 100 research outputs with ‘comics’ as a keyword according to Altmetric.

I queried the data from the Altmetric Explorer, looking for all outputs with this keyword between 13/12/2017 and 13/12/2018. I then refined the data to concentrate only on the Top 100 outputs about comics.

To see the complete Top 100, you can download the dataset I shared on figshare at https://doi.org/10.6084/m9.figshare.7467116.v1.

Below you can quickly take a look at the top 20 outputs with keyword “comics” ordered by their Altmetric Attention score :

Altmetric Attention Score Title Journal/Collection Title Publication Date
524 Ten simple rules for drawing scientific comics PLoS Computational Biology 04/01/2018
286 Comixify: Transform video into a comics 09/12/2018
154 Teaching Confidentiality through Comics at One Spanish Medical School AMA Journal of Ethics 01/02/2018
99 Bruised and Battered: Reinforcing Intimate Partner Violence in Comic Books Feminist Criminology 17/05/2018
84 Of Microscopes and Metaphors: Visual Analogy as a Scientific Tool The Comics Grid: Journal of Comics Scholarship 10/10/2018
79 The potential of comics in science communication JCOM – Journal of Science Communication 23/01/2018
65 Alter egos: an exploration of the perspectives and identities of science comic creators JCOM – Journal of Science Communication 16/01/2018
61 Using comics to change lives The Lancet 01/01/2018
50 The Question Concerning Comics as Technology: Gestell and Grid The Comics Grid: Journal of Comics Scholarship 24/09/2018
47 A survey of comics research in computer science 16/04/2018
41 Is There a Comic Book Industry? Media Industries 05/06/2018
38 The Utility of Multiplex Molecular Tests for Enteric Pathogens: a Micro-Comic Strip Journal of Clinical Microbiology 24/01/2018
38 Farting Jellyfish and Synergistic Opportunities: The Story and Evaluation
of Newcastle Science Comic
The Comics Grid: Journal of Comics Scholarship 20/03/2018
35 Pitfalls in Performing Research in the Clinical Microbiology Laboratory: a Micro-Comic Strip Journal of Clinical Microbiology 25/09/2018
34 Neural Comic Style Transfer: Case Study 05/09/2018
31 Comics and the Ethics of Representation in Health Care … AMA Journal of Ethics AMA Journal of Ethics 01/02/2018
29 Undemocratic Layout: Eight Methods of Accenting Images The Comics Grid: Journal of Comics Scholarship 25/05/2018
29 Communicating Science through Comics: A Method Publications 30/08/2018
26 Of Cornopleezeepi and Party Poopers: A Brief History of Physicians in Comics … AMA Journal of Ethics AMA Journal of Ethics 01/02/2018
26 On the Significance of the Graphic Novel to Contemporary Literary Studies: A Review of The Cambridge Companion to the Graphic Novel The Comics Grid: Journal of Comics Scholarship 19/09/2018
DOI Altmetric Details Page URL
10.1371/journal.pcbi.1005845 https://www.altmetric.com/details/31266263
https://www.altmetric.com/details/52485006
10.1001/journalofethics.2018.20.2.medu1-1802 https://www.altmetric.com/details/32564583
10.1177/1557085118772093 https://www.altmetric.com/details/41904868
10.16995/cg.130 https://www.altmetric.com/details/49471637
10.22323/2.17010401 https://www.altmetric.com/details/32104944
10.22323/2.17010201 https://www.altmetric.com/details/31748235
10.1016/s0140-6736(17)33258-0 https://www.altmetric.com/details/31292645
10.16995/cg.133 https://www.altmetric.com/details/48839521
https://www.altmetric.com/details/37717650
10.3998/mij.15031809.0005.102 https://www.altmetric.com/details/43846275
10.1128/jcm.01916-17 https://www.altmetric.com/details/32171741
10.16995/cg.119 https://www.altmetric.com/details/34631498
10.1128/jcm.01144-18 https://www.altmetric.com/details/48881364
https://www.altmetric.com/details/47890394
10.1001/journalofethics.2018.20.2.fred1-1802 https://www.altmetric.com/details/32521484
10.16995/cg.102 https://www.altmetric.com/details/42619367
10.3390/publications6030038 https://www.altmetric.com/details/47265663
10.1001/journalofethics.2018.20.2.mhst1-1802 https://www.altmetric.com/details/32529286
10.16995/cg.138 https://www.altmetric.com/details/48647607

To see the complete Top 100, you can download the dataset I shared on figshare at https://doi.org/10.6084/m9.figshare.7467116.v1.

I am obviously very pleased to see The Comics Grid included in the Top 100.

It is interesting to note the diversity of countries associated to the profiles (where the metadata was available) giving attention to the outputs. According to Altmetric, there were 4,588 tweets about research outputs with ‘comics’ as keyword between 13/12/17 and 13/12/18 by 2,866 unique tweeters in 98 different countries. The map looks like this:

Countries and Number of Profiles that Gave Attention to Research Outputs with 'Comics' Keyword between 13/12/17 and 13/12/18 according to Altmetric. Chart by Altmetric Explorer.
Countries and Number of Profiles that Gave Attention to Research Outputs with ‘Comics’ Keyword between 13/12/17 and 13/12/18 according to Altmetric. Chart by Altmetric Explorer.

 

I shared the countries data on figshare at https://doi.org/10.6084/m9.figshare.7467455.v1.

For more information and context on Altmetric and using the Altmetric Explorer, see my 2016 post here. Many other posts about alternative metrics and the Altmetric Explorer can be found throghout my blog.

References

Priego, Ernesto (2018): Altmetric Top 100 Outputs with ‘Comics’ Keyword between 13/12/17 and 13/12/18. figshare. Dataset. https://doi.org/10.6084/m9.figshare.7467116.v1

Priego, Ernesto (2018): Countries and Number of Profiles that Gave Attention to Research Outputs with ‘Comics’ Keyword between 13/12/17 and 13/12/18 according to Altmetric. figshare. Dataset. https://doi.org/10.6084/m9.figshare.7467455.v1

Metricating #respbib18 and #ResponsibleMetrics: A Comparison

I’m sharing summaries of Twitter numerical data from collecting the following bibliometrics event hashtags:

  • #respbib18 (Responsible use of Bibliometrics in Practice, London, 30 January 2018) and
  • #ResponsibleMetrics (The turning tide: A new culture of responsible metrics for research, London, 8 February 2018).

 

#respbib18 Summary

Event title Responsible use of Bibliometrics in Practice
Date 30-Jan-18
Times 9:00 am – 4:30 pm  GMT
Sheet ID RB
Hashtag #respbib18
Number of links 128
Number of RTs 100
Number of Tweets 360
Unique tweets 343
First Tweet in Archive 23/01/2018 11:44 GMT
Last Tweet in Archive 01/02/2018 16:17 GMT
In Reply Ids 15
In Reply @s 49
Unique usernames 54
Unique users who used tag only once 26 <–for context of engagement

Twitter Activity

#respbib18 twitter activity last three days
CC-BY. Originally published as https://twitter.com/ernestopriego/status/958424112547983363

 

#ResponsibleMetrics Summary

Event title The turning tide: A new culture of responsible metrics for research
Date 08-Feb-18
Times 09:30 – 16:00 GMT
Sheet ID RM
Hashtag #ResponsibleMetrics
Number of links 210
Number of RTs 318
Number of Tweets 796
Unique tweets 795
First Tweet in Archive 05/02/2018 09:31 GMT
Last Tweet in Archive 08/02/2018 16:25 GMT
In Reply Ids 43
In Reply @s 76
Unique usernames 163
Unique usernames who used tag only once 109 <–for context of engagement

Twitter Activity

#responsiblemetrics Twitter activity last three days
CC-BY. Originally published as https://twitter.com/ernestopriego/status/961639382150189058

#respbib18: 30 Most Frequent Terms

 

Term RawFrequency
metrics 141
responsible 89
bibliometrics 32
event 32
data 29
snowball 25
need 24
use 21
policy 18
today 18
looking 17
people 16
rankings 16
research 16
providers 15
forum 14
forward 14
just 14
practice 14
used 14
community 13
different 12
metric 12
point 12
using 12
available 11
know 11
says 11
talks 11
bibliometric 10

#ResponsibleMetrics: 30 Most Frequent Terms

Term RawFrequency
metrics 51
need 36
research 29
indicators 25
panel 16
responsible 15
best 13
different 13
good 13
use 13
index 12
lots 12
people 12
value 12
like 11
practice 11
context 10
linear 10
rankings 10
saying 10
used 10
way 10
bonkers 9
just 9
open 9
today 9
universities 9
coins 8
currency 8
data 8

Methods

Twitter data mined with Tweepy. For robustness and quick charts a parallel collection was done with TAGS. Data was checked and deduplicated with OpenRefine. Text analysis performed with Voyant Tools. Text was anonymised through stoplists; two stoplists were applied (one to each dataset), including usernames and Twitter-specific terms (such as RT, t.co, HTTPS, etc.), including terms in hashtags. Event title keywords were not included in stoplists.

No sensitive, personal nor personally-identifiable data is contained in this data. Any usernames and names of individuals were removed at data refining stage and again from text analysis results if any remained.

Please note that both datasets span different number of days of activity, as indicated in the summary tables. Source data was refined but duplications might have remained, which would logically affect the resulting term raw frequencies, therefore numbers should be interpreted as indicative only and not as exact measurements.  RTs count as Tweets and raw frequencies reflect the repetition of terms implicit in retweeting.

So?

As usual I share this hoping others might find interesting and draw their own conclusions.

A very general insight for me is that we need a wider group engaging with this discussions. At most we are talking about a group of approximately 50 individuals that actively engaged on Twitter on both events.

From the Activity charts it is noticeable that tweeting recedes at breakout times, possibly indicating that most tweeting activity is coming from within the room– when hashtags create wide engagement, activity is more constant and does not exactly reflect the timings of actual real-time activity in the room.

It seems to me that the production, requirement, use and interpretation of metrics for research assessment directly affects everyone in higher education, regardless of their position or role. The topic should not be obscure or limited to bibliometricians and RDM, Research and Enterprise or REF panel people.

Needless to say I do not think everyone ‘engaged’ with these events or topics is or should be actively using the hashtag on Twitter (i.e. we don’t know how many people followed on Twitter). An assumption here is that we cannot detect nor measure anything if there is not a signal– more folks elsewhere might be interested in these events but if they did not use the hashtag they were logically not detected here. That there is no signal measurable with the selected tools does not mean there is not a signal elsewhere, and I’d like this to be a comment on metrics for assessment as well.

In terms of frequent terms it remains apparent (as in other text analyses I have performed on academic Twitter hashtag archives) that frequently tweeted terms remain ‘neutral’ nouns, or adjectives if they are a keyword in the event’s title, subtitle or panel sessions (e.g. ‘responsible’). When a term like ‘snowball’ or ‘bonkers’ appears, it stands out. Due to the lack of more frequent modifiers, it remains hard to distant-read sentiment or critical stances, or even positions. Most frequent terms do come from RTs, not because of consensus in ‘original’ Tweets.

It seems that if we wanted to demonstrate the value added by live-tweeting or using an event’s hashtag remotely, quantifying (metricating?) the active users, tweets over time, days of activity and frequent words would not be the way to go for all events, particularly not for events with relatively low Twitter activity.

As we have seen, automated text analysis is more likely to reveal mostly-neutral keywords, rather than any divergence of opinion on or additions to the official discourse. We would have to look at those words less repeated, and perhaps to replies that did not use the hashtag, but this is not recommended as it would complicate things ethically: though it is generally accepted that RTs do not imply endorsement, less frequent terms in Tweets with the hashtag could single-out individuals, and if a hashtag was not included on a Tweet it should be interpreted the Tweet is not meant to be part of that public discussion/corpus.

 

 

 

 

A #HEFCEmetrics Twitter Archive

#hefcemetrics top tweeters

I have uploaded a new dataset to figshare:
Priego, Ernesto (2014): A #HEFCEmetrics Twitter Archive. figshare.
http://dx.doi.org/10.6084/m9.figshare.1196029

“In metrics we trust? Prospects & pitfalls of new research metrics” was a one-day workshop hosted by the University of Sussex, as part of the Independent Review of the Role of Metrics in Research Assessment. It took place on Tuesday 7 October 2014 at the Terrace Room, Conference Centre, Bramber House, University of Sussex, UK.

The file contains a dataset of 1178 Tweets tagged with #HEFCEmetrics (case not sensitive). These Tweets were published publicly and tagged with #HEFCEmetrics between 02/10/2014 10:18 and 08/10/2014 00:27 GMT.

The Tweets contained in the file were collected using Martin Hawksey’s TAGS 6.0. The file contains 3 sheets.

Please note the data in this file is likely to require further refining and even deduplication. The data is shared as is. The contents of each Tweet are responsibility of the original authors. This dataset is shared to encourage open research into scholarly activity on Twitter.

For more information refer to the upload itself.

If you use or refer to this data in any way please cite and link back using the citation information above.

1:AM London Altmetrics Conference: A #1AMconf Twitter Archive

1:AM  London 2014 logo

I have uploaded a new dataset to figshare:

Priego, Ernesto (2014): 1:AM London Altmetrics Conference: A #1AMconf Twitter Archive .  figshare.
http://dx.doi.org/10.6084/m9.figshare.1185443

1:AM London, “the 1st Altmetrics Conference: London”, took place 25th—26th September 2014 at the Wellcome Collection, London, UK.

The  file contains a dataset of 4267 Tweets tagged with #1AMconf (case not sensitive). These Tweets were published publicly and tagged with #1AMconf  between Thursday September 18 17:29:56 +0000 2014 and Sunday September 28 16:07:49 +0000 2014.

Only users with at least 2 followers were included in the archive. Retweets have been included. An initial automatic deduplication was performed but data might require further deduplication. The Time column (D) has times in British Summer Time (BST).

Please go to the file cited above for more information.

 

Ebola: Publisher, Access and License Types of the 100 Most Mentioned Papers

I made a quick alluvial diagram showing the publisher, access and license types of the top 100 papers in our dataset.

Alluvial Diagram Showing the Publishers of the Top 100 Ebola Papers According to Altmetric as of Wed Aug 06 2014 16:44:28 GMT+0000 (UTC)  By License and Access Type

Source:
Priego, Ernesto; Lewandowski, Tomasz; Atenas, Javiera; Andrés Delgado; Isabel Galina; Levin, John; Murtagh, John; Brun, Laurent; Whitton, Merinne; Pablo de Castro; Sarah Molloy; Petersen, Sigmund; Gutierrez, Silvia (2014): Articles with Ebola mentioned online anytime as tracked by Altmetric, with crowdsourced type of access and license. figshare.
http://dx.doi.org/10.6084/m9.figshare.1137162

Retrieved 10:22, Aug 15, 2014 (GMT)

Ebola: Access and Licenses of 497 Papers Crowdsourced in 7 Days

From  (2014): Articles with Ebola mentioned online anytime as tracked by Altmetric, with crowdsourced type of access and license. figshare. http://dx.doi.org/10.6084/m9.figshare.1137162

Yesterday I shared a spreadsheet containing references to 497 papers on Ebola including the access and license type of each paper. The access and license types of each paper were crowdsourced. Fourteen volunteers participated in completing the dataset.

On Wednesday 6 August 2014 I shared a dataset on a Google spreadsheet of references to 497 papers on Ebola exported from an Altmetric Explorer report (see my previous post here).

One of the intentions of sharing the dataset, apart from sharing a file containing links to 497 scientific articles on Ebola mentioned online, was to crowdsource the access and license type of each paper. I promoted the file and the task amongst my followers on Twitter.

The task was to manually click on each link and personally verify which papers were open access, which were paywalled, which were ‘free to read’, etc., and to verify under which licenses they were published. We also added another column for ‘Publisher’. Contributors were asked to add their names and Twitter usernames on a column next to the Access, License and Publisher rows they had completed.

By Wednesday 13 August 2014, the whole dataset was complete (only a few Publisher rows remained to be completed, which I did). I closed the shared Google spreadsheet for editing and did a little bit of manual data refining; and verified some of the access and licenses types. I then downloaded it and did a bit more refining on Excel; and edited the spreadsheet so it contained a documentation ReadMe sheet and two extra sheets; one sheet with only the Open Access (in this case we included SA, ND and NC Creative Commons Licenses; though as we know fully-fledged Open Access requires CC-BY licenses) and another one with only the CC-BY entries for easier location of the open papers. I shared it last night on figshare, including everyone who helped crowdsource as co-authors of the spreadsheet:

Priego, Ernesto; Lewandowski, Tomasz; Atenas, Javiera; Andrés Delgado; Isabel Galina; Levin, John; Murtagh, John; Brun, Laurent; Whitton, Merinne; Pablo de Castro; Sarah Molloy; Petersen, Sigmund; Gutierrez, Silvia (2014): Articles with Ebola mentioned online anytime as tracked by Altmetric, with crowdsourced type of access and license. figshare.
http://dx.doi.org/10.6084/m9.figshare.1137162
Retrieved 07:39, Aug 14, 2014 (GMT)

Last night I did a quick chart about the number of papers per type of access. It was late so it may contain errors. One of the reasons why the spreadsheet has been shared openly is so that others can do their own analyses and contrast any information about it.

Number of Ebola Papers in Dataset Per Access Type chart CC-BY Ernesto Priego
Number of Ebola Papers in Dataset Per Access Type. Click to enlarge.

 

Access type Number of papers in dataset per access type
All Open Access (includes NC; 95 CC-BY) 133
Paywalled 138
Free to Read but not OA (All Rights Reserved research papers) 211
“Advance Access” (Free to read but not OA) 1
News Items (Free to Read but not OA) 6
DOIs not found or unresolved 4

[Please note total is not 497 in the charts above as some license/access types were either not present or unclear; for example there’s cases of papers labeled as “Open Access” but the license for that article was absent of hard to find. In any case this chart needs to be revised and editorial decisions need to be taken about what will count as what. The charts are shared in the knowledge errors can still remain].

Depending on your interests, there is a series of different analyses that could be done from the data. I’ll be working on that; but since we have shared the dataset openly, why not see what you can do with it? (Don’t forget to cite the dataset!)