Friday, June 30, 2017

Dramatic Growth of Open Access June 30, 2017

Correction: DOAJ will soon surpass 2.5 million articles, not a quarter of a billion as originally reported. 


Open access continues to demonstrate robust growth on a global scale, in terms of works that are made available open access, ongoing growth in infrastructure (new repositories, journals, book publishers), strong growth for new initiatives such as SocArxiv, BioRxiv, the Directory of Open Access Books, SCOAP3, as well as ongoing strong growth in established services such as BASE, PubMed / PubMedCentral, Internet Archive (check out the new Collections including a Trump archive and FactChecker), DOAJ (almost 2.5 million articles searchable at the article level), RePEC and arXiv. Ongoing growth in infrastructure and OA policy give every reason to expect this growth to be ongoing.

Open Data Version

Morrison, Heather, 2014, "Dramatic Growth of Open Access", hdl:10864/10660, Scholars Portal Dataverse, V17,


This edition of the Dramatic Growth of Open Access highlights two of the new kids on the OA block - SocArxiv and BioRxiv, modeled on early OA success story arXiv, topping the quarterly growth by percentage with percentage growth of about 30% each! SocArxiv now has 1,200 documents and BioRxiv 12,800.

Similarly, a relative newcomer, the Directory of Open Access Books, is in both first and second place for annual growth by percentage with 68% growth for OA books and 40% of OA publishers in the past year for a total of 8,172 open access books and 217 OA book publishers.

SCOAP3, a global initiative to transform high-energy physics publishing to open access, is showing remarkable growth, 39% in the last year and 8% in the last quarter for a total of 15,790 articles funded.

To celebrate the growth of all OA services two pictures are presented of the growth of the largest collective OA search engine that I am aware of. Together, the 5,000 content providers who contribute metadata to the Bielefeld Academic Search Engine (BASE) have made available over 112 million documents. Around 60% of these are open access, so the number of OA documents in the world can be said to be somewhere about 67 million. BASE also posts their own online statistics table and chart - check it out here.

I wish I had the time to applaud and celebrate the growth of each and every OA service, but with 5,000 services contributing to BASE (and others that don't), if I worked on this 365 days a year I would have to cover 14 initiatives every day. So please feel free to help out by applauding and celebrating the services most relevant to you - the journals in your discipline, your institutional repository, the services you find most helpful to search.

Below you will find tables listing the top services by quarterly (5% or more) and annual growth (10% or more). For the full numbers download the open data version (link above). As usual Internet Archive is well represented, with 5 items in the list of the top 13 services by quarterly growth and the top 18 services by annual growth. Internet Archive also offers 2 intriguing new services under Collections - a Trump Archive with over a thousand videos and a Fact Checker collection with over 400 items, available at

Of course PubMed and PubMedCentral are up there in the growth charts, in this quarter for total number of items (5% quarterly growth) as well as what looks (to me) like hesitant new steps by a substantial number of journals, with a 26% increase in the number of contributing journals that provide some OA and a 14% increase in the number of journals that provide OA to selected articles. The number of journals providing immediate free access and/or all articles open access continues to increase, so this is clearly growth, not backsliding.

DOAJ is included in the top growth services with 14% growth in the number of articles searchable at article level. DOAJ now has over 2.49 million articles searchable at the article level and should soon surpass 2.5 million articles.

arXiv and RePEC are on the list for strong growth in articles, and ROARMAP for growth in OA policies.
Quarterly growth (percentage) June 2017
32% SocArxiv preprints 1,200
29% BioRxiv all articles 12,280
18% # of academic peer-reviewed books (DOAB) 8,172
18% # publishers (DOAB) 217
8% SCOAP3 articles 15,790
8% Internet Archive Software 178,635
7% Video (movies)  (Internet Archive) 3,437,542
7% Texts  (Internet Archive) 12,821,051
5% Images (Internet Archive) 1,476,743
5% # of content providers (BASE) 5,621
5% Audio (recordings)  (Internet Archive) 3,477,033
5% Webpages (Internet Archive) (in billions) 298
5% PubMedCentral (number of items) 4,400,000

Annual growth (percentage) 06/30/17
68% # of academic peer-reviewed books (DOAB) 8,172
40% # publishers (DOAB) 217
39% SCOAP3 number of archives 15,790
34% Video (movies)  (Internet Archive) 3,437,542
33% Internet Archive: Software 178,635
29% # of content providers (BASE) 5,621
27% Texts  (Internet Archive) 12,821,051
26% PMC journals some OA 609
25% Internet Archive: Images 1,476,743
20% # of documents (BASE) 112,458,360
17% Audio (recordings)  (Internet Archive) 3,477,033
17% RePEc journal articles 1,491,037
14% # of articles searchable at article level (DOAJ) 2,493,835
14% PMC select deposit journals 4,296
13% RePEC downloadable 2,143,844
13% Total Policies (ROARMAP) 872
13% PMC # items 4,400,000
10% arXiv 1,278,739

 This post is part of the Dramatic Growth of Open Access Series

Feel free to copy and share - with love.  Note that images are compressed by the software to reduce file size, and they are also quickly outdated. You are welcome to use the images, but my recommendation is to download the data and make your own graphics. It's easier than you think with tools like modern spreadsheet software.

Wednesday, June 28, 2017

Critical Data Literacy, why and how: an Open Education Resource (OER)

This OER was developed for presentation at the Data Power 2017 conference held at Carleton University, Ottawa, Ontario June 22 - 23. This is primarily a framework for how to go about teaching critical data literacy in the student-centered tradition of Freire, supplemented by the work of Tygel and colleagues. A sample introduction developed for Canadian university students, and a few references, are included. My definition of critical data literacy as used in this OER is: 
critical data literacy is the ability to understand and critique how the beliefs and values of people and groups (including government) influence what data is created, how it is shared and how it used by to tell compelling stories by storytellers whose beliefs and values shape the kind of stories they choose to tell and how they tell the stories. Critical data literacy also means having the ability to create and tell one's own stories using data. 
This OER is released under the terms of copy and share - with love, my latest statement on sharing which can be found at the bottom of this post. The Freire tradition of popular education involves starting with the lived experience of students. In this context, following is what I recommend for anyone who wishes to develop a full critical data literacy program based on the framework. I think that this framework could be adapated for teaching at any level, from community-based learning (led by community groups or organizers or as a participatory action research project) to graduate classes (that's where I teach). Some of the details would change. For example, if you are teaching at a university, some parts of the process are likely to involve formal evaluation (marking), but if you are teaching to the general public or a community group, this would not make sense. Please adjust as needed for your own context.

The overall approach:
  1. Identify your student group. Think about what kinds of issues or problems they might have that could potentially be helped by data, the kind of data stories they might be familiar with. 
  2. Develop an introduction to critical data literacy. Tygel and colleagues (2015, 2016) found that this was necessary. One way to think about the difference between critical data literacy and basic literacy (reading) is that people who do not know how to read in recent history are likely to be aware of the existence of reading as something that other people do. Data literacy / critical data literacy is not at this point in time as broadly understood as reading.
  3. Plan the 3 phases of the framework that follow directly from the Freire tradition: investigation, thematisation, and problematisation. In these phases, students should lead the learning process (active learning), pursuing problems and questions of their own devising. The teacher's role is to provide support. 
  4. Plan a systematisation (synthesis) wrap-up approach that makes sense for your student group. In some cases this might be left for the students to decide the approach, and the teacher only helps to guide the students towards this closure. In a formal educational setting, this might involve a pre-determined assignment.
  5. Implement!
The 5 phases are: introduction, investigation, thematisation, problematisation, and systematization (synthesis). Details follow. The introduction section is the most fully developed as this is the only teaching portion that involves imparting knowledge; all others begin with the student.


As noted above, it will not be obvious to everyone what data literacy or critical data literacy is or why they should learn about it, as discovered by Tygel and colleagues (2015, 2016). For this reason, an introduction to the topic may be helpful. In this phase one might invite in guest speakers from the community who use data in their storytelling and/or to provide examples of data storytelling. This is also where definitions of critical data literacy could be introduced. In addition to my definition (see above), I like this definition of data literacy from the Data Journalism Handbook  because it includes the element of critical thinking; not every definition that I have seen includes this, to me a significant omission.
data literacy is the ability to consume for knowledge, produce coherently and think critically about data [emphasis added] (Grey, Bounearu & Chambers (2012)
Following is a sample introduction developed for an audience of Canadian university students. If you are teaching a different type of student group, I recommend that you develop your own introduction tailored to your group. If you do and you are willing to share this with others, please send me a link (via e-mail to Heather dot Morrison at uottawa dot ca) or as a comment to this post and I will include a link to your work in this post. If you would like to use this introduction as is, please see the link to the full presentation.

Introduction slide 1

This slide presents two conflicting stories that are told using basically the same underlying data. One of these (tax freedom day) will be very familiar to the audience, while the other will not as it is relatively new. 

This slide illustrates two very different perspectives on taxation in Canada. On the left, we see the Fraser Institute’s Tax Freedom Day. The Fraser Institute, a right-wing think tank, uses data to tell their story of over-taxed Canadians, working more than half the year for the government before earning a dime for themselves. The idea of tax freedom day has been very effective in Canada over the past few decades. On the right, we see one of the images from the Broadbent Institute’s report The Brass Tax which was published very recently. The left-wing Broadbent Institute challenges the numbers behind the Fraser Institute’s analysis, argues that Canadian taxation is pretty reasonable compared to other countries, and presents a different picture. In this case this graph illustrates Canada’s progressive approach to taxation and makes the point that people with little to no income pay no income tax and only a small percentage of Canadians age 25 to 54 are in the top income tax bracket, paying more than 30% of income in taxes. These are 2 groups of people with a different vision of what society should be like, using the same underlying data to tell 2 very different stories. If we go directly to the data source, will this eliminate the impact of the storyteller? Let’s see.

The following two slides might be more effective as a live demo or in-class lab activity. 

One of the underlying datasets used by both groups is the statistics provided by OECD. If you go to the OECD website there are some neat online tools that let us quickly visualize data in different ways. One of the elements of the data story told by the Fraser Institute is that individual families pay too much in taxes. I wondered if there has been any change in the portion of tax revenue contributed through personal and corporate taxes over the years. Here is what I found using the OECD website. It seems that more tax is gathered from personal rather than corporate taxes, but over the past few years the portions don’t seem to have changed much. This is the default view that shows trends from 2000 – 2015. If this had fit what I already believed, I suspect I would have stopped here. But I seem to recall a relative decrease in corporate taxation over the past few decades so I decided to slide the years covered…

And this is what I found. If we slide the start date of the visualization tool back to 1965, it does appear that there has been a relative increase in tax revenue from personal sources and a relative decrease in tax revenue from corporate sources. This shows how easy it would be for two people with different perspectives on what a data trend is likely to be to go to exactly the same dataset and make a slight change to how the data is visualized to tell two very different stories. 

Kaulfuss uses OECD data to tell a story about U.S. health care spending on a blog called Beyond Economics. The story  is that the U.S. spends two and a half times the OECD average on health. It doesn’t surprise me that the U.S. spends more than the OECD average on health, but I am surprised that the difference is this much. What I found even more intriguing is the author’s claim that U.S. public spending on health is above the OECD average. Who knew? Disclaimer: what I am doing here is presenting stories told through data, I have not examined the data itself so cannot comment on the accuracy of the story.

Wikipedia has a section called Health Care in Canada. Here in Canada many of us – I include myself – think highly of our public health care system, and I think I see this perspective here. This section states that “most health statistics in Canada are at or above the G8 average” in a paragraph that is followed by the table pictured above. The table draws from a number of data sources and appears to me to demonstrate above-average data literacy skills. However…

When you look at the statistics that are presented and calculate the averages, Canada is above average on 3 of 8 measures. This is not “most”. This suggests a need for data literacy. If you look at the specific measures where we are above average, an argument can be made that being above average in life expectancy is a good thing. However, an above-average infant mortality rate is probably not such a good thing. We are also slightly above average on % of government revenue spent on health, but what does this mean and is it a good thing? Looking at some of the areas where we are below average –such as the  # of doctors & nurses per population & % of health costs paid by government – might give one reason to re-consider our narrative that we Canadians are above average in public health. This illustrates a need for critical data literacy. In other words, our beliefs might be getting in the way of understanding what is our existing data tells us.

Some approaches and suggestions  for creating a meaningful introduction     

The reason for the introduction section is because as Tygel and colleagues found there is a need to start with some explanation about what data is and how people use it. There are many potential approaches to introducing the topic such as having guest speakers come to explain how they make use of data and data visualization. 

Suggested sample activity

One activity that would fit here is to have students create their own demonstrations. In the case of tax data, students could do a google search for tax data and limit to images. This search will yield lots of material to work on. The idea is to have students find out who created the visualization and what the story behind the visualization is. If this is done for evaluation purposes, I recommend a pass/fail approach because student success will depend a lot on which images are selected. Being there to hear the findings of all the students is sufficient for this learning exercise. A teacher in an area where computers are not readily available could bring in copies of materials to work with. This introductory phase may be more relevant for some student groups than others, for example university students. If this doesn’t seem to fit, you could skip this stage. 

Investigation, Thematisation & Problematisation

Two key points to keep in mind in these 3 phases: 1) the core focus should be lived experience not imparting abstract knowledge and 2) teaching involves helping people seek and find answers. This is important because in teaching data literacy one might be tempted by starting with the data, teaching people how to understand and work with data. Keynote speaker Gwen Phillips (and BC First Nations data activist) at the Data Power 2017 provided a brilliant example of why not to start with the data: the existing data might not be what is wanted at all. As Gwen said, we should measure what do want (e.g. youth vitality) not just what we don't want (e.g. teen suicide). This introduces a challenge to develop new metrics, but one that seems worthy of pursuit. If we start by teaching about existing data we risk missing the opportunity to identify gaps like this.

Disclosure: in understanding the following 3 phases, it may be helpful to know that although I teach at a university and am very engaged in pedagogy, I do not have an education degree and do not consider myself an expert on pedagogy. If you would like to know more about how to teach in the Freire tradition, I suggest starting with the Tygel references below and if desired supplementing with general educational books and articles covering the Freire tradition. My contributions below are limited to providing a very quick introduction and making the connection with critical data literacy.


The investigation phase is the first of 3 phases that follow the Freire tradition. The idea is to begin with lived experience, with real-world problems. If this approach is used for self-teaching by community groups independently or with an academic consultant as a participatory action research project, this is closest to the classic Freire scenario and the best example of a pure investigation stage. To modify this for an education setting, students could either choose problems or issues of direct interest to them, for example student debt, or they might brainstorm a particular target group whose problems they are familiar with such as First Nations, a salient issue here in Canada as many of us struggle to implement the recommendations of our Truth & Reconciliation commission. Classroom activities could include a brainstorm session, individual or small group reflection, and/or presentation of the results of the investigation stage.

Thematisation is the first analytic stage. Before searching for what data is available, the idea is to focus on the real-world issue and figure out what kind of data might help to understand or resolve the issue. Examples based on today’s case studies on taxation and health spending could include learning what sorts of taxes are collected and by which governments, or comparing public collective health spending with individual spending.

After thematisation, with some back-and-forth, comes problematisation. This is where we get into research on what kinds of data actually exist that is relevant to the problem, who collects the data and why. Some examples of the types of data sources students might look into at this point if they choose to focus on taxation and spending:
  • Canada Revenue Agency
  • OECD
  • Federation and provincial budgets
  • Academic Research 
  • NGO / Think Tank research (e.g. Fraser Institute and Broadbent Institute) 
One question that might be raised is whether the existing data is actually sufficient or not, that is, the scope of the inquiry is not focused just on understanding what data is available. but rather what is needed to understand and resolve the problem of interest. 

Finally, in the systematization stage we put what we have together to come up with an action plan. The nature of the action plan might vary quite a bit depending on the students. An activist community group might want to develop an action campaign or an infographic or other data story to facilitate an existing action campaign. One approach to action could involve citizen data collection. In a graduate class on information policy, like the classes that I teach at the University of Ottawa's School of Information Studies, developing a policy briefing and recommendations for evaluation as academic work might make sense. 


Fraser Institute (n.d.). Tax freedom day calculator. Retrieved June 9, 2017 from
Grey, J., Bounegru, L., & Chambers, L. (2012). Data Journalism Handbook. OKFN. (as cited in Tygel & Kirsch 2016)
Kaulfuss, R. (2017). Health care: human right or expensive entitlement? Beyond economics. Retrieved June 15, 2017 from
OECD (2017), Tax revenue (indicator). doi: 10.1787/d98b8cf5-en (Accessed on 15 June 2017)
Shillington, R. & Shaban, R. (2017). The brass tax: busting myths about overtaxed Canadians. Ottawa: Broadbent Institute. Retrieved June 9, 2017 from

Tygel, A.; Campos, M.; De Alvear, C. (2015). Teaching open data for social movements: a research strategy. The Journal of Community Informatics 11:3. Retrieved June 19, 2017 from
Tygel, A.; Kirsch, R. (2016). Contributions of Paulo Freire for a critical data literacy: a popular education approach. The Journal of Community Informatics 12:3 pp. 108 – 121. Retrieved June 19, 2017 from
Wikipedia (n.d.). Healthcare in Canada. Retrieved June 15, 2017 from 

Terms:  Please copy and share with love.

What does this mean? In brief, I have no interest in using intellectual property law to prevent anyone from using or re-using my work with intentions such as furthering the collective knowledge of humanity (truth with justice and compassion), protecting or restoring the environment or making the conditions of life of humanity better. That is what I mean by with love. If your motives in using my work are something other than love, such as making a profit for yourself or a corporation that you work for, subverting truth, justice, or compassion, then note that I reserve all rights under copyright. Please use attribution as appropriate. For example, if you use my work in an academic or journalist context, you need to acknowledge me as author in order to avoid plagiarism (and confusion).

This post is part of the Creative Globalization series