taxonomy | Thought splinters

Educational Tools and Learning Theory

Sun, 12 Jan 2020 00:00:00 +0000

This article suggests a procedure to categorize the enormous amount of educational tools in the market. My approach uses a framework of three well-grounded learning theories: Instruction, Cognition, and Construction based on presentation modes (one-way teaching), dialog settings (two-way education), and collaborative scenarios (learning by mastering complex situations). As a proof of concept, I will use the Top Tools for Learning (TT4L), compiled by Jane Hart as a result of the 13th Annual Learning Tools Survey (published 18 September 2019).

Inspired by the yearly ranking of Top Tools for Learning (TT4L) compiled by Jane Hart, I wanted to know if there are global changes or trends in e-learning over the years. The ranked list of tools goes back 13 years and is available for all past years via the Wayback Machine. As a plan for analysis and for presenting results, I imagine a similar procedure as I have used in comparing static website generators over the years.

Inconsistencies in Jane’s categories

To compare the different rankings of the educational tools, I will need a classification of all apps/web services based on pedagogical reasons. For my purposes, the categorizations used by Jane Hart (see right column) is not useful:

It is the functionality of the tool itself, which motivates her division in different classes primarily. Therefore all categories contain the word ‘tool’ in their name. She distinguishes four main types:
1. Instructional tools
2. Content development tools
3. Social tools
4. Personal (and professional) tools
Her sub-categorization under the four main headings is not consistent and has changed over the years. Sometimes she uses ‘Content’ as a fifth type, or she includes ‘professional’ under the heading of ‘personal tool.’
Some tools are platform-dependent (e.g., Sharepoint, Keynote).
Even proprietary name for hardware is ranked in her list (e.g., iPhone, iPad, SMART board instead of smartphone, tablet, whiteboard).

Another much better division can be drawn from her beautiful infographics (see below) on top tools for higher education 2019. Here Hart distinguishes by pedagogical reasons between

Didactics (Formal Learning)
Discovery (Informal Learning)
Discourse (Social Learning) and
Doing (Studying).

Aside from the fact that ‘formal’ and ‘informal learning’ has another meaning (see the definition by the Council of Europe on formal, non-formal, and informal learning). I do not understand why ‘studying’ is different from structured learning in an academic setting. And why should ‘studying’ be linked just with the doing-bubble?

Usage of top tools for higher edudation in 2019. Infographics by C4LPT (2019): https://www.toptools4learning.com/edu100/.

Developing educational motivated categories

Three different learning/teaching modes. Infographics by Peter Baumgartner (2018)

Without going here into detail, I will use my categorization of learning theories published among others in The Zen Art of Teaching (PDF, 545 kB). This article outlines three different kinds of teaching modes:

Instruction or Learning I: Using static learning materials for reading, listening, and watching.
Cognition or Learning II: Using dynamic and elaborated didactical educational interactions for guided practice.
Construction or Learning III: Using collaborative scenarios for mastering unstructured situations.

This categorization results in different educational aspects for each of the three teaching/learning modes:

Kind of knowledge acquisition: static –dynamic/procedural – social.
Teaching modes: transfer – dialog/discussion – collaboration.
Learning strategies: study – practice – mastering.
Assessment methods: correct answers – applying adequate techniques, processes, and procedures – success criteria.
Teaching strategies: explaining – observing and helping – collaborating.

Even though my suggestion is coarse-grained as it distinguishes only three types, it has to overcome several problems of operationalizations:

One tool – several functionalities

Sometimes it is not easy to classify tools unambiguously into one of the three categories. Modern tools incorporate many features that may address different types of learning modes. Take, for example, the use of a learning management system (LMS). You can use it for various activities (reading PDFs, work through quizzes, or writing collaboratively into a wiki). In those inconclusive situations, I will utilize the highest predominant functionality of the tools. ‘Highest’ means that Learning II is more elevated than Learning I. This is not a quality rating of the different learning modes as all three modes have their value under certain conditions. ‘Higher’ or ‘lower’ address here the complexity of student activities.

But what is the ‘predominant functionality’ of a tool? I assume that every educational technology, every technology-supported learning environment, or internet application, implement a theoretical learning model – irrespective of opinions and beliefs of developers and teachers. In the case of LMS dominates guided learning interactivity (Learning II). Although teachers may use tools ingeniously for purposes not foreseen from the developers, I think that these inventing teaching strategies are not the mainstream.

Following other users on the web with the same subject interests, getting in contact with them, commenting their blogs, etc. entitles for Learning II but does not qualify for Learning III. I will rate only rate an educational setting as Learning III if learners produce (individually or collaboratively) an artifact for learning purposes.

General tools not intended primarily for education

In Jane Hart’s list of top tools are many products and web services not primarily developed for education, like word processors, email clients, file-sharing platforms, note-taking applications, browsers, search engines, etc. The reason for this broad mixture is the content of the questionnaire and their target group. Not learners or teachers, but mainly IT professionals and learning designers, addresses the survey. This orientation leads to two biases, as mentioned by Jane Hart:

only 22% of voters came from colleges and universities, hence the Top 200 list is therefore skewed towards workplace learning.
since only 6% of respondents came from schools, the EDU100 list this year focuses on tools for Higher Education.

Snippet of survey form for compiling the top tool list for 2020

Another issue favoring general tools are the questions themselves. See, for instance, the voting form for 2020 on the right ahnd side of this article:

A valid entry needs to include 10 different DIGITAL tools (resources, services, etc) – listed in any order (emphasis not mine).

So the questions ask for digital tools, not for digital educational tools. The consequence is that the top list with a few exceptions does not include subject-oriented software (e.g., apps for learning math, science, languages). This bias is an essential drawback as many e-learning scenarios use these kinds of content-related software.

The other side of the same coin is that there is a bias towards personal knowledge management applications. Software like email clients, browsers, search engines, etc., are unquestionably also essential parts of a learning ecology. Using these tools always goes together with (informal) learning experiences. But an educational taxonomy of tools for education should focus on apps or web services developed for e-learning. To distinguish those personal productivity instruments from tools explicitly dedicated to learning, I have designated them with Learning 0. Under this category, I also have classified support tools like converters or ‘neutral’ web resources like portals for icons, images, or other material.

Summarizing this subsection is the following types of tools designated with Learning 0:

General productivity: Word processors, email clients, browsers, search engines, converters, translators, grammar checkers, image/icon/theme libraries, and other supporting tools.
Personal knowledge management: Note-taking software, calendars, project management, skill validation.
Hardware: Smartphones, tablets, interactive whiteboards.

Developing learning material or using it?

Another complication arises from the fact that the educational judgment of developer tools can be seen two-fold:

from the authoring perspective, e.g., the resource development view
from the angle of using the developed product

There are content development tools which can be used by learner and teacher alike. Those apps or web services with various perspectives (e.g., blog services, screen-casting, curation tools) I will classify from their ownership. If it is easily possible that students get their personal instance, then I will designate it as Learning III.

With ‘easy’ I mean

that it is free available at least in a freemium variant for personal usage. This definition of ‘free available’ does not apply if learners are entitled to free usage only via an institutional account.
that it is easy to install either through the download of packaged software without any special additional technical requirements, such as using the terminal or running a server, changing functionality programmatically, or only through a simple online registration).
that it can be used easily in educational settings (no specialized knowledge necessary, learn time only 1-2hrs, no specialized hardware, platform-independent).

Note that every case has to be judged individually. For a correct classification with the mentioned rules, one has to look into pricing models, installation procedures, user interface, and typical application patterns.

Conclusion

To summarize my suggestion for an educational categorization of digital tools, I will define four types of apps, respectively web services:

Learning 0: Tools supporting personal knowledge management, productivity, and portals for web resources.
Learning I: Transfer of knowledge. The focus of learner engagement is primarily oriented to content to acquire factual knowledge.
Learning II: Applying knowledge, practicing procedures. The emphasis here is learner interactivity with dynamic content and dialog with teacher and co-learner.
Learning III: Collaboratively solving challenges. Essential is producing a useful output, doing something creatively and cooperatively.
Learning IV: Multi-purpose tool with features for Learning III and either Learning 0 or Learning I.

… to be continued

Update 2021-05-20: Originally, I have planned to reorganize Jane Hart’s data according to my set of categories. But unfortunately, because of other priorities, I didn’t have time so far. But the follow-up of this post is still on my schedule.

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.

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.