The components of Open Science
Open Access
Provide open access to research data and content. With open data anyone can freely use, modify and share the data for any purpose. In this context, the term exclusively refers to scientific research data. The content on the web is made available under an open licence and in a structured, non-proprietary format (i.e. XML, PNG, LaTeX, CSV). Sharing data allows for a more efficient replication process by simplifying data exchange and preventing unnecessary data acquisition. Plus, open data enables laboratories to collaborate and accumulate larger data sets in a cost-saving manner for more meaningful results. Smaller research groups are given the means to participate in the discussion. As a consequence, the entire scientific process is shaped more inclusively, allowing contributors to debate on an equal footing regardless of funding.
There are multiple ways to open access:
- the diamond way (publishing open access directly without article processing charges -> free for authors and readers)
- the golden way (publishing open access directly with article processing charges -> authors pay, free for readers)
- te green way (publishing open access indirectly when the original article is behind a paywall -> subscription reqired for readers)
For any questions regarding open access publishing or green open access options, you are welcome to contact the open access experts of your library.
For funding of open acces articles or books, many university libraries offer an open access fund.
Open Data
Provide open access to research data and content. With open data anyone can freely use, modify and share the data for any purpose. In this context, the term exclusively refers to scientific research data. The content on the web is made available under an open licence and in a structured, non-proprietary format (i.e. XML, PNG, LaTeX, CSV). Sharing data allows for a more efficient replication process by simplifying data exchange and preventing unnecessary data acquisition. Plus, open data enables laboratories to collaborate and accumulate larger data sets in a cost-saving manner for more meaningful results. Smaller research groups are given the means to participate in the discussion. As a consequence, the entire scientific process is shaped more inclusively, allowing contributors to debate on an equal footing regardless of funding.
Structuring and describing your data in an intuitive and (ideally) standardized way makes it easy to understand and reuse your data - for other researchers, but also for your future self. To do so, be FAIR (Findable, Accessible, Interoperable, Reusable) with your data. This is one basic pillar of reproducibility.
Research data management can be a lot of effort, but it can pay off. For detailled consulting regarding research data management and infrasructure, the eResearch Center can help you.
For making your data available, the best way is publishing them under a free license (e.g., we recommend for most cases CC-By) in a research data repositoy. The best repository for your data might be a subject specific repository, because it fits exactly your needs (e.g., Open Neuro for brain imaging data). But there are also generic repositories like zenodo, which is frequently used in science. If you wish to use your university’s service or need direct advice, the curated institutional data repository data_UMR might be the best choice for you.
Open Methods
Many scientific publications are characterised by a very brief documentation of the methods implemented to answer a research question. However, a full description of procedures, data gathering and analysis steps is often crucial to reproduce, and sometimes to even understand the findings reported in scientific manuscripts. In particular, when working with “open data” it is sometimes necessary to first achieve a profound understanding of the data processing steps that lead to the data resources in their present form before being ready to actually use them. “Open methodology” provides a framework for managing this type of information in a transparent and compressive way. It comprises the full documentation of:
- How data is created in the first place (i.e., apparatus and settings used to gather the data)
- The concrete processing steps (e.g., filtering, averaging, handling of missing values, etc.) used, for instance, to reduce the dimensionality of the data
- The inferential (or exploratory) statistical considerations and detailed model specifications used to derive the parameters and coefficients necessary to answer a research question.
Resource Identification Initiative
Why is the understanding of measures and methods so important?
Open Source
The principle “open source” refers to a software development approach in which the software’s entire source code is available to the public. Thereby, any user cannot only investigate how the software was constructed but also improve the code if bugs were found or write add-ons to the existing code that are thought to be useful. This central quality clearly distinguishes the open source environment from traditional software development where the source code typically is not available to the public or at least not open to changes [1]. The exact guidelines for the accepted modifications can be found in the licenses agreement of each open source software [2]. Furthermore, open source projects do not have classical project management structures or schedules but rather focus on the joint forces of widely distributed people instead, who work independently but in a complementary fashion [3]
Automatizing your preprocessing and analysis pipelines by coding and opening this code is another basic pillar of reproducibility.
You can publish your code on version control platforms, e.g., GitHub or your insitutionally hosted GitLab.
[1] Kraker, P., Leony, D., Reinhardt, W. and Beham, G. (2011). The case for an open science in technology enhanced learning. Int. J. Technology Enhanced Learning, Vol. 3, No. 6, pp.643–654. https://doi.org/10.1504/IJTEL.2011.045454
[2] Sowe, S. K., Stamelos, I., & Angelis, L. (2008). Understanding knowledge sharing activities in free/open source software projects: An empirical study. Journal of Systems and Software, 81(3), 431-446. https://doi.org/10.1016/j.jss.2007.03.086
[3] Scacchi, W., Feller, J., Fitzgerald, B., Hissam, S., & Lakhani, K. (2006). Understanding free/open source software development processes. Software Process: Improvement and Practice, 11(2), 95-105. http://dx.doi.org/10.1002/spip.255
Open Educational Resources (OER)
Open educational resources (OER), also known as Open Content or Open Learning Materials, are educational tools made freely accessible to the public. UNESCO defines OER as “any type of educational materials that are in the public domain or introduced with an open license”. The definition also reveals the main ideal behind this project: “anyone can legally and freely copy, use, adapt and re-share them.” ORE comprise all digital objects created for learning purposes, for example articles, textbooks and digital equivalents. They also refer to methodological resources such as open source software tools and teaching material, e. g. instructional designs, didactic plans, case studies, curricula and simple elements such as pictures, links and short texts used to support teaching. Aim of the project is to loosen the economic and organizational constraints of the education and training sector and give broader access to education.
Open Peer Review
Open peer review is a transparent and collaborative review process. It differs from traditional peer review in that the identities of authors and reviewers are often public, allowing for broader scrutiny and constructive feedback from experts and the public. This approach enhances transparency, reduces bias, and fosters collaboration in scholarly publishing.
For Marburg University’s open science services, please feel free to contact Dr. Lydia Riedl