4 Comments

1. JIE ZHOU on 12 de June de 2026 at 09:12

   CommeMy Journey into Open Science

   The several-week course An Introduction to Open Science has now come to a successful end. For me, this is not merely the conclusion of a general education course, but a valuable opportunity to re-examine the essence of academic research and reshape my mindset as a doctoral student majoring in Library and Information Science. I am truly honoured to participate in this programme, and I would like to express my gratitude to all teachers and fellow students for the fruitful exchanges and discussions throughout the sessions. As a learner from China and also a university lecturer, my understanding of academic research has deepened considerably during this course.

   Coincidentally, while the course was ongoing, an incident in China sparked heated discussions across academic circles and public opinion: a student from Jilin University filed a public complaint against an academician’s team over research data fraud and irregularities in data processing. This is not an isolated case; it lays bare the widespread flaws in the current global research evaluation system. Nowadays, numerous universities and research institutions still take publication volume, journal rankings and impact factors as the core criteria to assess researchers’ competence and determine their career prospects. Under the paper-centric evaluation culture, many early-career researchers are forced to prioritise final publication results, leaving them little time to delve deeply into their research. The exploratory essence of academic work has gradually been overshadowed. This incident prompted me to reflect: how can we return to the true nature of research and establish a standardised academic order? It was with this question in mind that I embarked on the in-depth study of Open Science.

   Before taking this course, my understanding of Open Science was rather one-sided. I simply equated Open Science with Open Access (OA), believing that its sole purpose was to break down journal paywalls and make academic papers freely accessible to the public. Influenced by the traditional academic environment, I took it for granted that high-impact journal publications were mandatory requirements for academic assessments and professional title evaluations. I even mistakenly thought that the sharing philosophy of Open Science would conflict with the traditional journal-based evaluation system. At that time, I failed to realise that Open Science covers far more than just published articles. Research data, experimental codes, research workflows, academic evaluation and citizen science all fall within its scope. As a researcher focusing on user information behaviour, I initially regarded Open Science merely as a trending topic and did not explore its profound connections with my own research field.

   This course completely changed my preconceptions. We systematically learned core theories including UNESCO’s Eight Open Pillars of Open Science, the FAIR principles for research data, the European Open Science Cloud (EOS), a full range of OpenAIRE services, as well as persistent identifiers such as DOI and ORCID. Meanwhile, we completed a series of hands-on practices: drafting Data Management Plans (DMP), building research projects on the Open Science Framework (OSF), archiving research outputs on Zenodo, carrying out sensitive data anonymisation, sorting out open access guidelines, and reusing metadata from OpenAlex and OpenAIRE.

   Two parts of the curriculum impressed me the most. The first was the discussion on the Coalition for Advancing Research Assessment (CoARA) and research assessment reform. We analysed the drawbacks of over-reliance on impact factors, and recognised that the value of research cannot be judged solely by the number of publications or journal tiers. Research reproducibility, social impact and interdisciplinary collaboration are equally important criteria for evaluating academic achievements.

   The second was the discussion on the boundaries of data sharing. The core maxim — as open as possible, as closed as necessary — taught me to strike a balance between data sharing, privacy protection, intellectual property rights and patent interests. These theories and practical skills are no longer abstract textbook knowledge, but practical tools that can be applied to real research work.

   This learning experience has thoroughly transformed my views on academic publishing, data management and even my entire academic career. I used to believe that a single journal article represented the entirety of a research project, while raw data, analytical codes and experimental records were just supplementary attachments. Now I fully understand that research data, open-source tools and complete experimental workflows are all core academic outputs. Complying with the FAIR principles to standardise data management is the foundation of ensuring research reproducibility and upholding academic integrity. When it comes to publishing, I no longer blindly pursue high-impact journals, and have started to explore diversified publishing channels.

   My doctoral research focuses on user information behaviour, and I have found a strong intersection between this field and Open Science. The searching habits, resource access patterns and data sharing willingness of users on platforms like OpenAIRE, OSF and Zenodo are typical research objects for user information behaviour studies. The development of Open Science relies heavily on the support of Library and Information Science. In turn, my empirical research can help optimise open science platforms and improve public academic services. The two fields reinforce and empower each other.
   Nevertheless, I am clearly aware that Open Science still faces many practical challenges across the global academic community, and such difficulties are shared by scholars both in Europe and China. First of all, there is an inherent conflict between evaluation systems. Major European research funding bodies and universities, as well as domestic research institutions, still adopt journal-based metrics as core assessment criteria. Even though we embrace the philosophy of Open Science, we are constrained by academic and career pressures, resulting in a situation where we agree with the ideas but face limitations in practice.

   Secondly, data management poses practical challenges. I frequently reuse public metadata from OpenAlex and OpenAIRE in my research, and I also come into contact with datasets involving personal privacy and commercial confidentiality. How to realise open sharing in compliance with regulations while protecting intellectual property and avoiding patent risks is a common problem for all researchers.
   Thirdly, Open Science has relatively high barriers to adoption. Its functional platforms are complex to use, and researchers have long been accustomed to closed research models. Therefore, awareness of data sharing and transparent research practices still needs to be improved worldwide.
   Reflecting on the current academic environment, I have gained a deeper understanding of the essence of research. The original mission of academic research is to explore truth, solve practical problems and drive social progress — it should never be conducted merely for the sake of publishing papers. Nowadays, generative artificial intelligence is widely used, and some people take advantage of it to churn out large numbers of low-quality articles, which exacerbates academic bubbles and dilutes the true value of research. In this context, Open Science serves as an effective solution. The FAIR principles require full transparency and traceability of data, and research workflows are fully documented, which technically and institutionally restrict data fraud and academic irregularities. Furthermore, Open Science advocates a diversified research evaluation system. Even a research idea, an academic discussion or a set of experimental methods deserves to be disseminated and valued. It guides the academic community to move beyond the obsession with final results and fully demonstrate the multi-dimensional value of research.

   Combining what I have learned from the course with my work and research, I have formulated a set of phased implementation plans for Open Science: 1. Standardise the use of persistent research identifiers. I will register ORCID for myself and assign DOIs to all research outputs to ensure the traceability of my academic identity and achievements. 2. Comply with relevant requirements to write formal Data Management Plans. I will classify research data and conduct data anonymisation, and archive datasets and analytical codes on Zenodo or OSF within the scope of compliance. 3. Prioritise open access channels for academic publications, and standardise the citation of third-party metadata and datasets. 4. Incorporate user behaviour on open science platforms into my doctoral empirical research, so as to provide references for platform optimisation.

   Finally, based on my major in user information behaviour, I would like to share an in-depth insight: every human information behaviour is driven by internal motivation. A willingness to share and a spirit of collaboration are the most essential personal qualities for practising Open Science. Open Science is far more than a set of tools and rules; it represents a brand-new research culture. It calls on researchers to abandon the mindset of working in isolation, and actively share research data, academic ideas and phased findings. From the perspective of information behaviour research, fostering the willingness to share and the awareness of collaboration is the first step to make Open Science take root. Only when researchers genuinely aspire to communicate and share can a sound open research ecosystem be built.

   Open Science is not a passing academic trend, but an inevitable direction for global research in the future. It will not replace traditional journal publishing; instead, it acts as a powerful complement to build a more transparent, fair and robust academic ecosystem. This journey has turned me from an onlooker into an advocate, practitioner and researcher of Open Science. In the years ahead, I will keep practising Open Science in my doctoral studies and university teaching, and confront existing contradictions and challenges with a steadfast commitment to the original aspiration of academic research. I also hope to leverage my professional strengths in Library and Information Science to contribute to the popularisation of Open Science and the improvement of the academic atmosphere.nt *

2. Alejandro Saavedra García on 18 de June de 2026 at 11:43

   When I enrolled in the Ticket to Open Science course, I already had some exposure to Open Science in practice. I had been involved in open science projects and was familiar with the basic vocabulary: FAIR principles, open repositories, preregistration. But I knew my understanding was fragmented and, in many areas, simply insufficient. I have recently started a doctoral programme in Library and Information Science at UC3M, and when I had to choose among the transversal doctoral courses on offer, this one stood out. What finally pushed me to sign up was a podcast in which the coordinator had participated, and I knew it was time to deepen my understanding of it.
   My discipline sits, almost by definition, at the intersection of knowledge production and knowledge access. Open Science is not a peripheral concern in Library and Information Science; it is close to the core of what the field is about. I came into the course already convinced that Open Science is a powerful tool for researchers building their careers, for institutions that support research, and for the quality and credibility of science itself.
   The single most impactful realisation of the course was understanding the scale and logic of the publishing industry. I knew, in a vague way, that academic publishing was a business. What I had not appreciated was the extent of it: profit margins close to 30%, revenues approaching ten billion euros a year, individual journal subscriptions exceeding $28,000, and a system in which researchers produce content for free, peer-review it for free, and then often cannot access the results of their own labour without institutional subscriptions. The paradox, that publicly funded science becomes a private good, is not an accident or a side effect. It is the business model. Understanding that changed the way I think about where I publish, how I license my work, and what it actually means when funders mandate Open Access.
   I had assumed, without much reflection, that publishing in high-impact journals was roughly equivalent to doing good research, that the two things tracked each other well enough. The module on Responsible Research Assessment dismantled that assumption carefully and systematically.When journal prestige becomes the primary metric of research quality, researchers optimize for prestige rather than for rigour, transparency, or relevance. The incentive system and the actual goals of research pull in different directions. I had experienced the consequences of this pressure without having a clear framework for naming it. Now I do.
   Most of the Open Science toolkit fits well with Library and Information Science, and I am genuinely enthusiastic about applying it. But one area where I feel genuine tension is pre-registration. The standard model assumes that you can define your hypotheses and analysis plan before data collection begins, and then stick to them. In my experience of doctoral research so far, the process is far more iterative than that. As I read more, talk to supervisors, and engage with the data, my research questions sharpen and sometimes shift. Hypotheses I thought were clear at the start of a project have been refined, sometimes substantially, by what I learned along the way. Pre-registration feels like it was designed for experimental research with a clean separation between design and execution. For exploratory, qualitative, or theoretically evolving research, the fit is less obvious. I do not think this means pre-registration has no value in my context, but I think it needs to be applied thoughtfully and honestly rather than treated as a universal prescription.
   Reproducibility is the area where I am making the most immediate changes. I work primarily in R, and I am improving my skills trying to avoid undocumented scripts, no consistent folder structure, no version control. I am now working in Quarto, which allows me to combine code, analysis and narrative in a single reproducible document. I have set up a GitHub repository for my analysis code with proper documentation, and I am linking it to Zenodo so that each version has a citable DOI. I now have the knowledge to do a drafted a Data Management Plan for my thesis using ARGOS, distinguishing clearly between data I can share openly and data that carries legal or ethical restrictions. For publications, I will consult Open Policy Finder and Dulcinea before submitting anywhere, with the default intention of depositing accepted manuscripts in e-Archivo.
   My most persistent gap is around the publishing process itself. I still feel uncertain about many of the practical details: embargo periods, exactly which version of a manuscript I can self-archive in a given repository, how transformative agreements work at UC3M, and what “hybrid” Open Access actually means in terms of my rights as an author. The course gave me the frameworks and the tools to find answers to these questions, Open Policy Finder, Dulcinea, Sherpa/RoMEO … but navigating them in real cases still feels like something I will need to learn by doing, probably with some mistakes along the way. I also remain concerned about the APC landscape: Diamond Open Access is the most equitable model, but in Library and Information Science, as in many fields, the journals that carry the most weight in career evaluation are not always the ones with the most open and ethical publishing practices.
   The most durable shift is probably in how I think about what a research contribution actually is. Before the course, I thought in terms of publications. Now I think in terms of a much broader set of outputs: data, code, protocols, teaching materials, public engagement, even a well-documented failed experiment. Each of these is a contribution to cumulative scientific knowledge; only some of them result in papers. That shift changes how I document my work, how I think about my narrative CV, and how I would eventually assess the quality of someone else’s research.
   I think one of the most important changes the Open Science movement could drive is the reform of how research is evaluated. The impact factor is, at its core, a perverse metric: to score well, you need to be cited, and citation culture encourages researchers to position their work as the solution to deficiencies in the work of others. It is an index that structurally incentivises a zero-sum framing of science. A genuinely better evaluation system would be more horizontal, one in which Open Science indicators function as quality checks. Did you pre-register? Did you share your data and code? Did you deposit your outputs in accessible repositories? Did you document your methodology clearly enough for someone else to replicate it? These are not metrics that replace quality judgement, but they are indicators of the kind of rigour and transparency that should underpin quality judgement. A science evaluated this way would reward doing things well, not just doing things quickly in prestigious venues.

3. Nuaman Moideen Kutty on 18 de June de 2026 at 13:56

   My Journey into Open Science

   Taking part in the “Ticket to Open Science” course has been a genuinely eye-opening experience for me. What started as an academic requirement gradually became a space for reflection on how research is actually done, shared, and valued. As a doctoral student in Library and Information Science, I found myself constantly rethinking familiar assumptions about publishing, evaluation, and even what counts as a “real” research output.

   Before the course, I mostly associated Open Science with open access to articles. However, through the discussions and practical exercises, I came to see a much broader ecosystem — from FAIR data principles and DMPs to tools like OSF, Zenodo, ORCID, and the wider infrastructure supporting research transparency. What stayed with me most is the idea that openness is not just about access, but about responsibility, reproducibility, and collaboration across the entire research process.

   This course has helped me rethink my own research practice and how I want to develop it in the future. I now see Open Science not as an optional add-on, but as a meaningful direction for more honest, transparent, and socially relevant research.

4. JAIME GONZALEZ MENOCAL on 18 de June de 2026 at 14:44

   When I first started the TicketToOpenScience course at Universidad Carlos III de Madrid, I didn’t understand completely the open science movement. For me, Open Science was mainly focused on the free access to literature without paywalls. I was unaware of the entire scope of open science and how necessary it is. Over the past 2 months, my educational journey has been focused on understanding that this movement is a comprehensive framework of the structure, documentation, assessment and distribution of the academic work we produce.
   A great challenge during this course was analyzing how Open Science principles fit within my own discipline. Generalizing all of these aspects is a very difficult task, since each area of knowledge have different in the way they conduct experiments or express results. As a pre-doctoral researcher in robotics, my work is reliant on hardware setups, prototyping, materials and programming. Sometimes, Open Science principles are human oriented, given that other areas such as social sciences or medicine are human oriented, but it is hard to extrapolate those ideas when the object of the experiments is not human.
   However, this is not the case with every single element, on the contrary. The core elements of reproducibility is of great importance in robotics, because we are constantly basing our current physical setups and hardware iterations on our own previous experiments, so we need to ensure that our methodologies are transparent and repeatable. For example: A slight variation in the print parameters of an FDM 3D printer, or an undocumented shift in the mechanical properties of unorthodox filaments can completely alter the behavior of a printed structure. Therefore, while we may not deal with datasets in the traditional sense, the need for open documentation regarding hardware and software remains critical.
   During the practical exercises on planning and pre-registration, this problem between abstract frameworks and engineering realities became particularly evident to me, when I completed the Open Science Framework (OSF) pre-registration for my current research project. Navigating the OSF platform, I was initially surprised and challenged by how the framework leaned toward social sciences. Fitting the experimental parameters of an engineering study into the categories designed for human was challenging. However, working through that friction proved to be an invaluable exercise. The pre-registration process forced me to explicitly organize the architecture of my study. It ordered ideas in my mind regarding metadata and the meaningful structure of an experiment. These are elements that are incredibly easy to take for granted when deeply immersed in the immediate, practical demands of laboratory work. The OSF exercise demonstrated that Open Science tools, even though they require adaptation, enforce a level of methodological discipline that improves research quality.
   This discipline extends into how I manage my daily workflows now. With the concepts of reproducible research, this course has made me reflect on my documentation habits as well. Managing complex bibliographic databases and ensuring that the formatting of my research is completely reproducible is a tough task, but it aligns directly with these goals. Utilizing environments like VS Code to handle LaTeX and compile dynamic documents with Quarto is not just about typesetting; it is a foundational practice in making sure that the methodological narrative of my thesis remains transparent, version-controlled, and accessible to future researchers in my lab.
   Beyond the mechanics of laboratory documentation, this course taught me so much about the broader scholarly communication ecosystem, particularly regarding the ethical, legal, and social issues. In the academic environment, the pervasive “publish or perish” culture creates intense pressure to publish articles at an impossible rate. Examining the rise of predatory journals and paper mills highlighted a severe vulnerability in the scientific process. From an engineering perspective, the impact of predatory publishing is incredibly tangible and very frustrating. For example, if a researcher like me tries to replicate a project based on a paper pushed through a predatory journal without rigorous peer review, the consequence is not merely a flawed citation, but rather weeks of wasted laboratory time, wasted funding on physical materials, and failed prototypes. Furthermore, analyzing Open Access publishing models introduced a significant tension that I still grapple with. While the ethical imperative of making publicly funded research freely available to society is undeniable, the current financial model often feels contradictory. The costs of Article Processing Charges (APCs) of the major publishers shift the financial stress directly on the grants that fund our work. Money is always a tough subject, and it is ironic that the thousands of euros required to publish a single paper as “Gold Open Access” could instead be used to fund months of actual laboratory research or purchase necessary hardware. Moving forward, my strategy is to prioritize reputable venues and utilize green open access routes wherever possible, ensuring my work is accessible without feeding into an inherently unequal financial system.
   This reflection on value naturally leads to the future of research assessment. The shift towards Responsible Research Assessment (RRA) and the introduction of the narrative CV resonated deeply with my experience in engineering. In soft robotics, the true impact of a researcher’s work is rarely captured entirely by citation counts or h-indices. Developing a novel testing rig, optimizing fabrication parameters, or writing the underlying control code takes months of rigorous work. Traditional metrics often fail to recognize the immense value of producing open hardware designs or sharing compiled documentation. The narrative CV offers a vital opportunity to articulate the actual story of this technological development, valuing the creation of reproducible systems as highly as the final published paper.
   As I look toward the remainder of my PhD and my future in academia, I am adopting a pragmatic approach to Open Science. The iterative and messy reality of daily experimental engineering means that applying strict open frameworks to every minor laboratory development is not quite feasible. Instead, I plan to deploy these practices strategically, focusing my efforts on major milestones, specifically when it comes to serious, high-impact publishing, and as I move toward the final compilation of my thesis.
   By reserving comprehensive Open Science practices for the culmination of my doctoral work, I can ensure maximum transparency and societal impact without paralyzing my daily experimental progress. Ultimately, knowing that there is dedicated support from UniOS and the library provides a necessary safety net as I navigate this landscape. This course has shifted my perspective from viewing Open Science as an abstract ideal to recognizing it as a powerful, structural tool. I now have a clearer vision of how to balance the messy reality of engineering innovation with the essential academic responsibilities of reproducibility and open collaboration.