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In this symposium, we will explore how Open Science can help to fight injustice, by centering decolonial and anticolonial Open Science initiatives. Together, we aim to raise awareness about the neglect justice in mainstream Open Science practices and explore actionable pathways to embed justice and equity as fundamental values of Open Science.

For this, we will specially focus on three topics related to injustice and Open Science:

• Expanding the definition of ‘science’ in Open Science to be more inclusive to diverse knowledge production approaches.
• Diamond open access publishing as an alternative to working with commercial publishers.
• Advocating for contextual, rather than absolute, openness as a means of centering justice.

Do you ever feel that the data you need for your research is accessible but it’s not in a convenient table, such as company reports or building plans?

Perhaps the information you need is spread out across many different documents?

If only we could read and extract structured data from thousands of written documents. 

In this course, we explore how to accomplish this task by combining web scraping, Optical Character Recognition (OCR), and Natural Language Processing (NLP). Over four weeks, we provide online lessons and interactive sessions to learn the fundamentals of these key technologies.

Chancen durch Offenheit – eine Reise durch Urheberrecht, Creative Commons und was offene Lizenzen  ermöglichen.

“An approximate answer to the right question is worth a great deal more than a precise answer to the wrong question” said the renowned statistician John Tukey as early as 1969.

Based on my own experience in statistical consultations, much confusion occurs due to a mismatch between research question and data/methods. However, even more fundamentally, the research question is often not even clearly articulated at the outset – perhaps because researchers anticipate that the right question can only be answered approximately. But how can we discuss what data and methods are suitable, if we are unclear or vague about the question to be answered? It seems that now, in the era of big data characterised by an abundance of data and a similar abundance of methods for analysing the data, the issue of asking the right question receives a new urgency.

Have any doubts or questions about your obligations as a Horizon Europe (HE) grant holder in terms of Open Access to publications and Research Data Management? We will present the HE requirements, followed by some time for your questions to our experts. You will also get a preview of the main tools and services OpenAIRE provides to help project coordinators and research support staff on the requirements’ compliance.

Learn about:
– Mandatory and recommended Open Science requirements in HE.
– Compliance with the HE Open Access to publications mandate.
– Managing and sharing Research Data in HE projects.
– Delivering Data Management Plans and reporting publications and datasets in HE.
– OpenAIRE tools and services to support HE projects.

The basic goal of this course is to provide participants with the methodological foundations and advanced knowledge in qualitative research in business. After attending the course, participants should be able to

• understand the philosophical and methodological foundations of qualitative research and to classify, differentiate and choose different qualitative research methods;
• assess goals and purposes, as well as strengths and weaknesses of qualitative research;
• understand and raise key questions for planning and preparing a qualitative research design, data collection and analysis;
• identify, analyze and manage core issues during the planning, execution, analysis and writing of qualitative studies;
• to differentiate rigorous from non-rigorous qualitative management research.

ReproducibiliTea is a journal club initiative that has resulted in journal clubs about open science and replicability in academic departments all over the world (see https://reproducibilitea.org/ for more history and information). IGDORE is now proud to announce our own ReproducibiliTea journal club fully online, allowing people to join from wherever they happen to be in the world!

This journal club is suited for beginners to open science. To fully understand what the open science movement is built upon, we will cover important basic concepts and discuss the most seminal works. Each session will be based on an article which all participants read in advance. Depending on the session topic, other resources such as Youtube videos or optional reading may be provided to facilitate the discussions. These will always be optional; you are never expected to prepare more than reading the session article.

Everyone is welcome – from student to Nobel Prize laureate; from intern to CEO. No prior knowledge is required to participate, but the article discussions will probably be more valuable to you if you have a university-level understanding of quantitative and/or qualitative research methodology

Der zweitägige Workshop richtet sich an Personen, die in ihrem Arbeitsbereich oder an ihrer Einrichtung Grundlagen zum Forschungsdatenmanagement vermitteln wollen und davon ausgehend FDM-Services für ihre Standorte auf- oder ausbauen möchten. Neben didaktischen Ansätzen, Methoden und dem Seminaraufbau werden folgende Themen behandelt:

• Forschungsdaten-Lebenszyklus
• Forschungsdaten-Policies
• Datenmanagementplan
• Strukturierung von Daten
• Dokumentation
• Speicherung und Backup
• Langzeitarchivierung
• Zugriffssicherheit
• Publikation von Forschungsdaten
• Nachnutzung von Forschungsdaten
• Rechtliche Aspekte

Die Einheiten werden von praktischen Übungen begleitet, durch die das neu gewonnene Wissen verfestigt und auf eigene Anwendungsfälle übertragen werden kann. Dabei lernen die Teilnehmenden nützliche Werkzeuge und Plattformen für eine effektive Arbeit mit ihren Daten kennen. Verwendete Methoden sind Theorieimpulse, Einzelübungen, Plenums- und Gruppenarbeit, Diskussions- und Reflexionsrunden sowie E-Learning-Elemente.

Chancen durch Offenheit – eine Reise durch Urheberrecht, Creative Commons und was offene Lizenzen  ermöglichen.

Zotero ist als kostenlose, open-source Software zur Verwaltung bibliografischer Daten und damit verknüpfter Forschungsliteratur nicht nur in den USA, sondern auch im deutschsprachigen Raum sehr beliebt. Als Software ist Zotero sehr anpassungsfähig und kann auch als niedrigschwelliges Werkzeug zur Verwaltung der eigenen Forschungsdaten eingesetzt werden. In diesem Workshop werden wir anhand von Datensätzen, die die Teilnehmer/innen mitbringen, Strategien zum Management unterschiedlicher Forschungsdaten mit Hilfe von Zotero diskutieren und ausprobieren. Wie können wir unsere Forschungsdaten organisieren, so dass wir sie über einen längeren Zeitraum selbst verwalten und ggf. zur Übermittlung an ein Datenrepositorium vorbereiten können? Im Workshop entwerfen wir auch eine Handreichung, die wir mit Kolleginnen und Kollegen im 4Memory Konsortium teilen können.

In this workshop we will delve into the core of reproducible science and its transformative potential for science. The workshop begins with two keynoteson reproducibility, one byAltuna Akalin(MDC – Software reproducibility of data processing and machine learning in the era of AI) and the other byFrieder Paulus(Lübeck University – Contextualization of reproducibility in the organization of academic work) – both of which will also be livestreamed, to make them easily accessible for researchers from all Helmholtz Centers. Following the keynotes, attendees can choose one oftwo hands-on workshopsthat will equip them with the tools and insights needed to make reproducibility a cornerstone of their scientific endeavors.

Im interaktiven Workshop werden die vielfältigen Möglichkeiten erkundet, wie Künstliche Intelligenz die Welt der Open Educational Resources (OER) transformiert und deutlich erleichtern kann. Dieser Workshop richtet sich an Hochschullehrende, die ihre Lehrmaterialien auf innovative Weise gestalten möchten. Die Teilnehmenden erfahren, wie KI dabei helfen kann, OER effektiver zu gestalten und den Arbeitsaufwand zu reduzieren. Von rechtlichen Fragestellungen bis hin zur praktischen Anwendung verschiedener Tools werden relevante Themen praxisnah beleuchtet. Dieser Workshop bietet die Gelegenheit, Bildungsinhalte mit neuen, innovativen Methoden zu gestalten und zugleich sicher entlang rechtlicher Aspekte handeln zu können.

Join us for the 11th EIFL online meet-up of open science trainers, which will focus on crowdsourcing, curating and sharing information about open science with researchers, librarians, and other open science actors.

Computer science is a key component for data science applications and research data management as methods and procedures rely on it. For instance, to enable fast access to information, data sets must be stored efficiently in data structures. Clever modelling and algorithmic processing hereby guarantee a fast search and selection of information of even big data sets. This course will provide insights into computer science basics and gives an overview about relevant topics for data science.

“The Collaborations Workshops series brings together researchers, developers, innovators, managers, funders, publishers, policy makers, leaders and educators to explore best practices and the future of research software.”We are delighted that you would like to attend the one-day symposium Qualitative Open Science: Challenges, Opportunities, Tensions, and Synergies funded by Open Science NL.”

In order to analyze, store, or publish digital research data, powerful software tools or even custom-made software are required. However, it is often quite challenging when the different tools are not interoperable or the overall workflows are not fully digitized, as this results in time-consuming processes with limited opportunities of collaboration. Therefore, a key objective should be to make data processing more efficient and to automate workflows. And this is where the tinkering begins!

The course exposes participants to recent developments in the field of machine learning (ML) and discusses their ramifications for business and economics. ML comprises theories, concepts, and algorithms to extract patterns from observational data. The prevalence of data (“big data”) has led to a surge in the interest in ML to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent in decision-makers’ mindset and ground, to a large extent, on ML. Familiarizing course participants with these concepts and enabling them to apply cutting-edge ML algorithms to real-world decision problems in management, policy development, and research is the overarching objective of the course. 

ReproducibiliTea is a journal club initiative that has resulted in journal clubs about open science and replicability in academic departments all over the world (see https://reproducibilitea.org/ for more history and information). IGDORE is now proud to announce our own ReproducibiliTea journal club fully online, allowing people to join from wherever they happen to be in the world!

This journal club is suited for beginners to open science. To fully understand what the open science movement is built upon, we will cover important basic concepts and discuss the most seminal works. Each session will be based on an article which all participants read in advance. Depending on the session topic, other resources such as Youtube videos or optional reading may be provided to facilitate the discussions. These will always be optional; you are never expected to prepare more than reading the session article.

Everyone is welcome – from student to Nobel Prize laureate; from intern to CEO. No prior knowledge is required to participate, but the article discussions will probably be more valuable to you if you have a university-level understanding of quantitative and/or qualitative research methodology

Programming is the essential tool for managing data sets and conducting data science methods. Handling huge data sets manually is impossible, so we can only prepare, curate, analyze and evaluate them by programmable means. In addition, programming is crucial for documenting, creating graphical output, and presenting results (e.g., on the web). In order to write programs, we need to a programming language – but what is that?

Lonni Besançon, Assistant Professor of Visualization at Linköping University, talks about self correcting in science.