Open Data as Open Educational Resources Case studies of emerging practice is a collection of narratives reflecting good practices in the use of open data in teaching and learning.

Open science reduces waste and accelerates the discovery of knowledge, solutions, and cures for the world's most pressing needs. Shifting research culture toward greater openness, transparency, and reproducibility is challenging, but there are incremental steps at every stage of the research lifecycle that can improve rigor and reduce waste. Visit cos.io to learn more.

You can become a more visible, effective and impactful researcher by sharing your research data and publications openly. In this course, you will learn the objectives, main concepts, and benefits of Open Science principles along with practices for open data management and open data sharing.

Since research increasingly relies on software which is used to model and simulate, and to deal with the ever growing volume of research data, the course will also introduce FAIR software practices.

You'll learn to establish links between publications, data, software and methods, how to attach a persistent identifier and metadata to your results, and methods for clarifying usage rights. You will also discover ways to apply these principles to your daily research and adapt existing routines. Finally, you'll uncover potential barriers to sharing research and discuss possible solutions.

This course will help you grasp the key principles of Open Science, with answers to questions like:

How can researchers effectively store, manage, and share research data?
What kinds of open access publishing are most effective?
How can researchers increase the visibility and impact of their research?
How can the use of social media contribute to the visibility and impact of research?
How can researchers be acknowledged for the research software they write?
You will apply the topics of the course to a variety of case studies on Open Science adoption, which you will then discuss among fellow students. You will also be presented with a hands-on guide to publishing your research with open access. This will help you to apply Open Science principles in your daily work. It will enable you to implement and benefit from the Open Science policies that are currently being developed by governments and research institutions.

This course is aimed at professionals. Those who will see the most benefit include academic researchers at different levels: PhD students, postdoctoral researchers, and professors; researchers working for governments; researchers working for commercial enterprises; MSc and BSc students interested to learn about the principles of Open Science.

The development of this course is supported by the VRE4EIC project with funding from the European Union's Horizon 2020 Research and Innovation Programme.

You can become a more visible, effective and impactful researcher by sharing your research data and publications openly. In this course, you will learn the objectives, main concepts, and benefits of Open Source principles along with practices for open data management and open data sharing.

You’ll learn to establish links between publications data and methods, how to attach a persistent identifier and metadata to your results, and methods for clarifying usage rights. You will also discover ways to apply these principles to your daily research and adapt existing routines. Finally, you’ll uncover potential barriers to sharing research and discuss possible solutions.

This course will help you grasp the key principles of Open Science, with answers to questions like:

How can researchers effectively store, manage, and share research data?
What kinds of open access publishing are most effective?
How can researchers increase the visibility and impact of their research?
How can the use of social media contribute to the visibility and impact of research?
You will apply the topics of the course to a variety of case studies on Open Science adoption, which you will then discuss among fellow students. You will also be presented with a hands-on guide to publishing your research with open access. This will help you to apply Open Science principles in your daily work. It will enable you to implement and benefit from the Open Science policies that are currently being developed by governments and research institutions.

This course is aimed at professionals. Those who will see the most benefit include academic researchers at different levels: PhD students, postdoctoral researchers, and professors; researchers working for governments; researchers working for commercial enterprises; MSc and BSc students interested to learn about the principles of Open Science.

A group of fourteen authors came together in February 2018 at the TIB (German National Library of Science and Technology) in Hannover to create an open, living handbook on Open Science training. High-quality trainings are fundamental when aiming at a cultural change towards the implementation of Open Science principles. Teaching resources provide great support for Open Science instructors and trainers. The Open Science training handbook will be a key resource and a first step towards developing Open Access and Open Science curricula and andragogies. Supporting and connecting an emerging Open Science community that wishes to pass on their knowledge as multipliers, the handbook will enrich training activities and unlock the community’s full potential.

In this first release of the Open Science Training Handbook, some initial feedback from the community is already included.

Open science describes the movement of making any research artefact available to the public and includes, but is not limited to, open access, open data, and open source. While open science is becoming generally accepted as a norm in other scientific disciplines, in software engineering, we are still strugglingin adapting open science to the particularities of our discipline, rendering progress in our scientific community cumbersome. In this chapter, we reflect upon the essentials in open science for software engineering including what open science is, why we should engage in it, and how we should do it. We particularly draw from our experiences made as conference chairs implementing open science initiatives and as researchers actively engaging in open science to critically discuss challenges and pitfalls, and to address more advanced topics such as how and under which conditions to share preprints, what infrastructure and licence model to cover, or how do it within the limitations of different reviewing models, such as double-blind reviewing. Our hope is to help establishing a common ground and to contribute to make open science a norm also in software engineering.

This paper investigates the conceptual relationship between openness and reproducibility using a model-centric approach, heavily informed by probability theory and statistics. We first clarify the concepts of reliability, auditability, replicability, and reproducibility–each of which denotes a potential scientific objective. Then we advance a conceptual analysis to delineate the relationship between open scientific practices and these objectives. Using the notion of an idealized experiment, we identify which components of an experiment need to be reported and which need to be repeated to achieve the relevant objective. The model-centric framework we propose aims to contribute precision and clarity to the discussions surrounding the so-called reproducibility crisis.

This lesson series is for the Openscapes Champions program, an open data science mentorship program for science teams.

Openscapes Champions is a professional development and leadership opportunity for teams to reimagine data analysis & stewardship as a collaborative effort, develop modern skills that are of immediate value to them, and cultivate collaborative and inclusive research communities. Cohorts are ~7 research teams (~35 total participants including team leads and members) that convene remotely to explore open data science tooling and practices together. This is a remote-by-design program since its launch in 2019.

The Series is written (and always improving) to be used as a reference, to teach, or as self-paced learning.

Openscapes is co-directed by Julia Stewart Lowndes and Erin Robinson. It is operated by the National Center for Ecological Analysis & Synthesis (NCEAS) and was incubated by a Mozilla Fellowship awarded to Lowndes in 2018.

The movement towards open science is a consequence of seemingly pervasive failures to replicate previous research. This transition comes with great benefits but also significant challenges that are likely to affect those who carry out the research, usually early career researchers (ECRs). Here, we describe key benefits, including reputational gains, increased chances of publication, and a broader increase in the reliability of research. The increased chances of publication are supported by exploratory analyses indicating null findings are substantially more likely to be published via open registered reports in comparison to more conventional methods. These benefits are balanced by challenges that we have encountered and that involve increased costs in terms of flexibility, time, and issues with the current incentive structure, all of which seem to affect ECRs acutely. Although there are major obstacles to the early adoption of open science, overall open science practices should benefit both the ECR and improve the quality of research. We review 3 benefits and 3 challenges and provide suggestions from the perspective of ECRs for moving towards open science practices, which we believe scientists and institutions at all levels would do well to consider.

In the last decade Open Science principles have been successfully advocated for and are being slowly adopted in different research communities. In response to the COVID-19 pandemic many publishers and researchers have sped up their adoption of Open Science practices, sometimes embracing them fully and sometimes partially or in a sub-optimal manner. In this article, we express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output. We provide evidence of the misuses of these principles at different stages of the scientific process. We call for a wider adoption of Open Science practices in the hope that this work will encourage a broader endorsement of Open Science principles and serve as a reminder that science should always be a rigorous process, reliable and transparent, especially in the context of a pandemic where research findings are being translated into practice even more rapidly. We provide all data and scripts at https://osf.io/renxy/.

Data sharing, open-source designs for medical equipment, and hobbyists are all being harnessed to combat COVID-19.

Opensciency is core open science curriculum material, drafted to introduce those beginning their open science journey to important definitions, tools, and resources; and provide for participants at all levels recommended practices. The material is made available under a CC-BY 4.0 International license and is structured into five modules:

- Ethos of Open Science
- Open Tools and Resources
- Open Data
- Open Software
- Open Results

Today, intellectual property (IP) scholars accept that IP as an approach to information production has serious limits. But what lies beyond IP? A new literature on “intellectual production without IP” (or “IP without IP”) has emerged to explore this question, but its examples and explanations have yet to convince skeptics. This Article reorients this new literature via a study of a hard case: a global influenza virus-sharing network that has for decades produced critically important information goods, at significant expense, and in a loose-knit group—all without recourse to IP. I analyze the Network as an example of “open science,” a mode of information production that differs strikingly from conventional IP, and yet that successfully produces important scientific goods in response to social need. The theory and example developed here refute the most powerful criticisms of the emerging “IP without IP” literature, and provide a stronger foundation for this important new field. Even where capital costs are high, creation without IP can be reasonably effective in social terms, if it can link sources of funding to reputational and evaluative feedback loops like those that characterize open science. It can also be sustained over time, even by loose-knit groups and where the stakes are high, because organizations and other forms of law can help to stabilize cooperation. I also show that contract law is well suited to modes of information production that rely upon a “supply side” rather than “demand side” model. In its most important instances, “order without IP” is not order without governance, nor order without law. Recognizing this can help us better ground this new field, and better study and support forms of knowledge production that deserve our attention, and that sometimes sustain our very lives.

The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of this study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, the results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

Preregistration, the act of specifying a research plan in advance, is becoming a central step in the way science is conducted. Preregistration for infant researchers might be different than in other fields, due to the specific challenges having to do with testing infants. Infants are a hard-to-reach population, usually yielding small sample sizes, they have a low attention span which usually can limit the number of trials, and they can be excluded based on hard to predict complications (e.g., parental interference, fussiness). In addition, as effects themselves potentially change with age and population, it is hard to calculate an a priori effect size. At the same time, these very factors make preregistration in infant studies a valuable tool. A priori examination of the planned study, including the hypotheses, sample size, and resulting statistical power, increase the credibility of single studies and thus add value to the field. It might arguably also improve explicit decision-making to create better studies. We present an in-depth discussion of the issues uniquely relevant to infant researchers, and ways to contend with them in preregistration and study planning. We provide recommendations to researchers interested in following current best practices.

In this webinar, Tamarinde Haven provides an overview of the process of preregistration in qualitative research. We review the process of preregistration, how we partnered with a community of qualitative researchers to develop a template for qualitative research through a Delphi study, and a guide to the fields that were included in the final form.

This webinar addresses questions related to writing, reviewing, editing, or funding a study using the Registered Report format, featuring Chris Chambers and ...

Interpreting the first results from the Reproducibility Project: Cancer Biology requires a highly nuanced approach. Reproducibility is a cornerstone of science, and the development of new drugs and medical treatments relies on the results of preclinical research being reproducible. In recent years, however, the validity of published findings in a number of areas of scientific research, including cancer research, have been called into question (Begley and Ellis, 2012; Baker, 2016). One response to these concerns has been the launch of a project to repeat selected experiments from a number of high-profile papers in cancer biology (Morrison, 2014; Errington et al., 2014). The aim of the Reproducibility Project: Cancer Biology, which is a collaboration between the Center for Open Science and Science Exchange, is two-fold: to provide evidence about reproducibility in preclinical cancer research, and to identify the factors that influence reproducibility more generally.

Currently, there is a growing interest in ensuring the transparency and reproducibility of the published scientific literature. According to a previous evaluation of 441 biomedical journals articles published in 2000–2014, the biomedical literature largely lacked transparency in important dimensions. Here, we surveyed a random sample of 149 biomedical articles published between 2015 and 2017 and determined the proportion reporting sources of public and/or private funding and conflicts of interests, sharing protocols and raw data, and undergoing rigorous independent replication and reproducibility checks. We also investigated what can be learned about reproducibility and transparency indicators from open access data provided on PubMed. The majority of the 149 studies disclosed some information regarding funding (103, 69.1% [95% confidence interval, 61.0% to 76.3%]) or conflicts of interest (97, 65.1% [56.8% to 72.6%]). Among the 104 articles with empirical data in which protocols or data sharing would be pertinent, 19 (18.3% [11.6% to 27.3%]) discussed publicly available data; only one (1.0% [0.1% to 6.0%]) included a link to a full study protocol. Among the 97 articles in which replication in studies with different data would be pertinent, there were five replication efforts (5.2% [1.9% to 12.2%]). Although clinical trial identification numbers and funding details were often provided on PubMed, only two of the articles without a full text article in PubMed Central that discussed publicly available data at the full text level also contained information related to data sharing on PubMed; none had a conflicts of interest statement on PubMed. Our evaluation suggests that although there have been improvements over the last few years in certain key indicators of reproducibility and transparency, opportunities exist to improve reproducible research practices across the biomedical literature and to make features related to reproducibility more readily visible in PubMed.