This is a read only archive of pad.okfn.org. See the shutdown announcement for details.

• Include live coverage of open science ( https://github.com/OKScienceDE/Open_Science_101/issues/1 ) #meta
• Clarify that Open Science refers to all scholarly domains, not just "Science" ( https://github.com/OKScienceDE/Open_Science_101/issues/3 ) #meta: Gather current definitions of Open Science,define framework of what Open Science means for this project (make emphasis on reference to other areas of knowledge). 
• Discuss entry points ( https://github.com/OKScienceDE/Open_Science_101/issues/4 ) #meta: any entry point possible, citizen scientist, undergraduates, PhD, postdocs, PI, Professor. Also, start easy with maybe some text disseminated from Google docs (maybe even crowdsourced), or Open Data in Zenodo, or if you can program open source code on GitHub  Why does entry have to be through coding? I am a scientist AND do NOT code. Nor do I feel it is necessary to, either for learning about open science or practicing open science. Yes, you're right, I will turn the order around.
• Open Educational Resources ( https://github.com/OKScienceDE/Open_Science_101/issues/8 ):
• A discussion with OER community is important: http://open-educational-resources.de/
• relevant questions (to be decided on):
• Where to store those OER? 
• What to store, and how to categorize?
• OER for whom: higher education, scientists (PhDs), industry workers? -> is this distinction necessary?
• Which OER formats?
• objectives:
• to know what OER concerning Open Science to use and where to find them
• Open Access ( https://github.com/OKScienceDE/Open_Science_101/issues/9 ): taken from Wikipedia https://en.wikipedia.org/wiki/Open_access - Open access refers to online research outputs that are free of all restrictions on access (e.g., access tolls) and free of many restrictions on use (e.g. certain copyright and license restrictions). Open access can be applied to all forms of published research output, including peer-reviewed and non peer-reviewed academic journal articles, conference papers, theses,book chapters, and monographs.
• Preprints ( https://github.com/OKScienceDE/Open_Science_101/issues/49#issuecomment-223326951 ): Preprints are scientific manuscripts that are not peer reviewed but already saved openly on preprint servers and can subsequently also be published in traditional scientific journals (however, many of these manuscripts will never be peer reviewed). Preprint servers, like arXiv ( https://arxiv.org/ ) and biorXiv ( http://biorxiv.org/ ) are becoming increasingly more popular, not only in physics and mathematics (where they have a long tradition) but also in life sciences. With preprints (or something similar) also post-publication peer reviews are possible, where peer review is done in the open after the manuscript has been published. Some platforms already do that (F1000, ScienceOpen).
• Open Peer Review ( https://github.com/OKScienceDE/Open_Science_101/issues/11 )
• Short description of peer review:
• Peer review is the quality control of science. A manuscript for a scientific publication is submitted to a journal the editor sends it to two or three scientists in the field (peers), who assess the article upon its scientific question, theory, execution of the experiments, methods, analysis, and novelty. The editor then decides upon the reviews of the peers if the article is acceptep for publishing, needs some rework, or is rejected.
• Problems with classical Peer Review:
• Can introduce bias
• Reviewers can hide behind anonymity
• Work can be "scooped" by the reviewers and published themselves
• No real incentive to do a lot or good Peer Reviews
• Peer Review types:
• Classic: authors known, reviewers blind
• Double blind: authors and reviewers blind
• Open Peer Review:
• all parties are known to ensure an open and ethically correct communication
• the reviews are published together with the manuscript, and optionally are signed by the reviewers
• Incentivize Peer Review by collecting points (e.g. Publons) and getting credit (see ORCID - Publons integration)
• Post publication peer review (see Preprints)
• Citizen Science ( https://github.com/OKScienceDE/Open_Science_101/issues/12 ): Citizen Science not necessarly open.. -> Focus on how to promote openness in citizen science?
• Include lay persons/professional amateurs into scientific projects
• Or lay persons/professional amateurs (or societies like wildlife conservation societies etc.) create a scientific project outside of academia/industry by themselves
• Crowdsourcing of projects:
• Give computing time (or gaming time) to the project, e.g. SETI, Foldit
• Collect data, e.g. bird watching, digitalization of historical data
• Analysis of data according to specific methods
• Important for Citizen Science are open methods and open educational resources
• Genetic manipulations of bacteria in a smaller scale
• Incentives for Open Science ( https://github.com/OKScienceDE/Open_Science_101/issues/13 ) #meta
• this might also be a meta topic that is important at various stages of the research cycle)
• here, we should distinguish between the diverse parties: students, scientists (PhDs),... >>> which is probably true for most of the topics, isn't it? but yes, in case of incentives the categories might be farther apart
• crucial questions is: why should scientists switch modes of working, turn towards working along the principles of open science and thereby maybe sacrifice reputation (in the classical sense via classical metrics) and thus mayne even give up on carreer opportunities?
• I (Daniel) am concentrating on what individual stakeholders can do (in the short and medium term) to advance open science themselves or to encourage others to do so - see https://openscience.uni-bielefeld.de/tag/stakeholders . Not sure precisely how that fits into the stuff discussed here, but it basically tackles the same problem, just from a slightly different angle -> https://github.com/OKScienceDE/Open_Science_101/issues/41
• there are many options for incentives that might be valuable for different stakeholdes (probably mainly based on the individual characters and their situation), such as:
• out of conviction: science is open by default
• reproducibility is a desired state to show the quality of your research
• discourse leads to new ideas, better ideas?
• synergy effects between research projects / research strands
• other important aspects:
• manifestos (Leiden Manifesto) to put down the principles
• policy "incentives": policy stakeholders writing down open principles as a must-criteria (e.g. Open Access in EU-funded research lately)
• "cultural change" needs to take place so that old mechanisms (like reputation according to IF) need to loose power
• Open Research Ideas, Research Proposals and Open Research ( https://github.com/OKScienceDE/Open_Science_101/issues/14 )#meta
• the idea behind this is produce research output (i.e. to publish) before the classic research article in scientific journals
• infact to produce output right from the beginning of a research project and thereby put transparency and openness at the core of the project
• output can be:
• research ideas
• proposals (grant applications)
• research-related articles
• anything else in later stages
• motivations to do so:
• get feedback for your research ideas
• maybe find collaborators
• get publication credit
• attract attention and visibility (attractive to fundings agents of any kind)
• link early-stage outputs (such as ideas and proposal) to output at later stages 
• Infrastructure for Open Science ( https://github.com/OKScienceDE/Open_Science_101/issues/15 ) #meta
• Infrastructure for Open Science (meta topic that is crucial in most of the stages of the research cycle)
• worth adding something about the study group open science 101 guide: https://github.com/mozillascience/studyGroupLessons/issues/7 ? I'll add an issue too! 
• provide infrastructure for research-related activities, such as:
• data handling: collection, storage, processing (grid?) and analysis of data
• experiments: labs, workflows
• publishing: data (repositories), articles (repositories, journals, etc.), workflows
• issues to pay attention to:
• interoperability among various types of data
• licensing, attribution, etc.
• access
• cost
• Open Notebook Science ( https://github.com/OKScienceDE/Open_Science_101/issues/18 ): Documenting the day-to-day research process is common and good practice in science. In general scientist only publish a small selection of the findings by compiling this into a manuscript. In Open Notebook Science the primary documentation is made public after a while or ideally in real time.
• Open Data ( https://github.com/OKScienceDE/Open_Science_101/issues/29 ): In order to make scientific studies reproducible as well to facilitate the reuse of results the data sets of a scientific project need to be made publicly available under an open license. Depending on the nature of the data there might be dedicated repositories or general purpose repositories.
• Open Licenses (https://github.com/OKScienceDE/Open_Science_101/issues/47): In order to give the legal basis for the reuse of scientific outcomes (data sets, source code, publications) those items have to be made public using open licenses. These licenses include the Creative Common licenses, FLOSS (free, libre, open source) licenses for source code and data base licenses like Open Data Commons Open Database License.
• Open Source Software ( https://github.com/OKScienceDE/Open_Science_101/issues/30 ):
• copied from Wikipedia https://en.wikipedia.org/wiki/Open-source_software: Open-source software (OSS) is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose. Open-source software may be developed in a collaborative public manner.  This has the advantage that bugs can be found more easily and fixed.
• relevance in science
• possibility of minting DOIs
• software citation practices
• related to Open Data
• Include suggestions as to what individual stakeholders can do to encourage Open Science #meta
• Open Source Hardware -> equally important as software
• relevance in science
• challenges with licenses and formats
• challenges with quality control
• Create online database of Open Science resources ( https://github.com/OKScienceDE/Open_Science_101/issues/20 )#meta: From @konrad: While I do not doubt that might be useful I personally would speak against such a collection of as part of this project. The main aim should be to compile (timeless) core principles. Maintaining the content of database would be out of scope. Maybe an option for a later spin-off.
• Open Science Definitions ( https://github.com/OKScienceDE/Open_Science_101/issues/3 )
• Clarify that Open Science refers to all scholarly domains, not just "Science" #meta (Mario & Selina): Gather current definitions of Open Science,define what Open Science means for this project (emphasising on inclusion of other areas of knowledge). 
• From Wikipedia "Open science is the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society, amateur or professional."
• From Foster Open Science ( www.fosteropenscience.eu ): "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."
• From Michael Nielsen's TED talk ( https://www.youtube.com/watch?v=DnWocYKqvhw ): ""Open science is the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process."
• From Leibniz Science 2.0 Research Alliance: https://www.leibniz-science20.de  
• From https://opensource.com/resources/open-science "Open science is the growing movement to make science open"
• Open Science history / framing ( https://github.com/OKScienceDE/Open_Science_101/issues/50 ): Perhaps an overarching "where have we come from, why is this important, where are we now" part, to help frame the rest of the 101. Lots of examples you can pull from here.

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