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+ +
+ +A demo of [interactive documents with Thebe](https://readthedocs.org/thebe/en/latest/), which is the tool that power's [Curvenote](https://curvenote.com)'s live computational documents. Services like Curvenote could power interactivity with a community-managed BinderHub service. + +
+
+ +## How to sustain these services? + +This brings up an important question: how would you sustain services like these? mybinder.org is built as an entirely free service both to build and to use environments. This is massively accessible, but is not scalable nor sustainable. Would community stakeholders pay for privileged access to BinderHubs that could reproduce and share their computational narratives? Would publishers be willing to pay a percentage of the cloud and management costs associated with reproduction? + +One thing seems clear - there is an imperative to make reproducibility and interaction frictionless. This is both to ensure the scientific integrity of the work being done, and also to make computational ideas more accessible to the world. Technologies and service partnerships like these can help ensure the broader communitys right to reproduce the work of others. + +Some of these ideas were recently explored in a talk recorded for [AGU 2024](https://agu.confex.com/agu/fm24/meetingapp.cgi/Paper/100644). + +
+ +
+A talk from 2i2c team member [Jim Colliander](https://2i2c.org/author/jim-colliander/) discussing the right to reproduce computational ideas, the importance of enabling frictionless reproducibility, and how we might sustain such a service. +
+
+ +## Concluding thoughts + +Much of the above is already possible, if a little bit clunky to execute. However, given the growing interest in open infrastructure, iterative science, and improving the publishing ecosystem, I'm hopeful that we'll have opportunities to build these service connections soon. I'd love to those from the more "formal" side of scholarly publishing to understand where these ideas have holes or significant new development. I'd also love feedback on sustainability models to ensure these services can be relied on as part of the publishing ecosystem. In the meantime, hopefully these ideas serve as an inspiration for what is possible, and where we might be heading with 2i2c's service and the broader Jupyter ecosystem. \ No newline at end of file From 5fd622735d2448c1352fdf3ebfa7d4ccb35dc770 Mon Sep 17 00:00:00 2001 From: choldgraf Date: Tue, 7 Jan 2025 10:31:32 -0800 Subject: [PATCH 2/3] Minor updates --- .../frictionless-reproducibility/index.md | 49 ++++++++++++------- 1 file changed, 31 insertions(+), 18 deletions(-) diff --git a/content/blog/2025/frictionless-reproducibility/index.md b/content/blog/2025/frictionless-reproducibility/index.md index 498691f2..a681b7ce 100644 --- a/content/blog/2025/frictionless-reproducibility/index.md +++ b/content/blog/2025/frictionless-reproducibility/index.md @@ -1,6 +1,6 @@ --- date: 2024-12-16 -title: Towards frictionless reproducibility with BinderHub and 2i2c +title: Towards frictionless, sharable, and sustainable reproducibility with Binder and JupyterHub --- -2i2c has been experimenting with ways to enable communities to share their work with one another more effectively. This post is an exploration of the basic workflow we're interested in, how it could be enabled with open technology in the Jupyter ecosystem, how it might fit in with the broader ecosystem of services around scientific communication, and how we might try to sustain it. +2i2c has been experimenting with empowering communities to share their work with one another. This post is an exploration of the basic workflow we're interested in, how it could be enabled with open technology in the Jupyter ecosystem, how it might fit in with the broader ecosystem of services around scientific communication, and how we might try to sustain it. ## The core pieces of data-driven discovery -For any individual to ask questions and create knowledge with data, they need access to a few key resources in order to do their work. +For any individual to create and share knowledge with data, they need access to a few key resources: - **Data** is the raw material that drives computational ideas. -- **Computation** provides the core hardware for manipulating data with code. -- **Software** the tools that code uses to leverage data and computation for specific outcomes. -- **Code** utilizes software and computation to reproduce a computation. -- **Narrative** allows all of the above to be communicated in a compelling and understandable way. +- **Computation** provides the mechanism for manipulating data with code. +- **Software** provides tools that leverage data and computation for specific outcomes. +- **Code** orchestrates software and computation to analyze and explore data. +- **Narratives** allow all of the above to be communicated in a compelling and understandable way. +- **Interfaces** bring together all of the above to facilitate workflows in data-driven discovery and sharing. -**Jupyter**'s goal is to weave all of the above together with an ecosystem of tools that are modular, flexible, and interoperable. **2i2c**'s [community hub platform](/platform) gives community leaders the ability to integrate and customize all of these resources to be useful for particular communities in research and education. This usually comes in the form of a **Jupyter Book** to teach communtiy workflows to users, with connections to a **JupyterHub** that provides the data, compute, and software tools needed for data-driven discovery. +**Jupyter**'s goal is to weave all of the above together with an ecosystem of tools that are modular, flexible, and interoperable. **2i2c**'s [community hub platform](/platform) gives community leaders the ability to integrate and customize all of these resources to be useful for communities in research and education. This usually comes in the form of a **Jupyter Book** to teach communtiy workflows to users, with connections to a **JupyterHub** that provides the data, compute, and software tools needed for data-driven discovery. {{< figure src="images/discover.png" title="2i2c community hubs use Jupyter technology to help members learn community workflows, and access all the resources needed for a community's data-driven discovery.">}} ## Sharing ideas means sharing computation to reproduce them -When a community member discovers something new, they want to share it with others in their community. This is usually an ongoing process by which community members are giving information and receiving information in return to iteratively drive their exploration and development. +When a community member discovers something new, they want to share it with others in their community. This is an ongoing process where community members are giving and receiving information to iteratively drive their exploration and development. -These ideas are driven by data and computation, and so it is critical to **share the resources needed to reproduce the computation** when sharing new ideas with community members. This allows others to reproduce the main ideas in order to validate the core claims, as well as interact with the code and software to better understand and build upon the ideas. +These ideas are driven by data and computation, and so it is critical to **share the resources needed to reproduce the computation**. This has two benefits: -We can leverage cloud infrastructure to facilitate this process. At a basic level, community hubs are a good way to standardize the software and resource environment so that content produced in one user's session can be easily reproduced by another user. [nbgitpuller](https://nbgitpuller.readthedocs.io/en/latest/) is the most common tool for distributing content within a JupyterHub. As long as the content was made on a JupyterHub, and shared with another user on the same hub, they'll be able to get their own copy and interact with it. +1. It allows others to **validate the core claims** by inspecting code and reproducing the results. +2. It allows others to **learn more effectively** by providing a more rich, interactive space to engage with the ideas. +3. It allows others to **build on top of work more easily** by providing an interactive starting point that lets readers get further with new ideas. + +We can leverage cloud infrastructure to facilitate this process. At a basic level, community hubs are a good way to standardize the environment so that content produced in one user's session can be easily reproduced by another user. [nbgitpuller](https://nbgitpuller.readthedocs.io/en/latest/) is the most common tool for distributing content within a JupyterHub. As long as the content was made on a JupyterHub, and shared with another user on the same hub, they'll be able to get their own copy and interact with it. {{< figure src="images/share.png" title="The simplest sharing workflow uses nbgitpuller to distribute content via links that community members click. Doing so will give them their own copy of the content running in the hub's environment.">}} @@ -48,20 +53,26 @@ We can leverage cloud infrastructure to facilitate this process. At a basic leve However, many authors need more control over the environment needed to reproduce a computation. For example, they might need a _specific version_ of a software package, or they may need a non-standard library that isn't available on their community hub's base environment. How can they share their computational narratives in a way that _bundles the environment_ with the content? This is what [**the Binder Project**](https://mybinder.org) was built for. It provides a tool called [BinderHub](https://binderhub.readthedocs.io/en/latest/), which is an **engine for sharable, reproducible computational environments**. It allows an author to package up their computational narratives and code, and share access to a cloud environment that allows readers to reproduce and interact with the computation. -Earlier this year our team has been prototyping how to [connect BinderHub services with a JupyterHub](../../2024/jupyterhub-binderhub-gesis/) and how to [deploy BinderHub services alongside each of our community hubs](../../2024/nasa-ephemeral-hubs/). +In 2024 our team prototyped how to [connect BinderHub services with a JupyterHub](../../2024/jupyterhub-binderhub-gesis/) and how to [deploy BinderHub services alongside each of our community hubs](../../2024/nasa-ephemeral-hubs/). Doing so would allow community members to **build an environment needed to reproduce their computation and share it via their hub**. {{< figure src="images/reproduce.png" title="BinderHub allows an author to package narrative, code, computational resources, and a software environment into a link that others can use to access a fully reproducible environment for their ideas.">}} -## Empowering communities to bring their own reproducibility when publishing +## Could we empower communities to bring their own reproducibility when publishing? -When community members have a big idea that they'd like to share more formally, want to **publish** rather than just **share**. Publishing is more structured, invites particular kinds of feedback, and requires more quality assurance. There's a huge ecosystem of publishers and services that support formal publishing, and they ensure things like discoverability, long-term archivability, versioning, peer review, DOI referencing, etc. +When community members have a polished idea to share more formally, they want to **publish** rather than just **share**. Publishing is more structured, invites particular kinds of feedback, and requires more quality assurance. There's a huge ecosystem of publishers and services that support formal publishing, and they ensure things like discoverability, long-term archivability, versioning, peer review, DOI referencing, etc. However, publishers don't have access to the core infrastructure used to make the original discovery, nor do they have the expertise to manage this kind of infrastructure. But 2i2c's community members already have a reproducible environment where they've been doing their discovery. Could we leverage this infrastructure for publishing? 2i2c has an opportunity to empower publishers to reproduce computational ideas by **allowing authors to bring their reproducible environments with them**. By leveraging the same core technologies, authors can re-use their hub infrastructure to provide the resources needed for reproducibility. -This would require treating a community's hub as an **external service for reproducibility** that could be leveraged by one of the many publishing platforms out there. For example, [Curvenote](https://curvenote.com) already builds heavily on the Jupyter and MyST ecosystems and could use a community's hub to provide interactivity to a Curvenote document. Authors could publish pre-prints on [Biorxiv](https://www.biorxiv.org/) that included Binder links back to their community hub [Binder links in arxiv has already been piloted with the Loren Frank lab and HHMI](../../2024/hhmi-spyglass-mysql/)). Larger publishers like [PLOS](https://plos.org/) could allow authors to provide metadata about BinderHubs that can reproduce their content, and weave this into their user interface for displaying publications. These are all _possible_ outcomes that haven't been realized, but I don't think they'd be that far off. +This would require treating a community's hub as an **external service for reproducibility** that could be leveraged by one of the many publishing platforms out there. For example: + +- [Curvenote](https://curvenote.com) already builds heavily on the Jupyter and MyST ecosystems and could use a community's hub to provide interactivity to a Curvenote document. +- Authors could publish pre-prints on [Biorxiv](https://www.biorxiv.org/) that included Binder links back to their community hub [Binder links in arxiv has already been piloted with the Loren Frank lab and HHMI](../../2024/hhmi-spyglass-mysql/)). +- Larger publishers like [PLOS](https://plos.org/) could allow authors to provide metadata about BinderHubs that can reproduce their content, and weave this into their user interface for displaying publications. + +These are all _possible_ outcomes that haven't been realized, but I don't think they'd be that far off. {{< figure src="images/publish.png" title="We could re-use the same tools for data-driven discovery to facilitate the reproducibility of computational narratives and publications. The same core infrastructure could be re-used to provide comptutational environments that are re-used in a variety of publishing tools and services. (note this is all hypothetical, no-such service integrations yet exist, and these are just a sample of relevant publishing organizations in this space)">}} @@ -80,9 +91,9 @@ A demo of [interactive documents with Thebe](https://readthedocs.org/thebe/en/la ## How to sustain these services? -This brings up an important question: how would you sustain services like these? mybinder.org is built as an entirely free service both to build and to use environments. This is massively accessible, but is not scalable nor sustainable. Would community stakeholders pay for privileged access to BinderHubs that could reproduce and share their computational narratives? Would publishers be willing to pay a percentage of the cloud and management costs associated with reproduction? +This brings up an important question: how would you sustain services like these? [mybinder.org](https://mybinder.org) is an entirely free service both to build and to use environments. This is massively accessible, but is not scalable nor sustainable, and doesn't provide rich computation needed for more complex analyses. Would community stakeholders pay for privileged access to BinderHubs that could reproduce and share their computational narratives? Would publishers be willing to pay a percentage of the cloud and management costs associated with reproduction? -One thing seems clear - there is an imperative to make reproducibility and interaction frictionless. This is both to ensure the scientific integrity of the work being done, and also to make computational ideas more accessible to the world. Technologies and service partnerships like these can help ensure the broader communitys right to reproduce the work of others. +One thing seems clear - there is an imperative to make reproducibility and interaction frictionless. This is both to ensure the scientific integrity of the work being done, and also to make computational ideas more accessible to the world. Technologies and service partnerships like these can help ensure the broader community's right to reproduce the work of others. Some of these ideas were recently explored in a talk recorded for [AGU 2024](https://agu.confex.com/agu/fm24/meetingapp.cgi/Paper/100644). @@ -95,4 +106,6 @@ A talk from 2i2c team member [Jim Colliander](https://2i2c.org/author/jim-collia ## Concluding thoughts -Much of the above is already possible, if a little bit clunky to execute. However, given the growing interest in open infrastructure, iterative science, and improving the publishing ecosystem, I'm hopeful that we'll have opportunities to build these service connections soon. I'd love to those from the more "formal" side of scholarly publishing to understand where these ideas have holes or significant new development. I'd also love feedback on sustainability models to ensure these services can be relied on as part of the publishing ecosystem. In the meantime, hopefully these ideas serve as an inspiration for what is possible, and where we might be heading with 2i2c's service and the broader Jupyter ecosystem. \ No newline at end of file +Much of the above is already possible, if a little bit clunky to execute. However, given the growing interest in open infrastructure, iterative science, and improving the publishing ecosystem, I'm hopeful that we'll have opportunities to build these service connections soon. + +I'd love to hear from the more "formal" side of scholarly publishing to understand where these ideas have holes or significant new development. I'd also love feedback on sustainability models to ensure these services can be relied on as part of the publishing ecosystem. In the meantime, hopefully these ideas serve as an inspiration for what is possible, and where we might be heading with 2i2c's service and the broader Jupyter ecosystem. \ No newline at end of file From 00ea50a8ebd660b54606cf05b3e5a33df816fcf4 Mon Sep 17 00:00:00 2001 From: choldgraf Date: Mon, 13 Jan 2025 14:01:31 -0800 Subject: [PATCH 3/3] Update Binder publishing post with new ideas and figures --- .../images/create.png | Bin 0 -> 92076 bytes .../images/discover.png | Bin 328471 -> 50200 bytes .../images/elements.png | Bin 0 -> 58822 bytes .../images/interact.png | Bin 0 -> 102937 bytes .../images/join.png | Bin 0 -> 48556 bytes .../images/learn.png | Bin 0 -> 52905 bytes .../images/lifecycle.png | Bin 0 -> 44458 bytes .../images/publish.png | Bin 328078 -> 56981 bytes .../images/reproduce.png | Bin 227316 -> 58089 bytes .../images/share.png | Bin 245488 -> 103917 bytes .../images/sustain.png | Bin 0 -> 79605 bytes .../frictionless-reproducibility/index.md | 120 ++++++++++++++---- 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@@ -89,9 +160,12 @@ A demo of [interactive documents with Thebe](https://readthedocs.org/thebe/en/la
-## How to sustain these services? -This brings up an important question: how would you sustain services like these? [mybinder.org](https://mybinder.org) is an entirely free service both to build and to use environments. This is massively accessible, but is not scalable nor sustainable, and doesn't provide rich computation needed for more complex analyses. Would community stakeholders pay for privileged access to BinderHubs that could reproduce and share their computational narratives? Would publishers be willing to pay a percentage of the cloud and management costs associated with reproduction? +## How could we sustain external sharing and publishing? + +This brings up an important question: how would you sustain services like these? [mybinder.org](https://mybinder.org) is an entirely free service both to build and to use environments. This is massively accessible, but is not scalable nor sustainable, and doesn't provide rich computation needed for more complex analyses. Would community stakeholders pay for privileged access to BinderHubs that could reproduce and share their computational narratives? Would publishers be willing to pay a percentage of the cloud and management costs associated with reproduction? Could we use this to sustain a larger public service like mybinder.org? + +{{< figure src="images/sustain.png" title="A theoretical sustainability model where communities pay for the computation that powers the reproducibility of their publications, and contribute a small % of their costs towards mybinder.org for the broader use of the service." >}} One thing seems clear - there is an imperative to make reproducibility and interaction frictionless. This is both to ensure the scientific integrity of the work being done, and also to make computational ideas more accessible to the world. Technologies and service partnerships like these can help ensure the broader community's right to reproduce the work of others.

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a/content/blog/2025/frictionless-reproducibility/index.md b/content/blog/2025/frictionless-reproducibility/index.md index a681b7ce..217f59a5 100644 --- a/content/blog/2025/frictionless-reproducibility/index.md +++ b/content/blog/2025/frictionless-reproducibility/index.md @@ -17,40 +17,111 @@ title: Towards frictionless, sharable, and sustainable reproducibility with Bind } -2i2c has been experimenting with empowering communities to share their work with one another. This post is an exploration of the basic workflow we're interested in, how it could be enabled with open technology in the Jupyter ecosystem, how it might fit in with the broader ecosystem of services around scientific communication, and how we might try to sustain it. +Over the years, 2i2c has managed infrastructure for more than 100 communities in research and education. Over this time, we've seen patterns emerge in how communities attract new members, collaborate with them, and create new knowledge. This post is a reflection on this process, where 2i2c has supported it historically, and where we think we can enhance our service to better-support this workflow. -## The core pieces of data-driven discovery +## Elements of data-driven discovery -For any individual to create and share knowledge with data, they need access to a few key resources: +For any individual to create and share knowledge with data, they need access to a few key resources[^1]: - **Data** is the raw material that drives computational ideas. - **Computation** provides the mechanism for manipulating data with code. -- **Software** provides tools that leverage data and computation for specific outcomes. -- **Code** orchestrates software and computation to analyze and explore data. -- **Narratives** allow all of the above to be communicated in a compelling and understandable way. -- **Interfaces** bring together all of the above to facilitate workflows in data-driven discovery and sharing. +- **Tools** domain-specific software that leverage data and computation for specific outcomes. -**Jupyter**'s goal is to weave all of the above together with an ecosystem of tools that are modular, flexible, and interoperable. **2i2c**'s [community hub platform](/platform) gives community leaders the ability to integrate and customize all of these resources to be useful for communities in research and education. This usually comes in the form of a **Jupyter Book** to teach communtiy workflows to users, with connections to a **JupyterHub** that provides the data, compute, and software tools needed for data-driven discovery. +Users leverage these resources by creating two types of content: -{{< figure src="images/discover.png" title="2i2c community hubs use Jupyter technology to help members learn community workflows, and access all the resources needed for a community's data-driven discovery.">}} +[^1]: For a more in-depth discussion of the relationship between tools, ideas, data, and computation, [Granger & Perez, 2021](https://ieeexplore.ieee.org/document/9387490). -## Sharing ideas means sharing computation to reproduce them +- **Code** operates these resources in order to run analyses, generate visualizations and reports, etc. +- **Narratives** contextualize the work of code to tell a story. +- **Computational Narratives** are a special-case of code and narratives that recognize the unique interplay between computation and scientific ideas for data-driven discovery. + +Finally, **interfaces** allow users to manage all of the above to facilitate workflows in data-driven discovery and sharing. + +{{< figure src="images/elements.png" title="Key elements needed for data-driven discovery include data, computation, tools, code, narratives, and interfaces that bring them all together." >}} + +**Jupyter**'s goal is to build an ecosystem of tools and services that are modular, flexible, and interoperable, with a focus on interactive, dynamic, human-in-the-loop workflows. **2i2c**'s [hub platform](/platform) provides communities with their own community hub in the cloud to customize for their own workflows and to make them accessible to whomever they wish in order to create and share knowledge. + +## The knowledge creation cycle requires building connections inside and outside of communities + +Patterns emerge when you work with many different communities. Some grow and generate ideas more readily, while others do not. Why is this? + +One consistent theme is that **successful communities make it easy to discover, co-create, and share both inside and outside of a community.**. When done well, this forms a cycle where new community members can quickly discover a community's ideas, learn its workflows, access its infrastructure, create their own knowledge, and share it with others. + +{{< figure src="images/lifecycle.png" title="The community knowledge creation lifecycle involves a cycle of learning and community growth, followed by a cycle of knowledge creation and sharing that is then discovered by new community members." >}} + +This isn't a complete or universal truth for how communities create and share knowledge, but we find it to be useful in thinking about how organizations like 2i2c can support communities along the way. There are some ways that 2i2c already facilitates this cycle, and others where we'd like to get better. The rest of this post explores each stage and where we think we can be more valuable. + +### Discover: Making community impact accessible and interesting + +The first step for most users is to learn about a community's big ideas and thematic areas. This is a crucial part of a community's ability to communicate the impact it is trying to have, and to share what it has accomplished so far. + +{{< figure src="images/discover.png" title="Landing pages are a key way that communities signal the kinds of work they do and why it is valuable." >}} + +2i2c's communities have facilitated the discovery of their work in a few ways: + +1. **Landing pages**. Usually the first thing that newcomers see when they are trying to understand what a community does. For example, [here's the Cryocloud landing page](https://cryointhecloud.com/). +2. **Galleries**. Provide a way for community members to share their analyses in a discoverable way, with frictionless connections to their hub to allow others to quickly explore and iterate on their work. For example, [the Pangeo gallery](https://gallery.pangeo.io/) allowed users to submit notebooks that would be automatically executed and built into a page on the Pangeo website. +3. **Blogs**. Allow communities to informally share their ideas and updates. We'll discuss this in the "Share" section below. + +**How we'd like to improve discovery**. Historically, communities have used a variety of tools for each of these workflows. For example, [Hugo](https://gohugo.io/) for landing pages and custom community code for galleries. We'd like to unify each of these workflows natively with [Jupyter Book 2](https://next.jupyterbook.org), which is built on the [MyST Document Engine](https://mystmd.org). This will require adding more functionality to [the MyST engine](https://mystmd.org) to allow for expressive (but still lightweight) interfaces like you'd find on an organization's webpage. + +## Learn: Showing the core tools and practices behind a community's work + +The next step in a newcomer's journey is to learn about the tools, practices, and workflows that a community hub enables. This is crucial for learning what kinds of skills and resources are needed to ask the questions that are of interest to a community, and how to learn them. For example, the [Project Pythia cookbooks](https://cookbooks.projectpythia.org/) provide an overview of core workflows in geospatial anlaytics, and are powered by the Pythia hub infrastructure. + +{{< figure src="images/learn.png" title="Community documentation provides an accessible way for newcomers to learn what kinds of questions the community asks, how to ask them with code, and how to learn more." >}} + +This is usually managed through **examples and tutorials** in the form of **computational narratives**. The two most common frameworks for documenting with computational narratives are [Jupyter Book](https://jupyterbook.org/) or [Quarto](https://quarto.org/). + +**How we'd like to improve learning**. Our priorites over the past several months have been to [prepare Jupyter Book 2 for launch](https://next.jupyterbook.org) so that communities can leverage the [new MyST Document Engine](https://mystmd.org) to build their documentation. Here's the [Jupyter Book 2 priorities board](https://github.com/orgs/jupyter-book/projects/1), which is both our priorities for Jupyter Book 2, and the community's priorities in case you'd like to contribute. Our hope is to **unify landing pages, galleries, blogs, and documentation with a single community site powered by Jupyter Book 2**. + +## Interact: Giving users all the tools they need to perform community workflows + +Once a new member learns about community workflows, they need a space to dig more deeply into problems, sharpen their skills, and answer new questions with data. To do so, they need access to key data, computation, and tools that help them get started. Communities use [a community hub](https://jupyterhub.readthedocs.io) to provide all of these resources, and community administrators can give access to a hub to let a newcomer learn more deeply. + +{{< figure src="images/interact.png" title="Allowing readers to directly interact with a community's resources removes the friction to learning these workflows, building on top of them, and growing their connection with a community." >}} + +We encourage communities to configure their Jupyter Books to _directly point users to their JupyterHub_, allowing them to pull book content into their own interactive session running in the cloud. This provides a frictionless way to turn _readers_ into _creators_ that can start to generate their own knowledge on a hub. + +**Where we'd like to improve the transition from learning to interactivity**. Historically, 2i2c hubs and community documentation have not been strictly connected. Some communities have done so on their own (e.g., by using JupyterHub launch buttons to point provide links to their community JupyterHub in a Jupyter Book), but we'd like to standardize and scale this practice by providing out-of-the-box configuration for new community hubs. We'd also like to make it easier for communities to define environments for their members so that they have fast access to the right tools to get started. + +### Join: Converting community learners into community practitioners + +Once a community member has participated in the community for long enough, they pass some threshold for "membership" and are given access to a more complete set of resources needed for community workflows. This might mean allowing them to spin up complex computing resources like GPUs or [Dask Gateway clusters](https://gateway.dask.org/), or giving them data resources like more local storage or [cloud scratch buckets](https://infrastructure.2i2c.org/howto/features/buckets/). At this point, a newcomer has been converted into a "member". + +{{< figure src="images/join.png" title="Community hubs can extend access to new users as they wish, transforming learnings into community members. All community members get access to the same tools, data, and computation to create and share community knowledge." >}} + +**How we'd like to improve membership**. Historically, 2i2c has provided a single set of hub resources to all of its users. We'd like to enable workflows that allow community leaders to define different tiers of access that map onto community member archetypes (e.g., newcomer, early member, member, expert, etc). This would allow communities to match the right resources to the level of trust they have in a given user to avoid misuse or runaway costs. + +### Create: Providing community members the tools needed to ask new questions + +Members of a community must build on top of the standardized workflows that are described in community documentation, and leverage a combination of data, computation, and tooling to answer new questions in their domain. 2i2c's hubs are designed to provide community members all of the resources needed to do so. + +{{< figure src="images/create.png" title="By having access to all the tools needed for community workflows, members can ask their own questions and create new ideas and insights." >}} + +**How we'd like to improve knowledge creation with a community hub**. Many of 2i2c's communities spend most of their time in this stage of knowledge creation, and the tools and workflows they use here are driven by communities themselves. The main areas we'd like to improve here are with service integrations for organizations and services that align with the values of open science. For instance, [data management services like Source Cooperative](https://source.coop/). + +### Share: Allowing community members to easily share computational narratives When a community member discovers something new, they want to share it with others in their community. This is an ongoing process where community members are giving and receiving information to iteratively drive their exploration and development. -These ideas are driven by data and computation, and so it is critical to **share the resources needed to reproduce the computation**. This has two benefits: +These ideas are driven by data and computation, and so it is critical to **share the resources needed to reproduce the computation**. This has three benefits: 1. It allows others to **validate the core claims** by inspecting code and reproducing the results. 2. It allows others to **learn more effectively** by providing a more rich, interactive space to engage with the ideas. 3. It allows others to **build on top of work more easily** by providing an interactive starting point that lets readers get further with new ideas. -We can leverage cloud infrastructure to facilitate this process. At a basic level, community hubs are a good way to standardize the environment so that content produced in one user's session can be easily reproduced by another user. [nbgitpuller](https://nbgitpuller.readthedocs.io/en/latest/) is the most common tool for distributing content within a JupyterHub. As long as the content was made on a JupyterHub, and shared with another user on the same hub, they'll be able to get their own copy and interact with it. +Community hubs leverage cloud infrastructure to facilitate this process. They standardize the environment so that content produced in one user's session can be easily reproduced by another user. [nbgitpuller](https://nbgitpuller.readthedocs.io/en/latest/) is the most common tool for distributing content within a JupyterHub. As long as the content was made on a JupyterHub, and shared with another user on the same hub, they'll be able to get their own copy and interact with it. {{< figure src="images/share.png" title="The simplest sharing workflow uses nbgitpuller to distribute content via links that community members click. Doing so will give them their own copy of the content running in the hub's environment.">}} -## Sharing computational environments means being able to (re)build them +{{< figure src="images/share.png" title="Because all community members have access to the same resources, it is easy to share the computational narratives." >}} + +**Where we'd like to improve sharing**. We'd like to streamline a user's ability to share content with services like nbgitpuller, and unify the experience with more complex content sharing services like [Binder](https://mybinder.org). For example, we'd like to build [blogging functionality into Jupyter Book 2](https://github.com/jupyter-book/mystmd/issues/840), and make it easy to generate sharable links directly from within a JupyterHub. + +### Reproduce: Bundling an environment with a sharable computational narrative -However, many authors need more control over the environment needed to reproduce a computation. For example, they might need a _specific version_ of a software package, or they may need a non-standard library that isn't available on their community hub's base environment. How can they share their computational narratives in a way that _bundles the environment_ with the content? +As an author's ideas are refined, it often comes with more specific needs for the environment and resources needed to reproduce their idea. For example, they might need a _specific version_ of a software package, or they may need a non-standard library that isn't available on their community hub's base environment. How can they share their computational narratives in a way that _bundles the environment_ with the content? This is what [**the Binder Project**](https://mybinder.org) was built for. It provides a tool called [BinderHub](https://binderhub.readthedocs.io/en/latest/), which is an **engine for sharable, reproducible computational environments**. It allows an author to package up their computational narratives and code, and share access to a cloud environment that allows readers to reproduce and interact with the computation. In 2024 our team prototyped how to [connect BinderHub services with a JupyterHub](../../2024/jupyterhub-binderhub-gesis/) and how to [deploy BinderHub services alongside each of our community hubs](../../2024/nasa-ephemeral-hubs/). @@ -58,27 +129,27 @@ Doing so would allow community members to **build an environment needed to repro {{< figure src="images/reproduce.png" title="BinderHub allows an author to package narrative, code, computational resources, and a software environment into a link that others can use to access a fully reproducible environment for their ideas.">}} -## Could we empower communities to bring their own reproducibility when publishing? +**Where we'd like to improve reproducibility**. We'd like to build on last year's work to make streamline the experience of generating sharable Binder links for a user's work so that others in a community can reproduce their results with custom environments rather than the standardized community environments. We'd also like to make it possible for members to **share external Binder links** so that non-members can reproduce and interact with their ideas, in a way that minimizes the risk of abuse or runaway cloud costs. + +### Publish: Connecting computation with the broader publishing ecosytem When community members have a polished idea to share more formally, they want to **publish** rather than just **share**. Publishing is more structured, invites particular kinds of feedback, and requires more quality assurance. There's a huge ecosystem of publishers and services that support formal publishing, and they ensure things like discoverability, long-term archivability, versioning, peer review, DOI referencing, etc. However, publishers don't have access to the core infrastructure used to make the original discovery, nor do they have the expertise to manage this kind of infrastructure. But 2i2c's community members already have a reproducible environment where they've been doing their discovery. Could we leverage this infrastructure for publishing? -2i2c has an opportunity to empower publishers to reproduce computational ideas by **allowing authors to bring their reproducible environments with them**. By leveraging the same core technologies, authors can re-use their hub infrastructure to provide the resources needed for reproducibility. +**How we'd like to improve connections with the publishing ecosystem**. 2i2c has an opportunity to empower publishers to reproduce computational ideas by **allowing authors to bring their reproducible environments with them**. By leveraging the same core technologies, authors can re-use their hub infrastructure to provide the resources needed for reproducibility. This would require treating a community's hub as an **external service for reproducibility** that could be leveraged by one of the many publishing platforms out there. For example: - [Curvenote](https://curvenote.com) already builds heavily on the Jupyter and MyST ecosystems and could use a community's hub to provide interactivity to a Curvenote document. -- Authors could publish pre-prints on [Biorxiv](https://www.biorxiv.org/) that included Binder links back to their community hub [Binder links in arxiv has already been piloted with the Loren Frank lab and HHMI](../../2024/hhmi-spyglass-mysql/)). +- Authors could publish pre-prints on [Biorxiv](https://www.biorxiv.org/) that included Binder links back to their community hub [Binder links in arxiv has already been piloted with the Loren Frank lab and HHMI](../../2024/hhmi-spyglass-mysql/). - Larger publishers like [PLOS](https://plos.org/) could allow authors to provide metadata about BinderHubs that can reproduce their content, and weave this into their user interface for displaying publications. -These are all _possible_ outcomes that haven't been realized, but I don't think they'd be that far off. - -{{< figure src="images/publish.png" title="We could re-use the same tools for data-driven discovery to facilitate the reproducibility of computational narratives and publications. The same core infrastructure could be re-used to provide comptutational environments that are re-used in a variety of publishing tools and services. (note this is all hypothetical, no-such service integrations yet exist, and these are just a sample of relevant publishing organizations in this space)">}} +{{< figure src="images/publish.png" title="Making a claim publicly requires a new set of workflows around facilitating review, archiving and versioning, discovery, etc. This is served by an ecosystem of publishers that could re-use the reproducibility services of a community's hub. (note these are hypothetical for now, but we think publishing platforms like these are a good starting point!)" >}} The best part is that the BinderHub provider could be anybody - if a community had their own capacity to manage Binder infrastructure they could do so, or they could use a service provider like [2i2c](https://2i2c.org) to manage it for them. In this way, a community could leverage its pre-existing infrastructure to provide reproducibility for published scholarly work. -There's a lot that we could do to remove the friction from workflows like the above. It should be **as easy as possible for publishing stakeholders to interface with a community's BinderHub** and integrate it into publishing services and products. This is especially true for more modern publishing platforms like [Curvenote](https://curvenote.com) that are already built on the Jupyter and MyST ecosystems. The more integrated a publisher's infrastructure is with modern computational technology, the more power they should be able to get out of an author's reproducible environment. +It should be **as easy as possible for publishing stakeholders to interface with a community's BinderHub** and integrate it into publishing services and products. This is especially true for more modern publishing platforms like [Curvenote](https://curvenote.com) that are already built on the Jupyter and MyST ecosystems. The more integrated a publisher's infrastructure is with modern computational technology, the more power they should be able to get out of an author's reproducible environment.

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zV47mjM?6E+Pbj~jV2p2RUA=rAif(cxr>54I5(B$~zR8veE`8l-gQ8Rc1FC^dM}%ls z2g()cpDrZ}fyP(E_Q)w>Q&3~Z=)aBt`&sh4yZf{IPcEWm0pF)m&w|ZOho@m^(&z)o z)Ra3oqgu#w*Op$=lQVzrsjm*Nji^;6LnR55*j4?l33H9*W*sS?XW&K#G}om*Re=!e z4-wmzLPs~}cNV)`c93IVk_$3plva)t9_g1&q>FmB!G{M;!TAH(bL)!3(=vdPO*H0X z#0+m@A`1Z>U7ZK#H@JW{i$Y1pJVp&yd3kxe#2t>0kFT%Yt+z5~xy8zA0g7briruSd zBfE-@xNt?JK%_@eVSD9Ig8l|yRbHUAAS_uxisJ8Y{)vpU~ujCd| + figure iframe { + margin-left: auto; + margin-right: auto; + } + figure { + display: flex; + flex-direction: column; + margin-left: auto; + margin-right: auto; + max-width: 500px; + } + + +2i2c has been experimenting with ways to enable communities to share their work with one another more effectively. This post is an exploration of the basic workflow we're interested in, how it could be enabled with open technology in the Jupyter ecosystem, how it might fit in with the broader ecosystem of services around scientific communication, and how we might try to sustain it. + +## The core pieces of data-driven discovery + +For any individual to ask questions and create knowledge with data, they need access to a few key resources in order to do their work. + +- **Data** is the raw material that drives computational ideas. +- **Computation** provides the core hardware for manipulating data with code. +- **Software** the tools that code uses to leverage data and computation for specific outcomes. +- **Code** utilizes software and computation to reproduce a computation. +- **Narrative** allows all of the above to be communicated in a compelling and understandable way. + +**Jupyter**'s goal is to weave all of the above together with an ecosystem of tools that are modular, flexible, and interoperable. **2i2c**'s [community hub platform](/platform) gives community leaders the ability to integrate and customize all of these resources to be useful for particular communities in research and education. This usually comes in the form of a **Jupyter Book** to teach communtiy workflows to users, with connections to a **JupyterHub** that provides the data, compute, and software tools needed for data-driven discovery. + +{{< figure src="images/discover.png" title="2i2c community hubs use Jupyter technology to help members learn community workflows, and access all the resources needed for a community's data-driven discovery.">}} + +## Sharing ideas means sharing computation to reproduce them + +When a community member discovers something new, they want to share it with others in their community. This is usually an ongoing process by which community members are giving information and receiving information in return to iteratively drive their exploration and development. + +These ideas are driven by data and computation, and so it is critical to **share the resources needed to reproduce the computation** when sharing new ideas with community members. This allows others to reproduce the main ideas in order to validate the core claims, as well as interact with the code and software to better understand and build upon the ideas. + +We can leverage cloud infrastructure to facilitate this process. At a basic level, community hubs are a good way to standardize the software and resource environment so that content produced in one user's session can be easily reproduced by another user. [nbgitpuller](https://nbgitpuller.readthedocs.io/en/latest/) is the most common tool for distributing content within a JupyterHub. As long as the content was made on a JupyterHub, and shared with another user on the same hub, they'll be able to get their own copy and interact with it. + +{{< figure src="images/share.png" title="The simplest sharing workflow uses nbgitpuller to distribute content via links that community members click. Doing so will give them their own copy of the content running in the hub's environment.">}} + +## Sharing computational environments means being able to (re)build them + +However, many authors need more control over the environment needed to reproduce a computation. For example, they might need a _specific version_ of a software package, or they may need a non-standard library that isn't available on their community hub's base environment. How can they share their computational narratives in a way that _bundles the environment_ with the content? + +This is what [**the Binder Project**](https://mybinder.org) was built for. It provides a tool called [BinderHub](https://binderhub.readthedocs.io/en/latest/), which is an **engine for sharable, reproducible computational environments**. It allows an author to package up their computational narratives and code, and share access to a cloud environment that allows readers to reproduce and interact with the computation. +Earlier this year our team has been prototyping how to [connect BinderHub services with a JupyterHub](../../2024/jupyterhub-binderhub-gesis/) and how to [deploy BinderHub services alongside each of our community hubs](../../2024/nasa-ephemeral-hubs/). +Doing so would allow community members to **build an environment needed to reproduce their computation and share it via their hub**. + +{{< figure src="images/reproduce.png" title="BinderHub allows an author to package narrative, code, computational resources, and a software environment into a link that others can use to access a fully reproducible environment for their ideas.">}} + +## Empowering communities to bring their own reproducibility when publishing + +When community members have a big idea that they'd like to share more formally, want to **publish** rather than just **share**. Publishing is more structured, invites particular kinds of feedback, and requires more quality assurance. There's a huge ecosystem of publishers and services that support formal publishing, and they ensure things like discoverability, long-term archivability, versioning, peer review, DOI referencing, etc. + +However, publishers don't have access to the core infrastructure used to make the original discovery, nor do they have the expertise to manage this kind of infrastructure. But 2i2c's community members already have a reproducible environment where they've been doing their discovery. Could we leverage this infrastructure for publishing? + +2i2c has an opportunity to empower publishers to reproduce computational ideas by **allowing authors to bring their reproducible environments with them**. By leveraging the same core technologies, authors can re-use their hub infrastructure to provide the resources needed for reproducibility. + +This would require treating a community's hub as an **external service for reproducibility** that could be leveraged by one of the many publishing platforms out there. For example, [Curvenote](https://curvenote.com) already builds heavily on the Jupyter and MyST ecosystems and could use a community's hub to provide interactivity to a Curvenote document. Authors could publish pre-prints on [Biorxiv](https://www.biorxiv.org/) that included Binder links back to their community hub [Binder links in arxiv has already been piloted with the Loren Frank lab and HHMI](../../2024/hhmi-spyglass-mysql/)). Larger publishers like [PLOS](https://plos.org/) could allow authors to provide metadata about BinderHubs that can reproduce their content, and weave this into their user interface for displaying publications. These are all _possible_ outcomes that haven't been realized, but I don't think they'd be that far off. + +{{< figure src="images/publish.png" title="We could re-use the same tools for data-driven discovery to facilitate the reproducibility of computational narratives and publications. The same core infrastructure could be re-used to provide comptutational environments that are re-used in a variety of publishing tools and services. (note this is all hypothetical, no-such service integrations yet exist, and these are just a sample of relevant publishing organizations in this space)">}} + +The best part is that the BinderHub provider could be anybody - if a community had their own capacity to manage Binder infrastructure they could do so, or they could use a service provider like [2i2c](https://2i2c.org) to manage it for them. In this way, a community could leverage its pre-existing infrastructure to provide reproducibility for published scholarly work. + +There's a lot that we could do to remove the friction from workflows like the above. It should be **as easy as possible for publishing stakeholders to interface with a community's BinderHub** and integrate it into publishing services and products. This is especially true for more modern publishing platforms like [Curvenote](https://curvenote.com) that are already built on the Jupyter and MyST ecosystems. The more integrated a publisher's infrastructure is with modern computational technology, the more power they should be able to get out of an author's reproducible environment. + +