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The post CC Licenses, Data Governance, and the African Context: Conversations and Perspectives appeared first on Creative Commons.
]]>What started as an organic exchange in various spaces has revealed something larger: a strong appetite to move these conversations into the open. At stake are not only questions about CC licenses but deeper issues of data sovereignty, equity, governance, and power in global knowledge systems. This blog post summarizes the themes emerging from those discussions and asks a broader question: how must “open” evolve to remain just, relevant, and community-centered?
A Shift
CC licenses were designed to reduce friction in sharing knowledge. For many years, CC’s focus has been on legality, access, and reuse. By all accounts, we’ve been successful in meeting these goals and objectives. But in today’s digital and AI-driven landscape—particularly in the Global South—that framing is no longer sufficient.
Across the discussions, participants raised concerns that CC licenses, especially CC BY and CC0, are sometimes (inadvertently) enabling extractive practices. African language datasets, cultural knowledge, and community-generated data are increasingly being reused in ways that benefit global institutions and corporations, while the originating communities see little agency, recognition, or return. This governance and equity issue rightly challenges some long-held assumptions about openness. When data producers are required to share their data with a specific permissive license, it introduces a potential conflict between the requirement to share and whether that specific data should be shared at all.
Key Challenges Identified
Colleagues highlighted the following challenges and concerns that are arising in their context and within their communities:
- A perception gap around extractive use
CC licenses are often viewed as neutral tools, but in practice they can amplify existing power imbalances (as we know, infrastructure is not neutral!). For example, marginalized language and data communities may lack the leverage to negotiate how open resources are reused. Yes, open data can lead to communities having better access to information about where they live like air and water quality, but that same data can be used by large corporate entities to make decisions on where, for example, to build a new factory.
- Equity blind spots in traditional openness
In the context of the CC licenses, openness has historically been framed as a legal condition answering the question: can something be reused, modified, or shared? But we know that openness is much more than a set of legal tools; it is a set of values, a way of belonging, a wish for a better future. As large AI models continue to train on the billions of works and datasets made available via the CC licenses in the commons without giving back and while hoarding power, communities are responding by asking for openness that also accounts for agency, consent, reciprocity, and governance.
Data Governance and the Limits of One-Size-Fits-All Licensing
One of the most challenging threads in these discussions centers on data governance, particularly for African languages and community-curated datasets.
Several tensions stand out:
- Funders often mandate CC BY or CC0 for publicly funded research, leaving little room for community-specific governance models or the potential for a powerful interplay between CC licenses and community-created fit-for-purpose open licenses like NOODL.
- CC licenses, by design, cannot prevent extractive reuse once content is made open.
- Local languages, cultural data, and community knowledge are not interchangeable with generic datasets—but licensing frameworks often treat them as such.
Openness is not binary, and context matters. Standardization matters and can amplify efforts to make knowledge accessible but only works when paired with governance. CC has worked with major funders of research to harmonize CC BY or CC0 across funders, but this work is built around the assumption that the license terms are adequate for all data and data distribution contexts. When there is no governance, what is the cost of harmonization? This community of researchers are asking whether CC can use its influence not only to promote CC licenses and legal tools but also to validate and support alternative, community-driven approaches where CC licenses fall short.
Open resources do not exist outside systems of power. Historically, openness has favored those with infrastructure, capital, and technical capacity—often institutions in the Global North. Simply making something open does not make it equitable, accessible, or just.
If the idealized version of openness has not delivered on its promise, is it time for CC to redefine it? What role can CC play in holding space, convening dissent, and legitimizing plural approaches to openness?
Where Do We Go From Here?
These conversations are not about arriving at neat conclusions. In fact, the goal is the opposite: to resist premature certainty and instead listen, reflect, and adapt.
For us as a community, this may mean:
- Being clearer about where CC licenses work and, just as importantly, where they don’t
- Acknowledging the limits of license-centric thinking
- Using the CC platform to amplify community-led definitions of openness
- Accepting that “the new open” may be more complex, more contextual, and intentionally less frictionless
The future of open knowledge depends on trust, dialogue, and shared governance.
A special thank you to Vukosi Marivate, University of Pretoria; Chijioke Okorie, Data Science Law Lab, University of Pretoria; and Melissa Omino, CIPIT, Strathmore University; as well as members of the CC board of directors for convening these dialogues and sharing their perspectives with us at Creative Commons.
We want to know: Does this resonate with you? What are you seeing within your own context and community? We plan on continuing this dialogue throughout 2026 as we celebrate our 25th anniversary. What better time to reflect on our past contributions and challenge our thinking about the future.
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CC Signals: A Refresher
It is within this environment that we continue to develop CC signals.
We introduced the CC signals concept last June during a live webinar, and further explored the motivation behind this work in our report From Human Content to Machine Data. We also shared the outcomes of our open feedback period following the CC signals kickoff. Since then, we’ve been experimenting in partnership with values-aligned stakeholders and developing pilot projects to test ideas raised by the community.
The goal of CC signals is to help creators and custodians of collections express how they want their content or data to be used in AI development in ways that uphold reciprocity, recognition, and sustainability. Today’s AI systems depend on vast amounts of human-created content, often collected without the awareness or involvement of those who made it. This has concentrated power and undermined trust in the social contract of the commons.
CC signals responds by promoting community agency while preserving Creative Commons’ core commitment to access and openness. Ultimately, through CC signals and other interventions that infuse concepts of reciprocity in standards and practices, we envision an open internet where participation is equitable, creators are respected, and innovation advances the commons—not unchecked extraction.
CC Signals: Where Are We Now?
CC signals is an evolving, values-driven framework—currently being tested through a series of pilot efforts. Our strategy is to explore modular approaches across legal, technical, and normative dimensions to encourage responsible AI development practices. This allows CC signals to adapt as norms, technologies, and standards continue to evolve.
At present, two key implementations are underway:
- Implementing CC signals on Mozilla Data Collective: We are working in partnership with our friends at Mozilla, looking at how implementation of CC signals would work on the Mozilla Data Collective platform, which is purpose-built to enable ethical dataset sharing and fair value exchange. Our plan is to test various ways of incorporating some measure of legal enforceability into CC signals. We also hope to use this as an opportunity to test which CC signal elements are most popular and impactful, and which ones have the biggest impact on AI developer behavior.
- Adapting the CC signals contribution element in the RSL framework: Using the framework of the ecosystem contribution signal element, we are working with the RSL Collective to embed the notion of reciprocal contribution into this evolving standard. As a platform that will let rightsholders set machine-readable licensing terms for their content, integrating the contribution element ensures that standards such as RSL provide mechanisms for AI developers to contribute back to the commons at the collective or community level, not simply a one-to-one payment.
Beyond CC signals itself, we are also exploring whether updates to CC’s license infrastructure could further strengthen and support the commons in the age of AI.
Looking Ahead
We are actively seeking expressions of interest from dataset custodians who are interested in participating in the Mozilla Data Collective pilot project. If that’s you, we’d love to hear from you.
We are also exploring sector-specific CC signals integrations, particularly within cultural heritage and science.
Ultimately, CC signals are incarnations of what we want to see in the world—more recognition for authorship, sustainable commons communities, mutual commitments to shared resources. We are focused on building a vocabulary and vision for the values we think a successful commons needs to thrive.
This work is resource-intensive. We need your support to ensure this work continues to be led by public interest organizations. Please donate today.
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]]>The post Integrating Choices in Open Standards: CC Signals and the RSL Standard appeared first on Creative Commons.
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Strange Bedfellows
That brings us to Really Simple Licensing (RSL). Publicly launched in September 2025, today the RSL Collective releases the RSL 1.0 standard. RSL is an open standard that lets publishers define machine-readable licensing terms for their content, including attribution, pay per crawl, and pay per inference compensation. This is an example of emerging technical systems used by websites to automate compensation for when their digital content—such as text, images, and structured data—is accessed by machines. We’ve been referring to these systems as pay-to-crawl. Think of it as the web’s attempt to answer the question: what tools are needed when bots become the biggest readers? If you are new to the concept, we recently published an issue brief that breaks it down in plain language.
On the surface, Creative Commons and pay-to-crawl systems are strange bedfellows. We have always been a champion of the open web and are concerned about a world where knowledge is harder to access. But we also recognize that responsible, interoperable systems can create leverage where none previously existed. Thoughtfully designed, pay-to-crawl systems may help curb extractive behavior by powerful actors while keeping the web open for everyone else.
Attribution + Compensation
In its early version 1.0 draft, RSL included attribution as one condition for machine access and reuse. From the standard:
Attribution-Only License
The publisher permits free reuse of the content on its site, provided that visible credit and a functional link to the original source are included.
This is important as one example of more choices given to web publishers beyond the binary no access or all access. The inclusion of attribution also mirrors some elements of the proposed CC signal Credit.
You must give appropriate credit based on the method, means, and context of your use.
Attribution + Reciprocity
But as the CC signals framework recognizes, attribution alone is not enough to address the very present power imbalances between AI developers and the commons. We need new tools that ensure the commons thrives and is sustained.
We believe now is the time to act to infuse concepts of reciprocity in standards that are ready for adoption. That’s why we worked with the RSL Collective ahead of the release of version 1.0 to integrate a contribution component to the standard, which is described as:
A good faith monetary or in-kind contribution that supports the development or maintenance of the assets, or the broader content ecosystem.
This is not about turning access into a tollbooth. It’s about acknowledging that extraction without reinvestment leads to collapse. There is a meaningful difference between paying a fee and giving back. One is transactional. The other is about responsibility.
When AI systems derive immense value from the digital commons, contribution isn’t compensation. It’s participation in the social contract that made that value possible in the first place.
Contribution could be in the form of:
- A donation back to a non-profit that stewards the dataset;
- Support for the broader ecosystem that sustains the work;
- Openly licensing the model, or sharing a modified dataset back to the original steward;
- Or other models we haven’t yet imagined.
A Big Step: Many More to Come
The future of the web is being negotiated right now, in standards documents, in product decisions, and in design choices that shape how power flows online. Collaboration is vital if we’re going to achieve a systems-level response to rebalance power in the digital commons.
There’s much more work to be done, particularly in developing what adherence to contribution means in different contexts. But we’re excited about where this is going.
Our door is open. We welcome ideas, critiques, and collaboration. If you have ideas, consider engaging with us on LinkedIn or joining CC’s community platform on Zulip.
Our year-end fundraising campaign is happening right now. While you are here, please consider making a donation to support this work.
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]]>The post AI and the Commons: A Reading List appeared first on Creative Commons.
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Here at CC, we have the goal of defending and sustaining the digital commons in the face of developments in artificial intelligence.
We’ve recently introduced a new framework, CC signals, to offer a new way for stewards of large collections of content to indicate their preferences for how machines (and the humans controlling them) should contribute back to the commons.
As we develop our approach, we’re taking inspiration from the work of our partners, community, and other stakeholders. We’re particularly interested in efforts to understand:
- How AI scrapers are reshaping the web
- Copyright, labor, surveillance, and resistance
- The effects of a new economy of data licensing
- Emerging ideas for more ethical AI and consensual data governance
We’re reading (a lot!) on these topics, to help ensure that CC signals become part of a diverse set of solutions for protecting the commons in the unfolding AI future. Here’s some of the writing that’s shaping our thinking:
- Cloudflare launches a marketplace that lets websites charge AI bots for scraping – Maxwell Zeff, TechCrunch https://techcrunch.com/2025/07/01/cloudflare-launches-a-marketplace-that-lets-websites-charge-ai-bots-for-scraping/
- Legal frictions for data openness: Reflections from a case-study on re-use of the open web for AI training – Ramya Chandrasekhar https://hal.science/hal-05009616v1
- AI Training, the Licensing Mirage, and Effective Alternatives to Support Creative Workers – Derek Slater, Tech Policy Press https://www.techpolicy.press/ai-training-the-licensing-mirage-and-effective-alternatives-to-support-creative-workers/
- “Wait, not like that”: Free and open access in the age of generative AI – Molly White, Citation Needed https://www.citationneeded.news/free-and-open-access-in-the-age-of-generative-ai/
- Telling AI to go away (but politely) – Nick Jackson, dxw https://www.dxw.com/2025/04/telling-ai-to-go-away-but-politely/
- Can AI Be Consentful? Rethinking Permission in the Age of Synthetic Everything – Giada Pistilli, Hugging Face https://huggingface.co/blog/giadap/consentful-ai
- AI Should Help Fund Creative Labor – Mariana Mazzucato, Project Syndicate https://www.project-syndicate.org/onpoint/how-ai-profits-can-help-fund-cultural-production-by-mariana-mazzucato-and-fausto-gernone-2025-07
- Licensing, Levies, and the Limits of Copyright – Paul Keller, OpenFuture https://openfuture.eu/blog/licensing-levies-and-the-limits-of-copyright/
We’d love for you to read and learn alongside us, share your thoughts, and contribute other articles and resources to this list! Connect with us on LinkedIn, Bluesky, or Mastodon.
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]]>The post We Asked, You Answered: How Your Feedback Shapes CC Signals appeared first on Creative Commons.
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In June we kicked off a public feedback period on our proposal for CC signals. CC signals is a preference signals framework designed to sustain the commons and ensure the continued sharing of knowledge in the age of AI.
The goal is to give holders of large datasets a way to set criteria for how their data may be used within AI training models. To give an example, a dataset holder may wish to require that any AI training that uses their data gives credit back to the original source (e.g. attribution), or that the resulting AI model is open. Like the CC licenses, CC signals builds on the idea of ‘some rights reserved’ and that creators and knowledge holders deserve meaningful choices in how their work is used. You can learn more on our website.
Since our kickoff event, we have been listening closely to feedback. We heard from hundreds of creators, librarians, technologists, legal experts, and open advocates. We asked for feedback and you delivered! Your voices – supportive, skeptical, frustrated, or curious – are essential in shaping how CC signals develops. We’d like to summarize what we heard and how this feedback is being incorporated and addressed.
What We Heard
Across the conversations, several themes emerged:
Concerns that CC is prioritizing AI companies over creators. A recurring concern is that CC signals seem to give legitimacy to AI training without doing enough to protect creators.
Confusion and disagreement about the CC licenses and AI training. We heard frustration that the CC licenses are not being interpreted or enforced in ways that some creators expected.
Strong calls for opt-outs. Many wondered why the draft CC signals did not include an opt-out option.
Asking politely for AI developers to give back in exchange for datasets is not enough. We heard doubts that CC signals would work in practice, given the widespread evidence of AI companies ignoring copyright, licenses, and even technical protocols like robots.txt.
Broader critique of AI’s role in society. There is a spectrum of views on AI across the CC community. Many of you stand firmly at the anti-AI end. For these voices, no technical framework, like CC signals, feels adequate without stronger laws and regulations.
We haven’t been clear on who this tool is meant to serve and the use cases it is meant to address. Naturally, the needs of an individual creator, like an artist, are quite different from those operating at an institutional or collective level. We heard loud and clear that CC signals, as currently conceived, does not meet the diverse needs of individual creators.
Requests for clarity. Many asked for more details about implementation and interoperability, including our long-term vision for CC signals as part of our broader mission.
We understand how deeply personal these issues are for many of you, especially artists and creators who feel their work is being taken without consent and are looking for ways to fight back. That frustration is real, and we take it seriously.
What We’re Doing Next
Improving clarity around CC’s position. We know many of you are worried that CC has “taken sides” or is being influenced by AI companies. We want to be clear: the driving motivation of CC signals is to defend and sustain the commons by developing practical tools for knowledge holders. Going forward, we will aim to clarify our guiding principles and positions in ways that translate to product decisions.
Strengthening messaging and education. We are committed to expanding resources on how the CC licenses and CC signals could interact, examples of how signals could work in practice, and deeper dives into questions of copyright within the AI landscape. If you haven’t already, take a look at our legal primer on understanding the CC licenses and AI training. The better informed the CC community is about AI and the commons at large, the more effective we can be as a community to defend the commons.
Clarifying the use cases for CC signals. This phase of CC signals is designed to serve large and open dataset holders, not the individual creator. Your feedback helped us recognize that this focus was not easy to square with our decision to leverage technical protocols used by anyone with a website. As a result, the target audience for CC signals was not clear. As we decide on next steps in product development, we plan to focus on specific use cases to put our goals and objectives into practice.
Deepening global engagement and inviting stakeholders into product development. We plan to continue conversations with diverse audiences to inform the future of CC signals through an iterative process. The rest of this year will be focused on exploring and testing possible integrations of CC signals with pilot adopters. From this, we hope to extrapolate findings as we explore wider adoption of CC signals in the future.
Maintaining transparency in development. Our GitHub repository will stay open and up to date. We are creating a roadmap that will be shared publicly and will provide consistent updates (either on the blog or via a virtual town hall) on our progress. This feedback loop is not over; it will be built into how CC signals will evolve.
Looking Ahead
The future of the commons depends on tools that reflect shared values of openness, fairness, and agency. We know many of you remain skeptical.
CC signals is not final. It is an experiment in building a new layer of choice in an age where the rules are rapidly shifting. We will keep listening, adjusting, and collaborating until we arrive at something that genuinely serves the commons.
Thank you to everyone who took the time to write, question, challenge, and support us. Please stay engaged. Together, we can ensure that Creative Commons continues to stand where it always has: with the community, for the commons.
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Thanks to everyone who attended our CC signals project kickoff last week. We’re receiving plenty of feedback, and we appreciate the insights. We are listening to all of it and hope that you continue to engage with us as we seek to make this framework fit for purpose.
Some of the input focuses on the specifics of the CC signals proposal, offering constructive questions and suggesting ideas for improving CC signals in practice. The most salient type of feedback, however, is touching on something far deeper than the CC signals themselves – the fact that so much about AI seems to be happening to us all, rather than with or for us all, and that the expectations of creators and communities are at risk of being overshadowed by powerful interests.
This sentiment is not a surprise to us. We feel it, too. In fact, it is why we are doing this project. CC’s goal has always been to grow and sustain the thriving commons of knowledge and culture. We want people to be able to share with and learn from each other, without being or feeling exploited. CC signals is an extension of that mission in this evolving AI landscape.
We believe that the current practices of AI companies pose a threat to the future of the commons. Many creators and knowledge communities are feeling betrayed by how AI is being developed and deployed. The result is that people are understandably turning to enclosure. Eventually, we fear that people will no longer want to share publicly at all.
CC signals are a first step to reduce this damage by giving more agency to those who create and hold content. Unlike the CC licenses, they are explicitly designed to signal expectations even where copyright law is silent or unclear, when it does not apply, and where it varies by jurisdiction. We have listened to creators who want to share their work but also have concerns about exploitation. CC signals provide a way for creators to express those nuances. The CC signals build on top of developing standards for expressing AI usage preferences (e.g., via robots.txt). Creators who want to fully opt out of machine reuse do not need to use a CC signal. CC signals are for those who want to keep sharing, but with some terms attached.
The challenge we’re all facing in this age of AI is how to protect the integrity and vitality of the commons. The listening we’ve been doing so far, across creator communities and open knowledge networks, has led us here, to CC signals. Our shared commitment is to protect the commons so that it remains a space for human creativity, collaboration, and innovation, and to make clear our expectation that those who draw from it give something in return.
Our goal is to advocate for reciprocity while upholding our values that knowledge and creativity should not be treated as commodities.
Our goal is to find a path between a free-for-all and an internet of paywalls.
Copyright will not get us there. Nor should it. And we don’t think the boundaries of copyright tell us everything we need to know about navigating this moment. Just this week, Open Future released a report that calls for going beyond copyright in this debate, on the path to a healthy knowledge commons.
This is the beginning of the conversation, not the end. We are listening. From what we have heard, CC signals, or something like it, is the best practical mechanism to avoid the dual traps of total exploitation or total enclosure, both of which damage the commons. We have shared our current progress because we want to learn how to design it to meet your needs. We invite you to continue sharing feedback so we can shape CC signals together in a way that works for diverse communities.
In the months ahead, we’ll be providing more detail about how CC signals are developing, including key themes we are hearing, along with the questions we are exploring and our next steps.
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]]>The post Introducing CC Signals: A New Social Contract for the Age of AI appeared first on Creative Commons.
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Creative Commons (CC) today announces the public kickoff of the CC signals project, a new preference signals framework designed to increase reciprocity and sustain a creative commons in the age of AI. The development of CC signals represents a major step forward in building a more equitable, sustainable AI ecosystem rooted in shared benefits. This step is the culmination of years of consultation and analysis. As we enter this new phase of work, we are actively seeking input from the public.
As artificial intelligence (AI) transforms how knowledge is created, shared, and reused, we are at a fork in the road that will define the future of access to knowledge and shared creativity. One path leads to data extraction and the erosion of openness; the other leads to a walled-off internet guarded by paywalls. CC signals offer another way, grounded in the nuanced values of the commons expressed by the collective.
Based on the same principles that gave rise to the CC licenses and tens of billions of works openly licensed online, CC signals will allow dataset holders to signal their preferences for how their content can be reused by machines based on a set of limited but meaningful options shaped in the public interest. They are both a technical and legal tool and a social proposition: a call for a new pact between those who share data and those who use it to train AI models.
“CC signals are designed to sustain the commons in the age of AI,” said Anna Tumadóttir, CEO, Creative Commons. “Just as the CC licenses helped build the open web, we believe CC signals will help shape an open AI ecosystem grounded in reciprocity.”
CC signals recognize that change requires systems-level coordination. They are tools that will be built for machine and human readability, and are flexible across legal, technical, and normative contexts. However, at their core CC signals are anchored in mobilizing the power of the collective. While CC signals may range in enforceability, legally binding in some cases and normative in others, their application will always carry ethical weight that says we give, we take, we give again, and we are all in this together.
“If we are committed to a future where knowledge remains open, we need to collectively insist on a new kind of give-and-take,” said Sarah Hinchliff Pearson, General Counsel, Creative Commons. “A single preference, uniquely expressed, is inconsequential in the machine age. But together, we can demand a different way.”
Now Ready for Feedback
More information about CC signals and early design decisions are available on the CC website. We are committed to developing CC signals transparently and alongside our partners and community. We are actively seeking public feedback and input over the next few months as we work toward an alpha launch in November 2025.
Get Involved
Join the discussion & share your feedback
To give feedback on the current CC signals proposal, hop over to the CC signals GitHub repository. You can engage in a few ways:
- Read about the technical implementation of CC signals
- Join the discussion to share feedback about the CC signals project
- Submit an issue for any suggested direct edits
Attend a CC signals town hall
We invite our community to join us for a brief explanation of the CC signals framework, and then we will open the floor to you to share feedback and ask questions.
Tuesday, July 15
6–7 PM UTC
Register here.
Tuesday, July 29
1–2 PM UTC
Register here.
Friday, Aug 15
3–4 PM UTC
Register here.
Support the movement
CC is a nonprofit. Help us build CC signals with a donation.
The age of AI demands new tools, new norms, and new forms of cooperation. With CC signals, we’re building a future where shared knowledge continues to thrive. Join us.
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]]>The post CC Learning and Training: 2024 Year in Review appeared first on Creative Commons.
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Creative Commons training efforts strengthen our mission to “empower individuals and communities around the world through technical, legal, and policy solutions that enable the sharing of education, culture, and science in the public interest.” In 2024, our Learning & Training team focused on: 1) piloting new partnerships, 2) expanding training options, and 3) reaching new communities. We are pleased that our 2024 training and engagement efforts supported national governments, universities, secondary education institutions, NGOs, librarians, cultural heritage professionals, and web developers spanning almost every continent. See below for highlights, and contact us if you would like to collaborate in 2025.
- Piloting new partnerships:
- CC worked in partnership with the University of Nebraska at Omaha on “Introduction to Open Educational Resources,” our first professional development microcredential course.
- We partnered with Library Juice Academy to create an additional microcredential course launching in January 2025. Registration is currently open until all seats are filled.
- Similar to our partnership with the US Midwest Higher Education Compact (MHEC), we partnered with BCcampus to support subsidized CC Certificate training for additional higher education institutions.
- Expanding training options:
- CC provided increased support for our CC Certificate program and its 1,892 alumni across 68 countries. The CC Certificate program trains advocates, educators, librarians, and cultural heritage professionals in copyright, open licensing and open practices for advocacy. This year, CC awarded 35 scholarships for Certificate courses; hosted 24 webinars covering topics such as Digitized Representation by Heritage Institutions, Accessible OER, AI, copyright, and CC licenses; and provided multiple avenues of engagement for Certificate alumni, including both formal and informal mentorship options and a monthly newsletter.
- CC increased our customized training offerings, providing workshops, tutorials, talks and training for: the AAC&U’s Institute on OER, Africa Journals Online, CEU Press and Ukrainian Partners, California Zero Textbook Cost (ZTC) program grantees, CC Rwanda, the Connecticut Museum of Culture and History, Ghanaian secondary school educators focused on Open Education For Climate and Environmental Sustainability, ISTE Live 2024, the Missouri’s A&OER Conference pre-conference training day, OER and Social Justice for Affordable Learning Kentucky, Utrecht University, Wikimania, in partnership with Ukrainian and Netherlands librarians, a Willoughby Institute faculty workshop at Dickinson College, and a WordPress Campus Conference Lightning Talk. See additional engagements including keynotes and moderated discussions in our CC Open Education Year in Review.
- Reaching new communities:
- To reach more librarians, CC collaborated with the American Library Association to provide a 4-week webinar series for ALA’s Core division, supporting librarians’ professional development. CC also hosted presentations at ALA LibLearnX and ALA Annual.
- To reach more communities involved in Open Science and Open Data, CC hosted trainings including: an Open Climate Data Recommendations and CC Licensing 101 workshop for Open Education week, Open Data training for the California Department of Transportation, an open data workshop for ocean and climate researchers in partnership with Intertidal Agency, the Michael J. Fox Foundation, and the Estonian Ministry of Economic Affairs and Communications.
- CC provided training for additional government audiences with a CC 101 for the U.S. Department of State’s Bureau of Educational and Cultural Affairs.
Reflecting on 2024, we are grateful for the friendships and collaborations forged, and the new communities we had the pleasure of meeting. As we continue working toward the three goals in 2025, we hope to connect! If you would like to partner with CC, host a CC training for your institution, or get CC support for your community of practice, please let us know. Learn more on our website and email learning [at] creativecommons.org for more information. We’d be delighted to help you continue to grow your knowledge expertise in opening access to research, science, education, and culture.
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The Creative Commons (CC) Certificate courses are widely considered an essential resource for open access education and for increasing capacity for individuals and institutions using the CC licenses to increase open access.
The CC Certificate program offers in-depth courses about CC licenses, open practices, and the ethos of the Commons. Courses are composed of various readings, quizzes, discussions, and practical exercises to develop learners’ open skills. Currently we offer a CC Certificate for Open Culture, a CC Certificate for Academic Librarians, and a CC Certificate for Educators. Courses are open to everyone, from university students and entry-level professionals to experts in the fields of library science, education, and cultural heritage.
With the goal of reducing the barrier of participating in one of these essential trainings, CC is proud to have recently awarded eight scholarships. These scholarships would not be possible without your donations. We invite you to donate today so that we can continue offering these scholarships. You may also want to consider joining our Open Infrastructure Circle so that we can increase participation in these trainings globally.
Join us in congratulating the following scholarship recipients and keep reading to learn more about their journey in the open community:
ABIR Mohammed Galib Hasan, Bangladesh
Galib is a PhD Researcher in Hokkaido University, Japan. His primary research areas are: Educational Technology, Open Education and Generative AI. He was a founding member of the CC Bangladesh Chapter, serving as the Education Lead since 2018. Galib also served on the program committee of CC Global Summit in 2019 and 2020.
Bukola James, Nigeria
Bukola James is a certified librarian, Wikimedian, and community coordinator for the African Wikipedian Alliance. She also serves as the co-lead for the Open Culture Platform’s Outreach Working Group and as a Sub-Saharan Liaison Officer for the Wikimedia Foundation Peer Learning Program. Additionally, Bukola is a communications expert for the EduWiki Newsletter and a special adviser for the EduWiki User Group. She holds the position of co-team and project lead within African Wiki Women and other impactful initiatives.
Chaidir Amir, Indonesia
Chaidir is a professional librarian who has been working in libraries since 2023. He has certifications and competence in library management based on information and communication technology. He is an active member of multiple library forums and associations. Chaidir also serves as an accreditation assessor and library training facilitator.
Jes Graham, South Africa
Jes is a 28-year-old, disabled, non-binary South African who works at the University of Cape Town in open education, specifically in the development and production of open textbooks. Their driving motto for their work is to “be conscientiously creative in the pursuit of developing and sharing accessible knowledge through design.” To this end, Jes combines their skills in graphic design, Disability Studies, and editorial work and publishing to develop open educational resources (with a strong focus on multiple forms of accessibility) from a South African perspective. In their current work at the University of Cape Town, Jes has developed foundational skills in CC licensing, but aims to advance this knowledge to more deeply integrate CC licensing in their own work and support others in the local design and academic community.
John Okewole, Nigeria
John is an open education advocate working locally by encouraging colleagues to engage openness as a culture and attitude, and globally as a CC Global Network member and member of CC’s Open Education Platform). Some of his recent contributions include acting as a member of the Working Group 4 — Beyond Copyright: the Ethics of Open Sharing and serving as a co-lead of the CC Open Education Platform’s working group on the UNESCO Recommendation on OER. John is a Commonwealth Scholar who has completed an MA in Online and Distance Education at the Open University, UK and he also has a certificate in Designing and Facilitating E-Learning (Level 5) at the Open Polytechnic of New Zealand.
Jonas Bäckelin, Sweden
Jonas is currently the Content Manager on the Creative Commons Sverige team and the Sweden Chapter Lead. Outside of his CC work, Jonas is the solution manager and learning designer at Adda Kompetens, a part of the Swedish Association of Local Authorities and Regions (SALAR). He also serves as the moderator of the Upskilling och Reskilling committee at Swedish JobTech.
Tina Kalan, Slovenija
After working in the public school system and the national library, Tina has found her place in the academic library world. Her work is very dynamic, including everything from cataloging to information literacy courses. An important part of her workload is bibliographies, and her goal is to provide support to patrons, from students to researchers, in questions related to open access, open science and research assessments.
Tri Astari, Indonesia
Tri Astari is a lecturer who creates educational content under CC licenses, driven by a strong desire to make knowledge easily accessible. In addition, she is a member of Wikimedia Indonesia.
Congratulations again to the recipients. If you are interested in the CC Certificate courses, we invite you to register for 2025
The post Meet the Recipients of the Fall 2024 CC Certificate Scholarship appeared first on Creative Commons.
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We’re proud to announce Creative Commons’ Legal Tools have been reviewed and accepted into the Digital Public Goods Alliance (DPGA) DPG Registry. The DPGA is a multi-stakeholder initiative, endorsed by the United Nations Secretary-General, that is working to accelerate the attainment of the UN Sustainable Development Goals in low- and middle-income countries. DPGA does this by facilitating the discovery, development, use of, and investment in digital public goods (DPGs) in order to create a more equitable world.
Being recognized as a DPG increases the visibility, support for, and prominence of open projects that have the potential to tackle global challenges. To become a digital public good, all projects are required to meet the DPG Standard to ensure that projects truly encapsulate open source principles.
Creative Commons provides and stewards the CC licenses and public domain tools that give every person and organization in the world a free, simple, and standardized way to grant copyright permissions for creative and academic works. In addition, the licenses support proper attribution and enable others to copy, distribute, and make use of those works. CC legal tools are digital public infrastructure that make the legal sharing of DPGs possible.
At Creative Commons, we are thrilled to have our Legal Tools recognised as DPGs as they can empower people to dramatically improve access to open content. By advocating for the use and implementation of DPGs, global communities can work together in prioritizing and mobilizing resources to help solve global challenges. CC’s legal tools and our programs play a critical role in helping to advance the DPG ecosystem.
For any inquiries about CC’s involvement in the Digital Public Goods Alliance, please reach out to Cable Green. For more information on the Digital Public Goods Alliance please reach out to hello@digitalpublicgoods.net.
Join us by supporting this ongoing work. You have the power to make a difference in a way that suits you best. By donating to CC, you are not only helping us continue our vital work, but you also benefit from tax-deductible contributions. Making your gift is simple – just click here. Thank you for your support.
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