by jsendak | Mar 31, 2024 | DS Articles
I am pleased, excited, and humbled to announce that I am stepping into the role of Executive Director of rOpenSci starting April 1.
First, let me give my gratitude to our outgoing Executive Director and friend Karthik Ram for his leadership and mentorship running rOpenSci the past decade. He’s been a steady hand and visionary that helped our community accomplish so much together in this time. Thanks to him and the rest of the team for entrusting me with this role.
I’ve had the pleasure of being an rOpenSci member for many years – as a volunteer, an editor for rOpenSci’s software peer-review since 2015, and director of the peer review program since 2019. I’m a disease ecologist at an NGO, where I work every day with the tools for reproducible analysis the rOpenSci community creates.
I am deeply committed to rOpenSci’s values of open and reproducible research using shared data, of creating infrastructure and a welcoming, inclusive community that enables researchers and developers to support each other in doing impactful research and work for public good.
As director I’m looking forward to working with our tremendous community of researchers, developers, reviewers, editors, mentors, Champions, and staff. My goal is to strengthen the pillars of our work and expand them through new partnerships: bringing diverse new voices and talent into the open-source ecosystem, improving research software through peer review, building networks of mutual support among research software developers, and creating new infrastructure and tools at the frontier of reproducible practice.
I know this community will continue to do great things together, and I’m glad I get to be part of it!
Read founding director Karthik Ram’s goodbye message
Continue reading: Hello from our New Executive Director!
Comprehensive Analysis Of The New Executive Director Announcement
The content disseminated indicates the appointment of a new Executive Director for rOpenSci. The new Executive Director, who has been associated with rOpenSci for many years, has taken over from the founding Executive Director – Karthik Ram.
Implications and Future Developments
This developmental change in leadership could influence the organisation in multiple ways, with expectations of new ideas and a fresh vision brought to the table. The incoming Executive Director, having knowledge and experience as a volunteer, an editor and a program director, should facilitate a smooth transition and continuity for the community, while adding a fresh perspective.
The Long-term Implications:
- New Opportunities: The incoming Executive Director has highlighted specific goals which point towards exploring new partnerships and bringing diverse new voices into the open-source ecosystem. This represents ample opportunities for inclusion and growth.
- Strengthened Role of the Community: The ongoing commitment to the values of rOpenSci and dedication to creating a more welcoming inclusive community could result in a more productive community, with increased support and co-operation among its diverse members.
- Boost for Open and Reproducible Research: This leadership transition may also enhance the promotion of open and reproducible research using shared data.
Future Developments:
- There could be an advancement in the creation of new infrastructure and tools at the frontier of reproducible practice.
- The incoming director may also facilitate improvements in the research software through a focused approach of peer review.
- The building of network support among research software developers can also see a surge, providing the boost for more bilateral collaborations.
Actionable Advice
Here are some suggested actions for the members of the rOpenSci community to make the most of this transition:
- Embrace the new leadership and its vision. Be open to new ideas, partnerships and approaches.
- Engage proactively with novel initiatives and activities. Participation and contribution will be essential for building a more vibrant, supportive and enriching community.
- Continue to support and promote reproducible research and shared data. These are fundamental values of the rOpenSci community that should continue to be a priority.
- Utilise the increased avenues for collaboration, knowledge sharing and networking. The building of support networks and focus on inclusivity can result in personal and professional growth for all members.
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by jsendak | Mar 31, 2024 | DS Articles
This article serves as a detailed guide on how to master advanced Python techniques for data science. It covers topics such as efficient data manipulation with Pandas, parallel processing with Python, and how to turn models into web services.
Mastering Advanced Python Techniques for Data Science: Long-Term Implications and Future Developments
Python is undoubtedly a fundamental tool in the field of data science, hence mastering advanced techniques in Python enhances efficient manipulation of data, parallel processing, and other numerous benefits. The future of Data Science lies in the advancement of these techniques and their efficient application.
Long-Term Implications
The advanced Python techniques for data science bring about long-term implications for data scientists and the broader field of data analysis. This could significantly change how we approach data handling, manipulation, and processing for the years to come.
- Enhanced Data Manipulation: Efficient data manipulation with tools such as Pandas enables data scientists to handle large datasets, standardize data inputs, and perform complex data transformations more conveniently.
- Optimized Processing: Python’s capability for parallel processing boosts the speed of executing data analysis, thereby allowing data scientists to focus more on interpreting the results rather than waiting for them.
- Interoperability: The ability to turn Python models into web services extends the use of these models beyond Python environments. This could lead to a more interconnected data science landscape where models can be applied interchangeably in various platforms.
Future Developments
In a rapidly progressing field like data science, staying updated with future developments is crucial. The evolution of Python and its applications in data science are seemingly endless, as these continue to be shaped by emerging trends in technology.
- Machine Learning Integration: As machine learning becomes increasingly important in data analysis, Python is expected to integrate more machine learning capabilities into its structure.
- Advanced Visualization Tools: Expect the development of more advanced visualization tools in Python for more detailed and creative data representation.
- Cloud-based Data Handling: With the increasing trend of data on the cloud, Python may develop more tools for cloud-based data handling and analysis.
Actionable Advice
Stay Updated: Continually update your knowledge about Python’s advancements and how they affect data science. Participate in online forums, attend virtual workshops, and read publications on Python and Data Science. This ongoing learning process is integral to making the most out of your Python skills.
Practice: The theoretical knowledge of Python is best complemented with practical experience. Regularly engage in Python projects that involve data manipulation, parallel processing, and other advanced techniques to keep your skills sharp.
Collaborate: Collaboration allows you to learn from peers and experts in the field. Participate in collaborative Python projects, and be open to sharing your knowledge and learning from others.
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by jsendak | Mar 31, 2024 | DS Articles
Master API Ecosystem Management: Unlock insights on managing complex API networks for enhanced digital performance and business growth.
Mastering API Ecosystem Management: The Path to Enhanced Business Performance and Growth
As businesses navigate the digital era, API (Application Programming Interface) ecosystems have become pivotal for operating in a more integrated, automated, and efficient way. Understanding and effectively managing these complex API networks are critical to unlocking enhanced digital performance and stimulating business growth.
Long-Term Implications
As the digital world continuously evolves, the utility and role of API ecosystems are projected to become increasingly significant. In the foreseeable future, superior API management could yield:
- Enhanced Business Integration: By facilitating seamless communication between diverse software systems, APIs can help in achieving enhanced business integration, resulting in more streamlined workflows.
- Improved Decision-making: Collecting and analyzing data from various digital touchpoints becomes easier with effective API management, paving the way for data-driven decision-making and strategy formulation.
- Increased Business Agility: By automating data exchange between systems and accelerating development processes, API ecosystems can significantly boost a business’s agility, enabling it to swiftly respond to changing market dynamics.
Possible Future Developments
API ecosystems, due to their growing importance in the digital landscape, are expected to witness several advancements in the coming years:
- Advancements in API Security: With the escalating risk of cyber threats, advancements in API security mechanisms would be a prerequisite to safeguard sensitive data and ensure reliable inter-system communication.
- Growth of API Marketplaces: As the use of APIs becomes increasingly widespread, expect the growth of API marketplaces where businesses can discover, share, and purchase APIs catering to their unique requirements.
- Emergence of AI in API Management: AI technologies could potentially optimize the API management process, from monitoring API performance to identifying and rectifying issues in real-time.
Actionable Advice
To maximize the benefits from API ecosystem management, businesses should:
- Invest in Skill Development: Businesses should prioritize training their teams on advanced API management techniques and tools to ensure efficient and secure use of APIs.
- Stay Up-to-date with Latest Trends: As technology advances swiftly, staying abreast of emerging trends in API management can help businesses gain a competitive edge.
- Leverage Key Metrics: By monitoring key API performance metrics, companies can gain valuable insights, enable proactive problem-solving, and ensure high-quality service delivery.
In conclusion, mastering API Ecosystem Management can undoubtedly unlock significant growth potential for businesses in the digital realm. Investing in skill development, keeping abreast of the latest trends and using key metrics can enhance digital performance and stimulate business growth.
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by jsendak | Mar 30, 2024 | DS Articles
Dear rOpenSci friends, it’s time for our monthly news roundup!
You can read this post on our blog.
Now let’s dive into the activity at and around rOpenSci!
rOpenSci HQ
Leadership changes at rOpenSci
After 13 years at the helm of rOpenSci, our founding executive director Karthik Ram is stepping down.
Noam Ross, rOpenSci’s current lead for peer review, will be our new Executive Director.
Karthik will remain a key advisor to rOpenSci.
We thank him for his years of leadership and service to the community!
Read Karthik’s farewell post, and Noam’s post about his new role on our blog
rOpenSci Dev Guide 0.9.0: Multilingual Now! And Better
We’re delighted to announce we’ve released a new version of our guide,
“rOpenSci Packages: Development, Maintenance, and Peer Review”!
A highlight is that our guide is now bilingual (English and Spanish), thanks to work by Yanina Bellini Saibene, Elio Campitelli and Pao Corrales, and thanks to support of the Chan Zuckerberg Initiative, NumFOCUS, and the R Consortium.
Read the guide in Spanish.
Our guide is now also getting translated to Portuguese thanks to volunteers.
We are very grateful for their work!
Read more in the blog post about the release.
Thanks to all contributors who made this release possible.
Interview with Code for Thought podcast
Our community manager, Yanina Bellini Saibene, talked with Peter Schmidt of the Code for Thought podcast, about the importance of making computing materials accessible to non-English speaking learners.
Listen to the episode.
Find our more about rOpenSci multilingual publishing project.
Coworking
Read all about coworking!
Join us for social coworking & office hours monthly on first Tuesdays!
Hosted by Steffi LaZerte and various community hosts.
Everyone welcome.
No RSVP needed.
Consult our Events page to find your local time and how to join.
And remember, you can always cowork independently on work related to R, work on packages that tend to be neglected, or work on what ever you need to get done!
Software
New packages
The following three packages recently became a part of our software suite, or were recently reviewed again:
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nuts, developed by Moritz Hennicke together with Werner Krause: Motivated by changing administrative boundaries over time, the nuts package can convert European regional data with NUTS codes between versions (2006, 2010, 2013, 2016 and 2021) and levels (NUTS 1, NUTS 2 and NUTS 3). The package uses spatial interpolation as in Lam (1983) doi:10.1559/152304083783914958 based on granular (100m x 100m) area, population and land use data provided by the European Commission’s Joint Research Center. It is available on CRAN. It has been reviewed by Pueyo-Ros Josep and Le Meur Nolwenn.
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quadkeyr, developed by Florencia D’Andrea together with Pilar Fernandez: Quadkeyr functions generate raster images based on QuadKey-identified data, facilitating efficient integration of Tile Maps data into R workflows. In particular, Quadkeyr provides support to process and analyze Facebook mobility datasets within the R environment. It has been reviewed by Maria Paula Caldas and Vincent van Hees.
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weatherOz, developed by Rodrigo Pires together with Anna Hepworth, Rebecca O’Leary, Jonathan Carroll, James Goldie, Dean Marchiori, Paul Melloy, Mark Padgham, Hugh Parsonage, Keith Pembleton, and Adam H. Sparks: Provides automated downloading, parsing and formatting of weather data for Australia through API endpoints provided by the Department of Primary Industries and Regional Development (DPIRD) of Western Australia and by the Science and Technology Division of the Queensland Governments Department of Environment and Science (DES). As well as the Bureau of Meteorology (BOM) of the Australian government precis and coastal forecasts, agriculture bulletin data, and downloading and importing radar and satellite imagery files. It has been reviewed by Laurens Geffert and Sam Rogers.
Discover more packages, read more about Software Peer Review.
New versions
The following nineteen packages have had an update since the last newsletter: frictionless (v1.0.3), aRxiv (0.10), cffr (v1.0.0), chromer (v0.8), drake (7.13.9), GSODR (v4.0.0), lightr (v1.7.1), lingtypology (v1.1.17), magick (2.8.3), melt (v1.11.2), nodbi (v0.10.4), nuts (v1.0.0), paleobioDB (v1.0.0), quadkeyr (v0.1.0), rtweet (v2.0.0), ruODK (v1.4.2), spocc (v1.2.3), tarchetypes (0.8.0), and targets (1.6.0).
Software Peer Review
There are thirteen recently closed and active submissions and 6 submissions on hold. Issues are at different stages:
Find out more about Software Peer Review and how to get involved.
On the blog
Software Review
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rOpenSci Dev Guide 0.9.0: Multilingual Now! And Better by Maëlle Salmon, Mark Padgham, and Noam Ross. Updates in version 0.9.0 of the online book ‘rOpenSci Packages: Development, Maintenance, and Peer Review’. Other languages: rOpenSci Dev Guide 0.9.0: ¡Ahora multilingüe! Y mejor (es).
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rOpenSci Code of Conduct Annual Review by Yanina Bellini Saibene, Mark Padgham, Kara Woo, and Natalia Morandeira. Updates for version 2.5 of rOpenSci’s Code of Conduct.
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Marketing Ideas For Your Package by Yanina Bellini Saibene and Maëlle Salmon. Now that you have created your package, presenting it to the world is a crucial step to gain visibility and attract users. In this blog post we suggest a series of activities and tools for advertizing your package. This post was discussed on the R Weekly highlights podcast hosted by Eric Nantz and Mike Thomas.
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rOpenSci Champions Pilot Year: Projects Wrap-Up by Yanina Bellini Saibene. Our first cohort of Champions complete the program. In this blog post, we share each champions projects, their achievements and outreach activities.
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From the Founding Director: My Farewell to rOpenSci by Karthik Ram. Karthik Ram steps down as Executive Director after 13 years. Other languages: Del Director Fundador: Mi despedida de rOpenSci (es).
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Hello from our New Executive Director! by Noam Ross. Noam Ross takes the helm as rOpenSci’s new Executive Director. Other languages: Hola de nuestro nuevo Director Ejecutivo (es).
Tech Notes
Calls for contributions
Calls for maintainers
If you’re interested in maintaining any of the R packages below, you might enjoy reading our blog post What Does It Mean to Maintain a Package?.
Calls for contributions
Also refer to our help wanted page – before opening a PR, we recommend asking in the issue whether help is still needed.
Package development corner
Some useful tips for R package developers.
Reminder: R Consortium Infrastructure Steering Committee (ISC) Grant Program Accepting Proposals until April 1st!
The R Consortium Call for Proposal might be a relevant funding opportunity for your package!
Find out more in their post.
If you can’t prepare your proposal in time, the next call will start September 1st.
@examplesIf for conditional examples in package manuals
Do you know you can make some examples of your package manual conditional on, say, the session being interactive?
The @examplesIf roxygen2 tag is really handy.
What’s more, inside the examples of a single manual page, you can seamlessly mix and match @examples and @examplesIf pieces.
‘argument “..2” is missing, with no default’
Mike Mahoney posted an important PSA on Mastodon:
if you’re getting a new error message ‘argument “..2” is missing, with no default’ on #rstats 4.3.3, it’s likely because you have a trailing comma in a call to glue::glue()
seeing this pop up in a few Slacks so figured I’d share
https://github.com/tidyverse/glue/issues/320
Thanks, Mike!
Useful hack: a CRAN-specific .Rbuildignore
The .Rbuildignore file lists the files to not be included when building your package, such as your pkgdown configuration file.
Trevor L. Davis posted a neat idea on Mastodon: using a CRAN-specific .Rbuildignore, so that CRAN submissions omit some tests and vignettes to keep the package under the size limit.
Regarding tests themselves, remember you can skip some or all on CRAN (but make sure you’re running them on continuous integration!).
Key advantages of using the keyring package
If your package needs the user to provide secrets, like API tokens, to work, you might be interested in wrapping or recommending the keyring package (maintained by Gábor Csárdi), that accesses the system credential store from R.
See this recent R-hub blog post.
A package for linting roxygen2 documentation
The compelling roxylint package by Doug Kelkhoff allows you to check some aspects of your roxygen2 docs, such as the use of full stops and sentence case.
See the list of current rules.
Last words
Thanks for reading! If you want to get involved with rOpenSci, check out our Contributing Guide that can help direct you to the right place, whether you want to make code contributions, non-code contributions, or contribute in other ways like sharing use cases.
You can also support our work through donations.
If you haven’t subscribed to our newsletter yet, you can do so via a form. Until it’s time for our next newsletter, you can keep in touch with us via our website and Mastodon account.
Continue reading: rOpenSci News Digest, March 2024
Long-term implications and possible future developments
With numerous intriguing updates and developments mentioned in the rOpenSci news article, here are several long-term implications and possible future directions.
Leadership Changes at rOpenSci
The change in leadership from Karthik Ram to Noam Ross, both integral individuals in rOpenSci, is likely to generate some shift in the approach and direction of the organization. As Noam takes over the helm, there might be changes to the strategic roadmap for rOpenSci, and the organization’s priorities may evolve, leading to the implementation of new initiatives and the modification of existing practices.
Enhanced Guide In Multiple Language
The fact that rOpenSci’s guide is now bilingual (English and Spanish) has the potential to dramatically expand the organization’s reach to non-English speaking audience. The ongoing translation to Portuguese suggests a broader aim of making rOpenSci accessible to as many global users as possible. This implies that more language versions may also be developed in the future.
Coworking and Community Building
rOpenSci’s coworking initiative helps to foster a sense of community, where users can collaborate, learn from one another, and also help in improving and maintaining various R packages. It can result in creativity and productivity enhancement, and knowledge exchange, fostering a more robust R user base.
New Packages
The inclusion of new packages like ‘nuts’, ‘quadkeyr’, and ‘weatherOz’ to rOpenSci demonstrates growth and adaptability of the open source software that it provides. This would make rOpenSci a more versatile and valuable platform for open science, particularly for researchers working on data related to Europe’s regional data, quadkey-identified data, and Australian weather data respectively.
Actionable advice based on these insights
If you are an existing member of rOpenSci, considering the leadership change, explore any new strategic directions that Noam Ross plans to implement, and find out how you can align your help with those plans. For all users, the availability of the guide in different languages means that there are fewer barriers to using rOpenSci’s resources, so take this opportunity to deepen your understanding.
Engage in the coworking sessions by rOpenSci which offer an opportunity to learn from and connect with other users across the globe. Explore the newly added packages and check if any could serve beneficial for your research or contributions. Lastly, consider if you could contribute to rOpenSci, whether by code or non-code contributions, proactive participation will only enhance your skills and increase your understanding of open science.
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by jsendak | Mar 30, 2024 | DS Articles
Begin your MLOps journey with these comprehensive free resources available on GitHub.
Embarking on Your MLOps Journey with Comprehensive Free Resources on GitHub
It’s no secret that Machine Learning Operations (MLOps) is rapidly becoming a significant necessity in the world of technology and business. With the increasing relevance of data-driven decision making, integrating machine learning (ML) systems into business systems has become a cornerstone of modern business strategy. Thankfully, numerous comprehensive and free resources are available on GitHub to make your start in MLOps smoother and more effective.
Long-term implications and future developments in MLOps
Machine Learning Operations, or MLOps, aims to bridge the gap between the development of ML models and their operation in production systems. With businesses relying more on machine learning models for data analysis and decision making, the need for a framework to manage these models becomes crucial. The long-term implications of MLOps are far-reaching and exciting.
MLOps is set to become an integral part of business strategy in more industries. We anticipate a future where businesses across sectors will rely on MLOps for the functional and efficient operation of their ML systems in production environments. This suggests a potential for an exponential rise in the demand for MLOps skills and resources.
The democratization of machine learning through MLOps opens the door to a future where ML models are as ubiquitous as software applications are today. In this future, expecting businesses to have incorporated ML models into their operations will be as commonplace as expecting businesses to have a website.
Actionable Advice Based on the Anticipated MLOps Future Developments
Leverage the available resources
With an unprecedented array of free resources available on GitHub for kick-starting your journey into MLOps, the first piece of advice is to take advantage of these resources. They present beginners with an invaluable opportunity to understand the terrain before diving in fully. Experiment with different models, understand the best practices, and identify the pitfalls to avoid while managing ML models.
Devote ample time to learning MLOps
Given the anticipated rise in the significance of MLOps in business and technology, it is crucial for tech savvy individuals and businesses alike to devote ample time to understand and learn this field. Far from being just a trend or buzzword, MLOps will likely become an essential component of technology and business operation.
Stay adaptable and keep learning
The field of MLOps, like most tech fields, is continuously evolving. What works today may be outdated tomorrow. To ensure long-term success in this field, it is crucial to stay adaptable and open to learning new things. Monitor trends, follow new research, join discussions, and continue to learn.
Implement ML with a clear plan
Before deploying ML models into business operations, have a clear plan. Understand the problem you’re trying to solve, the resources at your disposal, and the best ML model for the task. Then use MLOps as your guiding principle in developing and deploying the ML model.
The resources available on GitHub provide an excellent starting point for this journey, providing a wealth of information and support for those ready to dive into the riveting world of MLOps.
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by jsendak | Mar 30, 2024 | DS Articles
Digital transformation in finance is the process of implementing advanced digital technologies to boost financial processes.
Digital Transformation in Finance: The Future Beyond
The digital transformation in finance indicates a paradigm shift towards the extensive utilization of sophisticated digital technologies to enhance financial processes. This transformation is reshaping the finance industry in numerous ways, leaving its indelible mark on all affiliated business operations, forecasting a landscape of technology-enhanced capabilities.
Future Implications
The digital transformation in the finance sector is not merely a passing trend. It alters the way finance industries function, fostering transparency, speed, and efficiency in operations. As companies continue to engage in digital transformation, the potential effects on the global financial landscape are profound.
Increased Automation
One of the primarily anticipated long-term implications of digital transformation in finance is the rise of automation. Automated financial operations will unleash increased productivity, optimizing various tasks such as data entry, compliance checks, and report generation. This can lead to lower operational costs and time-saving.
New Job Opportunities
While automation does eliminate some roles, it simultaneously creates new ones. With digital transformation, new skill sets will be in demand, such as data analysis, cybersecurity, AI and machine learning expertise. This implies a shift in the job market, promoting upskilling and retraining of the workforce.
Improved Customer Experience
Digital transformation also contributes to improving customer experience by providing fast, stress-free, and seamless services. The adaption of digital processes means 24/7 availability, reducing wait times and making service accessibility more convenient.
Potential Future Developments
Acceleration of AI Integration
Artificial Intelligence (AI) is expected to play a more significant role in reshaping financial services. AI can optimize numerous financial operations, from credit scoring and fraud detection to customer service and financial advising.
Increase in Cybersecurity Investments
As financial operations continue to digitize, the sector becomes a prime target for cyber-attacks. Therefore, cybersecurity will likely become a critical investment area to ensure safe and secure transactions.
Greater Regulatory Scrutiny
With the rapid digital transformation, regulatory bodies will likely scrutinize financial institutions more rigorously. Compliance to data protection regulations and other directives will become critical for operations.
Actionable Advice
- Embrace digital technology: Financial institutions must proactively adopt digital solutions, keeping an open mind for modern technologies like AI and machine learning.
- Invest in cybersecurity: To manage digital risks, firms should increase investment in cybersecurity infrastructure and policies.
- Focus on customer experience: Make customer satisfaction a priority by providing seamless, efficient, and secure services.
- Retrain workforce: Encourage workforce to learn new skills related to technological advancements.
- Compliance Reviews: Regularly review your digital operations to ensure they comply with all regulatory bodies and data protection laws.
In conclusion, digital transformation in finance is setting significant trends. Financial institutions should monitor these developments closely and adapt accordingly to stay competitive and relevant in the technological era.
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