Where do you find these manuscripts? Is it like a website or do you find it randomly??
hey, thanks for the curiosity! lenghty answer below the cut :)
1)
medieval manuscripts are typically owned by libraries and showcased on the library's websites. so one thing i do is i randomly browse those digitized manuscript collections (like the collections of the bavarian state library or the bodleian libraries, to name just two), which everybody can do for free without any special access. some digital collections provide more useful tools than others (like search functions, filters, annotations on each manuscript). if they don't, the process of wading through numerous non-illustrated manuscripts before i find an illustrated one at all can be quite tedious.
2)
there are databases which help to navigate the vast sea of manuscripts. the one i couldn't live without personally use the most is called KdIH (Katalog der deutschsprachigen illustrierten Handschriften des Mittelalters). it's a project which aims to list all illustrated medieval manuscripts written in german dialects. the KdIH provides descriptions of the contents of each manuscript (with a focus on the illustrations), and if there's a digital reproduction of a manuscript available anywhere, the KdIH usually links to it. the KdIH is an invaluable tool for me because of its focus on illustrated manuscripts, because of the informations it provides for each manuscript, and because of its useful search function (once you've gotten over the initial confusion of how to navigate the website). the downside is that it includes only german manuscripts, which is one of the main reasons for the over-representation of german manuscripts on my blog (sorry about that).
3)
another important database for german manuscripts in general (i.e. not just illustrated ones) is the handschriftencensus, which catalogues information regarding the entirety of german language manuscripts of the middle ages, and also links to the digital reproductions of each manuscript.
4)
then there are simply considerable snowball effects. if you do even just superficial research on any medieval topic at all (say, if you open the wikipedia article on alchemy), you will inevitably stumble upon mentions of specific illustrated manuscripts. the next step is to simply search for a digital copy of the manuscript in question (this part can sometimes be easier said than done, especially when you're coming from wikipedia). one thing to keep in mind is that a manuscript illustration seldom comes alone - so every hint to any illustration at all is a greatly valuable one (if you do what i do lol). there's always gonna be something interesting in any given illustrated manuscript. (sidenote: one very effective 'cheat code' would be to simply go through all manuscripts that other online hobbyist archivers of manuscript illustrations have gone through before - like @discardingimages on tumblr - but some kind of 'professional pride' detains me from doing so. that's just a kind of stubbornness though. like, i want to find my material more or less on my own, not just the images but also the manuscripts, and i apply arbitrary rules to my search as to what exactly that means.)
5)
whatever tool or strategy i use to find specific illustrated manuscripts-- in the end, one unavoidable step is to actually manually skim through the (digitized) manuscript. i usually have at least a quick look at every single illustrated page, and i download or screenshot everything that is interesting to me. this process can take up to an hour per manuscript.
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in conclusion, i'd say that finding cool illuminated manuscripts is much simpler than i would have thought before i started this blog. there are so many of them out there and they're basically just 'hidden in plain side', it's really astounding. finding the manuscripts doesn't require special skills, just some basic experience with/knowledge of the tools available. the reason i'm able to post interesting images almost daily is just that i spend a lot of time doing all of this, going through manuscripts, curating this blog, etc. i find a lot of comfort in it, i learn a lot along the way, and i immensely enjoy people's engagement with my posts. so that's that :)
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The Data Migration Odyssey: A Journey Across Platforms
As a database engineer, I thought I'd seen it all—until our company decided to migrate our entire database system to a new platform. What followed was an epic adventure filled with unexpected challenges, learning experiences, and a dash of heroism.
It all started on a typical Monday morning when my boss, the same stern woman with a flair for the dramatic, called me into her office. "Rookie," she began (despite my years of experience, the nickname had stuck), "we're moving to a new database platform. I need you to lead the migration."
I blinked. Migrating a database wasn't just about copying data from one place to another; it was like moving an entire city across the ocean. But I was ready for the challenge.
Phase 1: Planning the Expedition
First, I gathered my team and we started planning. We needed to understand the differences between the old and new systems, identify potential pitfalls, and develop a detailed migration strategy. It was like preparing for an expedition into uncharted territory.
We started by conducting a thorough audit of our existing database. This involved cataloging all tables, relationships, stored procedures, and triggers. We also reviewed performance metrics to identify any existing bottlenecks that could be addressed during the migration.
Phase 2: Mapping the Terrain
Next, we designed the new database design schema using schema builder online from dynobird. This was more than a simple translation; we took the opportunity to optimize our data structures and improve performance. It was like drafting a new map for our city, making sure every street and building was perfectly placed.
For example, our old database had a massive "orders" table that was a frequent source of slow queries. In the new schema, we split this table into more manageable segments, each optimized for specific types of queries.
Phase 3: The Great Migration
With our map in hand, it was time to start the migration. We wrote scripts to transfer data in batches, ensuring that we could monitor progress and handle any issues that arose. This step felt like loading up our ships and setting sail.
Of course, no epic journey is without its storms. We encountered data inconsistencies, unexpected compatibility issues, and performance hiccups. One particularly memorable moment was when we discovered a legacy system that had been quietly duplicating records for years. Fixing that felt like battling a sea monster, but we prevailed.
Phase 4: Settling the New Land
Once the data was successfully transferred, we focused on testing. We ran extensive queries, stress tests, and performance benchmarks to ensure everything was running smoothly. This was our version of exploring the new land and making sure it was fit for habitation.
We also trained our users on the new system, helping them adapt to the changes and take full advantage of the new features. Seeing their excitement and relief was like watching settlers build their new homes.
Phase 5: Celebrating the Journey
After weeks of hard work, the migration was complete. The new database was faster, more reliable, and easier to maintain. My boss, who had been closely following our progress, finally cracked a smile. "Excellent job, rookie," she said. "You've done it again."
To celebrate, she took the team out for a well-deserved dinner. As we clinked our glasses, I felt a deep sense of accomplishment. We had navigated a complex migration, overcome countless challenges, and emerged victorious.
Lessons Learned
Looking back, I realized that successful data migration requires careful planning, a deep understanding of both the old and new systems, and a willingness to tackle unexpected challenges head-on. It's a journey that tests your skills and resilience, but the rewards are well worth it.
So, if you ever find yourself leading a database migration, remember: plan meticulously, adapt to the challenges, and trust in your team's expertise. And don't forget to celebrate your successes along the way. You've earned it!
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Effective Data Insights — A Game Changer for Businesses
Data insights are crucial for businesses to make informed decisions and remain competitive in the ever-evolving market. With the increase in the volume, variety, and velocity of data, it has become necessary for organizations to have the capability to analyze data and derive insights that help them make data-driven decisions. This article will explore the importance of data insights for businesses, steps to achieving effective data insights, best practices, tools, challenges, and future of data insights.
Introduction to Data Insights
According to KnowledgeHound, Data insights refer to the process of analyzing and interpreting data to extract meaningful information that can be used to make informed decisions.
It involves using various tools and techniques to identify patterns, trends, and relationships in data. There are many resources available for beginners who want to learn about data analytics, including online courses and guides.
These resources cover topics such as the role of a data analyst, tools used in data analysis, and the entire data analysis process. With the increasing demand for professionals with skills in data analytics, learning this field can be a great way to kickstart a career.
Importance of Data Insights for Businesses
Data insights are crucial for businesses as they provide valuable information that can be used to make informed decisions. By analyzing customer data from various channels, businesses can gain insights into customer behavior and preferences, which can help them provide a more personalized experience.
Historical data analysis can also help businesses anticipate fluctuations in consumer demand and make better business decisions. Companies that embrace data analytics initiatives can experience significant financial returns. Data analytics helps businesses optimize their performance by identifying areas for improvement and making strategic investments.
Implementing data analytics into the business model means companies can stay competitive in today’s market by making informed decisions based on real-time data.
Steps to Achieving Effective Data Insights
Achieving effective data insights involves several steps.
Firstly, it is important to align the data strategy with the business strategy and identify relevant business drivers that could be positively impacted by data and analytics.
Secondly, organizations need to implement processes such as data cataloging and governance and embrace culture changes to achieve effective analytics programs.
Thirdly, businesses should use deep learning to get value from unstructured data.
Finally, carrying out various analyses on the data is essential to obtain insights. The four types of data analysis include descriptive, diagnostic, predictive, and prescriptive analysis.
By following these steps, businesses can turn their data into actionable insights that can be used to make informed decisions.
Best Practices for Data Insights
To achieve the best results from data insights, businesses should follow some best practices.
It is important to define business objectives and identify the key performance indicators that will be used to measure success.
Building high-performance analytics teams and promoting data literacy within the organization can help ensure that everyone understands how to use data effectively.
Collecting, storing, and organizing data correctly is essential for accurate analysis.
Segmenting the audience can help businesses gain a better understanding of their customers’ behavior and preferences.
Using data storytelling can help promote insights by making complex data more accessible and understandable.
Utilizing new infrastructure technology and more advanced analytics can help businesses stay ahead of the competition.
By following these best practices, businesses can turn their data into actionable insights that drive growth and success.
Tools for Data Insights
There are many tools available for data insights that businesses can use to analyze and interpret their data. Some of the most widely used business analytics tools include Microsoft Power BI, Tableau, Qlik Sense, Excel and KnowledgeHound.
These tools are designed to help businesses visualize and analyze their data to gain insights into customer behavior, market trends, and other key metrics. These tools offer a range of features such as data visualization, predictive modeling, machine learning algorithms, and more.
By using these tools effectively, businesses can turn their data into actionable insights that drive growth and success.
Challenges in Data Insights
There are several challenges that businesses face when it comes to data insights.
Managing vast amounts of data can be a challenge, as it requires the right tools and techniques to analyze and interpret the data effectively.
Seelecting the right analytics tool can be difficult, as there are many options available and each has its own strengths and weaknesses.
Data visualization can be challenging, as it requires businesses to present complex data in a way that is easy to understand.
Dealing with data from multiple sources can be a challenge, as it requires businesses to integrate different types of data into a single system.
Low-quality data can also pose a challenge, as it can lead to inaccurate insights and decisions.
Other challenges include cultural dynamics within the organization, inaccessible data, lack of system integration, excessive costs, complexity and skills gaps.
By addressing these challenges effectively through proper planning and implementation of best practices for data insights, businesses can turn their data into actionable insights that drive growth and success.
Future of Data Insights
The future of data insights is promising, with several trends emerging that are expected to shape the industry in the coming years.
Businesses are expected to emphasize business intelligence, edge data, and cloud-native technologies.
Data democratization, artificial intelligence, and real-time data analytics are expected to become more prevalent.
Adaptive AI systems and metadata-driven data fabric are also expected to gain traction.
Real-time automated decision making and no-code solutions are also predicted to be important trends in the future of data insights.
Data quality and observability will continue to be important factors in ensuring accurate insights from data analysis.
By staying up-to-date with these trends and adopting new technologies and techniques as they emerge, businesses can stay ahead of the competition and turn their data into actionable insights that drive growth and success.
Also Read: Different Types of Survey Data Collection Methods You Should Know
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