Comprised of the Pike Nickel Centre, West and East showings, this nickel project has currently been traced by drillings over more than 4.5 kilometers. In addition to the presence of strategic minerals, the Gaboury project also contains several gold showings.

Mosaic Minerals Starts Drilling on Gaboury Nickel Project in Quebec

EMP Metals Corp. ("EMP Metals" or the "Company") is pleased to announce that it has closed its previously announced non-brokered private placement financing (see news releases dated October 24, 2023 and October 25,

EMP METALS CLOSES PRIVATE PLACEMENT FINANCING OF $9,757,600

Dele Alake, the Minister of Solid Minerals Development, announced this significant development after the weekly Federal Executive Council meeting at the

Nigeria signs MoU with German mining company

With the global energy transition under way, critical minerals are fast replacing fossil fuels as the most sought after natural resources. Critical minerals such as copper, lithium, manganese and nickel are essential to the development of technologies behind solar PV,

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The Mosaic Co. has reported a possible tear in the liner of the troubled New Wales phosphogypsum stack at its Mulberry facility east of Tampa, the same location of a sink hole in 2016, although findings have yet to be confirmed.

The Mosaic Co. reports possible issues with liner at troubled Mulberry gypstack facility

As London-listed Rainbow Rare Earths reports to shareholders on having achieved various milestones in the financial year ended June 30, it is set to focus on the production of separated rare earth oxides at a back-end pilot plant in the US and complete a definitive feasibility study (DFS) by the end of 2024.

Rainbow sets 2024 priorities, including to raise $3.4m

Macroeconomic factors, demand for green metals and environmental, social and governance (ESG) endeavours are all factors that are influencing investment in mining, speakers outlined during the ...

Macroeconomic factors, green metals demand and ESG impacting on mining investment

VANCOUVER, BC, Oct. 18, 2023 /CNW/ - Vortex Metals Inc. (TSXV: VMS) (FSE: DM8) (OTC: VMSSF) ("Vortex" or the "Company") is pleased to announce its participation in the 121 Mining Investment Conference in New York City, occurring on October 23-24.

Vortex Metals Announces Attendance at the 121 Mining Investment Conference in New York

VANCOUVER, BC, Oct. 18, 2023 /PRNewswire/ - Vortex Metals Inc. (TSXV: VMS) (FSE: DM8) (OTC: VMSSF) ("Vortex" or the "Company") is pleased to announce its participation in the 121 Mining Investment Conference in New York City, occurring on October 23-24.

Detailed price information for Mosaic Minerals Corp (MOC-CN) from The Globe and Mail including charting and trades.

Mosaic Minerals Begins Prospection Work on Amanda and Announces Correction Regarding the Annual General Meeting

Get the most recent info and news about Mosaic on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

The Mosaic Company produces and distributes crop nutrients to the agricultural communities located in North America and other countries. The Company offers products including concentrated phosphates and potash.

Mosaic

Scientific American Supplement, No. 799, April 25, 1891 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . MARBLE AND MOSAIC. here MARBLE AND MOSAIC. By T.R. SPENCE. I do not propose to enter into any historical details as to the first and subsequent application of mosaics. In a general sense we understand mosaic as a combination of various more or less imperishable materials—fixed together by cement or other adhesive substances—and laid over walls, floors, etc., with a view to permanent decorative effect. The substance of the tesseræ is of many kinds, namely, glass, cheap and precious marbles, hard stone, and burnt clay, these mentioned being mainly in use for architectural purposes. For decorative schemes we collect as many gradations of color as are obtainable in such durable materials in their natural or manufactured state, and thus form a color palette which we regard in the same sense as a painter would his pigments. Of course, the first proceeding is to prepare a design on a small scale, which shall embrace your notions of color only. Then follows a full-sized cartoon, which I need hardly add shall embrace your best efforts in drawing. A tracing is made of the latter and transferred to sheets of cardboard. This cardboard is cut to the size of certain sections of your design, and, for convenience, should not be more than, say, 20 in. square. Of course, it will not always be square, but will bear the same relation to your complete cartoon as a map of the counties would to that of all England. Now, working from the small design (of color), the tesseræ are cut to the forms required, laid face downward, and glued on to the cardboard sections containing your enlarged cartoon. When the design is all worked out on these sections they are ready for fixing on walls or floor by laying them home on a float of cement. When the cement sets, the cardboard sticking to the face is washed off, and the joints of tesseræ flushed over with cement and cleaned off, leaving all joints filled up level. There are other processes used for the same end. The technical processes need not occupy our attention at present. There is one process that may appeal to you, and that is executing the work by floating on a limited expanse of cement, and sticking on the tesseræ at once. It has the advantage of enabling the artist or architect to see the effect of his efforts under the fixed conditions of light and height. in situ I shall confine myself to vitreous or glass mosaic, which for durability, extended scales of primary colors and their numerous semi-transparent gradations is unequaled by any substance yet used for wall or floor decoration. I am surprised, having all these fine qualities, it is not more used by architects. If you require proofs of its triumphs, go to St. Mark's, of Venice, and stand under its mellow golden roof. There you will find its domes and vaulted aisles, nave and transepts entirely overlaid with gold mosaic, into which ground is worked—in the deepest and richest colors and their gradations that contemporary manufacturers could produce—subjects selected from the creation down to the life of Christ, in addition containing a complete alphabet of early Christian symbolism. The roof surfaces being one succession of over-arching curves become receptive of innumerable waves of light and broad unities of soft shadows, giving the whole an incomparable quality of tone and low juicy color. Never use your gold but on curved or undulating surfaces. Flat planes of gold only give the effect of a monotonous metallic yellow, and can never be beautiful, owing to the absence of the variations that come with waves of shadow. By letting out the reins of imagination we might feel that in this a tenth century Giorgione has given off the mental impressions of all the golden autumn of his life. His material gave him an advantage over his great followers of the fifteenth and sixteenth centuries, insomuch that glass has a living and glowing quality of light not existing in the somewhat clouded purity of oil or fresco. In St. Mark's we have an example of the superb treatment in deepest and most Titianesque scales applied to curved forms, but to find a similarly complete example of the use of lighter tones and on flat surfaces, we must turn to Ravenna. I can give you no adequate description of the wall mosaics of Ravenna. In the sense of delicate color they remind me of some of the subtile harmonies of many of the finest works of the modern French school—of the Impressionists and others who combine that quality with a true instinct for design. In standing before them you feel that the Dagnan Bouverets, the Mersons, the Cazins, the Puvis de Chavannes, etc., of the fifth century have had a hand in the conception and realization of the beautiful compositions to be found on the nave walls of the two churches of St. Appollinare Nuovo and St. Appollinare in Classe. Here all the scales are of delicate degrees of light tones, supreme in their beauty, completeness, and, most important to us, their true decorative instinct. In the Baptistery we find what I may term a third essay in color, by weaving in rich, dark, and glowing colors on figures and bold sinuous forms of ornament in such a skillful and judicious manner that the whole dome seems to be alive with harmonies, although they are mostly primaries. As you know, rules for the disposition of color are futile, yet some details that struck me as eminently satisfactory may interest you. In all cases the tesseræ are of small dimensions, about a quarter of an inch square. The stucco joints are large and open, surfaces far from level, but undulating considerably. The tesseræ stick up in parts, brilliant edges showing. Absence of flatness gives play to the light. The gray of the stucco joints brings the whole composition together, serving as cool grays in a picture to give tender unity. Gold, apart from backgrounds and large surfaces, is used very cleverly in small pieces in borders of garments, and more especially in thin outlines to make out the drawing and certain flowing forms of ornament. Brilliant pieces of glass actually moulded at the kiln into forms of jewels add brilliancy to crowns, borders, etc. These stick boldly out from the surface. I noticed in the Baptistery below the springing of the dome a frieze about 2 ft. 6 in. deep, having the ground entirely in black, through which was woven in thin gold lines a delicate foliated design. This, in conjunction with the upper surfaces in dark, rich color, had a most delightful effect. We, as students, can learn most from the Ravenna examples, for great are the needs of light and silvery color in this country, where gray and gloomy days far outnumber those in which the sun gives liberally of his light. I may say, in passing, as our subject is really a matter of decoration, that our nineteenth century efforts in this direction are all of a somewhat gloomy tendency. We fill our rooms with imitations of somber Spanish leather, stain and paint our woodwork in leathery and muddy tones, to arrive at what is now a sort of decorator's god. Quaintness is the name of that god. Many are the sins for which he has to answer. Had we not better worship a deity called beauty, whose place is a little higher up Parnassus? Why should we not in our endeavors attempt in some measure to transfix the brilliant harmonies that follow the sun in his liberal and gracious course? This muddy quaintness is certainly pleasant for brief periods, when lamps are low and fire light gilds and deepens its parts. Turn the sunlight on these so-called triumphs of the modern decorator's art, and then you feel the lack of many a phase of color that might have been borrowed from the thousand and one examples that in nature he vivifies and makes brilliant. Referring again to the Ravenna mosaics, I can only add that at the present day an extended palette of colored glass is available. The technical difficulties are not great, and there is no question as to the fine qualities of design and color that are to be obtained in this material. The great point in this, as in all other schemes of decoration, is the art, the mental quality of conception, and the sense of color and fitness. If we hold the precious heritage of an artist's mind—that divine and rare something which gives form, color, and completeness to a story, a dream or a vision—then very little difficulty follows in making vitreous mosaic a valued servant in the realization of a fine creation. It is the function of architects to design suitable spaces for color decoration, so bound in by dignified mouldings and other details of his constructive art, in such a manner that the addition of decorative color shall in no way mar the scheme of his complete work, but shall (under these well ordered distributions) have set on them the seal and crown of color which is inseparable from a perfect piece of architecture. In such spaces he may dream his dreams, tell his stories, and stamp on them for centuries his subtilest and divinest thoughts. May I not urge that to such spaces must be given the best that is in you? for once placed so shall they remain unchanged through generations, time being powerless to add any mellow garment of tone or softening quality whatever. I mistook the title of the subject in thinking that it was mosaic only, and at the last moment found it was marble and mosaic. However, the same dominant principles shall underlie the treatment of marble. It is a question of the finer instincts for form and color. In recent years the demand for choice decorative materials has been the means of opening out many marble quarries all over the world. Transit being easy, a large scale of varieties is available. One fine addition is the Mexican onyx. My feeling is that the most beautiful marbles are those where the soft and sinuous veins melt and die into the general body, comparatively sharp markings dying right away at the edges into innumerable gradations. Marbles having strong and hardly marked veins present great difficulties in distribution. If they are near, they offend you with their coarseness; and, placed at a distance, the hard vein lines have very little decorative value. I should say use these in narrow slips, with very little moulded profile or as parts of intazzio. Mouldings should be specially designed for different marbles. I should say mainly on the principle of sudden contrasts; that is, large members with very little curve bound with members very small in detail, thus obtaining sharp lines, having little surface to be influenced or distorted by the veined markings, and serving to sharpen up and give form to the broader members (which show the color qualities of the marble), much as you sharpen up an ink drawing by underlining. These small members serve the architect's purpose for the expression of vertical and horizontal lines, and where decisive and cutting shadows are required in the composition of his work. If delicate carving forms part of your design, I should say statuary is the best, as you have no veins to distort your detail. I need hardly add that economy should be studied in using precious marbles, without injuring the durability of the work. Contours may be built up in thin sections. Intazzio is a beautiful form of treating marble on an inexpensive ground. Gem-like effects may be obtained by inlaying with smaller pieces, following such ornamental forms as your inventive brains shall dictate. Perhaps the pockets of your clients will be the chief dictator. Heraldic emblazonings, inlaid in marble, are highly effective. The conditions of the heraldry necessitate the use of many varieties, but in such small quantities that on a large simple field they are rarely out of harmony. In addition they map out a large and interesting variety that will save the worry of creation of designs coming entirely from your own brain, and you know the worry of an architect's life makes him hail with pleasure at times a rest from the strain of creation. This heraldic work may be seen to perfection in the chapel of the tombs of the Medici at Florence. At the Pitti Palace are some tables which you may know where marble intazzio can no further go. Alabaster does not appeal to me, it is somewhat sugary in results. If you are fortunate enough to have a sculptor who is a sort of nineteenth century Donatello, let him work his will on statuary or such restful marble. The celebrated monument in the church of S. Giovanni Paulo, at Venice, which Ruskin says is the finest monument in the world, if my recollection serves me correctly, is in white marble, and its beauty comes entirely from the sculptor's art. Such monuments give you much better than any words of mine ample suggestions for marble treatment. I may quote such names as Nicolo Pisano and Verocchio. Photos of some of their work I have brought. Note Pisano's beautiful white altar at Bologna, and Mina de Fiesole's work in Florence. They all show the sculptor as supreme. Why should not we encourage individual young sculptors more? Give them portions of your work in which they can put all the fervor and enthusiasm of young manhood. Their powers may not be ripe, but they possess a verve and intensity that may have forever fled when in later years the imagination is less enthusiastic and the pulses slower. I am sure there are many young sculptors now wanting commissions who have been trained at the academy, and better still, in the best French schools. I maintain that the contemporary French school of sculpture is in its line equal to any school of sculpture that has ever existed, not excepting that of Phidias or that of the Italian Renaissance of the fifteenth and sixteenth centuries. I believe history will confirm this. Why not give these men an opportunity, and help on the movement to found a truly English school of sculpture, rather than give all such work to trading firms of carvers, who will do you any number of superficial feet, properly priced and scheduled, and in the bills of quantities, of any style you please, from prehistoric to Victorian Gothic? Of course, this is our British way of founding a great school. There is one method of treatment that appeals to me very strongly, and that is the application of colored metals to marble, more especially bronze and copper. I may quote as a successful example near the Wellington Memorial at St. Paul's. Another suggestion—although it is not used in combination with marble, but it nevertheless suggests what might be done in the way of bronze panels—that is, the Fawcett Memorial, by Gilbert, in the west chapel at Westminster Abbey. A paper recently read before the Architectural Association, London.— . From the Architect About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2004). Scientific American Supplement, No. 799, April 25, 1891. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/11649/pg11649-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

MARBLE AND MOSAIC

In this blog post, I’m going to take you step-by-step through the process of running MosaicML’s competitor, MPT-7B, on your own AWS SageMaker instance. ChatGPT Are you excited about the capabilities of ChatGPT, but have concerns about exposing your sensitive data to OpenAI? Luckily, there are alternatives that you can run on your own infrastructure. One such alternative is MosaicML’s MPT-7b, a competitor to ChatGPT, which we’ll explore in this blog post. Introduction to MosaicML and MPT-7B MosaicML, recently , has been making waves in the ML community with their MPT-7B model, a supposed competitor to ChatGPT. Despite its promise, running this model can be daunting due to sparse documentation and its heavy resource requirements. However, one can run MPT-7B on AWS SageMaker in a Jupyter notebook, an environment that is beginner-friendly and highly flexible for rapid iteration. This setup allows you to test the model’s feasibility and hardware requirements before deciding to move into production. acquired by Databricks for $1.3 billion Running MPT-7B on AWS SageMaker Running MPT-7B in a Jupyter notebook on AWS SageMaker provides several benefits. Not only can you pay just for what you use and turn it off when you’re done, but the ability to easily rerun portions of your code without having to reload the model saves time during iterative development. But do beware! If you forget to stop your notebook instances, the charges can quickly add up. While this method is relatively convenient, there are some considerations you must take into account. Firstly, loading the model can take up to 20 minutes even on a high-performance GPU, making this process somewhat time-consuming. Also, the cost is a factor to consider, as the running cost is at least $4 per hour. You’ll need to run MPT-7B on at least a instance; anything smaller does not seem feasible. If you opt for EC2 instead of SageMaker, you’ll have to ask AWS for permission to use a instance. p3.2xlarge p3.2xlarge In the next sections I’ll take you step-by-step through how to run the MPT-7B model in your very own SageMaker jupyter notebook: Step 1 - Open the SageMaker Console Fire up the AWS Console and search for SageMaker: Step 2 - Create a notebook instance From the left-side menu, select : Notebook->Notebook instances Click the button: Create notebook instance Specify an instance name. Choose the instance type . Unfortunately, it seems that an instance as powerful as is required, or else your instance may run out of memory or take an excessive amount of time to respond to even the simplest questions. However, please note that this instance will cost approximately $4/hr, so it’s important to monitor your usage carefully. m1.p3.2xlarge m1.p3.2xlarge Create a new IAM role: If your test environment doesn’t have any particularly sensitive data in it then you can grant access to . Otherwise, you’ll need to be more explicit. Any S3 bucket Click the button: Create notebook instance The notebook will then be in a status. This will likely last for about 10 minutes: Pending In the meantime, we’ll download a notebook so that we can upload it after the AWS SageMaker instance has finished provisioning. Step 3 - Download the notebook Head on over to the notebook at and download it: MPT-7B on AWS SageMaker.ipynb In this notebook you’ll notice two main code blocks. The first block loads the MPT-7B tokenizer and model: from torch import cuda, bfloat16
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig

device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'

tokenizer = AutoTokenizer.from_pretrained("mosaicml/mpt-7b-chat",
                                          trust_remote_code=True)

config={"init_device": "meta"}
model = AutoModelForCausalLM.from_pretrained("mosaicml/mpt-7b-chat",
                                             trust_remote_code=True,
                                             config=config,
                                             torch_dtype=bfloat16)

print(f"device={device}")
print('model loaded') The tokenizer is used to encode the question sent to the model and decode the response from the model. Additionally, we obtain the device specification for our GPU so that we can configure the model to utilize it later: import time
from IPython.display import Markdown
import torch
from transformers import StoppingCriteria, StoppingCriteriaList

# mtp-7b is trained to add "<|endoftext|>" at the end of generations
stop_token_ids = [tokenizer.eos_token_id]

# Define custom stopping criteria object.
# Source: https://github.com/pinecone-io/examples/blob/master/generation/llm-field-guide/mpt-7b/mpt-7b-huggingface-langchain.ipynb
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor,scores: torch.FloatTensor,
             **kwargs) -> bool:
  for stop_id in stop_token_ids:
    if input_ids[0][-1] == stop_id:
      return True
    return False

stopping_criteria = StoppingCriteriaList([StopOnTokens()])

def ask_question(question, max_length=100):
  start_time = time.time()

  # Encode the question
  input_ids = tokenizer.encode(question, return_tensors='pt')

  # Use the GPU
  input_ids = input_ids.to(device)

  # Generate a response
  output = model.generate(
    input_ids,
    max_new_tokens=max_length,
    temperature=0.9,
    stopping_criteria=stopping_criteria
  )

  # Decode the response
  response = tokenizer.decode(output[:, input_ids.shape[-1]:][0],
                              skip_special_tokens=True)

  end_time = time.time()
  duration = end_time - start_time

  display(Markdown(response))

  print("Function duration:", duration, "seconds") Note the use of , which is needed or else the model will just start babbling on, even after it has answered our question. stopping_critera See if you want to explore the different options. model generate parameters Now, let’s upload this notebook to SageMaker. Step 4 - Upload the notebook Hopefully by this time your SageMaker notebook instance has finished being provisioned. When it has, click the link: Open Jupyter Then, click the button in the top-right corner of your screen and select the notebook that you just downloaded: Upload Set the kernel to : conda_python3 Step 5 - Run the notebook Select : Cell -> Run All An hourglass logo will then appear in the browser tab: You’ll then need to wait about 10 minutes for the model to be downloaded: After it runs, you’ll see the answer to the question : Explain to me the difference between nuclear fission and fusion Since the model and tokenizer have already been loaded above, you can simply modify the code block and click the button to ask any other questions. This will save you from spending 10 minutes each time you want to test a new question. ask_question Run Step 6 - Stop the notebook As soon as you have finished testing the model, you’ll want to head back to your list of notebook instances and stop it. If you don’t, $4/hr will add up very quickly 💸 Performance Comparison In terms of performance, my preliminary tests suggest that MPT-7B’s results might not be as good as ChatGPT’s. It does a decent job answering questions like: , What is the capital of France? Explain to me the difference between nuclear fission and fusion . Write python code that converts a csv into pdf But for questions like “What is the capital of Belize?” it fails pretty horribly: I am currently collecting more data and will conduct a comprehensive comparative analysis in a follow-up blog post. In that post, I will compare the question and answer performance of MPT-7B, MPT-30B, Falcon-40b, and ChatGPT using actual conversation history. From Testing to Production Once you’re ready to transition from testing to production, SageMaker offers an additional benefit - the ability to create an endpoint for your model. With SageMaker, you can auto-scale based on demand to the endpoint, optimizing your resources. Additional Tips Be mindful that it’s easy for your process to get forked while running in a Jupyter notebook and run out of memory. If this happens, simply shut down the kernel and run all the commands again. If you’re curious about running this model on a platform other than AWS, Google Colab Pro is another viable option at $9/month. However, based on our testing, we found that we exhausted the provided credits within just a few hours. 😳 Another challenge you may face is the inability to utilize the on SageMaker due to a CUDA version incompatibility. Unfortunately, AWS’s current P3 instances do not include a recent CUDA version. Therefore, if you wish to utilize the Triton optimization, you will need to create an EC2 container with command line access. However, it’s important to note that you will also require special permission from AWS Support to run an instance with 8 VCPUs. In a future post, I will provide a detailed guide on how to integrate Triton and utilize a more cost-effective GPU cloud provider, such as . Triton optimization Lambda Labs Final Thoughts While MosaicML’s MPT-7B offers a viable alternative to OpenAI’s ChatGPT, it presents its own set of challenges. Running the model can be time-consuming, expensive, and the available documentation is lacking. However, the ability to keep the model in-house and protect your data from being exposed to OpenAI can be compelling for certain use cases. SageMaker offers great convenience for quickly testing the model and provides the flexibility to transition to production when you’re ready. Whether you’re just starting with MPT-7B or have been using it for a while, we hope this guide has provided valuable insights. Stay tuned for our next blog post, where we’ll delve deeper into the performance comparisons between MPT-7B, MPT-30B, Falcon-40b, and ChatGPT. See the following links if you are interested in learning more about or its larger variant, . MPT-7B MPT-30B And remember, whether you’re working with ChatGPT or MPT-7B, the key is to ensure your use case is served without compromising data privacy and cost-effectiveness. Happy tinkering! Want a Turn-Key Solution for Using MPT or ChatGPT with Your Data? At , we offer ChatGPT for businesses. If you’re interested in leveraging ChatGPT, MPT, or other models with your company’s data, please get in touch with us. MindfulDataAI.com

How to Run MPT-7B on AWS SageMaker: MosaicML's ChatGPT Competitor

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Since it burst into prominence in 2021, the Metaverse has provided millions of individuals with a fascinating realm where people can reside, interact, and learn from the comfort of their homes. This incredible world has acted as an enhanced version of real life, where, by assuming a digital avatar, individuals are able to explore an entirely virtual space with complete freedom. The concept of the Metaverse, which was once merely an idea that excited, burst into prominence in late 2021 when social media behemoth Facebook . Meta's spearheading of the Metaverse movement saw them create a . In somewhat of a response, other tech giants like Microsoft, Nvidia, Google, and Qualcomm have also committed billions of dollars to fuel the development of the Metaverse. rebranded itself as Meta $10 billion investment plan The unprecedented influx of investment that followed Meta's led strategic market insights firm Precedence Research to predict that the Metaverse market size was one that could in value by 2030. Since 2021, the Metaverse has expanded, and industries such as gaming, E-Commerce, education, and entertainment are dominating the space. This has provided a platform for impressive growth and innovation. generate up to $1.3 trillion Alex Hernandez, the visionary CEO of Avem Finance —a blockchain platform that tokenizes real-world assets using the Ethereum Virtual Machine (EVM)— shares an inspiring perspective on the Metaverse: " " In the boundless expanse of the Metaverse, a universe of opportunities awaits discovery. As its expansion accelerates, so does its explosive potential, propelling us into an era of unprecedented growth. The Metaverse is on the verge of transcending boundaries, with visionary minds driving its evolution. Aligned with this optimistic outlook, numerous companies have ventured —and continue to venture— into the . Each brings their unique offerings to the table. From gaming and education to entertainment and fitness solutions, these companies are revolutionizing the virtual world. A prime example in the fitness space is Fitburn, a company that has ingeniously blended gaming elements into the Metaverse's fitness landscape. Metaverse This fusion of technology, wellness, entertainment, and gaming in the Metaverse exemplifies the transformative potential of this vast digital realm. As we explore the Metaverse further in this article, we will encounter an array of groundbreaking ventures, with each contributing to the immersive and dynamic landscape of the Metaverse that exists today. Entertainment in the Metaverse In 2021, the global Metaverse in entertainment market size was valued at $13.8 billion. This figure is projected to reach $221.7 billion between 2022 to 2031. A Compound Annual Growth Rate (CAGR) of 32.3%. Personally, I think these are conservative numbers, and for good reasons. Until two or three years ago, the Metaverse was largely a concept. Now, it is a reality. Events are now regular fixtures in this virtual world. A typical example is DJ Marshmello's live concert in the Fortnite game, which had over 10 million people in attendance. A major example of a Metaverse entertainment company is Roblox. Originally an online gaming platform, the company made its mark on the music industry when it hosted its first virtual concert that had Lil Nas X debut his song "Holiday" to . Metafluence is another Metaverse company, and it combines entertainment and influencer marketing to connect audiences in the Metaverse. Roblox players Artificial Intelligence (AI) and Virtual Reality (VR) Revolutionizing Gaming in the Metaverse AI and VR are the dynamic duo of the Metaverse. The rapid growth of the Metaverse can be attributed to the synergistic combination of these technologies. The Metaverse being a virtual reality environment, provides users with a platform for interactions that are augmented by technologies like AI and Augmented Reality (AR). And with the explosive popularity of the Metaverse as a gaming hub, these technologies are playing a pivotal role in creating exceptional gaming experiences for gaming and Metaverse enthusiasts. where challenges are more engaging and continuously dynamic. The AI algorithms further enhance gaming experiences by personalizing the gameplay of players based on their behavior. The algorithm continuously adapts to meet the preferences of the player, regardless of how it evolves. VR, on the other hand, takes gaming to new levels, as it offers players truly immersive experiences. The technology allows players to interact with virtual environments (re: Metaverse) in stunning ways. Game developers use the power of AI to create intricate and immersive worlds An example of this fusion in technologies is Project Lambo, an innovative gaming platform within the metaverse. The platform seamlessly integrates AI, VR, and Non-Fungible Token (NFT) sharing to deliver captivating and enjoyable experiences for gamers. Gaming Protocols and Web3 Powered Gaming in the Metaverse Gaming protocols play a crucial role in governing gameplay and facilitating data exchange among players in the Metaverse. These protocols serve as a foundation for game development, enabling interoperability and the seamless transfer of assets across different games. A noteworthy example of a pioneering gaming protocol is Winr Games. Game developers can leverage the WINR Software Development Kit (SDK) to swiftly build on-chain games, tapping into the protocol's deep multi-asset liquidity pool. This protocol empowers developers to unlock the potential of the Metaverse by integrating their games with an innovative gaming protocol that promotes liquidity, rewards, and a thriving ecosystem. Still in the conversation about gaming protocols, the convergence of Web3 technology and the Metaverse has brought about an exciting era in virtual gaming. The decentralized infrastructure that prevails within this immersive virtual world grants gamers full control and true ownership of their earned or purchased gaming assets. , players can trade these unique assets, thereby introducing scarcity and enabling new avenues for monetization. By leveraging the power of NFTs A popular example of a Metaverse gaming platform is Fortnite. Fortnite provides a shared virtual space where gamers can interact with each other and explore virtual environments. Live events like concerts and movie screenings are also hosted in the game. Xandar is another gaming platform built in the Metaverse. The game seamlessly integrates AI, and blockchain technology, to provide its metaverse interoperability function, which enables players to venture through vast metaverses within the platform's expansive multiverse. Conclusion The ever-evolving landscape of the Metaverse has seen AI, VR, and blockchain-backed platforms take center stage. These technologies signify a paradigm shift within the gaming and entertainment industries, introducing a new era of user immersion, interoperability, true ownership, and play-to-earn economics. As a result of this, individuals now have an alternate means to entertain themselves and monetize their virtual experiences. The Metaverse is a dynamic and continuously expanding realm. As we continue to embrace its potential, we forge a path towards decentralized gaming and a wide range of applications that will empower players and users alike while also pushing the boundaries of creativity and collaboration. The convergence of these technologies is reshaping the future of entertainment, and with each advancement, the metaverse is being propelled to new heights and is vast potential unlocked for the benefit of everyone.

The Metaverse Mosaic: Unraveling Niche Platforms and Their Unique Virtual Experiences

A Short History of The Commercial Internet: From Mosaic to Web3

It struck me today that people are like mosaics. I’m sure I cannot be the first person to make this connection, but it’s a new idea for me. A mosaic is defined (by my Oxford Dictionary) as “a picture or pattern produced by arranging small colored pieces of hard material…” and “a combination of diverse elements forming a more or less coherent whole.” At distance, a mosaic appears as a whole or complete picture. The whole may be a geometric or abstract pattern, a landscape, or a portrait. Anything. As you get closer, you start to notice the mosaic’s pieces. It turns out that the “picture” is composed of many smaller elements or pieces, which may differ in size, shape, color or shade, and texture. The pieces may be smooth and uniform or jagged and irregular, and the “whole” may contain uniform pieces in one area and jagged pieces in another. Some parts may look like broken pieces or fragments and these broken fragments may appear to be leftovers from some other earlier whole object or rejects from another thing. The closer you get, the more you learn how the picture is more diverse, detailed and complicated than it appears from a distance. Mosaics are mounted on some kind of skeleton structure, base, or . And the mosaic’s pieces are held together by a medium in which they rest and bond, forming a “more or less whole.” foundation coherent A “more or less coherent whole”? I’ll take that: the more and the less. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Notes Toward a Unified Theory of the Coherent Person (Mosaic)

Table of Links Abstract and 1 Introduction 2 Related Work 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 5 The DeepSea System 5.1 Map View
5.2 Core View 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.3 Limitations 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 5 THE DEEPSEE SYSTEM From our design challenges and user tasks, we developed DeepSee, an interactive workspace for studying deep ocean ecosystems. Our system tightly integrates three coordinated views. In the Map View (Sect. 5.1), users explore maps at near-centimeter resolution of the ocean floor overlaid with locations of and information about previous dives and sampled cores. After selecting cores of interest, users can compare geochemical, physical, and biological attributes across horizons between cores in the Core View (Sect. 5.2) or analyze interpolated attribute values in 3D between cores in the Interpolation View (Sect. 5.3). 5.1 Map View The Map View (Fig. 3) plots previously sampled cores as a scatter plot using latitude/longitude on top of maps representing geological landscapes (e.g., bathymetry) and surface biological and ephemeral “soft” features (e.g., photomosaics and high-resolution lidar). By visualizing both region-level (map) and core-level (measurement) data simultaneously, users gain more context about spatial trends in the sampling environment by familiarizing themselves with data from previous cruises to help make new inferences about potential future sampling locations. Users start with filtering cores (Fig. 3A) by named location, date range, and core fate (T6). For example, a scientist not familiar with a previous field site could explore names and date ranges to understand when cores were sampled, or a scientist returning to the site could be interested in a specific geological feature or set of cores previously sampled for a follow-up investigation (T9). Then, users navigate the plot by clicking and dragging to pan and zoom (Fig. 3B) (T7). Cores remain the same size at all levels of zoom to avoid screen clutter and help users track them as the view changes. We also provide several details on demand (Fig. 3C). We label the plot with latitude and longitude grid lines and compute the width and height of the current viewport in metric units. This gives users a baseline for the size of the geological and biological features they see on the map as well as the distance between sampled cores at all times (T5). On hover, users can see the named location, fate, date, and exact latitude and longitude of any core. Users can also switch the map on the fly (Fig. 3D) and the boundaries of the viewport will persist, helping users maintain context as the view changes (T5). Once a site of interest has been located, we built an in-situ drawing tool on the map with six colors and undo/redo capabilities (Fig. 3E) to support evidence marshaling through annotation (T3). For example, a user could draw an outline on a bathymetric map around a surface expression of biological activity, write a small note to remember/share their intent, then switch to a seafloor mosaic image to see the same drawn feature projected on the sediment composition (T2). The user naturally concludes their initial exploration by selecting cores (Fig. 3F) to visualize in the Core View or Interpolation View (T8). After clicking and dragging over any number of points, we draw the rectangular convex hull that encases the selected points as a white border with a triangle at the top that always points North (T5). This border is drawn again in the Interpolation View to help orient the user at all times when comparing with the Map View 5.2 Core View The Core View (Fig. 4) arranges columns of horizontal bar charts that visualize a single core-level (measurement) or sample-level (parameter) attribute as individual bars at each horizon of the selected cores in the Map View. This plot enables rapid comparison of attributes in one dimension (depth) to quickly gain insights into biological and geochemical parameter distributions, helping users investigate subsurface gradients or trends among selected cores from a single expedition or multiple expeditions simultaneously. To add a chart of a single attribute for each selected core in the Map View, users search for and select attributes using a dropdown menu. The bar charts show core IDs, fates, dates, and are aligned vertically by depth horizon to facilitate comparison across bar charts along the same horizontal baseline (T4). For example, with a small number of cores selected around a vent in the Map View, a user could use the Core View to ask, “Which cores have high sulfide values? Do all cores with high sulfide also have high relative taxa ‘X’ abundance? If not, what other geochemical parameters are different that might explain this difference?” (T1, T2) leading to predictive analysis such as “As we move further away from this vent, do we see these values increasing or decreasing? Are all orange mats associated with relative taxa ‘X’ abundance?” (T10) We chose horizontal bars to visually mirror the measurement of parameters by depth in the sediment. Where horizons are different sizes, the smallest step size (typically 1 cm) is used. If a core does not have sample data at that resolution (e.g., parameters measured every 3 cm), we duplicate the bar by the number of smallest steps at that scale and repeat the horizon label to make this clear for the user. Users have a choice of six colorblind-friendly palettes (Viridis, Cividis, Greyscale, Inferno, Plasma, Magma). Finally, users can directly deselect cores from this view, to narrow the space of cores for subsequent analysis in the Interpolation View (T8). This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu);
(2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu);
(3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu);
(4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com);
(5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu);
(6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu);
(7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu);
(8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu);
(9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org);
(10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org);
(11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org);
(12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu);
(13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu);
(14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu);
(15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu);
(16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov);
(17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Methodology 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 4.3 Design Challenges and User Tasks 5 The DeepSea System 5 The DeepSea System 5.1 Map View 5.2 Core View 5.1 Map View 5.2 Core View 5.3 Interpolation View and 5.4 Implementation 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.2 Expert Interviews 7.3 Limitations 7.3 Limitations 7.4 Lessons Learned 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 8 Conclusions and Future Work, Acknowledgments, and References 5 THE DEEPSEE SYSTEM From our design challenges and user tasks, we developed DeepSee, an interactive workspace for studying deep ocean ecosystems. Our system tightly integrates three coordinated views. In the Map View (Sect. 5.1), users explore maps at near-centimeter resolution of the ocean floor overlaid with locations of and information about previous dives and sampled cores. After selecting cores of interest, users can compare geochemical, physical, and biological attributes across horizons between cores in the Core View (Sect. 5.2) or analyze interpolated attribute values in 3D between cores in the Interpolation View (Sect. 5.3). 5.1 Map View The Map View (Fig. 3) plots previously sampled cores as a scatter plot using latitude/longitude on top of maps representing geological landscapes (e.g., bathymetry) and surface biological and ephemeral “soft” features (e.g., photomosaics and high-resolution lidar). By visualizing both region-level (map) and core-level (measurement) data simultaneously, users gain more context about spatial trends in the sampling environment by familiarizing themselves with data from previous cruises to help make new inferences about potential future sampling locations. Map View Users start with filtering cores (Fig. 3A) by named location, date range, and core fate (T6) . For example, a scientist not familiar with a previous field site could explore names and date ranges to understand when cores were sampled, or a scientist returning to the site could be interested in a specific geological feature or set of cores previously sampled for a follow-up investigation (T9) . Then, users navigate the plot by clicking and dragging to pan and zoom (T6) (T9) (Fig. 3B) (T7). Cores remain the same size at all levels of zoom to avoid screen clutter and help users track them as the view changes. We also provide several details on demand (Fig. 3C). We label the plot with latitude and longitude grid lines and compute the width and height of the current viewport in metric units. This gives users a baseline for the size of the geological and biological features they see on the map as well as the distance between sampled cores at all times (T5). On hover, users can see the named location, fate, date, and exact latitude and longitude of any core. Users can also switch the map on the fly (Fig. 3D) and the boundaries of the viewport will persist, helping users maintain context as the view changes (T5) . (T7). (T5) Once a site of interest has been located, we built an in-situ drawing tool on the map with six colors and undo/redo capabilities (Fig. 3E) to support evidence marshaling through annotation (T3). For example, a user could draw an outline on a bathymetric map around a surface expression of biological activity, write a small note to remember/share their intent, then switch to a seafloor mosaic image to see the same drawn feature projected on the sediment composition (T2). The user naturally concludes their initial exploration by selecting cores (Fig. 3F) to visualize in the Core View or Interpolation View (T8). After clicking and dragging over any number of points, we draw the rectangular convex hull that encases the selected points as a white border with a triangle at the top that always points North (T5). This border is drawn again in the Interpolation View to help orient the user at all times when comparing with the Map View 5.2 Core View The Core View (Fig. 4) arranges columns of horizontal bar charts that visualize a single core-level (measurement) or sample-level (parameter) attribute as individual bars at each horizon of the selected cores in the Map View. This plot enables rapid comparison of attributes in one dimension (depth) to quickly gain insights into biological and geochemical parameter distributions, helping users investigate subsurface gradients or trends among selected cores from a single expedition or multiple expeditions simultaneously. Core View To add a chart of a single attribute for each selected core in the Map View, users search for and select attributes using a dropdown menu. The bar charts show core IDs, fates, dates, and are aligned vertically by depth horizon to facilitate comparison across bar charts along the same horizontal baseline (T4). For example, with a small number of cores selected around a vent in the Map View, a user could use the Core View to ask, “Which cores have high sulfide values? Do all cores with high sulfide also have high relative taxa ‘X’ abundance? If not, what other geochemical parameters are different that might explain this difference?” (T1, T2) leading to predictive analysis such as “As we move further away from this vent, do we see these values increasing or decreasing? Are all orange mats associated with relative taxa ‘X’ abundance?” (T10) We chose horizontal bars to visually mirror the measurement of parameters by depth in the sediment. Where horizons are different sizes, the smallest step size (typically 1 cm) is used. If a core does not have sample data at that resolution (e.g., parameters measured every 3 cm), we duplicate the bar by the number of smallest steps at that scale and repeat the horizon label to make this clear for the user. Users have a choice of six colorblind-friendly palettes (Viridis, Cividis, Greyscale, Inferno, Plasma, Magma). Finally, users can directly deselect cores from this view, to narrow the space of cores for subsequent analysis in the Interpolation View (T8). (T4). (T1, T2) (T10) (T8). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Authors: Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu).

Explore the Ocean Floor in High-Resolution with DeepSee

Table of Links Abstract and 1 Introduction 2 Related Work 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 5 The DeepSea System 5.1 Map View
5.2 Core View 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.3 Limitations 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 4.2 Workflow and Data To quantify the distribution of microbial activity in deep sea sediments, microbial ecologists, geochemists and geologists first collect biological and geological samples (often sediment, rock or pore water) from the deep ocean. These samples are then used to examine the geochemical, sedimentological, and biological characteristics of the environment. The sampled cores and associated parameters are both sparse and multidimensional (i.e., cores with a fixed location in 3D, sampled at time points ranging from days to decades). Collecting samples begins with an oceanographic research expedition, where scientists use various human- or robot-operated tools to map the study area and execute sampling dives. For the examples presented in this paper, AUVs (Autonomous Underwater Vehicles) equipped with multibeam sonar collected 1 m lateral resolution bathymetry and acoustic backscatter covering an ≈ 30 km2 area including multiple hydrothermal vent fields. With this context mapping completed, a Low Altitude Survey System (LASS) fielded on an ROV (Remotely Operated Vehicle) then mapped one vent field at much higher resolution, yielding 5 cm resolution bathymetry from a multibeam sonar (Fig. 2A), 1 cm resolution bathymetry from LIDAR, and 5 cm photomosaics from a color stereo camera rig (Fig. 2B) [37]. With these maps, scientists then planned the exploration and execution of sampling dives with ROVs or HOVs (Human Occupied Vehicles) to collect sediment samples from the ocean floor and bring them back to the ship for downstream processing. The samples DeepSee currently visualizes are sediment push cores (PC), hollow polycarbonate tubes 7 cm diameter and 30 cm tall, usually sectioned shipboard into 1 cm or 3 cm tall horizons. There are two types of data attributes: (1) core-level (Table 1, left) and sample-level (Table 1, right). For core-level analysis, there can be more than a dozen separate measurements conducted for a single horizon with a core; considering a core can have between 7-12 horizons, there are over 84 identification data points to interpret per core. For sample-level analysis, each horizon is processed for a wide variety of geochemical parameters, including sulfide, sulfate, iron, and other chemicals associated with metabolism or the state of the environment. Each horizon is also preserved and sequenced for a quantitative measure of how many microbial community members (taxa, e.g., ANME archaea or sulfate-reducing Desulfobacterota) live there. This typically results in over 500 discrete taxonomic, geochemical, or physical parameters per core, used to identify spatial trends in distributions (Sect. 4.3). Finally, cores are often collected in pairs or clusters for different experiments and assigned a core fate descriptor, e.g., “Geochem” or “Live”, which differentiates what type of metadata and parameters are associated with the core. In DeepSee, these data were stratified into three hierarchical levels and visualized in different ways: Region-level data. Region-level data (Fig. 2) are predominantly map-format, including low-altitude survey system (LASS) multi beam amplitude and bathymetry (5 cm) [31], AUV bathymetry (1 m lateral resolution), photo mosaics (5 cm resolution), and LIDAR bathymetry (2 cm). Together, these data types reveal the geological landscape (bathymetry) and surface biological and ephemeral “soft” features (photo mosaics and high-resolution LIDAR). Core-level data. Core-level data (Table 1, left) are unique x,y points within a methane seep where subsurface sediments were sampled. Cores comprise a minimum of 11 parameters stored in a CSV file, including core IDs (e.g., dive number, core number, GPS location), measurements (e.g., temperature, sulfate, sulfide), and associated data (e.g., core type, habitat description). Sample-level data. Sample-level data (Table 1, right) is typically parameters subdivided within cores into horizons of 2-3 cm each. DeepSee treats each horizon (row) as a sample belonging to a core within a seep. Recent cruises have returned cores with up to 257 depth horizons described in CSV format by 21 physicochemical parameters (e.g. pH), 12 geochemical measurements (e.g. methane concentration), and over 500 biological parameters (microbial identity and abundances). This paper is available on arxiv under CC BY 4.0 DEED license. [3] The example data is provided by Speth et al. [45]. Authors:
(1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu);
(2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu);
(3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu);
(4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com);
(5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu);
(6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu);
(7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu);
(8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu);
(9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org);
(10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org);
(11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org);
(12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu);
(13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu);
(14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu);
(15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu);
(16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov);
(17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Methodology 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 4.3 Design Challenges and User Tasks 5 The DeepSea System 5 The DeepSea System 5.1 Map View 5.2 Core View 5.1 Map View 5.2 Core View 5.3 Interpolation View and 5.4 Implementation 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.2 Expert Interviews 7.3 Limitations 7.3 Limitations 7.4 Lessons Learned 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 8 Conclusions and Future Work, Acknowledgments, and References 4.2 Workflow and Data To quantify the distribution of microbial activity in deep sea sediments, microbial ecologists, geochemists and geologists first collect biological and geological samples (often sediment, rock or pore water) from the deep ocean. These samples are then used to examine the geochemical, sedimentological, and biological characteristics of the environment. The sampled cores and associated parameters are both sparse and multidimensional (i.e., cores with a fixed location in 3D, sampled at time points ranging from days to decades). Collecting samples begins with an oceanographic research expedition , where scientists use various human- or robot-operated tools to map the study area and execute sampling dives. For the examples presented in this paper, AUVs (Autonomous Underwater Vehicles) equipped with multibeam sonar collected 1 m lateral resolution bathymetry and acoustic backscatter covering an ≈ 30 km2 area including multiple hydrothermal vent fields. With this context mapping completed, a Low Altitude Survey System (LASS) fielded on an ROV (Remotely Operated Vehicle) then mapped one vent field at much higher resolution, yielding 5 cm resolution bathymetry from a multibeam sonar (Fig. 2A), 1 cm resolution bathymetry from LIDAR, and 5 cm photomosaics from a color stereo camera rig (Fig. 2B) [37]. With these maps, scientists then planned the exploration and execution of sampling dives with ROVs or HOVs (Human Occupied Vehicles) to collect sediment samples from the ocean floor and bring them back to the ship for downstream processing. The samples DeepSee currently visualizes are sediment push cores (PC) , hollow polycarbonate tubes 7 cm diameter and 30 cm tall, usually expedition push cores (PC) sectioned shipboard into 1 cm or 3 cm tall horizons . There are two types of data attributes: (1) core-level (Table 1, left) and sample-level (Table 1, right). For core-level analysis, there can be more than a dozen separate measurements conducted for a single horizon with a core; considering a core can have between 7-12 horizons, there are over 84 identification data points to interpret per core. For sample-level analysis, each horizon is processed for a wide variety of geochemical parameters , including sulfide, sulfate, iron, and other chemicals associated with metabolism or the state of the environment. Each horizon is also preserved and sequenced for a quantitative measure of how many microbial community members ( taxa , e.g., ANME archaea or sulfate-reducing Desulfobacterota) live there. This typically results in over 500 discrete taxonomic, geochemical, or physical parameters per core, used to identify spatial trends in distributions (Sect. 4.3). Finally, cores are often collected in pairs or clusters for different experiments and assigned a core fate descriptor, e.g., “Geochem” or “Live”, which differentiates what type of metadata and parameters are associated with the core. In DeepSee, these data were stratified into three hierarchical levels and visualized in different ways: horizons parameters chemicals taxa core fate Region-level data. Region-level data (Fig. 2) are predominantly map-format, including low-altitude survey system (LASS) multi beam amplitude and bathymetry (5 cm) [31], AUV bathymetry (1 m lateral resolution), photo mosaics (5 cm resolution), and LIDAR bathymetry (2 cm). Together, these data types reveal the geological landscape (bathymetry) and surface biological and ephemeral “soft” features (photo mosaics and high-resolution LIDAR). Region-level data. Core-level data. Core-level data (Table 1, left) are unique x,y points within a methane seep where subsurface sediments were sampled. Cores comprise a minimum of 11 parameters stored in a CSV file, including core IDs (e.g., dive number, core number, GPS location), measurements (e.g., temperature, sulfate, sulfide), and associated data (e.g., core type, habitat description). Core-level data. Sample-level data. Sample-level data (Table 1, right) is typically parameters subdivided within cores into horizons of 2-3 cm each. DeepSee treats each horizon (row) as a sample belonging to a core within a seep. Recent cruises have returned cores with up to 257 depth horizons described in CSV format by 21 physicochemical parameters (e.g. pH), 12 geochemical measurements (e.g. methane concentration), and over 500 biological parameters (microbial identity and abundances). Sample-level data. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [3] The example data is provided by Speth et al. [45]. Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Authors: Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu).

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