Generated speech from text using onyx voice.

📊 BNB ML Features Dataset (2019-2025) 255,121 rows of processed BNB market data. 📈 Contents: 6 timeframes: 5m to 1d 19 engineered features Exchange token dynamics Regime detection labels 🎯 Use cases: BNB-specific trading models Binance ecosystem analysis Stablecoin pair research Largest single-asset dataset in this collection.

📊 Solana ML Features Dataset (2019-2025) 245,003 rows of processed SOL market data. 📈 Contents: 7 timeframes: 5m to 1d 19 features including tail_lambda High-volatility regime labels Momentum and mean-reversion signals 🎯 Use cases: Altcoin-specific models Cross-asset correlation studies Scalping strategy development Covers SOL's full price history.

📊 Ethereum ML Features Dataset (2019-2025) 245,003 rows of processed ETH market data with ML-ready features. 📈 Contents: 7 timeframes: 5m, 15m, 30m, 1h, 4h, 12h, 1d 19 engineered features Volatility expansion signals Drawdown and momentum indicators 🎯 Use cases: DeFi strategy optimization ETH/BTC correlation analysis Risk-adjusted position sizing Full 5-year history. Clean, labeled data.

📊 Bitcoin ML Features Dataset (2019-2025) 245,003 rows of processed Bitcoin market data with ML-ready features. 📈 Contents: 7 timeframes: 5m, 15m, 30m, 1h, 4h, 12h, 1d 19 engineered features Hindsight oracle labels (optimal_exposure) Tail risk indicators, volatility ratios, regime detection 🎯 Use cases: Train exposure prediction models Backtest BTC trading strategies Volatility regime classification Full 5-year history. Production-ready format.

990K premium labeled trading bars for BTC/ETH/SOL/BNB (2019-2025). Includes validated Machine Learning features: volatility regimes, tail risk lambda, and hindsight-optimal exposure. Verified IC Score: 0.775. Perfect for ML/RL bot development.

The question "Are permanent magnets AC or DC?" is based on a common misunderstanding of electrical terminology. The terms AC (Alternating Current) and DC (Direct Current) are used to describe the flow of electrical energy, not the static, ever-present field created by a permanent magnet. Therefore, the direct answer is that permanent magnets are neither AC nor DC. They are a distinct physical phenomenon defined by the unchanging alignment of their internal magnetic domains. However, understanding the relationship between permanent magnets and AC/DC is essential, as these magnets are integral to the devices that generate and utilize both types of currents. The Nature of Permanent Magnets A permanent magnet operates entirely independently of any external electrical power source. Static Field: A permanent magnet creates a static, unchanging magnetic field. The North pole always remains the North pole, and the South pole always remains the South pole. The field does not oscillate or reverse direction over time. Internal Alignment: The magnetic field is generated by the uniform alignment of tiny magnetic regions (domains) within the material. Once this alignment is achieved during manufacturing, it remains locked in place, providing a constant magnetic force. Because they do not involve the flow of current, they cannot be categorized using the terms DC (constant flow) or AC (oscillating flow). Permanent Magnets and Direct Current (DC) Permanent magnets are fundamental to many DC-powered devices, especially those involving continuous, non-oscillating motion. DC Motors: In a simple DC motor, the stationary permanent magnets (stator) provide the constant magnetic field that interacts with the magnetic field generated by the electromagnet in the rotating part (rotor). This interaction creates the continuous torque necessary for rotation. The direct current provides the steady, non-reversing power needed for the rotor's electromagnet. DC Generators: Conversely, in a DC generator, the mechanical motion forces the rotor's windings to cut the constant field lines created by the permanent magnets, inducing a steady (though often pulsating) direct current. Permanent Magnets and Alternating Current (AC) Permanent magnets are also crucial to the generation and operation of AC power, though the relationship is slightly more complex. AC Generators (Alternators): The most common way to generate large-scale AC power (like in power plants) involves rotating wire coils within a powerful, constant magnetic field. Whether that field is supplied by an electromagnet or powerful permanent magnets (as in some smaller generators), the static field is what the windings "cut" to induce the current. Because the windings rotate, the induced voltage constantly changes direction, creating AC. Permanent Magnet Synchronous Motors (PMSM): Used extensively in modern electric vehicles and high-efficiency appliances, these AC motors use powerful rare-earth magnets embedded in the rotor. The motor's electronic controller sends alternating current to the stator windings, creating a rotating magnetic field that constantly chases the static field of the permanent magnets, resulting in highly efficient motion. In conclusion, while permanent magnets themselves are neither AC nor DC, they provide the essential, static magnetic platform needed to transform both AC electricity into motion (in motors) and mechanical motion into AC or DC electricity (in generators).

The electric vehicle (EV) is a marvel of modern engineering, and at its core lies the electric motor—the component responsible for converting electrical energy into motion. A key question in understanding EV technology is whether these powerful engines rely on permanent magnets. The simple answer is Yes, a vast and growing number of high-performance EV motors absolutely rely on powerful permanent magnets, specifically the rare-earth type. However, the EV industry is diverse, and not all motors use them. The choice of motor type defines the vehicle's performance, cost, and efficiency, and it hinges on the role of permanent magnetism. The Rise of the Permanent Magnet Synchronous Motor (PMSM) The dominant motor type in modern, mainstream EVs (including many models from Tesla, Toyota, and GM) is the Permanent Magnet Synchronous Motor (PMSM). How it Works: In a PMSM, powerful rare-earth permanent magnets (usually Neodymium) are embedded directly into the rotor (the spinning part). The stationary coils (stator) receive alternating current (AC) from the inverter, creating a rotating magnetic field that constantly "chases" the fixed magnetic field of the rotor magnets. This continuous chasing action provides highly efficient torque. The Efficiency Edge: The main advantage of the PMSM is its high efficiency, particularly at lower speeds and in stop-and-go driving conditions (which is typical for city driving). Because the permanent magnets create their own field without needing external electricity, the motor wastes less energy generating the field, leading to better battery range. The Alternative: Induction Motors (IM) Not all EVs use permanent magnets. Some manufacturers, notably Tesla in its earlier and some current larger vehicles, utilize the AC Induction Motor (IM). How it Works: IMs do not use permanent magnets. Instead, they rely purely on electromagnetism. The stator's rotating field induces a magnetic field in the rotor's windings, and the interaction between the two fields creates torque. The Cost and Heat Advantage: Induction motors are generally cheaper to manufacture and do not rely on scarce rare-earth materials. They also perform well at extremely high speeds and are more robust under high-temperature conditions. The Trade-Off: IMs tend to be less efficient than PMSMs, particularly at partial load or low speed, because energy must constantly be expended (wasted as heat) to induce the magnetic field in the rotor. The Future: The Hybrid Approach To combine the best of both worlds—the efficiency of the PMSM and the high-speed robustness of the IM—many new vehicles are adopting hybrid motor designs, such as the Permanent Magnet-Assisted Synchronous Reluctance Motor (PMa-SynRM). These motors use a smaller amount of permanent magnets to boost the efficiency of a reluctance motor (a type of motor that uses the shape of its rotor for torque). This strategy reduces reliance on expensive rare-earth materials while maintaining high efficiency. In summary, the most common and efficient EV motors today are, indeed, built around powerful permanent magnets. While cost and sustainability concerns drive innovation toward magnet-free alternatives, the performance and efficiency benefits of the rare-earth magnet remain the gold standard for electric propulsion.

describes our first full training run, which tried to invert an earlier task. Instead of turning CIF output into JSON, we aimed for Qwen 2.5 to take a description of a crystal structure and return a valid CIF. The logged metrics looked promising, with progress up to 756 tokens planned, but we should have watched the raw policy outputs more closely. Between steps 70 and 100, the policy learned that repeating tokens could earn a good reward, so initial CIF-like tokens appeared for a while before the output degraded into repetition. Example outputs showed many repeated lines of the same data fields, rather than a valid CIF structure. This degradation is common in LLM RL post-training. The next run will add a stronger divergence penalty and better monitoring to track raw policy outputs more reliably. More updates will follow.

Explore how speed, mobile design, and game quality influence Filipino players when choosing online casino apps like Pusta88 Casino.

Phase diagram of Fe13Sb3N2; eabovehull: 0.162266 eV/atom; predicted_stable: False

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.04 THz

Fe13Sb3N2 (requested SG: P4mm #99, calculated SG: P1 #1, optimized: 400 steps, cell relaxed (isotropic))

Phase diagram of Fe16BiN2; eabovehull: 0.305757 eV/atom; predicted_stable: False

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.04 THz

Fe16N2Bi (requested SG: P-42m #111, calculated SG: P1 #1, optimized: 182 steps, cell relaxed (isotropic))

MatterGen generated Fe7B4W5 crystal (space group: Pm #6, crystal system: monoclinic)

MatterGen generated Fe(BW)2 crystal (space group: P2_1/m #11, crystal system: monoclinic)

MatterGen generated Fe3B5W crystal (space group: P1 #1, crystal system: triclinic)

Supercell 3x3x3 of MnFe3Co (Space group: Cm, 108 symmetry operations)

Standalone, embeddable HTML with MatterViz Trajectory viewer

Phase diagram of MnFe3Co; eabovehull: 0.224780 eV/atom; predicted_stable: False

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); imaginary modes detected; min freq = -1.79 THz

Mn1Fe3Co1 (requested SG: Pm #6, calculated SG: Amm2 #38, optimized: 48 steps, cell relaxed (isotropic))

Phase diagram of MnFe3Co; eabovehull: 0.076678 eV/atom; predicted_stable: False

Crystal structure for Mn1Fe3Co1 | Space group: 8 | Atoms: 5

AI-discovered magnetic material: Mn1Fe3Co1 (performance score: 0.900) | Space group: 8 (resolved) | Generated from scratch | Properties: Tc: 645K, Ms: 0.19T, $7/kg | Discovered in 10 iterations

Supercell 3x3x3 of MnFe2CoW (Space group: P3m1, 162 symmetry operations)

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); imaginary modes detected; min freq = -4.39 THz

AI-discovered magnetic material: Fe2CoMnW (performance score: 0.810) | Space group: 156 (resolved from structure) | AI-generated from scratch using crystal structure prediction | Key properties: Tc: 555K, Ms: 0.11T, MAE: 5.50mJ/m^3, Cost: $21/kg, E_hull: 0.262eV/atom, Dynamically stable | Discovered in 3 AI iterations | This material demonstrates that high magnetic performance can be achieved with relatively low cost and a small unit cell size. The high Curie temperature and magnetic anisotropy energy suggest potential for magnetic applications requiring thermal stability and strong anisotropy. The dynamic stability is a positive sign for synthesis feasibility. However, the elevated energy above hull suggests that further optimization or doping might be needed to improve thermodynamic stability. This insight highlights a trade-off between achieving strong magnetic properties and maintaining low energy above hull in this chemical composition and structure.

Calculate Magnetic Anisotropy Energy (MAE) using DFT

A new API is available to calculate magnetic anisotropy energy (MAE) using first-principles DFT with ABACUS. It’s designed for researchers who need accurate MAE values and are willing to run longer calculations. Expect 30 minutes to 2 hours per job, depending on system size and convergence. The service runs on an A100 GPU and is priced as a paid API.

Updated how we do route names. Previously, route names were the route's method (GET, POST, etc.) and the route path. This was limiting and unnecessary. We still store method and path, and now you are

🚀 The 7-Day Price Volatility Dataset: Market Dynamics Tracker 🔍 Dataset Description & Fields This dataset is designed to capture short-term market liquidity and volatility by comparing the initial price of an item to its price exactly one week later. It is a powerful tool for analyzing pricing strategies, predicting short-term market trends, and identifying products with significant price instability. Interpretation: A positive percentage (e.g., ) indicates the item has experienced appreciation (price increase) over the week, potentially signaling rising demand or reduced supply. A negative percentage (e.g., ) indicates depreciation (price decrease), possibly due to overstocking, new competition, or sales events. A value close to signifies strong price stability. 💡 Advanced Analytical Uses The simple structure of this data facilitates a wide range of analytical tasks: Market Timing: Determine the optimal days or conditions under which prices are most likely to drop (or surge) over a 7-day window. Product Segmentation: Group products by their volatility index to understand which market segments are most sensitive to change. Pricing Strategy Audit: Assess the effectiveness of promotional campaigns by observing the seven-day rebound or stabilization after an initial price change. Risk Assessment: Use the change percentage as a risk indicator for inventory management or investment decisions. First time time it was scraped at 02-12-2025 and next time at 09-12-2025.

🚀 The 7-Day Price Volatility Dataset: Market Dynamics Tracker 🔍 Dataset Description & Fields This dataset is designed to capture short-term market liquidity and volatility by comparing the initial price of an item to its price exactly one week later. It is a powerful tool for analyzing pricing strategies, predicting short-term market trends, and identifying products with significant price instability. Interpretation: A positive percentage (e.g., ) indicates the item has experienced appreciation (price increase) over the week, potentially signaling rising demand or reduced supply. A negative percentage (e.g., ) indicates depreciation (price decrease), possibly due to overstocking, new competition, or sales events. A value close to signifies strong price stability. 💡 Advanced Analytical Uses The simple structure of this data facilitates a wide range of analytical tasks: Market Timing: Determine the optimal days or conditions under which prices are most likely to drop (or surge) over a 7-day window. Product Segmentation: Group products by their volatility index to understand which market segments are most sensitive to change. Pricing Strategy Audit: Assess the effectiveness of promotional campaigns by observing the seven-day rebound or stabilization after an initial price change. Risk Assessment: Use the change percentage as a risk indicator for inventory management or investment decisions. First time time it was scraped at 01-12-2025 and next time at 08-12-2025.

Baseball is more than just a sport — it is a way to enjoy time with friends, sharpen reflexes, and stay active. But to truly enjoy the game and perform well, you need a good bat. A proper baseball bat

Investing in the right yoga mat can completely transform your at-home fitness or yoga sessions. Whether you’re stretching, doing full yoga flows, or simply practicing mindfulness, a reliable mat impro

Bitcoin is projected to stay elevated over the next year, trading in a broad, gentle upward range rather than a big boom or crash. The forecast for weekly data through December 2026 suggests a modest rise into early/mid‑2026, with a peak just under 94,000 USD, followed by a return to a wide, range‑bound zone around today’s level (roughly 86,000 to 92,000). By the end of 2026, the price is seen near 89,400 USD, about 3–4% higher than the latest reading.

Forecasts for Bitcoin Price with 52-period horizon (weekly)

WTI crude oil is expected to drift higher over the next year, moving from about $59–60 per barrel today to roughly $70–71 by late 2026, with plenty of ups and downs along the way. Prices are likely to stay roughly in a $60–75 range, with brief dips into the mid‑50s if shocks hit supply or demand weakens. The move higher is supported by geopolitics and risk premiums tied to attacks on oil infrastructure and potential limits on Venezuela’s exports, but near‑term gains are capped by softer demand and large inventories.

Forecasts for Crude Oil Price (WTI) with 52-period horizon (weekly)

Forecasts for Copper Price with 12-period horizon (monthly)

Forecasts for Silver Futures with 12-period horizon (monthly)

Forecasts for Gold Futures with 12-period horizon (monthly)

Observed and forecasted housing market data for December 2025 (monthly).

HubSpot Zimbabwe Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Zimbabwe. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Zambia Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Zambia. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot South Africa Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within South Africa. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Kosovo Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Kosovo. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Vietnam Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Vietnam. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Venezuela Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Venezuela. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Uruguay Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Uruguay. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Uganda Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Uganda. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Ukraine Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Ukraine. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Taiwan Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Taiwan. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Trinidad and Tobago Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Trinidad and Tobago. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

API for first-principles calculations and properties

Welcome

Interactive browser visualizations for materials science, by @janosh

Random bulk crystal generation with PyXtal and Orb v3