Abstract

MarketScan databases in the OMOP data model (https://www.ohdsi.org/data-standardization/the-common-data-model/)

Integrate map with high quality SNPs, bin markers and traditional markers. (XLSX 28 kb)

Verify the high quality SNPs between parents by using Sequenom MassARRAY. (XLSX 10 kb)

A Dataset of Binary Maps of Active Regions with Polarity Inversion Lines

  Dataset Summary

This dataset provides hourly binary segmentation maps (4096×4096 resolution) derived from Solar Dynamics Observatory (SDO) / Helioseismic and Magnetic Imager (HMI) line-of-sight magnetograms. The maps highlight regions containing Active Regions (ARs) and Polarity Inversion Lines (PILs). The dataset spans observations from May 13, 2010 to December 31, 2024 and is intended for image… See the full description on the dataset page: https://huggingface.co/datasets/nasa-ibm-ai4science/surya-bench-ar-segmentation.

Summary of production and alignment for 280 IBM Syn10 lines. (XLSX 36 kb)

Genetic and physical coordinate for Syn4 and Syn10 population. (XLSX 13078 kb)

Context

This project is to analyze how weather would affect bike-sharing demand in urban areas. To complete this project, first collect and process related weather and bike-sharing demand data from various sources, perform exploratory data analysis on the data, and build predictive models to predict bike-sharing demand. Finally, all results will be combined and connected to a dashboard displaying an interactive map and associated visualization of the weather and the estimated bike demand.

QMapDataset is a dataset generator designed to help build machine learning models for qubit mapping. In many current quantum computers, qubits are not fully connected — for example, in superconducting quantum processors. In a quantum circuit, a two-qubit gate may need to be applied between qubits that are not directly connected in the hardware. To handle this, a series of SWAP gates (which effectively permute qubits) must be inserted. However, this increases the circuit depth, computation time, and overall execution cost. The qubit-mapping problem aims to find an optimal assignment between the physical qubits of the hardware and the logical qubits in the quantum circuit to minimize the number of required SWAP gates.

QMapDataset samples a representative set of quantum circuits, including benchmark algorithms (Grover, QFT, Shor, etc.) and random circuits with varying numbers of qubits and depths. Additionally, data augmentation is applied via qubit permutations to further enrich the dataset. This release contains 4,000 samples (2,000 for the IBM Eagle-3 architecture and 2,000 for the IBM Heron-1 architecture). If you need more data, you can generate it using the QMapDataset Python code available at: https://github.com/rscadrien/QMapDataset

Each sample includes three files: circuit.json, hardware.json, and mapping.json.

circuit.json contains information about the quantum circuit, including metadata on the sampling procedure (type of algorithm, number of permutations, etc.), the number of logical qubits, the circuit depth, and counts of single-qubit and two-qubit gates.

hardware.json contains information about the hardware, including processor type, number of physical qubits, basis gates, coupling map, and qubit properties for each qubit (T1, T2, frequency, anharmonicity, readout error, probability of measuring 0/1 when 1/0 was prepared), as well as single- and two-qubit gate error rates.

mapping.json contains the optimized qubit mapping computed using the IBM transpiler at its highest optimization level. This file serves as the label for the machine-learning model.

We hope that this dataset will support the development of improved machine-learning models for qubit mapping.

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

Summary of Mo17 production and alignment results. (XLSX 9 kb)

The Location as a Service (LaaS) industry is experiencing robust growth, projected to reach a market size of $50.85 billion in 2025, exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24.11%. This expansion is driven by several key factors. The increasing adoption of mobile devices and the proliferation of location-based applications fuel the demand for precise and reliable location data. Furthermore, the rise of the Internet of Things (IoT) and the need for real-time location tracking across various industries, including logistics, transportation, and asset management, are significantly boosting market growth. The development of advanced technologies like GPS, Wi-Fi positioning, and sensor fusion is enhancing location accuracy and providing more sophisticated location intelligence. Finally, the growing focus on improving operational efficiency and enhancing customer experiences through location-based services is driving further adoption across diverse sectors. The LaaS market is segmented by various service types, including indoor positioning, map data services, location analytics, and geofencing. Major players like Ubiquicom, GL Communications Inc, HPE Aruba, IBM, Google, and Zebra Technologies are actively shaping the market landscape through technological innovations and strategic partnerships. While the industry faces challenges such as data privacy concerns and the need for consistent data quality across diverse platforms, the overall market trajectory remains strongly positive. The forecast period (2025-2033) is expected to witness continued growth driven by expanding applications in smart cities, autonomous vehicles, and augmented reality experiences. The competitive landscape is dynamic with ongoing mergers, acquisitions, and technological advancements fostering market evolution and increasing accessibility of LaaS solutions. Key drivers for this market are: Growing Demand for Geo-based Marketing, Technological Advancements Aided by Emergence of BLE and UWB for Indoor Services; Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Potential restraints include: Trade-offs Between Privacy/Security and Regulatory Constraints. Notable trends are: FMCG and E-Commerce Sector Expected to Witness Significant Growth.

We refined a suite of hydrodynamic and individual-based models to understand how climate change may impact red king crab (Paralithodes camtschaticus) recruitment in Bristol Bay, Alaska. We coupled a biophysical individual-based model (IBM) and a Regional Ocean Modeling System (ROMS) circulation model to estimate connectivity between the location of red king crab larval release and benthic settlement location in the eastern Bering Sea including Bristol Bay. We conducted ROMS hindcasts for two representative years: 1999 (cold) and 2005 (warm), and a forecast for a predicted warm year: 2037. Scientific output includes ROMS model files, IBM data files, and a red king crab habitat map. We modified an existing blue king crab (Paralithodes platypus) individual-based model (IBM) that was originally based on snow crab to represent the appropriate biology for red king crab. The biophysical model (ROMS model coupled to an IBM) used was a modified version of the ICHTHYOP modeling tool and was adapted to the Bering Sea system. Salinity, temperature, sea level, and current fields obtained from ROMS were used to force the red king crab IBM over the same time frame and spatial resolution as the physical model. The biology of the early life history stages of red king crab from larval release to settlement was represented through the following mechanisms or processes: larval abundance at release, spatial distribution of larvae at release, hatching time, vertical movement, growth, horizontal movement, post-larval settlement rules, and mortality (i.e., habitat availability). The “Connectivity” folder consists of the connectivity map, information of the connectivity map vertices, and connectivity matrices for each month/year simulation. Note that supplemental metadata can be found in the file Metadata – Matrices_ RedKing Crab.txt. The “IBM output (netcdf)” folder contains trajectory information for each particle in for each initial condition/month/year simulation. The “Initial Conditions” folder contains a map showing the particle location in the initial conditions and the lat, long, and depth information for each simulated particle. The “Settlement” folder contains maps showing particle density at release and settlement for each simulation using the habitat map grid spatial resolution.

HRIRN3L1 is the High Resolution Infrared Radiometer (HRIR) Nimbus-3 Level 1 Meteorological Radiance Data (NMRT) product and contains infrared radiances converted to equivalent black-body temperature or "brightness" temperature values. The data, originally written on IBM 360 machines, were recovered from magnetic tapes, also referred to as Nimbus Meteorological Radiation Tapes (NMRT). The data are archived in their original IBM 36-bit word proprietary format, also referred to as a binary TAP file. The HRIR instrument was designed to perform two major functions: first to map the Earth's cloud cover at night to complement the television coverage during the daytime portion of the orbit, and second to measure the temperature of cloud tops and terrain features. The HRIR flown on Nimbus-3 was modified to allow nighttime and daytime cloud cover mapping by use of dual band-pass filter which transmits 0.7 to 1.3 micron, and 3.4 to 4.2 micron radiation. The HRIR instrument was launched on the Nimbus-3 satellite and was operational from April 14, 1966 through July 22, 1969. Nighttime operation was made in the 3.4 to 4.2 micron near infrared region. Daytime operation was based on the predominance of reflected solar energy in the 0.7 to 1.3 micron region. Change-over from nighttime to daytime operation was accomplished automatically (or by ground station command), by actuating a relay in the early stages of the radiometer electronics. The system gain was reduced in the daytime mode to compensate for the higher energy levels. This product was previously available from the NSSDC with the identifier ESAD-00222 (old ID 69-037A-02C).

Context

Overwatch is a 6v6 FPS (first-person shooter) team game with great variety between heroes who can be played as. Overwatch League (OWL) is the professional esports league of Overwatch. When watching the OWL matches this year, I noticed the power-rankings and predictive statistics by IBM Watson extremely intriguing, so I determined to introduce the datasets into Kaggle. I, myself, really want to replicate the predictions and rankings, then testing with the stats lab.

Content

The datasets include players, head-to-head match-ups, and maps. The player historical statistics should contain OWL games from 2018 till now. It's centered around each player, and player's picked hero, its team name, performance, match IDs, etc.

Acknowledgements

Overwatch League Stats Lab has updated and downloadable csv files. And here are some interesting and inspiring questions to look at: https://overwatchleague.com/en-us/news/23303225.

Inspiration

Other than the power rankings and outcome predictions, I plan to look at teamfight stats, first elimination, and first death to compare the team's power.

For Players: 1. Match Report dashboard 2. Rate Ranks dashboard: Who led the league in solo kills/10 mins in 2018 as Junkrat? (min. 60 mins played) 3. Career Totals dashboard 4. Single Records dashboard

For Heroes: 1. Which 4 heroes did one play for 10% or more of his time on assault map attack rounds in the season? 2. Which hero increased in usage from 8% at the end of Stage 4 of 2018 to over 45% in the inaugural season playoffs?

For Matches: 1. Which team played the most matches that ended in a 3-2 score during the 2021 regular season? 2. Which team is entering the 2021 season on a 7-map loss streak? 3. Which team has the fastest completion time on Rialto?

Discover the booming Spain Location-Based Services (LBS) market! Explore its €880 million 2025 valuation, 13.75% CAGR, key drivers, and leading companies. Understand market segmentation and future trends for LBS in Spain. Recent developments include: February 2023: Mercedes-Benz and Google unveiled an extensive and visionary partnership aimed at revolutionizing the automotive industry and elevating the digital luxury car experience to new heights. In an industry-first move, Mercedes-Benz is set to develop its distinct navigation system, harnessing the advanced capabilities of the Google Maps Platform to craft an unparalleled driving experience. This groundbreaking collaboration will grant Mercedes-Benz exclusive access to Google's cutting-edge geospatial technologies, providing users with an array of exceptional features. These include comprehensive location data, automatic route optimization, up-to-the-minute traffic updates, and even predictive traffic insights, among other remarkable functionalities., January 2023: Mapbox, the leading platform for mapping and location services, joined forces with Toyota Motor Europe to introduce Cloud Navigation powered by Mapbox Dash. This transformative partnership brings an unprecedented level of real-time information to Toyota's Yaris, Yaris Cross, and Aygo X models, enhancing the driving experience in terms of efficiency, convenience, and safety. Alongside precision lane-level navigation, drivers can access a wealth of features such as live parking availability, speed limit alerts, and warnings for speed cameras. Furthermore, an upcoming pilot program will enable Toyota drivers to conveniently handle parking and fuel payments directly through their infotainment systems, further streamlining the driving experience.. Key drivers for this market are: Growing Demand for Geo-based Marketing, Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Potential restraints include: Growing Demand for Geo-based Marketing, Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Notable trends are: Indoor Location Segment is Expected to Hold Significant Share of the Market.

Genome wide QTL identification between IBM Syn4 and Syn10 populations. (XLSX 118 kb)

The Interact Public Safety Systems market is booming, projected to reach $12.39 billion by 2025, with a 17.82% CAGR. Discover key drivers, trends, and restraints shaping this dynamic sector, including insights on software, services, deployment types, and leading companies like Splunk, SAS, and IBM. Explore regional market shares and future growth potential. Recent developments include: June 2024 - CENTEGIX and GeoComm, a provider of authoritative indoor and outdoor geographic information systems (GIS), announced a partnership to bolster school safety and facilitate the sharing of mapping data among their mutual customers. This collaboration highlights the commitment of both companies to empower their customers with complete ownership and control over their facility maps and related mapping data., May 2024 - Presight, a big data analytics company powered by generative artificial intelligence (AI), signed a Memorandum of Understanding (MOU) with Obvious Technologies (OODA World) at the International Security National Resilience (ISNR) exhibition. The partnership aims to revolutionize crisis, emergency, and disaster management by integrating advanced data analytics and AI into emergency response systems. Presight, together with OODA World, works towards establishing an ecosystem partnership that leverages its combined capabilities and analytical tools to improve prevention, preparation, response, and recovery in emergencies., May 2024 - NEC X announced a new investment in Multitude Insights, the provider of a transformative AI-powered solution enabling greater collaboration and faster case resolution for law enforcement agencies and first responders. This collaboration with NEC X is more than just an investment; it is a robust partnership and a unique opportunity to leverage tech innovation and resources to enhance public safety.. Key drivers for this market are: Growing Number of Global Catastrophic Accidents, Crime Rates, and Terrorist Activities, Rising Adoption of Advanced Technologies and Growth in Smart Cities. Potential restraints include: Growing Number of Global Catastrophic Accidents, Crime Rates, and Terrorist Activities, Rising Adoption of Advanced Technologies and Growth in Smart Cities. Notable trends are: Cloud to be the Leading Deployment Type.

Scottish Marine and Freshwater Science Vol 8 No 20 Both the quantification and modelling of harbour seal movement are required to predict the consequence of environmental change on population distribution and connectivity. Two modelling approaches were considered. The first is an empirical Inter-Haulout Transition Rate (I-HTR) model which estimates the population probability of an individual moving from one haulout site to another. The second is a mechanistic Individual Based Model (IBM) of movement which uses seal physiology in a simulated quasi-realistic environment to predict movement patterns. The scope of the IBM development is to demonstrate its 'proof of concept'. To become a useful management tool, an IBM of appropriate complexity must be developed and tested, with the best possible estimates of parameters used to construct the model, e.g. from bioenergetic studies of captive seals or realistic estimates of habitat preference. The validation and checking of IBMs in general is an area of active research and for the seal IBM, appropriate checks may include comparison of model predictions in terms of summary properties and emergent properties of observational data, e.g. general patterns of spatial distribution. The prototype IBM has proven the concept and development work should continue to test whether the available data (seal and environmental), statistical selection and fitting techniques can ultimately progress to producing a robust management tool. However, an important future challenge is to sufficiently map and quantify the dynamics of the geographical resources that seals require, such as haulout sites and foraging areas. Approaches could include the use of synoptic physical and biological data to predict those regions that may be preferred for foraging.