IntroductionIt has proven a hard challenge to stimulate climate action with climate data. While scientists communicate through words, numbers, and diagrams, artists use movement, images, and sound. Sonification, the translation of data into sound, and visualization, offer techniques for representing climate data with often innovative and exciting results. The concept of sonification was initially defined in terms of engineering, and while this view remains dominant, researchers increasingly make use of knowledge from electroacoustic music (EAM) to make sonifications more convincing.MethodsThe Aesthetic Perspective Space (APS) is a two-dimensional model that bridges utilitarian-oriented sonification and music. We started with a review of 395 sonification projects, from which a corpus of 32 that target climate change was chosen; a subset of 18 also integrate visualization of the data. To clarify relationships with climate data sources, we determined topics and subtopics in a hierarchical classification. Media duration and lexical diversity in descriptions were determined. We developed a protocol to span the APS dimensions, Intentionality and Indexicality, and evaluated its circumplexity.ResultsWe constructed 25 scales to cover a range of qualitative characteristics applicable to sonification and sonification-visualization projects, and through exploratory factor analysis, identified five essential aspects of the project descriptions, labeled Action, Technical, Context, Perspective, and Visualization. Through linear regression modeling, we investigated the prediction of aesthetic perspective from essential aspects, media duration, and lexical diversity. Significant regressions across the corpus were identified for Perspective (ß = 0.41***) and lexical diversity (ß = −0.23*) on Intentionality, and for Perspective (ß = 0.36***) and Duration (logarithmic; ß = −0.25*) on Indexicality.DiscussionWe discuss how these relationships play out in specific projects, also within the corpus subset that integrated data visualization, as well as broader implications of aesthetics on design techniques for multimodal representations aimed at conveying scientific data. Our approach is informed by the ongoing discussion in sound design and auditory perception research communities on the relationship between sonification and EAM. Through its analysis of topics, qualitative characteristics, and aesthetics across a range of projects, our study contributes to the development of empirically founded design techniques, applicable to climate science communication and other fields.

This dataset provides a detailed insight into the daily activities of citizens in a futuristic smart city. It covers various aspects such as:

Demographics (Age, Gender) Mobility (Mode of Transport, Walking Steps) Lifestyle & Social Engagement (Work, Shopping, Entertainment, Social Media) Health & Well-being (Calories Burned, Sleep Hours) Energy & Sustainability (Home Energy Consumption, Carbon Footprint, Charging Station Usage) With 1000 rows and 15 columns, this dataset is ideal for data analysis, machine learning, and visualization projects related to urban mobility, sustainability, health trends, and behavioral analytics.

This dataset can be used to:

✅ Analyze citizen behavior trends

✅ Understand sustainable urban mobility

✅ Predict energy consumption patterns

✅ Identify health and social media habits

Visualizing project management : models and frameworks for mastering complex systems is a book. It was written by Hal Mooz and published by Wiley in 2005.

Resources for Advanced Data Analysis and VisualizationResearchers who have access to the latest analysis and visualization tools are able to use large amounts of complex data to find efficiencies in projects, designs, and resources. The Data Analysis and Assessment Center (DAAC) at ERDC's Information Technology Laboratory (ITL) provides visualization and analysis tools and support services to enable the analysis of an ever-increasing volume of data.Simplify Data Analysis and Visualization ResearchThe resources provided by the DAAC enable any user to conduct important data analysis and visualization that provides valuable insight into projects and designs and helps to find ways to save resources. The DAAC provides new tools like ezVIZ, and services such as the DAAC website, a rich resource of news about the DAAC, training materials, a community forum and tutorials on how to use data analysis and other issues.The DAAC can perform collaborative work when users prefer to do the work themselves but need help in choosing which visualization program and/or technique and using the visualization tools. The DAAC also carries out custom projects to produce high-quality animations of data, such as movies, which allow researchers to communicate their results to others.Communicate Research in ContextDAAC provides leading animation and modeling software which allows scientists and researchers may communicate all aspects of their research by setting their results in context through conceptual visualization and data analysis.Success StoriesWave Breaking and Associated Droplet and Bubble FormationWave breaking and associated droplet and bubble formation are among the most challenging problems in the field of free-surface hydrodynamics. The method of computational fluid dynamics (CFD) was used to solve this problem numerically for flow about naval vessels. The researchers wanted to animate the time-varying three-dimensional data sets using isosurfaces, but transferring the data back to the local site was a problem because the data sets were large. The DAAC visualization team solved the problem by using EnSight and ezVIZ to generate the isosurfaces, and photorealistic rendering software to produce the images for the animation.Explosive Structure Interaction Effects in Urban TerrainKnown as the Breaching Project, this research studied the effects of high-explosive (HE) charges on brick or reinforced concrete walls. The results of this research will enable the war fighter to breach a wall to enter a building where enemy forces are conducting operations against U.S. interests. Images produced show computed damaged caused by an HE charge on the outer and inner sides of a reinforced concrete wall. The ability to quickly and meaningfully analyze large simulation data sets helps guide further development of new HE package designs and better ways to deploy the HE packages. A large number of designs can be simulated and analyzed to find the best at breaching the wall. The project saves money in greatly reduced field test costs by testing only the designs which were identified in analysis as the best performers.SpecificationsAmethyst, the seven-node Linux visualization cluster housed at the DAAC, is supported by ParaView, EnSight, and ezViz visualization tools and configured as follows:Six computer nodes, each with the following specifications:CPU: 8 dual-core 2.4 Ghz, 64-bit AMD Opteron Processors (16 effective cores)Memory: 128-G RAMVideo: NVidia Quadro 5500 1-GB memoryNetwork: Infiniband Interconnect between nodes, and Gigabit Ethernet to Defense Research and Engineering Network (DREN)One storage node:Disk Space: 20-TB TerraGrid file system, mounted on all nodes as /viz and /work

Algorithms for Quantitative Pedology (AQP) is a collection of code, ideas, documentation, and examples wrapped-up into several R packages. The theory behind much of the code can be found in Beaudette, D., Roudier, P., & O'Geen, A. (2013). Algorithms for quantitative pedology: A toolkit for soil scientists. Computers & Geosciences, 52, 258-268. doi: 10.1016/j.cageo.2012.10.020. The AQP package was designed to support data-driven approaches to common soils-related tasks such as visualization, aggregation, and classification of soil profile collections. To contribute code, documentation, bug reports, etc. contact Dylan at dylan [dot] beaudette [at] usda [dot] gov. AQP is a collaborative effort, funded in part by the Kearney Foundation of Soil Science (2009-2011) and USDA-NRCS (2011-current). The AQP suite of R packages are used to generate figures for SoilWeb, Series Extent Explorer, and Soil Data Explorer. Soil data presented were derived from the 100+ year efforts of the National Cooperative Soil Survey, c/o USDA-NRCS. Resources in this dataset:Resource Title: aqp: Algorithms for Quantitative Pedology (CRAN). File Name: Web Page, url: https://CRAN.R-project.org/package=aqp The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks . These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb https://casoilresource.lawr.ucdavis.edu/soilweb-apps/. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.

This project contains the data, code, and files for my Master dissertation for the MA Digital Cultures at UCC 2018/19. I complied a data set from the Union List of Artist Names (ULAN) online repository (http://www.getty.edu/research/tools/vocabularies/ulan/) with SSMS and created a data visualization with Gephi. The thesis can be found at https://ckdigitalarts.com/dissertation-documentation/.

Theater project is an artwork. It is housed at the Museum of Modern Art (New York) and was created in 1947. It measures 21.59 cm in width and 15.24 cm in height.

Participatory Budgeting is a democratic process in which community members directly decide how to spend part of a public budget. Council Members choose to join Participatory Budgeting New York City (PBNYC), giving at least $1 million from their budget for the whole community to participate in decision-making. It’s a yearlong process of public meetings, to ensure that people have the time and resources to make informed decisions. Community members discuss local needs and develop proposals to meet these needs. Through a public vote, residents then decide which proposals to fund.

More info can be found at http://council.nyc.gov/pb/

Author: Andrew J. FeltonDate: 5/5/2024

This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis and figure production for the study entitled:

"Global estimates of the storage and transit time of water through vegetation"

Please note that 'turnover' and 'transit' are used interchangeably in this project.

Data information:

The data folder contains key data sets used for analysis. In particular:

"data/turnover_from_python/updated/annual/multi_year_average/average_annual_turnover.nc" contains a global array summarizing five year (2016-2020) averages of annual transit, storage, canopy transpiration, and number of months of data. This is the core dataset for the analysis; however, each folder has much more data, including a dataset for each year of the analysis. Data are also available is separate .csv files for each land cover type. Oterh data can be found for the minimum, monthly, and seasonal transit time found in their respective folders. These data were produced using the python code found in the "supporting_code" folder given the ease of working with .nc and EASE grid in the xarray python module. R was used primarily for data visualization purposes. The remaining files in the "data" and "data/supporting_data"" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here.

Code information

Python scripts can be found in the "supporting_code" folder.

Each R script in this project has a particular function:

01_start.R: This script loads the R packages used in the analysis, sets thedirectory, and imports custom functions for the project. You can also load in the main transit time (turnover) datasets here using the source() function.

02_functions.R: This script contains the custom function for this analysis, primarily to work with importing the seasonal transit data. Load this using the source() function in the 01_start.R script.

03_generate_data.R: This script is not necessary to run and is primarilyfor documentation. The main role of this code was to import and wranglethe data needed to calculate ground-based estimates of aboveground water storage.

04_annual_turnover_storage_import.R: This script imports the annual turnover andstorage data for each landcover type. You load in these data from the 01_start.R scriptusing the source() function.

05_minimum_turnover_storage_import.R: This script imports the minimum turnover andstorage data for each landcover type. Minimum is defined as the lowest monthlyestimate.You load in these data from the 01_start.R scriptusing the source() function.

06_figures_tables.R: This is the main workhouse for figure/table production and supporting analyses. This script generates the key figures and summary statistics used in the study that then get saved in the manuscript_figures folder. Note that allmaps were produced using Python code found in the "supporting_code"" folder.

The Public Assistance (PA) Funded Projects Details dataset contains a list of funded (obligated) PA projects, called project worksheets. Unobligated projects (still in formulation) are not represented. The Applicant ID is provided for this dataset to be used with the OpenFEMA “Public Assistance Applicants - v1” dataset.rnFEMA provides supplemental Federal disaster grant assistance for debris removal, emergency protective measures, and the repair, replacement, or restoration of disaster-damaged, publicly owned facilities and the facilities of certain Private Non-Profit (PNP) organizations through the PA Program (CDFA Number 97.036). The PA Program also encourages protection of these damaged facilities from future events by providing assistance for 406 hazard mitigation measures during the recovery process.rnThis is raw, unedited data from FEMA's Emergency Management Mission Integrated Environment (EMMIE) and as such is subject to a small percentage of human error. The financial information is derived from EMMIE and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations, and application of business rules, this financial information may differ slightly from official publication on public websites such as www.usaspending.gov . This dataset is not intended to be used for any official federal reporting.rnThe data has been incorporated into a graphic visualization at Public Assistance Program Summary of Obligations: https://www.fema.gov/data-visualization/public-assistance-program-summary-obligations. Questions pertaining to the data visualizations should be addressed to EnterpriseAnalytics@fema.dhs.gov.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

Explore Teach for America (Project) through data from visualizations to datasets, all based on diverse sources.

Visual Project Software Market Outlook

The global visual project software market size was valued at approximately $3.2 billion in 2023 and is projected to grow to $8.7 billion by 2032, registering a compound annual growth rate (CAGR) of 11.5% over the forecast period. This significant growth is driven by multiple factors, including the increasing need for efficient project management tools, the rising complexity of projects, and the growing adoption of cloud-based solutions.

One of the primary growth factors for the visual project software market is the escalating complexity and scale of projects across various industries. As businesses expand and globalize, managing projects has become more intricate, requiring advanced software solutions that can handle multiple tasks, timelines, resources, and team collaborations efficiently. Visual project software offers enhanced visualization, which is crucial for better planning, monitoring, and execution of projects. The visual representation of data helps in quick decision-making and improves overall project transparency.

Another significant growth driver is the rising adoption of cloud-based solutions. Cloud technology has transformed the way businesses operate by offering scalable resources, reducing IT infrastructure costs, and enabling remote accessibility. Visual project software hosted on the cloud allows for real-time updates, collaboration across different geographical locations, and seamless integration with other cloud services. This has become increasingly important in the era of remote work, where teams are distributed across various locations yet need to stay connected and work efficiently.

Technological advancements in AI and machine learning also contribute to the market's growth. Integrating AI capabilities within visual project software can automate routine tasks, provide predictive analytics, and offer insights based on historical data. This not only enhances productivity but also helps in identifying potential risks early on. The implementation of machine learning algorithms can further refine project planning and execution by learning from past project data and suggesting optimal paths for future projects.

Regionally, North America holds the largest share in the visual project software market, driven by the early adoption of advanced technologies and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid industrialization, increasing investments in IT infrastructure, and the rise of small and medium enterprises (SMEs) in this region are key contributors to this growth. Europe, Latin America, and the Middle East & Africa also present significant opportunities due to the growing awareness and adoption of digital project management tools.

Component Analysis

The visual project software market is segmented by component into software and services. The software segment includes project management software, collaboration tools, and analytics software, while the services segment encompasses implementation, consulting, and support services. The software segment holds the largest market share due to the high demand for comprehensive project management solutions that can streamline operations and enhance efficiency. These software solutions offer a range of functionalities, including task scheduling, resource allocation, budget management, and progress tracking, which are essential for successful project execution.

Within the software segment, project management software is expected to dominate the market. These tools provide a centralized platform for planning, executing, and monitoring projects, making them indispensable for organizations of all sizes. Collaboration tools are also gaining traction as they facilitate real-time communication and coordination among team members, thereby improving productivity and reducing delays. Analytics software, which offers insights and data-driven decision-making capabilities, is becoming increasingly important as organizations seek to optimize their project outcomes.

The services segment is also witnessing significant growth, driven by the need for expertise in implementing and maintaining complex software solutions. Implementation services ensure that the software is correctly configured to meet the specific needs of the organization, while consulting services offer strategic advice on best practices and process improvements. Support services are crucial for addressing technical issues and ensuring smooth operation of the so

This set of contract awards includes data on commitments against contracts that were reviewed by the Bank before they were awarded (prior-reviewed Bank-funded contracts) under IDA/IBRD investment projects and related Trust Funds. This dataset does not list all contracts awarded by the Bank, and should be viewed only as a guide to determine the distribution of major contract commitments among the Bank's member countries. "Supplier Country" represents place of supplier registration, which may or not be the supplier's actual country of origin. Information does not include awards to subcontractors nor account for cofinancing. The Procurement Policy and Services Group does not guarantee the data included in this publication and accepts no responsibility whatsoever for any consequences of its use. The World Bank complies with all sanctions applicable to World Bank transactions.

3D Visualization Software for Design Market Outlook

The global market size for 3D visualization software for design was valued at USD 2.81 billion in 2023 and is expected to reach USD 8.76 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.5% during the forecast period. This significant growth can be attributed to several factors, including the escalating demand for high-quality 3D models in various industries and the integration of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) into design software.

One of the primary growth factors driving the market is the increasing adoption of 3D visualization software in the architecture, engineering, and construction (AEC) sector. As urbanization accelerates and smart city initiatives gain traction, the need for precise and detailed design plans has surged. This software enables architects and engineers to create immersive, interactive models that enhance project accuracy and efficiency. Additionally, the ability to perform virtual walkthroughs and simulations has proven invaluable in minimizing errors and reducing project timelines, thereby fueling market growth.

In the media and entertainment industry, the demand for realistic visual effects and interactive content has been a significant driver for the 3D visualization software market. The software is increasingly used in film production, animation, and video game development to create high-fidelity visual content that captivates audiences. With the rise of augmented reality (AR) and virtual reality (VR) technologies, the potential applications of 3D visualization software have expanded even further, enabling more immersive and engaging user experiences.

The integration of 3D Modeling, 3D Visualization, and 3D Data Capture technologies has revolutionized the way industries approach design and development. These technologies enable the creation of highly detailed and accurate digital representations of objects, which can be manipulated and analyzed in a virtual environment. This capability is particularly beneficial in sectors such as architecture, engineering, and construction, where precision and detail are paramount. By capturing real-world data and converting it into 3D models, professionals can visualize complex structures and systems, identify potential issues, and make informed decisions before any physical work begins. This not only enhances the overall quality and efficiency of projects but also reduces costs and minimizes risks associated with design errors.

The manufacturing and automotive industries are also major contributors to the growth of this market. The adoption of 3D visualization software in these sectors enhances product design and prototyping processes, enabling companies to visualize and test products before physical production. This not only accelerates the development cycle but also reduces costs associated with prototype iterations. As industries increasingly prioritize innovation and efficiency, the reliance on advanced 3D visualization tools continues to grow.

Regionally, North America holds a dominant position in the 3D visualization software market, driven by the high adoption rate of advanced technologies and significant investments in research and development. However, the Asia-Pacific region is expected to witness the highest CAGR during the forecast period, owing to rapid industrialization, growing construction activities, and increasing demand for high-quality visual content. European markets are also steadily growing, supported by advancements in the automotive and manufacturing sectors.

Component Analysis

The 3D visualization software for design market is segmented into software and services components. The software segment is the largest, driven by the increasing demand for advanced visualization tools across various industries. These software solutions provide designers and engineers with robust capabilities to create, modify, and analyze complex 3D models. The integration of AI and ML into these tools further enhances their functionality, enabling features like predictive modeling and automated error detection, which significantly streamline the design process.

Within the software segment, there are various types of applications tailore

Context

The Global Footprint Network organization has been publishing the Overshoot Day, which is a day on which our planet runs out of resources it can renew in a year, each year since 1970.

The provide a lot of environmental data on their site and the list of Overshoot Day dates can be seen on this page.

I took this list and added a few extra details, like number of days that passed and were remaining on each Overshoot Day, or the corresponding percentages of the elapsed and remaining portion of the year.

Content

Provided is an Excel file I used to compute the additional fields as well as an exported data. You can

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

You can also read a short write-up and example data visualization on my site.

Inspiration

I've been working on a data visualization WordPress plugin, so this was a useful dataset to use for testing.

This data informs each solar park construction project. These are all parcels included in the construction permit application for the original project. When the central is carried out, it impacts only part of the project. The establishment of power plants in service is another set of data: N_PARC_PHOTOVOLTAIQUE. (http://catalogue.geo-ide.developpement-durable.gouv.fr/catalogue/srv/fre/catalog.search#/metadata/fr-120066022-jdd-60a878eb-8135-4b94-b95f-12b0e388c739) DDTM 34 only deals with applications for land-based power plants (except for one of the old applications: case of Vendres). The above-ground power plants are instructed by the municipalities.

KAPSARC Models, Apps, and Tools metadata.

project_name

Project full name.

Name

model_id

Model ID.

ID

web_ availability

The web application availability of the model.

Yes, No, WIP

project_status

The project current status.

Ongoing, Ended, Sunset

sme

Subject matter expert involved in the project.

Name

sme_status

Subject matter expert employment status.

Onboard, Offborad

program_director

Program director of the project.

Name

project_type

Project type. [ Model, App, Visualization tool ]

Model, App, Visualization, Data

models_on_kwebsite

Model available on KAPSARC data & tools webpage.

Yes, No.

models_on_kwebsite_url

Model URL on KAPSARC data & tools webpage.

URL

model_on_github

Model uploaded on GitHub.

Yes, No.

model_on_github_url

Model URL on GitHub.

URL

model_date

Model submission date on GitHub.

Date

appVis_on_GitHub

App or Visualization tool uploaded on GitHub.

Yes, No.

appVis_on_GitHub_URL

App or Visualization tool URL on GitHub.

URL

appVis_date

App or Visualization tool submission date on GitHub.

Date

data_on_github

Data uploaded on GitHub.

Yes, No.

data_classification

Data classification type. [ Public, Confidential, Subscription ]

Public, Confidential, Subscription

tracking_status

Tracking status for the project.

Done, Pending.

version

Latest version submitted for the project.

Version number

run_count_script_enabled

Model run count enabled for the project, on HeapAnalytics or GoodgleAnalytics.

Yes, No.

model_theme

What theme does the model falls under?

[ Energy, Economics, Environment ]

model_overview

Brief description of the model

string

solution

Solutions that the model provides

string

key_inputs

Model main inputs

string

key_outputs

Model main outputs

string

temporal_dimension

Time period that the model covers

string

spatical_dimension

Regions that the model covers

string

application

Applications of the model

string

soilDB is one of the Algorithms for Quantitative Pedology (AQP) suite of R packages, and comprises a collection of functions for reading data from USDA-NCSS (National Cooperative Soil Survey) soil databases including SoilWeb, Series Extent Explorer, and Soil Data Explorer. This package provides methods for extracting soils information from local PedonPC and AK Site databases (MS Access format), local NASIS databases (MS SQL Server), and the SDA webservice. Currently USDA-NCSS data sources are supported, however, there are plans to develop interfaces to outside systems such as the Global Soil Mapping project. Resources in this dataset:Resource Title: Website pointer to soilDB: Soil Database Interface. File Name: Web Page, url: https://cran.r-project.org/web/packages/soilDB/index.html

🏏 IPL Ball-by-Ball Dataset with Pitch & Player Stats (2020–2025)

This dataset includes ball-by-ball delivery data from Indian Premier League (IPL) matches between 2020 and March 2025, enriched with pitch condition information, and match-level batting and bowling performance statistics.

It's ideal for building machine learning models, conducting sports analytics, training fantasy cricket prediction systems, and exploring performance trends in T20 cricket.

📁 Dataset Overview

Files Included:

• Ipl match data - enriched.xlsx – Full ball-by-ball data with pitch types
• all_matches_batting_stats.csv – Player-wise batting stats per match
• all_matches_bowling_stats.csv – Player-wise bowling stats per match

🔍 Key Features

• ⚾ Ball-by-ball records for every delivery across 5 IPL seasons
• 🌱 Pitch condition data: Spin-friendly, Batting-friendly, etc.
• 🧢 Structured performance stats for batting and bowling per match
• 🏟️ Match metadata: Venue, city, date, teams, overs
• ✅ Well-cleaned, analysis-ready Excel and CSV formats

🎯 Use Cases & Keywords

• Fantasy Cricket Prediction Model
• T20 Match Analytics
• IPL Data Visualization Projects
• Cricket Simulation Engine
• Player Form and Consistency Analysis
• Pitch Impact on Performance
• Machine Learning with Cricket Data
• Python/Excel-based IPL Stats Projects

📝 Example Columns

From Ball-by-Ball: - match_id, date, venue, batter, bowler, runs_batter, runs_extras, wicket_taken, dismissal_kind, pitch_type

From Batting Stats: - player, team, runs_scored, balls_faced, fours, sixes

From Bowling Stats: - player, team, overs_bowled, runs_conceded, wickets

📜 License

CC0 1.0 Universal (Public Domain Dedication)
You are free to use this dataset in personal, academic, or commercial projects with no restrictions.

description: This image represents a LiDAR (Light Detection and Ranging) bathymetric mosaic (mean 4 meter gridded) collected along the coastline of southwestern Puerto Rico. The Tenix LADS Corporation (TLI) acquired bathymetric LIDAR for NOAA from 4/07/2006 to 5/15/2006. Data was acquired with a LADS (Laser Airborne Depth Sounder) Mk II Airborne System from altitudes between 1,200 and 2,200ft at ground speeds between 140 and 175 knots. The 900 Hertz Nd: YAG (neodymium-doped yttrium aluminum garnet) laser (1064 nm) acquired 4x4 meter spot spacing and 200% seabed coverage. In total, 265 square nautical miles of LiDAR were collected between -50 m (topographic) and up to 70 m (depth), requiring a total of 102 flight hours (134 hours, including flight time to and from San Juan airport). Environmental factors such as wind strength and direction, cloud cover, and water clarity influenced the area of data acquisition on a daily basis. The data was processed using the LADS Mk II Ground System and data visualization, quality control and final products were created using CARIS HIPS and SIPS 6.1 and CARIS BASE Editor 2.1 The project was conducted to meet the IHO (International Hydrograph Organization)Order 1 accuracy standards, dependant on the project area and depth. All users should individually evaluate the suitability of this data according to their own needs and standards.; abstract: This image represents a LiDAR (Light Detection and Ranging) bathymetric mosaic (mean 4 meter gridded) collected along the coastline of southwestern Puerto Rico. The Tenix LADS Corporation (TLI) acquired bathymetric LIDAR for NOAA from 4/07/2006 to 5/15/2006. Data was acquired with a LADS (Laser Airborne Depth Sounder) Mk II Airborne System from altitudes between 1,200 and 2,200ft at ground speeds between 140 and 175 knots. The 900 Hertz Nd: YAG (neodymium-doped yttrium aluminum garnet) laser (1064 nm) acquired 4x4 meter spot spacing and 200% seabed coverage. In total, 265 square nautical miles of LiDAR were collected between -50 m (topographic) and up to 70 m (depth), requiring a total of 102 flight hours (134 hours, including flight time to and from San Juan airport). Environmental factors such as wind strength and direction, cloud cover, and water clarity influenced the area of data acquisition on a daily basis. The data was processed using the LADS Mk II Ground System and data visualization, quality control and final products were created using CARIS HIPS and SIPS 6.1 and CARIS BASE Editor 2.1 The project was conducted to meet the IHO (International Hydrograph Organization)Order 1 accuracy standards, dependant on the project area and depth. All users should individually evaluate the suitability of this data according to their own needs and standards.