During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion along the southeast US coastline and implications for vulnerability to future storms.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

Data taken from https://www.kaggle.com/datasets/andrewsundberg/college-basketball-dataset and updated with data from https://barttorvik.com/

TEAM: The Division I college basketball school

CONF: The Athletic Conference in which the school participates in (A10 = Atlantic 10, ACC = Atlantic Coast Conference, AE = America East, Amer = American, ASun = ASUN, B10 = Big Ten, B12 = Big 12, BE = Big East, BSky = Big Sky, BSth = Big South, BW = Big West, CAA = Colonial Athletic Association, CUSA = Conference USA, Horz = Horizon League, Ivy = Ivy League, MAAC = Metro Atlantic Athletic Conference, MAC = Mid-American Conference, MEAC = Mid-Eastern Athletic Conference, MVC = Missouri Valley Conference, MWC = Mountain West, NEC = Northeast Conference, OVC = Ohio Valley Conference, P12 = Pac-12, Pat = Patriot League, SB = Sun Belt, SC = Southern Conference, SEC = South Eastern Conference, Slnd = Southland Conference, Sum = Summit League, SWAC = Southwestern Athletic Conference, WAC = Western Athletic Conference, WCC = West Coast Conference)

G: Number of games played

W: Number of games won

ADJOE: Adjusted Offensive Efficiency (An estimate of the offensive efficiency (points scored per 100 possessions) a team would have against the average Division I defense)

ADJDE: Adjusted Defensive Efficiency (An estimate of the defensive efficiency (points allowed per 100 possessions) a team would have against the average Division I offense)

BARTHAG: Power Rating (Chance of beating an average Division I team)

EFG_O: Effective Field Goal Percentage Shot

EFG_D: Effective Field Goal Percentage Allowed

TOR: Turnover Percentage Allowed (Turnover Rate)

TORD: Turnover Percentage Committed (Steal Rate)

ORB: Offensive Rebound Rate

DRB: Offensive Rebound Rate Allowed

FTR : Free Throw Rate (How often the given team shoots Free Throws)

FTRD: Free Throw Rate Allowed

2P_O: Two-Point Shooting Percentage

2P_D: Two-Point Shooting Percentage Allowed

3P_O: Three-Point Shooting Percentage

3P_D: Three-Point Shooting Percentage Allowed

ADJ_T: Adjusted Tempo (An estimate of the tempo (possessions per 40 minutes) a team would have against the team that wants to play at an average Division I tempo)

WAB: Wins Above Bubble (The bubble refers to the cut off between making the NCAA March Madness Tournament and not making it)

POSTSEASON: Round where the given team was eliminated or where their season ended (R68 = First Four, R64 = Round of 64, R32 = Round of 32, S16 = Sweet Sixteen, E8 = Elite Eight, F4 = Final Four, 2ND = Runner-up, Champion = Winner of the NCAA March Madness Tournament for that given year)

SEED: Seed in the NCAA March Madness Tournament

YEAR: Season

The average monthly rental rate for data center capacity in primary markets in the United States rose sharply to over *** U.S. dollars per kilowatt in the first half of 2024. The surge in rental rates reflects the increasing pressure on data center capacity due to rising demand and supply chain constraints. Northern Virginia leads the U.S. data center market As of the second half of 2023, the Northern Virginia region boasted the lowest data center vacancy rate among major markets in North America at just *** percent. Spread across Loudon, Fairfax, Fauquier, and Prince William Counties, the region has long been a leading data center hub, with hyperscale operators attracted by favorable local infrastructure conditions and proximity to major metropolitan areas. Challenges and opportunities in data center construction The second half of 2023 saw ***** megawatts of data center capacity under construction in Northern Virginia. However, the market could face limitations in future expansion, with the growing impact on local resources prompting local officials to rethink approaches to data center planning processes. Meanwhile, the Portland and Eastern Oregon region has emerged as an important west coast hub. Sometimes referred to as the Silicon Forest, the region has a reported *** megawatts under construction as of the second half of 2023.

The dataset includes Land Use/Land Cover types throughout the Chenier Eco-Region in Southwest Louisiana. Using the 2015 National Aerial Imagery Program (NAIP) dataset (1m) as the basemap, E-Cognition image objects were derived from the multiresolution segmentation algorithm at 75 and 250 segments. Attempts to refine the data training methods using E-cognition, to extrapolate automating categories of this information to the entire map resulted with exceedingly low accuracy. Therefore, a raster was produced by piecing together several data resources, which provide reliable data for specific LandUse/LandCover (LULC) categories. This process involved stitching together more reliable sources for specific categories to apply to higher resolution (75) segmentation product. Reference datasets include; 12,000 aerial points assigned to image objects derived from 75 segmentation settings (previously used to develop scripts for data training), mask created from National Wetlands Inventory (NWI) 2008 including water, wetland forested, upland forested and scrub/shrub categories, Bureau of Ocean Energy and Management (BOEM) marsh classes, National Land Cover Dataset (NLCD) urban areas, and Cropescape Data Layer (CDL) data. The raster produced from this process was applied to the vector image objects derived from the 250 segmentation settings, using a majority filter (greater then; 50 percent). The series of draft shapefiles were manually edited and merged, resulting in the final dataset. This vector dataset was then converted into a 10 meter raster datase(https://doi.gov/10.5066/F7KW5DJW). We used the Tabulate Area tool within the Spatial Analyst Tools in ArcGIS 10.4 (ESRI, Redlands, CA) to estimate the percentage of classified grasslands occurring on each soil type. Soil types with the highest percentages of grasslands occurring on them were identified. Most of these soils occurred in Calcasieu parish. Because each parish has different soil “MUSYSM” we could not just select by “MUSYSM”, so we had to manually identify those soils across parish lines that were identified previously. The following structured query language statement was built to identify those crosswalks between parishes."SOILDATA_Merge_Clip_Project.MUNAME" LIKE '% silt loam, 0 to 1 percent slopes%' OR "SOILDATA_Merge_Clip_Project.MUNAME " LIKE '% silt loams, 0 to 1 percent slopes%' OR "SOILDATA_Merge_Clip_Project.MUNAME" = 'Crowley-Vidrine complex' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Mr' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Mn' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Ju' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Mt' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'MoA' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Pa' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Co' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Pt' OR "SOILDATA_Merge_Clip_Project.MUSYM" = 'Cu'This selection was then used to mask the LULC areas to further prioritize. Prioritization or ranking of the LULC types was then accomplished by reclassifying the LULC types between 1 and 10 with 10 being the highest priority areas. The priority ranking was then grouped into High, Medium and Low areas of potential grassland areas to restore (High 10-8; Medium 7-4; Low 3-1).CodeClass Name Reclass_Rank Rank_Group10 Herbaceous Marsh 2 Low11 Fresh Marsh 1 Low 12 Intermediate Marsh 1 Low13 Brackish Marsh 1 Low14 Saline Marsh 1 Low20 Upland Forest 4 Medium21 Upland Forested Evergreen 7 Medium22 Upland Forested Deciduous 4 Medium23 Upland Forested Mixed 4 Medium30 Upland SS 7 Medium31 Upland SS Evergreen 8 High32 Upland SS Deciduous 7 Medium33 Upland SS Mixed 7 Medium40 Wetland Forest 3 Low41 Wetland Forested Evergreen 5 Medium42 Wetland Forested Deciduous 5 Medium43 Wetland Forested Mixed 4 Medium50 Wetland SS 5 Medium51 Wetland SS Evergreen 5 Medium52 Wetland SS Deciduous 5 Medium53 Wetland SS Mixed 5 Medium60 Swamp 1 Low70 Agriculture 8 High71 Row Crop 8 High72 Rice 8 High73 Sugarcane 8 High74 Grassland 10 High75 Pasture 9 High76 Orchard 8 High80 Urban 1 Low81 High Density Developed 1 Low82 Medium Density Developed 2 Low83 Low Density Developed 5 Medium90 Barren 1 Low100 Water 1 Low

On January 11, 2011, NOAA Fisheries implemented a new fishery management system for the West Coast Groundfish Trawl Catch Share Program as specified in the Magnuson-Stevens Fishery Conservation and Management Act. The trawl catch share program, also called the trawl rationalization program, consists of an Individual Fishing Quota (IFQ) program for the shorebased trawl fleet and cooperative programs for the at-sea mothership and catcher/processor trawl fleets. The new catch shares system divides the total amount of an overall allowable catch or quota into shares (QS) controlled by individual fishermen or groups of fishermen (cooperatives). For the Shorebased trawl fishery, NOAA Fisheries issues quota pounds (QPs) at the beginning of each year to QS accounts, based on the sector allocation and QS holdings (expressed as %) for each QS permit owner.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

Dividend-Per-Share Time Series for Suburban Propane Partners LP. Suburban Propane Partners, L.P., through its subsidiaries, engages in the retail marketing and distribution of propane, renewable propane, fuel oil, and refined fuels in the United States. The company operates through four segments: Propane, Fuel Oil and Refined Fuels, Natural Gas and Electricity, and All Other. The Propane segment is involved in the retail distribution of propane for space heating, water heating, cooking, and clothes drying for use as a motor fuel in internal combustion engines to power over-the-road vehicles, forklifts, and stationary engines, as well as to fire furnaces as a cutting gas to the industrial customers; and for tobacco curing, crop drying, poultry brooding, and weed control in the agricultural markets. It also engages in the wholesale distribution of propane to industrial end users. Its Fuel Oil and Refined Fuels segment engages in the retail distribution of fuel oil, diesel, kerosene, and gasoline to residential and commercial customers for use in primarily as a source of heat in homes and buildings. The Natural Gas and Electricity segment markets natural gas and electricity to residential and commercial customers in the deregulated energy markets in New York and Pennsylvania. The All Other segment sells, installs, and services a range of home comfort equipment, including whole-house heating products, air cleaners, humidifiers, and space heaters. The company serves residential, commercial, industrial, and agricultural customers primarily in the east and west coast regions of the United States, as well as portions of the midwest region of the United States and Alaska. Suburban Propane Partners, L.P. was founded in 1945 and is based in Whippany, New Jersey.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010-2014. The first new shoreline for the state includes data from 2010 along the North Shore and South Coast from lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX). Shorelines along the South Shore and Outer Cape are from 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Shorelines along Nantucket and Martha’s Vineyard are from a 2012 U.S. Army Corps of Engineers Post Sandy Topographic lidar survey. The second new shoreline for the North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha’s Vineyard, and South Coast west of Buzzards Bay is from 2013-2014 lidar data collected by the U.S. Geological Survey's (USGS) Coastal and Marine Geology Program. Shorelines were extracted from these lidar surveys using several different methods dependent on the location of the shoreline and whether or not wave data were available.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the Colville River and Point Barrow for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010-2014. The first new shoreline for the state includes data from 2010 along the North Shore and South Coast from lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX). Shorelines along the South Shore and Outer Cape are from 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Shorelines along Nantucket and Martha’s Vineyard are from a 2012 U.S. Army Corps of Engineers Post Sandy Topographic lidar survey. The second new shoreline for the North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha’s Vineyard, and South Coast west of Buzzards Bay is from 2013-2014 lidar data collected by the U.S. Geological Survey's (USGS) Coastal and Marine Geology Program. Shorelines were extracted from these lidar surveys using several different methods dependent on the location of the shoreline and whether or not wave data were available.

The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010-2014. The first new shoreline for the state includes data from 2010 along the North Shore and South Coast from lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX). Shorelines along the South Shore and Outer Cape are from 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Shorelines along Nantucket and Martha’s Vineyard are from a 2012 U.S. Army Corps of Engineers Post Sandy Topographic lidar survey. The second new shoreline for the North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha’s Vineyard, and South Coast west of Buzzards Bay is from 2013-2014 lidar data collected by the U.S. Geological Survey's (USGS) Coastal and Marine Geology Program. Shorelines were extracted from these lidar surveys using several different methods dependent on the location of the shoreline and whether or not wave data were available.

These datasets represent USGS-led coastal wetland vegetation survey and mapping efforts at Metzger Marsh, part of the Ottawa National Wildlife Refuge (Ohio, USA) along the coast of western Lake Erie between 1994 and 2022. Vegetation quadrat data provide percent cover estimates per sampling quadrat and overall mean percent cover (MPC) values per species by vegetation type from 1994, and 1996-2010. Vegetation mapping (a.k.a., "photointerpretation") geospatial datasets provide full site cover visualizations and feature class information by vegetation type from 1994,1996-2002, and 2022.

This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset includes shorelines from 65 years ranging from 1947 to 2012 for the north coast of Alaska between the Colville River and Point Barrow. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and lidar elevation data(USGS). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's National Assessment of Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Santa Barbara Coastal (SBC) contains animal cover of Diopatra ornata measurements in percent units and were aggregated to a yearly timescale.

Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. The Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) in cooperation with the Massachusetts Office of Coastal Zone Management, has compiled reliable historical shoreline data along open-facing sections of the Massachusetts coast under the Massachusetts Shoreline Change Mapping and Analysis Project 2013 Update. Two oceanfront shorelines for Massachusetts (approximately 1,800 km in total length) were (1) delineated using 2008/09 color aerial orthoimagery, and (2) extracted from topographic LIDAR datasets (2007) obtained from NOAA's Ocean Service, Coastal Services Center. The new shorelines were integrated with existing Massachusetts Office of Coastal Zone Management and USGS historical shoreline data in order to compute long- and short-term rates using the latest version of the Digital Shoreline Analysis System (DSAS).

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the Colville River and Point Barrow for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.