Dataset Containing 173 College Common Data Sets

Contains Common Data Sets for the Following Schools:

• Alabama State University
• Angelo State University
• Arapahoe Community College
• Arkansas Tech University
• Aurora University
• Baldwin Wallace University
• Beloit College
• Bemidji State University
• Berea College
• Binghamton University
• Boston University
• Bucknell University
• Cabrini University
• California Baptist University
• California State University, Bakersfield
• California State University, Long Beach
• California State University, Los Angeles
• California State University, Sacramento
• Carnegie Mellon University
• Case Western Reserve University
• Christopher Newport University
• Clark University
• Colby College
• College of Charleston
• Collin College
• Colorado College
• Colorado School of Mines
• Colorado State University-Pueblo
• Columbia College
• Concordia University Texas
• Cornell University
• Davidson College
• Delaware Technical Community College
• DeSales University
• Dickinson College
• Drake University
• Drew University
• Duquesne University
• East Central University
• Eastern Washington University
• Embry Riddle Aeronautical University-Daytona Beach
• Fairfield University
• Florida Gulf Coast
• Florida International University
• Fort Hays State University
• Georgia Institute Of Technology
• Gettysburg College
• Hamilton College
• Hollins University
• Humboldt State University
• Iowa State University
• Jackson State University
• John Jay College of Criminal Justice
• Kennesaw State University
• Lafayette College
• Lane College
• Lee University
• Le Moyne College
• Lenoir Rhyne University
• Life University
• Loyola University Maryland
• Lubbock Christian University
• Lycoming College
• Lynn University Common Data Set
• Malone University
• Marlboro College
• Maryville University
• Massachusetts Maritime Academy
• Metropolitan State University of Denver
• Michigan Technological University
• Middlebury College
• Millersville University
• Mississippi State University
• Mott Community College
• Neumann University
• Northeastern State University
• Northern Arizona University
• Northern Kentucky University
• Nyack College
• Oklahoma Christian University
• Oklahoma State University
• Old Dominion University
• Oral Roberts University
• Pepperdine University
• Pomona College
• Prescott College
• Providence College
• Reed College
• Regis University
• Rensselaer Polytechnic Institute
• Rice University
• Rochester College
• Rutgers University
• Saint Vincent College
• San Francisco State University
• Santa Clara University
• Scripps College
• Seton Hill University
• Sewanee
• Shippensburg University
• Simpson University
• Slippery Rock University
• Smith College
• Sonoma State University
• Southeastern Community College
• Southeastern Oklahoma State University
• Southwestern Oklahoma State University
• Springfield College
• St. Ambrose University
• Stanford University
• Stephen F. Austin State University
• SUNY Oneonta
• SUNY Potsdam
• Sweet Briar College
• Taylor University
• Temple University
• Tennessee Wesleyan University
• Texas A&M University - Kingsville
• Texas A&M University
• Texas Wesleyan University
• The College at Brockport
• The College of New Jersey
• The University of Scranton
• The University of Southern Mississippi
• The University of Tennessee
• Trinity University
• Tufts University
• Tulane University
• Tulsa Community College
• University at Buffalo
• University Enrollment
• University of California - Davis
• University of California, Riverside
• University of Colorado Boulder, 2015
• University of Delaware
• University of Kentucky
• University of Louisville
• University of Maine
• University of Missouri
• University of Montana
• University of Mount Olive: 2016
• University of Nebraska Kearney
• University of Nebraska-Lincoln
• University of Nevada, Reno
• University of New Hampshire
• University of New Mexico
• University of North Alabama
• University of North Carolina at Charlotte
• University of Pennsylvania
• University of Pikeville
• University of Puget Sound
• University of Science and Arts
• University of Texas Rio
• University of the Sciences in Philadelphia
• University of Wisconsin
• University Wide Common Data Set 2015
• Villanova University
• Virginia Commonwealth University
• Washburn University
• Washington and Lee University
• Washington College
• Weber State University
• Wellesley College
• Wesleyan University
• Westfield State University
• Westminster College
• Wheaton College
• Whitman College
• Widener University
• Worcester Polytechnic Institute
• Xavier University of Louisiana
• Xavier University
• Yale University

This retention schedule indicates the minimum length of time listed records series must be retained by a universities before destruction or archival preservation. The URRS does not take the place of an agency’s retention schedule, but it is to be used as a guide by the agency in creating and updating its schedule. Records series listed on the URRS are those that are commonly created or received by universities. The retention periods given in the URRS are required minimums. The commission also recommends them as appropriate maximum retention periods.

TWDB will recruit and define the active Subsidence User Community by identifying and communicating with various organizations and stakeholders that own or use subsidence data. UT will support engagement activities to communicate with the subsidence user community and capture common analytical workflows and capabilities desired by the community. These analytical workflows will be developed for subsidence data with cooperation with TWDB and USGS as the subject matter experts to assist with the design of these analyses. TACC, UT Austin will provide reuseable and shareable workflow environment. • Deliverable 3.1 - Host a virtual subsidence workshop with stakeholders to introduce TWDB program to provide subsidence data management and storage services. Include focus group sessions to capture the concerns, capabilities requests, feature preferences, data licensing and access options, and potential data management needs of the community. • Deliverable 3.2 - Develop training materials (e.g., videos, slides, reports) and host them on the virtual machines that serve as a primary point of access to the state of Texas subsidence data collections. • Deliverable 3.3 - Host either a virtual or a face-to-face user workshop to present training materials and assist members of the Subsidence User Community with initial data upload for subsidence related data files using guidelines for acceptable metadata and formats. Additional resources for the Task 3.0 Deliverables

This site provides access to download an ArcGIS geodatabase or shapefiles for the 2017 Texas Address Database, compiled by the Center for Water and the Environment (CWE) at the University of Texas at Austin, with guidance and funding from the Texas Division of Emergency Management (TDEM). These addresses are used by TDEM to help anticipate potential impacts of serious weather and flooding events statewide. This is part of the Texas Water Model (TWM), a project to adapt the NOAA National Water Model [1] for use in Texas public safety. This database was compiled over the period from June 2016 to December 2017. A number of gaps remain (towns and cities missing address points), see Address Database Gaps spreadsheet below [4]. Additional datasets include administrative boundaries for Texas counties (including Federal and State disaster-declarations), Councils of Government, and Texas Dept of Public Safety Regions. An Esri ArcGIS Story Map [5] web app provides an interactive map-based portal to explore and access these data layers for download.

The address points in this database include their "height above nearest drainage" (HAND) as attributes in meters and feet. HAND is an elevation model developed through processing by the TauDEM method [2], built on USGS National Elevation Data (NED) with 10m horizontal resolution. The HAND elevation data and 10m NED for the continental United States are available for download from the Texas Advanced Computational Center (TACC) [3].

The complete statewide dataset contains about 9.28 million address points representing a population of about 28 million. The total file size is about 5GB in shapefile format. For better download performance, the shapefile version of this data is divided into 5 regions, based on groupings of major watersheds identified by their hydrologic unit codes (HUC). These are zipped by region, with no zipfile greater than 120mb: - North Tx: HUC1108-1114 (0.52 million address points) - DFW-East Tx: HUC1201-1203 (3.06 million address points) - Houston-SE Tx: HUC1204 (1.84 million address points) - Central Tx: HUC1205-1210 (2.96 million address points) - Rio Grande-SW Tx: HUC2111-1309 (2.96 million address points)

Additional state and county boundaries are included (Louisiana, Mississippi, Arkansas), as well as disaster-declaration status.

Compilation notes: The Texas Commission for State Emergency Communications (CSEC) provided the first 3 million address points received, in a single batch representing 213 of Texas' 254 counties. The remaining 41 counties were primarily urban areas comprising about 6.28 million addresses (totaling about 9.28 million addresses statewide). We reached the GIS data providers for these areas (see Contributors list below) through these emergency communications networks: Texas 9-1-1 Alliance, the Texas Emergency GIS Response Team (EGRT), and the Texas GIS 9-1-1 User Group. The address data was typically organized in groupings of counties called Councils of Governments (COG) or Regional Planning Commissions (RPC) or Development Councils (DC). Every county in Texas belongs to a COG, RPC or DC. We reconciled all counties' addresses to a common, very simple schema, and merged into a single geodatabase.

November 2023 updates: In 2019, TNRIS took over maintenance of the Texas Address Database, which is now a StratMap program updated annually [6]. In 2023, TNRIS also changed its name to the Texas Geographic Information Office (TxGIO). The datasets available for download below are not being updated, but are current as of the time of Hurricane Harvey.

References: [1] NOAA National Water Model [https://water.noaa.gov/map] [2] TauDEM Downloads [https://hydrology.usu.edu/taudem/taudem5/downloads.html] [3] NFIE Continental Flood Inundation Mapping - Data Repository [https://web.corral.tacc.utexas.edu/nfiedata/] [4] Address Database Gaps, Dec 2017 (download spreadsheet below) [5] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/] [6] TNRIS/TxGIO StratMap Address Points data downloads [https://tnris.org/stratmap/address-points/]

In the academic year 2023/24, California was the most popular state for international students, with 140,858 international students studying there. New York, Texas, Massachusetts, and Illinois rounded out the top five leading states for international students in the United States.

Common bottlenose dolphin (Tursiops truncatus) photographic-identification surveys were conducted by the Texas A&M University at Galveston (TAMUG) Marine Mammal Behavioral Ecology Group, on the Texas coast and included Galveston Bay, West Bay, Matagorda Bay, Corpus Christi Bay, the lower Laguna Madre, and adjacent coastal waters from 1990 to 2001. Photos were initially collected using 35mm film cameras and converted into slides or developed into print photographs and archived in physical storage. The slide film and hardcopy images of individuals with distinct dorsal fin contours were later digitized and embedded with metadata. This dataset includes the digital photographs of dolphin groups, and individual dorsal fin images embedded with metadata including general geographic locations, dolphin mother-calf associations, calf identification, and dolphin group composition. This dataset also includes some tabular data that were recorded for the West Bay study area related to the digital photos.

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.

Osteosarcoma is the most common type of bone cancer that occurs in adolescents in the age of 10 to 14 years. The dataset is composed of Hematoxylin and eosin (H&E) stained osteosarcoma histology images. The data was collected by a team of clinical scientists at University of Texas Southwestern Medical Center, Dallas. Archival samples for 50 patients treated at Children’ s Medical Center, Dallas, between 1995 and 2015, were used to create this dataset. Four patients (out of 50) were selected by pathologists based on diversity of tumor specimens after surgical resection. The images are labelled as Non-Tumor, Viable Tumor and Necrosis according to the predominant cancer type in each image. The annotation was performed by two medical experts. All images were divided between two pathologists for the annotation activity. Each image had a single annotation as any given image was annotated by only one pathologist. The dataset consists of 1144 images of size 1024 X 1024 at 10X resolution with the following distribution: 536 (47%) non-tumor images, 263 (23%) necrotic tumor images and 345 (30%) viable tumor tiles.

This dataset includes shorelines from 151 years ranging from 1850 to 2001 for the Texas east coastal region from Sabine Pass at the Louisiana border to Aransas Pass at the southern end of San Jose Island. Shorelines were compiled from topographic survey sheets, also known as T-sheets (National Oceanic and Atmospheric Administration (NOAA)), aerial photographs (Bureau of Economic Geology, The University of Texas (UT BEG) at Austin), and lidar data (United States Geological Survey/National Aeronautics & Space Administration and UT BEG). 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 (USGS) 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. . 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. .

X-ray CT Scans of the whole body of Schistocerca emarginatum (Texas: Travis Co., Austin, Brackenridge Field Laboratory; 1 August 2003; A. D. Smith) for Dr. John Abbott of the Department of Integrative Biology, The University of Texas at Austin, Dr. Timothy Rowe of the Department of Geological Sciences, The University of Texas at Austin, and Digimorph. Specimen scanned by Matthew Colbert 8 August 2003. This specimen was scanned from back to front, so after reconstruction the images were renumbered and flipped to conform with typical ‘front-to-back’ orientation. Voxel size X and Y = 0.03906 mm; Z = 0.085 mm. Total slices = 1035. Please acknowledge The University of Texas High-Resolution X-ray CT Facility (UTCT), John Abbott, and NSF grant IIS-9874781 when using these data.

The Extrasolar Planets Encyclopedia is a working tool, providing all the latest detections and data that have been announced by professional astronomers, Which is intended to be used to facilitate progress in exoplanetology. Ultimately, researchers willing to make a quantitative, scientific use of the catalog can make their own judgement on the likelihood of the data and the detections. The stellar data (positions, distances, V and other magnitudes, mass, metallicities etc) are taken from Simbad or from professional papers on exoplanets. Ongoing large extrasolar planets ('exoplanets') projects include:

 Anglo-Australian Planet Search <http://www.phys.unsw.edu.au/~cgt/planet/AAPS_Home.html> California & Carnegie Planet Search <http://exoplanets.org/> Geneva Extrasolar Planet Search Programmes <http://obswww.unige.ch/~udry/planet/planet.html> Transatlantic Exoplanet Survey <http://www.astro.caltech.edu/~ftod/tres/tres.html> University of Texas - Dept. of Astronomy <http://www.as.utexas.edu/astronomy/research/ss.html> 

Papaya is a JavaScript based CT scan image viewer for the web that is compatible across a range of popular web browsers, including mobile devices and does not require additional software installation to use. This open source CT scan image viewer supports .nii and .nii.gz files. Papaya is developed by the Research Imaging Institute at the University of Texas Health Science Center.

This archived provides scripts and input files used for the implementation of a novel approach to conduct process-based Flood Frequency Analysis using a Stochastic Storm Transposition (SST) and an Integrated Surface-Subsurface Hydrological Model (ISSHM). As a proof-of-concept, this study uses the ISSHM, Advanced Terrestrial Simulator (Amanzi-ATS) model, and the SST model, RainyDay, to conduct flood frequency analysis by simulating the flood response to 5,000 annual synthetic storm events in a ~2000 km2 Southeast Texas watershed. The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include TXT, CSV, DAT, SBATCH, SHP, TIF, NetCDF, and HDF5 files, which can be read through Python scripts. The input files for the ATS model and RainyDay model have .XML and .SST extensions, respectively, and can be edited in any commonly used text editors. This archive contains: Scripts and data files essential for generating the ATS model input. It uses the Watershed Workflow package to produce both mesh and ATS input files. Jupyter notebooks designated for the ATS model evaluation, covering both long-term simulations and 40 rainfall-runoff events. Input files required to simulate SST storm events using RainyDay.

This dataset provides a map that shows contours for likely flood extent related to elevation within a watershed. The files were generated using a commonly accepted approach to terrain-based analyses for determining flood extent, called Height Above Natural Drainage (HAND), to analyze terrain information in the dataset . The complete 126 file set includes watersheds based on the national HUC-12 (Hydrologic Unit Code). Files are named using the unique HUC-12 code identifier used by the US Geological Survey (https://water.usgs.gov/GIS/huc.html). Each datafile is formatted as a raster GeoTIFF derived from 1-meter LIDAR https://tnris.org/stratmap/elevation-lidar/ Datasets were generated using the HAND-TauDEM workflow that can be accessed publicly in a github repository at https://github.com/dhardestylewis/HAND-TauDEM Files were processed using open-source software, including TauDEM and Python GIS libraries. Data was discretized in one foot intervals (1 ft ~= 0.3048 m) in order to reduce file size (see separate dataset for raw Height Above Nearest Drainage). (2021-03-25)

Description: Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are important. Southeast Texas is still at substantial risk of flooding and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. To make these data more useful for flood monitoring and mapping, Lamar University developed a program to measure elevation and coordinates for the various sensor locations. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various thresholds at critical points used by flood decision-makers for reference at each site. These sensors, in the first phase of this program, were deployed throughout a 7-county region spanning nearly 6000 square miles in Southeast Texas. The latitude and longitude of the sensors, along with their elevations, were determined using survey-grade Global Navigation Satellite System (GNSS) technology. This is an accurate, rapid, and relatively low-cost surveying method. Various Continually Operating Reference Stations (CORS) were examined during post-processing to achieve the most accurate horizontal and vertical results. After differential corrections were applied, accuracies of 0.4 in. (or better) were achieved. Each site's critical points and thresholds were also established using this method. The thresholds, elevations, and positions of these sensors and their surrounding critical points are transmitted to various dashboards on websites. These data are used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA). This data may also be used in the development of flood hydrological models in Southeast Texas watersheds and sub-basins. This program currently involves the Flood Coordination Study team which is part of the Center for Resiliency at Lamar University in collaboration with various entities such as the U.S. Department of Homeland Security Science and Technology Directorate, the Southeast Texas Flood Control District, and various other regional agencies, municipalities, and industries.

Steps to reproduce: A Trimble GEOX7 Global Navigation Satellite System (GNSS) handheld device, which employs Trimble H-StarTM technology, and a ZIPLEVEL PRO-2000 High Precision Altimeter was used to determine the coordinates and elevations of the sensors and surrounding critical points. Post-processing of the GNSS data used the Trimble GPS Pathfinder Office software. The closest CORS base stations were used for differential corrections and the NAD 1983 (2011) (epoch 2010.00) horizontal datum was used as the geographic coordinate system. Furthermore, orthometric heights were calculated using GEOID 18 which is referenced to the North American Vertical Datum of 1988 (NAVD 88). ArcGIS Pro 3 was used to create a map of the sensors and critical points, as well as a watershed delineation relative to Southeast Texas landmarks. Data were gathered in Southeast Texas watersheds and sub-watersheds in order to monitor and map the elevation and movement of water in the drainages. Vertical and horizontal positions of the 74 flood sensors installed in the first phase of the project and their surrounding critical points, including the node (solar panels, battery, and transmission device), the bottom of the posts that nodes attached (bottom of the node from now on), top of the bank, the bottom of the ditch, the bottom of the bridge's deck, and the center of the road and edges, have been gathered accordingly. Also, the relative elevations between these points are important and were collected.

The Texas Department of Insurance (TDI) collects and reports information about billing rates for emergency service providers by procedure code as set by the political subdivisions. The procedure codes include Healthcare Common Procedure Coding System (HCPCS) Codes and any other codes reported by the political subdivisions. This dataset lists the codes and the rates for residents of that political subdivision and for non-residents if that rate differs. There is a row for each procedure code and the rates set by a political subdivision. Political subdivisions with more than one code with a rate set will be listed in multiple rows. The data includes the year and quarter the information applies to as well as the date the political subdivision submitted their report.

The Texas Legislature amended Texas Insurance Code Chapter 38 via Senate Bill 2476 during the 88th session to add reporting “relating to consumer protections against certain medical and health care billing by emergency medical services providers. A political subdivision may submit to the department a rate set, controlled, or regulated by the political subdivision for emergency services.”

► For contact information, refer to dataset:  Emergency Services Billing Rates - Contact List.

► For National Provider Identifier Standard (NPI) information reported in each political subdivision, refer to dataset: Emergency Services Billing Rates - NPI.

► For ZIP codes within political subdivisions, refer to dataset:  Emergency Services Billing Rates - ZIPs.

Users are responsible for reviewing and updating data before the submission deadlines. Information entered or found in this dataset is subject to change. Visit TDI’s web site disclaimer for more information.

For more information related to this data, visit TDI’s FAQ page.

Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are important. Southeast Texas is still at substantial risk of flooding and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. To make these data more useful for flood monitoring and mapping, Lamar University developed a program to measure elevation and coordinates for the various sensor locations. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various thresholds at critical points used by flood decision-makers for reference at each site. These sensors, in the first phase of this program, were deployed throughout a 7-county region spanning nearly 6000 square miles in Southeast Texas. The latitude and longitude of the sensors, along with their elevations, were determined using survey-grade Global Navigation Satellite System (GNSS) technology. This is an accurate, rapid, and relatively low-cost surveying method. Various Continually Operating Reference Stations (CORS) were examined during post-processing to achieve the most accurate horizontal and vertical results. After differential corrections were applied, accuracies of 0.4 in. (or better) were achieved. Each site's critical points and thresholds were also established using this method. The thresholds, elevations, and positions of these sensors and their surrounding critical points are transmitted to various dashboards on websites. These data are used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA). This data may also be used in the development of flood hydrological models in Southeast Texas watersheds and sub-basins. This program currently involves the Flood Coordination Study team which is part of the Center for Resiliency at Lamar University in collaboration with various entities such as the U.S. Department of Homeland Security Science and Technology Directorate, the Southeast Texas Flood Control District, and various other regional agencies, municipalities, and industries.