This asset includes Superfund site-specific sampling information including location of samples, types of samples, and analytical chemistry characteristics of samples. Information is associated with a particular contaminated sate as there is no national database of this information.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Plant Macrofossil. The data include parameters of plant macrofossil (population abundance) with a geographic location of Iowa, United States Of America. The time period coverage is from 14473 to 14447 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

SFLD (Structure-Function Linkage Database) is a hierarchical classification of enzymes that relates specific sequence-structure features to specific chemical capabilities.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity.

The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions.

This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed.

See https://www.epa.gov/smartgrowth/smart-location-mapping for more information.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 24, 2013. Database and Browser containing quantitative information on all the metal-containing sites available from structures in the PDB distribution. This database contains geometrical and molecular information that allows the classification and search of particular combinations of site characteristics, and answer questions such as: How many mononuclear zinc-containing sites are five coordinate with X-ray resolution better than 1.8 Angstroms?, and then be able to visualize and manipulate the matching sites. The database also includes enough information to answer questions involving type and number of ligands (e.g. "at least 2 His"), and include distance cutoff criteria (e.g. a metal-ligand distance no more than 3.0 Angstroms and no less than 2.2 Angstroms). This database is being developed as part of a project whose ultimate goal is metalloprotein design, allowing the interactive visualization of geometrical and functional information garnered from the MDB. The database is created by automatic recognition and extraction of metal-binding sites from metal-containing proteins. Quantitative information is extracted and organized into a searchable form, by iterating through all the entries in the latest PDB release (at the moment: September 2001). This is a comprehensive quantitative database, which exists in SQL format and contains information on about 5,500 proteins.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

CDD is a protein annotation resource that consists of a collection of annotated multiple sequence alignment models for ancient domains and full-length proteins. These are available as position-specific score matrices (PSSMs) for fast identification of conserved domains in protein sequences via RPS-BLAST. CDD content includes NCBI-curated domain models, which use 3D-structure information to explicitly define domain boundaries and provide insights into sequence/structure/function relationships, as well as domain models imported from a number of external source databases.

Software tool as an annotated database of protein phosphorylation sites in eukaryotes. Contains experimentally identified and conserved p-sites which were collected from phosphoproteomic studies.

Site Leader, Inc. Whois Database, discover comprehensive ownership details, registration dates, and more for Site Leader, Inc. with Whois Data Center.

The RIDB is an API accessible database of US Gov't Recreation site data contributed by twelve participating agencies in the Recreation One Stop program. This data is used on Recreation.gov and is available to the public for various other uses.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

To determine the impact of the intron-exon architecture and splice site strength on splice site selection, we created a database of alternative splice sites (ALTssDB) using the Human Exon Splicing Event Database HEXEvent, the Intron DB, and GeneBase. MaxEntScan, a computational tool was used to assign splice site scores. To minimize variability, we focused on competing alternative 5′ or 3′ splice site pairs of internal exons with only one alternative splice pattern. Thus, two alternative 5′ splice sites compete for a common 3′ss, or two alternative 3′ splice sites compete for a common 5′ss. As a result, ALTssDB reports the location of the major splice site and its competing alternative 3′ or 5′ splice site, corresponding exon sizes, usage levels, splice site scores and flanking intron lengths. Methods ALTssDB was created using EST data from the Human Exon splicing Events (HEXEvent) database. HEXEvent contains information regarding the location of competing splice sites, the resulting exon sizes, alternative splice site usage levels and the gene associated with each mRNA. The HEXEvent data was filtered to obtain a dataset comprising of only pairs of competing 5' and 3' splice sites separately. This database was subsequently modified to include splice site junction information and MaxENT scores using MaxEntScan. Using an R script and IntronDB dataset, (a database detailing eukaryotic intron features) flanking intron lengths were added to the database. Alternative splicing events were further filtered to include only events that have 10 or more EST counts.

This is a list of sites from the “SASU (Site Assessment and Support Unit) Case Management” System, which is also known as the LUST Database. Sites on this list include, but are not limited to: sites with known releases from regulated Underground Storage Tank (UST) systems, sites with releases from non-regulated UST system sources, releases from non-UST related sources, sites with potential releases for which impacts or sources of release were not confirmed at time of entry into the system.

The “SASU Case Management” system was initially developed in the late 1980’s/early 1990’s to track sites that the Site Assessment and Support Unit (SASU) worked on. At the time SASU contained the LUST Program. Overtime the “SASU Case Management” System evolved to primarily track releases from regulated Leaking Underground Storage Tanks (LUST) and is currently maintained by the DEEP’s Corrective Action Unit (CAU).

This dataset does not replace a full review of other environmental datasets available through CT Open Data, HazConnect (https://connecticut.hazconnect.com/listincidentpublic.aspx), files publicly available either using the DEEP on-line Public Portal (https://filings.deep.ct.gov/DEEPDocumentSearchPortal/) and/or at the DEEP Record Center File Room (https://portal.ct.gov/DEEP/About/Environmental-Quality-Records-Records-Center-File-Room).

We know there may be errors in the data although we strive to minimize them. Examples of errors may include: misspelled or incomplete addresses and/or missing data.

A separate dataset ( https://data.ct.gov/Environment-and-Natural-Resources/List-of-Contaminated-or-Potentially-Contaminated-S/u76p-weqj/data ) is also published for: List of Contaminated or Potentially Contaminated Sites - Remediation Division. The two database systems are maintained by different Divisions within the agency. There may be sites in both databases due to an overlap in responsibilities of the two Divisions.

TassDB stores extensive data about alternative splice events at GYNGYN donors and NAGNAG acceptors. Currently, 114,554 tandem splice sites of eight species are contained in the database, 5,209 of which have EST/mRNA evidence for alternative splicing. Users can search by Transcript Accession Number and Gene Symbol, SQL Query, and Tandem Donor/Tandem Acceptor pairs.

HAMAP stands for High-quality Automated and Manual Annotation of Proteins. HAMAP profiles are manually created by expert curators. They identify proteins that are part of well-conserved protein families or subfamilies. HAMAP is based at the SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.

DISCOVERAQ_Maryland_Ground_Edgewood_Data contains data collected at the Edgewood ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

DISCOVERAQ_Maryland_Ground_Essex_Data contains data collected at the Essex ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

As large-scale cross-linking data becomes available, new software tools for data processing and visualization are required to replace manual data analysis. XLink-DB serves as a data storage site and visualization tool for cross-linking results. XLink-DB accepts data generated with any cross-linker and stores them in a relational database. Cross-linked sites are automatically mapped onto PDB structures if available, and results are compared to existing protein interaction databases. A protein interaction network is also automatically generated for the entire data set. The XLink-DB server, including examples, and a help page are available for noncommercial use at http://brucelab.gs.washington.edu/crosslinkdbv1/. The source code can be viewed and downloaded at https://sourceforge.net/projects/crosslinkdb/?source=directory.

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

AQA's NZ quarry database.

Developed with support from GNS Science.

Quarry data is updated periodically. AQA accepts no liability for incorrect data.

Please email any corrections to tech@aqa.org.nz

​

Q_INDEX: Unique Identifier – DO NOT CHANGE

NAME: Quarry Name

CLASS: Type of Quarry. Options:

• Commercial – open for general sales
• Farm – on-farm, typically a permitted activity if material extracted doesn’t leave the property
• Forestry – used exclusively for forestry roading/structures and located in a forest
• Satellite site - a different extraction area within the same quarry operation
• Other
• REVIEW - requires input from the technical team

ACTIVITY: Indicator of the level of activity at the quarry. Options:

• Operating
• Operating (part-time)
• Recently operated – operated within the last 2-3 years
• Under care and maintenance – active site maintenance but no quarrying
• Historic – has not been used for 5+ years
• Proposed

PRODUCTION_CLASS: Annualised production estimate. Options:

• Farm (<10,000tpa)
• Small (10,000-50,000tpa)
• Medium (50,000-200,000tpa)
• Large (200,000-1,000,000tpa)
• Super (>1,000,000tpa)

OPERATOR: Company operating the quarry

COMMODITY_TYPE: Rock type – taken from the GNS QMAP

COMMODITY_GROUP: Type of quarry. Options:

• Hard Rock
• Gravel
• Sand
• Mineral

REVIEW_STATUS: Indicator of whether the site’s information has been checked by the technical team. Options:

• OPEN – checked and OK to release for public use
• CLOSED – not for public release
• REVIEW – OK for public release but requires further information or reviewing
• DELETE – used by the technical team to delete a site that isn’t and wasn’t ever a quarry. Note that HISTORIC sites are not deleted.

NZTM_EAST: Easting coordinate in NZGD 2000 New Zealand Transverse Mercator projection

NZTM_NORTH: Northing coordinate in NZGD 2000 New Zealand Transverse Mercator projection

WGS84_LONG: Longitude in WGS84 projection (used by Google Earth)

WGS84_LAT: Latitude in WGS84 projection (used by Google Earth)

TERRAUTH: NZ Territorial Authority in which the quarry land is situated.

REGION: NZ Regional Authority in which the quarry land is situated.

QMAP_MAPNAME: QMAP Rock type indicated for the site. E.g. “Manaia Hill Group sandstone and siltstone (Waipapa Composite Terrane)”

QMAP_LITHO: Rock type general classification (what a quarry would describe their rock as) e.g. “sandstone, siltstone”

Note: “sandstone” is used as the preferred geological term instead of “greywacke”.