The NYS Department of Environmental Conservation (DEC) collects and maintains several datasets on the locations, distribution and status of species of plants and animals. Information on distribution by county from the following three databases was extracted and compiled into this dataset. First, the New York Natural Heritage Program biodiversity database: Rare animals, rare plants, and significant natural communities. Significant natural communities are rare or high-quality wetlands, forests, grasslands, ponds, streams, and other types of habitats. Next, the 2nd NYS Breeding Bird Atlas Project database: Birds documented as breeding during the atlas project from 2000-2005. And last, DEC’s NYS Reptile and Amphibian Database: Reptiles and amphibians; most records are from the NYS Amphibian & Reptile Atlas Project (Herp Atlas) from 1990-1999.

COCONUT (Collection of Open Natural Products) Online

COlleCtion of Open NatUral producTs (COCONUT) is an aggregated dataset comprising elucidated and predicted natural products (NPs) from open repositories. It offers a user-friendly web interface for browsing, searching, and efficiently downloading NPs. The latest database integrates more than 63 open NP resources, providing unrestricted access to data free of charge. Each entry in the database represents a "flat" NP structure, accompanied by information on its known stereochemical forms, relevant literature, producing organisms, natural geographical distribution, and precomputed molecular properties.

Natural products are small bioactive molecules produced by living organisms with potential applications in pharmacology and various industries. The significance of these compounds has driven global interest in NP research across diverse fields. However, despite the growing number of general and specialized NP databases, no comprehensive online resource has consolidated all known NPs in one place—until COCONUT. This became a resource facilitating NP research, enabling computational screening and other in-silico applications.

Summary of the COCONUT database's statistics:

COCONUT was meticulously assembled from a range of public databases and primary sources, including:

Marine and Freshwater Fish specimens deposited in Osaka Museum of Natural History, Part 1: Specimens collected from Kansai District.

Photographs and other visual media provide valuable pre- and post-event data for natural hazards. Research, mitigation, and forecasting rely on visual data for post-analysis, inundation mapping and historic records. Instrumental data only reveal a portion of the whole story; photographs explicitly illustrate the physical and societal impacts from an event. This resource provides high-resolution geologic and damage photographs from natural hazards events, including earthquakes, tsunamis, slides, volcanic eruptions and geologic movement (faults, creep, subsidence and flows). The earliest images date back to 1867. Each event also links to NCEI's Global Historical hazards databases, which provide details for these events.

This dictionary covers the three kingdoms: Animal, Vegetal, Mineral. It contains 50,000 species with numerous synonyms in French, English and Latin and many breeds and varieties. Minerals are given with their chemical formula. About 7,900 definitions in French are included.This dictionary gathers many disciplines and topics such as: Mammals, Fishes, Birds, Insects, Reptiles, Shellfishes, Trees, Plants, Flowers, Fruits, Vegetables, Minerals, Rocks, Gems, etc. It also includes synonyms and linguistic variants.Languages : French - English (GB, US) - LatinNumber of entries: 133,500 Number of terms per language: between -20% and -25% approx. with respect to the number of entries (i.e. ca. 50,000 terms)Disciplines: about 105Format: .DBF files, sorted alphabetically in French and EnglishA viewer is also available upon demand for an additional cost of 2676 euros. This software enables a spontaneous search French => English and English => French in the database according to different criteria:- by beginning of term, - by included word,- by discipline,- by class,- by kingdom,- through a free search: French, English, Latin words and synonyms in the 3 languages, chemical formula, i.e. 435,000 access points. For each term obtained, the database corpus is instantly displayed with the total of terminological data available for that term.Viewing format: .FIC (Windev)Please note that the prices indicated here are dependent from the number of entries available which is growing constantly. Please contact us for further details.

Explore the booming Vector Database market, projected to hit $3.04 billion by 2025 with a 23.7% CAGR. Discover key drivers, applications like NLP and Computer Vision, and leading companies shaping AI's future.

This file contains the data set used to develop a random forest model predict background specific conductivity for stream segments in the contiguous United States. This Excel readable file contains 56 columns of parameters evaluated during development. The data dictionary provides the definition of the abbreviations and the measurement units. Each row is a unique sample described as R** which indicates the NHD Hydrologic Unit (underscore), up to a 7-digit COMID, (underscore) sequential sample month. To develop models that make stream-specific predictions across the contiguous United States, we used StreamCat data set and process (Hill et al. 2016; https://github.com/USEPA/StreamCat). The StreamCat data set is based on a network of stream segments from NHD+ (McKay et al. 2012). These stream segments drain an average area of 3.1 km2 and thus define the spatial grain size of this data set. The data set consists of minimally disturbed sites representing the natural variation in environmental conditions that occur in the contiguous 48 United States. More than 2.4 million SC observations were obtained from STORET (USEPA 2016b), state natural resource agencies, the U.S. Geological Survey (USGS) National Water Information System (NWIS) system (USGS 2016), and data used in Olson and Hawkins (2012) (Table S1). Data include observations made between 1 January 2001 and 31 December 2015 thus coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (https://modis.gsfc.nasa.gov/data/). Each observation was related to the nearest stream segment in the NHD+. Data were limited to one observation per stream segment per month. SC observations with ambiguous locations and repeat measurements along a stream segment in the same month were discarded. Using estimates of anthropogenic stress derived from the StreamCat database (Hill et al. 2016), segments were selected with minimal amounts of human activity (Stoddard et al. 2006) using criteria developed for each Level II Ecoregion (Omernik and Griffith 2014). Segments were considered as potentially minimally stressed where watersheds had 0 - 0.5% impervious surface, 0 – 5% urban, 0 – 10% agriculture, and population densities from 0.8 – 30 people/km2 (Table S3). Watersheds with observations with large residuals in initial models were identified and inspected for evidence of other human activities not represented in StreamCat (e.g., mining, logging, grazing, or oil/gas extraction). Observations were removed from disturbed watersheds, with a tidal influence or unusual geologic conditions such as hot springs. About 5% of SC observations in each National Rivers and Stream Assessment (NRSA) region were then randomly selected as independent validation data. The remaining observations became the large training data set for model calibration. This dataset is associated with the following publication: Olson, J., and S. Cormier. Modeling spatial and temporal variation in natural background specific conductivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 4316-4325, (2019).

This database provides species lists, general descriptions of the species, their distributions, and their abundances. It can search through any number of nature reserves, preserves, state parks and state beaches for plant, mammal, bird, reptile, fish and amphibian data. It can also search by species or larger taxonomic category.

The River Macrophytes Database (RMD) is a Microsoft Access database constructed to house data on the plant communities of rivers in Great Britain and Northern Ireland. It includes data from over 7000 survey sites and is the most comprehensive database of its kind. Data have been collected from all over the UK between 1977 and the present day, following the methods of Holmes et al. (1999). The data held in the RMD are the result of collaborative work across all four statutory nature conservation bodies: Scottish Natural Heritage (SNH), Natural England (NE), Natural Resources Wales (NRW, formerly CCW) and the Northern Ireland Environment Agency (NIEA). The River Macrophytes Database can be downloaded from the JNCC website: https://hub.jncc.gov.uk/assets/0a26368d-400c-44e1-beaf-d4b89b7badcd#extent-detail

I assembled this dataset from various published sources to evaluate how the social-ecological system (SES) in Adirondack Park, New York changed through time and the interplay of public goods (Common Pool Resources, CPRs), public land rules and private land rights, and related concepts over 260 years (1760-2020). The database was the basis for a doctoral dissertation titled "Blue Lining: Assessing the Resilience of Adirondack Park, New York Using Polycentricity and Panarchy Frameworks." The goal of the dissertation was to assess patterns and changes in institutional rules, actors and arrangements before and after establishment of the public Adirondack Forest Preserve in 1885 and Adirondack Park in 1892 as those actors and rules were modified and as both internal and external events influenced the SES as it moved through different phases of the adaptive cycle through space and time (see panarchy). Using the database, I identified which organizations and events contributed to natural resource and CPR policy. The dissertation can be downloaded here: https://experts.esf.edu/esploro/outputs/99917370604826.

Instream Flow and Natural Lake Level water rights of the Colorado Water Conservation Board

Introduction and Rationale:Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update SPAN. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in the final version of SPAN.Contents:Spatial dataNational rasters of land cover in the conterminous United States: 2012-2021Rasters of pixels mismatched between CDL and NVC: 2012-2021Resources in this dataset:Resource Title: SPAN land cover in the conterminous United States: 2012-2021 - SCINet File Name: KammererNationalRasters.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). Spatial Products for Agriculture and Nature ('SPAN') land cover in the conterminous United States from 2012-2021. This raster dataset is available in GeoTIFF format and was created by joining agricultural classes from the USDA-NASS Cropland Data Layer (CDL) to national vegetation from the LANDFIRE National Vegetation Classification v2.0 ('Remap'). Pixels of national vegetation are the same in all rasters provided here and represent land cover in 2016. Agricultural pixels were taken from the CDL in the specified year, so depict agricultural land from 2012-2021. Resource Title: Rasters of pixels mismatched between CDL and NVC: 2012-2021 - SCINet File Name: MismatchedNational.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). This dataset includes pixels that were classified as agriculture in the NVC but, in the CDL, were not agriculture (or were a conflicting agricultural class). For more details, we refer users to the linked publication describing our geospatial processing and validation workflow.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node455886/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

Natural Gas Imports: Pipeline: Canada data was reported at 236.194 Cub ft bn in Aug 2018. This records a decrease from the previous number of 242.492 Cub ft bn for Jul 2018. Natural Gas Imports: Pipeline: Canada data is updated monthly, averaging 215.490 Cub ft bn from Jan 1973 (Median) to Aug 2018, with 548 observations. The data reached an all-time high of 372.142 Cub ft bn in Dec 2007 and a record low of 40.723 Cub ft bn in Jun 1983. Natural Gas Imports: Pipeline: Canada data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.RB013: Natural Gas Imports.

The Database collects the different types of data concerning wildlife management in the protected areas of the Lazio Region. It is currently organized into 5 sections: crop fauna damage events, amounts compensated for crop fauna damage, livestock fauna damage events, amounts compensated for livestock fauna damage, amounts invested in wildlife damage prevention activities. The Database aims to build the cognitive support necessary to guide intervention strategies and the allocation of resources.

Data for modeling

This publication is a 6 CD-ROM compilation of data describing the bedrock, surficial, and engineering geology of a portion of the proposed Alaska Natural Gas Transportation System corridor. Maps or reports with scales of 1:250,000 or greater that cross or come within 5 miles of the centerline were included in this compilation. Two maps with scales smaller than 1:250,000 were also included: OFR 82-1071 and OFR 98-133. A complete bibliography of references used in this compilation is on the first CD-ROM of this project in ANGTSgis/RefsCited.doc. This project consists of an ArcView 3.x (AV) .apr file and a Microsoft Access 2000 (MS Access) database. The ArcView project and the Access database are linked using AccessLink, which runs from ArcView. A free, limited version of AccessLink is included with this compilation on the first CD-ROM in ANGTSgis/Extras/AccessLinkLTD.avx. This version of the ANGTS AV project and MS Access database covers the area from Prudhoe Bay to Delta Junction. Scanned versions of documents or maps are linked to the AV 3.x project and to the Access database.

Additional file 1. Overlap (in percent) of compound content between open natural products databases.

This collection provides international data on natural gas. Data organized by country. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

Vascular plant specimens deposited at the Kanagawa Prefectural Museum of Natural History