The Great Lakes Environmental Database (GLENDA) houses environmental data collected by EPA Great Lakes National Program Office (GLNPO) programs that sample water, aquatic life, sediments, and air to assess the health of the Great Lakes ecosystem. GLENDA is available to the public on the EPA Central Data Exchange (CDX). A CDX account is required, which anyone may create. GLENDA offers “Ready to Download Data Files” prepared by GLNPO or a “Query Data” interface that allows users to select from predefined parameters to create a customized query. Query results can be downloaded in .csv format. GLNPO programs providing data in GLENDA include the Great Lakes Water Quality Survey and Great Lakes Biology Monitoring Program (1983-present, biannual monitoring throughout the Great Lakes to assess water quality, chemical, nutrient, and physical parameters, and biota such as plankton and benthic invertebrates), the Great Lakes Fish Monitoring and Surveillance Program (1977-present, annual analysis of top predator fish composites to assess historic and emerging persistent, bioaccumulative, or toxic chemical contaminants), the Cooperative Science and Monitoring Initiative (2002-present, intensive water quality and biology sampling of one lake per year focusing on key challenges and data gaps), the Great Lakes Integrated Atmospheric Deposition Network (1990-present, monitoring Great Lakes air and precipitation for persistent toxic chemicals), the Lake Michigan Mass Balance Study (1993-1996, analyzed the atmosphere, tributaries, sediments, water column, and biota of Lake Michigan for nutrients, atrazine, PCBs, trans-nonachlor, and mercury modelling), and the Great Lakes Legacy Act (1996-present, evaluations of sediment contamination in Areas of Concern). GLENDA is updated frequently with new data.

DNV is a risk and classification company with roots dating back to the founding of Det Norske Veritas (DNV) in 1864. DNV operates in the oil, gas, and renewable energy sectors.

The data produced by DNV is stored in their own Environmental Monitoring database (MOD). It comprises approximately 2.8 million species occurrence records, as well as chemical and geology records. This information comes from grab sampling conducted in areas around oil drilling stations. GBIF Norway is working with DNV to publish the species abundance data in the MOD database.

The grab sampling process is done on a yearly basis around the months of May and June, but not all stations are sampled each year. In general sampling is done around each station every third year, and in some areas samples have been repeated since the 1990s.

LocalitiesList of 1627 localities used to generate the Maxent models in "Do ecological niche models accurately identify climatic determinants of species ranges?" Provided data include the longitude and latitude for each locality and bioclimatic variables ('bio1' to 'bio19'), as extracted from the 'WorldClim' database (for definitions see: http://www.worldclim.org/bioclim). Further information about WorldClim at Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. http://dx.doi.org/10.1002/joc.1276

Tabular data associated with the article "Ecological condition of mountain lakes in the conterminous United States and vulnerability to human development". All tabular data for lake, catchment, and watershed characteristics and population condition estimates are included. This dataset is associated with the following publication: Handler, A., M. Weber, M. Dumelle, L. Jansen, J. Carleton, B. Schaeffer, S. Paulsen, T. Barnum, A. Rea, A. Neale, and J. Compton. Ecological condition of mountain lakes in the conterminous United States and vulnerability to human development. ECOLOGICAL INDICATORS. Elsevier Science Ltd, New York, NY, USA, 173: 113402, (2025).

The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.

This dataset is uploaded to FigShare by Jacob B. Socolar in support of a forthcoming publication. The University of Chicago Press has granted permission to JBS to upload and publish these data. USERS OF THESE DATA SHOULD CITE THE ORIGINAL PUBLICATION:Theodore A. Parker III, Douglas F. Stotz, and John W. Fitzpatrick (1996) Ecological and Distributional Databases for Neotropical Birds. University of Chicago Press, Chicago. ISBN: 9780226646763See also:https://www.press.uchicago.edu/ucp/books/book/chicago/E/bo3618705.html The file "Parker_Stotz_Fitzpatrick_1996.zip" unarchives to a folder called "Parker_Stotz_Fitzpatrick_1996". That folder contains three items:1) readme.txt is a read-me document that contains some additional details in addition to repeating the information below.2) license.pdf is a copy of the license granted to Jacob Socolar by University of Chicago Press to re-publish these data on figshare.com (license states "datadryad.org or a similar repository")3) The 'databases' folder contains the files published as:Theodore A. Parker III, Douglas F. Stotz, and John W. Fitzpatrick (1996) Ecological and Distributional Databases for Neotropical Birds. University of Chicago Press, Chicago. ISBN: 9780226646763See also:https://www.press.uchicago.edu/ucp/books/book/chicago/E/bo3618705.html These databases were originally published as a companion to the book "Neotropical Birds: Ecology and Conservation", by Douglas F. Stotz, John W. Fitzpatrick, Theodore A. Parker III, and Debra K. Moskovits, copyright 1996 University of Chicago Press.If using the data provided in this folder, please cite the references provided above. This item on FigShare (with date 2019) should not replace or supersede the above citations.

To evaluate effects of human influence on the health of Puget Sound's pelagic ecosystems, we propose a sampling program across multiple oceanographic basins measuring key attributes of the pelagic foodweb. We will quantify seasonal abundance and composition of pelagic biota from lower trophic levels (e.g., bacteria and phytoplankton) to middle trophic levels (e.g., zooplankton, small pelagic fi...

InvaCost is the most up-to-date, comprehensive, standardized and robust data compilation and description of economic cost estimates associated with invasive species worldwide1. InvaCost has been constructed to provide a contemporary and freely available repository of monetary impacts that can be relevant for both research and evidence-based policy making. The ongoing work made by the InvaCost consortium2,3,4 leads to constantly improving the structure and content of the database (see sections below). The list of actual contributors to this data resource now largely exceeds the list of authors listed in this page. All details regarding the previous versions of InvaCost can be found by switching from one version to another using the “version” button above. IMPORTANT UPDATES: 1. All information, files, outcomes, updates and resources related to the InvaCost project are now available on a new website: http://invacost.fr/2. The names of the following columns have been changed between the previous and the current version: ‘Raw_cost_estimate_local_currency’ is now named ‘Raw_cost_estimate_original_currency’; ‘Min_Raw_cost_estimate_local_currency’ is now named ‘Min_Raw_cost_estimate_original_currency’; ‘Max_Raw_cost_estimate_local_currency’ is now named ‘Max_Raw_cost_estimate_original_currency’; ‘Cost_estimate_per_year_local_currency’ is now named ‘Cost_estimate_per_year_original_currency’3. The Frequently Asked Questions (FAQ) about the database and how to (1) understand it, (2) analyse it and (3) add new data are available at: https://farewe.github.io/invacost_FAQ/. There are over 60 questions (and responses), so there’s probably yours.4. Accordingly with the continuous development and updates of the database, a ‘living figure’ is now available online to display the evolving relative contributions of different taxonomic groups and regions to the overall cost estimates as the database is updated: https://borisleroy.com/invacost/invacost_livingfigure.html5. We have now added a new column called ‘InvaCost_ID’, which is now used to identify each cost entry in the current and future public versions of the database. As this new column only affects the identification of the cost entries and not their categorisation, this is not considered as a change of the structure of the whole database. Therefore, the first level of the version numbering remains ‘4’ (see VERSION NUMBERING section).

CONTENT: This page contains four files: (1) 'InvaCost_database_v4.1' which contains 13,553 cost entries depicted by 66 descriptive columns; (2) ‘Descriptors 4.1’ provides full definition and details about the descriptive columns used in the database; (3) ‘Update_Invacost_4.1’ has details about the all the changes made between previous and current versions of InvaCost; (4) ‘InvaCost_template_4.1’ (downloadable file) provides an easier way of entering data in the spreadsheet, standardizing all the terms used on it as much as possible to avoid mistakes and saving time at post-refining stages (this file should be used by any external contributor to propose new cost data).

METHODOLOGY: All the methodological details and tools used to build and populate this database are available in Diagne et al. 20201 and Angulo et al. 20215. Note that several papers used different approaches to investigate and analyse the database, and they are all available on our website http://invacost.fr/.

VERSION NUMBERING: InvaCost is regularly updated with contributions from both authors and future users in order to improve it both quantitatively (by new cost information) and qualitatively (if errors are identified). Any reader or user can propose to update InvaCost by filling the ‘InvaCost_updates_template’ file with new entries or corrections, and sending it to our email address (updates@invacost.fr). Each updated public version of InvaCost is stored in this figShare repository, with a unique version number. For this purpose, we consider the original version of InvaCost publicly released in September 2020 as ‘InvaCost_1.0’. The further updated versions are named using the subsequent numbering (e.g., ‘InvaCost_2.0’, InvaCost_2.1’) and all information on changes made are provided in a dedicated file called ‘Updates-InvaCost’ (named using the same numbering, e.g., ‘Updates-InvaCost_2.0’, ‘Updates-InvaCost_2.1’). We consider changing the first level of this numbering (e.g. ‘InvaCost_3.x’ ‘InvaCost_4.x’) only when the structure of the database changes. Every user wanting to have the most up-to-date version of the database should refer to the latest released version.

RECOMMENDATIONS: Every user should read the ‘Usage notes’ section of Diagne et al. 20201 before considering the database for analysis purposes or specific interpretation. InvaCost compiles cost data published in the literature, but does not aim to provide a ready-to-use dataset for specific analyses. While the cost data are described in a homogenized way in InvaCost, the intrinsic disparity, complexity, and heterogeneity of the cost data require specific data processing depending on the user objectives (see our FAQ). However, we provide necessary information and caveats about recorded costs, and we have now an open-source software designed to query and analyse this database6.

CAUTION: InvaCost is currently being analysed by a network of international collaborators in the frame of the InvaCost project2,3,4 (see https://invacost.fr/en/outcomes/). Interested users may contact the InvaCost team if they wish to learn more about or contribute to these current efforts. Users are in no way prevented from performing their own independent analyses and collaboration with this network is not required. Nonetheless, users and contributors are encouraged to contact the InvaCost team before using the database, as the information contained may not be directly implementable for specific analyses.

RELATED LINKS AND PUBLICATIONS:

1 Diagne, C., Leroy, B., Gozlan, R.E. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7, 277 (2020). https://doi.org/10.1038/s41597-020-00586-z

2 Diagne C, Catford JA, Essl F, Nuñez MA, Courchamp F (2020) What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63: 25–37. https://doi.org/10.3897/neobiota.63.55260

3 Researchgate page: https://www.researchgate.net/project/InvaCost-assessing-the-economic-costs-of-biological-invasions

4 InvaCost workshop: https://www.biodiversitydynamics.fr/invacost-workshop/

5 Angulo E, Diagne C, Ballesteros-Mejia L. et al. (2021) Non-English languages enrich scientific knowledge: the example of economic costs of biological invasions. Science of the Total Environment 775:144441. https://doi.org/10.1016/j.scitotenv.2020.144441

6Leroy B, Kramer A M, Vaissière A-C, Courchamp F and Diagne C (2020) Analysing global economic costs of invasive alien species with the invacost R package. BioRXiv. doi: https://doi.org/10.1101/2020.12.10.419432

This dataset was created for the following publication: Cheruvelil, K.S., S. Yuan, K.E. Webster, P.-N. Tan, J.-F. Lapierre, S.M. Collins, C.E. Fergus, C.E. Scott, E.N. Henry, P.A. Soranno, C.T. Filstrup, T. Wagner. Under review. Creating multi-themed ecological regions for macrosystems ecology: Testing a flexible, repeatable, and accessible clustering method. Submitted to Ecology and Evolution July 2016. This dataset includes lake total phosphorus (TP) and Secchi data from summer, epilimnetic water samples, as well as 52 geographic variables at the HU-12 scale; it is a subset of the larger LAGOS-NE database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS-NE compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.054.1 for lake water chemistry data and LAGOSGEO version 1.03 for geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. The dataset is a subset of the following integrated databases: LAGOSLIMNO v.1.054.1 and LAGOSGEO v.1.03. For full documentation of these databases, please see the publication below: Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M. Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K. Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan, J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A. Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse. GigaScience 4:28 doi:10.1186/s13742-015-0067-4 .

The Ecosystem Mapping Layer was created by the Taranaki Regional Council to support the identification and analysis of potential ecosystems and associated threat categories within the region. The dataset combines multiple data sources to provide accurate spatial information essential for conservation planning and ecosystem management. This layer aids in the understanding of regional ecosystems and the threats they face, contributing to informed decision-making in environmental monitoring and resource management.Title: Ecosystem Mapping LayerDate created: 05/10/2020Last updated: 12/02/2024Layers:Potential Ecosystems: Feature layer representing the distribution of potential ecosystems in the region.Potential Ecosystem Threat Categories: Feature layer identifying the threat levels faced by different ecosystems.Purpose: To provide accurate spatial data on potential ecosystems and their associated threats for environmental conservation and resource management in the Taranaki Region.Language: EnglishFormat: Vector (Polygon)Type: Feature LayerSpatial Coverage: Taranaki Region, New ZealandProjection: NZGD2000 / New Zealand Transverse Mercator 2000Source: Derived from multiple environmental data sources and updated with aerial photography for accuracy.Version Control: v1.0

The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneWhat can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location.This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme.Developing the World Terrestrial EcosystemsWorld Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes.Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields.The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation More information about World Terrestrial Ecosystems can be found in this Story Map.

Environmental Radiation Data (ERD) is an electronic and print journal compiled and distributed quarterly by the Office of Radiation and Indoor Air's National Air and Radiation Environmental Laboratory (NAREL) in Montgomery, Alabama. It contains data from RadNet (previously known as ERAMS.)

The vulnerable marine ecosystem (VME) concept emerged from discussions at the United Nations General Assembly (UNGA) and gained momentum after UNGA Resolution 61/105. VMEs constitute areas that may be vulnerable to impacts from fishing activities.

The vulnerable marine ecosystems database was developed in collaboration with the regional bodies with mandates to manage deep-sea fisheries in areas beyond national jurisdiction (ABNJ). It is a compilation of information on management measures taken to reduce current or potential impact on areas where VMEs are known or likely to occur.

The database is designed to facilitate the work of scientists and managers working on these fisheries and also to promote transparency and accessibility of work that has been done in relation to VMEs to the general public.

The content of the database is linked to the data providers (e.g. RFMOs and other multi-lateral bodies) and users have access the primary source of the information through direct links.

This database was developed specifically in response to a request from the UN General Assembly (61/105, paragraph 90) to create a database of information on vulnerable marine ecosystems (VMEs) in ABNJ. It has been developed within the FAO Deep-sea Fisheries Programme to promote the use of the International Guidelines for the Management of Deep-Sea Fisheries in the High Seas that provides guidance to States and Regional Fisheries Management Organizations or Arrangements (RFMO/As) to ensure the long-term conservation and sustainable use of marine living resources in the deep seas. This includes preventing Significant Adverse Impacts on vulnerable marine ecosystems.

For more information please visit - https://www.fao.org/in-action/vulnerable-marine-ecosystems/en/

The Water Interdisciplinary Biology and Ecology database “WIBE” database presents data from different scientific projects proposing the monitoring of the ecological status of waters by a multi-biomarker study. This database gathers biological data of selected bioindicator species and environmental contextual data. In this work, data on physico-chemical parameters and concentrations of trace elements and organic pollutants were collected in the waters as well as biomarkers of effect and exposure to pollutants in marine organisms. The collected data, after various analyses, were cleaned and reworked to meet FAIR and open data principles. We focused on developing a data dictionary linked to existing ontologies and compliant with standards in order to make it as reusable as possible for the ecotoxicology research community. All datasets are available on the public repository of the National Biodiversity Data Centre. The current dataset can be used by port and coastal water managers but also by marine ecotoxicology researchers who will benefit from the first, to our knowledge, completely open database on marine biomarkers allowing the monitoring of coastal water contamination and thus the proposal of remediation measures if necessary.

This dataset is part of a dataset series that establishes an ecosystem service maps (national scale) for a set of services prioritised through stakeholder consultation and any intermediate layers created by Environment Systems Ltd in the cause of the project. The individual dataset resources in the datasets series are to be considered in conjunction with the project report: https://www.npws.ie/research-projects/ecosystems-services-mapping-and-assessment The project provides a National Ecosystem and Ecosystem Services (ES) map for a suite of prioritised services to assist implementation of MAES (Mapping and Assessment of Ecosystems and their services) in Ireland. This involves stakeholder consultation for identification of services to be mapped, the development of a list of indicators and proxies for mapping, as well as an assessment of limitations to ES mapping on differing scales (Local, Catchment, Region, National, EU) based on data availability. Reporting on data gaps forms part of the project outputs. The project relied on the usage of pre-existing data, which was also utilised to create intermediate data layers to aid in ES mapping. For a full list of the data used throughout the project workings, please refer to the project report.

The Database of Sources of Environmental Releases of Dioxin-like Compounds in the United States was developed by EPA to be a repository of certain specific chlorinated dibenzo-p-dioxin/dibenzofuran (CDD/CDF) emissions data from all known sources in the US. The database contains information that can be analyzed to track emissions of CDD/CDF over time, compare specific profiles between and among source categories, and develop source specific emission factors that can then be used to develop emission estimates.

The purpose of the cartographic data resulting from analyses of the ecological connectivity of natural environments in the St. Lawrence lowlands is to equip users by making it possible to integrate the concepts of ecological connectivity and the quality of the habitat of natural terrestrial environments into conservation issues. It is a knowledge tool for recognizing natural environments of importance for ecological connectivity in the St. Lawrence Lowlands region. This data is the result of research conducted by McGill University and its partners (Apex Resource Management Solutions, Quebec Center for Biodiversity Science and Habitat) on behalf of the Ministry of the Environment, the Fight against Climate Change, and its partners (Apex Resource Management Solutions, Wildlife and Parks) (MELCCFP).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Ecological footprint calculated in number of planets per capita. The method is based on the regional ecological footprint, as calculated by the IUU IdF (Nov 2005). Within the 5 sectors considered in the calculation: food, services, goods, mobility and housing, the latter two have been recalculated on the basis of communal data. The property sector has been allocated with a weighting according to the resources of the communal population. The other two sectors were distributed in proportion to the population. C_dep_iau c_dep_iau_Labels c_arobase_d_Classes_Empr_ecolo C_Com_iau c_Com_iau_2_ Tags C_Com_iau_Labels c_Com_iau_2

This dataset contains data from the Long Term Ecological Research (LTER) Project at AWI. These data are derived from PANGAEA.