The WP5 team of the DIAMAS project designed a follow-up survey to investigate the funding
practices of IPSPs more deeply. We examined the proportion of Diamond publishing within the
same IPSP by output type, and the capability to plan for the future. We also enquired about
spending priorities, reasons for fundraising and the amount of work required, and asked about
views on institutional publishing funding.

The project sent the follow-up survey to respondents of the DIAMAS survey (metadata and
aggregated data available here) who agreed to be contacted. Emails used unique identifiers,
enabling us to merge databases and easily recover information gathered from the first survey
for more advanced cross-analysis.

This follow-up survey was open during the last two months of 2023 and successfully garnered
469 answers. After cleaning (mainly deleting blank surveys and duplicates), we retained 383
relevant answers, a response rate of 56%.

A compilation of different molecule databases ready to be used in HERMES. We have compiled different open-access DBs and adapted their format to the HERMES requisite columns. Since all databases share the "Name" and "MolecularFormula" columns, merges between databases can be easily generated.

More databases and merges will be added in the future. If you have any suggestions or want to contribute, feel free to contact us!

All rights reserved to the original authors of the databases.

Description of the files:

ECMDB.csv: Entries from E. coli Metabolome Database. 3760 compounds.

Merge_KEGG_ECMDB.csv: a merge between all metabolites from KEGG pathways associated to E.coli K12 with the ECMDB.csv from above. 6107 compounds.

Merge_LipidMaps_LipidBlast.csv: a merge between lipid entities from LipidMaps LMSD and the metadata (just Name and Molecular Formula) of LipidBlast entries. 163453 compounds.

norman.xls: Entries from NORMAN SusDat, containing common and emerging drugs, pollutants, etc. 52019 compounds.

PubChemLite_31Oct2020.csv Adapted column names from https://zenodo.org/record/4183801. 371,663 compounds related to exposomics.

MS1_2ID.csv. Merge of HMDB, ChEBI and NORMAN compounds. 183911 compounds related to Human Metabolism, drugs, etc..

COCONUT_NP.csv: parsed collection of entries from the COlleCtion of Open Natural ProdUcTs (COCONUT).406752 compounds.

DiTriPeptides.csv: a list of all theoretically possible dipeptides (400) and tripeptides (8000) and their associated molecular formulas. 8400 compounds.

The Wyoming Landscape Conservation Initiative Literature Database is a collection of publication records that reference the Wyoming Landscape Conservation Initiative (WLCI). The database was developed in an effort to provide the WLCI community with a more functional publication citation index and storage system. By maintaining the literature database, the WLCI community has the opportunity to streamline their workflow and gain further insight into the work that has been published on in regards to the WLCI effort. The xDD API, ScienceBase Catalog, USGS Publications Warehouse, ORCiD website, USGS Staff Profiles website, WLCI website, and the homepages of WLCI scientists were used to search and compile records. The records were entered into a shared library using Zotero, a reference management software. Zotero, along with other software options like Mendeley, RefWorks, EndNote, etc., facilitate the creation of literature databases. In order to confirm each record in the literature database represented a WLCI publication, each publication was searched for mention of the WLCI. To ensure each record included adequate information, records were automatically cross-referenced using the Zotero software and common digital identifiers (DOI or ISSN) or information was added manually. To improve the database, the Zotero duplication tool was used to detect and merge duplicate records and specific tags were incorporated into each record to indicate where the WLCI was referenced in a publication, how the WLCI was referenced in a publication, and/or through which search method the publication was found. The WLCI library can be accessed through three ways; by downloading the Zotero desktop application and syncing it to the online WLCI library, by accessing the WLCI library in the online Zotero application, and through the Zotero API. The library is open to the public. However, the records can only be added or edited by WLCI community members. Because the database is hosted by a third party provider, this data release offers access to the database records in the form of a CSV and RDF file.

Luxury Goods Buyers Mailing List – Target Affluent U.S. Consumers by AmeriList

Unlock access to one of the most powerful consumer databases available, the AmeriList Luxury Goods Buyers File, featuring over 4 million verified U.S. consumers who actively purchase and engage with premium products and brands. These high-income, brand-loyal shoppers represent the top tier of consumer spending, encompassing individuals who invest in designer fashion, fine jewelry, luxury automobiles, upscale travel, and high-end home décor. When you choose AmeriList, you’re connecting with a trusted leader in consumer intelligence and direct marketing data, ensuring accuracy, deliverability, and response-driven results.

About the Luxury Goods Buyers Database

• The AmeriList Luxury Goods Buyers Database was created for marketers targeting affluent, style-conscious consumers who actively purchase high-end and designer products.

• Features millions of verified luxury shoppers, individuals who invest in premium fashion, jewelry, décor, automobiles, and upscale lifestyle brands.

• Each record is validated, standardized, and enhanced through AmeriList’s data hygiene and verification process, ensuring accurate, responsive results.

• Updated monthly with NCOA and CASS certification, guaranteeing deliverable, compliant, and up-to-date consumer data.

• Provides detailed demographic and lifestyle selects, including income, location, age, gender, and buying behavior—perfect for segmentation and precision targeting.

Ideal for direct mail, email, and digital campaigns aimed at luxury buyers and high-income households.

What Makes This List Unique

• Affluent, Verified Consumers: Every record represents a high-value consumer with known purchasing power and a demonstrated interest in luxury categories, from couture fashion to luxury travel and automotive.

• True Multi-Channel Reach: Use this database for direct mail, email, or telemarketing campaigns. Coordinate messaging across channels for a consistent, high-impact brand presence.

• Data Hygiene & Quality Assurance: The AmeriList team updates and validates the file monthly, performing address hygiene (NCOA, CASS, DSF2) and duplicate suppression. This ensures that your campaigns reach deliverable, current consumers.

• Exclusive Market Segmentation: Identify buyers of specific categories such as jewelry, handbags, watches, décor, luxury automobiles, and upscale lifestyle goods. Layer additional selects like age, income, marital status, or homeowner type to refine your audience further.

• Backed by a Leading Data Provider: AmeriList has more than two decades of expertise in consumer data compilation and direct marketing. All files adhere to privacy and compliance best practices, ensuring data integrity and ethical usage.

Ideal Marketing Applications

• Fashion & Accessories Marketing: Reach stylish consumers who actively purchase from designer labels, luxury boutiques, and high-end apparel brands.

• Fine Jewelry & Watches: Promote premium jewelry collections, limited-edition watches, and bespoke pieces to buyers who value craftsmanship and exclusivity.

• Luxury Home & Décor Brands: Target homeowners with the taste and means to invest in luxury furniture, custom interiors, and designer décor.

• Premium Automobiles & Transport: Reach affluent drivers who appreciate performance, innovation, and luxury craftsmanship, perfect for premium car brands, leasing programs, or lifestyle accessories.

• Travel, Leisure & Hospitality: Market high-end travel packages, resorts, private charters, and curated experiences to consumers who prioritize luxury and personalized service.

• High-End Beauty & Fragrance: Connect with consumers who prefer prestige beauty, skincare, and niche fragrances that define elegance and exclusivity.

How the Data Is Compiled

AmeriList’s data compilation combines multiple, verified data sources to ensure accuracy and responsiveness. The Luxury Goods Buyers file is derived from:

• Self-reported lifestyle and interest surveys
• Purchase transactions and online registrations
• Brand and loyalty program participation
• Public records and demographic overlays
• Third-party and proprietary data partnerships

Each data element is verified, standardized, and enhanced to meet AmeriList’s strict quality standards. Before release, the entire database undergoes merge/purge processes, duplicate removal, and suppression against existing client data to prevent redundancy and enhance performance.

All addresses are validated through CASS Certification and the National Change of Address (NCOA) database, ensuring postal deliverability and compliance with USPS standards.

Marketing Advantages:

• Pinpoint Precision – Reach only those who are statistically most likely to respond to luxury offers.
• High Deliverability – Every record meets rigorous hygiene protocols for accurate, mailable, and emailable data.
• Increased ROI – Reduce wasted impressions and focus your spend on high-va...

Abstract:

Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use.

BSSL Description and Usefulness

The Brazilian Soil Spectral Library (BSSL), available at https://bibliotecaespectral.wixsite.com/english, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data.

The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative.

The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability.

Coverage

The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a .xlsx format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (20 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range.

Accessing original data source

Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors:

a) Refer to sheet two for name and code-based searches;

b) Visit the website https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes or locate the contributors' list by Brazilian state;

c) Visit the website of the Brazilian Soil Spectral Service – Braspecs http://www.besbbr.com.br/, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found.

Proceeding to data analysis

We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011).

The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition.

a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh;

b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;

c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings;

d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample;

e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm;

f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and

g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.

The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.

We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°.

Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard.

For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory.

The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm^-1 to 600 cm^-1, with a resolution of 2 cm^-1. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.

Dataset characterization

The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:

Sheet 1. BSSL_Soil_Attributes_Dataset

Column 1. ID_unique: Sequential code assigned to every record;

Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data;

Column 3. Vis_NIR_SWIR_availability: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;

Column 4. MIR_availability: availability of spectral data in the middle infrared range;

Column 5. Sampling: type of soil sampling;

Column 6. Depth_cm: soil surface layer depth in centimeters;

Column 7. Region: Brazilian geographical region of samples' source;

Column 8. Municipality: Brazilian municipality of samples' source;

Column 9. State: Brazilian Federation Unit of samples'

Environmental Data Service & Ecosystem Data Analytics

Simplify your research data collection with trusted environmental data service managed by TERN Australia. We host a diverse range of ecosystem data and environmental datasets, including ecoacoustics, leaf area index, imagery and more. Our environment database supports open science by making research data accessible to scientists, educators, policymakers and decision-makers worldwide. Start exploring today through our easy-to-use tools and portals.

The TERN research data collection provides analysis-ready environmental data that facilitates ecological research projects for both established and emerging scientists from Australia and around the world. The resources we provide support scientific investigation in environment and climate research fields while helping to inform sustainable decision-making initiatives. Access our environmental data service now to power your next research project.

If you’d like to learn more about TERN’s data, tools, or research services – or if you have a specific enquiry – visit the TERN Portal, call us on 07 3365 9097, or email us at tern@uq.edu.au.

Explore our Data Portals

Open access ecosystem data collections are available via the TERN Data Discovery Portal and sub-portals:

Access all TERN Environment Data

Discover datasets published by TERN’s observing platforms and collaborators. Search geographically, then browse, query, and extract data via the TERN Data Discovery Portal.

Search EcoPlots Data

Search, integrate, and access Australia’s plot-based ecology survey data.

Download ausplotsR

Extract, prepare, visualise, and analyse TERN Ecosystem Surveillance monitoring data in R.

Search EcoImages

Search and download Leaf Area Index (LAI), Phenocam, and Photopoint images.

Explore our Data Services

Tools that support the discovery, analysis, and re-use of ecosystem data include:

Visualise the Data

In partnership with ANU, we provide 50 landscape and ecosystem datasets presented graphically.

Access CoESRA Virtual Desktop

A virtual desktop environment that enables users to create, execute, and share environmental data simulations.

Submit data with SHaRED

Our user-friendly tool to upload your data securely to our environment database so you can contribute to Australia’s ecological research.

Other Data Portals, Tools, and Services

The Soil and Landscape Grid of Australia uses the best available data from existing environment databases, new sensor measurements, and innovative spatial modelling. It presents fine spatial resolution (3 arc-seconds or approximately 90 x 90 m pixels) digital soil and landscape attribute maps.

The Australian Cosmic-Ray Neutron Soil Moisture Monitoring Network (CosmOz) delivers soil moisture data for 16 sites covering about 30 hectares, with measurements taken to depths of between 10-50 cm. Led by CSIRO, this ecosystem data network is expanding to 23 sites to provide even greater environmental insights.

The TERN Mangrove Data Portal offers a diverse range of historical and contemporary remotely-sensed datasets on the extent and change of mangrove ecosystems across Australia. It includes multi-scale field measurements of mangrove floristics, structure and biomass, as well as a wide variety of airborne imagery collected since the 1950s, and multispectral and hyperspectral imagery from drones, aircraft, and satellites. This project has been recognised for its contribution to the Sustainable Development Goals and strengthens Australia’s long-term environment database.

TERN’s ausplotsR is an R Studio package designed for extracting, preparing, visualising, and analysing TERN’s Ecosystem Surveillance monitoring data. Researchers can directly access plot-based ecosystem data on vegetation and soils across Australia, using simple functions to merge information into species occurrence matrices for advanced analysis, such as calculating basal area or fractional cover.

The annual Australia’s Environment products summarise large volumes of observations on the trajectory of our natural resources and ecosystems. Through the data explorer, users can view and download maps, accounts, and charts by region and land use type. The portal also provides national summary reports and report cards for different types of administrative and geographical regions, all underpinned by TERN’s trusted environmental data service.

Meet the Data Services & Analytics Team Leaders

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Data Services and Analytics Platform Lead

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Solution Architect

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Senior Software Engineer

Get in Touch with TERN for Analysis-Ready Ecosystem Data

Our research data collection makes it easier for scientists and researchers to investigate and answer questions by providing them with open data, research and management tools, infrastructure, and site-based research tools.

The TERN Data Discovery Portal provides open access ecosystem data and is a cornerstone of our environmental data service. Our tools support data discovery, analysis, and re-use. The services which we provide facilitate research, education, and management. We maintain a network of monitoring sites and sensor data streams for long-term research as part of our environment database.

By choosing TERN, you gain reliable access to high-quality ecosystem data, curated tools and a leading environmental data service that drives global research and education. Have questions about TERN’s data collections, tools, or services? Connect with our team by visiting the TERN Portal, calling 07 3365 9097, or emailing tern@uq.edu.au for personalised support.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset contains the spatial and non-spatial (attribute) components of the Namoi subregion Asset List as an .mdb file, which is readable as an MS Access database or as an ESRI Personal Geodatabase.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. A report on the WAIT process for the Namoi is included in the zip file as part of this dataset (Namoi_Phase01_Final.pdf).

Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Namoi subregion are found in the "AssetList" table of the database.

Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "NAM_AssetList_v4_20150115_description.doc", located in the zip file as part of this dataset.

The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Detailed information describing the database structure and content can be found in the document "NAM_AssetList_v4_20150115_description.doc" located in the zip file as part of this dataset.

Some of the source data used in the compilation of this dataset is restricted. This dataset is not to be published in its current form.

A spreadsheet that contains the Namoi Asset List and the Water Dependent Asset Register is available for public release:

Water-dependent asset register and asset list for the Namoi subregion on 15 January 2015

ID: de112f5b-a4e8-4238-b8e6-662ca8c0ea51

Purpose

The Asset List Database was developed to spatially identify water dependent assets found within the Namoi subregion.

Dataset History

On 20 April 2015 the title of this database was changed from "Namoi_AssetList_Database_v4_20150115".

This dataset replicates the spatial and tabular content and structure of the previous version of the Namoi asset list ("Asset list for Namoi - CURRENT"; ID: 538c717c-c04a-4720-8bcd-96fbdf7f0d80) with the exception that decisions made by the Namoi Project Team concerning Materiality Test 2 (water dependency) have been incorporated into the AssetList table, which are used to define the water dependent asset register.

Date Notes

22/07/2014 Initial database for asset related tables and feature classes, and imported element data from element list database

5/09/2014 Updated database with updated WSP/GWMP/RegRiv assets/elements; additional WSP plus point water volume data and additional RegRiv plus point water volume data

18/11/2014 Merge some assets with non standard classification to standard classification

18/11/2014 add additional point groundwater economic data ( 121 new elements)

18/11/2014 add additional point surface water economic data (49 new elements)

15/01/2015 Incorporate materiality decisions (M2) from project team into AssetList table

Dataset Citation

Bioregional Assessment Programme (2015) Asset database for the Namoi subregion on 15 January 2015. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/c32e70ad-9357-4297-a5dd-e1f1e1f5255f.

Dataset Ancestors

• Derived From Asset list for Namoi - CURRENT

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From NSW Office of Water Surface Water Licences in NIC linked to locations v1 (22 April 2014)

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Missing SW Licensing Data in the Namoi PAE 20140711

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted

• Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

• Derived From Australia - Species of National Environmental Significance Database

• Derived From NSW Office of Water Groundwater Licence Extract NIC- Oct 2013

• Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal

• Derived From NSW Office of Water Groundwater Entitlements Spatial Locations

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public)

• Derived From NSW Office of Water Groundwater licences extract linked to spatial locations NIC v3 (13 March 2014)