TERN Ecosystem Data Services and Analytics

TERN Research Data Repository

Simplify your research data collection with the help of the research data repository managed by the Terrestrial Ecosystem Research Network. Our collection of ecosystem data includes ecoacustics, bio acoustics, lead area index information and much more.

The TERN research data collection provides analysis-ready environment data that facilitates a wide range of ecological research projects undertaken by established and emerging scientists from Australia and around the world. The resources which we provide support scientific investigation in a wide array of environment and climate research fields along with decision-making initiatives.

Explore Our Ecosystem Data Portals

Open access ecosystem data collections 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 the 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, anaylsis and re-use of data:

Visualise the data

We’ve teamed up with ANU to 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 provides relevant, consistent, comprehensive, nation-wide data in an easily-accessible format. It provides detailed digital maps of the country’s soil and landscape attributes at a finer resolution than ever before in Australia.

The annual Australia’s Environment products summarise a large amount of observations on the trajectory of our natural resources and ecosystems. Use the data explorer to view and download maps, accounts or charts by region and land use type. The website also has national summary reports and report cards for different types of administrative and geographical regions.

TERN’s ausplotsR is an R Studio package for extracting, preparing, visualising and analysing TERN’s Ecosystem Surveillance monitoring data. Users can use the package to directly access plot-based data on vegetation and soils across Australia, with simple function calls to extract the data and merge them into species occurrence matrices for analysis or to calculate things like basal area and fractional cover.

The Australian Cosmic-Ray Neutron Soil Moisture Monitoring Network (CosmOz) delivers soil moisture data for 16 sites over an area of about 30 hectares to depths in the soil of between 10 to 50 cm. In 2020, the CosmOz soil moisture network, which is led by CSIRO, is set to be expanded to 23 sites.

The TERN Mangrove Data Portal provides a diverse range of historical and contemporary remotely-sensed datasets on extent and change of mangrove ecosystems across Australia. It includes multi-scale field measurements of mangrove floristics, structure and biomass, a diverse range of airborne imagery collected since the 1950s, and multispectral and hyperspectral imagery captured by drones, aircraft and satellites.

The TERN Wetlands and Riparian Zones Data Portal provides access to relevant national to local remotely-sensed datasets and also facilitates the collation and collection of on-ground data that support validation.

ecocloud provides easy access to large volumes of curated ecosystem science data and tools, a computing platform and resources and tools for innovative research. ecocloud gives you 10GB of persistent storage to keep your code/notebooks so they are ready to go when you start up a server (R or Python environment). It uses the JupyterLabs interface, which includes connections to GitHub, Google Drive and Dropbox.

Analysis Ready Ecosystem Data

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

The TERN data portal provides open access ecosystem data. 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 site and sensor data streams for long-term research as part of our research data repository.

Despite a growing recognition of the importance of data to the economy and to science, investment in repositories to manage and disseminate that data in easily accessible and understandable ways is scarce. Keeping repository services active and up-to-date for a long time period is difficult due to this funding situation. As a result, repositories must continually provide proof of their value, their Return on Investment (ROI) to their sponsors; yet doing so has always been difficult, problematic and not always successful. In this work, an analysis of approaches for assessing the ROI of several scientific data repositories has identified various techniques that repositories use to report on the impact and value of their data products and services. A survey of selected repositories rated the set of metrics identified and rated each by its importance as well as the ease with which the metric could be measured. The discussion is broken down into considerations for calculating costs, perceived value of repositories and suggested metrics that would allow a repository to calculate an ROI. The authors, representatives of environmental data repositories, concluded that easily obtainable data use metrics, such as data downloads, etc., have limited value while more informative analyses would require additional resources.

Link Function: information

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository and data management support organization for the environmental scientist. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, these data and code were used to provide a general description of EDI’s collection of data and its data management philosophy and placement in the repository landscape. They show how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, they provide measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats. The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

The United States Fish and Wildlife Service Delta Juvenile Fish Monitoring Program (DJFMP) has monitored juvenile Chinook Salmon Oncorhynchus tshawytscha and other fish species within the San Francisco Estuary (Estuary) since 1976 using a combination of surface trawls and beach seines. Since 2000, three trawl sites and 58 beach seine sites have been sampled weekly or biweekly within the Estuary and lower Sacramento and San Joaquin Rivers. As part of the Interagency Ecological Program (IEP) that manages the Estuary, the DJFMP has tracked the relative abundance and distribution of naturally and hatchery produced juvenile Chinook Salmon of all races as they outmigrate through the Sacramento-San Joaquin Delta for over four decades. The data that DJFMP collected has been used not only to help inform the management of Chinook Salmon, but also to monitor the status of native species of interest such as the previously listed Sacramento Splittail Pogonichthys macrolepidotus and invasive species such as Mississippi Silverside Menidia audens and Largemouth Bass Micropterus salmoides. DATA CORRECTION/UPDATE: Previous data versions 244.6, 244.7, and 244.8 contained an error and resulted in duplicated records of hatchery Chinook Salmon in the datasets. These datasets were removed from the data repository and the error was corrected in version 244.9 and after.
DNA Data: Full more details of the DNA methods and results for juvenile Chinook salmon included in this dataset, please check out Blankenship, S.M., J. Israel, E. Buttermore, and K. Reece. 2021. Knights Landing, California Department of Fish and Wildlife, Genetic Determination of Population of Origin 2017 through 2019 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/85fbc988c0b1362e84c318e69c7a939e. For more information on the Delta Juvenile Fish Monitoring Program: https://www.fws.gov/project/delta-juvenile-fish-monitoring-program

The concept of ecosystem services (ES) is attracting increased attention as a way to communicate the value of nature for human well-being by using a language that reflects dominant political and economic views. Progress in ES research has been rapid (Guerry et al. 2015), and there is an increasing information demand for diverse groups of decision makers (Schaefer et al. 2015; Bouwma et al. 2017). Incorporating ES information into decision making, however, is a long-term project and requires successfully addressing a number of challenges. One challenge is to efficiently exploit available information sources for decision advice. In Schmidt and Seppelt (2018) we reviewed how information contained in ES databases can support policy instruments to better take nature’s benefits into account. Here the data compiled within Schmidt and Seppelt (2018) was made available. In total 29 databases with global coverage were reviewed that contain information of 36,014 studies, projects and methods within more than 600,000 entries. Additionally, I identified 93 indicators of information demand for six major policy instruments which deal with or are directly related to the use of natural resources or land. Database entries were than matched with indicators of information demand. The resulting dataset encompasses the total number of data entries of ES databases that could be thematically linked to information requirements from indicators of information demand. Also, data was made available that provides broader insights into the content, design and impact of reviewed ES databases. Facilitating data discovery and linking ES databases with policy instruments are essential steps for the incorporation of ES information into decision making.

Preface:

This is a part of my contribution to the Kiva, to help them continue and expand their initiative to alleviate global poverty.

Context:

In order for Kiva to best set its investment priorities, help inform lenders, and understand their target communities, knowing the level of poverty for each borrower is crucial. However, attaining individual-level information is time-consuming, labor-intensive and expensive.

Therefore, I propose a method that combines machine learning and satellite imagery to predict poverty. This approach is easier, less expensive and scalable since satellite data is often inexpensive and open source. Before developing this model we have to first collect the data that would be relevant for prediction regionalized poverty, which is why I have created this dataset.

Content:

This data set is a collection of the following:

1. Environmental Data: Vegetation indices, soil characteristics, evaporation
2. Climate Data: Temperature, precipitation, elevation
3. Socioeconomic and Demographic Data: Population Density, Access to major cities, Nightlight, Land usage
4. Conflict Data: Conflicts, death tolls, civilian casualties
5. Natural Disaster Data (coming shortly)

I have provided the data in the following formats:

• Individual Level Data: For each region in the loans (kiva_loans) and MPI study set, I have extracted all of the information listed above. The data is provided in the csv files; MPIData_augmented.csv, kivaData_augmented.csv
• Stacked satellite images for each country with a loan. For each country, a satellite image is provided as a .grd file in the Satellite Imagery folder

Usage:

If you are unfamiliar with working with satellite images I suggest you utilize the csv files. I will put out a tutorial on working with the satellite images in the near future. If you have any questions, please post them an I will try to answer them as soon as I can. If you have any questions related to the data, please refer to the data dictionary.

Documentation:

Documentation for this dataset is an ongoing process given the complex and extensive process to preparing this dataset, amd the wide range of data sources. If you have a particular question please post it and I'll answer it as soon as possible.

Upcoming Work:

As I mentioned above, I am going to use this data to build a machine learning model that will be able to predict the poverty for any region in any impoverished country! Stay tuned!

This portal contains environmental radiological monitoring data collected in response to the nuclear emergency following the March 11th, 2011 Tohoku earthquake and tsunami. Available data sets include field measurements, field samples, and analysis results. It is designed to contain data sets from other large-scale response efforts should they occur.

Link Function: information

Data repository for Seasonality modulates coral trophic plasticity in an extreme, multi-stressor environment in Limnology and Oceanography.

This data repository contains: A) MATLAB source code B) Sample locations and chemical measurements used for Be-10 and C-14 analysis C) Dose recovery test results for luminescence protocol D) Sample locations and dosimetry information for luminescence samples

Metafile contains github links for R code and input datasets (including those generated in our analysis and those publicly available) for probabilistic and deterministic crop footprint generation and field-level simulation analysis. This dataset is associated with the following publication: McCaffrey, K., E. Paulukonis, S. Raimondo, S. Sinnathamby, S. Purucker, and L. Oliver. A multi-scale approach for identification of potential pesticide use sites impacting vernal pool critical habitat in California. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 857(1): 159274, (2023).

This dataset contains an inventory for the paper entitled "ecocomDP: A flexible data design pattern for ecological community survey data" (O'Brien et al), submitted to Ecological Informatics. The paper describes an approach for harmonizing and reformatting community survey data such as organism abundance or cover measurements. Data currently using this data model and workflow approach are from the repository of the Environmental Data Initiative (EDI), the Long Term Ecological Research (LTER) Network, and the National Ecological Observatory Network (NEON). Data were assembled for this analysis in late 2020. The inventory is composed of two tables, describing data from EDI (including LTER) and data from NEON. The EDI inventory includes information for 70 datasets: identifiers for both the original and converted datasets, and basic coverage information such as temporal coverage (range of years and a measurement of sampling evenness), spatial coverage (maximum bounding coordinates and area of the "bounding box"), and taxonomic coverage (taxonomic classes). The NEON inventory contains information from 11 continent-wide NEON data products, divided into individual field sites to be more spatially compatible with EDI and LTER data. Taxonomic coverage is by group (e.g., algae, birds) rather than explicit taxonomic classes. Spatial coverage is the area of a field sampling site polygon. Temporal coverage includes the same minimum and maximum sampling years and temporal evenness measures as for the EDI data plus a count of months during that period when sampling occurred. At the time of data download, NEON data was considered provisional, however identifiers are persistent and now deliver final, "released" data.

 Also included in the data package is a script to reformat inventory data and create Figure 3 of the paper.

Load cell data of Ocean Sentinel (OS) mooring, OS locations data and environmental conditions data (wave, wind, current) measured in the 2013 field test.

This repository contains files of spatial environmental data layers which were used as predictors of nesting habitat suitability for six biodiversity indicator bird species in Finland: (i) three hawk species, the European honey buzzard (Pernis apivorus), the northern goshawk (Accipiter gentilis) and the common buzzard (Buteo buteo), and (ii) three woodpecker species, the white-backed woodpecker (Dendrocopos leucotos), the lesser spotted woodpecker (Dryobates minor) and the Eurasian three-toed woodpecker (Picoides tridactylus). These six bird species have been shown to provide useful indicators of different conservation and biodiversity values of boreal forests, such as occurrences of red listed polypores, and indicative of forest characteristics related to old-growth forests such as representative occurrences of dead wood. The modelling spesifically targetted the nest sites of the bird species where the role of critical suitable environmental conditions is elevated. The data of nesting sites of the bird species are not open access data due to its sensitivity, but can be requested for research purposes by sending a query to the head of the Zoology unit at the Finnish Museum of Natural History. However, the Maxent results of the nesting habitat suitability for bird species across the whole of Finland are available in Zenodo (https://doi.org/10.5281/zenodo.4779108). Please also note that the environmental data is split - due to its large size - into two repository locations, this one containing the local site variables and 500 m buffer variables, and the other containing the 1 km buffer variables and the two climate variables. Taken together, 40 different environmental data layers were developed and their information sampled for a 96 x 96 m resolution lattice system covering the whole Finland. These 40 data layers were organised for the modeling into the following groups of predictor variables: (1) Data on forest structure and other forest stand characteristics (96 x 96 m cell) (8 variables), (2) Data on land cover at the forest stand (96 x 96 m cell) (8 variables), (3) Data on forest characteristics in the 500 m landscape buffer area (3 variables), (4) Data on forest characteristics in the 1 km landscape buffer area (3 variables), (5) Data on land cover in the 500 m landscape buffer area, (6) Data on land cover in the 500 m landscape buffer area, and (7) Climate data (2 variables). These data were used to examine what are the key determinants of the nesting site suitability of the six indicator bird species and to developed predictive maps across the whole Finland for the locations of most optimal nesting forest areas. The nesting habitat suitability modelling was done using the MaxEnt model. The forest structure and habitat quality predictor variables were developed based on national forest data gathered from three sources: (i) Finnish Forest Center (FFC), (ii) Metsähallitus Parks & Wildlife (MPW), and (iii) the multi-source national forest inventory carried out by the Natural Resources Institute Finland (LUKE). The land cover variables were measured using the CORINE Land Cover 2018 system, from the database produced, maintained and distributed by Syke. The two climate variables were initially for the SUMI project by the Finnish Meteorological Institute and further applied in this modelling study. The environmental variables, the six indicator bird species and the processses, steps and choices included in the MaxEnt modelling are described in full detail in the following publication: Virkkala, Raimo, Leikola, Niko, Kujala, Heini, Kivinen, Sonja, Hurskainen, Pekka, Kuusela, Saija, Valkama, Jari and Heikkinen, Risto K.: Developing fine-grained nationwide predictions of valuable forests using biodiversity indicator bird species, Ecological Applications, in press. The details of the environmental data are also described in the read_me.doc file uploaded into this repository.

Link Function: information

This dataset contains the high resolution 3D bathymetry of 347 global reservoirs, which represents 50% of the overall global storage capacity. It also provides the Area-Elevation (A-E) and Elevation-Volume (E-V) relationships for these reservoirs.

The repository contains the outputs of the notebook "Variational data assimilation with deep prior (CIRC23)" published in The Environmental Data Science Book.

This dataset tracks the updates made on the dataset "MDE Environmental Justice Screening Tool" as a repository for previous versions of the data and metadata.