Start with the question your analysis needs to answer, then search the repository or research network whose study designs and data match it. ForestGEO is a route to standardized forest-plot census data; the USDA Forest Service Research Data Archive preserves research data publications, including work from Experimental Forests and Ranges; and USGS or Data.gov records can lead to federal datasets. A catalog listing is only a starting point: check its design, time coverage, methods, documentation, access conditions, and reuse terms before using or combining data.
Where to find long-term forest experiment data
No single repository covers every forest experiment. Use the source that best matches the geography, study design, and evidence your question requires.
ForestGEO for standardized forest plots
ForestGEO provides long-term forest research resources, including field and data protocols, a data model, data-entry software, and a guide. Its overview describes a network of 84 sites in 29 countries, representing 7 million trees and 12,000 species; these figures can change, so check the current ForestGEO overview. Site pages can help identify candidate plots, including by ecological zone and number of censuses.
The Explore Data page reported that 30 of 78 sites hosted data in its portal when accessed in 2026. This is a portal-coverage snapshot, not a permanent inventory of the network. Other plots may require a direct request to the plot’s principal investigator.
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USDA Forest Service Research Data Archive
The Forest Service Research Data Archive publishes and preserves short- and long-term research data from Forest Service Research and Development, the Joint Fire Science Program, and the Aldo Leopold Wilderness Research Institute. Its collection includes data from Experimental Forests and Ranges. Each archived data publication includes at least one dataset, complete metadata, and supporting documentation selected by the researcher. The data file itself may be hosted by another archive, so follow the record’s data link and check its terms.
USGS and Data.gov catalog records
Federal catalog records can help locate specific public datasets. One example is USGS’s “Long-term Experimental Forest Growth and Drought Data”, a compilation described as covering multi-decadal growth across eight U.S. experimental forests with replicated density treatments. The record identifies annual treatment-level basal area increment (BAI, mm²/year) as the growth measure and the self-calibrated Palmer Drought Severity Index (scPDSI) as an annual drought-severity index. It lists John B. Bradford as contact and identifies the dataset as public. The record’s update date is 2020-08-14, and the catalog says it was last checked on 2026-08-01; verify the current record and data files before relying on them.
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Forest Research for UK long-term experiments
Forest Research’s long-term experiments project focuses on conserving silviculture and genetics field experiments so they can be revisited for sustainable forest management questions. Its aims include maintaining sites and records, supporting collaboration, and advancing long-term forest science. Treat the project page as a way to identify relevant experiments and contacts, not as a catalog of downloadable datasets.
LTER for ecological data packages
The LTER data portal describes ecological data packages contributed by past and present network sites. It provides dataset DOIs to support citation and advises users to consult the data policy, metadata, and data providers; overlooking that context can lead to misinterpretation. Check the current portal location, policy, and individual record details.
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How to request ForestGEO data
ForestGEO’s portal supports network partners managing data and allows other researchers to request access. The request process depends on whether the selected data are public or restricted.
- Choose a data type, then select a region and plot in the portal.
- Read the terms and conditions for the selected data.
- Submit the request with a project description, planned analyses, and a description of the data needed.
- If the data are not public, the plot investigator reviews the request and may approve or reject it. For public data, the portal says it sends automatic login and download information.
For plots not represented in the portal, contact the plot’s principal investigator directly. Do not assume that finding a plot or catalog record means its files are immediately downloadable.
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How to evaluate whether a forest dataset fits
Assess the record against the question before downloading, requesting, or analyzing it. A dataset can be well documented yet unsuitable for a particular inference if its design, time interval, or measurement methods do not match what the analysis needs.
Study design and research fit
- Determine whether the record describes a manipulative experiment, observational monitoring, or a compilation of studies.
- Check whether the treatment structure, spatial scale, forest type, and site conditions address the question you plan to test.
- Distinguish treatment-level values from plot-level or tree-level observations; the unit represented affects which comparisons are valid.
Time coverage and sampling cadence
- Confirm start and end dates, gaps, census frequency, and whether the interval can resolve the process of interest.
- ForestGEO’s digital resources page reports tree censuses every five years at its sites, with specialized surveys at some sites. Confirm the cadence for the particular plot and dataset: a five-year census may not capture annual changes or short-lived events without additional data. See ForestGEO Digital Resources.
Variables, units, and measurement methods
- Read variable definitions and units, treatment levels, taxonomic conventions, and measurement protocols rather than relying on column names alone.
- Look for changes in methods or definitions over time. A variable with the same name may not be comparable across records if the measurement procedure changed.
- ForestGEO’s shared protocols are intended to support comparisons among plots, but confirm that the specific records use compatible protocols and include the variables your analysis requires.
Metadata and supporting documentation
- Read the full metadata, methods, and supporting files, including notes on quality control, missing values, and transformations where provided.
- The Forest Service archive describes its data publications as including complete metadata and researcher-provided documentation. LTER likewise cautions users to consult metadata and providers to reduce the risk of misinterpretation.
- If an essential detail is absent or ambiguous, contact the investigators or data provider before interpreting the data.
Access, reuse, and citation
- Check whether the files are public, request-only, or subject to plot-specific terms. Access to a record does not by itself establish unrestricted reuse.
- Keep the dataset title, authors or investigators, repository, version or update date, DOI or persistent identifier, and the date you accessed it.
- Follow the repository’s citation instructions. LTER provides dataset DOIs; use a DOI or other persistent identifier from the actual record when available.
Can data from different forest plots be compared?
Yes, when the selected records support the comparison. Standardized protocols can make cross-plot work more feasible, but they do not remove the need to verify what was measured, when it was measured, and how the methods align. A shared variable label or a common repository is not, by itself, evidence that two records can be pooled.
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Compare candidate records along these dimensions before combining them:
- Geography and forest type: Are differences among plots relevant to the question or confounded with it?
- Design: Are the data experimental or observational, and do treatment levels and replication correspond?
- Temporal span and cadence: Do the records overlap sufficiently, and are intervals compatible with the process being studied?
- Variables and protocols: Do units, definitions, taxonomic rules, and measurement procedures align?
- Access and use terms: Do the applicable conditions permit the intended analysis and combination?
- Documentation: Is there enough metadata and investigator context to interpret differences and gaps?
There is no established universal quality score for ranking these repositories or their datasets. Judge each candidate against the requirements of the intended analysis, and document any harmonization decisions rather than treating unlike measurements as equivalent.
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