Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
93
datasets available to search
ShareScore release 0.9.0
Dataset results
93 results for “forest age”
Age of Nonstructural Carbohydrates in Four Tree Species at Harvard Forest 2015
We estimated the mean age of sugars within and between different organs of four temperate tree species using the radiocarbon (carbon-14) bomb spike approach. Radial patterns of carbon-14 in the stemwood and coarse roots showed that sugars tended to became older when moving towards the pith.
Concentration and Age of Nonstructural Carbon Reserves in Two Trees at Harvard Forest 2012
We know surprisingly little about whole-tree nonstructural carbon (NSC; primarily sugars and starch) budgets. Even less well understood is the mixing between recent photosynthetic assimilates (new NSC) and previously stored reserves. And, NSC turnover times are poorly constrained. We characterized the distribution of NSC in the stemwood, branches, and roots of two temperate trees, and we used the continuous label offered by the radiocarbon (14C) bomb spike to estimate the mean age of NSC in different tissues. NSC in branches and outermost stemwood growth rings had the 14C signature of the current growing season. However, NSC in older above- and below-ground tissues was enriched in 14C, indicating that it was produced from older assimilates. Radial patterns of 14C in stemwood NSC showed strong mixing of NSC across the youngest growth rings, with limited.
Weight, sex, age, beam diameter, antler points and teat length for harvested deer from 1984-2025 in Black Rock Forest, Cornwall, NY.
Data from white-tailed deer harvested within Black Rock Forest, Cornwall, New York are collected annually. Trained staff measure mass, antler beam diameter, and teat length (since 2010), estimate age via dentition, count antler points, and assess sex on all field-dressed deer. Heart girth, measured as chest circumference, was recorded from 1984 to 1998.
Disturbance effects on soil processes in the Andrews Experimental Forest (1995 Stand Age Study)
This study was designed to determine how edges generated by clear-cutting old-growth forests influence patterns of soil carbon and nitrogen cycling and the distribution of ectomycorrhizal mats and to determine how these patterns change with time after harvest. A secondary objective was to measure how clear-cutting within different climatic regimes influenced soil nitrogen and carbon cycling.
Age structure, developmental pathways, and fire regime characterization of Douglas-fir/western hemlock forests in the central western Cascades of Oregon
These data are the raw forest stand- and age-structure data from 124 stands in the central western Cascades of Oregon used to construct a conceptual model of stand development under the mixed-severity fire regime that has operated extensively in this region.
High elevation forest age structure across an elevational gradient in the Greater Yellowstone Ecosystem
<p>Dataset for Blomdahl et al. 2022. Drivers of forest change in the Greater Yellowstone Ecosystem. Journal of Vegetation Science. </p> <p>See publication for site description and methods. </p> <p>Descriptions for variables in “trees_seedlings.csv”:</p> <p><strong>Plot_ID: </strong>Plot identifier. Nomeclature follows transect name and plot number. ECO="Ecotone" transect, SBM="South Bird Mountain" transect.</p> <p><strong>Year_Sampled: </strong>Samples collected 2017-2019.</p> <p><strong>Tree_ID: </strong>Identifier for unique trees and seedlings. </p> <p><strong>Core: </strong>Tree core sample identifier. Applies only to trees (cores not taken from seedlings). Generally, 2 cores were taken per Tree >5 cm DCH, though sometimes up to 4 were collected if a sample was rotten.</p> <p><strong>Sample_ID: </strong>Identifier for unique samples, some of which come from the same tree (for unique individuals: "Tree_ID"). Applies to trees and seedlings.</p> <p><strong>Form: </strong>Stems >5 cm diameter at coring height (DCH), coring height=30 cm; Seedlings >30: Stems <5 cm DCH and >30 cm in height (sometimes referred to as "saplings"); Seedlings <30: Stems <30 cm in height</p> <p><strong>Species: </strong>ABLA=<em>Abies</em> <em>lasiocarpa</em>, PIAL=Pinus <em>albicaulis</em>, PICO=<em>Pinus</em> <em>contorta</em>, PIEN=<em>Picea</em> <em>engelmannii</em>, PSME=<em>Pseudotsuga</em> <em>menziesii</em></p> <p><strong>Diam_30_cm: </strong>Diameter (cm) at 30 cm sample height.</p> <p><strong>Diam_0_cm: </strong>Diameter (cm) at 0 cm sample height (i.e., the base). Only seedlings were measured at base, not trees.</p> <p><strong>Seedling_Ht_cm: </strong>Length of seedling stem (cm).</p> <p><strong>Bark_Thick_cm: </strong> Bark thickness (cm). Not recorded in 2018. Bark thickness assumed to be <0.1 cm for seedlings.</p> <p><strong>Live_Dead: </strong>Live/Dead status when sampled. L=Live, D=Dead.</p> <p><strong>Canopy: </strong>Canopy position. D=Dominant, C=Codominant. S=Suppressed. Not recorded in 2017. All seedlings assumed suppressed.</p> <p><strong>Outer_Ring: </strong>Last complete year of growth, generally one year prior to Year_Sampled for live trees. Mortality year for dead trees.</p> <p><strong>Inner_Ring:</strong> Year of innermost ring measured in tree core sample measured at 30 cm sample height. Does not apply to seedlings, which were sampled as cross sections, and therefore the pith was always measureable.</p> <p><strong>Pith_30: </strong>Year of the first ring of the tree or sapling, measured at 30 cm sampling height. </p> <p><strong>Pith_0: </strong>Year of the first ring of the seedling, measuring at 0 cm sampling height (i.e., the base). Applies only to seedlings, which were destructively sampled at the base.</p> <p><strong>Estab_Year: </strong>Estimated year of establishment for trees and saplings, same as Pith_0 for seedlings. See methods of Blomdahl et al., 2022, for how establishment year was estimated.</p> <p><strong>Age:</strong> Estimated age of the tree.</p>
Figure 3 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area
Figure 3. PCoA ordinal configuration of soil macrofaunal communities from different habitats by Euclidean distance similarity index. In the code of the samples, the prefix means the code of the habitat, and the suffix means the number of the sample.
Figure 2 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area
Figure 2. One-way ANOVA of taxonomic richness and abundance (A) and Margalef 's richness index R and Shannon– Weaver diversity index H' (B) across different habitats (mean ± SE). Means with different scripts are significantly different by Dunnett's T3 test (A) and LSD test (B), α = 0.05.
FIG. 7 in Multi-aged forest fragments in Atlantic France that are surrounded by meadows retain a richer epiphyte lichen flora
FIG. 7. — The beta diversity indicated significant lichen species replacement on larger trees in the interiors of the FFs surrounded by meadows (A). In contrast, lichen species replacement was significant on thinner trees from the exteriors of the FFs surrounded by meadows (B).
FIG. 6 in Multi-aged forest fragments in Atlantic France that are surrounded by meadows retain a richer epiphyte lichen flora
FIG. 6. — The gamma diversity indicated that the highest number of lichen species was recorded on larger trees in the interiors of the FFs surrounded by meadows (legend is as in Fig. 2).
FIG. 3 in Multi-aged forest fragments in Atlantic France that are surrounded by meadows retain a richer epiphyte lichen flora
FIG. 3. — The significant effect of host tree species (A) and shrub cover (B) on lichen species abundance according to a summary of the GLMMs. The GLMM results are presented for the interior forest at the tree level within FFs surrounded by meadows, taking into account the larger tree category (trees that range in circumference between 0.56 and 2.97).
FIG. 5 in Multi-aged forest fragments in Atlantic France that are surrounded by meadows retain a richer epiphyte lichen flora
FIG. 5. — The significant effect of tree circumference on the number of lichen species according to the summary of the GLMMs. The GLMM results are presented at the tree level within FFs surrounded by crops, taking into account the larger tree category (details as in Fig. 2).
FIG. 2 in Multi-aged forest fragments in Atlantic France that are surrounded by meadows retain a richer epiphyte lichen flora
FIG. 2. — The significant effects of: A, B, moss coverage; C, D, tree circumference; and E, F, host tree species on lichen abundance according to a summary of the GLMMs. The values of the estimator (E), standard error (SE), and Wald chi-squared test (chisq), the degrees of freedom (dfs) and significance (p) are presented. The GLMM results are presented for the larger tree category (trees that range in circumference between 0.56 and 2.97) at the tree and forest levels at the exteriors of the FFs surrounded by crops.
FIG. 1 in Multi-aged forest fragments in Atlantic France that are surrounded by meadows retain a richer epiphyte lichen flora
FIG. 1. — The location of the study area within the Poitou-Charentes region (western France). Source: Google Earth Pro V 7.3.2.5776. (14 December 2015). France. 45°21'34.14"N, 0°12'32.38"W, Eye alt 340.93 km. SIO, NOAA, U.S. Navy, NGA, GEBCO. US Dept of State Geographer. Landsat/Copernicus 2018. http://www. earth.google.com (13 February 2019).
Forest cover, age and densification rate for Southern China at 30 m resolution
<p>The dataset includes Landsat based forest cover maps from 1986-2020 that were used to calculate forest age and densification rate.</p> <p>Forest probability as the output from a Random Forest model was used to reflect forest cover, and shows how likely an area resembles a dense forest. We set a threshold of 50% probability to define an area as dense forest, and the number of years from the year the threshold is crossed until 2018 as the forest age. Note that the area must remain above 50% until 2018 to qualify as forest. The years before the threshold is crossed are used to calculate the densification rate. It is defined as the mean probability change per year in the period where an area is between a probability of 20% and 50%. If the area falls below 20% the count is reset.</p>
Carbon storage and carbon-equivalent albedo impact for US forests, by age and forest type
<p>These tables document estimates of carbon storage (Mg/ha +/- Standard Error) and carbon-equivalent albedo impacts (same units) of US forests by age and forest type (Healey et al., in review). Carbon estimates are derived from field measurements made by the USDA Forest Service on approximately 125,000 forested field plots (Domke et al., 2022). Soil organic carbon is omitted from these estimates, but all other above- and below-ground pools are included. Albedo impacts (time-dependent emissions equivalent, TDEE; Bright et al., 2016) were developed by applying atmospheric kernels (Bright and O'Halloran) to a new Landsat blue sky albedo product for the Landsat archive (Erb et al., 2022), as described by Healey et al. (in review). Standard error is supplied for each age/forest type bin for carbon storage, but upper and lower standard error bounds are specified for TDEE because log transformation creates an asymmetrical uncertainty envelope. </p> <p> </p> <p>Bright, Bogren, Bernier, Astrup, (2016). Carbon-equivalent metrics for albedo changes in land management contexts: Relevance of the time dimension. <em>Ecol. Appl.</em> 26, 1868–1880</p> <p>Bright, R. M., & O'Halloran, T. L. (2019). Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1. 0. <em>Geoscientific Model Development, </em>12(9), 3975-3990.</p> <p>Domke, Walters, Nowak, Greenfield, Smith, Nichols, Ogle, Coulston, Wirth (2022). Greenhouse Gas Emissions and Removals From Forest Land, Woodlands, Urban Trees, and Harvested Wood Products in the United States, 1990–2020. (US Dept. Ag. For. Service, Madison, WI; <a href="https://doi.org/10.2737/FS-RU-382">https://doi.org/10.2737/FS-RU-382</a>).</p> <p>Erb, Li, Sun, Paynter, Wang, & Schaaf, (2022). Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain. <em>Remote Sensing</em>, <em>14</em>(21), 5320.</p> <p>Healey, Yang, Erb, Bright, Domke, Frescino, Schaaf, (in review) New satellite observations expose albedo dynamics offsetting half of carbon storage benefits in US forests.</p>
Data from: Gross primary productivity from leaf-age-dependent light use efficiency (LA-LUE) model over pantropical evergreen broadleaved forests
Open the record for dataset details and reuse information.
Data from: Provenance variation in functional traits of European forest trees: Meta-analysis reveals effects of taxa and age despite critical research gaps
Open the record for dataset details and reuse information.
Data and R code for “Tree regeneration response to a shifting soil nutrient economy depends on mycorrhizal association and age”, Forest Ecology and Management, 2022
Atmospheric nitrogen (N) deposition has led to an increase in N cycling and N availability. This increase in inorganic N is likely to impact forest ecosystems, although the responses remain uncertain. Most tree species are associated with one of two mycorrhiza types – arbuscular mycorrhizae (AM) or ectomycorrhiza (ECM). Due to functional differences in their ability to access soil nutrients, we might expect that increased N cycling and availability of inorganic N would benefit AM associated species, with negative or neutral impact for ECM associated species. This study addresses how the abundance of the regeneration layer responds to those shifting soil conditions, based on their mycorrhizal association. We used a long-term experiment located in a temperate deciduous forest, where the native acidic soils are low in nutrients. Soil treatments began in 2009, by adding lime and/or phosphate to raise pH and increase the availability of N and P. All trees ≥ 6.0 cm in 2010 were tagged and have been monitored with annual censuses. To quantity the density of the regeneration layer, seedlings and saplings were recorded in the control and limed plots in 2019. Trees that had recruited into the canopy (DBH ≥ 6.0 cm) were measured in 2020 in all treatment plots. Seedlings older than one year responded to the treatments as predicted, as AM seedlings increased by 42% in the lime treatment (1.53 ± 0.38 individuals per m2 in control, to 2.17 ± 0.56 in lime) and ECM seedlings decreased by 49% in response to liming (0.61 ± 0.14 individuals per m2 in control, to 0.31 ± 0.04 in lime). AM saplings also responded positively to liming, increasing 254% from control to lime (from 0.013 ± 0.003 to 0.046 ± 0.018 individuals per m2), while ECM sapling abundance was neutral (0.063 ± 0.015 individuals per m2 in control, and 0.054 ± 0.009 in lime). The highest number of ECM recruits was found in the control plots (34.4%), followed by phosphate (25.8%), lime + phosphate (21.5%), then lime (18.3%).
Uneven Aged Management Project (UAMP), Andrews Experimental Forest
The study consists of 16 treatment units – stands ranging in size from 7 to 11 ha – within the H.J. Andrews Experimental Forest. Each unit is has assigned one of four alternative thinning regimes – unthinned (Control), light thin with patch openings (GAP), light thin (Light) or heavy thin (Heavy),. The stands are to be managed under these alternative regimes over several harvest cycles as the experiment evaluates alternative thinning regimes to convert uniform, even-aged stands, to structurally heterogeneous uneven-aged structure. Vegetation data collection occurred pretreatment (1997 or 1998) and post-treatment (2001, 2003, 2005 and 2010).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.