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.
2,603
datasets available to search
ShareScore release 0.9.0
Dataset results
2,603 results for “Ecological data”
Data from: Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes
Open the record for dataset details and reuse information.
Data from: Ecological and evolutionary significance of primates’ most consumed plant families
Open the record for dataset details and reuse information.
Data for: Zebra finch song ecology: monitoring of breeding, observational transects, focal and year-round acoustic recordings, and a large-scale simultaneous playback experiment
Open the record for dataset details and reuse information.
Data from: Working groups, gender and publication impact of Canada’s ecology and evolution faculty
Open the record for dataset details and reuse information.
Data from: Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies
Open the record for dataset details and reuse information.
Data from: A new Paleogene fossil and a new dataset for waterfowl (Aves: Anseriformes) clarify phylogeny, ecological evolution, and avian evolution at the K-Pg boundary
Open the record for dataset details and reuse information.
Ranking ecological contingencies from high-order factorial data demonstrate tidy control of biodiversity from facilitation cascades in estuaries on the South Island of New Zealand
Open the record for dataset details and reuse information.
Data from: Long-term cattle grazing shifts the ecological state of forest soils
Open the record for dataset details and reuse information.
Data from: Demographic, behavioral, and ecological data from a long-term field study of wild baboons in Amboseli, Kenya
Open the record for dataset details and reuse information.
Data from: Dispersal in a house sparrow metapopulation: an integrative case study of genetic assignment calibrated with ecological data and pedigree information
Open the record for dataset details and reuse information.
Data from: The impacts of climate change, energy policy, and traditional ecological practices on future firewood availability for Diné (Navajo) People
Open the record for dataset details and reuse information.
R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys
Open the record for dataset details and reuse information.
Data from: The spatial patterns of community composition, their environmental drivers and their spatial scale dependence vary markedly between fungal ecological guilds
Open the record for dataset details and reuse information.
Data from: Evidence of repeated zoonotic pathogen spillover events at ecological boundaries
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%).
Maple River Dam Removal Stream Ecology Data
Over the past two decades, dam removal has become an increasingly important aspect of aquatic ecology. The motivation for the removal broadly ranges from purely ecological terms, restoring stream or river connectivity, through safety concerns, as a result of crumbling infrastructure, to economic concerns, increasing local tourism, recreational fishing, or canoeing and kayaking. As a result of this work, ecological studies have arisen that monitor the changes to the riverine ecosystem as a result of removal. Unfortunately, given the uncertain nature of funding and public concerns over dam removal, long term longitudinal studies that cover multiple trophic levels are difficult to find. Fortunately, the University of Michigan Biological Station has been involved in the ecological monitoring of a headwater river (the Maple River) in the northern part of the lower peninsula of Michigan. Through the tremendous intellectual resources available at the station, the physical, chemical, and some biological aspects of this river’s ecology has been measured for eight years prior to the dam removal, during the dam removal, and for two years post-dam removal. The results presented here show that the ecology of the river recovered within this two-year period, but had a different ecological state driven. This new state is primarily driven increases in flow, ammonia, silica, and increases in the populations of the macroinvertebrate feeding guilds of filterers and scrapers. In addition, decreases in stream pH and water temperature contributed to this new state. The results and observations presented here may provide some guidance for other long term monitoring studies.
Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts (Instructor Materials)
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. To be useful for management, ecological forecasts need to be both accurate enough for managers to be able to rely on them for decision-making and include a representation of forecast uncertainty, so managers can properly interpret the probability of future events. To improve forecast accuracy, forecasts can be updated with observational data once they become available, a process known as data assimilation. Recent improvements in environmental sensor technology and an increase in the number of sensors deployed in ecosystems have increased the availability of data for assimilation to develop and improve forecasts for natural resource management. In this module, students will explore how assimilating data with different amounts of observation uncertainty and at different temporal frequencies affects forecasts of lake water quality, using data from the U.S. National Ecological Observatory Network (NEON). The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module7/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module7) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module7_R) of the module, and both code r
Social data from "Ecology, The Economy of Nature" (10th edition, 2025): Addressing diversity in undergraduate ecology textbooks"
This data package includes social data on the diversity and composition of people depicted in the text, photographs, and illustrations of the ecology textbook Ecology: The Economy of Nature (10th ed.) by Relyea (2025). The data were utilized in an article published in Frontiers in Ecology and the Environment, which explores the use of undergraduate textbooks as tools for building a diverse community of ecologists (Richards et al., 2025). Relyea, R. (2025). Ecology: The Economy of Nature (10th ed.). W.H. Freeman. ISBN 9781319524838 Richards JH, Charton KT, McFarlane SL, Widell AF, Haddad NM, Wyer MB, Damschen EI (2025) Rethinking the undergraduate textbook as a tool to build a diverse community of ecologists. Frontiers in Ecology and the Environment 23(3), e2819, https://doi.org/10.1002/fee.2819.
Small mammals surveys, 1981 - 1996, Adirondack Long-Term Ecological Monitoring Program Project No. 10 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative
Small mammals are important in forested ecosystems: they are key predators on seeds and invertebrates, provide food for larger predators and act as disease vectors. The objective of this study was to document small mammal abundance and population changes in managed and unmanaged forests of Huntington Wildlife Forest (HWF). Seven sites were sampled from 1981-1996. Fifty traps per site (250 total) were deployed for 4 nights and checked in the mornings. All captured small mammals were identified, sexed, weighed, and measured for reproductive condition, tagged, and brought into the lab for processing. Females with embryos or placental scars were noted in the lab. Over a five-year period, 671 deer mice; 261 woodland jumping mice, 594 southern redbacked voles, 248 short-tailed shrews, 373 masked shrews, 75 smoky shrews and small numbers of other species were captured and sexed/aged. According to Prachar and Sage (1988), weights of deer mice, redbacked voles, woodland jumping mice, short-tailed shrews, masked shrews and smoky shrews differed among years and age classes for 1983-1987. Weights differed between sexes for mice and voles but not shrews. Placental scar/embryo counts of mice and voles did not differ among years, habitats, mammal age classes or sexes. Reproductive rates of shrews exhibited patterns of fluctuation from 1983-1987.
Songbird surveys , 1952 - 1964, 1983 - 2008 Adirondack Long-Term Ecological Monitoring Program Project No. 2 Breeding Birds by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
Study objectives were to (1) Document long-term trends in relative abundance and diversity of breeding forest birds (songbirds) in forest stands with different harvest histories and (2) Identify bird species that can be used as indicators of habitat change or degradation. Declines in neotropical migrants have been linked to changes in habitat quantity and quality across species' range. Songbirds that nest and forage in different habitat types or at different heights in the forest canopy may not be affected equally by forest change or management. We detected breeding songbirds using point-counts at Huntington Wildlife Forest (HWF) in the central Adirondack Mountains of New York during 1983-2000 and modeled on an original songbird point count dataset from Webb et al. (1977). Relative abundance (RA, the number of individual birds/count) was measured in sites with differing management histories, from an unmanaged >300-year-old stand to a stand cut with the shelterwood/overstory removal method just prior to sampling in 1983). Over eighty bird species were detected during the study duration. Songbird ecology and habitat characteristics can be used to understand long-term changes in relative abundance as related to forest change.
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.