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.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “Wildfire management”
Soil microbial and physicochemical data from watersheds impacted by different management practices or wildfire in the Southern Appalachian Mountains, 2023
Four forested watersheds in Western North Carolina with different management practices or disturbance were sampled in the summer of 2023 to compare soil physicochemical, microbial, and functional differences. These data include mineral soil physicochemical properties (location, elevation, aspect, gravimetric moisture content, pH, total carbon and nitrogen, total organic carbon, dissolved organic carbon and nitrogen, total dissolved nitrogen, dissolved inorganic nitrogen (NO3 and NH4), and microbial biomass carbon and nitrogen), soil microbial properties (16S ASV community sequences, ITS ASV community sequences, extracellular enzyme activity, carbon mineralization rates, and ammonium mineralization rates), and organic soil properties (total organic carbon, total carbon and nitrogen, 16S ASV sequences, pH, and moisture). Together, this dataset provides context to understanding the impacts of different management practices and relevant disturbances, such as severe wildfire, on soil in the Southern Appalachian region.
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Managing for ecosystem response to drought and wildfire on the Colorado Plateau
<p>These data are responses to survey questions (see below) given to staff associated with natural resource management on the Colorado Plateau, U.S.A. The survey was intended to understand how managers perceived: 1) the degree to which managed lands have experienced drought, wildfire, and related stressors; 2) the current and future responses of ecosystems to these stressors and associated changes to natural resource condition; 3) the role of natural resource management interventions in preparing for stressors and ecosystem response; 4) limitations and barriers to management interventions. The survey consisted of quantitative questions, including a combination of close-ended (yes or no) questions, select-all-that-apply questions, ranked choice questions, and 3-point, 5-point, and 11-point Likert-scale questions. The survey also included qualitative open-ended and fill-in-the-blank questions. The survey was implemented within the Qualtrics program hosted at Northern Arizona University in Flagstaff, Arizona. The numbered questions below (Q1, Q2, etc.) correspond to the columns in the spreadsheet, with possible answers chosen by survey participants indicated below each question. Each participant is a row in the spreadsheet. All identifying information of the participants has been removed to maintain their anonymity. </p>
Prioritizing wildfire fuel management in California
<p><strong><span>The resources available for managing wildfire risk are insufficient and ultimately finite , while the risk of catastrophic fires is enormous and growing. Prioritization of responses is thus critical, but the basis for comparing the costs and societal benefits of alternative investments in wildfire mitigation is woefully inadequate. Here, we assess and compare the costs of landscape-scale fuel treatment to the benefits of avoided destruction of property and smoke-related health impacts, and identify areas where the net benefits are greatest statewide. We find that re-prioritizing treatment areas could increase net benefits by a factor of more than 7 relative to historical treatments, with average net benefits in the top decile of areas (i.e., 28,000 km<sup>2</sup>)<sup> </sup>of >$225k per km<sup>2 </sup>(as compared to an estimated $90k per km<sup>2</sup> of past treatments). By integrating physical, epidemiological, and economic methods, our results reveal large opportunities for improving the cost-effectiveness of wildfire fuel treatments, and demonstrate a general framework that can be applied by land managers in all wildfire-prone areas.</span></strong></p>
Managing for ecosystem response to drought and wildfire on the Colorado Plateau
Open the record for dataset details and reuse information.
Data from: Incorporating uncertainty into forest management planning: timber harvest, wildfire and climate change in the boreal forest
In an effort to ensure the sustainability of their forests, boreal forest managers often use forest planning models to make future projections of timber supply and other key services, such as habitat for wildlife. Projecting the fate of these services has proven to be challenging, however, as major uncertainties exist regarding the principal drivers of boreal ecosystem dynamics, including the future spatial and temporal distribution of wildfire and timber harvesting. Existing forest planning models are not well suited to dealing with this uncertainty because they produce deterministic projections based on central tendencies of these drivers. Here we present a new approach for incorporating uncertainty into forest management planning, which we demonstrate using two landscapes in the Canadian boreal forest. Our approach takes the assumptions contained within the latest forest management plans for each of these landscapes, including parameterizations of their deterministic forest planning models, and converts these assumptions into equivalent parameterizations of a stochastic, spatially-explicit state-and-transition simulation model (STSM). We then use Monte Carlo simulations with the STSM to "stress-test" the forest management plan with respect to a range of possible future uncertainties, including uncertainties in future levels and patterns of wildfire and timber harvest, along with the possible changes in wildfire that might result from future climate change. Our analysis demonstrates the importance of incorporating stochastic variability into projections of future ecosystem condition. The STSM projections that acknowledged variability in wildfire and timber harvest differed from the deterministic forest planning model projections that were based solely on mean values. Our analysis also suggests that there is an increased risk of shortfalls in timber harvest, for both boreal landscapes, associated with future projections for changes in wildfire due to climate change, and that management strategies aimed at reducing the future level of timber harvest offer an opportunity to mitigate these risks. We believe our approach provides a new risk-based framework for incorporating uncertainty into forest management, including the effects of climate change.
Datasets used in "Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management"
<p>Data sets used to train, validate, and test machine learning models for the prediction of 10-hour dead fuel moisture content. The data sets are for 375 m and 1 km resolutions. MADIS climatography data is also included. The datasets may be processed using the code supplied at https://github.com/NCAR/fmc_viirs. </p>
Data from: Land management practices associated with house loss in wildfires
Open the record for dataset details and reuse information.
Data from: Incorporating uncertainty into forest management planning: timber harvest, wildfire and climate change in the boreal forest
Open the record for dataset details and reuse information.
Leveraging wildfire to augment forest management and amplify forest resilience
Open the record for dataset details and reuse information.
Data from: Changes in ecosystem properties after postfire management strategies in wildfire affected areas
<p>1. Forest are highly vulnerable to global change drivers, such as an increase in wildfire events. Learning more about how and why different postfire management strategies regulate the ability of forest ecosystem properties (e.g., plant diversity and function) to simultaneously recover after wildfire and provide multiple ecosystem functions is of critical importance.</p> <p>2. This study aims to evaluate how unburned, burned managed and burned unmanaged plots regulate the responses of multiple forest ecosystem properties (e.g., plant diversity, nutrient cycling, soil carbon stocks, water regulation, and decomposition and wood production) and overall multifunctionality to wildfires. In September 2017, we selected two postfire management strategies in a 3 km2 watershed previously affected by a wildfire in July 2012: contour-felled log debris (CFD), log erosion barriers area (LEB), and also unburned and unmanaged plots (BNA). We randomly distributed 12 plots among the three postfire management strategies (3 plots per treatment) and unburned.</p> <p>3. The results showed that multiple forest ecosystem properties were significantly affected by wildfire and that specific postfire management treatment (e.g., LEB and CFD) can be used to efficiently support plant diversity and ecosystem functioning. Our results revealed that the general indicators of ecosystem functions decreased in Mediterranean forests after wildfires and postfire management strategies (LEB and CFD) significantly helped to recover the ecosystems' short-term community-level properties and ecosystem functions (5 years after a wildfire event) to prefire levels. </p> <p>4. Synthesis and applications: These findings demonstrate that multiple ecosystem functions are affected by wildfires in Mediterranean forests and show that postfire management treatments can promote multifunctionality and plant diversity. Our results unfold the potential of LEB and CFD as effective strategies for recovering community-level properties and forest functions in the short term.</p>
Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: a Bayesian Network Modeling Approach
Open the record for dataset details and reuse information.
And it burns, burns, burns, the ring-of-fire: Reviewing and harmonizing terminology on wildfire management and policy
Open the record for dataset details and reuse information.
Data from: Changes in ecosystem properties after postfire management strategies in wildfire affected areas
Open the record for dataset details and reuse information.
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.