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.
1,271
datasets available to search
ShareScore release 0.7.1
Dataset results
1,271 results for “tropical forest”
Ecological and morphological traits determine community-wide responses of birds to climate change in a tropical dry forest
<p><strong><span>Description</span></strong></p> <p><span>Raw data and species distribution maps for conducting work on bird community changes caused by climate change in the largest block of tropical dry forests in South America. In addition to the R scripts for the climate modeling analyses and the subsequent analyses in the work. </span></p> <p> </p> <p><strong><span>File contents</span></strong></p> <p><span>Centoids.zip: Data with the centroids of the distributions of each species in the current scenario and the six future climate scenarios.</span></p> <p><span>Climate_valeus_scenrios.zip: Climate variable values for all the cells in the Caatinga grid. </span></p> <p><span>Correlation_species_variable.zip: Correlation for selecting the climate variables used for each species. </span></p> <p><span>Dataset_traits.csv: Species traits used in the analyses. </span></p> <p><span>Maps_species_distributions.zip: Distribution maps for all species, in the current climate scenario and the six future scenarios.</span></p> <p><span>occurrence_birds.zip: Occurrence data used to build the models for each species<br><br>PGLS.html: PGLS analysis correlating the percentage of change in the distribution area of </span><span>each species and species traits.</span></p> <p><span>Species_tree.zip: Species phylogeny built from BirdTree and used as input in PGLS</span></p> <p><span>Species_variables.csv: Individual variables used to build climate models for each species</span></p>
Elevated ENSO extremes are reducing tropical forest invertebrate diversity and function
<p>These datasets accompany the article "Elevated ENSO extremes are reducing tropical forest invertebrate diversity and function" and associated Github R code.</p>
Data from "Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics"
<p>Datasets and script of the manuscript “Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics” authored by R. Muñoz*, M. Enríquez, F. Bongers, R.D. López-Mendoza, C. Miguel-Talonia & J.A. Meave*, published in Frontiers in Forests and Global Change (2023).</p> <p>* Correspondence: R. Muñoz (rod.munozaviles@gmail.com) & J.A. Meave (jorge.meave@ciencias.unam.mx)</p> <p>The original publication can be found in https://doi.org/10.3389/ffgc.2023.1082207</p> <p> </p> <p><strong>TERMS OF USE FOR THE CURRENT DATASETS AND SCRIPTS</strong></p> <p>All data and scripts associated with the current publication are intended ONLY for the reproduction and validation of the analyses conducted in the manuscript cited above. Use of this data for other purposes (for example, other publications or meta-analyses) is strictly forbidden without prior consent from the corresponding authors (R. Muñoz and/or J.A. Meave, contact details above).</p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The ZIP folder is structured in the following manner:</p> <p>– Munoz et al 2023 Frontiers.zip</p> <p> – READ ME.txt</p> <p> – Script Munoz et al 2023 Frontiers.R</p> <p> – Data source</p> <p> – Dataset Munoz et al 2023 Frontiers stand data.csv</p> <p> – Dataset Munoz et al 2023 Frontiers species matrix.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ONI.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ENSO events.csv</p> <p> </p> <p><strong>DESCRIPTION OF SCRIPT</strong></p> <p>The script provided in the root of the ZIP folder (Script Munoz et al 2023 Frontiers.R) allows to reproduce the analyses, figures and tables supporting the original publication in Frontiers. When executed in full, the script generates a new folder named “Figures” where all figures are stored in their raw, unedited version. The figures for publication were later edited in Adobe Illustrator to enhance their visual appearance.</p> <p> </p> <p><strong>DESCRIPTION OF DATASETS</strong></p> <p>Four datasets are provided in this ZIP file (“Data source” folder):</p> <p>1. Dataset Munoz et al 2023 Frontiers stand data.csv (<em>Stand data</em>)</p> <p>2. Dataset Munoz et al 2023 Frontiers species matrix.csv (<em>Species matrix</em>)</p> <p>3. Dataset Munoz et al 2023 Frontiers ONI.csv (<em>ONI</em>)</p> <p>4. Dataset Munoz et al 2023 Frontiers ENSO events.csv (<em>ENSO events</em>)</p> <p> </p> <p><em>STAND DATA </em>contains information about the seven forest attributes included in the study, per substrate and year. It contains the following variables:</p> <ol> <li>Year: Year of measurement</li> <li>Plot: Plot code</li> <li>Set: Can only be “MatCan” (Mature Canopy)</li> <li>Subset: Either “Lim” (limestone) or “Phy" (phyllite)</li> <li>Dynamics: Whether there is a previous measurement allowing the estimation of dynamic rates (e.g., net change; FALSE/TRUE) </li> <li>Basal: Basal area expressed in m2/ha</li> <li>DeltaBasal: Annual net change in basal area</li> <li>R.basal: Annual change in basal area due to recruitment</li> <li>G.basal: Annual change in basal area due to growth</li> <li>M.basal: Annual change in basal area due to mortality</li> <li>AGB: Aboveground biomass expressed in Mg/ha, estimated from the allometric equation of Chave et al. 2014 (including DBH, height and WD)</li> <li>DeltaAGB: Annual net change in AGB</li> <li>R.agb: Annual change in AGB due to recruitment</li> <li>G.agb: Annual change in AGB due to growth</li> <li>M.agb: Annual change in AGB due to mortality</li> <li>Dens: Tree density expressed in individuals/ha</li> <li>DeltaDens: Annual net change in tree density</li> <li>R.dens: Annual change in tree density due to recruitment</li> <li>G.dens: Annual change in tree density due to “growth”. Here, “growth” is a term introduced to account for small differences in tree densities between years due to changes in the extrapolation factor of a tree. Due to the nested sampling design of the vegetation survey, sometimes trees change their extrapolation factor as they grow larger. Thus, is a tree changes extrapolation factor, those differences (that are neither recruitment or mortality) are added up here.</li> <li>M.dens: Annual change in tree density due to mortality</li> <li>Species: Species richness expressed in spp/plot. Redundant with “q0” column.</li> <li>DeltaSpecies: Annual net change in species richness</li> <li>R.species: Annual change in species richness due to recruitment</li> <li>M.species: Annual change in species richness due to mortality</li> <li>Height: Average plot canopy height expressed in m</li> <li>q0: Hill number of order 0 expressed in species effective number (species richness)</li> <li>q1: Hill number of order 1 expressed in species effective number (typical species)</li> <li>q2: Hill number of order 2 expressed in species effective number (dominant species)</li> </ol> <p> </p> <p><em>SPECIES MATRIX</em> contains an abundance matrix per species, plot and year. It contains the following variables:</p> <ol> <li>PlotYear: This column actually does not have a name to it in the file, but is the first column in the dataset, It contains the three-character identifier for the plot and the four numbers of the year of measurement. For instance, “BER2008” would represent the observations made for the plot BER in 2008.</li> <li>treat: This indicates whether the plot is located on limestone (1) or phyllite (2) substrate</li> <li>sp001-sp127: indicates the abundance (in number of individuals per plot) of a given species. Species numbers were assigned randomly, thus they do not match the order of the table provided in Supplementary Material 3 of the publication in Frontiers.</li> </ol> <p> </p> <p><em>ONI</em> contains the Oceanic El Niño Index values per month and year. It is a “year by month” contingency matrix, where years are presented in the rows name, and months are presented in the columns name. ONI values are given in Celsius degrees, and they represent the 3-month rolling average of the temperature anomaly in the Nino3.4 region. The data source and details of this dataset can be found at the NOAA webpage (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).</p> <p> </p> <p><em>ENSO EVENTS</em> contains the occurrence of events of El Niño (warm and dry episodes) and La Niña (cold and wet episodes). It contains the following variables:</p> <ol> <li>Year: Year</li> <li>Month: Month</li> <li>ONI: Oceanic El Niño Index (see ONI dataset description above)</li> <li>Year.cont: Time as a continuous variable (instead of having years and months separately, for plotting)</li> <li>Nino: El Niño (warm and dry) episode occurrence (“1” indicates occurrence)</li> <li>Nina: La Niña (cold and wet) episode occurrence (“1” indicates occurrence)</li> </ol>
Fig. 2 in Longhorned Beetles (Coleoptera: Cerambycidae and Disteniidae) Collected in the Canopy of a Dry Tropical Lowland Forest in Panama
Fig. 2. Number of Cerambycidae and Disteniidae specimens collected over 60 weeks in the MNP canopy.
Nutrient availability and stoichiometry mediate microbial effects on soil carbon sequestration in tropical forests
<p>A one-year <em>in situ</em> soil incubation experiment using mesh bags (mesh size of 38 µm, allowing colonization of bacteria and penetration of fungal hyphal but precluding root penetration) with fertile sugarcane soil and infertile sand along an elevational gradient characterized by varied climatic and biotic conditions in a tropical forest. Biomarkers, including phospholipid fatty acids, carbon-nitrogen-phosphorus acquiring hydrolases, glomalin-related proteins and amino sugars were measured to characterize the production and accumulation of microbial living biomass, exo-enzymes, extracellular glycoprotein and microbial necromass, and to elucidate their contributions to SOC sequestration.</p>
Phosphorus addition decreases microbial residual contribution to soil organic carbon pool in a tropical coastal forest
<p>This is the data supporting the study of 'Phosphorus addition decreases microbial residual contribution to soil organic carbon pool in a tropical coastal forest'. Data in the excel sheet were used for the figures and tables in the article. </p>
Drivers of beta diversity along a precipitation gradient in tropical forests of the Cauca River Canyon in Colombia
<p>Abundance data are provided for each of the species found in each of the plots.</p>
Hysteresis of tropical forests in the 21st century
<p>Data underlying the article:</p> <p>Staal, A., Fetzer, I., Wang-Erlandsson, L., Bosmans, J.H.C., Dekker, S.C., van Nes, E.H., Rockström, J. & Tuinenburg, O.A. (2020). Hysteresis of tropical forests in the 21st century. Nature Communications 11, 4978. DOI: 10.1038/s41467-020-18728-7</p>
Dataset of "Ecoenzymatic stoichiometry reveals increased phosphorus limitation of microbial metabolism in tropical forests along elevation gradients"
<p> DOI reserved for Kuang</p>
Data from: Environmental variation predicts patterns of phenotypic and genomic variation in an African tropical forest frog
<p>Central African rainforests are predicted to be disproportionately affected by future climate change. How species will cope with these changes is unclear, but rapid environmental changes will likely impose strong selection pressures. Here we examined environmental drivers of phenotypic and genomic variation in the central African puddle frog (<i>Phrynobatrachus auritus</i>) to identify areas of elevated environmentally-associated turnover where populations may have the greatest capacity to adapt. We also compared current and future climate models to pinpoint areas of high genomic vulnerability where allele frequencies will have to shift the most in order to keep pace with future climate change. Analyses of body size, relative leg length, and head shape suggest that seasonal aspects of temperature and precipitation significantly influence phenotypic variation, whereas geographic distance and precipitation seasonality are the most important drivers of SNP allele frequency variation. However, neither landscape barriers nor the effects of past Pleistocene refugia had any influence on genomic differentiation. Most phenotypic and genomic differentiation coincided with key ecological gradients across the forest-savanna ecotone, montane areas and a coastal to interior rainfall gradient. Areas of greatest vulnerability were found in the lower Sanaga basin and southeastern region of Cameroon. In contrast with past conservation efforts that have focused on hotspots of species richness or endemism, our findings highlight the importance of preserving environmentally heterogeneous landscapes to preserve putatively adaptive variation and ongoing evolutionary processes in the face of climate change.</p>
Nitrogen addition increases aboveground silicon and phytolith concentrations in understory plants of a tropical forest
<p><strong><i>Purpose</i></strong>: Silicon (Si) is a beneficial element for plants and plays important roles in the biogeochemical cycle of mineral elements. Yet, few studies have focused on the impact of nitrogen (N) deposition on plant Si uptake and the Si biocycle.</p> <p><strong><i>Methods</i></strong>:<strong> </strong>We designed an experiment dealing with canopy and understory N addition in a tropical forest to comprehensively assess the response of Si cycle to N addition focusing on understory plants and topsoil. After six years of treatment, we compared the effects of different treatments on the concentrations of Si-related variables in the leaves of dominant understory species, and topsoil. The foliar elemental stoichiometry, plant water-use efficiency, and soil properties were also assessed to explore potential mechanisms underpinning an N-regulated biological feedback loop of Si.</p> <p><strong><i>Results</i></strong>: The concentrations of SiO<sub>2</sub> and phytolith in the leaves of the understory plants increased under N addition, while those of phytolith and plant-available Si in the topsoil were not altered. The significant accumulation of Si in the leaves and the unaltered plant-available Si concentrations in the soil were associated with enhanced plant Si uptake and decreased soil phytolith return due to increased phosphorus (P) limitation, soil acidification, and decreased leaf C:N ratios caused by N addition.</p> <p><strong><i>Conclusion</i></strong>: Our findings suggest that N addition affected plant phytolith production and its coupling with the Si biocycle in the forest which may stimulate plant Si accumulation and decrease soil Si return. Nitrogen enrichment will alter the biological Si cycle in forest ecosystems at regional and global scales.</p>
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.