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.
854
datasets available to search
ShareScore release 0.9.0
Dataset results
854 results for “Disparity”
Leaf and Soil Nitrogen Following Lymantria dispar Defoliation in Central Massachusetts 2018-2019
In this study we investigated relationships between ecosystem nitrogen (N) cycling and tree defoliation during a recent 2015-2018 irruption of invasive Lymantria dispar caterpillars, which can cause tree stress and sometimes mortality following multiple years of defoliation. Nitrogen is a critical nutrient that limits the growth of caterpillars and plants in temperate forests. We assessed the associations between N concentrations, soil solution inorganic N availability, and defoliation intensity by L. dispar at the scale of individual trees in the Amherst, MA area and forest plots in the Quabbin Reservoir area.
Lymantria dispar Defoliation and Mortality Survey at the Quabbin Watershed in Central Massachusetts 2017-2022
For most of the 20th century, the invasive Lymantria dispar was the most serious insect threat to forests and shade trees in the northeastern United States, but outbreaks have been sporadic and light since 1989, after the successful establishment of a fungal pathogen, Entomophaga maimaiga. However, in 2016 a surprising new outbreak of Lymantria dispar began in southern New England, resulting in dramatic oak (Quercus spp.) mortality across thousands of forested hectares by 2018. In 2017, during the height of the outbreak, a rapid assessment of defoliation across 486 plots in six clusters (aka ‘hotspots’) across the Quabbin Watershed Forest in central Massachusetts was conducted. These sample points can be related to satellite-based defoliation estimates, and the tree and site data analyzed for predictors of defoliation severity. In 2022, we returned to 204 of these plots to assess oak mortality, understory vegetation, and oak regeneration.
Oak Forest Response to Lymantria dispar Defoliation in Central Massachusetts since 2019
Invasive forest insects are a major global change driver, and the Northeastern U.S. is an invasion hotspot. Lymantria dispar is one of the region’s most destructive defoliators. After thirty years of quiescence, a surprisingly severe outbreak began in 2015 in southern New England, and by 2018 had caused dramatic oak mortality across thousands of forested acres. Lymantria dispar is considered a generalist, but in New England, oaks (Quercus sp.) are its preferred host trees. Oaks are key overstory trees in eastern North America. In New England, they have been a dominant component of the forest for thousands of years, and play a leading role in providing habitat, timber, and carbon sequestration. However, oak prominence is declining throughout New England. The reasons for oak’s decreasing abundance are the subject of lively debate but the role of L. dispar is likely underappreciated. Therefore, we need to understand the causes and consequences of oak dieback and mortality to this disturbance event, so that we can better predict responses to future outbreaks. To address this need, we established a set of permanent plots in central Massachusetts, chosen to capture a range of defoliation severity.
Resources from: Disparate patterns of genetic divergence in three widespread corals across a pan-pacific environmental gradient highlights species-specific adaptation trajectories
<p>The following files are contained in this repository:</p> <p><br> README.Hume_et_al_2022.zenodov4.txt - This document.</p> <p>scripts.Hume_et_al_2022.zenodov4.pdf - Contains the scripts, or locations of the scripts, used to conduct the data analyses detailed in the associated manuscript.</p> <p>acknowledgements_local_authorities.Hume_et_al_2022.zenodov1.pdf - Acknowledgements of local authorities for the collection of samples used in the associated study.</p> <p>TaraPacific_SST_timeseries_mean_productsV2mai2021.Hume_et_al_2022.zenodov1.csv - The historical temperature data set used for the RDA, Mantel tests and gradient Forest analysis.</p> <p>Pocillopora_meandrina_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip - The Pocillopora SNPs referred to as 'raw' in the Methods of the associated manuscript. Compressed using genozip (https://genozip.readthedocs.io/index.html).</p> <p>Pocillopora_meandrina_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip.md5 - md5 of the the Pocillopora raw SNPs.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'linked' in the Methods of the associated manuscript.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora linked SNPs.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'unlinked' in the Methods of the associated manuscript.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora unlinked SNPs.</p> <p>Porites_lobata_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip - The Pocillopora SNPs referred to as 'raw' in the Methods of the associated manuscript. Compressed using genozip (https://genozip.readthedocs.io/index.html).</p> <p>Porites_lobata_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip.md5 - md5 of the the Pocillopora raw SNPs.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'linked' in the Methods of the associated manuscript.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora linked SNPs.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'unlinked' in the Methods of the associated manuscript.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora unlinked SNPs.</p> <p>PANAMA2021.raw.Hume_et_al_2022.zenodov2.vcf.gz - The Millepora SNPs referred to as 'raw' in the Methods of the associated manuscript.</p> <p>PANAMA2021.raw.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Millepora raw SNPs.</p> <p>Millepora_REF_orthologue_genes.Hume_et_al_2022.zenodov2.csv - The Millepora gene list referred to as 'target genes' in the Methods of the associated manuscript.</p> <p>Mil_transcriptom.Hume_et_al_2022.zenodov2.fa.gz - The Millepora de novo assembled transcriptome.</p> <p>Mil_transcriptom.Hume_et_al_2022.zenodov2.fa.gz.md5 - md5 of the Millepora de novo assembled transcriptome.</p> <p> </p> <p>mtORF Phylogeny</p> <p>TP-Johnston_mtORF-Pocillo.fa = all sequences</p> <p>TP-Johnston_mtORF-Pocillo.mafft.fa = mafft alignment</p> <p>TP-Johnston_mtORF-Pocillo.mafft.ML.nwk = ML tree newick</p> <p> </p> <p>Hellberg genotype network Porites</p> <p>TP-Hellberg_MM32-Porites.nex = all aligned sequences for this locus with indels encoded</p> <p>TP-Hellberg_MM100-Porites.nex = all aligned sequences for this locus with indels encoded</p> <p>TP-Hellberg_ATPaseB.nex = all aligned sequences for this locus with indels encoded,</p> <p>TP-Hellberg_POFAD.nex = POFAD multilocus genotypic distance,</p> <p>TP-Hellberg_Splitstree.nex= Multilocus genotype network in nexus format</p> <p><br> Gradient Forest Analysis</p> <p>Poc_abund.csv - Pocillopora SSH Occurrences per Site er Island</p> <p>Por_abund.csv - Porites SSH Occurrences per Site er Island</p> <p>mean_depth_por.csv - per site per island mean depth among Porites colonies</p> <p>mean_depth_poc.csv - per site per island mean depth among Pocillopora colonies</p>
Supplementary Data to "Disparate regulation of Smad3 phosphorylation and collagen gene transcription by full-length IL-33"
<p>These are Supplementary Figures for the article "Disparate regulation of Smad3 phosphorylation and collagen transcription by full-length IL-33"</p>
Data for The Disparities and Development Trajectories of Nations in Achieving the Sustainable Development Goals
<p>This dataset provides the source data for Tables and Figures in the main text and the supplementary information, and the code for the main figure of the article.</p>
Air Quality and Exposure Disparity Results for the Bronx, New York City
<p>This is the dataset accompanying the publication "Big Mobility Data Reveals Hyperlocal Air Pollution Exposure Disparities in the Bronx, New York". It contains mainly three parts: 1. day-to-day air quality prediction maps for exposure estimation; 2. street-level PM2.5 exposure and its disparity modeling results for all populations and for socio-demographic groups; 3. residence- and mobility-based exposure calculation for a sample of Bronx residents.</p>
INCOME DISPARITIES AMONG MICRO AND SMALL ENTERPRISES: THE DIGITAL DIVIDE IN INDONESIA
<p>Data related to small scale enterprise in relations to the digital aspects by prinvince in Indonesia source from Badan Pusat Statistik, Republic of Indonesia</p> <p>Data was collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381) </p>
Regional Disparities in AI Awareness
<p>The dataset consists of variables linked to the benefits, risks, and impacts of AI and regional development in a variety of areas. Sources of the data: Office for National Statistics (ONS), UK.</p>
Data to: "Despite impaired binocular function, binocular disparity integration across the visual field is spared in normal aging and glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello G., & Kwon, M. (in press) Despite impaired binocular function, binocular disparity integration across the visual field is spared in normal aging and glaucoma. IOVS</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2022.11.28.518250</p>
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
<p>Using data from New York City, we found that there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare essential workers had greater mobility during the pandemic. When adjusted for the percent of essential workers, these associations do not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percent older than 75 years old and percent of healthcare essential workers) was associated with a higher rate of COVID-19 cases per 100k, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged are not only at an increased risk for COVID-19 infection, but lack the privilege to fully engage in social distancing interventions.</p>
Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles
<p>Without comprehensive examination of available literature on health disparities and minority health (HDMH), the field is left vulnerable to disproportionately focus on specific populations or conditions, curtailing our ability to fully advance health equity. Using scalable open-source methods, we conducted a computational scoping review of more than 200,000 articles to investigate major populations, conditions, and themes in the literature as well as notable gaps. We also compared trends in studied conditions to their relative prevalence in the general population using insurance claims (42 million Americans). HDMH publications represent 1% of articles in MEDLINE. Most studies are observational in nature, though randomized trial reporting has increased five-fold in the last twenty years. Half of all HDMH articles concentrate on only three disease groups (cancer, mental health, endocrine/metabolic disorders), while hearing, vision, and skin-related conditions are among the least well-represented despite substantial prevalence. To support further investigation, we also present HDMH Monitor, an interactive dashboard and repository generated from the HDMH bibliome.</p>
Data from: Morphological disparity and evolutionary patterns of Cambrian hyoliths
<p>Hyolitha represents one of the major components of the Cambrian evolutionary fauna, first appearing in the Terreneuvian and rapidly diversifying soon after. Recent work has both enriched the hyolith fossil records and expanded our understanding of their biology, but studies documenting the evolutionary trajectory of Cambrian hyoliths remain scarce. Here we present the first study of changes in morphological disparity in Cambrian hyoliths over time with the aim of characterizing the evolutionary trajectory of hyoliths through their primary period of diversification. Our results show that hyoliths occupy distinct regions of morphospace at different times during the Cambrian, with an expansion in morphospace occupation associated with the increase in hyolith diversity in the early Cambrian. Both the Sinsk Event and multiple abiotic factors led to a decline in hyolith diversity in the Miaolingian, and morphological disparity also contracts in association with this reduction in diversity.</p>
Fig. 7 in Morphological disparity of early ammonoids: A geometric morphometric approach to investigate conch geometry
Fig. 7. Evolution of the relative contribution of ammonoid superfamilies to diversity and disparity (mean squared Euclidean distance to the centroid) through the Early and Middle Devonian; based on the analysis of the whorl profiles. A. Relative contribution of ammonoid superfamilies to diversity (sampled-in-bin). B. Fluctuations of the mean squared Euclidean distance to the centroid (black line with grey area showing the confidence intervals computed after 1000 bootstraps) and sampled-in-bin diversity (blue bars). C. Relative contribution of ammonoid superfamilies to disparity (mean squared Euclidean distance to the centroid). See Fig. 2 for interval labels.
Fig. 9 in Morphological disparity of early ammonoids: A geometric morphometric approach to investigate conch geometry
Fig. 9. Evolution of the relative contribution of ammonoid superfamilies to diversity and disparity (mean squared Euclidean distance to the centroid) through the Early and Middle Devonian ammonoid zones (biozones numbered from 1 to 30, see Fig. 2); based on the analysis of the whorl profiles. A. Relative contribution of ammonoid superfamilies to diversity (sampled-in-bin). B. Fluctuations of the mean squared Euclidean distance to the centroid (black line with grey area showing the confidence intervals computed after 1000 bootstraps) and sampled-in-bin diversity (blue bars). C. Relative contribution of ammonoid superfamilies to disparity (mean squared Euclidean distance to the centroid).
Fig. 6 in Morphological disparity of early ammonoids: A geometric morphometric approach to investigate conch geometry
Fig. 6. Evolution of the morphospace occupation through the seven intervals constituting the Early and Middle Devonian, showing the distribution of ammonoid superfamilies; based on the analysis of the whorl profiles (on each diagram, the horizontal axis corresponds to PC1 and the vertical axis to PC2). See Fig. 2 for interval labels.
Fig. 11 in Morphological disparity of early ammonoids: A geometric morphometric approach to investigate conch geometry
Fig. 11. Variations of the convex hull area computed for PC1 and PC2, based on the analysis of the whorl profiles through the Early and Middle Devonian. Comparison of the measured values with the expected values given diversity, computed by applying the null model of Whalen et al. (2020). A. Fluctuations computed at the interval resolution. B. Fluctuations computed at the biozone resolution. See Fig. 2 for interval labels and biozones.
Fig. 5 in Morphological disparity of early ammonoids: A geometric morphometric approach to investigate conch geometry
Fig. 5. Diagrams showing the morphospace occupation observed for the three stages constituting the Early and Middle Devonian (A–C), with level contours and density curves; based on the analysis of the whorl profiles. The grey dots correspond to the data recorded for the entire studied time interval (Early and Middle Devonian); the black dots refer to the data recorded for each of the studied stage (respectively, Emsian, Eifelian, and Givetian). The colours refer to the density of the data in the morphospace; the red-yellowwhite gradient indicates the decreasing density of occupied areas. Compare also with density curves (in grey) above and to the right of the diagrams.
Fig. 4 in Morphological disparity of early ammonoids: A geometric morphometric approach to investigate conch geometry
Fig. 4. Morphospace occupation observed for the Early and Middle Devonian, based on the analysis of the whorl profiles, with representative examples of shapes. The first two axes explain 95.7% of the variance.
Fig. 3. Ammonoid morphology and dataset. A in Morphological disparity of early ammonoids: A geometric morphometric approach to investigate conch geometry
Fig. 3. Ammonoid morphology and dataset. A. Morphology of an ammonoid; as an example, the outline of the whorl profile taken at the maximum conch diameter is highlighted by a thick black line (modified from De Baets et al. 2010). B. Dataset analysed here; compilation of drawings of whorl profile outlines corresponding to Early and Middle Devonian ammonoids from Morocco.
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.