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.
13,586
datasets available to search
ShareScore release 0.7.1
Dataset results
13,586 results for “specificity”
Stream specific conductance and temperature from small watersheds in the Andrews Forest
Stream specific conductance and water temperature are measured instantaneously every 5 minutes at most of the gauged watersheds within the H.J. Andrews Experimental Forest (Watersheds 1, 2, 3, 6, 7, 8, 10 and Mack Creek). Data is complementary to stream chemistry measurements from a proportional sampler (CF002) and long-term streamflow measurements (HF004) made at each of these watersheds.
Primary producer biomarker profiles of bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA) and their fatty acid (FA) collected from the Beaufort Sea coastal lagoons,2021-2024
Within Stefansson Sound in Prudhoe Bay, AK various organic matter sources were collected to determine multiple biomarker baseline profiles (i.e., bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA), fatty acids (FA)). Some organic matter sources were collected from Elson lagoon in Utqiaġvik, AK and Kaktovik and Jago lagoons in Kaktovik, AK to supplement low sample sizes in some organic matter source groups. Kelp, red algae, terrestrial plants, phytoplankton, and ice algae were collected in 2024 with some supplement samples collected in 2021 - 2023. Stable isotope values of δ13C and δ15N are reported as “del_13c” and “del_15n”, respectively. Individual fatty acids are reported as the percent relative to total fatty acids for 23 fatty acids: C11:0, C12:0, C14:0, C15:1, C15:0, C16:0, C16:1n7, C17:0, C17:1, C18:0, C18:1n9 trans, C18:2n6 cis, C18:1n7, C18:3n3, C20:0, C18:3n6, C20:4n6, C21:0, C22:0, C22:1n9, C23:0, C24:0, C22:6n3. Stable isotope values of δ13C are reported in the following essential amino acids: Valine (Val), Leucine (Leu), iLeu (isoleucine), Methionine (Met), Phenylalanine (Phe). Additionally, we used ice algal diatoms collected in the Arctic (landfast ice near Utqiaġvik, Alaska) and cultured in a laboratory setting at the University of Alaska Fairbanks to compare the CSIA-EAA fingerprints of field (composites) ice algal samples and isolate diatoms samples.
Specific Leaf Area in the Clearcut Site at Harvard Forest 2012
Clearcutting a forest ecosystem can result in a drastic reduction of the stand’s productivity. Despite the severity of this disturbance type, past studies have found that the productivity of young regenerating stands can quickly rebound, approaching that of mature undisturbed stands within a few years. One of the obvious reasons is increased leaf area with each year of recovery. However, a less obvious reason may be the variability in species composition and distribution during the natural regeneration process. The purpose of this study was to investigate to what extent the increase in GEP, observed during the first four years of recovery, in a naturally regenerating clearcut stand was due to 1) an overall expansion of leaf area, and 2) an increase in the canopy’s photosynthetic capacity stemming from either species compositional shifts or drift in physiological traits within species. We found that the multi-year rise in GEP following harvest was clearly attributed to the expansion of leaf area rather than a change in vegetation composition. Sizeable changes in relative abundance of species were masked by remarkably similar leaf physiological attributes for a range of vegetation types present in this early successional environment. Comparison of upscaled leaf-chamber to eddy-covariance-based light-response curves revealed broad consistency in both maximum photosynthetic capacity and quantum yield efficiency. The approaches presented here illustrate how chamber- and ecosystem-scale measurements of gas exchange can be blended with species-level leaf area data to draw conclusive inferences about changes in ecosystem processes over time in a highly dynamic environment.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, turbidity, and fluorescent dissolved organic matter at discrete depths in Carvins Cove Reservoir, Virginia, USA in 2020-2025
We monitored water quality in Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.3697 -79.958) with high-frequency (10-minute) sensors in 2020-2025. Carvins Cove Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source. This data package consists of datasets from two separate deployments. First, from July 2020 - August 2021, depth profiles of water temperature were measured on 1-meter intervals using HOBO temperature pendant loggers deployed from 0.1 m below the surface of the reservoir to 10 m depth, and also at 15 and 20 m depth. Additionally, water temperature was measured in the Sawmill Branch inflow at 0.5 m depth using HOBO temperature pendant loggers. Second, from 9 April 2021 - 31 December 2025, depth profiles of water temperature were measured on 1-meter intervals from 0.1 m below the surface of the reservoir to 11 m depth and additionally at 15 and 19 m. A YSI EXO2 sonde measured water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~1.5 m depth. A YSI EXO3 sonde measured water temperature, conductivity, specific conductance, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~9 m depth, which corresponds to the depth of a water outtake valve. The thermistors, EXO3 sonde, and pressure sensor were deployed at stationary, fixed elevations (referred to as positions) deployed off of the dam near the water outtake valves. Due to variable water levels in the reservoir, the depths of these sensors varied over time. In contrast, the EXO2 was deployed on a buoy from 2021-2022 and remained at 1.5 m depth as the water level fluctuated. However, in 2023, the buoy disappeared in a storm, and after that the EXO2 was deployed at a stationary elevation as the water level fluctuated around the sensor. The EXO2 was redeployed on the buoy in 2024. The monitoring site's maximum de
Secchi depth data and discrete depth profiles of water temperature, dissolved oxygen, conductivity, specific conductance, photosynthetic active radiation, oxidation-reduction potential, and pH for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Discrete depth profiles of water temperature, dissolved oxygen, oxidation-reduction potential, conductivity, specific conductance, and pH were collected with multiple handheld water quality probes and discrete depth profiles of photosynthetically active radiation (PAR) were collected with a LI-COR underwater light meter from 2013 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. All discrete depth profiles were collected on approximately 1-meter intervals. The data package consists of two datasets: 1) Secchi depth data; and 2) discrete depth profiles of multiple water quality variables measured by handheld sensors. The Secchi data and discrete depth profiles were measured at the deepest site of each reservoir adjacent to the dam, as well as other in-reservoir sites. Handheld sensor measurements were also collected at a gauged weir on the primary inflow tributary, other inflows, and outflows at Falling Creek Reservoir; inflows and outflows at Beaverdam Reservoir; and inflows at Carvins Cove Reservoir. In 2021, YSI handheld data were also collected from a littoral site in Beaverdam Reservoir. In 2025, YSI handheld data were collected monthly from June to October from nine littoral sites around the perimeter of Falling Creek Reservoir. From 2024 - 2025, additional within-reservoir depth profiles were collected in Carvins Cove Reservoir and multiple sites. Data were collected approximately fortnightly in the spring months (March - Ma
Time series of high-frequency profiles of depth, temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, turbidity, pH, oxidation-reduction potential, photosynthetically active radiation, colored dissolved organic matter, phycocyanin, phycoerythrin, and descent rate for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Depth profiles of water biogeochemical properties were collected with SeaBird Electronics (SBE) Conductivity, Temperature, and Depth (CTD) profilers from 2013-2025 at five drinking water reservoirs in southwestern Virginia, USA. The study reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of CTD depth profiles measured at the deepest site of each reservoir adjacent to the dam as well as other upstream reservoir sites. The profiles were collected approximately fortnightly in the spring months, weekly in the summer and early autumn, and monthly in the late autumn and winter. Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir were sampled every year in the dataset (2013-2025); Spring Hollow Reservoir was only sampled 2013-2017 and 2019; and Gatewood Reservoir was only sampled in 2016. Data availability differs across years due to additional sensors that have been added or replaced over time. From 2013-2016, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor and an ECO FLNTU sensor for turbidity and chlorophyll. From 2017-2025, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor, an ECO FLNTU sensor for turbidity and chlorophyll, a PAR-LOG ICSW sensor for photosynthetically active radiation, and a SBE 27 pH and ORP (oxidation-reduction potential) sensor. In 2022 and 2023, profiles were also taken with an additional CTD equipped with an SBE 43 Dissolved Oxygen sensor; an ECO Triplet Scattering Fluorescence sensor for
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, pressure, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Falling Creek Reservoir, Virginia, USA in 2018-2025
We monitored water quality in Falling Creek Reservoir (Vinton, Virginia, USA; 37.30325 -79.8373) with high-frequency (10-minute) sensors in 2018-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled of depth profiles of water temperature on 1-m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 m and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, turbidity, and pressure at ~1.6 m depth. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.
Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025
We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Taxon-specific seasonal net primary production (NPP) for macroalgae
This dataset provides estimates of seasonal net primary production (NPP) for all taxa of macroalgae sampled in fixed plots of the SBC LTER's seasonal kelp forest monitoring sites. The five reefs (Arroyo Quemada 34°28.048’N, 120°07.031’W; Carpinteria 34°23.474’N, 119°32.510’W; Isla Vista 34°23.275’N, 119°32.792’W; Mohawk 34°23.649’N, 119°43.762’W; and Naples 34° 25.342’N, 119° 57.102’W) ranged in depth from 5.8 m to 8.9 m (MLLW) and were chosen to represent a range of physical and biological characteristics known to influence subtidal macroalgal assemblages in the region. NPP of understory taxa was calculated using field measurements of irradiance and biomass (derived from abundance) and laboratory estimates of taxon-specific photosynthetic parameters. NPP for the giant kelp, Macrocystis pyrifera, was calculated using linear relationships between frond density in a given season and average NPP for that season.
SBC LTER: Reef: Long-term experiment: Taxon-specific seasonal net primary production (NPP) for macroalgae
This dataset provides estimates of seasonal net primary production (NPP) for all taxa of macroalgae sampled in fixed plots of the SBC LTER's long-term kelp removal experiment sites. The experiment was initiated in 2008 at 4 sites; a fifth site as added in 2011. Data collection is ongoing. NPP of understory taxa was calculated using field measurements of irradiance and biomass (derived from abundance) and laboratory estimates of taxon-specific photosynthetic parameters. NPP for the giant kelp, Macrocystis pyrifera, was calculated using linear relationships between frond density in a given season and average NPP for that season.
Specific Heat of Holmium in Gold and Silver at Low Temperatures - Data
<p>Data from measurements on the specific heat of a variety of Au:Ho and Ag:Ho alloys. This data is associated with the manuscript:</p> <p>Herbst, M., Reifenberger, A., Velte, C. <em>et al.</em> Specific Heat of Holmium in Gold and Silver at Low Temperatures. <em>J Low Temp Phys</em> <strong>202, </strong>106–120 (2021). https://doi.org/10.1007/s10909-020-02531-1</p> <p>For information on the motivation, measurement techniques, equipment, and data processing, please refer to this manuscript.</p>
Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans
<p><strong>Abstract: </strong><br>SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.</p> <p><strong>More Information:</strong></p> <ul> <li><strong>Preprint:</strong> <a href="https://www.researchsquare.com/article/rs-3304466/v1">Research Square.</a></li> <li><strong>Sample information</strong>: data_inventory.csv file.</li> <li><strong>Code</strong> code_github_repo.zip or at the <a href="https://github.com/ncborcherding/COVID_TCR">original github repo</a></li> <li><strong>Interactive Portal</strong>: <a href="https://cellpilot.emed.wustl.edu/">CellPilot</a></li> </ul>
Four Essential Components for FAIR Data: Capability & Category-Specific Requirements
<p>Adapted from Bailo (2019) and Peng (2023), this diagram illustrates FAIR requirements specific to data, metadata, and infrastructure, aligned with the definitions of individual FAIR principles. It highlights the critical role of enterprise capabilities—including processes, systems, standards, tools, and skills—in supporting FAIR data. These four components are essential for systematically enhancing the overall FAIRness of an organization's scientific data collection</p> <p> </p>
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>
Model-based fMRI reveals co-existing specific and generalized concept representations
Open the record for dataset details and reuse information.
Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)
<p>The zip file contains supplementary data for the publication - Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study, accepted for publication in Environmental Health Perspectives (DOI: 10.1289/EHP6174).</p> <p>The description of the files are noted below:</p> <p><strong>1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_SingleExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_SingleExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p> </p> <p><strong>General footnote for all files:</strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from single exposure epigenome-wide linear mixed models, with random intercept at the level of participant. Each model was adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator (for Lden models) and leukocyte composition. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites.</p> <p>Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p> <p> </p> <p><strong>2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_MultiExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_MultiExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p><strong>General table footnotes: </strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from multi-exposure epigenome-wide linear mixed models, with random intercept at the level of participant, and were adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator and leukocyte composition. Multi-exposure models included all five exposures (Aircraft, railway, road traffic Lden and respective truncation indicators, NO<sub>2</sub> and PM<sub>2.5</sub>) at the same time. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites. Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p>
Data from: Sex-specific recombination landscape in a species with holocentric chromosomes
<p>Male and female meiosis typically exhibit significant differences in crossover locations along chromosomes. It has been suggested that higher recombination rates at chromosome centers in females counteract centromere-associated meiotic drivers, increasing their chances of segregating into the oocyte rather than to the non-viable polar bodies. Our research, employing the first sex-specific recombination map for an organism lacking defined centromeres revealed parallel recombination landscapes across the sexes, supporting the meiotic drive hypothesis.</p>
Data for Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex
<p>Data for the paper: Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex, Nature 640, 2025</p> <p>In brief, this data archive includes information about the skeleton morphology and synaptic features of neurons whose cell bodies fell within a 100 micron by 100 micron column spanning all layers of mouse visual cortex. See <a href="https://www.microns-explorer.org/cortical-mm3">MICrONs-Explorer</a> for a full description of the broader volume and how it was collected.</p> <p>The data here include both data tables of cell locations, neuronal features, synapse lists, and more, as well as files containing morphological descriptions of all neurons used for the analysis in the initial version of the preprint. See the README.md file for more complete information about the individual files.</p> <p>Note: Data has been updated with post-publication files.</p>
Atomistic Structures discussed in "Segregation-enhanced grain boundary embrittlement of recrystallised tungsten evidenced by site-specific microcantilever fracture"
<p>The tar file Sigma7_GB.tar contains all data to reproduce the results shown and discussed in the Publication "Segregation-enhanced grain boundary embrittlement of recrystallised tungsten evidenced by site-specific microcantilever fracture", DOI: <a href="https://doi.org/10.1016/j.actamat.2023.119256">10.1016/j.actamat.2023.119256</a></p><p>It contains three folders for the grain boundary creation, decoration with P atoms, and fracture simulations.<br>The naming conventions and additional information are provided in README.txt files in the directories.</p>
Species-specific proteotypic peptides for characterization of non-tuberculosis mycobacteria
<p>Non-tuberculous mycobacteria are opportunistic bacteria that closely resemble <i>Mycobacterium tuberculosis,</i> causing respiratory infections in humans. While genotyping through genome sequencing is found accurate in detecting mycobacterial species but struggles with distinguishing bacterial co-infection. These challenges lead to delayed therapeutic intervention, drug resistance, and disease complications. Lately, mass spectrometry-based (MALDI-TOF-MS) proteomics has routinely been used in diagnosing mycobacterial species in clinical samples. However, it suffers accurate species detection owing to extensive bacterial cultures and poor specificity in polymicrobial infections. In contrast, due to its sensitivity, LC-MS/MS based proteomics is widely employed for accurate bacterial proteome mapping. In this study, in-depth proteomics of 9 NTM species with proteome database searches of 26 datasets was used. In total 20 million peptide spectrum matches were identified aiding to ≥40% proteome coverage in 7 NTMs with highest in <i>M. abscessus</i>. Further, metaproteomic analysis and rescoring of peptides resulted in high-confidence species-specificity proteotypic peptides in <i>M. smegmatis</i> (2342), <i>M. vaccae</i> (960), <i>M. abscessus</i> (75), <i>M. avium</i> subsp. <i>paratuberculosis</i> (3) and <i>M. fortuitum</i> (1). Finally, database search results were converted to spectral library format for easier future usage in targeted proteomic workflows. This workflow in deriving species-specific peptides with high confidence can be extended in distinguishing closely related bacterial species for enhancing microbial diagnostics.</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.