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352 results for “Data Enrichment”
Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data
<p>This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.<br> 1. Initial data used<br> We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).<br> 2. The calculation method<br> The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.<br> 3. Data sets<br> The data set contains two files:<br> - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)<br> - the yearly average of the EI (period 01/2003 - 12/2018)<br> <br> Two images are joined with this data set:<br> - a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)<br> (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).</p> <p> - a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).<br> (file: Figure-enrichment-index.pdf)<br> <br> An improved version of this index will be available in a near future.</p>
Data for Forb diversity globally is harmed by nutrient enrichment but can be rescued by large mammalian herbivory
Forbs (“wildflowers”) are important contributors to grassland biodiversity and services, but they are vulnerable to environmental changes that affect their coexistence with grasses. In a factorial experiment at 94 sites on 6 continents, we tested the global generality of several broad predictions arising from previous studies: (1) Forb cover and richness decline under nutrient enrichment, particularly nitrogen enrichment, which benefits grasses at the expense of forbs. (2) Forb cover and richness increase under herbivory by large mammals, especially when nutrients are enriched as grazing will release forbs from decreased grass competition under fertilization. (3) Forb richness and cover are less affected by nutrient enrichment and herbivory in more arid climates, because water limitation reduces the impacts of competition with grasses. We found strong evidence for the first, partial support for the second, and no support for the third prediction. Forb richness and cover are reduced by nutrient addition, with nitrogen having the greatest effect; forb cover is enhanced by large mammal herbivory, although only under conditions of nutrient enrichment and high herbivore intensity; and forb richness is lower in more arid sites, but is not affected by consistent climate-nutrient or climate-herbivory interactions. We also found that nitrogen enrichment disproportionately affects forbs in certain families (Asteraceae, Fabaceae). Our results underscore that anthropogenic nitrogen addition is a major threat to grassland forbs and the ecosystem services they support, but grazing under high herbivore intensity can offset these nutrient effects. For associated r code that goes along with this dataset, please refer to the following Zenodo repository: https://zenodo.org/records/14207290
Data from: Nutrient enrichment negatively impacts flowers and pollinators, especially in warmer climates
Using data from 14 plant and pollinator communities in three continents, we assessed the effect of different fertilization treatments on plant and pollinator abundance and richness. The 14 sites spanned a wide range of ambient temperature and soil fertility and replicated a long-term nutrient experiment identically. This allowed us to separate the role of nutrients across ambient site conditions to determine whether effects vary with environmental conditions. We found an interactive effect of temperature and soil fertility with nutrient enrichment, with N input having a more negative impact on plants and pollinators in warmer climates. Moreover, the effects on flower abundance were more pronounced in high fertility soils, while plant richness was more affected in poor fertility soils. In general, the effect of nutrient enrichment in colder climates was positive, and pollinators were more responsive to nutrient enrichment than plants. These results highlight the high susceptibility of warmer regions and particularly their pollinators to nutrient enrichment. This chapter is presented in a format in which Results and Discussion sections are written together, to match the format of the Journal selected for future submission.
Plant species percent cover data: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Raw and processed GO term data to support running GCEA analyses using ensemble-based nulls, as described in the manuscript, 'Overcoming bias in gene category enrichment analyses of brain-wide transcriptomic data'.
<p>Data to support a toolbox for performing gene category enrichment analyses, including against ensembles of null phenotypes.</p> <p>Descriptions of how these data files can be used for this purpose are in the documentation for the toolbox, at https://github.com/benfulcher/GCEA_FalsePositives</p>
Data Tables for Enrichment by Extragalactic First Stars in the Large Magellanic Cloud
<p>Machine-readable data tables for the article, "Enrichment by Extragalactic First Stars in the Large Magellanic Cloud"-- DOI: 10.1038/s41550-024-02223-w. Full descriptions of the tables are in the article, but we briefly summarize here-</p> <p>Table 1 (Table1.csv): This table provides general information on the stars for which we obtained long-exposure Magellan/MIKE data to derive their detailed elemental abundances. Columns include names, coordinates, SkyMapper g magnitudes from the Gaia XP spectra, and stellar parameters and metallicities with their respective random uncertainties.</p> <p>Table 2 (Table2.csv): This table provides names, radial velocities, metallicities, and selected elemental abundances with random uncertainties for stars listed in Table 1. [C/Fe]_c indicates carbon abundances that are corrected for the evolutionary state of the star following Placco et al. (2014). Abundances that are upper limits are flagged by the ul_[X/Fe] columns and have "nan" values for the uncertainty.</p> <p>Extended Data Table 1 (Extended_Data_Table1.csv): This table summarizes all of our observations, by providing names, coordinates, SkyMapper g magnitudes from the Gaia XP spectra, exposure times, dates of observation, and the instrument for these observations. This table includes stars observed with MagE, those observed with short-exposures with MIKE for just metallicities and carbon abundances, and those flagged as more metal-rich upon initial exposure and hence, not further observed. </p> <p>Extended Data Table 2 (Extended_Data_Table2.csv): This table provides names, followed by stellar parameters, metallicities, and carbon abundances, along with their respective uncertainties, for stars observed with MagE or MIKE for short exposures to just obtain a metallicity and carbon abundance. As in Table 2, abundances that are upper limits are flagged by the ul_[C/Fe] column and "nan" entries for the uncertainty.</p> <p>Supplementary Data 1 (Summary_Data_1.csv or Summary_Data_1.ascii): This table provides the suite of detailed element abundances and uncertainties from the long-exposure MIKE spectra of the stars in Table 1. Columns include the name, atomic number and ionization state of the element (element), the number of features used to estimate the elemental abundance (N), the solar abundance of that element (Solar), the absolute abundance (logeps), the chemical abundance scaled by the solar abundance relative to hydrogen ([X/H]), the ratio with respect to the iron abundance ([X/Fe]), the random uncertainty ([X/H]_err) and an upper limit flag (ul), and errors from propagating the uncertainties in the individual stellar parameters and the overall systematic and total uncertainty ([X/H]_errteff, [X/H]_errlogg, [X/H]_errvt, [X/H]_errsys, [X/H]_errtot). These are followed by the same columns, but with respect to iron (e.g., [X/Fe]_errteff, [X/Fe]_errlogg). Abundances of the CH molecule are indicated by 106.0 in the "element" column. This table is provided as a machine readable csv file and as an ascii file, the latter for easier visual readability.</p> <p>Supplementary Data 2 (Summary_Data_2.csv or Summary_Data_2.ascii): This table summarizes the chemical abundances from individual absorption features for the LMC stars with long-exposure MIKE spectra. The columns include the star name, the atomic number and ionization state of the element (species), the solar abundance of that element (Solar), followed by the wavelength, excitation potential, and loggf of the line (wavelength, expot, loggf), and then the measured equivalent width (EW), absolute abundance (logeps), and a flag indicating whether the abundance is an upper limit (ul). Abundances derived via spectral synthesis have "nan" entries for expot, loggf, and EW, and abundances of the CH molecular band are indicated by 106.0. This table is provided as a machine readable csv file and as an ascii file, the latter for easier visual readability. </p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
Supplementary material for 'Station to Station: Linking and Enriching Historical British Railway Data'
<p>Supplementary material for the <a href="https://github.com/Living-with-machines/station-to-station">station-to-station</a> Github repository, containing the underlying code and materials for the paper 'Station to Station: Linking and Enriching Historical British Railway Data', accepted to CHR2021 (Computational Humanities Research).</p> <p>Mariona Coll Ardanuy, Kaspar Beelen, Jon Lawrence, Katherine McDonough, Federico Nanni, Joshua Rhodes, Giorgia Tolfo, and Daniel C.S. Wilson. "Station to Station: Linking and Enriching Historical British Railway Data." In Computational Humanities Research (CHR2021). 2021.</p>
Data for 'Floral color and family drive contrasting plant-pollinator responses to nutrient enrichment' by Rebecca A. Nelson, Elizabeth T. Borer, and Eric W. Seabloom 2025. Collected in California grasslands 2023 and 2024.
Data for analysis of how flower color and family mediate plant-pollinator response to nutrient enrichment. Data on pollinator visitation and flower abundance were collected in three California grasslands in 2023 and 2024 from a factorial experimental in which combinations of nitrogen, phosphorus, and potassium with micronutrients were applied.
Plant aboveground biomass data: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Supplementary Data S1 for Rosen et al. Li and Ca enrichment in the Bristol Dry Lake brine, California
<p>Supplementary file S1: Chemical groundwater data used for analysis in the Journal article entitled "<strong>Li and Ca enrichment in the Bristol Dry Lake brine compared to brines from Cadiz and Danby Dry Lakes, Barstow-Bristol Trough, California, USA" </strong>by Michael R. Rosen, Lisa L. Stillings, Tyler Kane, Kate Campbell-Hay, and Matthew Vitale, P.G., Ray Spanjers, P.G.in the Journal Minerals</p> <p>Supplementary file S2: Chemical data from extractions from 10 clay samples from Bristol Dry lake by acid digestion. used in "<strong>Li and Ca enrichment in the Bristol Dry Lake brine compared to brines from Cadiz and Danby Dry Lakes, Barstow-Bristol Trough, California, USA" </strong>by Michael R. Rosen, Lisa L. Stillings, Tyler Kane, Kate Campbell-Hay, and Matthew Vitale, P.G., Ray Spanjers, P.G.in the Journal Minerals.</p> <p>Methods for all data are in the journal article.</p> <p>Data is for Bristol, Cadiz and Danby playas (Dry Lakes). Most data has been previously published either online in the California Division of Water Resources website, or in other USGS publications or journal articles. Some data from Cadiz Dry lake has not been previously published and publication of these data has been granted by Tetra Technologies Inc. and Standard Lithium LLC.</p>
Data from: Enriching the ant tree of life: enhanced UCE bait set for genome-scale phylogenetics of ants and other Hymenoptera
1. Targeted enrichment of conserved genomic regions (e.g., ultraconserved elements or UCEs) has emerged as a promising tool for inferring evolutionary history in many organismal groups. Because the UCE approach is still relatively new, much remains to be learned about how best to identify UCE loci and design baits to enrich them. 2. We test an updated UCE identification and bait design workflow for the insect order Hymenoptera, with a particular focus on ants. The new strategy augments a previous bait design for Hymenoptera by (a) changing the parameters by which conserved genomic regions are identified and retained, and (b) increasing the number of genomes used for locus identification and bait design. We perform in vitro validation of the approach in ants by synthesizing an ant-specific bait set that targets UCE loci and a set of "legacy" phylogenetic markers. Using this bait set, we generate new data for 84 taxa (16/17 ant subfamilies) and extract loci from an additional 17 genome-enabled taxa. We then use these data to examine UCE capture success and phylogenetic performance across ants. We also test the workability of extracting legacy markers from enriched samples and combining the data with published data sets. 3. The updated bait design (hym-v2) contained a total of 2,590-targeted UCE loci for Hymenoptera, significantly increasing the number of loci relative to the original bait set (hym-v1; 1,510 loci). Across 38 genome-enabled Hymenoptera and 84 enriched samples, experiments demonstrated a high and unbiased capture success rate, with the mean locus enrichment rate being 2,214 loci per sample. Phylogenomic analyses of ants produced a robust tree that included strong support for previously uncertain relationships. Complementing the UCE results, we successfully enriched legacy markers, combined the data with published Sanger data sets, and generated a comprehensive ant phylogeny containing 1,060 terminals. 4. Overall, the new UCE bait design strategy resulted in an enhanced bait set for genome-scale phylogenetics in ants and likely all of Hymenoptera. Our in vitro tests demonstrate the utility of the updated design workflow, providing evidence that this approach could be applied to any organismal group with available genomic information.
Data and Code for "Metal-enriched, sub-kiloparsec gas clumps in the circumgalactic medium of a faint z = 2.5 galaxy"
<p>This repository has code and data used in the paper "Metal-enriched, sub-kiloparsec gas clumps in the circumgalactic medium of a faint z = 2.5 galaxy" (http://arxiv.org/abs/1406.4239). If you find any of the data or code useful for a publication, please consider citing that paper.</p>
Data from: CIDER-Seq: unbiased virus enrichment and single-read, full length genome sequencing
<p>Raw and finished sequence data produced in the study: </p> <p>Mehta D, Hirsch-Hoffmann M, Patrignani A, Gruissem W, Vanderschuren H (2017) CIDER-Seq: unbiased virus enrichment and single-read, full length genome sequencing. <em><strong>bioRxiv</strong></em>. doi: https://doi.org/10.1101/168724</p>
Data for methylome sequencing: Enriching and Profiling Methylomes for Tumor Classification and Liquid Biopsies
<p>We benchmarked and demonstrated the versatility of FLEXseq (Fragment Ligation EXclusive methylation sequencing) across different sample types: genomic DNA from the K562 (leukemia) cell line, DNA mix-in titrations of four immune cell types (B cells, T cells, monocytes, and neutrophils), DNA titrations of three cancer cell lines (breast invasive carcinoma [BRCA], colon adenocarcinoma [COAD], and glioblastoma [GBM]) mixed with those four immune cell mixtures separately, input titrations of cell-free (cf) DNA from one plasma sample and DNA from formalin-fixed paraffin-embedded (FFPE) tissues, cfDNA from 106 cerebrospinal fluids (CSF) and 42 other body fluids, and DNA from 37 FFPE tissues.</p> <p>We sequenced all the samples mentioned above using FLEXseq. Paired-end reads were quality and length trimmed with cutadapt version 3.5, and all high-quality sequencing reads were then aligned to the hg38 reference genome using Bismark v0.23.0. We then filtered out reads with unmethylated cytosine in the non-CpG context with filter_non_conversion function. Next, we used the bismark_methylation_extractor function to extract the methylation calls (removing single-nucleotide polymorphisms [SNP]).</p> <p>We also used the bam2pat function from wgbs_tools, to convert bam files into .pat files for deconvolution, keeping reads covering at least three CpG sites. The .pat files preserve fragment-level data and were de-identified by removing SNPs using the mask_pat function. </p> <p>We used CNVkit (v0.9.10) to analyze and visualize genome-wide copy numbers. Our inputs into CNVkit were Bismark/Bowtie 2 aligned BAM files deduplicated by Bismark based on end positions and fragment lengths. We then generated log2copy ratio plots for all body fluid and FFPE samples based on the pooled reference and visualized them across all bins using the DNAcopy R package.</p> <p> </p>
Semantic Enrichment of the Laboratory Data Dictionary of the Study of Health in Pomerania (SHIP-START-4) with LOINC; Detailed Mapping Results
<p>Unlike West Germany, high morbidity and mortality have been observed in East Germany over the last century. The regional population-based Study of Health in Pomerania (SHIP) therefore investigates the long-term progression of sub-clinical findings, their determinants and prognostic values, to acquire knowledge that facilitates early diagnosis and thus helps prevent the progression of disease. The SHIP covers various areas of patient health. Each SHIP data set is accompanied by a data dictionary (DD) which provides descriptions of variables and definitions.</p> <p>This work shows the detailed mapping results of the semantic enrichment of the SHIP-START-4 medical laboratory data dictionary with LOINC codes. This work also provides detailed descriptions of the concepts applied in the semnatic enrichment. The results of this work serve as a critical step towards improving its interoperability and hence FAIRness for the SHIP laboratory-related measurements. </p>
Data from: Opposing responses of temporal stability of aboveground and belowground net primary productivity to water and nitrogen enrichment in a temperate grassland
<p><span>Changes in water and nitrogen availability, as important elements of global environmental change, are known to affect the temporal stability of aboveground net primary productivity (ANPP). However, evidences for their effects on the temporal stability of belowground net primary productivity (BNPP), and whether such effects are consistent between belowground and aboveground, are rather scarce. Here, we investigated the responses of temporal stability of both ANPP and BNPP to water and nitrogen addition based on a 9-year manipulative experiment in a temperate grassland in northern China. The results showed that the temporal stability of ANPP increased with water addition but decreased with nitrogen addition. By contrast, the temporal stability of BNPP decreased with water addition but increased with nitrogen enrichment. The temporal stability of ANPP was mainly determined by the soil moisture and inorganic nitrogen, which modulated species asynchrony, as well as by the stability of dominant species. On the other hand, the temporal stability of BNPP was mainly driven by the soil moisture and inorganic nitrogen that modulated ANPP of grasses, and by the direct effect of soil water availability. Our study provides the first evidence on the opposite responses of aboveground and belowground grassland temporal stability to increased water and nitrogen availability, highlighting the importance of considering both aboveground and belowground components of ecosystems for a more comprehensive understanding of their dynamics.</span></p>
Data from: Transcriptome analysis of apical meristem enriched bud samples for size dependent flowering commitment in Crocus sativus reveal role of sugar and auxin signalling
<p><strong>Background</strong></p> <p>Cultivation of <em>Crocus sativus</em> (saffron) faces challenges due to inconsistent flowering patterns and variations in yield. Flowering takes place in a graded way with smaller corms unable to produce flowers. Enhancing the productivity requires a comprehensive understanding of the underlying genetic mechanisms that govern this size based flowering initiation and commitment. Therefore, samples enriched with non-flowering and flowering apical buds from small (<6g) and large (>14g) corms were sequenced. </p> <p><strong>Methods and Results</strong></p> <p>Apical bud enriched samples from small and large corms were collected immediately after break of dormancy in July. RNA sequencing was performed using Illumina Novaseq 6000. <em>De-novo</em> transcriptome assembly and analysis using flowering committed buds from large corms at post-dormancy and their comparison with vegetative shoot primordia from small corms pointed out the major role of Auxin and ABA hormonal regulation. Many genes with known dual responses in flowering development and circadian rhythm like Flowering locus T and Cryptochrome 1 along with a transcript showing homology with small auxin upregulated RNA (SAUR) exhibited induced expression in flowering buds. Thorough prediction of <em>Crocus sativus</em> non-coding RNA repertoire has been carried out for the first time. Enolase was found to be acting as a major hub with protein-protein interaction analysis using Arabidopsis counterparts.</p> <p><strong>Conclusion</strong></p> <p>Transcripts belong to key pathways including phenylpropanoid biosynthesis, hormone signaling and carbon metabolism were found significantly modulated. KEGG assessment and protein-protein interaction analysis confirm the expression data. Findings unravel the genetic determinants driving the size-dependent flowering in <em>Crocus sativus</em>.</p>
Data and ARRIVE 2.0 checklist for the original article "Lockbox enrichment facilitates manipulative and cognitive activities for mice"
<p>This repository contains data (XLSX file) related to the original article "Lockbox enrichment facilitates manipulative and cognitive activities for mice", which was submitted for publication to Open Research Europe. Moreover, the ARRIVE checklist including the ARRIVE Essential 10 and the Recommended Set is provided in Version v2.</p>
Matlab example for Local Enrichment Analysis (LEA) analysis with real data
<p>Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of 8 distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T-cell morphology to transcriptional state based on their joint donor variability, and validate an inflammation-associated polarized T-cell morphology, and an age-associated loss of mitochondria in CD4+ T-cells. Taken together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease.</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.