cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28425866
title: Data for: Effects of floral trait dissimilarity and flower density on pollinator-mediated interactions at different spatial scales

description: Conducted in nine coastal dune communities, the study assesses how floral trait dissimilarity affects pollinator sharing and how the co-flowering environment—floral dissimilarity, conspecific flower density, and heterospecific flower density—impacts pollination outcomes. Key metrics include flower visitation rates, conspecific pollen reception, and the proportion of pollen tubes. The study also investigates whether these effects vary at the plot and site scales.MethodsField sampling and flower densityPlant communities were surveyed at nine sites along a 205 km coastal stretch. Each site was sampled over three randomized days during the rainy season (September–November 2016). Within each site, ten 20 m² plots were established, recording flowering species identity, plant counts, and open flower numbers. Conspecific and heterospecific flower densities were estimated at both the plot (20 m²) and site scale (200 m²).Floral trait dissimilarityFloral traits—including corolla diameter, tube opening, and flower length—were measured in 1–5 flowers per plant from at least five plants per species. Floral symmetry and clustering were also recorded. Floral reflectance spectra (300–700 nm) were measured using a spectrophotometer to model color perception by Hymenoptera, Diptera, and Lepidoptera. Floral trait dissimilarity between species pairs was estimated using Gower’s dissimilarity index, and functional distinctiveness was calculated using an abundance-weighted index.Pollinator observations and pollinator sharingPollinator observations were conducted in each plot for three 5-minute periods (15 min total per plot per day). Only visits involving contact with reproductive structures were recorded. Pollinators were identified to species or morphospecies, and unidentified specimens were collected for later identification. Pollinator visitation rate was calculated as visits per flower per 15 minutes. Pollinator sharing was estimated using Pianka’s niche overlap index, considering both pollinator identity and visitation frequency.Pollen receipt and pollination successTo assess pollen receipt and pollination success, one style per plant was collected from senesced flowers at the end of the day and stored in FAA solution. Styles were processed using NaOH softening and aniline blue staining, and pollen grains and pollen tubes were counted under a fluorescent microscope. Pollen tube production was used as a proxy for pollination success, and all data were relativized to account for species-specific differences.
DOI: 10.57745/rtvh8w
title: KO_Crypt.tab
DOI: 10.6084/m9.figshare.28425866.v2
title: Data for: Effects of floral trait dissimilarity and flower density on pollinator-mediated interactions at different spatial scales

description: Conducted in nine coastal dune communities, the study assesses how floral trait dissimilarity affects pollinator sharing and how the co-flowering environment—floral dissimilarity, conspecific flower density, and heterospecific flower density—impacts pollination outcomes. Key metrics include flower visitation rates, conspecific pollen reception, and the proportion of pollen tubes. The study also investigates whether these effects vary at the plot and site scales.MethodsField sampling and flower densityPlant communities were surveyed at nine sites along a 205 km coastal stretch. Each site was sampled over three randomized days during the rainy season (September–November 2016). Within each site, ten 20 m² plots were established, recording flowering species identity, plant counts, and open flower numbers. Conspecific and heterospecific flower densities were estimated at both the plot (20 m²) and site scale (200 m²).Floral trait dissimilarityFloral traits—including corolla diameter, tube opening, and flower length—were measured in 1–5 flowers per plant from at least five plants per species. Floral symmetry and clustering were also recorded. Floral reflectance spectra (300–700 nm) were measured using a spectrophotometer to model color perception by Hymenoptera, Diptera, and Lepidoptera. Floral trait dissimilarity between species pairs was estimated using Gower’s dissimilarity index, and functional distinctiveness was calculated using an abundance-weighted index.Pollinator observations and pollinator sharingPollinator observations were conducted in each plot for three 5-minute periods (15 min total per plot per day). Only visits involving contact with reproductive structures were recorded. Pollinators were identified to species or morphospecies, and unidentified specimens were collected for later identification. Pollinator visitation rate was calculated as visits per flower per 15 minutes. Pollinator sharing was estimated using Pianka’s niche overlap index, considering both pollinator identity and visitation frequency.Pollen receipt and pollination successTo assess pollen receipt and pollination success, one style per plant was collected from senesced flowers at the end of the day and stored in FAA solution. Styles were processed using NaOH softening and aniline blue staining, and pollen grains and pollen tubes were counted under a fluorescent microscope. Pollen tube production was used as a proxy for pollination success, and all data were relativized to account for species-specific differences.
DOI: 10.57745/vilwyc
title: KO_Enterocytes.tab
DOI: 10.57745/gajalk
title: WT_Crypt.tab
DOI: 10.57745/ayzwxu
title: WT_Enterocytes.tab
DOI: 10.57745/6osfor
title: metadata_normExpressionByCluster.tab
DOI: 10.57745/vux9fr
title: metadata_cellsCoordinates_UMAP.csv
DOI: 10.57745/jfsbl6
title: metadata_selected_cells.csv
DOI: 10.57745/tqyyzb
title: metadata_cellsClusterIdentity.tab
DOI: 10.57745/af16un
title: metadata_MarkerGenes.csv
DOI: 10.57745/xiwi7r
title: metadata_excluded_cells_nbUMI.tab
DOI: 10.57745/wnua92
title: metadata_clustersColor.tab
DOI: 10.57745/lzbsoa
title: metadata_cellsCoordinates_tsne.csv
DOI: 10.57745/axxunk
title: metadata_excluded_cells_nbGene.csv
DOI: 10.57745/rdh2jy
title: metadata_excluded_cells_pctRibo.csv
DOI: 10.57745/d2said
title: metadata_excluded_cells_pctMito.csv
DOI: 10.15468/dl.2e7gez
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-2 53.7,-1.9 53.7,-1.9 53.8,-2 53.8,-2 53.7))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pipistrellus pipistrellus (Schreber, 1774)"
 ]
}

 The dataset includes 18 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003169-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/x5b2pr
title: 002_IFNL_Recovery_scRNAseq__10a_GlobalHeterogeneity_Identified_Crypt.html
DOI: 10.57745/kbcp55
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_seuratObject_final.RDS
DOI: 10.17632/vn9963scv4.1
title: Effects of hyperuricemia on injured tendon healing: an in vivo study in a chicken model

description: Original data of study "Effects of hyperuricemia on injured tendon healing: an in vivo study in a chicken model ".
DOI: 10.57745/wgl67j
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/3jbmkr
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.15468/dl.smwxs5
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Pilularia minuta Durieu"
 ]
}

 The dataset includes 67 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003172-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/05osba
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.57745/sqhlq1
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv
DOI: 10.57745/9bur25
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_clustersColor.tab
DOI: 10.57745/yzmhks
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_tsne.tsv
DOI: 10.57745/uoc1gc
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByHTO.tsv
DOI: 10.57745/2dtbpc
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_sessionImage_final.RDATA
DOI: 10.5285/2e18811e-99f5-8475-e063-7086abc02b26
title: Microzooplankton taxonomic composition from CTD and OCEAN Sampler samples taken during Marine Productivity programme cruises RRS Discovery DI258, DI262 and DI264 cruises in the Northern North Atlantic and Irminger Sea between November 2001 and August 2002

description: These datasets contain microzooplankton taxonomic data from CTD and OCEAN Sampler samples collected during the DI258, DI262 and DI264 cruises in the northern North Atlantic, between November 2001 - August 2002. Taxonomic notes are provided for some of the species. All species are placed into size categories (less than 20.00 micrometres, 20.00 - 39.99 micrometres, 40.00 - 59.99 micrometres, 60.00 - 79.99 micrometres, 80.00 - 99.99 micrometres and greater than 100.00 micrometres), based on the average length measurement of species fixed in 10 percent Lugol's iodine. All species are also placed into taxonomic groupings (strombidiids, strobilidiids, tintinnids, <em>Myrionecta rubra</em> and all others). Abundance of each ciliate taxa is measured in cells per ml. Biomass of each ciliate taxa is expressed as nanograms of carbon per millilitre for each station. This was calculated by multiplying the ciliate abundance by the individual species biomass. Individual biomass was calculated from the average biovolume (micrometres cubed) for all species based on length and width measurements made on individual cells fixed in 10 percent Lugol's iodine and conversion to volume using geometric shapes that matched the cell shape. Species biovolumes (micrometres cubed) were converted to 2 percent Lugol's iodine. This was necessary as the biovolume/biomass conversion factor used was based on samples fixed in 2 percent Lugol's iodine (based on unpublished data, the biovolume of ciliates fixed in 10 percent Lugol's iodine is 70 percent that of the biovolume of ciliates fixed in 2 percent). Individual species biomass (picograms of carbon per individual cell) was calculated from biovolume using the equation of Putt and Stoecker (1989) who suggest a conversion factor of 190 femtograms of carbon per micrometre cubed. This is based on ciliates fixed in 2 percent Lugol's iodine. These data were collected for the Marine Productivity (MarProd) programme by scientists from the University of Liverpool School of Biological Sciences.
DOI: 10.57745/4mlqku
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByClusterAndHTO.tsv
DOI: 10.57745/vjne0h
title: .DS_Store
DOI: 10.57745/gk2sdr
title: .DS_Store
DOI: 10.57745/0dh5us
title: _perf._truncated_
DOI: 10.57745/u1vuvv
title: _vdrkill
DOI: 10.57745/vzxmhc
title: .DS_Store
DOI: 10.17632/2g8kchh347.1
title: Fate of Iodate on Adsorbed/Coprecipitated Ferrihydrite-Organic Matter Compl https://doi.org/10.17632/cfdzx2rh8z.1

description: Raw data for all figures in the manuscript of "Fate of Iodate on Adsorbed/Coprecipitated Ferrihydrite-Organic Matter".
DOI: 10.57745/ej12ls
title: _jobmode
DOI: 10.57745/esslog
title: _cmdline
DOI: 10.57745/uro0r2
title: _mrosource
DOI: 10.57745/ygzvgc
title: _finalstate
DOI: 10.17632/jb7yskm9d5.1
title: Significant decline in oxygen levels following the Great Oxidation Event

description: This database contains the iodine, Mg, Ca, Mn, Sr and REE concentration of carbonate from the Paleoproterozoic Daguandong and Huaiyincun Formation, North China, as well as the reference material GSR 12 and Jdo-1.
DOI: 10.57745/u6m0wj
title: feature_reference.tab
DOI: 10.57745/skfwx8
title: filtered_feature_bc_matrix.h5
DOI: 10.57745/3c88v0
title: .DS_Store
DOI: 10.17632/cf4krknfdm.1
title: Brno data

description: article data in MS Excell format
DOI: 10.57745/7vgjtz
title: metrics_summary.tab
DOI: 10.57745/agyeod
title: possorted_genome_bam.bam
DOI: 10.57745/ifhvm1
title: raw_feature_bc_matrix.h5
DOI: 10.57745/ahlaam
title: features.tsv.gz
DOI: 10.57745/ehq8yl
title: barcodes.tsv.gz
DOI: 10.57745/q2mucq
title: matrix.mtx.gz
DOI: 10.57745/x76lwd
title: cloupe.cloupe
DOI: 10.57745/ouu79h
title: molecule_info.h5
DOI: 10.57745/i6z0y5
title: possorted_genome_bam.bam.bai
DOI: 10.57745/ldj9vg
title: aggregate_barcodes.csv
DOI: 10.57745/ihsjzk
title: projection.tab
DOI: 10.57745/l0siuc
title: projection.tab
DOI: 10.57745/9pkt6y
title: projection.tab
DOI: 10.57745/y5zfmi
title: projection.tab
DOI: 10.17632/pt4bjfrjkg.1
title: SUPPLEMENTARY MATERIAL

description: Risk of Major Adverse Cardiovascular Events in Patients With Moderate‑to‑Severe Atopic Dermatitis: A United States Population‑Based Study
DOI: 10.57745/upvmdz
title: clusters.tab
DOI: 10.57745/c5nku5
title: clusters.tab
DOI: 10.57745/nwezjo
title: clusters.tab
DOI: 10.57745/vqvbsd
title: clusters.tab
DOI: 10.17632/tx6cr7xvyt.1
title: Data_for_Preparation and Study of Perovskite Solar Cells with Titanium-Tin Alloy-Based Electron Transport Layer

description: Data for Preparation and Study of Perovskite Solar Cells with Titanium-Tin Alloy-Based Electron Transport Layer
DOI: 10.57745/emkjqx
title: clusters.tab
DOI: 10.57745/e9agfs
title: clusters.tab
DOI: 10.57745/n0usuv
title: clusters.tab
DOI: 10.17632/rd7s6h6w32.1
title: LINF_080009100

description: Atg6/Beclin-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.57745/rzxa9v
title: clusters.tab
DOI: 10.57745/6foit2
title: clusters.tab
DOI: 10.57745/etfrzt
title: clusters.tab
DOI: 10.17632/t6g59wxk92.1
title: LINF_080009900

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.57745/xkz76d
title: clusters.tab
DOI: 10.57745/sr8hnf
title: clusters.tab
DOI: 10.57745/vvivb1
title: clusters.tab
DOI: 10.57745/d04tl4
title: clusters.tab
DOI: 10.57745/ao2d41
title: clusters.tab
DOI: 10.57745/nfbyrn
title: clusters.tab
DOI: 10.57745/sbmute
title: clusters.tab
DOI: 10.15468/dl.4z4byg
title: Occurrence Download

description: A dataset containing 6981 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abies sibirica Ledeb."
 ]
}

 The dataset includes 6981 records from 130 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003173-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/dpaafs
title: clusters.tab
DOI: 10.57745/mc7sky
title: clusters.tab
DOI: 10.57745/5qjmhu
title: clusters.tab
DOI: 10.57745/ug6ttw
title: differential_expression.tab
DOI: 10.57745/3hw9a9
title: differential_expression.tab
DOI: 10.57745/zgello
title: differential_expression.tab
DOI: 10.57745/mpatmf
title: differential_expression.tab
DOI: 10.57745/ge9ujb
title: differential_expression.tab
DOI: 10.57745/xgiar6
title: differential_expression.tab
DOI: 10.57745/6z05jk
title: differential_expression.tab
DOI: 10.57745/h760rb
title: differential_expression.tab
DOI: 10.57745/4gnd7v
title: differential_expression.tab
DOI: 10.57745/94x142
title: differential_expression.tab
DOI: 10.17632/pgvv3gf634.1
title: The Outlook of Emerging Technology in Education

description: The Outlook of emerging Technologies in Education underscored the role and implications of cutting-edge technologies, tools, innovations and trends in transforming educational systems globally. This paper, edited by Joshua Vidal, MSSc, MAEd, LPT, PhD (student), attempts to contribute to academic leaders and researchers, examines the integration of Artificial Intelligence (AI), Virtual Reality (VR), Gamification, and IoT in educational settings. This paper provides key findings that could be helpful for crafting and implementing a guide for AI in education. This, at the same time, underscored challenges such as the digital divide, privacy concerns, and the need for teachers personal development. Through case studies, this paper identified both the opportunities and limitations of technology integration in diverse educational contexts. This report provided practical recommendations for education, policymakers, and institutions to adopt emerging technologies effectively while mitigating associated risks. This, however, ensures that technological advancements serve as a tool for engaging and classroom enhanced experience. [This paper was produced with support from the Science Department and Robotics Elite team of Unida Christian Colleges, Imus, Cavite, Philippines and Electronic Paper for Science and Technology Education, The Publication Office.]
DOI: 10.57745/blofeu
title: differential_expression.tab
DOI: 10.57745/vcm4lz
title: differential_expression.tab
DOI: 10.57745/bxtult
title: differential_expression.tab
DOI: 10.57745/spqlll
title: differential_expression.tab
DOI: 10.57745/7wvjos
title: differential_expression.tab
DOI: 10.57745/an0wdg
title: differential_expression.tab
DOI: 10.57745/3wu3sb
title: differential_expression.tab
DOI: 10.57745/vslm43
title: differential_expression.tab
DOI: 10.57745/wnquim
title: differential_expression.tab
DOI: 10.17632/yyx5m9hzjw.1
title: LINF_080010300

description: Protein kinase-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.57745/bavanp
title: differential_expression.tab
DOI: 10.57745/hzakp8
title: projection.tab
DOI: 10.57745/o5kqa8
title: projection-1.tab
DOI: 10.57745/nadp1r
title: dispersion.tab
DOI: 10.17632/9g7zxffppr.1
title: LINF_080011100

description: Hypothetical protein - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.57745/ss2s0u
title: variance.tab
DOI: 10.57745/0shlkl
title: components.tab
DOI: 10.57745/ik1rmc
title: features_selected.csv
DOI: 10.57745/vvyqmf
title: projection.tab
DOI: 10.17632/t6chv2vdwm.1
title: Parametric residential heating energy, thermal comfort and CO2 concentration data for an energy-efficient semi-detached house in Ireland

description: The dataset is a synthetic, simulation-based collection of numerical data representing the energy performance, thermal comfort, and indoor air quality (specifically CO₂ concentration) of a low-energy, naturally ventilated semi-detached residential dwelling in Ireland, an oceanic temperate climate zone. Generated through 45,000 parametric simulations using EnergyPlus and the jEPlus tool, the dataset captures a wide range of building design and operational scenarios for an A-rated semi-detached house, the most prevalent residential archetype in Ireland. It is stored in a comma-separated values (.csv) format and hosted on Mendeley Data, with the data originating from University College Dublin, Ireland. The dataset comprises 19 input parameters and 24 output parameters, detailed as follows:

Input Parameters

Sampled via LHS from Irish EPCs and TUS data:
Window U-value (0.53–3.21 W/m²·K): Glazing heat loss.
Floor U-value (0.06–0.85 W/m²·K): Floor insulation.
Roof U-value (0.06–0.88 W/m²·K): Roof insulation.
Wall U-value (0.12–0.85 W/m²·K): Wall heat loss.
Orientation (0–360°): Solar exposure.
HSPT (18–22°C): Heating setpoint.
Lighting Density (1–6 W/m²): Lighting load.
Occupancy Density (0.0125–0.3289 persons/m²): Occupant load.
Equipment Density (16–24 W/m²): Equipment load.
HVAC Efficiency (0–1): Heating efficiency.
Infiltration (0.35–1.5 ACH): Air leakage.
Metabolic Rate (60–360 W/person): Occupant heat.
Clothing Value (0–1 clo): Thermal insulation.
SHGC (0.1–1): Solar gain factor.
WWR (40–90%): Window area ratio.
Window Open Time (10–180 min): Ventilation duration.
Window Close Time (10–180 min): Closed duration.
Window Area (0.1–1 factor): Openable area.
Ventilation (3.35–6.5 ACH): Air exchange rate.


Output Parameters

Calculated per simulation:
c0: Heating (kWh/year): Annual heating energy.
c1: Lighting (kWh/year): Annual lighting energy.
c2–c8: Thermal Discomfort (hours/year): Hours outside comfort for kitchen, dining, sitting, bedrooms 1–3, facility.
c9–c15: Setpoint Not Met (hours/year): Unmet heating hours for kitchen, dining, sitting, bedrooms 1–3, facility.
c16–c20: CO₂ &gt; 1000 ppm (hours/year): High CO₂ hours for kitchen, dining, bedrooms 1–3.
c21: EUI (kWh/m²·year): Energy intensity.
c22: Total Site Energy (kWh): Total energy use.
c23: CO₂ Average (hours/year): Mean CO₂ &gt; 1000 ppm hours.
c24–c33: Fanger Index: PMV (-3 to +3) for 10 zones (kitchen, dining, corridor, sitting, top toilet, bedrooms 1–3, top corridor, bath).

We see this dataset as a vital resource for understanding how heating energy, thermal comfort, and CO₂ levels interplay in low-energy, naturally ventilated homes in Ireland. It’s useful for us as architects to optimise building designs, for engineers to refine HVAC and ventilation systems, for policymakers to shape energy-efficient regulations, and for researchers like us to study occupancy impacts on indoor environmental quality.
DOI: 10.57745/vulezt
title: dispersion.tab
DOI: 10.57745/9n2enu
title: variance.tab
DOI: 10.57745/lnpaou
title: components.tab
DOI: 10.57745/siwftg
title: features_selected.csv
DOI: 10.57745/cma7ss
title: web_summary.html
DOI: 10.57745/9eiqhg
title: cellsorted_possorted_genome_bam.bam
DOI: 10.57745/jqqww1
title: features.tsv.gz
DOI: 10.57745/pfdqpr
title: barcodes.tsv.gz
DOI: 10.57745/cies0t
title: matrix.mtx.gz
DOI: 10.57745/cfzb5t
title: _filelist
DOI: 10.57745/lswswj
title: _log
DOI: 10.57745/qufwlc
title: _uuid
DOI: 10.57745/cjynzi
title: _versions
DOI: 10.57745/zgqqih
title: _invocation
DOI: 10.57745/w4twoz
title: _timestamp
DOI: 10.57745/b4yuvn
title: mm10.mri.tgz
DOI: 10.57745/ke7imp
title: _perf
DOI: 10.57745/bdyrxl
title: _sitecheck
DOI: 10.57745/nocdpn
title: _tags
DOI: 10.57745/fr5siw
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_seuratObject_final.RDS
DOI: 10.57745/uqaimp
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/wvmlkp
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/eyrzt4
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbGene.txt
DOI: 10.57745/ekdfwe
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.57745/yve4jh
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.57745/gnn5a5
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctMito.txt
DOI: 10.57745/w4nie1
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv
DOI: 10.57745/0xu2g9
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_clustersColor.tab
DOI: 10.57745/o0cloq
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_tsne.tsv
DOI: 10.57745/8cjrtn
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_selected_cells.txt
DOI: 10.57745/lki44t
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctRibo.txt
DOI: 10.57745/fvbtrw
title: 002_IFNL_Recovery_scRNAseq__05b_GlobalHeterogeneity_Enterocytes.html
DOI: 10.57745/nftzke
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_sessionImage_final.RDATA
DOI: 10.57745/x0iatp
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_seuratObject_final.RDS
DOI: 10.57745/95wr1e
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbUMI.txt
DOI: 10.57745/t0jqtr
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_normExpressionByCluster.tsv
DOI: 10.57745/0gr114
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_nbGene.txt
DOI: 10.57745/rihmms
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsClusterIdentity.tsv
DOI: 10.57745/oizfhc
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_MarkerGenes.tsv
DOI: 10.57745/vfgede
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_excluded_cells_pctMito.txt
DOI: 10.57745/7udneh
title: 002_IFNL_Recovery_scRNAseq_20230601_10X_RNAseq_WT_cellsCoordinates_umap.tsv