cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10817924
title: GEO gene expression dataset recompute for selected tumor samples

description:  

 

We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.

All uploaded files are UTF-8, `.csv`-formatted matrices. The `*_expected_count.csv.gz` files are unlogged, raw expression counts as reported by `rsem-quantify-expression` (see details below). The associated `*_metadata.csv.gz` files contain metadata pertinent to each column of the corresponding expression matrix.Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).

Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.

Each recompute has at least the `gene_id` column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.Each associated metadata has at least the following columns:- `geo_accession`: The GEO sample ID of the sample.- `sample_accession`: The ENA sample ID of the sample.- `run_accession`: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.

## Pipeline Details

The alignment and quantification was made with the `x.FASTQ` tool available [on Github](https://github.com/TCP-Lab/x.FASTQ) installed locally on an Arch Linux machine running the Linux `6.7.8-zen1-1-zen` kernel with a `11th Gen Intel i7-1185G7 (8)` CPU and a `Intel TigerLake-LP GT2 [Iris Xe Graphics]` GPU.
DOI: 10.5281/zenodo.10893902
title: GEO gene expression dataset recompute for selected tumor samples

description: We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.

All uploaded files are UTF-8, `.csv`-formatted matrices. The `*_expected_count.csv.gz` files are unlogged, raw expression counts as reported by `rsem-quantify-expression` (see details below). The associated `*_metadata.csv.gz` files contain metadata pertinent to each column of the corresponding expression matrix.Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).

Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.

Each recompute has at least the `gene_id` column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.Each associated metadata has at least the following columns:- `geo_accession`: The GEO sample ID of the sample.- `sample_accession`: The ENA sample ID of the sample.- `run_accession`: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.

## Pipeline Details

The alignment and quantification was made with the `x.FASTQ` tool available [on Github](https://github.com/TCP-Lab/x.FASTQ) installed locally on an Arch Linux machine running the Linux `6.7.8-zen1-1-zen` kernel with a `11th Gen Intel i7-1185G7 (8)` CPU and a `Intel TigerLake-LP GT2 [Iris Xe Graphics]` GPU.
DOI: 10.5281/zenodo.11108100
title: GEO gene expression dataset recompute for selected tumor samples

description: We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.

All uploaded files are UTF-8, .csv-formatted matrices. The *_expected_count.csv.gz files are unlogged, raw expression counts as reported by rsem-quantify-expression (see details below). The associated *_metadata.csv.gz files contain metadata pertinent to each column of the corresponding expression matrix.Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).

Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.

Each recompute has at least the gene_id column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.Each associated metadata has at least the following columns:



geo_accession: The GEO sample ID of the sample.

ena_sample: The ENA sample ID of the sample.

ena_run: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.


The remaining columns are derived from GEO metadata files and other ENA-provided data. Please refer to the x.FASTQ package for more information.

Pipeline Details

The alignment and quantification was made with the x.FASTQ tool available on Github installed locally on an Arch Linux machine on commit 3a93dd77a70df59c74f7b15216c26f12cd918e81 running the Linux 6.7.8-zen1-1-zen kernel with a 11th Gen Intel i7-1185G7 (8) CPU and a Intel TigerLake-LP GT2 [Iris Xe Graphics] GPU. Please note that no sample filtering or omissions were done based on sample quality or sequencing depth. However, sensible trimming (e.g. low-quality bases and common adapters) was performed on all the samples.

Reference genome was downloaded from Ensembl, version hg38. STAR was used to create the index genome with overhang set to 149.
DOI: 10.5281/zenodo.10822272
title: GEO gene expression dataset recompute for selected tumor samples

description: We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.

All uploaded files are UTF-8, `.csv`-formatted matrices. The `*_expected_count.csv.gz` files are unlogged, raw expression counts as reported by `rsem-quantify-expression` (see details below). The associated `*_metadata.csv.gz` files contain metadata pertinent to each column of the corresponding expression matrix.Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).

Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.

Each recompute has at least the `gene_id` column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.Each associated metadata has at least the following columns:- `geo_accession`: The GEO sample ID of the sample.- `sample_accession`: The ENA sample ID of the sample.- `run_accession`: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.

## Pipeline Details

The alignment and quantification was made with the `x.FASTQ` tool available [on Github](https://github.com/TCP-Lab/x.FASTQ) installed locally on an Arch Linux machine running the Linux `6.7.8-zen1-1-zen` kernel with a `11th Gen Intel i7-1185G7 (8)` CPU and a `Intel TigerLake-LP GT2 [Iris Xe Graphics]` GPU.
DOI: 10.5281/zenodo.10817923
title: GEO gene expression dataset recompute for selected tumor samples

description: We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.

All uploaded files are UTF-8, .csv-formatted matrices. The *_expected_count.csv.gz files are unlogged, raw expression counts as reported by rsem-quantify-expression (see details below). The associated *_metadata.csv.gz files contain metadata pertinent to each column of the corresponding expression matrix.Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).

Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.

Each recompute has at least the gene_id column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.Each associated metadata has at least the following columns:



geo_accession: The GEO sample ID of the sample.

ena_sample: The ENA sample ID of the sample.

ena_run: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.


The remaining columns are derived from GEO metadata files and other ENA-provided data. Please refer to the x.FASTQ package for more information.

Pipeline Details

The alignment and quantification was made with the x.FASTQ tool available on Github installed locally on an Arch Linux machine on commit 3a93dd77a70df59c74f7b15216c26f12cd918e81 running the Linux 6.7.8-zen1-1-zen kernel with a 11th Gen Intel i7-1185G7 (8) CPU and a Intel TigerLake-LP GT2 [Iris Xe Graphics] GPU. Please note that no sample filtering or omissions were done based on sample quality or sequencing depth. However, sensible trimming (e.g. low-quality bases and common adapters) was performed on all the samples.

Reference genome was downloaded from Ensembl, version hg38. STAR was used to create the index genome with overhang set to 149.
DOI: 10.5281/zenodo.11183835
title: GEO gene expression dataset recompute for selected tumor samples

description: We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.

All uploaded files are UTF-8, .csv-formatted matrices. The *_expected_count.csv.gz files are unlogged, raw expression counts as reported by rsem-quantify-expression (see details below). The associated *_metadata.csv.gz files contain metadata pertinent to each column of the corresponding expression matrix.Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).

Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.

Each recompute has at least the gene_id column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.Each associated metadata has at least the following columns:



geo_accession: The GEO sample ID of the sample.

ena_sample: The ENA sample ID of the sample.

ena_run: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.


The remaining columns are derived from GEO metadata files and other ENA-provided data. Please refer to the x.FASTQ package for more information.

Pipeline Details

The alignment and quantification was made with the x.FASTQ tool available on Github installed locally on an Arch Linux machine on commit 3a93dd77a70df59c74f7b15216c26f12cd918e81 running the Linux 6.7.8-zen1-1-zen kernel with a 11th Gen Intel i7-1185G7 (8) CPU and a Intel TigerLake-LP GT2 [Iris Xe Graphics] GPU. Please note that no sample filtering or omissions were done based on sample quality or sequencing depth. However, sensible trimming (e.g. low-quality bases and common adapters) was performed on all the samples.

Reference genome was downloaded from Ensembl, version hg38. STAR was used to create the index genome with overhang set to 149.
DOI: 10.17632/y9krhbm2hg.1
title: Comparing NC- and AC-FET with method specific LPS

description: The objective of the study was to compare the efficacy of AC-FET with that of NC-FET in terms of ongoing pregnancy, with method-specific LPS administered in the FET methods.

Patients undergoing NC- and AC-FET with method-specific LPS had comparable ongoing pregnancy rates as well as total pregnancy loss rates
DOI: 10.17632/zp2kp479n7.1
title: “In the Kissing-Place of Souls, Spiritual and Mathematical Insights with a Meditation on Song of Solomon 1:2,3”

description: This script generates and analyzes data representing various dimensions of human experience, ranging from emotional and spiritual phenomena to sensory perception and abstract concepts like eternity and timelessness. Let's break down the description:

1. Eternal Love:

The plot displays the intensity of love over time, represented by a sinusoidal function with an added constant term. It reflects the fluctuating nature of love, characterized by peaks and troughs.
Statistical analysis provides insights into the mean intensity of love and its standard deviation, offering measures of centrality and dispersion in the data.
2. Transcendence:

This plot illustrates the concept of transcendence over time, calculated as the cumulative sum of love intensity multiplied by a sinusoidal function representing spiritual alignment.
Statistical analysis reveals the mean level of transcendence and its variability, shedding light on the overall trend and variability in the data.
3. Sensuality and Passion:

The graph depicts the intensity of sensory experience related to sensuality and passion over time, incorporating both periodic oscillations and random fluctuations.
Statistical measures such as mean intensity and standard deviation quantify the average level and variability of sensory experience throughout the observed period.
4. Sanctuary and Refuge:

This plot represents the intensity of sanctuary and refuge over time, modeled using a sigmoid function that transitions from low to high values over a specified threshold.
Statistical analysis offers insights into the average intensity and variability of sanctuary and refuge, providing a quantitative understanding of the temporal dynamics of these phenomena.
5. Communion of Souls:

The plot illustrates the concept of communion among individuals' love intensities over time, calculated as the mean intensity across multiple individuals.
Statistical measures highlight the average level and variability of communion among souls, reflecting the collective experience of interconnectedness and unity.
6. Imagery of Eternity and Timelessness:

This three-dimensional surface plot visualizes the intensity of imagery related to eternity and timelessness across spatial coordinates, showcasing a gradient of values that symbolize the abstract nature of these concepts.
Statistical analysis offers insights into the mean intensity and variability of imagery associated with eternity and timelessness, providing quantitative descriptors of the spatial distribution of these phenomena.
DOI: 10.15468/dl.53j8xv
title: Occurrence Download

description: A dataset containing 61591 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Pinus mugo Turra)"
 ]
}

 The dataset includes 61591 records from 363 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014443-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48597/yu6p-hng8
title: Measurement of Sulphur_dioxide at Giordan Lighthouse

description: Measurement of Sulphur_dioxide at Giordan Lighthouse
DOI: 10.48597/6vcb-gbdj
title: Measurement of Sulphur_dioxide at Giordan Lighthouse

description: Measurement of Sulphur_dioxide at Giordan Lighthouse
DOI: 10.15468/dl.qu8xgu
title: Occurrence Download

description: A dataset containing 2118 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Pinus muricata D.Don)"
 ]
}

 The dataset includes 2118 records from 136 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014444-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48597/kbtj-f8wn
title: Measurement of Ozone at Giordan Lighthouse

description: Measurement of Ozone at Giordan Lighthouse
DOI: 10.5281/zenodo.10720089
title: System Parameter of 8-bus and 123-bus System
DOI: 10.5281/zenodo.10720090
title: System Parameter of 8-bus and 123-bus System
DOI: 10.48597/c9rq-6e78
title: Measurement of Ozone at Giordan Lighthouse

description: Measurement of Ozone at Giordan Lighthouse
DOI: 10.15468/dl.268ekh
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is New Zealand",
 "OccurrenceStatus is Present",
 "TaxonKey is Myricaria germanica Qaiser"
 ]
}

 The dataset includes 67 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014446-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/t7w2sd2t99.1
title: The Mahalanobis distance approach as a performance indicator for meteorological simulations in air quality studies: A proposal. Acosta et al.

description: Each file contains the different variables analyzed: Temperature, Relative Humidity, Wind Speed, and Direction, together with the values observed in the meteorological stations and the values simulated by WRF.
DOI: 10.17632/y2pmnjrnyr.1
title: PRRX2-Mediated Transcriptional Regulation of GLI2 Promotes Cell Proliferation, Invasion and Metastasis by Suppressing Cellular Senescence via Hedgehog Signaling in Pancreatic Cancer

description: Our discovery of the role of PRRX2 in pancreatic cancer and its mechanism provides new ideas for the diagnosis and treatment of pancreatic cancer.
DOI: 10.15468/dl.zevqk8
title: Occurrence Download

description: A dataset containing 8414 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alectoris barbara (Bonnaterre, 1790)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 8414 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014448-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zk5v87z2k5.3
title: Supplemental File 1_Dual biologics & inflammatory skin dz

description: Search Strategy and PRISMA Flow Diagram
DOI: 10.48597/eqky-g5m5
title: Measurement of Nitrogen_monoxide at Giordan Lighthouse

description: Measurement of Nitrogen_monoxide at Giordan Lighthouse
DOI: 10.48597/9cys-n232
title: Measurement of Nitrogen_monoxide at Giordan Lighthouse

description: Measurement of Nitrogen_monoxide at Giordan Lighthouse
DOI: 10.48597/ne33-gw65
title: Measurement of Nitrogen_monoxide at Giordan Lighthouse

description: Measurement of Nitrogen_monoxide at Giordan Lighthouse
DOI: 10.48597/au6w-ghw8
title: Measurement of Nitrogen_monoxide at Giordan Lighthouse

description: Measurement of Nitrogen_monoxide at Giordan Lighthouse
DOI: 10.48597/kebv-tbdj
title: Measurement of Nitrogen_monoxide at Giordan Lighthouse

description: Measurement of Nitrogen_monoxide at Giordan Lighthouse
DOI: 10.5281/zenodo.11175298
title: Three-dimensional Reconstructions and Quantitative Indicators for colloidal particles in Dry and Liquid Conditions in Scanning Transmission Electron Microscope (STEM)

description: This dataset accompanies the research presented in the paper:

Esteban, D.A., Wang, D., Kadu, A., Olluyn, N., Iglesias, A.S., Perez, A.G., Casablanca, J.G., Nicolopoulos, S., Liz-Marzán, L.M. and Bals, S., 2023. Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies. arXiv preprint arXiv:2311.05309. [link]

It provides a comprehensive collection of three-dimensional reconstructions and quantitative descriptors for small colloidal particles. These gold nanoparticles are arranged in tetrahedral and other intricate geometries under both dry and liquid conditions. The dataset contains 3D reconstructions and quantitative indicators such as centroids, volumes, surface areas, solidity measures, and principal axis lengths for assemblies with 4, 5, and 6 particles. 

The dataset includes: N4_dry_dart.rec and N4_liquid_dart.rec for the 3D reconstructions of an assembly with 4 particles in dry and liquid conditions respectively; N4_quant_descriptors_dry.mat and N4_quant_descriptors_liquid.mat providing quantitative descriptors for these conditions. Similar files are provided for assemblies with 5 and 6 particles, such as N5_dry_dart.rec, N5_liquid_dart.rec, N5_quant_descriptors_dry.mat, N5_quant_descriptors_liquid.mat, and the corresponding files for N6. 

This dataset can be used to study the structural dynamics of nanoparticle assemblies and studies in colloidal chemistry, materials science, and nanotechnology. The .rec files can be visualized using volume rendering software (e.g. Amira or Avizo), while the .mat files contain structured data for analysis in MATLAB. The supporting code and scripts for this dataset are available on the GitHub repository: https://github.com/ajinkyakadu/LiquidET_NatComm2024.
DOI: 10.5281/zenodo.11175299
title: Three-dimensional Reconstructions and Quantitative Indicators for colloidal particles in Dry and Liquid Conditions in Scanning Transmission Electron Microscope (STEM)

description: This dataset accompanies the research presented in the paper:

Esteban, D.A., Wang, D., Kadu, A., Olluyn, N., Iglesias, A.S., Perez, A.G., Casablanca, J.G., Nicolopoulos, S., Liz-Marzán, L.M. and Bals, S., 2023. Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies. arXiv preprint arXiv:2311.05309. [link]

It provides a comprehensive collection of three-dimensional reconstructions and quantitative descriptors for small colloidal particles. These gold nanoparticles are arranged in tetrahedral and other intricate geometries under both dry and liquid conditions. The dataset contains 3D reconstructions and quantitative indicators such as centroids, volumes, surface areas, solidity measures, and principal axis lengths for assemblies with 4, 5, and 6 particles. 

The dataset includes: N4_dry_dart.rec and N4_liquid_dart.rec for the 3D reconstructions of an assembly with 4 particles in dry and liquid conditions respectively; N4_quant_descriptors_dry.mat and N4_quant_descriptors_liquid.mat providing quantitative descriptors for these conditions. Similar files are provided for assemblies with 5 and 6 particles, such as N5_dry_dart.rec, N5_liquid_dart.rec, N5_quant_descriptors_dry.mat, N5_quant_descriptors_liquid.mat, and the corresponding files for N6. 

This dataset can be used to study the structural dynamics of nanoparticle assemblies and studies in colloidal chemistry, materials science, and nanotechnology. The .rec files can be visualized using volume rendering software (e.g. Amira or Avizo), while the .mat files contain structured data for analysis in MATLAB. The supporting code and scripts for this dataset are available on the GitHub repository: https://github.com/ajinkyakadu/LiquidET_NatComm2024.
DOI: 10.48597/92tf-eam2
title: Measurement of Nitrogen_monoxide at Giordan Lighthouse

description: Measurement of Nitrogen_monoxide at Giordan Lighthouse
DOI: 10.48597/53ba-deg9
title: Measurement of Ozone at Giordan Lighthouse

description: Measurement of Ozone at Giordan Lighthouse
DOI: 10.5281/zenodo.7330338
title: Raw data for"Early hydration of Belite-Ye'elimite-Ferrite cements: role of admixtures"

description: Raw data for "Early hydration of Belite-Ye’elimite-Ferrite cements: role of admixtures". https://doi.org/10.1016/j.conbuildmat.2023.130765

Diffraction data is attached here.

 
description: Acknowledgement :P18-RT-720 from the Andalusian regional government, are gratefully acknowledged. ALBA synchrotron is acknowledged for providing with beamtime at MSPD beamline.