cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7299599
title: Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+

description: This is the repository containing the code used to obtain the results shown in Bellato M, Cappellato M, Longhin F, Del Vecchio C, Brancaccio G, Cattelan AM, Brun P, Salaris C, Castagliuolo I, Di Camillo B. "Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+" Sci Rep 13, 16867 (2023).
We characterized through 16S rDNA-seq the microbiota in the upper airways of 192 subjects with a positive nasopharyngeal swab for SARS-CoV-2 to identify a microbial signature predictive of disease progression. Patients were divided in groups based on the presence of symptoms, the level of pneumonia, and whether or not they needed oxygen therapy or intubation. 
In the GitLab repository here there are all the scripts used to perform the preprocessing step and the downstream analysis. The GitLab repository (version 1.0) contains also the Docker image microbiomecovid:1.0.0 that can be used to reproduce the results shown in the paper (see the instruction here).
Here in Zenodo you can find the microbiomecovid_data.zip file containing two folders that need to be unzipped and put in the microbiomecovid local repository, downloaded from GitLab.
In particular, the original_data folder contains:

Raw_data: a folder with two FASTQ files for each sample, i.e., forward (R1) and reverse (R2) reads.
Metadata.xlsx: the table containing information on the - anonymized - subjects involved in the study.
QC Report.pdf: The report provided by the sequencing center.
Additionally, in the output folder, the following items can be found:

Preprocessing: a folder containing all the output file from step1 to step7, namely:

create input data for QIIME2;
import data in QIIME2;
remove primers;
denoising and imputation;
taxonomy classification;
phylogenetic tree reconstruction;
collapse at specific taxonomic level and normalize data.
Analysis:  a folder containing all the output files form step8 to step10 namely:

alpha and beta diversity results;
DA_output: a folder containing the differential abundance analysis performed for each taxonomic level and for each covariate;
Network_output: a folder containing the sparCC and Cytoscape networks and results.
For more info about all the bioinformatic pipeline see the GitLab repository.
Remember to set the path as ABSOLUTE_PATH_MICROBIOMECOVID.
DOI: 10.5281/zenodo.7299600
title: Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+

description: This is the repository containing the data and results shown in<br> <em><strong>"Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+" (2023)</strong></em>.
DOI: 10.5281/zenodo.7713313
title: Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+

description: This is the repository containing the code used to obtain the results shown in Bellato M, Cappellato M, Longhin F, Del Vecchio C, Brancaccio G, Cattelan AM, Brun P, Salaris C, Castagliuolo I, Di Camillo B. "Uncover a microbiota signature of upper respiratory tract in patients with SARS-CoV-2+" Sci Rep 13, 16867 (2023).
We characterized through 16S rDNA-seq the microbiota in the upper airways of 192 subjects with a positive nasopharyngeal swab for SARS-CoV-2 to identify a microbial signature predictive of disease progression. Patients were divided in groups based on the presence of symptoms, the level of pneumonia, and whether or not they needed oxygen therapy or intubation. 
In the GitLab repository here there are all the scripts used to perform the preprocessing step and the downstream analysis. The GitLab repository (version 1.0) contains also the Docker image microbiomecovid:1.0.0 that can be used to reproduce the results shown in the paper (see the instruction here).
Here in Zenodo you can find the microbiomecovid_data.zip file containing two folders that need to be unzipped and put in the microbiomecovid local repository, downloaded from GitLab.
In particular, the original_data folder contains:

Raw_data: a folder with two FASTQ files for each sample, i.e., forward (R1) and reverse (R2) reads.
Metadata.xlsx: the table containing information on the - anonymized - subjects involved in the study.
QC Report.pdf: The report provided by the sequencing center.
Additionally, in the output folder, the following items can be found:

Preprocessing: a folder containing all the output file from step1 to step7, namely:

create input data for QIIME2;
import data in QIIME2;
remove primers;
denoising and imputation;
taxonomy classification;
phylogenetic tree reconstruction;
collapse at specific taxonomic level and normalize data.
Analysis:  a folder containing all the output files form step8 to step10 namely:

alpha and beta diversity results;
DA_output: a folder containing the differential abundance analysis performed for each taxonomic level and for each covariate;
Network_output: a folder containing the sparCC and Cytoscape networks and results.
For more info about all the bioinformatic pipeline see the GitLab repository.
Remember to set the path as ABSOLUTE_PATH_MICROBIOMECOVID.
DOI: 10.5281/zenodo.10013164
title: ES07 - Others - Galende (Spain)

description: Data files for building: ES07 - Others - Galende (Spain)
Languages: Spanish, English
These files are part of the public benchmark repository created as a part of the crossCert EU project. 
This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.
The files are organised in the following folders  (note that not all files are always provided):

Main data  and Results, with:

Neutral data inventory.
Neutral results report.
Original EPC certificate.
Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.
Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.
Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.
Dynamic Model

Data to run a dynamic model of the building, if available.
The files have been redacted to exclude confidential information.
DOI: 10.5281/zenodo.10013163
title: ES07 - Others - Galende (Spain)

description: Data files for building: ES07 - Others - Galende (Spain)
Languages: Spanish, English
These files are part of the public benchmark repository created as a part of the crossCert EU project. 
This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.
The files are organised in the following folders  (note that not all files are always provided):

Main data  and Results, with:

Neutral data inventory.
Neutral results report.
Original EPC certificate.
Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.
Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.
Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.
Dynamic Model

Data to run a dynamic model of the building, if available.
The files have been redacted to exclude confidential information.
DOI: 10.57841/casdc.0002937
title: 国家级能源革命关键技术-榆林地区典型高浓度CO2排放解决方案研究
DOI: 10.17632/gy7kcvf98b.2
title: A machine learning model for multi-class classification of quenched and partitioned steel microstructure type by the k-nearest neighbor algorithm

description: QP steel microstructure type dataset
1. Resource reference list
2. Raw dataset
3. Dataset compatible to program code 
4. Program code (compatible in ipynb platform)
DOI: 10.17632/gy7kcvf98b.1
title: A machine learning model for multi-class classification of quenched and partitioned steel microstructure type by the k-nearest neighbor algorithm

description: QP steel microstructure type dataset 1. Resource reference list 2. Raw dataset 3. Dataset compatible to program code 4. Program code (compatible in ipynb platform)
DOI: 10.5281/zenodo.10013195
title: ES08 - Others - Zamora (Spain)

description: Data files for building: ES08 - Others - Zamora (Spain)
Languages: Spanish, English
These files are part of the public benchmark repository created as a part of the crossCert EU project. 
This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.
The files are organised in the following folders  (note that not all files are always provided):

Main data  and Results, with:

Neutral data inventory.
Neutral results report.
Original EPC certificate.
Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.
Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.
Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.
Dynamic Model

Data to run a dynamic model of the building, if available.
The files have been redacted to exclude confidential information.
DOI: 10.5281/zenodo.10013194
title: ES08 - Others - Zamora (Spain)

description: Data files for building: ES08 - Others - Zamora (Spain)
Languages: Spanish, English
These files are part of the public benchmark repository created as a part of the crossCert EU project. 
This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.
The files are organised in the following folders  (note that not all files are always provided):

Main data  and Results, with:

Neutral data inventory.
Neutral results report.
Original EPC certificate.
Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.
Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.
Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.
Dynamic Model

Data to run a dynamic model of the building, if available.
The files have been redacted to exclude confidential information.
DOI: 10.6096/aeroclo.1755
title: AEROCLO-sA F20 Uhsas

description: Dataset with UHSAS Measurements (Ultra High Sensitivity Aerosols Spectrometer)

The UHSAS measures aerosol number concentration between 0.06 and 1 μm in diameter in 99 logarithmically spaced bins.

Size bins are nominal and do not take into account the ambien refractive index.
DOI: 10.5281/zenodo.10013216
title: ES09 - Others - Ibeas de Juarros (Spain)

description: Data files for building: ES09 - Others - Ibeas de Juarros (Spain)
Languages: Spanish, English
These files are part of the public benchmark repository created as a part of the crossCert EU project. 
This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.
The files are organised in the following folders  (note that not all files are always provided):

Main data  and Results, with:

Neutral data inventory.
Neutral results report.
Original EPC certificate.
Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.
Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.
Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.
Dynamic Model

Data to run a dynamic model of the building, if available.
The files have been redacted to exclude confidential information.
DOI: 10.5281/zenodo.10013215
title: ES09 - Others - Ibeas de Juarros (Spain)

description: Data files for building: ES09 - Others - Ibeas de Juarros (Spain)
Languages: Spanish, English
These files are part of the public benchmark repository created as a part of the crossCert EU project. 
This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.
The files are organised in the following folders  (note that not all files are always provided):

Main data  and Results, with:

Neutral data inventory.
Neutral results report.
Original EPC certificate.
Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.
Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.
Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.
Dynamic Model

Data to run a dynamic model of the building, if available.
The files have been redacted to exclude confidential information.
DOI: 10.6084/m9.figshare.24432853
title: <b>A family-level phylogeny of liverworts (Marchantiophyta): new evidences from mitochondrial sequences</b>

description: <b>This database contains gaptured gene, alignment and tree files.</b>
DOI: 10.6084/m9.figshare.24432853.v2
title: <b>A family-level phylogeny of liverworts (Marchantiophyta): new evidences from mitochondrial sequences</b>

description: <b>This database contains gaptured gene, alignment and tree files.</b>
DOI: 10.6084/m9.figshare.24137652
title: Supplementary Material for: Plasma MicroRNAs related to metabolic syndrome in Mexican women

description: Introduction: The metabolic syndrome (MetS) is a cluster of abnormalities related to cardiovascular disease (CVD). Circulating miRNAs (c-miRNAs) are non-coding RNAs associated with different phenotypes, some of them integrating the MetS. The aim of the study is to compare the c-miRNAs profile in plasma between women with MetS and controls, and explore their possible association with dysregulation of metabolic pathways. Methods: The study was conducted in two phases. At the screening phase, miRNA composition in fasting plasma was compared between 8 participants with MetS and 10 healthy controls, using microarray technology. The validation phase included the analysis by qRT-PCR of 10 selected c-miRNAs in an independent sample (n=29). Results: We found 21 c-miRNAs differentially expressed between cases and controls. The concentration in plasma of the c-miRNAs hsa-miR-1260a, hsa-miR-4514 and hsa-miR-4687-5p were also correlated with risk factors for CVD. Differences of hsa-miR-1260a between cases and controls were validated using qRT-PCR. (Fold-change= 7.0; P= 0.00289). Conclusion: The signature of plasma c-miRNA differed between women with MetS and controls. The identified miRNAs regulate pathways related to the MetS such as insulin resistance and adipokine activity. The role of c-miR-1260a in the MetS remains to be elucidated.
DOI: 10.6084/m9.figshare.24137652.v1
title: Supplementary Material for: Plasma MicroRNAs related to metabolic syndrome in Mexican women

description: Introduction: The metabolic syndrome (MetS) is a cluster of abnormalities related to cardiovascular disease (CVD). Circulating miRNAs (c-miRNAs) are non-coding RNAs associated with different phenotypes, some of them integrating the MetS. The aim of the study is to compare the c-miRNAs profile in plasma between women with MetS and controls, and explore their possible association with dysregulation of metabolic pathways. Methods: The study was conducted in two phases. At the screening phase, miRNA composition in fasting plasma was compared between 8 participants with MetS and 10 healthy controls, using microarray technology. The validation phase included the analysis by qRT-PCR of 10 selected c-miRNAs in an independent sample (n=29). Results: We found 21 c-miRNAs differentially expressed between cases and controls. The concentration in plasma of the c-miRNAs hsa-miR-1260a, hsa-miR-4514 and hsa-miR-4687-5p were also correlated with risk factors for CVD. Differences of hsa-miR-1260a between cases and controls were validated using qRT-PCR. (Fold-change= 7.0; P= 0.00289). Conclusion: The signature of plasma c-miRNA differed between women with MetS and controls. The identified miRNAs regulate pathways related to the MetS such as insulin resistance and adipokine activity. The role of c-miR-1260a in the MetS remains to be elucidated.
DOI: 10.5281/zenodo.10013247
title: ES10 - Healthcare - Villagonzalo/Pedernales (Spain)

description: Data files for building: ES10 - Healthcare - Villagonzalo/Pedernales (Spain)
Languages: Spanish, English
These files are part of the public benchmark repository created as a part of the crossCert EU project. 
This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.
The files are organised in the following folders  (note that not all files are always provided):

Main data  and Results, with:

Neutral data inventory.
Neutral results report.
Original EPC certificate.
Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.
Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.
Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.
Dynamic Model

Data to run a dynamic model of the building, if available.
The files have been redacted to exclude confidential information.
DOI: 10.5281/zenodo.10013246
title: ES10 - Healthcare - Villagonzalo/Pedernales (Spain)

description: Data files for building: ES10 - Healthcare - Villagonzalo/Pedernales (Spain)
Languages: Spanish, English
These files are part of the public benchmark repository created as a part of the crossCert EU project. 
This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.
The files are organised in the following folders  (note that not all files are always provided):

Main data  and Results, with:

Neutral data inventory.
Neutral results report.
Original EPC certificate.
Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.
Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption  models.
Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.
Dynamic Model

Data to run a dynamic model of the building, if available.
The files have been redacted to exclude confidential information.
DOI: 10.6084/m9.figshare.24166719
title: Supplementary Material for: Hippocampal dynamic functional connectivity, HPA axis activity, and personality trait in bipolar disorder with suicidal attempt

description: Introduction Suicide in bipolar disorder (BD) is a multifaceted behavior, involving specific neuroendocrine and psychological mechanisms. According to previous studies, we hypothesized that suicidal BD patients may exhibit impaired dynamic functional connectivity (dFC) variability of hippocampal subregions and hypothalamic-pituitary-adrenal (HPA) axis activity, which may be associate with suicide-related personality traits. The objective of our study was to clarify this. Methods Resting-state functional magnetic resonance imaging data were obtained from 79 patients with BD, 39 with suicidal attempt (SA) and 40 without SA, and 35 healthy controls. The activity of the HPA axis was assessed by measuring morning plasma adrenocorticotropic hormone (ACTH) and cortisol (CORT) levels. All participants underwent personality assessment using Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Results BD patients with SA exhibited increased dFC variability between the right caudal hippocampus and the left superior temporal gyrus (STG) when compared with non-SA BD patients and HCs. BD with SA also showed significantly lower ACTH levels in comparison with HCs, which was positively correlated with increased dFC variability between the right caudal hippocampus and the left STG. BD with SA had significantly higher scores of Hypochondriasis, Depression and Schizophrenia than non-SA BD. Additionally, multivariable regression analysis revealed the interaction of ACTH × dFC variability between the right caudal hippocampus and the left STG independently predicted MMPI-2 score (depression evaluation) in suicidal BD patients. Conclusion These results suggested that suicidal BD exhibited increased dFC variability of hippocampal-temporal cortex and less HPA axis hyperactivity, which may affect their personality traits.