cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/gx4cxh
title: AnalyseAir_ExterieurBatterie_Aqara_Pourcentage_2022.tab
DOI: 10.57745/zgfczb
title: AnalyseAir_ExterieurBatterie_Aqara_Pourcentage_2023.tab
DOI: 10.57745/z52gau
title: AnalyseAir_ExterieurPile_Aqara_V_2019.tab
DOI: 10.57745/qx7ay1
title: AnalyseAir_ExterieurPile_Aqara_V_2020.tab
DOI: 10.57745/xfr35h
title: AnalyseAir_ExterieurPile_Aqara_V_2021.tab
DOI: 10.57745/yppeoh
title: AnalyseAir_ExterieurPile_Aqara_V_2022.tab
DOI: 10.57745/rumeo0
title: AnalyseAir_ExterieurHumiditeV2_Netatmo_Pourcentage_2023.tab
DOI: 10.57745/ttlo7s
title: AnalyseAir_ExterieurPile_Aqara_V_2023.tab
DOI: 10.57745/fpqyv8
title: AnalyseAir_ExterieurHumidite_Aqara_Pourcentage_2019.tab
DOI: 10.57745/jyvpip
title: AnalyseAir_ExterieurHumidite_Aqara_Pourcentage_2020.tab
DOI: 10.57745/vohkmt
title: AnalyseAir_ExterieurHumidite_Aqara_Pourcentage_2021.tab
DOI: 10.57745/ekyrxa
title: AnalyseAir_ExterieurHumidite_Aqara_Pourcentage_2022.tab
DOI: 10.57745/cebw83
title: AnalyseAir_ExterieurHumidite_Netatmo_Pourcentage_2019.tab
DOI: 10.57745/ygs40n
title: AnalyseAir_ExterieurHumidite_Aqara_Pourcentage_2023.tab
DOI: 10.57745/08bqgr
title: AnalyseAir_ExterieurHumidite_Netatmo_Pourcentage_2020.tab
DOI: 10.57745/a0cxux
title: AnalyseAir_ExterieurHumidite_Netatmo_Pourcentage_2021.tab
DOI: 10.34691/uchile/aydrzl/oygzql
title: count_trnascan.pl

description: Script will read any file in the current directory that was produced by script trnascan_all_v2.pl. Files are recognized if ending with “.fna.tscan”. It will construct a results file indicating the number of genes coding for each tRNA in each of the analyzed genomes.

Usage: perl count_trnascan.pl
DOI: 10.15468/1w07qy
title: RSPB - 2008 Farmland Bird Survey North East Wales - RSPB/CCW

description: NBN Gateway Dataset GA000898
DOI: 10.34691/uchile/aydrzl/ldog5e
title: codon_usage_by_gene_presence-ALL-V3.1.pl

description: Program will make histograms and boxplots for the distribution of values of each column of several 
 The list of must be in the file 
 its format must be:
 lines beginning with # are not considered
 first line not starting with # must have a single file containing the codon usages in all genomes format like in file average-b.txt
 following lines (not starting with #) must have three fields separated by tabs:
 First field : name of the gene
 Second field: first file, has data for genomes with the gene
 Third field : second file, has data for genomes withOUT the gen
 
 All data files need to be in the same format. Data divided be tab separated columns
 First line starts with "#" and indicates the name of each field

 corresponds to the gene that has been analyzed. Will be used in plots names
Results files will be constructed based on the and the names of each column and 
 A file with all plots will be saved to a file that additionally contains 
Results will be saved in . It must end with "/"

Additionally, the program adds an histogram. For that, the (size of bins), as well as the minimum value to count () and the number of bins to include () must also be added to the command line. If is set to zero, please use 0.0 instead of 0.

This script requires the following modules
 Statistics::Descriptive 
 Statistics::Ttest
Please consider that this script uses T-test to define statistical confidence of differences. This suppose a normal distribution of data. If your data is not normally distributed, please use codon_usage_by_gene_presence-ALL-Wilcoxon-V3.1.pl instead. P value is set to 0.00005. If a different value is required, please change the variable $pvalue accordingly.

Usage: perl codon_usage_by_gene_presence-ALL-V3.1.pl
DOI: 10.6084/m9.figshare.26064724.v1
title: Supplementary Material for: Three-Dimensional Fractional Moving Blood Volume (3D-FMBV): A robust bedside tool for evaluation of fetal multi-organ perfusion

description: Introduction: Three-dimensional-fractional moving blood volume (3D-FMBV) may provide superior non-invasive measurement of feto-placental perfusion compared to current methods. This study investigated the feasibility and repeatability of producing 3D-FMBV measurements of the placenta, fetal liver, kidney, and brain in a single ultrasound consultation. 
Methods: The placenta, fetal liver, kidney and brain were scanned in triplicate using 3D power Doppler ultrasound (3D-PDU) in 48 women ≥22 weeks gestation with healthy fetuses. 3D-FMBV was calculated by two analyzers. Feasibility was assessed as the percentage of cases where 3D-FMBV could be evaluated; repeatability (intraobserver and interobserver) using two-way mixed measure intraclass correlation coefficients (ICCs).
Results: 3D-FMBV was calculated for 100% of scanned organs. Intraobserver ICCs (95% CI) were good to excellent; 0.93 (0.88-0.96) and 0.87 (0.78-0.92) for placenta, 0.95 (0.92-0.97) and 0.98 (0.96-0.99) for fetal liver, 0.96 (0.94-0.98) and 0.91 (0.85-0.95) for fetal kidney and 0.98 (0.97-0.99) and 0.97 (0.95-0.98) for fetal brain. Interobserver ICCs (95% CI) were 0.50 (0.08-0.73), 0.92 (0.85-0.96), 0.89 (0.78-0.94) and 0.71 (0.46-0.85) for placenta, fetal liver, kidney and brain. 
Conclusion: Feto-placental perfusion assessment with 3D-FMBV is highly reliable in healthy pregnancies ≥22 weeks gestation and can be feasibly calculated in four feto-placental vascular beds in a single ultrasound consultation.
DOI: 10.5281/zenodo.12167869
title: UTDRM: unsupervised method for training debunked-narrative retrieval models (Dataset)

description: The repository contains T5 and ChatGPT generated claims for the paper "UTDRM: Unsupervised Method for Training Debunked-Narrative Retrieval Models" (EPJ Data Science Journal).

The synthetically generated false claims dataset is solely for evaluation purposes and will only be made available to academic researchers following careful vetting and a signed contract, in order to prevent public harm or the spread of misinformation.

Paper link:

https://link.springer.com/article/10.1140/epjds/s13688-023-00437-y

Model links:

https://huggingface.co/iknoor/UTDRM-MPNet 

https://huggingface.co/iknoor/UTDRM-RoBERTa

Please use the following reference if you use the dataset or models:

@article{singh2023utdrm,
  title={UTDRM: unsupervised method for training debunked-narrative retrieval models},
  author={Singh, Iknoor and Scarton, Carolina and Bontcheva, Kalina},
  journal={EPJ Data Science},
  volume={12},
  number={1},
  pages={59},
  year={2023},
  publisher={Springer Berlin Heidelberg}
}
DOI: 10.5281/zenodo.12167870
title: UTDRM: unsupervised method for training debunked-narrative retrieval models (Dataset)

description: The repository contains T5 and ChatGPT generated claims for the paper "UTDRM: Unsupervised Method for Training Debunked-Narrative Retrieval Models" (EPJ Data Science Journal).

The synthetically generated false claims dataset is solely for evaluation purposes and will only be made available to academic researchers following careful vetting and a signed contract, in order to prevent public harm or the spread of misinformation.

Paper link:

https://link.springer.com/article/10.1140/epjds/s13688-023-00437-y

Model links:

https://huggingface.co/iknoor/UTDRM-MPNet 

https://huggingface.co/iknoor/UTDRM-RoBERTa

Please use the following reference if you use the dataset or models:

@article{singh2023utdrm,
  title={UTDRM: unsupervised method for training debunked-narrative retrieval models},
  author={Singh, Iknoor and Scarton, Carolina and Bontcheva, Kalina},
  journal={EPJ Data Science},
  volume={12},
  number={1},
  pages={59},
  year={2023},
  publisher={Springer Berlin Heidelberg}
}
DOI: 10.5518/1110
title: Whole-system analysis reveals high greenhouse gas emissions from citywide sanitation in Kampala, Uganda

description: This file includes details of the data, assumptions and calculations used to construct the city-wide sanitation system level estimates of greenhouse gas emissions for Kampala. The sanitation system model is based on the excreta-flow diagram or SFD for Kampala and uses secondary data collected previously to summarise probably emissions from each element of the sanitation value chain in Kampala.
DOI: 10.34691/uchile/aydrzl/q8gtg9
title: plot-codonusage-vs-GC-V3-High_expression-vs-all-genes.pl

description: Program will take data on files and and make plots comparing codon usage with frequency of nucleotides in genomes. First line in each file has to be commented and indicate the column titles. This line must use the same titles as given by previous scripts so that the program can recognize them. Also, both files must use the same names for fields. Each file should contain the columns presenting the usage of each codon as well as columns presenting the content of each nucleotide. In linux, you can use the paste command to construct this file pasting the average codon file and the GC content files produced by the following scripts:
 Codon_statistics-V2.pl
 AGCT_count_V2.pl
For example:
 paste average-b.txt genomic_AGCT.tab &gt; usage-genomic_AGCT.tab
Results files will use the to construct result files in 
This program calculate linear regression using perl “Statistics::LineFit” module and uses GnuPlot to construct plots. Both must be installed.
After finishing, the program will unite all pdf files of the folder in and add to that file name a ".pdf" extension.
Additionally it will make a text file with the same name, but finishing in ".tab", that contains the slope, intercept and R² for each codon 

Usage: perl plot-codonusage-vs-GC-V3-High_expression-vs-all-genes.pl
DOI: 10.5281/zenodo.12165385
title: IuliaMargineanGitHub/Projecting-Heat-Stress-Vulnerable-Populations-at-Intra-Urban-Scales: Projecting heat stress vulnerable populations at intra-urban scales

description: This repository provides the data and scripts necessary for full reproduction of results, as well as example datasets that were used for data generation and analysis in the manuscript Marginean et al., "High-resolution Modelling and Projecting Local Dynamics of Differential Vulnerability to Urban Heat Stress".

The folders in this repository contain the following:



The input datasets:



shares of educational attainment in 2012 and 2020, by sex, age group and census tract

internal migration by sex, age group and census tracts

decadal mortality and fertility, by sex, age group, scenario, and census tract



Projected decadal data (2012 to 2050) by sex, age group, scenario and census tract

Scripts for generating high resolution projections for three Shared Socioeconomic Pathways (SSPs): one script for SSP 2 projections and one scripts for SSP 1 and SSP 3 projections

Example input data to reconstruct the projections in SSP 1, 2 and 3 for females ages 25 to 64
DOI: 10.34691/uchile/aydrzl/tgyhcs
title: batch-separate_file_by_gene_presence_and_plot_histograms.pl

description: Program checks for all files in directory that start with and end with . The aim is to select results files from rpsblast-summary-V4.pl. Then it runs separate_file_by_gene_presence-v2.pl for each of this files using the program indicated in variable $separate_prog and the codon usage file indicated in . This separates the codon usage of each genomes based on the presence or absence of the gen of interest. Then it uses the results files to run program indicated in variable $plot_prog (codon_usage_by_gene_presence-ALL-V3.1.pl) to construct superposed histograms and box plots comparing the distribution of codon usages in genomes that present or lack each gene. It additionally calculates statistical significance of differences between populations based on T-test (suppose normal distribution, if populations are not normally distributed, use batch-separate_file_by_gene_presence_and_plot_histograms-Wilcoxon.pl).
All files in the indicated directory will be used to make plots in the same page of each pdf.
Please correct the path in variables $separate_prog and $plot_prog if the corresponding scripts are not in the path or current directory

Usage: perl batch-separate_file_by_gene_presence_and_plot_histograms.pl
DOI: 10.15468/dl.bbwvkq
title: Occurrence Download

description: A dataset containing 18287 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acacia myrtifolia (Sm.) Willd., Acacia myrtifolia f. angustifolia Benth., Acacia myrtifolia f. celastrifolia (Benth.) Benth., Acacia myrtifolia var. angustifolia (Benth.) W.Fitzg., Acacia myrtifolia var. celastrifolia (Benth.) Benth., Acacia myrtifolia var. angustifolia (Benth.) Benth., Acacia myrtifolia var. myrtifolia, Acacia myrtifolia f. myrtifolia, Acacia myrtifolia f. normalis Benth., Acacia myrtifolia var. major Meisn.)"
 ]
}

 The dataset includes 18287 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0087188-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12168625
title: FAIR Metadata Concepts in DataCite Metadata Schema

description: Documentation Concepts that support the FAIR Principles are mapped to the DataCite Metadata Schema using json Paths.
DOI: 10.5281/zenodo.12168626
title: FAIR Metadata Concepts in DataCite Metadata Schema

description: Documentation Concepts that support the FAIR Principles are mapped to the DataCite Metadata Schema using json Paths.
DOI: 10.34691/uchile/aydrzl/0bbour
title: AGCT_count_V2.pl

description: Script counts the number of G, C, A and T present in all fasta entries of each genome fasta file present in the running folder. Additionally, the program calculates the fraction of nucleotides that correspond to each nucleotide type and the fraction of nucleotides that correspond to either G or C. The script is designed to select all fasta files with the text “_genomic.fna” in order to select full genomes and not annotated features such as RNAs or CDS. If you prefer to determine these parameters considering only CDS, please comment variables $pat, $notpat and $notpat2 in lines 15 to 17 of the script and uncomment the corresponding variables in lines 20 to 22 or change variables according to the text in the file names you are are analyzing.

Usage: perl AGCT_count_V2.pl
DOI: 10.15468/dl.yr3jqz
title: Occurrence Download

description: A dataset containing 154471 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Larix decidua Mill."
 ]
}

 The dataset includes 154471 records from 752 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0087148-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34691/uchile/aydrzl/i2jqds
title: plot-codonusage-vs-tRNAgenes-V3.pl

description: Program will take data on file and make plots comparing codon usage with the number of tRNA genes with complementary anticodons. First line in the data file has to be commented and have the column titles. This line must use the same titles as given by previous scripts so that this program recognize them. Each file should contain the columns presenting the usage of each codon as well as columns presenting the number of tRNA genes. In Linux, you can use the paste command to construct this file pasting the average codon file and the tRNA genes content files produced by the following scripts:
 Codon_statistics-V2.pl
 count_trnascan.pl
For example:
 paste average-b.txt tRNA_genes.tab &gt; usage-tRNAs.tab
Results files will use the to construct result files in 
This program calculate linear regression using perl “Statistics::LineFit” module and produces plots using GnuPlot. Both should be installed. After finishing, the program will unite all pdf files of the folder in and add to that file name a ".pdf" extension
Additionally it will make a text file with the same name, but finishing in ".tab", that contains the slope, intercept and R² for each codon. Some additional plots are made based on this file

Usage: perl plot-codonusage-vs-tRNAgenes-V3.pl
DOI: 10.34691/uchile/aydrzl/xyxwwu
title: Codon_counter-V1.pl

description: The script will count the number of codons in every gene in each fasta file of the folder and produce a result file for each genome containing the fraction of codons of each gene that correspond to each codon. Genes annotated as pseudogenes or presenting a number of nucleotides not dividable by three are not considered. The program is designed to use the “_cds_from_genomic.fna” files from GeneBank or RefSeq that have fasta headers in genebank format. This allows the script to recognize genes annotated with the “pseudo” flag as “true”. In order to work, the script requires the text that will be added to each result file and a file with the list of codons that it should look for. We have added a sample file named codons.txt that contains all possible codons, but users may want to erase stop codons from the list.

Usage: perl Codon_counter-V1.pl
DOI: 10.34691/uchile/aydrzl/xvwnop
title: statistics-all-columns-V2.pl

description: Script calculates a series of statistical values for each column of the indicated file. In this work it was used to estimate variability of codon usage between proteobacterial genomes based on standard deviations and the distance between the values of percentiles 90 and 10. The program additionally calculate other simple statistical values of columns such as mean, median, skewness, kurtosis and the value of selected percentiles. The script requires that the Perl module “Statistics::Descriptive” is installed.
 The file with data to be analyzed should be organized in tab separated columns. First column will be skipped (considering it contains the genome names or accession codes). First line in the file has to be commented and should contain the column titles. Other commented lines will be ignored.

Usage: perl statistics-all-columns-V2.pl
DOI: 10.34691/uchile/aydrzl/gtwzb7
title: batch-separate_file_by_gene_presence_and_plot_histograms-Wilcoxon.pl

description: Program checks for all files in directory that start with and end with . The aim is to select results files from rpsblast-summary-V4.pl. Then it runs separate_file_by_gene_presence-v2.pl for each of this files using the program indicated in variable $separate_prog and the codon usage file indicated in . This separates the codon usage of each genomes based on the presence or absence of the gen of interest. Then it uses the results files to run program indicated in variable $plot_prog (codon_usage_by_gene_presence-ALL-Wilcoxon-V3.1.pl) to construct superposed histograms and box plots comparing the distribution of codon usages in genomes that present or lack each gene. It additionally calculates statistical significance of differences between populations based on Mann-Whitney test also called Wilcoxon-Mann-Whitney test or Wilcoxon Rank Sum (non parametric, can be used to test differences between non-normal distributions).
All files in the indicated directory will be used to make plots in the same page of each pdf.
Please correct the path in variables $separate_prog and $plot_prog if the corresponding scripts are not in the path or current directory

Usage: perl batch-separate_file_by_gene_presence_and_plot_histograms-Wilcoxon.pl
DOI: 10.34691/uchile/aydrzl/0tjwex
title: codons.txt

description: Sample of the file required by Codon_counter-V1.pl
Contains a list of all codons.
DOI: 10.5281/zenodo.10673968
title: TIGAR experiments with background flow and Rossby wave forcing

description: A barotropic version of the TIGAR (Transient Inertia-Gravity And Rossby wave dynamics) model has been run at T42 resolution to simulate the effects of subtropical Rossby waves on equatorial waves in the presence of a zonal background flow. TIGAR solves the rotating shallow water equations by applying Hough harmonics as spectral basis functions thereby enabling the analysis of Rossby and Inertia-gravity wave dynamics. More details of the model are available at:  https://doi.org/10.1002/qj.4006 .
DOI: 10.5281/zenodo.12168306
title: The Interconnected Nature of Multiple Threats are Impacting Freshwater Biodiversity

description: Freshwater biodiversity is in crisis across the globe: the significant extraction, modification, and pollution of freshwater resources puts these communities and systems at great risk. Here, using probabilistic network analysis, we show that globally and across all taxonomic groups and geographic regions, threats to freshwater species are interconnected and do not occur in isolation. However, we also find that species in higher risk categories are more acutely threatened by single, dominant threats as compared to species at lower risk of global extinction. Determining when and which species are threatened by isolated threats or a suite of co-occurring threats provides important information for designing effective freshwater conservation strategies.
DOI: 10.34691/uchile/aydrzl/q7st40
title: rpsblast-summary-V4.pl

description: The aim of this script is to screen for a set of sequence patterns in all genomes present in the current directory. Screens are performed using rpsblast (tested with version 2.8.1+, included in Debian package ncbi-blast+) and patterns can be obtained from the CDD database at NCBI webpage. Nevertheless, if using the complete database the program will take too long to run for a data base with thousands of genomes. In such cases, it is recommended to construct a specific database that only includes the patterns that should be found in the genes of interest, as well as those expected to be absent from those genes. Full instructions for constructing your databases can be found at the CDD database webpage at NCBI. Briefly, you should run the makeprofiledb program. For example, supposing you have already downloaded the patterns of interest:

 makeprofiledb -in patterns_of_interest.txt -out database_name 

 where “patterns_of_interest.txt” is the file containing the names of the patterns of interest and “database_name” is the name your database will have.


Program will look for the indicated pattern(s) in all the files of the directory that follow the indicated rules. Information such as the patterns file and gene selection rules must be indicated in a . Please look at RPSB-criteria-example.txt for an example of . Additionally, it is necessary to indicate the base for the name of the results directory and for the results files. must end with "/".
 Each gene in the analyzed genomes will be selected if it has any of the required patterns in the inclusion criteria
 Each gene in the analyzed genomes will be rejected if it has any of the patterns in the rejection criteria

Usage: perl rpsblast-summary-V4.pl
DOI: 10.5281/zenodo.10673967
title: TIGAR experiments with background flow and Rossby wave forcing

description: A barotropic version of the TIGAR (Transient Inertia-Gravity And Rossby wave dynamics) model has been run at T42 resolution to simulate the effects of subtropical Rossby waves on equatorial waves in the presence of a zonal background flow. TIGAR solves the rotating shallow water equations by applying Hough harmonics as spectral basis functions thereby enabling the analysis of Rossby and Inertia-gravity wave dynamics. More details of the model are available at:  https://doi.org/10.1002/qj.4006 .
DOI: 10.5281/zenodo.11544723
title: TIGAR experiments with background flow and Rossby wave forcing

description: A barotropic version of the TIGAR (Transient Inertia-Gravity And Rossby wave dynamics) model has been run at T42 resolution to simulate the effects of subtropical Rossby waves on equatorial waves in the presence of a zonal background flow. TIGAR solves the rotating shallow water equations by applying Hough harmonics as spectral basis functions thereby enabling the analysis of Rossby and Inertia-gravity wave dynamics. More details of the model are available at:  https://doi.org/10.1002/qj.4006 .