cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1400340.10fu
title: SH1400340.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400355.10fu
title: SH1400355.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400364.10fu
title: SH1400364.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400365.10fu
title: SH1400365.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400366.10fu
title: SH1400366.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400367.10fu
title: SH1400367.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400373.10fu
title: SH1400373.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400374.10fu
title: SH1400374.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15151/esrf-es-2276858669
title: Swiss consortium in macromolecular X-ray crystallography structure determination for academic researchers: EPFL, UNIL, UNIGE, Zurich and SLS

description: This proposal builds upon the foundation established in BAG MX 2521 and MX 2644 and represents a collaborative effort among prominent Swiss academic institutions, including EPFL, UNIL, UNIGE, university of Zurich and SLS. Under the leadership of Dr. Florence Pojer, the head of the Protein Production and Structure core facility at EPFL, this initiative aims to streamline and enhance the process of determination of macromolecules structures by X-ray crystallography. Notably, the automated data collection established at ESRF has revolutionized our current pipeline, enabling the analysis of over 1000 crystals and the finalization at high-resolution of around 50 different structures in the past year alone. These achievements span diverse projects ranging from drug discovery to the design of novel protein folds.
DOI: 10.15151/esrf-es-2237118842
title: Unraveling the structural dynamics of Silver active sites in carbon nitride-based electrodes during electrochemical oxidation of benzyl alco

description: The primary objective of this experiment is to use operando X-ray absorption spectroscopy (XAS) to investigate the dynamic behaviour of silver single atoms supported on a carbon nitride (CNx) electrode during electrochemical oxidation of benzyl alcohol (vide infra). The study, in particular, focuses on understanding the interaction between Ag active sites and their surrounding environment, particularly the relationship between the electrode and reactants during reaction. Real-time monitoring of these interactions could provide valuable insights for optimizing electrochemical systems and enhancing the application of electrocatalytic biomass conversion.
DOI: 10.15468/dl.65c2qz
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Polyosma nhatrangensis"
 ]
}

 The dataset includes 6 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022779-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.69ccp9
title: Occurrence Download

description: A dataset containing 5862 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ardisia elliptica"
 ]
}

 The dataset includes 5862 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022780-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17553246
title: A high-resolution pan-European hydrological reanalysis

description: dataset to produce a longterm hydrological reanalysis with downscaled and bias-connected climate reanalysis. For more details refer here
DOI: 10.5281/zenodo.17553247
title: A high-resolution pan-European hydrological reanalysis

description: dataset to produce a longterm hydrological reanalysis with downscaled and bias-connected climate reanalysis. For more details refer here
DOI: 10.5281/zenodo.17556652
title: Protestant Epistemology as Juridical Contestation: How Evangelical Institution-Building Contests Colonial Legal Frameworks

description: Analyzing Catholic Affiliation Trends in Latin America with Google Colab

This repository contains a Python script designed for analyzing trends in Catholic affiliation across Latin American countries. It utilizes descriptive statistics, growth rate analysis, K-means clustering, and regression analysis to explore factors influencing religious change. This guide explains how to run the analysis using Google Colaboratory (Colab) and obtain results:

1. Open the Notebook in Google Colab:



Click on the Catholic_Affiliation_Analysis.ipynb file in this repository.

Click “Open in Colab” or upload to Colab manually.


2. Upload the Data:



Download the Catholic Affiliation in Latin America.xlsx file from the repository.

In your Google Colab notebook, use the file explorer (left sidebar) to upload the Excel file to the Colab environment.


3. Modify the Filepath:



Locate the code block where the filepath is defined:




python

Copy


if __name__ == "__main__":
    filepath = '/content/Catholic Affiliation in Latin America.xlsx'  # Corrected filepath
    df = main(filepath)





Change the "filepath" variable to the actual path where you've uploaded the Excel file. A good way to do that is to check the directory and copy the filepath to this section.


4. Run the Notebook:



Once the file path is set up correctly, run the Colab notebook.

Click the "Connect" button. Then the "Run all" option under the "Run" menu will automatically execute all the code blocks sequentially.

If the notebook's functions are running, you should begin seeing the code and graphs.


5. Inspect Results:



The results of each analysis section (Descriptive Statistics, Growth Rate Analysis, Clustering, etc.) will be printed to the output area of the corresponding code block.

The code will also automatically generate and save a visualization (4-panel figure) in the Colab environment. It outputs at "catholic_decline_analysis.png".


6. Download the Visualization:



To download the outputted png file, click the "Files" icon in the left sidebar of the Colab interface.

Locate the catholic_decline_analysis.png file.

Right-click on the file and select "Download" to save it to your local machine.


By following these steps, you can run the analysis, interpret the results, and download the visualization for your further investigation. If you encounter any issues during the process, please refer to the documentation for Pandas, statsmodels, scikit-learn, matplotlib, and seaborn. This tool has helped you run econometric analysis without any coding skills or complicated installs. Happy coding!
DOI: 10.15468/dl.3nbesu
title: Occurrence Download

description: A dataset containing 101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Camelus"
 ]
}

 The dataset includes 101 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022781-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17556663
title: Transformation cognitive dans le contexte de l'intelligence augmentée : Revue de littérature

description: Notre travail de recherche explorera l’impact de l’intelligence artificielle (IA) sur les processus cognitifs humains en matière de résilience et de transformation cognitive. Nous analyserons les concepts clés liés à la transformation cognitive à l'ère de l'intelligence augmentée, notamment l'intelligence, l'intelligence collaborative, l'intelligence artificielle et l'intelligence augmentée. Nous discuterons ensuite de la symbiose cognitive entre l’homme et l’IA, soulignant l'importance d'une collaboration enrichissante entre l'humain et la machine pour optimiser les capacités décisionnelles. Nous examinerons également comment l’intégration des technologies avancées, telles que l’IA, influence la neuroplasticité et la résilience cognitive dans des environnements complexes. Ces analyses ont permis de formaliser un modèle conceptuel intégratif qui fournit une vision structurée de la dynamique circulaire entre l'IA, la transformation cognitive, la résilience et la symbiose Homme-Machine.
DOI: 10.5281/zenodo.17556664
title: Transformation cognitive dans le contexte de l'intelligence augmentée : Revue de littérature

description: Notre travail de recherche explorera l’impact de l’intelligence artificielle (IA) sur les processus cognitifs humains en matière de résilience et de transformation cognitive. Nous analyserons les concepts clés liés à la transformation cognitive à l'ère de l'intelligence augmentée, notamment l'intelligence, l'intelligence collaborative, l'intelligence artificielle et l'intelligence augmentée. Nous discuterons ensuite de la symbiose cognitive entre l’homme et l’IA, soulignant l'importance d'une collaboration enrichissante entre l'humain et la machine pour optimiser les capacités décisionnelles. Nous examinerons également comment l’intégration des technologies avancées, telles que l’IA, influence la neuroplasticité et la résilience cognitive dans des environnements complexes. Ces analyses ont permis de formaliser un modèle conceptuel intégratif qui fournit une vision structurée de la dynamique circulaire entre l'IA, la transformation cognitive, la résilience et la symbiose Homme-Machine.
DOI: 10.15468/dl.ghwb2e
title: Occurrence Download

description: A dataset containing 14055704 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is China"
 ]
}

 The dataset includes 14055704 records from 5683 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022773-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f4q6zb
title: Occurrence Download

description: A dataset listing the 43277 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (AUS.9_1, AUS.11_1, AUS.10_1, AUS.5_1, AUS.2_1, AUS.8_1, AUS.6_1, AUS.7_1)",
 "Geometry POLYGON((155.47487 -31.77661,154.78382 -22.72375,146.49112 -12.21966,142.82851 -7.38225,133.63743 -8.28063,124.30814 -12.97982,123.75529 -15.46763,115.32438 -19.40667,110.62518 -24.72781,114.21869 -39.37825,149.53177 -46.49615,155.47487 -31.77661))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset's 43277 records were derived from 401 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031698-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ykcsjd
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025248-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rxzshv
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048159-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wptqxh
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073495-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gk56nj
title: Occurrence Download

description: A dataset containing 640 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Indigofera hochstetteri Baker"
 ]
}

 The dataset includes 640 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031830-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v4bf9y
title: Occurrence Download

description: A dataset containing 103175556 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 103175556 records from 11499 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0227095-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/iw73uu/2nwjgl
title: KAN_L_day.csv

description: Uploaded with pyDataverse 2024-04-24 20:42
DOI: 10.22008/fk2/iw73uu/lb5skn
title: KAN_L_month.csv

description: Uploaded with pyDataverse 2024-04-24 20:43
DOI: 10.22008/fk2/iw73uu/os0gni
title: KAN_L_hour.csv

description: Uploaded with pyDataverse 2024-04-24 20:42
DOI: 10.22008/fk2/iw73uu/r2qbl1
title: KAN_L_hour.nc

description: Uploaded with pyDataverse 2024-04-24 20:43
DOI: 10.22008/fk2/iw73uu/xpmncx
title: KAN_L_day.nc

description: Uploaded with pyDataverse 2024-04-24 20:43
DOI: 10.22008/fk2/iw73uu/aecn9n
title: KAN_L_month.nc

description: Uploaded with pyDataverse 2024-04-24 20:43
DOI: 10.22008/fk2/iw73uu/q9p9rc
title: KAN_M_day.csv

description: Uploaded with pyDataverse 2024-04-24 20:43
DOI: 10.22008/fk2/iw73uu/v3cb5g
title: KAN_M_hour.csv

description: Uploaded with pyDataverse 2024-04-24 20:43
DOI: 10.22008/fk2/iw73uu/4tzzra
title: KAN_M_month.csv

description: Uploaded with pyDataverse 2024-04-24 20:44
DOI: 10.22008/fk2/iw73uu/oliqlj
title: KAN_M_hour.nc

description: Uploaded with pyDataverse 2024-04-24 20:44
DOI: 10.22008/fk2/iw73uu/uoxquu
title: KAN_M_day.nc

description: Uploaded with pyDataverse 2024-04-24 20:44
DOI: 10.22008/fk2/iw73uu/eyc1ta
title: KAN_M_month.nc

description: Uploaded with pyDataverse 2024-04-24 20:44
DOI: 10.22008/fk2/iw73uu/wrmoti
title: KAN_U_day.csv

description: Uploaded with pyDataverse 2024-04-24 20:44
DOI: 10.22008/fk2/iw73uu/phvnwk
title: KAN_U_hour.csv

description: Uploaded with pyDataverse 2024-04-24 20:44
DOI: 10.22008/fk2/iw73uu/9qde4b
title: KAN_U_month.csv

description: Uploaded with pyDataverse 2024-04-24 20:44
DOI: 10.22008/fk2/iw73uu/l6tppp
title: KAN_U_day.nc

description: Uploaded with pyDataverse 2024-04-24 20:45
DOI: 10.22008/fk2/iw73uu/wxmehk
title: KAN_U_hour.nc

description: Uploaded with pyDataverse 2024-04-24 20:45
DOI: 10.22008/fk2/iw73uu/tdlddl
title: KAN_U_month.nc

description: Uploaded with pyDataverse 2024-04-24 20:45
DOI: 10.22008/fk2/iw73uu/aaz2v6
title: KPC_L_day.csv

description: Uploaded with pyDataverse 2024-04-24 20:45
DOI: 10.22008/fk2/iw73uu/2mvpci
title: KPC_L_hour.csv

description: Uploaded with pyDataverse 2024-04-24 20:45