cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17230561
title: Costa Rica. World Trade. Comercio Exterior. Importó, Exportó. Flujo Total, Valor Total, Valor Unitario

description: Este archivo describe el comercio exterior de Costa Rica según datos diponibles con detalle mensual desde 2001 01 hasta 2025 04

Incluye importaciones, exportaciones; y cada una de ellas en tres tipos de datos,según la disponibilidad y unidades de la información recopilada de trademap.org

 

Flujo total, la suma de la masa física de las mercancias agragada en un mismo tipo de unidad.

Valor Total,la suma en unidades de1.000 dolares corrientes de Estados Unidos de América

Valor Unitario, la division del valor total en le flujo total en la unidad disponible

 

Es de anotar que las unidades de medicion peden cambiar entre periodos.

El código arancelario usado es de 4 cifras junto a la descripción de la clasificación.

 

La hoja"A" contiene las convencines de color y de accino para cada una de las hojas

La hoja "0" entre las columnas "A hasta H" contine

A: País

B: Rol del Pais importador, o exportador

C: Tipo: Variable de la que trata la fila segun la "unidad de medida" correspondiente

-Flujo Total: Volumen, masa, total de mercancias

-Valor total: Cantidad económica de la transacción en miles de dóllares corrientes de EUA, 1000 USDC

-Valor unitario: Promedio de dóllares corrientes de EUA para una sola unidad de volumen o masa

D: Código.2: Codificación arancelaria de 2 cifras

E: Arancel.2: Nombre del renglón aracncelaria del "Código.2"

F: Código.4: Codificación arancelaria de 4 cifras

G: Arancel.4: Nombre del renglón aracncelaria del "Código.4"

H: Unidad de Medida de la Fila

Columnas I-KN: Datos de transacciones comerciales segun el encabezado del año y mes, pe año 2005 mes 3 "2005-03"

Columnas KL a CAA: Estadisticas calculadas para cada grupo de meses o años y autocorrelacion y correlacion contar el incremento de tiempo

 

Hoja "1": Dato estandarizado: dato centrado dividido en la desviación estandar con datos no cero o no vacios

Hoja "2": Datos en la escala estandarizada de "-1 hasta +1" con datos no cero o no vacios

Hoja "3": Dato actual dividido en el dato anterior

Hoja "4": Primera diferencia: dato menos dato anterior

Hoja "5": Seguda difrencia: Primera diferencia actual menos primera diferencia del periodo anterior: dato actual menos 2 veces el dato anterior más 2 vez el dato de hhace dos periodos

 

Este trabajo es posible al proyecto de investigacion con recursos en especie de la Universidad Distrital "Francisco José de Caldas" llamado "Comercio Exterior 2002 a 2025" del Grupo de investigacion Armónico por Wilson Alexander Pinzón Rueda, en el programa de Ingeniería de Producción por Ciclos Propedéiticos de la Facultad Tecnológica.
DOI: 10.5281/zenodo.17230560
title: Costa Rica. World Trade. Comercio Exterior. Importó, Exportó. Flujo Total, Valor Total, Valor Unitario

description: Este archivo describe el comercio exterior de Costa Rica según datos diponibles con detalle mensual desde 2001 01 hasta 2025 04

Incluye importaciones, exportaciones; y cada una de ellas en tres tipos de datos,según la disponibilidad y unidades de la información recopilada de trademap.org

 

Flujo total, la suma de la masa física de las mercancias agragada en un mismo tipo de unidad.

Valor Total,la suma en unidades de1.000 dolares corrientes de Estados Unidos de América

Valor Unitario, la division del valor total en le flujo total en la unidad disponible

 

Es de anotar que las unidades de medicion peden cambiar entre periodos.

El código arancelario usado es de 4 cifras junto a la descripción de la clasificación.

 

La hoja"A" contiene las convencines de color y de accino para cada una de las hojas

La hoja "0" entre las columnas "A hasta H" contine

A: País

B: Rol del Pais importador, o exportador

C: Tipo: Variable de la que trata la fila segun la "unidad de medida" correspondiente

-Flujo Total: Volumen, masa, total de mercancias

-Valor total: Cantidad económica de la transacción en miles de dóllares corrientes de EUA, 1000 USDC

-Valor unitario: Promedio de dóllares corrientes de EUA para una sola unidad de volumen o masa

D: Código.2: Codificación arancelaria de 2 cifras

E: Arancel.2: Nombre del renglón aracncelaria del "Código.2"

F: Código.4: Codificación arancelaria de 4 cifras

G: Arancel.4: Nombre del renglón aracncelaria del "Código.4"

H: Unidad de Medida de la Fila

Columnas I-KN: Datos de transacciones comerciales segun el encabezado del año y mes, pe año 2005 mes 3 "2005-03"

Columnas KL a CAA: Estadisticas calculadas para cada grupo de meses o años y autocorrelacion y correlacion contar el incremento de tiempo

 

Hoja "1": Dato estandarizado: dato centrado dividido en la desviación estandar con datos no cero o no vacios

Hoja "2": Datos en la escala estandarizada de "-1 hasta +1" con datos no cero o no vacios

Hoja "3": Dato actual dividido en el dato anterior

Hoja "4": Primera diferencia: dato menos dato anterior

Hoja "5": Seguda difrencia: Primera diferencia actual menos primera diferencia del periodo anterior: dato actual menos 2 veces el dato anterior más 2 vez el dato de hhace dos periodos

 

Este trabajo es posible al proyecto de investigacion con recursos en especie de la Universidad Distrital "Francisco José de Caldas" llamado "Comercio Exterior 2002 a 2025" del Grupo de investigacion Armónico por Wilson Alexander Pinzón Rueda, en el programa de Ingeniería de Producción por Ciclos Propedéiticos de la Facultad Tecnológica.
DOI: 10.7910/dvn/t0cenh
title: Federal University of Health Sciences Kwale, Delta State 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.

description: Federal University of Health Sciences Kwale, Delta State 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
description: Federal University of Health Sciences Kwale, Delta State 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
DOI: 10.15468/dl.ntrhnr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepisosteus oculatus",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032152-250920141307145/datasets/export for details.
DOI: 10.6084/m9.figshare.30239668
title: Original Variable Density Logging

description: Original Variable Density Logging (VDL) images data
DOI: 10.6084/m9.figshare.30239668.v1
title: Original Variable Density Logging

description: Original Variable Density Logging (VDL) images data
DOI: 10.15468/dl.wnvg7p
title: Occurrence Download

description: A dataset containing 642 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Macaca mulatta"
 ]
}

 The dataset includes 642 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032154-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k5wyvz
title: Occurrence Download

description: A dataset containing 1340 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Hong Kong)",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((74.00169 36.56675,94.43311 34.17917,95.24037 31.76773,101.27195 21.46231,105.9256 18.38225,110.61317 17.49323,131.97211 45.39873,77.80024 48.52811,74.00169 36.56675))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Macaca mulatta",
 "Year 2000-2025"
 ]
}

 The dataset includes 1340 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032156-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17230428
title: Mitochondrial calcium levels regulate SynGAP via mitochondrial dynamics
DOI: 10.5281/zenodo.17230427
title: Mitochondrial calcium levels regulate SynGAP via mitochondrial dynamics
DOI: 10.13026/b1c0-3506
title: Organ Retrieval and Collection of Health Information for Donation (ORCHID)

description: There are well-documented inefficiencies and inequities in the current system
of deceased donor organ transplantation. While much prior research has focused
on designing better allocation systems to distribute donated organs, more can
be done to study organ procurement, the process by which organs are recovered
from deceased donors. Improvements to organ procurement can greatly increase
the supply of a scarce public resource, improving health outcomes for the more
than 100,000 patients currently waiting for an organ transplant. Such changes
will also impact health equity, as a disproportionate number of waitlisted
patients come from marginalized communities. So far, research into organ
procurement has been hamstrung by a lack of transparent data. To address this
pressing issue, we have collected the first ever multi-center dataset on organ
procurement, covering ten years of clinical, financial, and administrative
information from six organ procurement organizations (OPOs). This novel
dataset can be used to identify opportunities to improve current practice,
build tools to help OPOs make better decisions, and recommend policy changes
to increase procurement.
DOI: 10.13026/rfeq-j318
title: Organ Retrieval and Collection of Health Information for Donation (ORCHID)

description: There are well-documented inefficiencies and inequities in the current system
of deceased donor organ transplantation. While much prior research has focused
on designing better allocation systems to distribute donated organs, more can
be done to study organ procurement, the process by which organs are recovered
from deceased donors. Improvements to organ procurement can greatly increase
the supply of a scarce public resource, improving health outcomes for the more
than 100,000 patients currently waiting for an organ transplant. Such changes
will also impact health equity, as a disproportionate number of waitlisted
patients come from marginalized communities. So far, research into organ
procurement has been hamstrung by a lack of transparent data. To address this
pressing issue, we have collected the first ever multi-center dataset on organ
procurement, covering ten years of clinical, financial, and administrative
information from six organ procurement organizations (OPOs). This novel
dataset can be used to identify opportunities to improve current practice,
build tools to help OPOs make better decisions, and recommend policy changes
to increase procurement.
DOI: 10.34740/kaggle/dsv/13212851
title: Predicting the Beats-per-Minute of Songs | Vault
DOI: 10.15468/dl.ty9m3v
title: Occurrence Download

description: A dataset containing 485 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chinese Taipei",
 "Geometry POLYGON((121.33716 25.42018,121.18395 25.31803,120.08357 24.24087,119.93499 23.01977,120.39929 21.84975,121.02144 21.76153,121.39288 22.76905,121.91753 24.58444,122.45147 25.16481,121.33716 25.42018))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Eurya chinensis",
 "Year 1993-2025"
 ]
}

 The dataset includes 485 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032157-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17230447
title: Modeling geoid and dynamic topography from tomography-based thermo-chemical mantle convection

description: This repository accompanies the paper "Cui R., Steinberger B., Fang J.: Modeling geoid and dynamic topography from tomography-based thermo-chemical mantle convection".

There are two files in this repository, which are the input files for the software CitcomS used in the paper.
DOI: 10.5281/zenodo.17230448
title: Modeling geoid and dynamic topography from tomography-based thermo-chemical mantle convection

description: This repository accompanies the paper "Cui R., Steinberger B., Fang J.: Modeling geoid and dynamic topography from tomography-based thermo-chemical mantle convection".

There are two files in this repository, which are the input files for the software CitcomS used in the paper.
DOI: 10.7910/dvn/womqtw
title: Abia State University, Uturu 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.

description: Abia State University, Uturu 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
description: Abia State University, Uturu 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
DOI: 10.7910/dvn/wuzex9
title: Adamawa State University Mubi 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.

description: Adamawa State University Mubi 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
description: Adamawa State University Mubi 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
DOI: 10.5281/zenodo.17204568
title: Supplementary Data for the Predictive Model for Atmospheric Substances and Trace Pollutants in the Environment Using Machine Learning (PASTEL)

description: Supporting datasets and ensemble members/submembers for the PASTEL model.

Brief description of individual entries:





v0_1_5_Awakens.csv — A merged dataset combining multiple airborne campaigns with supplementary 24-hour backward trajectory information. Represents the input samples used to train PASTEL.




Koppen_npy_files.zip — Numpy arrays containing merged land (Beck et al. 2023) and ocean (Walterscheid 2011) Köppen climate classifications at 0.5° x 0.5° global resolution. Includes a Matplotlib colormap (Python .pkl), following Beck et al. (2023), along with alternative Köppen representations.




worldcities.zip — Simplemaps basic dataset (see attribution and license within).




df_preprocessed.csv — A preprocessed version of v0_1_5_Awakens.csv containing additional derived features and statistics. Can be used to bypass preprocessing steps in the main PASTEL notebook.




AllTrajectories.zip — All 24-hour backward HYSPLIT trajectories generated for each sample in v0_1_5_Awakens.csv and df_preprocessed.csv, with varying meteorological inputs (see associated publication for details).




ne_10m_land.zip — Natural Earth shapefile containing 10-meter resolution land boundaries.




ERA5_32yr_monthly_avg.nc — NetCDF file containing 32-year monthly averages of ERA5 data (ozone, specific humidity, relative humidity, temperature) over the study period.




ensemble.zip — Ensemble members and submembers contributing to PASTEL predictions, along with derived statistics and plots (≈27 GB uncompressed).



License





Code (not included here, see linked repository): GNU General Public License v3.0 (GPLv3).




Data: Creative Commons Attribution–ShareAlike 4.0 International (CC-BY-SA 4.0).




Third-party data (Simplemaps, Natural Earth) is redistributed under their respective licenses (see included attributions).



Citation

If you use this dataset, please cite:



Geiser, Victor (2025). Supplementary data for the Predictive model for Atmospheric Substances and Trace pollutants in the Environment using machine Learning (PASTEL). Zenodo. https://doi.org/10.5281/zenodo.17204569


How to Use

These datasets are intended for use with the PASTEL model, but may also be of independent value for climate classification, atmospheric transport analysis, or ensemble modeling.

Size Warning

"ensemble.zip" is roughly 27GB uncompressed as statistics/plotting information for all members/submembers is included!

Contact

For questions regarding this dataset or publication please contact victor.w.geiser[at]gmail.com
DOI: 10.5281/zenodo.17204569
title: Supplementary Data for the Predictive Model for Atmospheric Substances and Trace Pollutants in the Environment Using Machine Learning (PASTEL)

description: Supporting datasets and ensemble members/submembers for the PASTEL model.

Brief description of individual entries:





v0_1_5_Awakens.csv — A merged dataset combining multiple airborne campaigns with supplementary 24-hour backward trajectory information. Represents the input samples used to train PASTEL.




Koppen_npy_files.zip — Numpy arrays containing merged land (Beck et al. 2023) and ocean (Walterscheid 2011) Köppen climate classifications at 0.5° x 0.5° global resolution. Includes a Matplotlib colormap (Python .pkl), following Beck et al. (2023), along with alternative Köppen representations.




worldcities.zip — Simplemaps basic dataset (see attribution and license within).




df_preprocessed.csv — A preprocessed version of v0_1_5_Awakens.csv containing additional derived features and statistics. Can be used to bypass preprocessing steps in the main PASTEL notebook.




AllTrajectories.zip — All 24-hour backward HYSPLIT trajectories generated for each sample in v0_1_5_Awakens.csv and df_preprocessed.csv, with varying meteorological inputs (see associated publication for details).




ne_10m_land.zip — Natural Earth shapefile containing 10-meter resolution land boundaries.




ERA5_32yr_monthly_avg.nc — NetCDF file containing 32-year monthly averages of ERA5 data (ozone, specific humidity, relative humidity, temperature) over the study period.




ensemble.zip — Ensemble members and submembers contributing to PASTEL predictions, along with derived statistics and plots (≈27 GB uncompressed).



License





Code (not included here, see linked repository): GNU General Public License v3.0 (GPLv3).




Data: Creative Commons Attribution–ShareAlike 4.0 International (CC-BY-SA 4.0).




Third-party data (Simplemaps, Natural Earth) is redistributed under their respective licenses (see included attributions).



Citation

If you use this dataset, please cite:



Geiser, Victor (2025). Supplementary data for the Predictive model for Atmospheric Substances and Trace pollutants in the Environment using machine Learning (PASTEL). Zenodo. https://doi.org/10.5281/zenodo.17204569


How to Use

These datasets are intended for use with the PASTEL model, but may also be of independent value for climate classification, atmospheric transport analysis, or ensemble modeling.

Size Warning

"ensemble.zip" is roughly 27GB uncompressed as statistics/plotting information for all members/submembers is included!

Contact

For questions regarding this dataset or publication please contact victor.w.geiser[at]gmail.com
DOI: 10.7910/dvn/zwnmdg
title: Adekunle Ajasin University, Akungba 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.

description: Adekunle Ajasin University, Akungba 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
description: Adekunle Ajasin University, Akungba 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
DOI: 10.15468/dl.rpeb9r
title: Occurrence Download

description: A dataset containing 7078 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Capsicum annuum annuum"
 ]
}

 The dataset includes 7078 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032158-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pn9f59
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Observation, Occurrence evidence)",
 "Geometry POLYGON((112.17557 -7.08705,112.45782 -7.08705,112.45782 -6.90452,112.17557 -6.90452,112.17557 -7.08705))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 32 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032159-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/bgqsgq
title: Akwa Ibom State University of Technology, Uyo 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.

description: Akwa Ibom State University of Technology, Uyo 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
description: Akwa Ibom State University of Technology, Uyo 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
DOI: 10.7910/dvn/w4gc03
title: Ambrose Alli University, Ekpoma 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.

description: Ambrose Alli University, Ekpoma 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
description: Ambrose Alli University, Ekpoma 2025/2026 Admission List Is Out Now Call Admin Office Call 08142094711, (08142094711) To Check And Further Admission Assistance Call +2348142094711.
DOI: 10.25919/tyen-ny06
title: NBDL Data Download