cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ekxzw5
title: Occurrence Download

description: A dataset containing 13265 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Salicornia europaea (Moss) Lambinon & Vanderp.",
 "HasCoordinate is true",
 "Continent is Europe",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000",
 "CoordinateUncertaintyInMeters is less than 10000"
 ]
}

 The dataset includes 13265 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005665-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.65zycv
title: Occurrence Download

description: A dataset containing 713979 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is AD AVH Data",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 713979 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0347185-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rczxtr
title: Occurrence Download

description: A dataset containing 3030 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus laurina Bonpl."
 ]
}

 The dataset includes 3030 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018440-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.29cs55
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aphragmus obscurus (Dunn) O.E.Schulz"
 ]
}

 The dataset includes 43 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060229-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6jnkzg
title: Occurrence Download

description: A dataset containing 476 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Nannotrigona mellaria (Smith, 1862)"
 ]
}

 The dataset includes 476 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0123574-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bcfzxc
title: Occurrence Download

description: A dataset containing 756 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Especies de Anopheles en el sur de México y su distribución geográfica actual con enfoque en taxonomía clásica y molecular",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 756 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018446-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m6v85n
title: Occurrence Download

description: A dataset containing 109 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Aniba puchury-minor (Mart.) Mez"
 ]
}

 The dataset includes 109 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0129862-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zhcctw
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "StateProvince is nuevo león",
 "HasCoordinate is true",
 "TaxonKey is Acacia Mill.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018431-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pkaw8w
title: Occurrence Download

description: A dataset containing 552778 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((38.54883 -28.14175,59.11523 -24.03844,58.93945 -16.79613,54.28125 -7.52805,42.76758 -7.35375,38.54883 -28.14175))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 552778 records from 200 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0117448-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xgnhj3
title: Occurrence Download

description: A dataset listing the 20 species recorded in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Especies de Anopheles en el sur de México y su distribución geográfica actual con enfoque en taxonomía clásica y molecular",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 20 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018443-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5kw37r
title: Occurrence Download

description: A dataset containing 26047 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cucumis melo L."
 ]
}

 The dataset includes 26047 records from 357 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000338-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60635/c39c75
title: San Blas, all data

description: The San Blas field station was established in 1977 and closed on June 1,1998.The station was situated on an island adjacent to Punta de San Blas, on the Caribbean coast of Panama 120 km east of the Panama Canal, midway to the Panama/Colombia border. That island is at the western end of Guna Yala, an indigenous reserve (also known as the Comarca de San Blas) that stretches along 160 km of the eastern coast of Panama. Smithsonian scientists started working in Guna Yala in 1970. The field station supported short-term biological studies in addition to as well as long-term meteorological and biological monitoring. Physical Monitoring activities began at the San Blas station in 1992.Monitoring activities included the measurement of air temperature, precipitation, solar radiation, wind speed and direction, sea level, water temperature and salinity.
DOI: 10.48597/hf3b-wkch
title: Measurement of Nitrogen oxides at SIRTA Atmospheric Research Observatory

description: Measurement of Nitrogen oxides at SIRTA Atmospheric Research Observatory
DOI: 10.6084/m9.figshare.28451567
title: Subgroup analysis

description: Supplementary table
DOI: 10.6084/m9.figshare.28451567.v4
title: Subgroup analysis

description: Supplementary table
DOI: 10.6084/m9.figshare.28451567.v5
title: Subgroup analysis

description: Supplementary table
DOI: 10.6084/m9.figshare.28457672
title: Earth greening and climate change reshaping the patterns of terrestrial water sinks and sources

description: We use multisource datasets from climate model projections, remote sensing, and local measurements to examine the impact of vegetation dynamics on water availability over the periods of 1982 to 2019 and 2015 to 2100.The datasets includes the results of our main findings.
DOI: 10.25625/12eq4t
title: EVaR optimization for portfolios using a primal-dual approach

description: A Python implementation, that optimizes the Entropic Value-at-Risk (EVaR) of a portfolio for given historical data using a feasible primal-dual algorithm.
DOI: 10.25625/12eq4t/8hmj1p
title: evar_final.ipynb

description: :unav
DOI: 10.5281/zenodo.10939110
title: Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

description: ML-GPS: Machine Learning-Assisted Genetic Priority Score

This Zenodo repository contains data and code associated with the publication:

Chen R, Duffy Á, Petrazzini BO, Vy HM, Stein D, Mort M, Park JK, Schlessinger A, Itan Y, Cooper DN, Jordan DM, Rocheleau G, Do R. Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score. Nat Commun. 2024 Oct 15;15(1):8891. doi: 10.1038/s41467-024-53333-y.

Important notes



You can interactively view the top 10% of ML-GPS predictions without download at https://rstudio-connect.hpc.mssm.edu/mlgps/.

For running Jupyter notebooks, please follow the instructions in the README of the GitHub repository at https://github.com/robchiral/ML-GPS.


Repository contents

Files needed to train ML-GPS and ML-GPS DOE:



Files needed for Jupyter notebooks.zip: Data files required for preprocessing and training.

Jupyter notebooks.zip: Notebooks for cleaning data, training models, and generating predictions.


Other files:



Predictions for all gene-phecode pairs.zip: ML-GPS and ML-GPS DOE scores for all analyzed gene-phecode pairs.

Summary statistics.zip: Genetic association summary statistics for all tested gene-phecode pairs.


Updated performance metrics




Model
Open Targets AUPRC
SIDER AUPRC


ML-GPS (non-DOE)
0.074
0.080


ML-GPS DOE (activator predictions)
0.029
0.042


ML-GPS DOE (inhibitor predictions)
0.067
0.064




Zenodo versions



Version 4: Updated notebooks and external data to use Open Targets 2024.9; summary statistics are unchanged

Version 3: Corrected error where DOE for rare and ultrarare variants was incorrectly incorporated

Version 2: Original release accompanying the publication
DOI: 10.5281/zenodo.10939111
title: Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

description: Gene-phecode pairs in the top 10% of ML-GPS scores can be accessed interactively at https://rstudio-connect.hpc.mssm.edu/mlgps/.

This respository contains the following data:



All ML-GPS and ML-GPS DOE predictions (Predictions for all gene-phecode pairs.zip)

Summary statistics for all 112 phecodes (Summary statistics.zip)

Cleaned genetics and drug datasets (Cleaned files to generate Open Targets and SIDER datasets.zip)

Input datasets for ML-GPS and ML-GPS DOE training and prediction (Inputs for ML-GPS training and prediction.zip)

Code to clean data and train models (Jupyter Notebooks.zip)


Dependencies can be found within each Jupyter Notebook; these analyses were performed using Python 3.12.
DOI: 10.5281/zenodo.13756169
title: Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

description: Gene-phecode pairs in the top 10% of ML-GPS scores can be accessed interactively at https://rstudio-connect.hpc.mssm.edu/mlgps/.

This respository contains the following data:



All ML-GPS and ML-GPS DOE predictions (Predictions for all gene-phecode pairs.zip)

Summary statistics for all 112 phecodes (Summary statistics.zip)

Cleaned genetics and drug datasets (Cleaned files to generate Open Targets and SIDER datasets.zip)

Input datasets for ML-GPS and ML-GPS DOE training and prediction (Inputs for ML-GPS training and prediction.zip)

Code to clean data and train models (Jupyter Notebooks.zip)


Dependencies can be found within each Jupyter Notebook; these analyses were performed using Python 3.12.
DOI: 10.5281/zenodo.14502857
title: Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

description: Gene-phecode pairs in the top 10% of ML-GPS scores can be accessed interactively at https://rstudio-connect.hpc.mssm.edu/mlgps/.

This respository contains the following data:



All ML-GPS and ML-GPS DOE predictions (Predictions for all gene-phecode pairs.zip)

Summary statistics for all 112 phecodes (Summary statistics.zip)

Cleaned genetics and drug datasets (Cleaned files to generate Open Targets and SIDER datasets.zip)

Input datasets for ML-GPS and ML-GPS DOE training and prediction (Inputs for ML-GPS training and prediction.zip)

Code to clean data and train models (Jupyter Notebooks.zip)


Dependencies can be found within each Jupyter Notebook; these analyses were performed using Python 3.12.

----------

New for version 3 (December 16, 2024): We have corrected an error in the direction-of-effect (DOE) model where effect directions for rare and ultrarare variants were not correctly incorporated into the model. We use the following framework to assign predicted mechanisms to variants/genes:



Gain of function and beta > 0: inhibitor drug

Gain of function and beta < 0: activator drug

Loss of function and beta > 0: activator drug

Loss of function and beta < 0: inhibitor drug


We updated the code and outputs in this version, resulting in an approximately 30% increase in AUPRC for ML-GPS DOE compared to the original results reported in our paper.
DOI: 10.5281/zenodo.14905752
title: Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

description: ML-GPS: Machine Learning-Assisted Genetic Priority Score

This Zenodo repository contains data and code associated with the publication:

Chen R, Duffy Á, Petrazzini BO, Vy HM, Stein D, Mort M, Park JK, Schlessinger A, Itan Y, Cooper DN, Jordan DM, Rocheleau G, Do R. Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score. Nat Commun. 2024 Oct 15;15(1):8891. doi: 10.1038/s41467-024-53333-y.

Important notes



You can interactively view the top 10% of ML-GPS predictions without download at https://rstudio-connect.hpc.mssm.edu/mlgps/.

For running Jupyter notebooks, please follow the instructions in the README of the GitHub repository at https://github.com/robchiral/ML-GPS.


Repository contents

Files needed to train ML-GPS and ML-GPS DOE:



Files needed for Jupyter notebooks.zip: Data files required for preprocessing and training.

Jupyter notebooks.zip: Notebooks for cleaning data, training models, and generating predictions.


Other files:



Predictions for all gene-phecode pairs.zip: ML-GPS and ML-GPS DOE scores for all analyzed gene-phecode pairs.

Summary statistics.zip: Genetic association summary statistics for all tested gene-phecode pairs.


Updated performance metrics




Model
Open Targets AUPRC
SIDER AUPRC


ML-GPS (non-DOE)
0.074
0.080


ML-GPS DOE (activator predictions)
0.029
0.042


ML-GPS DOE (inhibitor predictions)
0.067
0.064




Zenodo versions



Version 4: Updated notebooks and external data to use Open Targets 2024.9; summary statistics are unchanged

Version 3: Corrected error where DOE for rare and ultrarare variants was incorrectly incorporated

Version 2: Original release accompanying the publication
DOI: 10.5281/zenodo.14905930
title: calochortus_balsensis_spades_01.fasta.gz
DOI: 10.5281/zenodo.14905931
title: calochortus_balsensis_spades_01.fasta.gz
DOI: 10.60635/c35p58
title: Monthly summary - San Blas

description: The San Blas field station was established in 1977 and closed on June 1,1998.The station was situated on an island adjacent to Punta de San Blas, on the Caribbean coast of Panama 120 km east of the Panama Canal, midway to the Panama/Colombia border. That island is at the western end of Guna Yala, an indigenous reserve (also known as the Comarca de San Blas) that stretches along 160 km of the eastern coast of Panama. Smithsonian scientists started working in Guna Yala in 1970. The field station supported short-term biological studies in addition to as well as long-term meteorological and biological monitoring. Physical Monitoring activities began at the San Blas station in 1992.Monitoring activities included the measurement of air temperature, precipitation, solar radiation, wind speed and direction, sea level, water temperature and salinity.station in 1992.
DOI: 10.5281/zenodo.13844351
title: Pauli Decomposition via the Fast Walsh-Hadamard Transform v3

description: Scripts and data used to produce results for the paper "Pauli Decomposition via the Fast Walsh-Hadamard Transform" (https://arxiv.org/abs/2408.06206; Georges et al 2025 New J. Phys. https://doi.org/10.1088/1367-2630/adb44d) . 

In the previous version, there was a bug in generating kinetic energy matrix where one of the FFTs should have been transformed and not only conjugated. This did not practically change the timings but the input matrix was incorrect. Now it's fixed in "kinetic_energy_matrix.zip". All other files are the same as in the second version.
DOI: 10.5281/zenodo.13844352
title: Pauli Decomposition via the Fast Walsh-Hadamard Transform

description: scirpt and data used to produce results for the paper "Pauli Decomposition via the Fast Walsh-Hadamard Transform" (https://arxiv.org/abs/2408.06206).
DOI: 10.5281/zenodo.14747833
title: Pauli Decomposition via the Fast Walsh-Hadamard Transform v2

description: scirpt and data used to produce results for the paper "Pauli Decomposition via the Fast Walsh-Hadamard Transform" (https://arxiv.org/abs/2408.06206). 

In this second version, we uploaded scripts for two new tests: calcualtion of kinetic energy matrix (kinetic_energy_matrix.zip) and electron repulsion integrals (fe2s2.zip). All other files are the same as in version no. 1.
DOI: 10.5281/zenodo.14905815
title: Pauli Decomposition via the Fast Walsh-Hadamard Transform v3

description: Scripts and data used to produce results for the paper "Pauli Decomposition via the Fast Walsh-Hadamard Transform" (https://arxiv.org/abs/2408.06206; Georges et al 2025 New J. Phys. https://doi.org/10.1088/1367-2630/adb44d) . 

In the previous version, there was a bug in generating kinetic energy matrix where one of the FFTs should have been transformed and not only conjugated. This did not practically change the timings but the input matrix was incorrect. Now it's fixed in "kinetic_energy_matrix.zip". All other files are the same as in the second version.
DOI: 10.48597/5kfw-vp5p
title: Measurement of Nitrogen oxides at SIRTA Atmospheric Research Observatory

description: Measurement of Nitrogen oxides at SIRTA Atmospheric Research Observatory
DOI: 10.15493/bea.data.10000063
title: Location of Grain Silos

description: The dataset was digitised from records kept by the Johannesburg Stock Exchange to inform commodity prices and availabilities. The data set is in the public domain, and includes information on shipping distances to main commodity markets and capacities for different types of grain.
DOI: 10.25921/pkjs-5w29
title: GLobal Ocean Data Analysis Project (GLODAP) version 1.1 (NCEI Accession 0001644)

description: The GLobal Ocean Data Analysis Project (GLODAP) is a cooperative effort to coordinate global synthesis projects funded through NOAA/DOE and NSF as part of the Joint Global Ocean Flux Study - Synthesis and Modeling Project (JGOFS-SMP). Cruises conducted as part of the World Ocean Circulation Experiment (WOCE), Joint Global Ocean Flux Study (JGOFS) and NOAA Ocean-Atmosphere Exchange Study (OACES) over the decade of the 1990s have created an oceanographic database of unparalleled quality and quantity. These data provide an important asset to the scientific community investigating carbon cycling in the oceans.
DOI: 10.3334/cdiac/otg.gcp_sab_2005-2006
title: Partial pressure (or fugacity) of carbon dioxide, water temperature, salinity and other variables collected from surface underway observations during R/V Cape Hatteras cruises in the US South East Coastal Waters and North Atlantic Ocean from 2005-01-05 to 2006-05-27 (NCEI Accession 0051983)

description: This dataset includes surface underway, chemical, meteorological and physical data collected during R/V Cape Hatteras cruises in the US South East Coastal Waters and North Atlantic Ocean from 2005-01-05 to 2006-05-27. These data include partial pressure (or fugacity) of carbon dioxide - water, sea surface salinity, sea surface water temperature and barometric pressure. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer and shower head chamber equilibrator for autonomous carbon dioxide (CO2) measurement. These data were collected by Wei-Jun Cai of University of Delaware as part of the South Atlantic Bight (SAB) dataset. The survey was carried out on six cruises: 5-16 January 2005, 19-30 March 2005, 27 July - 5 August 2005, 7-17 October 2005, 16-21 December 2005, and 17-27 May 2006. In all of the sampling cruises except for the one in December 2005, the research vessel transected the whole SAB from coastline to about 500 m water depth. The survey focused on 5 cross-shelf transects that are named E-, D-, C-, B-, and A-transects respectively from north to south (Figure 1). In December 2005 the ship still transected the whole SAB, but did not cover D- and B-transects and did not go beyond the 200 m isobath. Sea level pressure was recorded using an on board R.M. Young® barometric pressure sensor. Sea surface temperature (SST) and salinity were recorded continuously with an on-board SeaBird® flow through thermosalinography. Surface water and atmospheric xCO2 were also measured underway during all cruises.
DOI: 10.3334/cdiac/otg.ndp088(v2012)
title: LDEO Database (Version 2012): Global Ocean Surface Water Partial Pressure of CO2 Database: Measurements Performed During 1957-2013 (NCEI Accession 0059946)

description: This dataset includes Surface underway, chemical, meteorological and physical data collected in global oceans from 1957-10-22 to 2013-03-21. These data include BAROMETRIC PRESSURE, Partial pressure (or fugacity) of carbon dioxide - water, SALINITY, SEA SURFACE TEMPERATURE and WATER TEMPERATURE. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer.

These data were collected by Alex Kozyr of Carbon Dioxide Information Analysis Center (CDIAC) and Stewart C. Sutherland and Taro Takahashi of Lamont-Doherty Earth Observatory (LDEO) as part of the LDEO Database (Version 2012) dataset. CDIAC associated the following cruise ID with this dataset: LDEO20120101
DOI: 10.3334/cdiac/otg.vos_exp2003
title: Partial pressure (or fugacity) of carbon dioxide, salinity and other variables collected from underway - surface observations using Barometric pressure sensor, Carbon dioxide (CO2) gas analyzer and other instruments from the EXPLORER OF THE SEAS in the Caribbean Sea, Gulf of Mexico and North Atlantic Ocean from 2003-02-08 to 2004-01-03 (NCEI Accession 0081032)

description: This dataset includes biological, chemical, meteorological, physical and underway - surface data collected from EXPLORER OF THE SEAS in the Caribbean Sea, Gulf of Mexico and North Atlantic Ocean from 2003-02-08 to 2004-01-03 and retrieved during cruise EX0304E - EX0350E. These data include AIR TEMPERATURE, BAROMETRIC PRESSURE, CARBON DIOXIDE - AIR, CARBON DIOXIDE - PARTIAL PRESSURE - DIFFERENCE, DISSOLVED OXYGEN, FLUORESCENCE, Partial pressure (or fugacity) of carbon dioxide, SALINITY, SEA SURFACE TEMPERATURE, WIND DIRECTION and WIND SPEED. The instruments used to collect these data include Barometric pressure sensor, Carbon dioxide (CO2) gas analyzer, Shower head chamber equilibrator for autonomous carbon dioxide (CO2) measurement and thermosalinographs. These data were collected by Rik Wanninkhof of NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML) as part of the VOS Explorer of the Seas Lines 2003 dataset.