cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.235tym
title: Occurrence Download

description: A dataset containing 416 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dianthus ciliatus",
 "Country is one of (Albania, Croatia, Italy, Montenegro)"
 ]
}

 The dataset includes 416 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045999-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17087140
title: A Comprehensive Framework for Augmenting JWST Observational Capabilities (Extended Version)"

description: Project Summary

Title: A Comprehensive Framework for Augmenting JWST Observational Capabilities

This technical memorandum presents an innovative framework to enhance the observational capabilities of the James Webb Space Telescope (JWST) through five synergistic technologies:

1. Space-Based Adaptive Optics (SBAO): Employs a high-density MEMS deformable mirror and pyramid wavefront sensor to correct micro-vibrational and thermal wavefront errors, achieving a 10²–10³ contrast enhancement for high-contrast exoplanet imaging.2. Quantum-Enhanced Interferometry: Utilizes entangled photon pairs to form a synthetic aperture, enabling an angular resolution of 0.1 microarcseconds, ideal for detailed astrophysical observations.3. AI-Driven Autonomous Scheduling: Implements a Deep Reinforcement Learning (DRL) agent for real-time rescheduling of transient events, improving observation efficiency by over 30%.4. Advanced Cryogenic Systems: Achieves sub-50mK temperatures for far-infrared detectors using a multi-stage cooling system, enabling studies of the cosmic dawn.5. Reconfigurable Metasurface Optics: Provides dynamic aberration correction and tunable spectral properties, reducing wavefront errors to λ/100 RMS.

Key Benefits:- Unprecedented angular resolution and contrast for exoplanet spectroscopy.- Enhanced operational efficiency and sensitivity for transient and far-infrared observations.- Post-launch adaptability through metasurfaces, ensuring long-term mission relevance.

Implementation Plan: A phased R&D program (2026–2035) is proposed, targeting Technology Readiness Level (TRL) 7-8 with an estimated cost of $50–100M. The roadmap includes component validation, subsystem integration, small-satellite demonstration, and on-orbit integration with JWST, in collaboration with NASA’s Jet Propulsion Laboratory, Lockheed Martin, and ESA.

Supporting Elements:- Mathematical Models: Equations for SNR improvement, angular resolution, scheduling efficiency, cooling power, and metasurface phase shifts.- Simulations: Python-based simulations for SBAO wavefront correction and AI scheduling, with graphical outputs.- Visuals: Enhanced TikZ diagrams with clear layouts to avoid text overlap, plus performance plots (e.g., SNR vs. residual error, scheduling efficiency vs. event rate).- References: 20 peer-reviewed sources supporting the technical feasibility.

Conclusion: This framework transforms JWST into a next-generation observatory, enabling groundbreaking science such as direct spectroscopic analysis of Earth-like exoplanets, with a clear path to implementation by 2035.
DOI: 10.5281/zenodo.17087139
title: A Comprehensive Framework for Augmenting JWST Observational Capabilities (Extended Version)"

description: Project Summary

Title: A Comprehensive Framework for Augmenting JWST Observational Capabilities

This technical memorandum presents an innovative framework to enhance the observational capabilities of the James Webb Space Telescope (JWST) through five synergistic technologies:

1. Space-Based Adaptive Optics (SBAO): Employs a high-density MEMS deformable mirror and pyramid wavefront sensor to correct micro-vibrational and thermal wavefront errors, achieving a 10²–10³ contrast enhancement for high-contrast exoplanet imaging.2. Quantum-Enhanced Interferometry: Utilizes entangled photon pairs to form a synthetic aperture, enabling an angular resolution of 0.1 microarcseconds, ideal for detailed astrophysical observations.3. AI-Driven Autonomous Scheduling: Implements a Deep Reinforcement Learning (DRL) agent for real-time rescheduling of transient events, improving observation efficiency by over 30%.4. Advanced Cryogenic Systems: Achieves sub-50mK temperatures for far-infrared detectors using a multi-stage cooling system, enabling studies of the cosmic dawn.5. Reconfigurable Metasurface Optics: Provides dynamic aberration correction and tunable spectral properties, reducing wavefront errors to λ/100 RMS.

Key Benefits:- Unprecedented angular resolution and contrast for exoplanet spectroscopy.- Enhanced operational efficiency and sensitivity for transient and far-infrared observations.- Post-launch adaptability through metasurfaces, ensuring long-term mission relevance.

Implementation Plan: A phased R&D program (2026–2035) is proposed, targeting Technology Readiness Level (TRL) 7-8 with an estimated cost of $50–100M. The roadmap includes component validation, subsystem integration, small-satellite demonstration, and on-orbit integration with JWST, in collaboration with NASA’s Jet Propulsion Laboratory, Lockheed Martin, and ESA.

Supporting Elements:- Mathematical Models: Equations for SNR improvement, angular resolution, scheduling efficiency, cooling power, and metasurface phase shifts.- Simulations: Python-based simulations for SBAO wavefront correction and AI scheduling, with graphical outputs.- Visuals: Enhanced TikZ diagrams with clear layouts to avoid text overlap, plus performance plots (e.g., SNR vs. residual error, scheduling efficiency vs. event rate).- References: 20 peer-reviewed sources supporting the technical feasibility.

Conclusion: This framework transforms JWST into a next-generation observatory, enabling groundbreaking science such as direct spectroscopic analysis of Earth-like exoplanets, with a clear path to implementation by 2035.
DOI: 10.15468/dl.f84jxm
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malachra capitata",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046003-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z4u86n
title: Occurrence Download

description: A dataset containing 93 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malachra capitata",
 "Country is India",
 "HasCoordinate is true"
 ]
}

 The dataset includes 93 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046002-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25671048.v3
title: Data and Code from "Trophic adaptation of large terrestrial omnivores to global change"

description: This package contains macroecological data on the trophic niche of terrestrial bears (Ursidae), as well as paleoecological data on the trophic niche of the European brown bear (<i>Ursus arctos</i>) based on stable isotope analyses.Along with the raw data we provide R code to reproduce the analysis from the paper. For details about how to run the R code please have a look at the README.txt file in the package. For details about the methods (e.g., sample collection or background of particular analyses) we refer to the original publication.
DOI: 10.15468/dl.xn5bqk
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Melanotaenia rubrostriata)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 16 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046004-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wqfsbd
title: Occurrence Download

description: A dataset containing 1496 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-4.99258 41.49882,-4.9923 41.53419,-5.04482 41.53433,-5.04537 41.49546,-4.99278 41.49498,-4.99258 41.49882))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2015-2025"
 ]
}

 The dataset includes 1496 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046005-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17009540
title: Fundamental and Revolutionary Theory of Cosmic Dynamics (A New Horizon in Physics)

description: **Fundamental and Revolutionary Theory of Cosmic Dynamics: A New Horizon in Physics**

 

This book introduces a bold new vision of the universe—one that challenges the foundations of modern cosmology. Instead of relying on invisible particles or hypothetical energy fields, it proposes that the key to understanding cosmic phenomena lies in the geometry of spacetime itself.

 

Through the concept of multi-ring curvature, the author presents a unified framework that explains gravitational lensing, galaxy rotation curves, and cosmic expansion—all without invoking dark matter or dark energy. Every equation is derived with mathematical rigor, and the theory is tested against real astronomical data, including CMB observations and lensing maps from Hubble and JWST.

 

The work is not just theoretical—it includes Python simulations, observational comparisons, and responses to anticipated scientific critiques. It’s a complete scientific manuscript, designed for researchers, physicists, and anyone seeking a deeper, geometry-driven understanding of the cosmos.

 

If you’re ready to explore a universe shaped not by what we cannot see, but by the curvature we can measure—this book opens t

he door.
DOI: 10.5281/zenodo.17009539
title: Fundamental and Revolutionary Theory of Cosmic Dynamics (A New Horizon in Physics)

description: **Fundamental and Revolutionary Theory of Cosmic Dynamics: A New Horizon in Physics**

 

This book introduces a bold new vision of the universe—one that challenges the foundations of modern cosmology. Instead of relying on invisible particles or hypothetical energy fields, it proposes that the key to understanding cosmic phenomena lies in the geometry of spacetime itself.

 

Through the concept of multi-ring curvature, the author presents a unified framework that explains gravitational lensing, galaxy rotation curves, and cosmic expansion—all without invoking dark matter or dark energy. Every equation is derived with mathematical rigor, and the theory is tested against real astronomical data, including CMB observations and lensing maps from Hubble and JWST.

 

The work is not just theoretical—it includes Python simulations, observational comparisons, and responses to anticipated scientific critiques. It’s a complete scientific manuscript, designed for researchers, physicists, and anyone seeking a deeper, geometry-driven understanding of the cosmos.

 

If you’re ready to explore a universe shaped not by what we cannot see, but by the curvature we can measure—this book opens t

he door.
DOI: 10.5281/zenodo.15264882
title: Normalized GEFS Week 4 Forecasts and ERA5 VPD Labels for ML

description: Machine Learning dataset from 2000-2019, specifically used to train UNET neural networks, containing the following training data processed to a CONUS-like domain, 10.5 to 59.5 latitude and -159.5 to -60.5 longitude, on half degree resolution from 11 ensemble members of 6-hourly GEFS data and vapor pressure deficit (VPD) labels created on the same domain from ERA5.

Training data are from years: 2000, 2001, 2003-2006, 2009-2012, 2016, 2017, and 2019

Validation data are from years: 2002, 2008, 2014, and 2018

Blind testing data are from years 2007, 2013, and 2015

The input data are created from week 4 forecasting data produced by the GEFS initialized on the first Wednesdays of the year. Input data included in this dataset are:



'soilw'   # Weekly mean volumetric soil moisture content @ bgrnd_depth

'pwat'    # Weekly mean precipitable water

'2t'      # Weekly mean 2 metre temperature

't'       # Weekly mean temperature at pressure (note two values at 850mb and 200mb)

sine of the number of the week of the year


Finally, the files are normalized by z-score normalization by pressure height (or surface) and variable. They are then saved into npy matrices sized [99, 199, 6] in the above order for NN training purposes. The VPD labels are the coresponding weekly mean VPD per gridcell derived form ERA5 data, and stored in npy files sized [99, 199, 1] for NN label purposes, intended to represent "observed" vpd on the corresponding week three forecast. They have an identical name to the input but are stored in the label directory. The zip files has been zipped in a way that contains subdirectories directories storing the npy files and identical data-label names as the following:

naming example - nn_dataset_YYYY_week_WW_ens_E_f_3.npy

where YYYY = year (2019)

where WW = week (1 through up to 48)

where E = GEFS ensemble number (0-10)

where f_3 means forecast week three (0-4 included in initial GEFS dataset)

Directories are named to divide npy files into:



training data

training labels

validation data

validation labels

testing data

testing labels


Lastly, an additional file called "norm_inference_vars" is concluded and contains the validation and testing input variable datasets standard deviations and means.
DOI: 10.5281/zenodo.17086850
title: Normalized GEFS Week 4 Forecasts and ERA5 VPD Labels for ML

description: Machine Learning dataset from 2000-2019, specifically used to train UNET neural networks, containing the following training data processed to a CONUS-like domain, 10.5 to 59.5 latitude and -159.5 to -60.5 longitude, on half degree resolution from 11 ensemble members of 6-hourly GEFS data and vapor pressure deficit (VPD) labels created on the same domain from ERA5.

Training data are from years: 2000, 2001, 2003-2006, 2009-2012, 2016, 2017, and 2019

Validation data are from years: 2002, 2008, 2014, and 2018

Blind testing data are from years 2007, 2013, and 2015

The input data are created from week 4 forecasting data produced by the GEFS initialized on the first Wednesdays of the year. Input data included in this dataset are:



'soilw'   # Weekly mean volumetric soil moisture content @ bgrnd_depth

'pwat'    # Weekly mean precipitable water

'2t'      # Weekly mean 2 metre temperature

't'       # Weekly mean temperature at pressure (note two values at 850mb and 200mb)

sine of the number of the week of the year


Finally, the files are normalized by z-score normalization by pressure height (or surface) and variable. They are then saved into npy matrices sized [99, 199, 6] in the above order for NN training purposes. The VPD labels are the coresponding weekly mean VPD per gridcell derived form ERA5 data, and stored in npy files sized [99, 199, 1] for NN label purposes, intended to represent "observed" vpd on the corresponding week three forecast. They have an identical name to the input but are stored in the label directory. The zip files has been zipped in a way that contains subdirectories directories storing the npy files and identical data-label names as the following:

naming example - nn_dataset_YYYY_week_WW_ens_E_f_3.npy

where YYYY = year (2019)

where WW = week (1 through up to 48)

where E = GEFS ensemble number (0-10)

where f_3 means forecast week three (0-4 included in initial GEFS dataset)

Directories are named to divide npy files into:



training data

training labels

validation data

validation labels

testing data

testing labels


Lastly, an additional file called "norm_inference_vars" is concluded and contains the validation and testing input variable datasets standard deviations and means.
DOI: 10.15468/dl.j2e57s
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malachra capitata",
 "Country is Philippines",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046006-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8fhkdh
title: Occurrence Download

description: A dataset containing 6553 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((1.47062 45.29638,1.46806 45.29268,1.46749 45.2847,1.46435 45.28499,1.45452 45.26932,1.45495 45.26091,1.45267 45.2582,1.44312 45.26746,1.43671 45.26533,1.42959 45.26875,1.42816 45.26746,1.42831 45.26433,1.43614 45.25678,1.43614 45.25065,1.42645 45.24994,1.41107 45.24225,1.42161 45.2327,1.40352 45.22558,1.40295 45.22387,1.4095 45.22159,1.42118 45.22073,1.43529 45.2176,1.43856 45.21689,1.43671 45.21988,1.43258 45.22757,1.44284 45.22971,1.45039 45.23341,1.45352 45.23028,1.46221 45.23156,1.46335 45.22771,1.46791 45.23056,1.47375 45.23156,1.47703 45.22714,1.48159 45.22672,1.48487 45.22358,1.49441 45.22273,1.50097 45.23071,1.49228 45.23811,1.49327 45.24039,1.50353 45.24438,1.50481 45.24709,1.5061 45.25293,1.51664 45.26134,1.51122 45.26547,1.51863 45.27644,1.49897 45.29154,1.48458 45.29667,1.47062 45.29638))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 6553 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046007-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc277xjy
title: CCDC 2063190: Experimental Crystal Structure Determination

description: Related Article: Nitish Kumar Sinha, Natesan Thirupathi|2021|Organometallics|40|3535|doi:10.1021/acs.organomet.1c00414
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27f0d3
title: CCDC 2068084: Experimental Crystal Structure Determination

description: Related Article: Guanming Liao, Xing Chen, Yali Qiao, Kanglei Liu, Nan Wang, Pangkuan Chen, Xiaodong Yin|2021|Org.Lett.|23|5836|doi:10.1021/acs.orglett.1c01983
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2883nb
title: CCDC 2093171: Experimental Crystal Structure Determination

description: Related Article: Caleb D. Fast, Nathan D. Schley|2021|Organometallics|40|3291|doi:10.1021/acs.organomet.1c00391
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2886md
title: CCDC 2093263: Experimental Crystal Structure Determination

description: Related Article: Issei Nishimura, Tomohiro Higashino, Hiroshi Imahori|2021|Chem.Commun.|57|9606|doi:10.1039/D1CC04215G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc288x1j
title: CCDC 2093927: Experimental Crystal Structure Determination

description: Related Article: Andrey V. Kolos, Yulia V. Nelyubina, Basker Sundararaju, Dmitry S. Perekalin|2021|Organometallics|40|3712|doi:10.1021/acs.organomet.1c00403
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc288x3l
title: CCDC 2093929: Experimental Crystal Structure Determination

description: Related Article: Andrey V. Kolos, Yulia V. Nelyubina, Basker Sundararaju, Dmitry S. Perekalin|2021|Organometallics|40|3712|doi:10.1021/acs.organomet.1c00403
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc288x4m
title: CCDC 2093930: Experimental Crystal Structure Determination

description: Related Article: Andrey V. Kolos, Yulia V. Nelyubina, Basker Sundararaju, Dmitry S. Perekalin|2021|Organometallics|40|3712|doi:10.1021/acs.organomet.1c00403
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc277xn2
title: CCDC 2063194: Experimental Crystal Structure Determination

description: Related Article: Nitish Kumar Sinha, Natesan Thirupathi|2021|Organometallics|40|3535|doi:10.1021/acs.organomet.1c00414
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc288x6p
title: CCDC 2093932: Experimental Crystal Structure Determination

description: Related Article: Andrey V. Kolos, Yulia V. Nelyubina, Basker Sundararaju, Dmitry S. Perekalin|2021|Organometallics|40|3712|doi:10.1021/acs.organomet.1c00403
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.