cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e225321
title: 2018 New Markets Tax Credit Public Data Release Source Data: COMMUNITY DEVELOPMENT FINANCIAL INSTITUTIONS FUND

description: The New Markets Tax Credit Program (NMTC Program) permits taxpayers to receive a credit against Federal income
taxes for making qualified equity investments in designated Community Development Entities (CDEs). Substantially all
of the qualified equity investment (QEI) must in turn be used by the CDE to provide investments in low-income
communities. The credit provided to the investor totals 39 percent of the cost of the investment and is claimed over a
seven-year credit allowance period.<br><br> • The CDFI Fund requires all CDEs that have been awarded NMTC allocations (Allocatees) to submit an annual report
detailing how they invested QEI proceeds in low-income communities.
<br><br>• These reports must be submitted to the CDFI Fund by the Allocatees, along with their audited financial statements,
within six months after the end of their fiscal year.
• All NMTC investments must meet statutory qualifications for their investors to be able to claim the tax credit.
• The vast majority of NMTC investments are made within statutorily defined “Low-Income Communities.”<br><br>  In addition to investments located in Low-Income Communities, investments can qualify for NMTCs by using
other statutory provisions designed to target certain areas or populations, including provisions for Rural Counties,
and Low-Income Targeted Populations.
<br><br>The data represented in this summary report and accompanying data file was submitted by Allocatees prior to
September 30, 2017. Allocatees must submit their annual report to the CDFI Fund within six months after the end
of their fiscal year (FY), therefore the data submitted by September 30, 2017 represents nearly all investments for
FY 2016.
description: Community Development Entities that have been awarded New Markets Tax Credit Program allocations.
DOI: 10.3886/e225321v1
title: 2018 New Markets Tax Credit Public Data Release Source Data: COMMUNITY DEVELOPMENT FINANCIAL INSTITUTIONS FUND

description: The New Markets Tax Credit Program (NMTC Program) permits taxpayers to receive a credit against Federal income
taxes for making qualified equity investments in designated Community Development Entities (CDEs). Substantially all
of the qualified equity investment (QEI) must in turn be used by the CDE to provide investments in low-income
communities. The credit provided to the investor totals 39 percent of the cost of the investment and is claimed over a
seven-year credit allowance period.<br><br> • The CDFI Fund requires all CDEs that have been awarded NMTC allocations (Allocatees) to submit an annual report
detailing how they invested QEI proceeds in low-income communities.
<br><br>• These reports must be submitted to the CDFI Fund by the Allocatees, along with their audited financial statements,
within six months after the end of their fiscal year.
• All NMTC investments must meet statutory qualifications for their investors to be able to claim the tax credit.
• The vast majority of NMTC investments are made within statutorily defined “Low-Income Communities.”<br><br>  In addition to investments located in Low-Income Communities, investments can qualify for NMTCs by using
other statutory provisions designed to target certain areas or populations, including provisions for Rural Counties,
and Low-Income Targeted Populations.
<br><br>The data represented in this summary report and accompanying data file was submitted by Allocatees prior to
September 30, 2017. Allocatees must submit their annual report to the CDFI Fund within six months after the end
of their fiscal year (FY), therefore the data submitted by September 30, 2017 represents nearly all investments for
FY 2016.
description: Community Development Entities that have been awarded New Markets Tax Credit Program allocations.
DOI: 10.5281/zenodo.15081880
title: MDDTA: A drug target binding affinity prediction method based on molecular dynamics simulation data enhancement.

description: PDBbind_2020_md.csv: Enhanced MD-PDBbind sample set used for training.




CASF2016.csv: CASF-2016 test set.




protein_emb_data.h5: ESM2 pre-training characterization file containing proteins from both the training and test sets.




protein_coords_data.h5: File containing protein pocket coordinates for both the training and test sets.




ligand_data.h5: File containing the coordinates of ligand small molecules and GraphMVP pre-training characterization for both the training and test sets.
DOI: 10.5281/zenodo.15126107
title: MDDTA: A drug target binding affinity prediction method based on molecular dynamics simulation data enhancement.

description: PDBbind_2020_md.csv: Enhanced MD-PDBbind sample set used for training.




CASF2016.csv: CASF-2016 test set.




protein_emb_data.h5: ESM2 pre-training characterization file containing proteins from both the training and test sets.




protein_coords_data.h5: File containing protein pocket coordinates for both the training and test sets.




ligand_data.h5: File containing the coordinates of ligand small molecules and GraphMVP pre-training characterization for both the training and test sets.
DOI: 10.15468/dl.cjd7uq
title: Occurrence Download

description: A dataset listing the 525 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Indonesia",
 "Elevation 0.0-9999.0m",
 "Geometry POLYGON((114.52489 -7.99647,114.42062 -8.10653,114.48434 -8.41354,115.00568 -8.72635,115.01437 -8.92909,116.59185 -9.09398,117.6335 -9.3498,119.4684 -10.1164,121.78056 -10.83184,124.26838 -11.56276,128.0195 -8.97292,128.18178 -6.85767,122.09303 -7.63779,116.13748 -7.58071,114.52489 -7.99647))",
 "Year 2015-2025"
 ]
}

 The dataset's 525 records were derived from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001513-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11180124
title: Games Metadata & Ratings (5K+ Dataset)
DOI: 10.15468/dl.ffgsmu
title: Occurrence Download

description: A dataset containing 9629 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Living Specimen, Occurrence evidence, Fossil)",
 "TaxonKey is Pseudolmedia Trécul"
 ]
}

 The dataset includes 9629 records from 236 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001518-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wsq5um
title: Occurrence Download

description: A dataset containing 806615 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((104.90033 41.49893,106.59834 39.42875,106.66812 38.31225,107.41245 37.05619,108.69178 36.42816,110.36652 36.70729,110.66891 37.89357,110.64565 38.42856,111.06434 37.5214,114.01841 37.80053,119.56153 39.07515,119.346 42.43741,120.16501 41.92014,123.7428 42.78225,123.31175 44.29096,119.77706 47.09285,106.84529 43.81679,104.90033 41.49893))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Animalia",
 "Year 2000-2024"
 ]
}

 The dataset includes 806615 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001510-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q8eggk
title: Occurrence Download

description: A dataset containing 162 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Culex pipiens pallens Coquillett, 1898"
 ]
}

 The dataset includes 162 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001519-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15093165
title: Neural mechanisms of confidence propagation in hierarchical partially-observable decision-making

description: Unprocessed, anonimized imaging data associated with the publication "Neural mechanisms of confidence propagation in hierarchical partially-observable decision-making", Katayama et al.
DOI: 10.5281/zenodo.15093166
title: Neural mechanisms of confidence propagation in hierarchical partially-observable decision-making

description: Unprocessed, anonimized imaging data associated with the publication "Neural mechanisms of confidence propagation in hierarchical partially-observable decision-making", Katayama et al.
DOI: 10.15468/dl.exed87
title: Occurrence Download

description: A dataset containing 1152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.65255 -1.01542,-72.35695 -1.01542,-72.35695 5.35253,-78.65255 5.35253,-78.65255 -1.01542))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Boana punctata (Schneider, 1799)"
 ]
}

 The dataset includes 1152 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001520-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14100304
title: Residual Matters: How Residual Learning Can Improve Climate Downscaling

description: Title: Residual Matters: How Residual Learning Can Improve Climate Downscaling

Description:This repository contains the code and data used in the project "Residual Matters: How Residual Learning Can Improve Climate Downscaling." This project explores how residual networks, particularly models like EDSR and VDSR, can improve the downscaling of climate data. The goal is to enhance the spatial resolution of climate variables—such as ERA5 2m temperature data—using deep learning approaches. By leveraging residual learning techniques, this project achieves higher accuracy in climate downscaling, particularly in complex terrain regions, providing more reliable data for climate predictions and research.

The repository includes:



Python scripts implementing EDSR and VDSR architectures for temperature data downscaling.

Requirements file (requirements.txt) listing all dependencies for reproducibility.

Sample data preprocessing and model training scripts.

Instructions for evaluating model performance.

LICENSE file under the GNU General Public License (GPL) v3, permitting reuse and modification.


License: GNU General Public License v3.0

Keywords: Climate Downscaling, Deep Learning, Super-Resolution, Residual Networks, EDSR, VDSR, ERA5

Usage Notes:To run the code, set up a Python environment using the dependencies listed in requirements.txt. The provided data files can be used as input examples, and the README offers detailed instructions on running the models. For further usage guidance, please refer to the README or the paper associated with this project.

Acknowledgments: This work is conducted as part of research at IIT Mandi, focusing on improving climate downscaling techniques with deep learning.
DOI: 10.5281/zenodo.14100303
title: Residual Matters: How Residual Learning Can Improve Climate Downscaling

description: Title: Residual Matters: How Residual Learning Can Improve Climate Downscaling

Description:This repository contains the code and data used in the project "Residual Matters: How Residual Learning Can Improve Climate Downscaling." This project explores how residual networks, particularly models like EDSR and VDSR, can improve the downscaling of climate data. The goal is to enhance the spatial resolution of climate variables—such as ERA5 2m temperature data—using deep learning approaches. By leveraging residual learning techniques, this project achieves higher accuracy in climate downscaling, particularly in complex terrain regions, providing more reliable data for climate predictions and research.

The repository includes:



Python scripts implementing EDSR and VDSR architectures for temperature data downscaling.

Requirements file (requirements.txt) listing all dependencies for reproducibility.

Sample data preprocessing and model training scripts.

Instructions for evaluating model performance.

LICENSE file under the GNU General Public License (GPL) v3, permitting reuse and modification.


License: GNU General Public License v3.0

Keywords: Climate Downscaling, Deep Learning, Super-Resolution, Residual Networks, EDSR, VDSR, ERA5

Usage Notes:To run the code, set up a Python environment using the dependencies listed in requirements.txt. The provided data files can be used as input examples, and the README offers detailed instructions on running the models. For further usage guidance, please refer to the README or the paper associated with this project.

Acknowledgments: This work is conducted as part of research at IIT Mandi, focusing on improving climate downscaling techniques with deep learning.
DOI: 10.5281/zenodo.15128430
title: Raw data of the experiment of shrimp exposed to surgical masks
DOI: 10.5281/zenodo.15128431
title: Raw data of the experiment of shrimp exposed to surgical masks
DOI: 10.15468/dl.vh78sg
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-105.81641830124165 20.357396331808427,-105.21622405362915 20.357396331808427,-105.21622405362915 20.957590579420934,-105.81641830124165 20.957590579420934,-105.81641830124165 20.357396331808427))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Larsonia pterophylla (Haeckel, 1879)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001522-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y6p8ky
title: Occurrence Download

description: A dataset containing 768839 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Abarema Pittier, Acacia Mill., Acaciella Britton & Rose, Adenopodia C.Presl, Alantsilodendron Villiers, Albizia Durazz., Anadenanthera Speg., Archidendron F.Muell., Archidendropsis I.C.Nielsen)",
 "Year 1950-2025"
 ]
}

 The dataset includes 768839 records from 840 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001517-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/svnv7z65sn.1
title: The Precision Oncology Approach to Molecular Cancer Therapeutics Targeting Oncogenic Signaling Pathways is a Means to an End

description: Cancer is a deadly genetic disease with diverse aspects of complexity, including cancer immune evasion, treatment resistance, and recurrence requiring optimized treatment to be cured. Molecular studies have revealed that tumors are profoundly heterogeneous in nature, leading to the complexity of cancer progression that is ultimately linked to its genetic machinery. It is important to note that patients with the same types of cancer respond differently to cancer treatments, indicating the need for patient-specific treatment options. This requires an in-depth genomic study of the patient's tumors to fully understand the driving factors of cancer for effective targeted therapy. Precision oncology has evolved as a form of cancer therapy focused on genetic profiling of tumors to identify molecular alterations involved in cancer development for tailored individualized treatment of the disease. Whole genome sequencing, tumor and cell-free DNA profiling, transcriptomics, proteomics and exploration of the cancer immune system form the basis of this field of cancer research and treatment. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of ongoing technological advancements in related fields of study in to assess its scope and importance in achieving effective cure against cancer.
DOI: 10.26188/28720421
title: Incorporated Chinese Community Voluntary Associations in Victoria, Australia

description: This database contains comprehensive information on 715 formally incorporated Chinese Voluntary Associations (CVAs) in the State of Victoria, Australia. Victoria is home to one of the largest and most vibrant ethnic Chinese communities in Australia, making this dataset particularly significant. The information provided includes organisation names, year of registration, membership numbers, deregistration years (where applicable), and mission statements (for some organisations). This rich collection of data offers researchers a unique opportunity to study the evolution and characteristics of Chinese community organisations in a key area of Chinese settlement and community development in Australia.While the database is based on self-reported information and may not perfectly reflect reality, it nonetheless provides a robust and detailed picture of the formally incorporated Chinese community organisations in Victoria. The dataset's strengths lie in its comprehensive coverage of registered CVAs and the inclusion of both active and deregistered organisations. However, it is important to note that informal, unincorporated associations are not included in this database.This valuable resource can support various research applications, including analyses of trends in organisation formation, patterns in organisational missions, membership dynamics, and comparative studies with other regions or ethnic communities. By making this database available, researchers can gain deeper insights into the Chinese diaspora's civic engagement and community-building efforts in one of Australia's most significant Chinese population centres, contributing to a better understanding of multicultural society and immigrant integration in urban contexts.
DOI: 10.5281/zenodo.17293779
title: Regimes of whistler waves' ducting in magnetized plasmas with a pair of narrow, field-aligned density depletions: simulation results

description: The repository contains the calculation results used in the paper “Regimes of whistler waves' ducting in magnetized plasmas with a pair of narrow, field-aligned density depletions“ (I. Elyasina et al.). Each file name corresponds to the figure number in the article for which the data were used. The files contain numerical data in HDF5 format, which can be opened using the HDFView program. The names of the arrays correspond to the physical quantities shown in the figures. All relevant descriptions are provided in the text of the article.
DOI: 10.5281/zenodo.17293780
title: Regimes of whistler waves' ducting in magnetized plasmas with a pair of narrow, field-aligned density depletions: simulation results

description: The repository contains the calculation results used in the paper “Regimes of whistler waves' ducting in magnetized plasmas with a pair of narrow, field-aligned density depletions“ (I. Elyasina et al.). Each file name corresponds to the figure number in the article for which the data were used. The files contain numerical data in HDF5 format, which can be opened using the HDFView program. The names of the arrays correspond to the physical quantities shown in the figures. All relevant descriptions are provided in the text of the article.
DOI: 10.13140/rg.2.1.5037.7447
title: The Cantabrian Thrust Belt: basin history of the North Gondwana passive margin - rifting, glaciation? more rifting and collision. (2010)
DOI: 10.15468/dl.2nzxzy
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Craspedacusta sowerbii Lankester, 1880",
 "Country is South Africa"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000413-170816103707345/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc111322432
title: Cuneiform Tablet 000244, CUN.000244

description: [Text Description:] Cuneiform Tablet, Letter, 000244 ❧ [Text or Publication Number Note:] CUN.000244 ❧ [Medium:] Clay ❧ [Script Note:] Cuneiform, Non-Alphabetic ❧ [Physical Object Description:] Clay tablet with cuneiform script
DOI: 10.15468/dl.sesziy
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nothoscordum ibiramense Ravenna"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064030-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gr90w6
title: Occurrence Download

description: A dataset containing 2916033 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Argentina",
 "Country is Paraguay"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2916033 records from 753 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011159-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc111322469
title: Cuneiform Tablet 000244, CUN.000244

description: [Text Description:] Cuneiform Tablet, Letter, 000244 ❧ [Text or Publication Number Note:] CUN.000244 ❧ [Medium:] Clay ❧ [Script Note:] Cuneiform, Non-Alphabetic ❧ [Physical Object Description:] Clay tablet with cuneiform script
DOI: 10.15468/dl.nwills
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trithemis festiva (Rambur, 1842)",
 "Country is one of (France, Germany, Spain, Portugal, Belgium, Netherlands, Switzerland, Denmark, Luxembourg, Norway, Sweden, Finland)",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Machine Observation, Observation)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002096-171025204727650/datasets/export for details.
DOI: 10.15468/dl.9puuec
title: Occurrence Download

description: A dataset containing 381 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Uruguay",
 "Country is Paraguay",
 "Country is Argentina",
 "Country is Bolivia (Plurinational State of)",
 "Country is Colombia"
 ]
 },
 "TaxonKey is Aechmea distichantha Lem."
 ]
}

 The dataset includes 381 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004623-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ft3pc
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aceria ilicis (Canestrini, 1890)"
 ]
}

 The dataset includes 47 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057650-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.