cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.etb3zk
title: Occurrence Download

description: A dataset containing 1513 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pometia pinnata J.R.Forst. & G.Forst.",
 "Country is Indonesia"
 ]
}

 The dataset includes 1513 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037337-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jd98qf
title: Occurrence Download

description: A dataset containing 113 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bubalus bubalis (Linnaeus, 1758)",
 "Country is Indonesia"
 ]
}

 The dataset includes 113 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037338-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14370562
title: A 1 km soil moisture data over eastern continental U.S. generated through assimilating SMAP data into the Noah-MP land surface model

description: The 1-km soil moisture analysis data over the domain covering much of the eastern continental U.S.are generated by assimilating 9-km SMAP data into the version 4.0.1 Noah-MP land surface model. The NetCDF formatted data is valid 6-hourly (e.g., 00, 06, 12, 18 UTC) for the entire year of 2016 and archived by each month. Additional variables including soil temperature, latent heat flux, sensible heat flux, and evapotransporation were concurrently simulated and also archived in the dataset.
DOI: 10.5281/zenodo.14370563
title: A 1 km soil moisture data over eastern continental U.S. generated through assimilating SMAP data into the Noah-MP land surface model

description: The 1-km soil moisture analysis data over the domain covering much of the eastern continental U.S.are generated by assimilating 9-km SMAP data into the version 4.0.1 Noah-MP land surface model. The NetCDF formatted data is valid 6-hourly (e.g., 00, 06, 12, 18 UTC) for the entire year of 2016 and archived by each month. Additional variables including soil temperature, latent heat flux, sensible heat flux, and evapotransporation were concurrently simulated and also archived in the dataset.
DOI: 10.5281/zenodo.14064232
title: Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.

description: ML_AB_H: Datasets of 64 water molecules and a single proton with 64 water molecules.

ML_AB_VP2: Datasets of a single V^2+ ion with 64 water molecules.

ML_AB_VP3: Datasets of a single V^3+ ion with 64 water molecules.

ML_AB_FeP2: Datasets of a single Fe^2+ ion with 64 water molecules.

ML_AB_FeP3: Datasets of a single Fe^3+ ion with 64 water molecules.

ML_AB_CuP1: Datasets of a single Cu^+ ion with 64 water molecules.

ML_AB_CuP2: Datasets of a single Cu^2+ ion with 64 water molecules.

ML_AB_RuP2: Datasets of a single Ru^2+ ion with 64 water molecules.

ML_AB_RuP3: Datasets of a single Ru^3+ ion with 64 water molecules.

ML_AB_AgP1: Datasets of a single Ag^+ ion with 64 water molecules.

ML_AB_AgP2: Datasets of a single Ag^2+ ion with 64 water molecules.

ML_AB_O2: Datasets of a single O2 ion with 64 water molecules.

ML_AB_O2N1: Datasets of a single O2^- ion with 64 water molecules.

ML_AB_water: Datasets of 64 water molecules presenting bulk water and 64 water molecules representing water slab.

All datasets were generated by VASP using PAW, plane wave basis sets with cutoff energy of 520 eV and RPBE+D3 exchange-correlation functional with zero-damping. All ab initio calculations were done on extended systems with periodic boundary conditions. See details in https://doi.org/10.48550/arXiv.2409.11000.
DOI: 10.5281/zenodo.14064233
title: Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.

description: ML_AB_H: Datasets of 64 water molecules and a single proton with 64 water molecules.

ML_AB_VP2: Datasets of a single V^2+ ion with 64 water molecules.

ML_AB_VP3: Datasets of a single V^3+ ion with 64 water molecules.

ML_AB_FeP2: Datasets of a single Fe^2+ ion with 64 water molecules.

ML_AB_FeP3: Datasets of a single Fe^3+ ion with 64 water molecules.

ML_AB_CuP1: Datasets of a single Cu^+ ion with 64 water molecules.

ML_AB_CuP2: Datasets of a single Cu^2+ ion with 64 water molecules.

ML_AB_RuP2: Datasets of a single Ru^2+ ion with 64 water molecules.

ML_AB_RuP3: Datasets of a single Ru^3+ ion with 64 water molecules.

ML_AB_AgP1: Datasets of a single Ag^+ ion with 64 water molecules.

ML_AB_AgP2: Datasets of a single Ag^2+ ion with 64 water molecules.

ML_AB_O2: Datasets of a single O2 ion with 64 water molecules.

ML_AB_O2N1: Datasets of a single O2^- ion with 64 water molecules.

ML_AB_water: Datasets of 64 water molecules presenting bulk water and 64 water molecules representing water slab.

All datasets were generated by VASP using PAW, plane wave basis sets with cutoff energy of 520 eV and RPBE+D3 exchange-correlation functional with zero-damping. All ab initio calculations were done on extended systems with periodic boundary conditions. See details in https://doi.org/10.48550/arXiv.2409.11000.
DOI: 10.5281/zenodo.14503891
title: Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.

description: ML_AB_H: Datasets of 64 water molecules and a single proton with 64 water molecules.

ML_AB_VP2: Datasets of a single V^2+ ion with 64 water molecules.

ML_AB_VP3: Datasets of a single V^3+ ion with 64 water molecules.

ML_AB_FeP2: Datasets of a single Fe^2+ ion with 64 water molecules.

ML_AB_FeP3: Datasets of a single Fe^3+ ion with 64 water molecules.

ML_AB_CuP1: Datasets of a single Cu^+ ion with 64 water molecules.

ML_AB_CuP2: Datasets of a single Cu^2+ ion with 64 water molecules.

ML_AB_RuP2: Datasets of a single Ru^2+ ion with 64 water molecules.

ML_AB_RuP3: Datasets of a single Ru^3+ ion with 64 water molecules.

ML_AB_AgP1: Datasets of a single Ag^+ ion with 64 water molecules.

ML_AB_AgP2: Datasets of a single Ag^2+ ion with 64 water molecules.

ML_AB_O2: Datasets of a single O2 ion with 64 water molecules.

ML_AB_O2N1: Datasets of a single O2^- ion with 64 water molecules.

ML_AB_water: Datasets of 64 water molecules presenting bulk water and 64 water molecules representing water slab.

All datasets were generated by VASP using PAW, plane wave basis sets with cutoff energy of 520 eV and RPBE+D3 exchange-correlation functional with zero-damping. All ab initio calculations were done on extended systems with periodic boundary conditions. See details in https://doi.org/10.48550/arXiv.2409.11000.
DOI: 10.5281/zenodo.12761214
title: Self-Admitted Technical Debt in Commit Messages: Comparing Java, Python, and R

description: The folder organization and datasets within each are as follows:

Collection Folder: the original dataset that we scraped is placed. We have removed the user names and email addresses to keep the users’ privacy. RQ1 Folder has three subfolders: 

❖     Manual Training: The initial manually labeled data we used to initially train the classifiers is included. Note that columns A-O in this dataset are all extracted from GitHub’s API. Column O (heading “message”) is the commit message itself. The following columns P and Q (heading “author_a” and “author_b”) are the final classification (upon which the Cohen Kappa was calculated). Column R (heading “notes”) contains some commentaries on specific cases that may be meaningful.

❖     Predicted: The results of the automatic classifiers (both 1st and 2nd round) are included. The additional columns are generated by the classifiers.

❖     Verifications contain the manually labeled data that we used as 1st and 2nd verification rounds. This is a simplified dataset with the commit’s sha and the parsed message. The authors classified columns E and F independently and individually. The labels stated here are those that the authors agreed to (without having access to column D). Note that column D was added afterward by sha-matching by another author to calculate the Cohen Kappa. The yellow rows are those with disagreements.

 RQ2_RQ3 Folder contains the manually labeled dataset for RQ2 and RQ3 (SATD Types and Activities). 

NOTE: Kindly note that many messages or classifications are multiline. This means that the cells have to be expanded to be capable of reading all text included in a cell.
DOI: 10.5281/zenodo.12761215
title: Self-Admitted Technical Debt in Commit Messages: Comparing Java, Python, and R

description: The folder organization and datasets within each are as follows:

Collection Folder: the original dataset that we scraped is placed. We have removed the user names and email addresses to keep the users’ privacy. RQ1 Folder has three subfolders: 

❖     Manual Training: The initial manually labeled data we used to initially train the classifiers is included. Note that columns A-O in this dataset are all extracted from GitHub’s API. Column O (heading “message”) is the commit message itself. The following columns P and Q (heading “author_a” and “author_b”) are the final classification (upon which the Cohen Kappa was calculated). Column R (heading “notes”) contains some commentaries on specific cases that may be meaningful.

❖     Predicted: The results of the automatic classifiers (both 1st and 2nd round) are included. The additional columns are generated by the classifiers.

❖     Verifications contain the manually labeled data that we used as 1st and 2nd verification rounds. This is a simplified dataset with the commit’s sha and the parsed message. The authors classified columns E and F independently and individually. The labels stated here are those that the authors agreed to (without having access to column D). Note that column D was added afterward by sha-matching by another author to calculate the Cohen Kappa. The yellow rows are those with disagreements.

 RQ2_RQ3 Folder contains the manually labeled dataset for RQ2 and RQ3 (SATD Types and Activities). 

NOTE: Kindly note that many messages or classifications are multiline. This means that the cells have to be expanded to be capable of reading all text included in a cell.
DOI: 10.17909/8gbq-gp71
title: Data from Detection of H2O and CO2 in the Atmosphere of the Hot Super-Neptune WASP-166b with JWST

description: Single NIRSpec G395M transit observation and single NIRISS SOSS transit observation of WASP-166b, for publication in "Detection of H2O and CO2 in the Atmosphere of the Hot Super-Neptune WASP-166b with JWST"
DOI: 10.5281/zenodo.13242625
title: Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness

description: Replication package for Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness
DOI: 10.5281/zenodo.13242626
title: Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness

description: Replication package for Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness
DOI: 10.5281/zenodo.14270737
title: Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness

description: Replication package for Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness
DOI: 10.5281/zenodo.14270753
title: Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness

description: Replication package for Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness
DOI: 10.5281/zenodo.14270760
title: Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness

description: Replication package for Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness
DOI: 10.5281/zenodo.14270925
title: Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness

description: Replication package for Voting on a Trade Agreement: Firm Networks and Attitudes Toward Openness
DOI: 10.5281/zenodo.14338663
title: FractiScope Technical Papers

description: Welcome to the FractiScope Technical Papers Repository!

Welcome to the FractiScope Technical Papers Repository on Zenodo! This is a collaborative space dedicated to advancing fractal intelligence in computation, artificial intelligence, and systems modeling. Here, you’ll find cutting-edge research, practical applications, and visionary ideas exploring the transformative potential of fractal-based innovations.

Our mission is to foster a global community of researchers, developers, and innovators working on solving complex, multi-scale problems through the principles of fractal intelligence. We encourage collaboration, discussion, and contributions that push the boundaries of what’s possible in science, technology, and computation.

Dive in, explore, and join us in shaping the future of fractal intelligence!
DOI: 10.5281/zenodo.14390346
title: FractiScope Technical Papers

description: Welcome to the FractiScope Technical Papers Repository!

Welcome to the FractiScope Technical Papers Repository on Zenodo! This is a collaborative space dedicated to advancing fractal intelligence in computation, artificial intelligence, and systems modeling. Here, you’ll find cutting-edge research, practical applications, and visionary ideas exploring the transformative potential of fractal-based innovations.

Our mission is to foster a global community of researchers, developers, and innovators working on solving complex, multi-scale problems through the principles of fractal intelligence. We encourage collaboration, discussion, and contributions that push the boundaries of what’s possible in science, technology, and computation.

Dive in, explore, and join us in shaping the future of fractal intelligence!
DOI: 10.5281/zenodo.14338662
title: FractiScope Technical Papers

description: Welcome to the FractiScope Technical Papers Repository!

Welcome to the FractiScope Technical Papers Repository on Zenodo! This is a collaborative space dedicated to advancing fractal intelligence in computation, artificial intelligence, and systems modeling. Here, you’ll find cutting-edge research, practical applications, and visionary ideas exploring the transformative potential of fractal-based innovations.

Our mission is to foster a global community of researchers, developers, and innovators working on solving complex, multi-scale problems through the principles of fractal intelligence. We encourage collaboration, discussion, and contributions that push the boundaries of what’s possible in science, technology, and computation.

Dive in, explore, and join us in shaping the future of fractal intelligence!
DOI: 10.5281/zenodo.14503908
title: FractiScope Technical Papers

description: Welcome to the FractiScope Technical Papers Repository!

Welcome to the FractiScope Technical Papers Repository on Zenodo! This is a collaborative space dedicated to advancing fractal intelligence in computation, artificial intelligence, and systems modeling. Here, you’ll find cutting-edge research, practical applications, and visionary ideas exploring the transformative potential of fractal-based innovations.

Our mission is to foster a global community of researchers, developers, and innovators working on solving complex, multi-scale problems through the principles of fractal intelligence. We encourage collaboration, discussion, and contributions that push the boundaries of what’s possible in science, technology, and computation.

Dive in, explore, and join us in shaping the future of fractal intelligence!
DOI: 10.3886/e104060
title: The Future Strikes Back. Using Future Treatments to Detect and Reduce Hidden Bias

description: Conventional advice discourages controlling for post-outcome variables in regression analysis. By contrast, we show that controlling for commonly available post-outcome (i.e. future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved confounder that affects treatment also affects the future value of the treatment. Future treatments thus proxy for the unmeasured confounder, and researchers can exploit these proxy measures productively. We establish several new results: Regarding a commonly assumed data-generating process involving future treatments, we (1) introduce a simple new approach and show that it strictly reduces bias; (2) elaborate on existing approaches and show that they can increase bias; (3) assess the relative merits of alternative approaches; (4) analyze true state dependence and selection as key challenges. (5) Importantly, we also introduce a new non-parametric test that uses future treatments to detect hidden bias even when future-treatment estimation fails to reduce bias. We illustrate these results empirically with an analysis of the effect of parental income on children’s educational attainment.
DOI: 10.3886/e104060v2-161741
title: The Future Strikes Back. Using Future Treatments to Detect and Reduce Hidden Bias

description: Conventional advice discourages controlling for post-outcome variables in regression
analysis. By contrast, we show that controlling for commonly available post-outcome (i.e. future)
values of the treatment variable can help detect, reduce, and even remove omitted variable bias
(unobserved confounding). The premise is that the same unobserved confounder that affects
treatment also affects the future value of the treatment. Future treatments thus proxy for the
unmeasured confounder, and researchers can exploit these proxy measures productively. We
establish several new results: Regarding a commonly assumed data-generating process
involving future treatments, we (1) introduce a simple new approach and show that it strictly
reduces bias; (2) elaborate on existing approaches and show that they can increase bias; (3)
assess the relative merits of alternative approaches; (4) analyze true state dependence and
selection as key challenges. (5) Importantly, we also introduce a new non-parametric test that
uses future treatments to detect hidden bias even when future-treatment estimation fails to
reduce bias. We illustrate these results empirically with an analysis of the effect of parental
income on children’s educational attainment.
DOI: 10.15468/dl.zechmq
title: Occurrence Download

description: A dataset containing 1879 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Pl@ntNet automatically identified occurrences",
 "Geometry POLYGON((-39.74421 -8.35787,-34.27017 -8.35787,-34.27017 -6.09911,-39.81336 -5.82253,-39.84793 -7.0902,-39.74421 -8.35787))"
 ]
}

 The dataset includes 1879 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037339-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x49ch4
title: Occurrence Download

description: A dataset containing 472 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Colombia",
 "TaxonKey is Tangara arthus R.Lesson, 1832"
 ]
}

 The dataset includes 472 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037340-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14279395
title: Intranasal administration of a multiplex targeting SCLMs@FEN1-hpDNAs with synergistic baicalein for pulmonary anti-viral treatment

description: Here we reported an anti-viral strategy for SARS-CoV-2 infection: intranasal administration of silica cross-linked micelles (SCLMs) encapsulating in vitro purified NES-FEN1-NES protein and a set of hpDNAs (shorted as SCLMs@FEN1-hpDNAs). The set of hpDNAs multiplex targeted SARS-CoV-2’s N gene RNA and the host Ctsl gene mRNA. In vitro experiments demonstrated the purified NES-FEN1-NES protein transfected into cells has efficient mRNA knockdown ability when guided by hpDNAs. The intranasal administration of SCLMs exhibited high pulmonary accessibility, and SCLMs@FEN1-hpDNAs effectively downregulated targeted gene expression in vivo. The efficacy is enhanced by synergistic intranasal baicalein by improving cellular uptake and anti-inflammatory effects. In a mouse model of SARS-CoV-2 infection, the combination with baicalein and SCLMs@FEN1-hpDNAs inhibited the virus replication, improved survival rates, and reduced neuroinflammation. This study highlights the potential of intranasal SCLMs@FEN1-hpDNAs with baicalein as a promising approach for treating respiratory diseases.
DOI: 10.5281/zenodo.14279396
title: Intranasal administration of a multiplex targeting SCLMs@FEN1-hpDNAs with synergistic baicalein for pulmonary anti-viral treatment

description: Here we reported an anti-viral strategy for SARS-CoV-2 infection: intranasal administration of silica cross-linked micelles (SCLMs) encapsulating in vitro purified NES-FEN1-NES protein and a set of hpDNAs (shorted as SCLMs@FEN1-hpDNAs). The set of hpDNAs multiplex targeted SARS-CoV-2’s N gene RNA and the host Ctsl gene mRNA. In vitro experiments demonstrated the purified NES-FEN1-NES protein transfected into cells has efficient mRNA knockdown ability when guided by hpDNAs. The intranasal administration of SCLMs exhibited high pulmonary accessibility, and SCLMs@FEN1-hpDNAs effectively downregulated targeted gene expression in vivo. The efficacy is enhanced by synergistic intranasal baicalein by improving cellular uptake and anti-inflammatory effects. In a mouse model of SARS-CoV-2 infection, the combination with baicalein and SCLMs@FEN1-hpDNAs inhibited the virus replication, improved survival rates, and reduced neuroinflammation. This study highlights the potential of intranasal SCLMs@FEN1-hpDNAs with baicalein as a promising approach for treating respiratory diseases.
DOI: 10.15468/dl.aw9e64
title: Occurrence Download

description: A dataset containing 278 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Petrocoptis grandiflora Rothm."
 ]
}

 The dataset includes 278 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037341-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14503909
title: Variability of Heavy Metals in Mountainous Rivers: Influence of Tidal Forces and Tropical Storms

description: Supplementary data
DOI: 10.5281/zenodo.14503910
title: Variability of Heavy Metals in Mountainous Rivers: Influence of Tidal Forces and Tropical Storms

description: Supplementary data
DOI: 10.15468/dl.88r36x
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Colombia",
 "PublishingOrg is 2a7e3080-28a9-11dd-97cd-b8a03c50a862",
 "TaxonKey is Tangara arthus R.Lesson, 1832"
 ]
}

 The dataset includes 30 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037342-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.85ww9s
title: Occurrence Download

description: A dataset containing 23878 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is Spain",
 "Geometry POLYGON((1.88884 41.25754,2.3646 41.25754,2.3646 41.59216,1.88884 41.59216,1.88884 41.25754))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EN, CR, VU, NT, LC)",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 2015-2015"
 ]
}

 The dataset includes 23878 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037343-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.