cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28758918.v1
title: Cardiomyopathy genetic testing: insights into demographics, counseling, and cascade screening

description: Genetic testing (GT) is clinically indicated for patients with suspected inherited cardiomyopathy (CM) but is underutilized. This single-center study included patients with CM who underwent outpatient GT from 2018 to 2023. Data were collected from the medical record. A total of 306 patients were included, with a mean age of 62 years and were predominantly male (63%) and White (59%). Heart failure (92%) cardiologists were most likely to order testing, followed by general cardiology (5%) providers at tertiary hospital clinics (89%). The most common phenotypes were hypertrophic (30%), amyloid (29%) and dilated (21%). There were 17% of patients who had a positive test and 53% of patients who had a variant of uncertain significance. Patients with a positive family history had a significantly higher positive yield compared to those without (<i>p</i> &lt; 0.01). Genetic counselors were underutilized (17%). For patients who had a positive test result, only 40% had cascade screening. Outpatient GT for CM was primarily ordered by heart failure cardiologists in tertiary hospital clinics. Cascade screening was low, which may reflect the limited use of genetic counselors. Further research is needed to understand implementation barriers to GT. We studied how genetic testing is used in patients with a heart muscle disorder to understand barriers that may limit its use. Our study included 306 patients who had outpatient genetic testing between 2018 and 2023. We found that heart failure cardiologists ordered most of the tests, while general cardiologists and electrophysiologists ordered fewer. Genetic testing was less common in clinics that care for underserved communities. Additionally, family screening with genetic testing was infrequent, possibly due to limited referrals to genetic counselors. These findings highlight key barriers to genetic testing and can help guide strategies to improve use.
DOI: 10.5281/zenodo.17590891
title: Strip roads in pine forest detected by four different remote sensing methods
DOI: 10.6084/m9.figshare.30391240
title: Innate immuno-response to nanoparticle uptake in liver and spleen mimics pathogen infection

description: Systemically administered nanoparticles (NPs) designed for biomedical applications are retained in liver and spleen where they become rapidly phagocyted by tissue macrophages leading to inflammation. To gain insight into the NP-immune cell interaction in liver spleen and lungs, we followed the distribution of molybdenum nanoparticles (MoNPs) <i>in vivo</i> by X-Ray Fluorescence Imaging (XRF) and examined the NP-macrophage interaction and physiological response in these organs. XRF imaging showed that intravenously administered MoNPs transiently accumulate in lungs, liver, and spleen. This leads to increments in the number of Kupffer cells (KC), natural killer (NK) cells, oxidative stress, and inflammation. Macrophage phenotype switched from pro- to an anti-inflammatory. In parallel genes with immunoregulatory and cytoprotective functions were expressed to maintain homeostasis. Nanoparticle uptake in spleen was operated by CD169/Siglec1 splenic macrophages indicating initiation of a secondary immune response. Silica coating reduced nanoparticle toxicity. The innate immunoresponse to NP uptake in liver and spleen is similar to viral or bacterial infections in these organs. A possible secondary immunoresponse to NPs can be primed in spleen aided by CD169/Siglec1 splenic macrophages. Silica coating of metal NPs tunes down this response.
DOI: 10.6084/m9.figshare.30391240.v1
title: Innate immuno-response to nanoparticle uptake in liver and spleen mimics pathogen infection

description: Systemically administered nanoparticles (NPs) designed for biomedical applications are retained in liver and spleen where they become rapidly phagocyted by tissue macrophages leading to inflammation. To gain insight into the NP-immune cell interaction in liver spleen and lungs, we followed the distribution of molybdenum nanoparticles (MoNPs) <i>in vivo</i> by X-Ray Fluorescence Imaging (XRF) and examined the NP-macrophage interaction and physiological response in these organs. XRF imaging showed that intravenously administered MoNPs transiently accumulate in lungs, liver, and spleen. This leads to increments in the number of Kupffer cells (KC), natural killer (NK) cells, oxidative stress, and inflammation. Macrophage phenotype switched from pro- to an anti-inflammatory. In parallel genes with immunoregulatory and cytoprotective functions were expressed to maintain homeostasis. Nanoparticle uptake in spleen was operated by CD169/Siglec1 splenic macrophages indicating initiation of a secondary immune response. Silica coating reduced nanoparticle toxicity. The innate immunoresponse to NP uptake in liver and spleen is similar to viral or bacterial infections in these organs. A possible secondary immunoresponse to NPs can be primed in spleen aided by CD169/Siglec1 splenic macrophages. Silica coating of metal NPs tunes down this response.
DOI: 10.5281/zenodo.17590890
title: Strip roads in pine forest detected by four different remote sensing methods
DOI: 10.15468/dl.n8mws8
title: Occurrence Download

description: A dataset containing 60285 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lama guanicoe (Müller, 1776)"
 ]
}

 The dataset includes 60285 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002169-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kr9ydr
title: Occurrence Download

description: A dataset containing 366240 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gesneriaceae"
 ]
}

 The dataset includes 366240 records from 1164 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011026-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zr49s9pzvb
title: XRF Spectra

description: XRF spectra acquired with IRIS
DOI: 10.15468/dl.duhqp6
title: Occurrence Download

description: A dataset containing 1510 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Branta hutchinsii",
 "Country is Sweden",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1510 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085563-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zr49s9pzvb.1
title: XRF Spectra

description: XRF spectra acquired with IRIS
DOI: 10.5281/zenodo.18226137
title: Dataset supporting "An ab initio answer to long-debated questions about superconducting Nb$_3$Sn"

description: This archive contains the datasets associated with the PRX Energy article by Cucciari et al. on superconducting Nb$_3$Sn. The data provided here are sufficient to reproduce all plots and numerical results presented in the paper.

The following software packages were used to generate the data (see the manuscript for further details):



Quantum ESPRESSO version 7.2 (https://www.quantum-espresso.org)

EPW version 5.7 (https://epw-code.org)

Wannier90 version 3.1.0 (https://wannier.org)

python-sscha version 1.4.1 (https://sscha.eu)


For any questions, clarifications, or additional information, please contact:

alessio.cucciari@uniroma1.it
DOI: 10.5281/zenodo.18226136
title: Dataset supporting "An ab initio answer to long-debated questions about superconducting Nb$_3$Sn"

description: This archive contains the datasets associated with the PRX Energy article by Cucciari et al. on superconducting Nb$_3$Sn. The data provided here are sufficient to reproduce all plots and numerical results presented in the paper.

The following software packages were used to generate the data (see the manuscript for further details):



Quantum ESPRESSO version 7.2 (https://www.quantum-espresso.org)

EPW version 5.7 (https://epw-code.org)

Wannier90 version 3.1.0 (https://wannier.org)

python-sscha version 1.4.1 (https://sscha.eu)


For any questions, clarifications, or additional information, please contact:

alessio.cucciari@uniroma1.it
DOI: 10.4225/08/5ac456a0a6154
title: Notes being incorporated into a report on laterite geochemistry for the Moora, Perth, Pinjarra, Collie and Pemberton 1:250 000 sheets - Supplementary Data

description: Supplementary files which accompany CRC LEME Open File Report 63. 
CSIRO Division of Exploration Geoscience Report Unnumbered. Second Impression 1998.
DOI: 10.15468/dl.f7jdff
title: Occurrence Download

description: A dataset containing 1977 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Salvelinus fontinalis",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1977 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028813-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16903821
title: Estrogen modulates reward prediction errors and reinforcement learning

description: This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate figures in the publication:

RawData_Figure1: Contains behavioral data from the rat temporal wagering task and estradiol levels from serum measured with an ELISA. Used, at least in part, to generate Figure 1, Figure 2, Figure 6, Extended Data 1, Extended Data 2, Extended Data 3, and Extended Data 10.

RawData_Figure5: Contains mass spectrometry fold change results comparing nucleus accumbens core (NAcc) proteomic expression in proestrus compared to diestrus and estrus compared to diestrus, immunohistochemistry quantification of SERT and DAT expression in the NAcc, and localization of DAT in the NAcc with electron microscopy. Used to generate Figure 5.

RawData_Figure6: Contains RNAscope data to validate the knockdown of Esr1 with two versions of shRNA, behavioral data after Esr1 knockdown, and measurement of estradiol and osmolality from serum. Used to generate Figure 6.

All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning.

Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.
DOI: 10.5281/zenodo.16903822
title: Estrogen modulates reward prediction errors and reinforcement learning

description: This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate figures in the publication:

RawData_Figure1: Contains behavioral data from the rat temporal wagering task and estradiol levels from serum measured with an ELISA. Used, at least in part, to generate Figure 1, Figure 2, Figure 6, Extended Data 1, Extended Data 2, Extended Data 3, and Extended Data 10.

RawData_Figure5: Contains mass spectrometry fold change results comparing nucleus accumbens core (NAcc) proteomic expression in proestrus compared to diestrus and estrus compared to diestrus, immunohistochemistry quantification of SERT and DAT expression in the NAcc, and localization of DAT in the NAcc with electron microscopy. Used to generate Figure 5.

RawData_Figure6: Contains RNAscope data to validate the knockdown of Esr1 with two versions of shRNA, behavioral data after Esr1 knockdown, and measurement of estradiol and osmolality from serum. Used to generate Figure 6.

All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning.

Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.
DOI: 10.5281/zenodo.16912310
title: Estrogen modulates reward prediction errors and reinforcement learning

description: This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate Figure 2 in the publication: photometry data measured with GRABDA, motion-corrected and aligned to the temporal wagering task offer cue event. This data is also used to generate parts of Extended Data 5 and Extended Data 6.

All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning.

Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.
DOI: 10.5281/zenodo.16912311
title: Estrogen modulates reward prediction errors and reinforcement learning

description: This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate Figure 2 in the publication: photometry data measured with GRABDA, motion-corrected and aligned to the temporal wagering task offer cue event. This data is also used to generate parts of Extended Data 5 and Extended Data 6.

All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning.

Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.
DOI: 10.5281/zenodo.16912772
title: Estrogen modulates reward prediction errors and reinforcement learning

description: This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate Figure 3 in the publication: photometry data measured with GRABDA, motion-corrected and aligned to the temporal wagering task offer cue event, and computational models fit to the rat behavioral data. This data is also used to generate part of Extended Data 5.

All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning.

Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.
DOI: 10.5281/zenodo.16912773
title: Estrogen modulates reward prediction errors and reinforcement learning

description: This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate Figure 3 in the publication: photometry data measured with GRABDA, motion-corrected and aligned to the temporal wagering task offer cue event, and computational models fit to the rat behavioral data. This data is also used to generate part of Extended Data 5.

All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning.

Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.
DOI: 10.15468/dl.w79d85
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028815-250811113504898/datasets/export for details.
DOI: 10.5281/zenodo.16912862
title: Estrogen modulates reward prediction errors and reinforcement learning

description: This data was used and described in the following paper: Golden C.E.M., Martin, A.C., Kaur, D., Mah, A., Levy, D.H., Yamaguchi, T., Lasek, A.W., Lin, D., Aoki, C., Constantinople, C.M. (2025). Estrogen modulates reward prediction errors and reinforcement learning. Nature Neuroscience. The dataset comprises raw data used to generate Figure 4 in the publication: photometry data measured with GRABDA, motion-corrected and aligned to the temporal wagering task offer cue event, behavioral data from sessions with and without optogenetic stimulation of dopaminergic terminals in the nucleus accumbens core, and example rat data for computational modeling.

All files are Matlab data (.mat) files. The code to analyze this data and generate all figures in Golden et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/EstrousRPEPaper}. Data was analyzed using Matlab 2024a with the following additional toolboxes: Curve Fitting, Optimization, Signal Processing, and Statistics and Machine Learning.

Funding: This work was supported by a K99/R00 Pathway to Independence Award (R00MH11-1926), a Klingenstein-Simons Fellowship in Neuroscience, and an NIH Director’s New Innovator Award (DP2MH126376) to C.M.C. C.G. was supported by a grant from the Simons Foundation (855332), F32MH125448, and 5T32MH019524. The mass spectrometric experiments were supported in part by NYU Langone Health, the Laura and Isaac Perlmutter Cancer Center support grant P30CA016087 from the National Cancer Institute, and the NIH Shared Instrumentation Grant 1S10OD010582-01A1 for the purchase of an Orbitrap Fusion™ Lumos™ Tribrid™ mass spectrometer.