cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.16912863
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.
DOI: 10.15468/dl.2emb4r
title: Occurrence Download

description: A dataset containing 116404 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Avena barbata"
 ]
}

 The dataset includes 116404 records from 926 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028683-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16914370
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 Extended Data 4, along with files uploaded separately. Each file is an individual rat's photometry data measured with GRABDA, motion-corrected and aligned to all of the events of the temporal wagering task.

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.16914371
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 Extended Data 4, along with files uploaded separately. Each file is an individual rat's photometry data measured with GRABDA, motion-corrected and aligned to all of the events of the temporal wagering task.

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.8wa26m
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028816-250811113504898/datasets/export for details.
DOI: 10.15468/dl.d8yxsa
title: Occurrence Download

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

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028818-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16913031
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 Extended Data 1, 2, 4, 5, 8, 9, and 10: 

RawData_ExtendedData1: estradiol levels in serum, measured with an ELISA

RawData_ExtendedData5: mCherry and GRABDA motion-corrected photometry signal aligned to the behavioral events for an example rat

RawData_ExtendedData7: GRABDA motion-corrected signal across rats aligned to delay beginning and end

RawData_ExtendedData8: percentage of overlap between TH and ChR2 for each micrograph taken from all rats

RawData_ExtendedData9: enrichment terms from STRING v12 for differentially expressed proteins

RawData_ExtendedData10: osmolality measurements from serum

There are also files used, along with files uploaded separately, to create Extended Data 4. Each file is an individual rat's photometry data measured with GRABDA that has been motion-corrected, aligned to all of the events of the temporal wagering task.

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.16913033
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 Extended Data 1, 2, 4, 5, 8, 9, and 10: 

RawData_ExtendedData1: estradiol levels in serum, measured with an ELISA

RawData_ExtendedData5: mCherry and GRABDA motion-corrected photometry signal aligned to the behavioral events for an example rat

RawData_ExtendedData7: GRABDA motion-corrected signal across rats aligned to delay beginning and end

RawData_ExtendedData8: percentage of overlap between TH and ChR2 for each micrograph taken from all rats

RawData_ExtendedData9: enrichment terms from STRING v12 for differentially expressed proteins

RawData_ExtendedData10: osmolality measurements from serum

There are also files used, along with files uploaded separately, to create Extended Data 4. Each file is an individual rat's photometry data measured with GRABDA that has been motion-corrected, aligned to all of the events of the temporal wagering task.

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.jfu9md
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Salvelinus fontinalis",
 "Country is Poland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 19 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028822-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16914533
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 Extended Data 6 in the publication: photometry data measured with GRABDA from the caudate putamen, motion-corrected and aligned to the events on the temporal wagering task.

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.16914534
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 Extended Data 6 in the publication: photometry data measured with GRABDA from the caudate putamen, motion-corrected and aligned to the events on the temporal wagering task.

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.3r4q72
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pseudocheirodon terrabae)",
 "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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028823-250811113504898/datasets/export for details.
DOI: 10.5281/zenodo.16921034
title: Table 1-2

description: Table 1. Summary of Rousettus aegyptiacus observation in Hail Region (Qa’a Tiltel, King Salman Bin Abdulaziz Royal Nature Reserve). This table presents details of the new record including date, time, coordinates, habitat type, estimated colony size, and type of evidence.

Table 2. Confirmed records of Rousettus aegyptiacus in Saudi Arabia with coordinates and references. This table compiles previous published records and photographic evidence from various regions in the country (Taima, Al Disah, Muleh, Bisha, and Abha), alongside the new confirmed Hail record documented in this study.
DOI: 10.15784/601966
title: Lithospheric thicknesses in Antarctica from Sp receiver functions

description: This dataset contains the depths of negative velocity gradients that are potentially interpretable as the lithosphere-asthenosphere boundary (LAB) beneath Antarctica, derived from common conversion point stacking of Sp receiver functions. The dataset provides constraints on lithospheric thickness variations, particularly in West Antarctica, where a vertically localized LAB is observed at depths of 70–100 km. In East Antarctica, two zones of well-defined LAB depths are present in regions near the coast at depths of 75-110 km (~140˚E) and 90-130 km (~115˚E). Interpretation of these results and comparison with prior studies are provided in the associated journal article (see References).
DOI: 10.15468/dl.56r3z7
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028824-250811113504898/datasets/export for details.
DOI: 10.15468/dl.bqg543
title: Occurrence Download

description: A dataset containing 187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chile",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oxalis arenaria"
 ]
}

 The dataset includes 187 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028827-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15784/601965
title: Channel Camp borehole CTD data (January 2020)

description: Seabird MicroCAT SBE37IMP data following hotwater drilling operations at the Channel site AMIGOS (Automated Meteorology-Ice-Geophysics Observing System) on the Thwaites Eastern Ice Shelf, Antarctica.
DOI: 10.15468/dl.5warbw
title: Occurrence Download

description: A dataset containing 21454 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Fossil, Specimen, Material sample)",
 "OccurrenceStatus is Present",
 "TaxonKey is Cribrariaceae"
 ]
}

 The dataset includes 21454 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028825-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8w9j5
title: Occurrence Download

description: A dataset containing 21454 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Fossil, Specimen, Material sample)",
 "OccurrenceStatus is Present",
 "TaxonKey is Cribrariaceae"
 ]
}

 The dataset includes 21454 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028826-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w7jaep
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028828-250811113504898/datasets/export for details.
DOI: 10.13012/b2idb-3538917_v1
title: Data for "Viral Delivery of Recombinases Activates Heritable Genetic Switches in Plants"

description: Viral vectors provide an increasingly versatile platform for transformation-free reagent delivery to plants. RNA viral vectors can be used to induce gene silencing, overexpress proteins, or introduce gene editing reagents; however, they are often constrained by carrying capacity or restricted tropism in germline cells. Site-specific recombinases that catalyze precise genetic rearrangements are powerful tools for genome engineering that vary in size and, potentially, efficacy in plants. In this work, we show that viral vectors based on tobacco rattle virus (TRV) deliver and stably express four recombinases ranging in size from ∼0.6 to ∼1.5 kb and achieve simultaneous marker removal and reporter activation through targeted excision in transgenic Nicotiana benthamiana lines. TRV vectors with Cre, FLP, CinH, and Integrase13 efficiently mediated recombination in infected somatic tissue and led to heritable modifications at high frequency. An excision-activated Ruby reporter enabled simple and high-resolution tracing of infected cell lineages without the need for molecular genotyping. Together, our experiments broaden the scope of viral recombinase delivery and offer insights into infection dynamics that may be useful in developing future viral vectors.
DOI: 10.7910/dvn/5mfd0y
title: Smart CVR 2023

description: Data from manuscript
DOI: 10.15468/dl.efjm9f
title: Occurrence Download

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

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028829-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4fd3ru
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028830-250811113504898/datasets/export for details.
DOI: 10.15468/dl.ecrp86
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Micralestes comoensis)",
 "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 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028834-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/njgwgv
title: IL33_JACC BTS_2025

description: Data from manuscript
DOI: 10.15468/dl.jmpagx
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Casuarina glauca",
 "Country is Cuba",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028831-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.taeyad
title: Occurrence Download

description: A dataset containing 6656 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Human Observation)",
 "Country is one of (United States of America, Canada)",
 "Issue is one of (Coordinate rounded, Geodetic datum assumed WGS84, Continent derived from coordinates)",
 "TaxonKey is Tephrosia virginiana"
 ]
}

 The dataset includes 6656 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028832-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.