cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18501827
title: Docking dataset: Caffeic acid with TAK1, AMPK, and TJ-related proteins

description: This dataset provides all molecular docking input and output files for caffeic acid docked to TAK1, AMPK, and tight junction–related proteins (ZO-2, Claudin-1, Claudin-3, and Claudin-4) using AutoDock Vina (v1.1.2; exhaustiveness = 32; three independent runs per target). The dataset includes prepared receptors and ligands, configuration files, log files, selected best-scoring poses, complex structures, and docking results for property-matched decoy molecules to support independent verification and reproducibility.

<Data Description for Molecular Docking Analysis>

This repository contains the complete dataset for molecular docking simulations between Caffeic acid and its target proteins (TAK1, AMPK, ZO-2, Claudin-1, -3, -4). This description provides a detailed roadmap of the uploaded files to facilitate peer review during the embargo period.

1. Repository OrganizationThe data are structured into six primary directories, one for each target protein: /TAK1/, /AMPK/, /ZO2/, /Claudin1/, /Claudin3/, /Claudin4/

Each directory contains all necessary files to replicate the docking process and verify the results presented in the manuscript.

2. Detailed File Descriptions (per Target Directory)To ensure transparency, each folder contains the following standardized file sets:

Receptor Preparation: - *_original.pdb: The initial protein structure.- *_prepared.pdb & *.pdbqt: The final receptor structures used for docking after removing water molecules and assigning charges.

Docking Execution:- *_config.txt: The AutoDock Vina configuration file. This includes the exact grid box center coordinates, dimensions, and exhaustiveness (32), allowing reviewers to verify   the search space. 

Results & Reproducibility:- *_out(Figure).pdbqt: The specific docking pose that corresponds to the visualization in the manuscript (e.g., Figure 8).- *_out2.pdbqt & *_out3.pdbqt: Results from independent docking runs to demonstrate the consistency of the binding mode.- *_log(Figure).txt, *_log2.txt, *_log3.txt: Raw log files showing the predicted binding affinities (kcal/mol) for each run.

Complex Structure:- *_complex.pdb: A pre-assembled protein–ligand complex file for immediate structural assessment.

3. Decoy-Based Validation DataThe /decoys/ subdirectory contains the following files to support the specificity of Caffeic acid:

*_decoy_Results.csv (or .xlsx): A compilation of docking scores for property-matched decoy molecules. These data provide a statistical baseline used to assess whether the docking score of caffeic acid is distinguishable from property-matched non-active analogs.

4. Correspondence with ManuscriptTable Data: All binding energy values reported in the manuscript tables correspond to the lowest-energy pose from the docking run designated for figure visualization (*_log(Figure).txt).

Visual Figures: The 3D interactions and 2D ligand–protein interaction maps (e.g., LigPlot+) shown in the figures were generated based on the *_out(Figure).pdbqt files.
DOI: 10.5281/zenodo.18501828
title: Docking dataset: Caffeic acid with TAK1, AMPK, and TJ-related proteins

description: This dataset provides all molecular docking input and output files for caffeic acid docked to TAK1, AMPK, and tight junction–related proteins (ZO-2, Claudin-1, Claudin-3, and Claudin-4) using AutoDock Vina (v1.1.2; exhaustiveness = 32; three independent runs per target). The dataset includes prepared receptors and ligands, configuration files, log files, selected best-scoring poses, complex structures, and docking results for property-matched decoy molecules to support independent verification and reproducibility.

<Data Description for Molecular Docking Analysis>

This repository contains the complete dataset for molecular docking simulations between Caffeic acid and its target proteins (TAK1, AMPK, ZO-2, Claudin-1, -3, -4). This description provides a detailed roadmap of the uploaded files to facilitate peer review during the embargo period.

1. Repository OrganizationThe data are structured into six primary directories, one for each target protein: /TAK1/, /AMPK/, /ZO2/, /Claudin1/, /Claudin3/, /Claudin4/

Each directory contains all necessary files to replicate the docking process and verify the results presented in the manuscript.

2. Detailed File Descriptions (per Target Directory)To ensure transparency, each folder contains the following standardized file sets:

Receptor Preparation: - *_original.pdb: The initial protein structure.- *_prepared.pdb & *.pdbqt: The final receptor structures used for docking after removing water molecules and assigning charges.

Docking Execution:- *_config.txt: The AutoDock Vina configuration file. This includes the exact grid box center coordinates, dimensions, and exhaustiveness (32), allowing reviewers to verify   the search space. 

Results & Reproducibility:- *_out(Figure).pdbqt: The specific docking pose that corresponds to the visualization in the manuscript (e.g., Figure 8).- *_out2.pdbqt & *_out3.pdbqt: Results from independent docking runs to demonstrate the consistency of the binding mode.- *_log(Figure).txt, *_log2.txt, *_log3.txt: Raw log files showing the predicted binding affinities (kcal/mol) for each run.

Complex Structure:- *_complex.pdb: A pre-assembled protein–ligand complex file for immediate structural assessment.

3. Decoy-Based Validation DataThe /decoys/ subdirectory contains the following files to support the specificity of Caffeic acid:

*_decoy_Results.csv (or .xlsx): A compilation of docking scores for property-matched decoy molecules. These data provide a statistical baseline used to assess whether the docking score of caffeic acid is distinguishable from property-matched non-active analogs.

4. Correspondence with ManuscriptTable Data: All binding energy values reported in the manuscript tables correspond to the lowest-energy pose from the docking run designated for figure visualization (*_log(Figure).txt).

Visual Figures: The 3D interactions and 2D ligand–protein interaction maps (e.g., LigPlot+) shown in the figures were generated based on the *_out(Figure).pdbqt files.
DOI: 10.15468/dl.yaw2jx
title: Occurrence Download

description: A dataset containing 1948 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lochmaea capreae"
 ]
}

 The dataset includes 1948 records from 138 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015866-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31143826
title: <b>EspF Acts as a Molecular Scaffold to Facilitate TRIM25-Mediated NLRP3 Inflammasome Activation During Mycobacterial Infection</b>

description: The mycobacterial ESX-1 (Type VII) secretion system is essential for virulence and induces the NLRP3 inflammasome activation. However, the specific bacterial effectors involved in this process, beyond the well-characterized EsxA (ESAT-6), remain largely unidentified. In this study, we identified the ESX-1 effectors EspF as a critical and conserved regulator of the NLRP3 inflammasome across pathogenic mycobacterial species. Our results demonstrate that EspF significantly augments the mature IL-1β and IL-18 release, caspase-1 activation, and gasdermin D (GSDMD)-mediated pyroptosis. Notably, infection with <i>M. smegmatis</i> or <i>M. bovis</i> BCG strains overexpressing EspF significantly enhanced NLRP3 inflammasome activation and pyroptosis, which were completely abolished in NLRP3-deficient cells. Mechanistically, EspF directly interacts with the NACHT and LRR domains of NLRP3. Furthermore, unbiased proteomic screening identified the host E3 ubiquitin ligase TRIM25 as a critical partner recruited by EspF to NLRP3, which mediated K63-linked NLRP3 ubiquitination. <i>In vivo</i>, mice infected with <i>M. bovis</i> BCG strains overexpressing EspF exhibited exacerbated lung lesions, increased inflammatory cell infiltration, and higher bacterial burdens. Collectively, these findings reveal EspF-TRIM25-NLRP3 axis is a novel mechanism of mycobacterial pathogenesis that drives hyperinflammation to facilitate bacterial survival and dissemination.
DOI: 10.6084/m9.figshare.31143826.v3
title: <b>EspF Acts as a Molecular Scaffold to Facilitate TRIM25-Mediated NLRP3 Inflammasome Activation During Mycobacterial Infection</b>

description: The mycobacterial ESX-1 (Type VII) secretion system is essential for virulence and induces the NLRP3 inflammasome activation. However, the specific bacterial effectors involved in this process, beyond the well-characterized EsxA (ESAT-6), remain largely unidentified. In this study, we identified the ESX-1 effectors EspF as a critical and conserved regulator of the NLRP3 inflammasome across pathogenic mycobacterial species. Our results demonstrate that EspF significantly augments the mature IL-1β and IL-18 release, caspase-1 activation, and gasdermin D (GSDMD)-mediated pyroptosis. Notably, infection with <i>M. smegmatis</i> or <i>M. bovis</i> BCG strains overexpressing EspF significantly enhanced NLRP3 inflammasome activation and pyroptosis, which were completely abolished in NLRP3-deficient cells. Mechanistically, EspF directly interacts with the NACHT and LRR domains of NLRP3. Furthermore, unbiased proteomic screening identified the host E3 ubiquitin ligase TRIM25 as a critical partner recruited by EspF to NLRP3, which mediated K63-linked NLRP3 ubiquitination. <i>In vivo</i>, mice infected with <i>M. bovis</i> BCG strains overexpressing EspF exhibited exacerbated lung lesions, increased inflammatory cell infiltration, and higher bacterial burdens. Collectively, these findings reveal EspF-TRIM25-NLRP3 axis is a novel mechanism of mycobacterial pathogenesis that drives hyperinflammation to facilitate bacterial survival and dissemination.
DOI: 10.6084/m9.figshare.31143826.v2
title: <b>EspF Acts as a Molecular Scaffold to Facilitate TRIM25-Mediated NLRP3 Inflammasome Activation During Mycobacterial Infection</b>

description: The mycobacterial ESX-1 (Type VII) secretion system is essential for virulence and induces the NLRP3 inflammasome activation. However, the specific bacterial effectors involved in this process, beyond the well-characterized EsxA (ESAT-6), remain largely unidentified. In this study, we identified the ESX-1 effectors EspF as a critical and conserved regulator of the NLRP3 inflammasome across pathogenic mycobacterial species. Our results demonstrate that EspF significantly augments the mature IL-1β and IL-18 release, caspase-1 activation, and gasdermin D (GSDMD)-mediated pyroptosis. Notably, infection with <i>M. smegmatis</i> or <i>M. bovis</i> BCG strains overexpressing EspF significantly enhanced NLRP3 inflammasome activation and pyroptosis, which were completely abolished in NLRP3-deficient cells. Mechanistically, EspF directly interacts with the NACHT and LRR domains of NLRP3. Furthermore, unbiased proteomic screening identified the host E3 ubiquitin ligase TRIM25 as a critical partner recruited by EspF to NLRP3, which mediated K63-linked NLRP3 ubiquitination. <i>In vivo</i>, mice infected with <i>M. bovis</i> BCG strains overexpressing EspF exhibited exacerbated lung lesions, increased inflammatory cell infiltration, and higher bacterial burdens. Collectively, these findings reveal EspF-TRIM25-NLRP3 axis is a novel mechanism of mycobacterial pathogenesis that drives hyperinflammation to facilitate bacterial survival and dissemination.
DOI: 10.15468/dl.2p9qet
title: Occurrence Download

description: A dataset containing 305865 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-85.91111 33.92889,-77.13422 34.29891,-77.25597 40.8613,-85.83769 40.74229,-85.91111 33.92889))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year is 2020"
 ]
}

 The dataset includes 305865 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015839-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28616207
title: IDWE_CHM (NRT_F)

description: A near-real-time (NRT) extension of the IDWE_CHM dataset with ongoing daily updates beyond 2023. This NRT product continues to apply the IDWE framework on incoming data, thereby extending the record in near real-time. Users can obtain timely precipitation estimates with the same ~0.1° resolution and methodology consistency as the historical dataset.For a comprehensive description of the project, please refer to:<br><b>An Incremental Dynamic Weighting Ensemble Framework for Long-Term and NRT Precipitation Prediction</b><br>https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619<br>The IDWE_CHM dataset provides <b>four precipitation variables</b>, all derived from the ensemble framework but with slightly different modeling approaches:<b>ENS_Reg</b> – A purely regression-based merged precipitation estimate. This product is generated by optimally weighting and combining the input datasets (ERA5-Land, IMERG, GSMaP, etc.) using regression, without additional classification. It serves as a baseline for the IDWE approach.<b>ENS_RegCla1</b>, <b>ENS_RegCla2</b>, <b>ENS_RegCla3</b> – Three variants of a hybrid <i>regression-plus-classification</i> approach (collectively called <b>ENS_RegCla</b>). These are produced by first applying the regression merging (as in ENS_Reg) and then using a classification step to adjust the estimates. The classification is enhanced with incremental learning, meaning the algorithm learns from errors over time. These three variants may correspond to different configurations or epochs of incremental learning, and they generally show improved skill in capturing precipitation occurrence and extremes compared to a regression-only merge.The updates of IDWE_CHM (NRT_F) are temporally coordinated with those of the five datasets integrated in the fusion process, with explicit synchronization maintained for the GPM_3IMERGDF dataset (available at: https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGDF.07), which exhibits relative latency compared to other fused datasets.
DOI: 10.6084/m9.figshare.28616207.v13
title: IDWE_CHM (NRT_F)

description: A near-real-time (NRT) extension of the IDWE_CHM dataset with ongoing daily updates beyond 2023. This NRT product continues to apply the IDWE framework on incoming data, thereby extending the record in near real-time. Users can obtain timely precipitation estimates with the same ~0.1° resolution and methodology consistency as the historical dataset.For a comprehensive description of the project, please refer to:<br><b>An Incremental Dynamic Weighting Ensemble Framework for Long-Term and NRT Precipitation Prediction</b><br>https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619<br>The IDWE_CHM dataset provides <b>four precipitation variables</b>, all derived from the ensemble framework but with slightly different modeling approaches:<b>ENS_Reg</b> – A purely regression-based merged precipitation estimate. This product is generated by optimally weighting and combining the input datasets (ERA5-Land, IMERG, GSMaP, etc.) using regression, without additional classification. It serves as a baseline for the IDWE approach.<b>ENS_RegCla1</b>, <b>ENS_RegCla2</b>, <b>ENS_RegCla3</b> – Three variants of a hybrid <i>regression-plus-classification</i> approach (collectively called <b>ENS_RegCla</b>). These are produced by first applying the regression merging (as in ENS_Reg) and then using a classification step to adjust the estimates. The classification is enhanced with incremental learning, meaning the algorithm learns from errors over time. These three variants may correspond to different configurations or epochs of incremental learning, and they generally show improved skill in capturing precipitation occurrence and extremes compared to a regression-only merge.The updates of IDWE_CHM (NRT_F) are temporally coordinated with those of the five datasets integrated in the fusion process, with explicit synchronization maintained for the GPM_3IMERGDF dataset (available at: https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGDF.07), which exhibits relative latency compared to other fused datasets.
DOI: 10.6084/m9.figshare.28616207.v12
title: IDWE_CHM (NRT_F)

description: A near-real-time (NRT) extension of the IDWE_CHM dataset with ongoing daily updates beyond 2023. This NRT product continues to apply the IDWE framework on incoming data, thereby extending the record in near real-time. Users can obtain timely precipitation estimates with the same ~0.1° resolution and methodology consistency as the historical dataset.For a comprehensive description of the project, please refer to:<br><b>An Incremental Dynamic Weighting Ensemble Framework for Long-Term and NRT Precipitation Prediction</b><br>https://figshare.com/projects/An_Incremental_Dynamic_Weighting_Ensemble_Framework_for_Long-Term_and_NRT_Precipitation_Prediction/241619<br>The IDWE_CHM dataset provides <b>four precipitation variables</b>, all derived from the ensemble framework but with slightly different modeling approaches:<b>ENS_Reg</b> – A purely regression-based merged precipitation estimate. This product is generated by optimally weighting and combining the input datasets (ERA5-Land, IMERG, GSMaP, etc.) using regression, without additional classification. It serves as a baseline for the IDWE approach.<b>ENS_RegCla1</b>, <b>ENS_RegCla2</b>, <b>ENS_RegCla3</b> – Three variants of a hybrid <i>regression-plus-classification</i> approach (collectively called <b>ENS_RegCla</b>). These are produced by first applying the regression merging (as in ENS_Reg) and then using a classification step to adjust the estimates. The classification is enhanced with incremental learning, meaning the algorithm learns from errors over time. These three variants may correspond to different configurations or epochs of incremental learning, and they generally show improved skill in capturing precipitation occurrence and extremes compared to a regression-only merge.The updates of IDWE_CHM (NRT_F) are temporally coordinated with those of the five datasets integrated in the fusion process, with explicit synchronization maintained for the GPM_3IMERGDF dataset (available at: https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGDF.07), which exhibits relative latency compared to other fused datasets.
DOI: 10.5281/zenodo.18502300
title: code
DOI: 10.5281/zenodo.18502301
title: code
DOI: 10.6084/m9.figshare.31273963
title: Supplemental data for "Statistical mechanics of a cold tracer in a hot bath"

description: Data used in Plots in for "Statistical mechanics of a cold tracer in a hot bath". Paper abstract: We study the dynamics of a zero-temperature particle interacting linearly with a bath of hot Brownian particles. Starting with the most general model of a linearly-coupled bath, we eliminate the bath degrees of freedom exactly to map the tracer dynamics onto a generalized Langevin equation, allowing for an arbitrary external potential on the tracer. We apply this result to determine the fate of a tracer connected by springs to N identical bath particles or inserted within a harmonic chain of hot particles. In the former "fully-connected" case, we find the tracer to transition between an effective equilibrium regime at large N and an FDT-violating regime at finite N, while in the latter "loop" model the tracer never satisfies an FDT. We then study the fully-connected model perturbatively for large but finite N, demonstrating signatures of irreversibility such as ratchet currents, non-Boltzmann statistics, and positive entropy production. Finally, we specialize to harmonic external potentials on the tracer, allowing us to exactly solve the dynamics of both the tracer and the bath for an arbitrary linear model. We apply our findings to show that a cold tracer in a hot lattice suppresses the fluctuations of the lattice in a long-ranged manner, and we generalize this result to linear elastic field theories.
DOI: 10.6084/m9.figshare.31273963.v1
title: Supplemental data for "Statistical mechanics of a cold tracer in a hot bath"

description: Data used in Plots in for "Statistical mechanics of a cold tracer in a hot bath". Paper abstract: We study the dynamics of a zero-temperature particle interacting linearly with a bath of hot Brownian particles. Starting with the most general model of a linearly-coupled bath, we eliminate the bath degrees of freedom exactly to map the tracer dynamics onto a generalized Langevin equation, allowing for an arbitrary external potential on the tracer. We apply this result to determine the fate of a tracer connected by springs to N identical bath particles or inserted within a harmonic chain of hot particles. In the former "fully-connected" case, we find the tracer to transition between an effective equilibrium regime at large N and an FDT-violating regime at finite N, while in the latter "loop" model the tracer never satisfies an FDT. We then study the fully-connected model perturbatively for large but finite N, demonstrating signatures of irreversibility such as ratchet currents, non-Boltzmann statistics, and positive entropy production. Finally, we specialize to harmonic external potentials on the tracer, allowing us to exactly solve the dynamics of both the tracer and the bath for an arbitrary linear model. We apply our findings to show that a cold tracer in a hot lattice suppresses the fluctuations of the lattice in a long-ranged manner, and we generalize this result to linear elastic field theories.
DOI: 10.15468/dl.qbhg22
title: Occurrence Download

description: A dataset containing 1619 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anemopsis californica",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 1619 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015867-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8aszc9
title: Occurrence Download

description: A dataset containing 426007 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scyliorhinus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 426007 records from 304 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015841-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uf2aao
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Maihueniopsis domeykoensis F.Ritter"
 ]
}

 The dataset includes 6 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010259-190320150433242/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ftmq9v
title: Occurrence Download

description: A dataset listing the 89 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "Geometry POLYGON((11.96747 51.50253,11.97579 51.50253,11.97579 51.50774,11.96747 51.50774,11.96747 51.50253))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 89 records were derived from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0269561-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.