cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.30719
title: Data used for manuscript 250682286 of European Journal of Environmental and Civil Engineering
title: Data used for manuscript 250682286 of European Journal of Environmental and Civil Engineering

description: The data are the data points for Figures 3 to 9 in the manuscript. They were computed by a self - written Mathematica program. They represent the consolidation response process of unsaturated soil under different influencing factors. Each table corresponds to the data points for one figure. The leftmost column is the x - axis, and the other columns are the y - axis.
description: The data are the data points for Figures 3 to 9 in the manuscript. They were computed by a self - written Mathematica program. They represent the consolidation response process of unsaturated soil under different influencing factors. Each table corresponds to the data points for one figure. The leftmost column is the x - axis, and the other columns are the y - axis.
DOI: 10.57760/sciencedb.30739
title: Dataset of 'Imaging through Scattering Media with a Non-symmetric U-Net'
title: Dataset of 'Imaging through Scattering Media with a Non-symmetric U-Net'

description: Deep learning has been widely applied in imaging through scattering media, showing advantages in penetration depth, resolution, dynamic adaptivity, et al. Due to requirements of network architecture and/or computation efficiency, the recorded speckle pattern is usually cropped or downsampled to reduce the size of the input, which often leads to severely degraded reconstruction under incoherent illumination, when target information is encoded in low-contrast speckles. In this study, we propose a non-symmetric U-Net to enhance information extraction from incoherent speckle patterns while maintaining a low cost in computation resources. The non-symmetric U-Net uses a deeper encoder to match the full-size speckle pattern and extract more feature information but a lightweight decoder to achieve target reconstruction. It has been demonstrated that the deep-contraction-shallow-expansion network structure can selectively enhance information extraction from high-frequency modulations in incoherent speckles that is overwhelmed by noise and can be easily averaged-out by downsampling. The study provides a new architectural paradigm for imaging with deep learning, which has significant research and application values in imaging fields with low coherence illuminations, such as autonomous driving detection and remote sensing.
description: Deep learning has been widely applied in imaging through scattering media, showing advantages in penetration depth, resolution, dynamic adaptivity, et al. Due to requirements of network architecture and/or computation efficiency, the recorded speckle pattern is usually cropped or downsampled to reduce the size of the input, which often leads to severely degraded reconstruction under incoherent illumination, when target information is encoded in low-contrast speckles. In this study, we propose a non-symmetric U-Net to enhance information extraction from incoherent speckle patterns while maintaining a low cost in computation resources. The non-symmetric U-Net uses a deeper encoder to match the full-size speckle pattern and extract more feature information but a lightweight decoder to achieve target reconstruction. It has been demonstrated that the deep-contraction-shallow-expansion network structure can selectively enhance information extraction from high-frequency modulations in incoherent speckles that is overwhelmed by noise and can be easily averaged-out by downsampling. The study provides a new architectural paradigm for imaging with deep learning, which has significant research and application values in imaging fields with low coherence illuminations, such as autonomous driving detection and remote sensing.
DOI: 10.15468/dl.vurtvy
title: Occurrence Download

description: A dataset containing 612 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.28_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Ictidomys tridecemlineatus"
 ]
}

 The dataset includes 612 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009128-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17428249
title: Data Release: Probing Spin-Orbit Resonances with the Binary Black Hole Population

description: This data release contains the analysis inputs and results for "Probing Spin-Orbit Resonances with the Binary Black Hole Population" (https://arxiv.org/abs/2502.04278).



injections.pkl: This unpacks into a dictionary that includes the sensitivity injections generated for the hierarchical analyses of simulated events at O4 sensitivity. The dictionary keys are the binary parameters, the total number of generated injections, and the joint draw probability for each injection.


>>> import pickle
>>> injections = pickle.load(open('injections.pkl','rb'))
>>> injections.keys()
dict_keys(['mass_1', 'cos_tilt_1', 'cos_tilt_2', 'mass_ratio', 'a_1', 'a_2', 'redshift', 'phi_12', 'prior', 'total_generated'])




hyper_pe.tar.gz: This tar file contains all the hierarchical inference results summarized in Tables IV-V of the paper, along with the 11 different simulated catalog realizations for GWTC-2 and GWTC-3. The file names specify the name of the population following the mapping below, the number of events included in the analysis, and the waveform and tilt model variations used. These are bilby result files in json format that can either be read in using json or using bilby. 





all_field_iso - Strong resonances (Population A - new seed, Population B - old seed)




field_frac_0.64 - Strong resonances + Isotropic




field_frac_1.00_wide - Weak resonances




field_frac_0.00_reflect - Isotropic




gwtc3_n69 - Real GWTC-3 results




field_frac_0.00_o3a - The 11 different GWTC-2-sized catalogs





If the filename includes "jeffreys", the Jeffreys prior was used. Otherwise, the flat prior was used.






field_frac_0.00_gwtc3 - The 11 different GWTC-3-sized catalogs





>>> import bilby
>>> import json
>>> result = bilby.core.result.read_in_result('all_field_iso_n199_mix_dynesty_simple_tilt_new_seed_reweighted_result.json')
>>> result2 = json.load(open('all_field_iso_n199_mix_dynesty_simple_tilt_new_seed_reweighted_result.json','r'))



old_seed.tar.gz: This tar file contains all the individual-event bilby result files in json format for the Strong Resonances Population B, along with .dat files that contain the posteriors on the spin angles evolved to f_ISCO using the SpinTaylor (PN) evolution and the surrogate dynamics, where available. They can be read in using the same commands given above for the hierarchical inference runs.

new_seed.tar.gz: This tar file contains all the individual-event bilby result files and evolved posterior samples for all the other populations
DOI: 10.5281/zenodo.17428250
title: Data Release: Probing Spin-Orbit Resonances with the Binary Black Hole Population

description: This data release contains the analysis inputs and results for "Probing Spin-Orbit Resonances with the Binary Black Hole Population" (https://arxiv.org/abs/2502.04278).



injections.pkl: This unpacks into a dictionary that includes the sensitivity injections generated for the hierarchical analyses of simulated events at O4 sensitivity. The dictionary keys are the binary parameters, the total number of generated injections, and the joint draw probability for each injection.


>>> import pickle
>>> injections = pickle.load(open('injections.pkl','rb'))
>>> injections.keys()
dict_keys(['mass_1', 'cos_tilt_1', 'cos_tilt_2', 'mass_ratio', 'a_1', 'a_2', 'redshift', 'phi_12', 'prior', 'total_generated'])




hyper_pe.tar.gz: This tar file contains all the hierarchical inference results summarized in Tables IV-V of the paper, along with the 11 different simulated catalog realizations for GWTC-2 and GWTC-3. The file names specify the name of the population following the mapping below, the number of events included in the analysis, and the waveform and tilt model variations used. These are bilby result files in json format that can either be read in using json or using bilby. 





all_field_iso - Strong resonances (Population A - new seed, Population B - old seed)




field_frac_0.64 - Strong resonances + Isotropic




field_frac_1.00_wide - Weak resonances




field_frac_0.00_reflect - Isotropic




gwtc3_n69 - Real GWTC-3 results




field_frac_0.00_o3a - The 11 different GWTC-2-sized catalogs





If the filename includes "jeffreys", the Jeffreys prior was used. Otherwise, the flat prior was used.






field_frac_0.00_gwtc3 - The 11 different GWTC-3-sized catalogs





>>> import bilby
>>> import json
>>> result = bilby.core.result.read_in_result('all_field_iso_n199_mix_dynesty_simple_tilt_new_seed_reweighted_result.json')
>>> result2 = json.load(open('all_field_iso_n199_mix_dynesty_simple_tilt_new_seed_reweighted_result.json','r'))



old_seed.tar.gz: This tar file contains all the individual-event bilby result files in json format for the Strong Resonances Population B, along with .dat files that contain the posteriors on the spin angles evolved to f_ISCO using the SpinTaylor (PN) evolution and the surrogate dynamics, where available. They can be read in using the same commands given above for the hierarchical inference runs.

new_seed.tar.gz: This tar file contains all the individual-event bilby result files and evolved posterior samples for all the other populations
DOI: 10.25345/c5h12vm8j
title: MassIVE MSV000099701 - Evaluation of Data Analysis Tools for DIA Phosphoproteomics in Mammalian and Bacterial Systems
title: Evaluate DIA phosphoproteomics workflow, including instruments, LC separations, and data analysis tools, in characterizing phosphorylation-rich and -sparse systems.
DOI: 10.5281/zenodo.17489447
title: The Φ Foundation — Authorship & Timestamp Proof (Private Deposit)

description: The Φ Foundation — Authorship & Timestamp Proof

**DOI (Private Deposit)**: reserved  **Version**: 1.2 — 2025-10-31  **Status**: Confidential (internal verification only)  **Maintainer**: The Φ Foundation  

#### EssenceThis private Zenodo record preserves **cryptographic proof of authorship and existence**of the Φ-Law theoretical framework — a model for universal coherence of energy, matter, and geometry.It serves as **timestamped evidence** of priority and integrity, **without** disclosing analytical content.

> “Coherence is the bridge between physics and meaning.”

#### Purpose- Establish authorship and timestamp of the Φ-Law research framework.  - Secure the internal repository via **dual-hash verification** (SHA-256 + BLAKE2b).  - Provide long-term, cross-platform reproducibility of proof.  - Maintain confidentiality while enabling integrity verification.
DOI: 10.5281/zenodo.17489448
title: The Φ Foundation — Authorship & Timestamp Proof (Private Deposit)

description: The Φ Foundation — Authorship & Timestamp Proof

**DOI (Private Deposit)**: reserved  **Version**: 1.2 — 2025-10-31  **Status**: Confidential (internal verification only)  **Maintainer**: The Φ Foundation  

#### EssenceThis private Zenodo record preserves **cryptographic proof of authorship and existence**of the Φ-Law theoretical framework — a model for universal coherence of energy, matter, and geometry.It serves as **timestamped evidence** of priority and integrity, **without** disclosing analytical content.

> “Coherence is the bridge between physics and meaning.”

#### Purpose- Establish authorship and timestamp of the Φ-Law research framework.  - Secure the internal repository via **dual-hash verification** (SHA-256 + BLAKE2b).  - Provide long-term, cross-platform reproducibility of proof.  - Maintain confidentiality while enabling integrity verification.
DOI: 10.34740/kaggle/dsv/13562425
title: SOLANA Price Data Binance API (2020–Now)
DOI: 10.5281/zenodo.17489505
title: INNOVATION FOR RESOURCE-EFFICIENT MANUFACTURING: A FRAMEWORK FOR SUSTAINABLE DEVELOPMENT
DOI: 10.5281/zenodo.17489506
title: INNOVATION FOR RESOURCE-EFFICIENT MANUFACTURING: A FRAMEWORK FOR SUSTAINABLE DEVELOPMENT
DOI: 10.15468/dl.uy2shb
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Cambodia, Indonesia, Lao People’s Democratic Republic, Philippines, Malaysia, Myanmar, Singapore, Thailand, Timor-Leste, Viet Nam, Brunei Darussalam)",
 "TaxonKey is Nyctereutes procyonoides"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009129-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/6v49-4z63
title: "Electricity-Gas Coupled Systems data"

description: "This dataset provides a case for research on electricity-gas coupled systems. The data originates from Belgium. It includes: (1) Power system: system topology, bus load, wind farm and photovoltaic station output; (2) Natural gas system: gas sources, pipelines, compressor stations, and load in the natural gas network; (3) Coupling components: the locations of 10 gas turbines and their gas consumption characteristic functions, along with the power consumption of 2 electrically driven compressors."
DOI: 10.15468/dl.vhjmg9
title: Occurrence Download

description: A dataset containing 119 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Machine Observation)",
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Leopardus colocolo, Leopardus colocola, Leopardus tigrinus, Leopardus tigrinus pardinoides, Leopardus pardalis, Leopardus pardalis nelsoni, Leopardus pardalis sonoriensis, Leopardus pardalis albescens, Leopardus pardalis pusaeus, Leopardus pardalis mitis, Leopardus pardalis aequatorialis, Puma yagouaroundi, Leopardus pardalis melanurus, Puma yagouaroundi yagouaroundi, Puma yagouaroundi panamensis, Puma yagouaroundi cacomitli, Puma yagouaroundi ameghinoi, Puma yagouaroundi melantho, Puma yagouaroundi fossata, Puma yagouaroundi eyra, Puma yagouaroundi tolteca, Herpailurus yaguarondi, Herpailurus yaguarondi tolteca, Herpailurus yagouaroundi yagouaroundi, Herpailurus yagouaroundi cacomitli, Herpailurus yagouarondi yagouarondi, Herpailurus yagouaroundi, Herpailurus yagouaroundi melantho, Herpailurus yagouaroundi fossata, Herpailurus yaguarondi fossata)"
 ]
}

 The dataset includes 119 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009130-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57783/tqc9-4j47
title: LibreChat-TEST
DOI: 10.5281/zenodo.17489516
title: Hyperspectral  Fingerprinting of Dune Sediments Across the Arabian Peninsula: Insights into Sediment Provenance and Transport Pathways from NASA's EMIT Data
DOI: 10.6084/m9.figshare.29900933
title: Trackloaded Dataset

description: The Trackloaded Dataset is a curated, open Linked Data collection representing African music artists, record labels, and cultural metadata published on Trackloaded.com.Each entity includes structured attributes such as artist or label name, biography, birth date, social media links, homepage, and external identity connections (e.g. Wikidata, Instagram, MusicBrainz, Discogs ).The dataset follows Linked Open Data (LOD) best practices and is published in RDF/Turtle, RDF/XML, and JSON-LD formats using FOAF, Music Ontology, Schema.org, and OWL vocabularies.<br>It supports real-time semantic discovery through a SPARQL endpoint, VoID metadata, and per-artist RDF exports — making it compatible with knowledge graphs, LLM ingestion pipelines, and cultural research applications.Data Access Points<b>SPARQL Endpoint</b>: https://trackloaded.com/sparql-endpoint.php<b>SPARQL Browser UI</b>: https://trackloaded.com/sparql-browser<b>VoID Metadata (Turtle)</b>: https://trackloaded.com/.well-known/void.ttl<b>JSON-LD Access Point</b>: https://trackloaded.com/sparql-endpoint.php?sparql_jsonld=1<b>RDF Sitemap (Turtle)</b>: https://trackloaded.com/?build_rdf_sitemapPer-Entity Linked Data Exampleshttps://trackloaded.com/tag/olamide/?rdf=ttlhttps://trackloaded.com/tag/rema/?format=jsonldhttps://trackloaded.com/tag/mavin-records/?rdf=ttlUse CasesSemantic web and linked data enrichmentAI / LLM training data for cultural modelsDigital heritage and cultural documentationCross-platform music analytics and recommendation systems
DOI: 10.6084/m9.figshare.29900933.v2
title: Trackloaded Dataset

description: The Trackloaded Dataset is a curated, open Linked Data collection representing African music artists, record labels, and cultural metadata published on Trackloaded.com.Each entity includes structured attributes such as artist or label name, biography, birth date, social media links, homepage, and external identity connections (e.g. Wikidata, Instagram, MusicBrainz, Discogs ).The dataset follows Linked Open Data (LOD) best practices and is published in RDF/Turtle, RDF/XML, and JSON-LD formats using FOAF, Music Ontology, Schema.org, and OWL vocabularies.<br>It supports real-time semantic discovery through a SPARQL endpoint, VoID metadata, and per-artist RDF exports — making it compatible with knowledge graphs, LLM ingestion pipelines, and cultural research applications.Data Access Points<b>SPARQL Endpoint</b>: https://trackloaded.com/sparql-endpoint.php<b>SPARQL Browser UI</b>: https://trackloaded.com/sparql-browser<b>VoID Metadata (Turtle)</b>: https://trackloaded.com/.well-known/void.ttl<b>JSON-LD Access Point</b>: https://trackloaded.com/sparql-endpoint.php?sparql_jsonld=1<b>RDF Sitemap (Turtle)</b>: https://trackloaded.com/?build_rdf_sitemapPer-Entity Linked Data Exampleshttps://trackloaded.com/tag/olamide/?rdf=ttlhttps://trackloaded.com/tag/rema/?format=jsonldhttps://trackloaded.com/tag/mavin-records/?rdf=ttlUse CasesSemantic web and linked data enrichmentAI / LLM training data for cultural modelsDigital heritage and cultural documentationCross-platform music analytics and recommendation systems
DOI: 10.6084/m9.figshare.29900933.v1
title: Trackloaded Artist Dataset

description: The Trackloaded Artist Dataset is a curated open data collection of Nigerian music artists and related metadata from Trackloaded.com. It includes artist names, biographies, birth dates, profile pages, social media handles, and external identity links such as Wikidata and Instagram.<br>The dataset is published as Linked Open Data (LOD) and is available in RDF/Turtle, RDF/XML, and JSON-LD formats. It is accessible through a SPARQL endpoint, VoID metadata, and an RDF sitemap for programmatic access.<br>**Data access points:**- SPARQL Endpoint: https://trackloaded.com/sparql-endpoint.php- SPARQL Browser UI: https://trackloaded.com/sparql-browser- VoID Metadata (Turtle): https://trackloaded.com/?void=1- RDF Sitemap (Turtle): https://trackloaded.com/?build_rdf_sitemap<br>**Per-Artist Linked Data examples:**- https://trackloaded.com/tag/olamide/?rdf=ttl- https://trackloaded.com/tag/olamide/?format=rdf<br>**Related resources:**- Zenodo DOI: https://doi.org/10.5281/zenodo.16777415- GitHub repository: https://github.com/trackloaded/data- Kaggle dataset: https://www.kaggle.com/datasets/trackloaded/artist-datasets- LOD Cloud profile: https://lod-cloud.net/dataset/trackloaded- Trackloaded dataset page: https://trackloaded.com/data
DOI: 10.5281/zenodo.17486594
title: Acetylcholine demixes heterogeneous dopamine signals for learning and moving - dataset 6

description: These data are used and described in the following paper: Jang H.J., Ward R.M., Golden C.E.M., Constantinople C.M. (2025). Acetylcholine demixes heterogeneous dopamine signals for learning and moving. Nature Neuroscience. 

The data set comprises:

1) Photometry recordings from the nucleus accumbens core in rats. 

Make a folder named data-published/PhotometryData/GRAB_DA_NAcc and save the files under this folder in order to use visualization scripts provided in the github link below.

Raw photometry signals are motion-corrected and pooled across sessions for each rat. Associated behavioral data for each rat are stored in *_bstruct.mat files.

Files are Matlab data (.mat) files. The code to analyze this data and generate figures in Jang et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/DMS_AChDA}. Data was analyzed using Matlab 2021b.
DOI: 10.5281/zenodo.17486595
title: Acetylcholine demixes heterogeneous dopamine signals for learning and moving - dataset 6

description: These data are used and described in the following paper: Jang H.J., Ward R.M., Golden C.E.M., Constantinople C.M. (2025). Acetylcholine demixes heterogeneous dopamine signals for learning and moving. Nature Neuroscience. 

The data set comprises:

1) Photometry recordings from the nucleus accumbens core in rats. 

Make a folder named data-published/PhotometryData/GRAB_DA_NAcc and save the files under this folder in order to use visualization scripts provided in the github link below.

Raw photometry signals are motion-corrected and pooled across sessions for each rat. Associated behavioral data for each rat are stored in *_bstruct.mat files.

Files are Matlab data (.mat) files. The code to analyze this data and generate figures in Jang et al., 2025 is available at {https://github.com/constantinoplelab/published/tree/main/DMS_AChDA}. Data was analyzed using Matlab 2021b.
DOI: 10.15468/dl.z6nsyy
title: Occurrence Download

description: A dataset containing 186 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amblyseius swirskii"
 ]
}

 The dataset includes 186 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009131-251025141854904/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.26244578
title: Global_Estuarine_Virome_analysis.zip

description: R script<b>1. System requirements</b>This script requires R software (version 4.3.1) to run.<b>2. Installation guide</b>R v4.3.1: Index of /bin/windows/base/old/4.3.1 (r-project.org)Rstudio: Download RStudio | The Popular Open-Source IDE from Posit<b>3. Instructions for use</b>Click the "Run" option at the top of each chunk to view the figures. It will take about half an hour to run all chunks by your laptop.<br><br><br>
DOI: 10.6084/m9.figshare.26244578.v4
title: Global_Estuarine_Virome_analysis.zip

description: R script<b>1. System requirements</b>This script requires R software (version 4.3.1) to run.<b>2. Installation guide</b>R v4.3.1: Index of /bin/windows/base/old/4.3.1 (r-project.org)Rstudio: Download RStudio | The Popular Open-Source IDE from Posit<b>3. Instructions for use</b>Click the "Run" option at the top of each chunk to view the figures. It will take about half an hour to run all chunks by your laptop.<br><br><br>
DOI: 10.6084/m9.figshare.26244578.v3
title: Global_Estuarine_Virome_analysis.zip

description: R script<b>1. System requirements</b>This script requires R software (version 4.3.1) to run.<b>2. Installation guide</b>R v4.3.1: Index of /bin/windows/base/old/4.3.1 (r-project.org)Rstudio: Download RStudio | The Popular Open-Source IDE from Posit<b>3. Instructions for use</b>Click the "Run" option at the top of each chunk to view the figures. It will take about half an hour to run all chunks by your laptop.<br><br><br>
DOI: 10.5281/zenodo.17489529
title: Abdullayeva Nasiba Ibrohimjon qizi
DOI: 10.5281/zenodo.17489530
title: Abdullayeva Nasiba Ibrohimjon qizi