cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive172062.20251217.10
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/1q40oudj
title: Flixster

description: The Flixster dataset is a collection of user-item interactions.
DOI: 10.13145/bacdive172063.20241212.9.2
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive172063.20251217.10
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/4txarv0x
title: Factors of Transferability for a Generic ConvNet Representation

description: The dataset is used for transfer learning and feature extraction.
DOI: 10.13145/bacdive172064.20241212.9.2
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive172064.20251217.10
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive172065.20241212.9.2
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive172065.20251217.10
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive172066.20241212.9.2
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/fqn04o9n
title: NAS-Bench Event Sequences

description: The NAS-Bench Event Sequences dataset is used for Event Sequence Classification.
DOI: 10.13145/bacdive172066.20251217.10
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive172067.20241212.9.2
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive172067.20251217.10
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive172068.20241212.9.2
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15468/dl.ykz88w
title: Occurrence Download

description: A dataset containing 72 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Iris tectorum, Evansia tectorum, Iris chinensis, Iris rosthornii, Iris tectorum alba, Iris tectorum tectorum, Iris tectorum alba, Iris tectorum alba, Iris tectorum aurescens, Iris tomiolopha, Tectiris tectorum)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1970",
 "Year is less than or equal to 2024",
 "CoordinateUncertaintyInMeters is greater than or equal to 0",
 "CoordinateUncertaintyInMeters is less than or equal to 4500",
 "BasisOfRecord is one of (Human Observation, Observation, Specimen, Material sample, Living Specimen)"
 ]
}

 The dataset includes 72 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079504-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13145/bacdive172068.20251217.10
title: Bacillus cereus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15468/dl.w3gvgk
title: Occurrence Download

description: A dataset containing 15448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Senegal",
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Charadriidae",
 "Year 2002-2025"
 ]
}

 The dataset includes 15448 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079505-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18445829
title: ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models

description: Concept-based interpretability methods like TCAV require clean, well-separated positive and negative examples for each concept. Existing music datasets lack this structure: tags are sparse, noisy, or ill-defined. We introduce ConceptCaps, a dataset of 21k music-caption-tags triplets with explicit labels from a 200-attribute taxonomy. Our pipeline separates semantic modeling from text generation: a VAE learns plausible attribute co-occurrence patterns, a fine-tuned LLM converts attribute lists into professional descriptions, and MusicGen synthesizes corresponding audio. This separation improves coherence and controllability over end-to-end approaches. We validate the dataset through audio-text alignment (CLAP), linguistic quality metrics (BERTScore, MAUVE), and TCAV analysis confirming that concept probes recover musically meaningful patterns. Dataset and code are available online.
DOI: 10.5281/zenodo.18445830
title: ConceptCaps: a Distilled Concept Dataset for Interpretability in Music Models

description: Concept-based interpretability methods like TCAV require clean, well-separated positive and negative examples for each concept. Existing music datasets lack this structure: tags are sparse, noisy, or ill-defined. We introduce ConceptCaps, a dataset of 21k music-caption-tags triplets with explicit labels from a 200-attribute taxonomy. Our pipeline separates semantic modeling from text generation: a VAE learns plausible attribute co-occurrence patterns, a fine-tuned LLM converts attribute lists into professional descriptions, and MusicGen synthesizes corresponding audio. This separation improves coherence and controllability over end-to-end approaches. We validate the dataset through audio-text alignment (CLAP), linguistic quality metrics (BERTScore, MAUVE), and TCAV analysis confirming that concept probes recover musically meaningful patterns. Dataset and code are available online.
DOI: 10.5281/zenodo.18475477
title: IODP Expedition 341 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18475478
title: IODP Expedition 341 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18475473
title: Applications of generative artificial intelligence in higher education: transformations in assessment and academic authorship
DOI: 10.5281/zenodo.18475474
title: Applications of generative artificial intelligence in higher education: transformations in assessment and academic authorship
DOI: 10.5281/zenodo.18475483
title: IODP Expedition 342 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18475482
title: IODP Expedition 342 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18475484
title: IODP Expedition 344 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18475485
title: IODP Expedition 344 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18463646
title: Data for: Euclidean-Monte-Carlo-informed ground-state preparation for quantum simulation of scalar field theory

description: This repository contains the numerical data underlying the figures in the manuscript “Euclidean-Monte-Carlo-informed ground-state preparation for quantum simulation of scalar field theory” (arXiv:2506.02313).

The data are provided as CSV files bundled in a single zip archive. Within the archive, subfolders correspond to individual figures, and the CSV files contain the numerical values of the plotted points/curves used to generate those figures. The deposited files include processed results derived from Monte Carlo calculations and subsequent analysis/optimizations. Raw Monte Carlo samples are not included. However, the method for generating these samples is described in the manuscript.
DOI: 10.5281/zenodo.18463645
title: Data for: Euclidean-Monte-Carlo-informed ground-state preparation for quantum simulation of scalar field theory

description: This repository contains the numerical data underlying the figures in the manuscript “Euclidean-Monte-Carlo-informed ground-state preparation for quantum simulation of scalar field theory” (arXiv:2506.02313).

The data are provided as CSV files bundled in a single zip archive. Within the archive, subfolders correspond to individual figures, and the CSV files contain the numerical values of the plotted points/curves used to generate those figures. The deposited files include processed results derived from Monte Carlo calculations and subsequent analysis/optimizations. Raw Monte Carlo samples are not included. However, the method for generating these samples is described in the manuscript.
DOI: 10.5281/zenodo.18475492
title: IODP Expedition 345 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18475493
title: IODP Expedition 345 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5517/ccdc.csd.cc27ysyy
title: CCDC 2084221: Experimental Crystal Structure Determination

description: Related Article: Victor B. Rybakov, Anton  V. Shchetinin, Eugene V. Babaev|2021|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2lc099
title: CCDC 2393860: Experimental Crystal Structure Determination

description: Related Article: V. B. Rybakov, A. V. Shchetinin, E. V. Babaev, S. Yu. Panshina, N. A. Alzhaparova, M. K. Ibraev|2024|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2qssmy
title: CCDC 2526271: Experimental Crystal Structure Determination

description: Related Article: langwang|2026|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2qssjv
title: CCDC 2526268: Experimental Crystal Structure Determination

description: Related Article: langwang|2026|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2qsskw
title: CCDC 2526269: Experimental Crystal Structure Determination

description: Related Article: langwang|2026|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2qsslx
title: CCDC 2526270: Experimental Crystal Structure Determination

description: Related Article: langwang|2026|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.17088975
title: Files associated with testing the branchwater-web webapp

description: Files associated with testing the branchwater-web webapp (https://branchwater.jgi.doe.gov/).

In brief: After a time test of branchwater-web with 17 genomes, metagenome results from five genome queries went through stringent testing for verification of match accuracy by creating metagenome assembled genomes (MAGs) from the publicly available SRA metagenomes. For each query genome, the CSV of full results from branchwater-web were downloaded and filtered to cANI levels denoting high match quality and sufficient matches for testing (0.96-98). Metagenomes were downloaded with nf-core/fetchngs v. 1.10.0 (15) and processed into MAGs with nf-core/mag v. 2.3.2, which only kept MAG bins that meet high quality criteria. For metagenomes with bins, the resulting MAGs were searched against NCBI via both sourmash and BBTools/sendsketch (17) to identify potential matches at the genus or species level. Independent taxonomic classification was also carried out using BAT bin annotation, which searches predicted ORFs against NCBI non-redundant protein database (NR from 12/12/2024).

The testing pipeline parameters are available on GitHub (https://github.com/USDA-ARS-GBRU/branchwater-web-MAGtest). Here, we include the associated files for each analysis.



The raw data for the processing time test can be found in Timed_test.csv

The full output (.CSVs) from branchwater-web for the five genome queries that underwent strigent testing can be found in branchwater_web_output.zip 

The MAG bins can be found in bins.zip.

Inputs and outputs for the sendsketch can be found in sendsketch_files.zip
DOI: 10.5281/zenodo.17088974
title: Files associated with testing the branchwater-web webapp

description: Files associated with testing the branchwater-web webapp (https://branchwater.jgi.doe.gov/).

In brief: After a time test of branchwater-web with 17 genomes, metagenome results from five genome queries went through stringent testing for verification of match accuracy by creating metagenome assembled genomes (MAGs) from the publicly available SRA metagenomes. For each query genome, the CSV of full results from branchwater-web were downloaded and filtered to cANI levels denoting high match quality and sufficient matches for testing (0.96-98). Metagenomes were downloaded with nf-core/fetchngs v. 1.10.0 (15) and processed into MAGs with nf-core/mag v. 2.3.2, which only kept MAG bins that meet high quality criteria. For metagenomes with bins, the resulting MAGs were searched against NCBI via both sourmash and BBTools/sendsketch (17) to identify potential matches at the genus or species level. Independent taxonomic classification was also carried out using BAT bin annotation, which searches predicted ORFs against NCBI non-redundant protein database (NR from 12/12/2024).

The testing pipeline parameters are available on GitHub (https://github.com/USDA-ARS-GBRU/branchwater-web-MAGtest). Here, we include the associated files for each analysis.



The raw data for the processing time test can be found in Timed_test.csv

The full output (.CSVs) from branchwater-web for the five genome queries that underwent strigent testing can be found in branchwater_web_output.zip 

The MAG bins can be found in bins.zip.

Inputs and outputs for the sendsketch can be found in sendsketch_files.zip
DOI: 10.5281/zenodo.18475499
title: IODP Expedition 346 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.5281/zenodo.18475500
title: IODP Expedition 346 Rig instrumentation

description: Operational rig information data were measured using a variety of sensors and compiled using the RigWatch software package. Approximately 50 channels of drilling/coring data are captured in real time during the expedition. Data are presented as ASCII files extracted from the proprietary RigWatch data files and are presented by expedition. RigWatch data in time or depth domain can be imported into graphics and analysis programs to be merged and correlated with core physical properties data to enhance assessment of poor core recovery intervals.
DOI: 10.3886/e241290
title: Health Outcomes Post-Eviction Moratoria (HOPE-M) [Harris and Travis County, TX], 2024, Time 1

description: During the COVID-19 pandemic eviction
moratoria were implemented. Eviction moratoria were short-term housing
interventions designed to limit the spread of COVID-19 and maintain housing
stability. Travis County residents experienced a strong local moratorium (i.e.
blocked all stages of eviction process) through December 2021. In contrast,
Harris County residents never experienced a local moratorium, and were
dependent on the state moratorium, which ended in May 2020. The Health Outcomes
Post-Eviction Moratoria (HOPE-M) Study is based on data collected from tenants
located in Harris and Travis County Texas that were exposed to these policies. The
sample is derived from two sources. The first source is from court records
among individuals who had eviction filing post state/local moratorium (Harris:
June 2020–December 2023; Travis: January 2022–December 2023). The source uses
an interactive mapping tool to identify neighborhoods with high eviction risk.
Individuals selected in these neighborhoods were at high risk for eviction but were
not filed during the time period above. The survey data set (n=1,818), which was
collected (March 2024 – July 2024) includes measures of housing, economic
hardship, psychosocial stressors and support, mental health, substance use,
general health, disability, and sociodemographic characteristics.<br>
description: N/A
description: Response Rates: 5.8% response rate. See Zapata et al., 2025.
description: See Zapata et al., 2025.
description: Tenants
from Travis and Harris Counties (Texas) who were derived using eviction records and
machine learning techniques from neighborhoods with the highest risk of housing
precarity.. Smallest Geographic Unit: County
description: mixed mode; telephone interview; web-based survey;
DOI: 10.5281/zenodo.18227487
title: Efficient and robust optimization of nuclear and electronic orbitals within the nuclear Hartree product representation