cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13012/b2idb-4925630_v1
title: Data for Resilience and environmental benefits of electric school buses as backup power for educational functions continuation during outages

description: School buses transport 20 million students annually and are currently undergoing electrification in the US. With Vehicle-to-Building (V2B) technology, electric school buses (ESBs) can supply energy to school buildings during power outages, ensuring continued operation and safety. This study proposes assessing the resilience of secondary schools during outages by leveraging ESB fleets as backup power across various US climate regions. The findings indicate that the current fleet of ESBs in representative cities across different climate regions in the US is insufficient to meet the power demands of an entire school or even its HVAC system. However, we estimated the number of ESBs required to support the school's power needs, and we showed that the use of V2B technology significantly reduces carbon emissions compared to backup diesel generators. While adjusting HVAC setpoints and installing solar panels have limited impacts on enhancing school resilience, gathering students in classrooms during outages significantly improved resilience in our case study in Houston, Texas. Given the ongoing electrification of school buses, it is essential for schools to complement ESBs with stationary batteries and other backup power sources, such as solar and/or diesel generators, to effectively address prolonged outages. Determining the deployment of direct current fast and Level 2 chargers can reduce infrastructure costs while maintaining the resilience benefits of ESBs. This dataset includes the simulation process and results of this study.
DOI: 10.6084/m9.figshare.26367214
title: Replication Package for 'An Empirical Study of Community-prescribed Security Configurations in Kubernetes'

description: The replication package contains data and scripts used to generate results reported in the paper. The replication package does not contain any data from Company-A to abide by the non-disclose agreement signed between the authors and Company-A.
DOI: 10.6084/m9.figshare.26367214.v6
title: Replication Package for 'An Empirical Study of Community-prescribed Security Configurations in Kubernetes'

description: The replication package contains data and scripts used to generate results reported in the paper. The replication package does not contain any data from Company-A to abide by the non-disclose agreement signed between the authors and Company-A.
DOI: 10.26093/cds/vizier.51660011
title: Stellar properties & RV variability for M-dwarfs

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal with title ' Mid-to-late M dwarfs lack Jupiter analogs.' (bibcode: 2023AJ....166...11P)
DOI: 10.26093/cds/vizier.51670174
title: The POKEMON Speckle Survey of Nearby M Dwarfs. III.

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal with title ' The POKEMON Speckle Survey of Nearby M Dwarfs. III. The Stellar Multiplicity Rate of M Dwarfs within 15pc' (bibcode: 2024AJ....167..174C)
DOI: 10.25345/c5f766k48
title: MassIVE MSV000096935 - GNPS_Investigating Chagas Disease tropism_Tolerance_Esophagus
title: DDA LC_MS/MS from the esophagus tissue of mice with or without T. cruzi infection
DOI: 10.13012/b2idb-2656127_v1
title: Data for Total irrigation by crop in the Continental United States from 2008 to 2020

description: This dataset provides estimates of total Irrigation Water Use (IWU) by crop, county, water source, and year for the Continental United States. Total irrigation from Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) is provided for 20 crops and crop groups from 2008 to 2020 at the county spatial resolution. 

In total, there are nearly 2.5 million data points in this dataset (3,142 counties; 13 years; 3 water sources; and 20 crops). This dataset supports the paper by Ruess et al (2024) "Total irrigation by crop in the Continental United States from 2008 to 2020", Scientific Data, doi: 10.1038/s41597-024-03244-w

When using, please cite as: 
Ruess, P.J., Konar, M., Wanders, N., and Bierkens, M.F.P. (2024) Total irrigation by crop in the Continental United States from 2008 to 2020, Scientific Data, doi: 10.1038/s41597-024-03244-w
DOI: 10.6084/m9.figshare.28278947
title: Global Surface Water Supply and Demand (1985 to 2020) Summarized in Watershed Basins

description: Data associated with manuscript: Historical Trends in Snowmelt Used for Irrigation by Kinnebrew et al. 2025, <i>Environmental Research: Food Systems </i>(https://doi.org/10.1088/2976-601X/adacec).These data represent surface water runoff (supply) and consumption (demand) from 1985 to 2020, summarized within watershed basins (HydroBASINS level 3; https://www.hydrosheds.org/products/hydrobasins). Surface water runoff data were derived from TerraClimate (https://doi.org/10.5061/dryad.vx0k6dk2h), irrigation surface water consumption were derived from the Global Crop Water Model (https://doi.org/10.5281/zenodo.14278069), and domestic & industrial consumption were from Hoekstra <i>et al.</i> (2012). Please see the manuscript methods for additional information.<br><b>Column names and descriptions:</b>1. <b>watershedNum:</b> the HUC12 identifier number from HydroBASINS level 32. <b>year:</b> data year, from 1985 to 20203. <b>month:</b> data month, e.g., 1 (January), 2 (February), 3 (March), etc.4. <b>CropWaterDemand_km3: </b>surface water irrigation consumption (in km<sup>3</sup>)5. <b>DomIndWaterDemand_km3:</b> surface water consumption (in km<sup>3</sup>) from domestic and industrial sources6. <b>TotalDemand_km3:</b> combined water consumption (in km<sup>3</sup>) from irrigation, domestic, and industrial sources7. <b>SnowRO_km3:</b> snowmelt runoff (in km<sup>3</sup>)8. <b>RainRO_km3:</b> rainfall runoff (in km<sup>3</sup>)9. <b>TotalRO_km3:</b> combined snowmelt and rainfall runoff (in km<sup>3</sup>)10. <b>SnowConsumed_TotDemand_km3:</b> snowmelt runoff (in km<sup>3</sup>) that supplied total (irrigation, industrial, and domestic) water consumption11. <b>RainConsumed_TotDemand_km3: </b>rainfall runoff (in km<sup>3</sup>) that supplied total (irrigation, industrial, and domestic) water consumption12. <b>AltConsumed_TotDemand_km3:</b> alternative water sources (in km<sup>3</sup>) that supplied total (irrigation, industrial, and domestic) water consumption13. <b>SnowConsumed_CropDemand_km3:</b> snowmelt runoff (in km<sup>3</sup>) that supplied irrigation water consumption14. <b>RainConsumed_CropDemand_km3:</b> rainfall runoff (in km<sup>3</sup>) that supplied irrigation water consumption15. <b>AltConsumed_CropDemand_km3:</b> alternative water sources (in km<sup>3</sup>) that supplied irrigation water consumption
DOI: 10.6084/m9.figshare.28278932
title: Metadata for Historical Trends in Snowmelt Used for Irrigation

description: Metadata associated with the datasets used in the manuscript: Historical Trends in Snowmelt Used for Irrigation by Kinnebrew et al. 2025, <i>Environmental Research: Food Systems </i>(https://doi.org/10.1088/2976-601X/adacec)<br>
DOI: 10.6084/m9.figshare.28278962
title: Peak Timing of Runoff (1985 to 2020) Summarized within Global Watershed Basins

description: Data associated with manuscript: Historical Trends in Snowmelt Used for Irrigation by Kinnebrew et al., 2025, <i>Environmental Research: Food Systems </i>(https://doi.org/10.1088/2976-601X/adacec).These data represent the temporal mass center of runoff, or peak runoff timing, for each year (1985 to 2020) and within each watershed basin (HydroBASINS level 3; https://www.hydrosheds.org/products/hydrobasins). The data were summarized from snowmelt, rainfall and total runoff data from TerraClimate (https://doi.org/10.5061/dryad.vx0k6dk2h). Please see the manuscript methods for additional information.<b>Column Names and Descriptions:</b>1. <b>watershedNum:</b> the HUC12 identifier number from HydroBASINS level 32. <b>year:</b> data year, from 1985 to 20203. <b>CT_Snow: </b>the month in which the mass center of snowmelt runoff occurred. Values correspond to month and day (see note below)4. <b>CT_Rain: </b>the month in which the mass center of rainfall runoff occurred. Values correspond to month and day (see note below)5. <b>CT_Total: </b>the month in which the mass center of total (snowmelt and rainfall) runoff occurred. Values correspond to month and day (see note below)<br><b>Converting values to month and day: </b>The whole number corresponds to the month (1: January, 2: February, 3: March, etc.). The fraction can be converted to the day of the month by multiplying it by the number of days in the month. A value of 1.3 would correspond to January 9, or a value of 9.7 would correspond to September 21.
DOI: 10.6084/m9.figshare.28278962.v1
title: Peak Timing of Runoff (1985 to 2020) Summarized within Global Watershed Basins

description: Data associated with manuscript: Historical Trends in Snowmelt Used for Irrigation by Kinnebrew et al., 2025, <i>Environmental Research: Food Systems </i>(https://doi.org/10.1088/2976-601X/adacec).These data represent the temporal mass center of runoff, or peak runoff timing, for each year (1985 to 2020) and within each watershed basin (HydroBASINS level 3; https://www.hydrosheds.org/products/hydrobasins). The data were summarized from snowmelt, rainfall and total runoff data from TerraClimate (https://doi.org/10.5061/dryad.vx0k6dk2h). Please see the manuscript methods for additional information.<b>Column Names and Descriptions:</b>1. <b>watershedNum:</b> the HUC12 identifier number from HydroBASINS level 32. <b>year:</b> data year, from 1985 to 20203. <b>CT_Snow: </b>the month in which the mass center of snowmelt runoff occurred. Values correspond to month and day (see note below)4. <b>CT_Rain: </b>the month in which the mass center of rainfall runoff occurred. Values correspond to month and day (see note below)5. <b>CT_Total: </b>the month in which the mass center of total (snowmelt and rainfall) runoff occurred. Values correspond to month and day (see note below)<br><b>Converting values to month and day: </b>The whole number corresponds to the month (1: January, 2: February, 3: March, etc.). The fraction can be converted to the day of the month by multiplying it by the number of days in the month. A value of 1.3 would correspond to January 9, or a value of 9.7 would correspond to September 21.
DOI: 10.13026/cec2-9w70
title: MUSIC (Sudden Cardiac Death in Chronic Heart Failure)

description: The MUSIC (MUerte Subita en Insuficiencia Cardiaca) study is a prospective,
multicentre, longitudinal study designed to assess risk predictors of cardiac
mortality and sudden cardiac death (SCD) in ambulatory patients with chronic
heart failure (CHF).

The study population consisted of 992 patients with CHF consecutively enrolled
from the specialized HF clinics of eight University Spanish Hospitals between
April 2003 and December 2004, and followed up for a median of 44 months (until
November 2008).  All patients had a 12-lead electrocardiogram (ECG),  a 24 h,
2-(4%) or 3-lead (96%) Holter ECG, chest X-ray, echocardiography, and blood
laboratory parameters performed at enrolment.

Primary outcomes were cardiac death, either sudden cardiac death (SCD) or pump
failure death (PFD) at the end of the follow-up period.
DOI: 10.13026/fa8p-he52
title: MUSIC (Sudden Cardiac Death in Chronic Heart Failure)

description: The MUSIC (MUerte Subita en Insuficiencia Cardiaca) study is a prospective,
multicentre, longitudinal study designed to assess risk predictors of cardiac
mortality and sudden cardiac death (SCD) in ambulatory patients with chronic
heart failure (CHF).

The study population consisted of 992 patients with CHF consecutively enrolled
from the specialized HF clinics of eight University Spanish Hospitals between
April 2003 and December 2004, and followed up for a median of 44 months (until
November 2008).  All patients had a 3-lead resting electrocardiogram (ECG),  a
24 h, 2-(4%) or 3-lead (96%) Holter ECG, chest X-ray, echocardiography, and
blood laboratory parameters performed at enrolment.

Primary outcomes were cardiac death, either sudden cardiac death (SCD) or pump
failure death (PFD) at the end of the follow-up period.
DOI: 10.13026/z3m7-rf58
title: MUSIC (Sudden Cardiac Death in Chronic Heart Failure)

description: The MUSIC (MUerte Subita en Insuficiencia Cardiaca) study is a prospective,
multicentre, longitudinal study designed to assess risk predictors of cardiac
mortality and sudden cardiac death (SCD) in ambulatory patients with chronic
heart failure (CHF).

The study population consisted of 992 patients with CHF consecutively enrolled
from the specialized HF clinics of eight University Spanish Hospitals between
April 2003 and December 2004, and followed up for a median of 44 months (until
November 2008).  All patients had a 3-lead resting electrocardiogram (ECG),  a
24 h, 2-(4%) or 3-lead (96%) Holter ECG, chest X-ray, echocardiography, and
blood laboratory parameters performed at enrolment.

Primary outcomes were cardiac death, either sudden cardiac death (SCD) or pump
failure death (PFD) at the end of the follow-up period.
DOI: 10.18430/m39arr
title: X-ray diffraction data for the crystal structure of AF9 YEATS domain in complex with dicrotonylated at K1007 and K1014 MOZ (9ARR)
DOI: 10.18430/m39aro
title: X-ray diffraction data for the crystal structure of AF9 YEATS domain in complex with acetylated at K1007 MOZ (9ARO)
DOI: 10.5281/zenodo.14717019
title: Personality Traits and Cognitive Ability in Political Selection

description: This package contains the data, programs and instructions to replicate manuscript Personality Traits and Cognitive Ability in Political Selection by Jokela et al. forthcoming at JEEA.
DOI: 10.5281/zenodo.14717020
title: Personality Traits and Cognitive Ability in Political Selection

description: This package contains the data, programs and instructions to replicate manuscript Personality Traits and Cognitive Ability in Political Selection by Jokela et al. forthcoming at JEEA.
DOI: 10.5281/zenodo.14517533
title: A CURE lab in an introductory biology course has minimal impact on student outcomes, self-confidence, and preferences compared to a traditional lab

description: This dataset includes data collected over three years from Biology students taking an introductory biology course for majors at Kutztown University of Pennsylvania. Data includes the version of the lab the student was enrolled in (CURE vs traditional), demographic information (gender, race, class level, transfer status, and prior research experience), academic performance (pre- and post-course test scores, final lab score, final lecture score, and final letter grade), retention in the Biology program (up to three years following course completion), and survey responses on a Likert-scale (including self-confidence in scientific abilities, interest in pursuing science, and preferences for lab courses, both pre- and post-course). All student identifying information has been removed.
DOI: 10.5281/zenodo.14728622
title: Replication package for: Does Functional Package Management Enable Reproducible Builds at Scale? Yes. - Diffoscopes

description: Code archive for the paper Does Functional Package Management Enable Reproducible Builds at Scale? Yes.

Archive of the diffoscopes needed to answer RQ3.
DOI: 10.5281/zenodo.14728623
title: Replication package for: Does Functional Package Management Enable Reproducible Builds at Scale? Yes. - Diffoscopes

description: Code archive for the paper Does Functional Package Management Enable Reproducible Builds at Scale? Yes.

Archive of the diffoscopes needed to answer RQ3.
DOI: 10.5281/zenodo.14736077
title: Replication package for: Does Functional Package Management Enable Reproducible Builds at Scale? Yes. - Build metadatas and logs

description: Code archive for the paper Does Functional Package Management Enable Reproducible Builds at Scale? Yes.

Archive of the build metadatas and logs.
DOI: 10.5281/zenodo.14736078
title: Replication package for: Does Functional Package Management Enable Reproducible Builds at Scale? Yes. - Build metadatas and logs

description: Code archive for the paper Does Functional Package Management Enable Reproducible Builds at Scale? Yes.

Archive of the build metadatas and logs.
DOI: 10.5517/ccdc.csd.cc2l3clp
title: CCDC 2386522: Experimental Crystal Structure Determination

description: Related Article: Jianfeng Wu, Jingsong Li, Qianqian Yang, Dan Liu, Jinkui Tang, Baoliang Zhang|2025|Inorg.Chem.|64|999|doi:10.1021/acs.inorgchem.4c04337
description: Related Article: Jianfeng Wu|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.cc2l3cmq
title: CCDC 2386523: Experimental Crystal Structure Determination

description: Related Article: Jianfeng Wu, Jingsong Li, Qianqian Yang, Dan Liu, Jinkui Tang, Baoliang Zhang|2025|Inorg.Chem.|64|999|doi:10.1021/acs.inorgchem.4c04337
description: Related Article: Jianfeng Wu|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.cc2l3cnr
title: CCDC 2386524: Experimental Crystal Structure Determination

description: Related Article: Jianfeng Wu, Jingsong Li, Qianqian Yang, Dan Liu, Jinkui Tang, Baoliang Zhang|2025|Inorg.Chem.|64|999|doi:10.1021/acs.inorgchem.4c04337
description: Related Article: Jianfeng Wu|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: 10.5281/zenodo.14081022
title: GNSS-RO Machine Learning Feature Sets used for Classification of Cubesat GNSS-RO Disturbances

description: General Description:

This dataset includes feature sets extracted from GNSS-RO profiles used for multiclass classifcation model training and testing the classifier from 

Dittmann, Chang, & Morton (202?) Machine Learning Classification of Ionosphere and RFI Disturbances in Spaceborne GNSS Radio Occultation Measurements.



In this work we apply a combination of physics-based feature engineering with data-driven supervised machine learning to improve classification of low earth orbit Spire Global GNSS radio occultation disturbances.


Included in this dataset:

data
├── converted_labels.pkl  #(feature set catalogs)
├── **.pkl
└── data
    ├── feature_set_all_single_file
     │   └── all_fdf_v2.pkl #(6 months of feature sets concatenated into single object)
    └── feature_sets
        ├── 2022.206.117.01.01.G23.SC001_0001.pkl #(individual profile feature sets)
        ├── 202***.pkl


References:



This conference proceedings and/or manuscript (TBA) for further description.

This notebook for reproducing this experiment's result and using these datasets.

The python library associated with this dataset.
DOI: 10.5281/zenodo.14081023
title: GNSS-RO Machine Learning Feature Sets used for Classification of Cubesat GNSS-RO Disturbances

description: General Description:

This dataset includes feature sets extracted from GNSS-RO profiles used for multiclass classifcation model training and testing the classifier from 

Dittmann, Chang, & Morton (202?) Machine Learning Classification of Ionosphere and RFI Disturbances in Spaceborne GNSS Radio Occultation Measurements.



In this work we apply a combination of physics-based feature engineering with data-driven supervised machine learning to improve classification of low earth orbit Spire Global GNSS radio occultation disturbances.


Included in this dataset:

data
├── converted_labels.pkl  #(feature set catalogs)
├── **.pkl
└── data
    ├── feature_set_all_single_file
     │   └── all_fdf_v2.pkl #(6 months of feature sets concatenated into single object)
    └── feature_sets
        ├── 2022.206.117.01.01.G23.SC001_0001.pkl #(individual profile feature sets)
        ├── 202***.pkl


References:



This conference proceedings and/or manuscript (TBA) for further description.

This notebook for reproducing this experiment's result and using these datasets.

The python library associated with this dataset.
DOI: 10.5281/zenodo.13183430
title: Flexico: Sustainable Machine Translation via Self-Adaptation

description: Data for reproducing the results of paper 'Flexico: Sustainable Machine Translation via Self-Adaptation'
DOI: 10.5281/zenodo.13183431
title: Flexico: Sustainable Machine Translation via Self-Adaptation

description: Data for reproducing the results of paper 'Flexico: Sustainable Machine Translation via Self-Adaptation'
DOI: 10.5281/zenodo.14728358
title: Flexico: Sustainable Machine Translation via Self-Adaptation

description: Data for reproducing the results of paper 'Flexico: Sustainable Machine Translation via Self-Adaptation'
DOI: 10.5281/zenodo.14736503
title: Flexico: Sustainable Machine Translation via Self-Adaptation

description: Data for reproducing the results of paper 'Flexico: Sustainable Machine Translation via Self-Adaptation'
DOI: 10.6084/m9.figshare.27936084
title: figshare 2024_12_14

description: GSE185381 bone marrow 29/Nov 2024
DOI: 10.6084/m9.figshare.27936084.v4
title: figshare 2024_12_14

description: GSE185381 bone marrow 29/Nov 2024
DOI: 10.6084/m9.figshare.27936084.v3
title: figshare 2024_12_14

description: GSE185381 bone marrow 29/Nov 2024
DOI: 10.15468/dl.wqk9sf
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 396d5f30-dea9-11db-8ab4-b8a03c50a862",
 "TaxonKey is Sylvilagus parentum Ruedas, 2017"
 ]
}

 The dataset includes 19 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001308-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gz6sy6
title: Occurrence Download

description: A dataset containing 532 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Month is one of (April, May, June, July)",
 "TaxonKey is Ceiba pubiflora (A.St.-Hil.) K.Schum."
 ]
}

 The dataset includes 532 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001327-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zba4e3
title: Occurrence Download

description: A dataset containing 262 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Greece",
 "Geometry POLYGON((11.77998 31.82281,39.91702 31.82281,39.91702 43.07281,11.77998 43.07281,11.77998 31.82281))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Periplaneta americana (Linnaeus, 1758)"
 ]
}

 The dataset includes 262 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001305-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.