cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6519246
title: SuperDARN data in netCDF format (2012-Nov)

description: 2012-Nov SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.18487493
title: LIP Quantification
DOI: 10.5281/zenodo.18487492
title: LIP Quantification
DOI: 10.15468/dl.hmbpem
title: Occurrence Download

description: A dataset containing 16392 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Globicephala macrorhynchus"
 ]
}

 The dataset includes 16392 records from 343 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012424-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18487495
title: THE ROLE OF RUSSIAN CHILDREN'S LITERATURE IN THE DEVELOPMENT OF UZBEK CHILDREN'S LITERATURE

description: The development of Uzbek children’s literature has been significantly influenced not only by national literary traditions but also by world literature, particularly Russian children’s literature. Uzbek writers drew substantial inspiration and creative experience from Russian literary models. This influence was primarily realized through translation activities, literary interactions, the adoption of stylistic and genre features, and the educational system. This article examines the role of Russian children’s literature in shaping Uzbek children’s literature, highlighting its impact on genre formation, narrative techniques, character creation, and the psychological portrayal of children.
DOI: 10.5281/zenodo.18487494
title: THE ROLE OF RUSSIAN CHILDREN'S LITERATURE IN THE DEVELOPMENT OF UZBEK CHILDREN'S LITERATURE

description: The development of Uzbek children’s literature has been significantly influenced not only by national literary traditions but also by world literature, particularly Russian children’s literature. Uzbek writers drew substantial inspiration and creative experience from Russian literary models. This influence was primarily realized through translation activities, literary interactions, the adoption of stylistic and genre features, and the educational system. This article examines the role of Russian children’s literature in shaping Uzbek children’s literature, highlighting its impact on genre formation, narrative techniques, character creation, and the psychological portrayal of children.
DOI: 10.5066/p14m9sk2
title: Visual encounter and environmental DNA (eDNA) surveys for amphibians in the Western United States

description: We used visual encounter surveys (VES) and environmental DNA (eDNA) surveys for amphibians in the western united states. Data are results of both VES and eDNA surveys for all amphibian species that occur in the locations surveyed.
DOI: 10.15468/dl.ej2zgt
title: Occurrence Download

description: A dataset containing 41049 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Calluna vulgaris, Calluna vulgaris)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 41049 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012425-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18407660
title: ReEDS: Utility-scale PV profiles

description: This record provides modeled hourly utility-scale photovoltaic (PV) capacity factor profiles for the contiguous United States (U.S.), generated using the National Solar Radiation Database (NSRDB), System Advisor Model (SAM), and Renewable Energy Potential (reV) model. These profiles are used in the Regional Energy Deployment System (ReEDS) model.

 

Additional details are provided in the ReEDS documentation.

Technology assumptions

Utility-scale PV capacity factor profiles use assumptions consistent with the 2024 Annual Technology Baseline (ATB) and Lopez et al. (2025) assumptions for 2035, namely:






1-axis tracking with 0° tilt

Inverter loading ratio: 1.34

DC losses: 10.4%

Ground cover ratio: 0.4


Values are given in units of  $[MW_{AC,grid}/MW_{DC,array}]$, where  $MW_{AC,grid}$ is the post-inverter output to the grid and  $MW_{DC,array}$ is the pre-inverter PV array capacity. To convert to units of  $[MW_{AC,grid}/MW_{AC,nameplate}]$, the profiles should be multiplied by the inverter loading ratio of 1.34  $[MW_{DC,array}/MW_{AC,nameplate}]$.

Temporal resolution



All capacity factor profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00) and represent instantaneous values on the hour. The profiles span 15 weather years (2007–2013 + 2016–2023). For leap years, the final day of the year (December 31) is dropped, such that each year contains 8760 hours.

Spatial resolution



Capacity factor profiles are provided for three siting access assumptions described by Lopez et al. (2025) ("limited", "reference", and "open") and two spatial resolutions. Files with names ending in "county" are resolved for individual U.S. counties; files with names ending in "ba" are resolved for the 134 ReEDS model zones. Profiles are further differentiated into five resource classes, with higher class numbers indicating higher average capacity factor.




 

File structure





county2zone.csv: U.S. counties comprising each of the 134 ReEDS model zones

cf_upv_{access scenario}_{ba or county}.h5: Hourly capacity factor profiles. Columns are labeled as {resource class}|p{zone number or 5-digit county FIPS code}.

sc_upv_{access scenario}_{ba or county}.csv: Available capacity [MW] associated with each class/region capacity factor profile



Capacity factor profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:

 





import h5py

import pandas as pd


def read_cf_profile(filepath):

    """

    Read a CF profile from `filepath` and return a pandas dataframe.


    Usage:

    `df = read_profile('/path/to/filename.h5')`

    """

    encoding = 'utf-8'

    with h5py.File(filepath, 'r') as f:

        df = pd.DataFrame(

            f['data'][:],

            columns=pd.Series(f['columns']).str.decode(encoding),

            index=f['index_0'],

        )

        df.index = pd.to_datetime(

            pd.Series(df.index, name='datetime').str.decode(encoding)

        )


   return df
DOI: 10.5281/zenodo.18421977
title: ReEDS: Distributed PV profiles

description: This record provides modeled hourly distributed photovoltaic (PV) capacity factor profiles for the contiguous United States (U.S.), generated using the National Solar Radiation Database (NSRDB), System Advisor Model (SAM), and Renewable Energy Potential (reV) model. These profiles are used in the Regional Energy Deployment System (ReEDS) model.

 

Additional details are provided in the ReEDS documentation.

Technology assumptions

Capacity factor profiles represent the average of residential and commercial distributed PV profiles and use the following assumptions:






Fixed roof mount with 16° tilt and 180° azimuth

Inverter loading ratio: 1.21

DC losses: 10.4%

Inverter efficiency: 96% for residential, 98% for commercial


Values are given in units of  $[MW_{AC,grid}/MW_{AC,nameplate}]$, where  $MW_{AC,grid}$ is the post-inverter output to the grid and  $MW_{AC,nameplate}$ is the post-inverter PV array capacity.

Temporal resolution



All capacity factor profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00) and represent instantaneous values on the hour. The profiles span 15 weather years (2007–2013 + 2016–2023). For leap years, the final day of the year (December 31) is dropped, such that each year contains 8760 hours.

Spatial resolution



Capacity factor profiles are resolved for individual U.S. counties. Columns are labeled as p{5-digit county FIPS code}.




File structure



Capacity factor profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:


 



import h5py

import pandas as pd


def read_cf_profile(filepath):

    """

    Read a CF profile from `filepath` and return a pandas dataframe.


    Usage:

    `df = read_profile('/path/to/filename.h5')`

    """

    encoding = 'utf-8'

    with h5py.File(filepath, 'r') as f:

        df = pd.DataFrame(

            f['data'][:],

            columns=pd.Series(f['columns']).str.decode(encoding),

            index=f['index_0'],

        )

        df.index = pd.to_datetime(

            pd.Series(df.index, name='datetime').str.decode(encoding)

        )


   return df
DOI: 10.60905/u5x660
title: Integrada la comisi��n conciliadora en la UdeA.

description: &lt;p&gt;Se crea una comisi��n entre directivos, estudiantes y profesores con el fin de estudiar y buscar soluciones al conflicto estudiantil que viene d��ndose desde el a��o pasado con el paro resultado de la elecci��n del decano de Derecho Luis Gabriel Botero. En paro se encuentran las facultades de Derecho, Ciencias y Humanidades, Econom��a, Ingenier��a, Odontolog��a, Qu��mica Farmac��utica, Ciencias de la Educaci��n, Bibliotecolog��a, Veterinaria, Zootecnia y Ciencias de la Comunicaci��n. Se realiz�� una rueda de prensa con estudiantes y profesores para generar el debate y buscar una soluci��n a fondo, ya que seg��n ellos hay incumplimientos por parte de las directivas de la universidad. De esta situaci��n se realiz�� un comunicado por parte del Consejo Superior Estudiantil donde se expresa la buena voluntad para el di��logo.&lt;/p&gt;
description: CONTEXTO DE LA NOTICIA: Campa��a presidencial de Pastrana; retenci��n en la fuente para salarios; crisis de hambruna en Biafra; Huelgas en empresas; EE.PP demandada por el municipio del Pe��ol; casas conservadoras en Medell��n.
DOI: 10.5281/zenodo.18435263
title: ReEDS: Electricity demand profiles 2025

description: This record provides modeled hourly state-level electricity demand profiles for the contiguous United States (U.S.), produced by Evolved Energy Research (EER) using the EnergyPathways model and used in the Regional Energy Deployment System (ReEDS) model.

Additional details are provided in the ReEDS documentation.

Demand assumptions

Demand profiles represent gross end-use load (i.e., distributed generation is not subtracted) in hour-ending format, in units of [MWh/h].

Profiles are generated bottom-up using sectoral electricity demand assumptions described in the EER 2024 Annual Decarbonization Perspective (ADP) and corresponding Technical Documentation. The ReEDS User Guide provides a lookup table between the scenario naming used in the ADP and ReEDS, and the ReEDS documentation provides a high-level description of the scenarios.

Temporal resolution

Demand profiles are defined for 7 model years (2021, 2025, 2030, 2035, 2040, 2045, 2050); for each model year, 15 weather years (2007–2013 + 2016–2023) of bottom-up hourly profiles are provided. "Model" years define the sectoral mix of demand-site technologies and "weather" years define the temperature and calendar days (weekdays / weekends / holidays); both impact the hourly profiles. All profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00). For leap years, the final day of the weather year (December 31) is dropped, such that each weather year contains 8760 hours.

File structure

Modeled electricity demand profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:

import h5pyimport pandas as pd
def read_demand_profile(filepath):
    """
    Read a demand profile from `filepath` and return a pandas dataframe.
    Usage:
    `df = read_profile('/path/to/filename.h5')`
    """
    encoding = 'utf-8'
    with h5py.File(filepath, 'r') as f:
        years = list(f.keys())
        first_year = years[0]
        not_states = ['columns', 'datetime']
        states = [i for i in list(f[first_year].keys()) if i not in not_states]
        dictout = {}
        for year in years:
            for state in states:
                dictout[year,state] = pd.Series(f[year][state][:])
        df = pd.concat(dictout, axis=1, names=('year','state'))
        df.index = pd.to_datetime(
            pd.Series(f[first_year]['datetime'], name='datetime')
            .str.decode(encoding)
        )
    return df
DOI: 10.5281/zenodo.18435264
title: ReEDS: Electricity demand profiles 2025

description: This record provides modeled hourly state-level electricity demand profiles for the contiguous United States (U.S.), produced by Evolved Energy Research (EER) using the EnergyPathways model and used in the Regional Energy Deployment System (ReEDS) model.

Additional details are provided in the ReEDS documentation.

Demand assumptions

Demand profiles represent gross end-use load (i.e., distributed generation is not subtracted) in hour-ending format, in units of [MWh/h].

Profiles are generated bottom-up using sectoral electricity demand assumptions described in the EER 2024 Annual Decarbonization Perspective (ADP) and corresponding Technical Documentation. The ReEDS User Guide provides a lookup table between the scenario naming used in the ADP and ReEDS, and the ReEDS documentation provides a high-level description of the scenarios.

Temporal resolution

Demand profiles are defined for 7 model years (2021, 2025, 2030, 2035, 2040, 2045, 2050); for each model year, 15 weather years (2007–2013 + 2016–2023) of bottom-up hourly profiles are provided. "Model" years define the sectoral mix of demand-site technologies and "weather" years define the temperature and calendar days (weekdays / weekends / holidays); both impact the hourly profiles. All profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00). For leap years, the final day of the weather year (December 31) is dropped, such that each weather year contains 8760 hours.

File structure

Modeled electricity demand profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:

import h5pyimport pandas as pd
def read_demand_profile(filepath):
    """
    Read a demand profile from `filepath` and return a pandas dataframe.
    Usage:
    `df = read_profile('/path/to/filename.h5')`
    """
    encoding = 'utf-8'
    with h5py.File(filepath, 'r') as f:
        years = list(f.keys())
        first_year = years[0]
        not_states = ['columns', 'datetime']
        states = [i for i in list(f[first_year].keys()) if i not in not_states]
        dictout = {}
        for year in years:
            for state in states:
                dictout[year,state] = pd.Series(f[year][state][:])
        df = pd.concat(dictout, axis=1, names=('year','state'))
        df.index = pd.to_datetime(
            pd.Series(f[first_year]['datetime'], name='datetime')
            .str.decode(encoding)
        )
    return df
DOI: 10.5281/zenodo.18423997
title: ReEDS: Electricity demand profiles 2023

description: This record provides modeled hourly state-level electricity demand profiles for the contiguous United States (U.S.), produced by Evolved Energy Research (EER) using the EnergyPathways model and used in the Regional Energy Deployment System (ReEDS) model.

Additional details are provided in the ReEDS documentation.

Demand assumptions

Demand profiles represent gross end-use load (i.e., distributed generation is not subtracted) in hour-ending format, in units of [MWh/h].

Profiles are generated bottom-up using sectoral electricity demand assumptions described in the EER 2022 Annual Decarbonization Perspective (ADP) and corresponding Technical Documentation. The ReEDS User Guide provides a lookup table between the scenario naming used in the ADP and ReEDS.

Temporal resolution

Demand profiles are defined for 7 model years (2021, 2025, 2030, 2035, 2040, 2045, 2050); for each model year, 7 weather years (2007–2013) of bottom-up hourly profiles are provided. "Model" years define the sectoral mix of demand-site technologies and "weather" years define the temperature and calendar days (weekdays / weekends / holidays); both impact the hourly profiles. All profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00). For leap years, the final day of the weather year (December 31) is dropped, such that each weather year contains 8760 hours.

File structure

Modeled electricity demand profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:

import h5py
import pandas as pd
def read_demand_profile(filepath):
    """
    Read a demand profile from `filepath` and return a pandas dataframe.
    Usage:
    `df = read_profile('/path/to/filename.h5')`
    """
    encoding = 'utf-8'
    with h5py.File(filepath, 'r') as f:
        years = list(f.keys())
        first_year = years[0]
        not_states = ['columns', 'datetime']
        states = [i for i in list(f[first_year].keys()) if i not in not_states]
        dictout = {}
        for year in years:
            for state in states:
                dictout[year,state] = pd.Series(f[year][state][:])
        df = pd.concat(dictout, axis=1, names=('year','state'))
        df.index = pd.to_datetime(
            pd.Series(f[first_year]['datetime'], name='datetime')
            .str.decode(encoding)
        )
    return df
DOI: 10.5281/zenodo.18423998
title: ReEDS: Electricity demand profiles 2023

description: This record provides modeled hourly state-level electricity demand profiles for the contiguous United States (U.S.), produced by Evolved Energy Research (EER) using the EnergyPathways model and used in the Regional Energy Deployment System (ReEDS) model.

Additional details are provided in the ReEDS documentation.

Demand assumptions

Demand profiles represent gross end-use load (i.e., distributed generation is not subtracted) in hour-ending format, in units of [MWh/h].

Profiles are generated bottom-up using sectoral electricity demand assumptions described in the EER 2022 Annual Decarbonization Perspective (ADP) and corresponding Technical Documentation. The ReEDS User Guide provides a lookup table between the scenario naming used in the ADP and ReEDS.

Temporal resolution

Demand profiles are defined for 7 model years (2021, 2025, 2030, 2035, 2040, 2045, 2050); for each model year, 7 weather years (2007–2013) of bottom-up hourly profiles are provided. "Model" years define the sectoral mix of demand-site technologies and "weather" years define the temperature and calendar days (weekdays / weekends / holidays); both impact the hourly profiles. All profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00). For leap years, the final day of the weather year (December 31) is dropped, such that each weather year contains 8760 hours.

File structure

Modeled electricity demand profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:

import h5py
import pandas as pd
def read_demand_profile(filepath):
    """
    Read a demand profile from `filepath` and return a pandas dataframe.
    Usage:
    `df = read_profile('/path/to/filename.h5')`
    """
    encoding = 'utf-8'
    with h5py.File(filepath, 'r') as f:
        years = list(f.keys())
        first_year = years[0]
        not_states = ['columns', 'datetime']
        states = [i for i in list(f[first_year].keys()) if i not in not_states]
        dictout = {}
        for year in years:
            for state in states:
                dictout[year,state] = pd.Series(f[year][state][:])
        df = pd.concat(dictout, axis=1, names=('year','state'))
        df.index = pd.to_datetime(
            pd.Series(f[first_year]['datetime'], name='datetime')
            .str.decode(encoding)
        )
    return df
DOI: 10.5281/zenodo.18461542
title: ReEDS: Electricity demand profiles 2022

description: This record provides hourly modeled state-level electricity demand profiles for the contiguous United States (U.S.), produced by Evolved Energy Research (EER) using the EnergyPathways model and used in the Regional Energy Deployment System (ReEDS) model.

Additional details are provided in the ReEDS documentation.

Demand assumptions

Demand profiles represent gross end-use load (i.e., distributed generation is not subtracted) in hour-ending format, in units of [MWh/h].

Profiles are provided for nine demand assumptions representing different combinations of electrification and technology advancement. Additional details are provided in the Electrification Futures Study and a related data publication. A separate data release provides related information for a subset of the demand profiles that can be assumed to be flexible in modeling of demand response. The Clean2035 scenarios are described by Denholm et al. (2022).

The ReEDS documentation provides a high-level description of the scenarios.

Temporal resolution

Demand profiles are defined for 41 model years (2010 through 2050); each model year uses the 2012 weather year to define the temperature and calendar days (weekdays / weekends / holidays). All profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00). The final day of the 2012 weather year (December 31) is dropped, such that each year contains 8760 hours.

File structure

Modeled electricity demand profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe:

import h5py
import pandas as pd
def read_demand_profile(filepath):
    """
    Read a demand profile from `filepath` and return a pandas dataframe.
    Usage:
    `df = read_profile('/path/to/filename.h5')`
    """
    encoding = 'utf-8'
    with h5py.File(filepath, 'r') as f:
        years = list(f.keys())
        first_year = years[0]
        not_states = ['columns', 'datetime']
        states = [i for i in list(f[first_year].keys()) if i not in not_states]
        dictout = {}
        for year in years:
            for state in states:
                dictout[year,state] = pd.Series(f[year][state][:])
        df = pd.concat(dictout, axis=1, names=('year','state'))
        df.index = pd.to_datetime(
            pd.Series(f[first_year]['datetime'], name='datetime')
            .str.decode(encoding)
        )
    return df