cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.c2tjms
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Type locality distributions from the World Register of Marine Species",
 "TaxonKey is Mycale alagoana"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016293-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8pmqqw
title: Occurrence Download

description: A dataset containing 333 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aonyx cinereus"
 ]
}

 The dataset includes 333 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016290-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18636110
title: THE CLASSIFICATION OF NEGATIVE WORDS IN ENGLISH LANGUAGE SENTENCE STRUCTURE

description: This article examines the classification of negative words in English sentence structure and their syntactic and semantic functions. Negation is a fundamental linguistic category that affects meaning, polarity, and interpretation of sentences. The study aims to classify negative words in English based on their grammatical roles, structural positions, and interaction with sentence components. Through descriptive and analytical methods, negative words are grouped into several categories, including negative particles, pronouns, adverbs, determiners, and conjunctions. The findings demonstrate that negative words play a crucial role in shaping sentence structure and meaning, and their distribution follows specific grammatical rules in Modern English.
DOI: 10.5281/zenodo.18636111
title: THE CLASSIFICATION OF NEGATIVE WORDS IN ENGLISH LANGUAGE SENTENCE STRUCTURE

description: This article examines the classification of negative words in English sentence structure and their syntactic and semantic functions. Negation is a fundamental linguistic category that affects meaning, polarity, and interpretation of sentences. The study aims to classify negative words in English based on their grammatical roles, structural positions, and interaction with sentence components. Through descriptive and analytical methods, negative words are grouped into several categories, including negative particles, pronouns, adverbs, determiners, and conjunctions. The findings demonstrate that negative words play a crucial role in shaping sentence structure and meaning, and their distribution follows specific grammatical rules in Modern English.
DOI: 10.5281/zenodo.13321506
title: Biopix: Biopix (31) DwCA

description: ####--- __EOL DwCA resource last updated: Apr 13, 2024 02:57 PM__ ---####
This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.18636124
title: Biopix: Biopix (31) DwCA
DOI: 10.15468/dl.qasg5u
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Merrilliodendron megacarpum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 41 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016295-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mcg4td
title: Occurrence Download

description: A dataset containing 2755 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Apodemus draco"
 ]
}

 The dataset includes 2755 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016297-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kdzwxx
title: Occurrence Download

description: A dataset containing 145 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Papaver kluanense"
 ]
}

 The dataset includes 145 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016296-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dd.u2f2ct
title: Dataset generated from Esri tool

description: Derived dataset generated by Esri geoprocessing tool using GBIF API
DOI: 10.15468/dl.w9aaf4
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Crenicichla isbrueckeri",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 18 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016298-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.72862r
title: Occurrence Download

description: A dataset containing 1797004 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Specimen)",
 "Geometry POLYGON((-127.42 31.95,-108.72 31.95,-108.72 37.53,-127.42 37.53,-127.42 31.95))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC-BY-NC 4.0, CC-BY 4.0, CC0 1.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Ochrophyta"
 ]
}

 The dataset includes 1797004 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016193-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/cw3945
title: FRB: Bivalve larvae of Malpeque Bay, Prince Edward Island

description: This resource includes species distribution records (presence) for taxa collected as part of a Fisheries Research Board study in an estuary in Prince Edward island.

Detailed description of the study which was aimed at studying all larval types in a single locality is contained in Sullivan (1948). The belief was that identification of a number of pelecypod larvae from one area would be of practical use in shellfish investigations all along our Atlantic coast. The study which took place over four summers in the 1940s, was done at the Prince Edward Island Biological Station during the months of May to October. All the materials were collected in Malpeque Bay, PEI. This sampling location was chosen as a particularly suitable locality because of the variety of its pelecypod fauna and because its small tides, slight currents, and sheltered waters made it ideal for the gathering of specimens.

At the time of the FRB study it was known that twenty-two species of bivalves occurred regularly in the bay. In the study, the larvae of twenty of these were described in all their stages and less complete information was presented on the remaining two, Pandora gouldiana and Yoldia limatula. Ten of these larvae had been described by other authors and twelve were described in the FRB report (Sullivan 1948) for the first time.
DOI: 10.15468/dl.hb5jzt
title: Occurrence Download

description: A dataset containing 55196 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Trilophozia quinquedentata, Tetralophozia setiformis, Schizophyllopsis sphenoloboides, Schistochilopsis hyperarctica, Scapania tundrae, Scapania spitzbergensis, Scapania spitsbergensis, Scapania simmonsii, Scapania parvifolia, Scapania paludicola, Scapania obcordata, Scapania kaurinii, Scapania hyperborea, Scapania degenii, Scapania crassiretis, Scapania brevicaulis, Radula prolifera, Protolophozia elongata, Protochilopsis grandiretis, Prasanthus suecicus, Orthocaulis cavifolius, Odontoschisma macounii, Neoorthocaulis binsteadii, Nardia breidleri, Marsupella spiniloba, Marsupella apiculata, Marchantia romanica, Lophoziopsis polaris, Lophoziopsis pellucida, Lophoziopsis jurensis, Lophozia murmanica, Lophozia heterocolpos, Jungermannia polaris, Gymnomitrion revolutum, Eocalypogeia schusterana, Douinia imbricata, Cephaloziella arctogena, Calycularia laxa, Blepharostoma brevirete, Arnellia fennica)"
 ]
}

 The dataset includes 55196 records from 338 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016244-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/gtmmbt
title: x1000-noHarq-prf100-pw100-mcs27

description: 1000 iterations of 20s radar pulses that has 100Hz PRF, and 100uS PW. 
1 UE on a gNB with noHarq and locked MCS values.
DOI: 10.6084/m9.figshare.30753314
title: Supplementary Table S1 and Supplementary Figures

description: This is the supplementary table and figures to the publication.
DOI: 10.6084/m9.figshare.30753314.v1
title: Supplementary Table S1 and Supplementary Figures

description: This is the supplementary table and figures to the publication.
DOI: 10.15468/dl.g8h4te
title: Occurrence Download

description: A dataset containing 1536 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepisanthes senegalensis",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 1536 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016299-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cj3q43
title: Occurrence Download

description: A dataset containing 1227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Passiflora tenuiloba"
 ]
}

 The dataset includes 1227 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016300-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18636136
title: GAMIFICATION AS AN EFFECTIVE TOOL IN DEVELOPING ENGLISH LANGUAGE SKILLS"

description: Gamification, which involves integrating game elements such as points, badges, leaderboards, levels, and rewards into educational activities, has emerged as a highly effective strategy for enhancing students’ English language learning. This approach not only increases motivation and engagement but also encourages autonomous learning and sustained participation in language practice. The study investigates the impact of gamified learning on reading, writing, listening, and speaking skills among university students from diverse backgrounds. A mixed-method design was applied, combining surveys, semi-structured interviews, and performance assessments to evaluate both quantitative outcomes (language proficiency scores) and qualitative insights (student perceptions and experiences). The results reveal that students participating in gamified activities showed higher engagement, improved vocabulary retention, better writing coherence, and enhanced communicative competence compared to peers in traditional classroom settings. Moreover, gamification appeared to positively influence students’ confidence and willingness to practice English actively, even in less interactive learning environments. These findings highlight that gamification is a practical, engaging, and scalable tool for modern language education and can be adapted to different educational contexts, including both urban and rural settings, providing equitable opportunities for learners to develop essential English skills.
DOI: 10.35079/hbm585.mvrt.396
title: Cell DIVE data from the skin of a 66-year-old black or african american male
DOI: 10.5281/zenodo.18636135
title: GAMIFICATION AS AN EFFECTIVE TOOL IN DEVELOPING ENGLISH LANGUAGE SKILLS"

description: Gamification, which involves integrating game elements such as points, badges, leaderboards, levels, and rewards into educational activities, has emerged as a highly effective strategy for enhancing students’ English language learning. This approach not only increases motivation and engagement but also encourages autonomous learning and sustained participation in language practice. The study investigates the impact of gamified learning on reading, writing, listening, and speaking skills among university students from diverse backgrounds. A mixed-method design was applied, combining surveys, semi-structured interviews, and performance assessments to evaluate both quantitative outcomes (language proficiency scores) and qualitative insights (student perceptions and experiences). The results reveal that students participating in gamified activities showed higher engagement, improved vocabulary retention, better writing coherence, and enhanced communicative competence compared to peers in traditional classroom settings. Moreover, gamification appeared to positively influence students’ confidence and willingness to practice English actively, even in less interactive learning environments. These findings highlight that gamification is a practical, engaging, and scalable tool for modern language education and can be adapted to different educational contexts, including both urban and rural settings, providing equitable opportunities for learners to develop essential English skills.
DOI: 10.15468/dl.d9t4kb
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Natsiatopsis thunbergiifolia",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016301-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18636113
title: PowerShell Commands
DOI: 10.5281/zenodo.18636114
title: PowerShell Commands
DOI: 10.5281/zenodo.18617805
title: THE THEME OF IMPOSSIBLE LOVE' IN THE AMAZIGH FOLKTALES OF AIT SEGHROUSHEN OF MOROCCO ISSLI AND TISSLIT AND THE JINN WHO FELL IN LOVE WITH THE GIRL AS CASE STUDY
DOI: 10.5281/zenodo.18617806
title: THE THEME OF IMPOSSIBLE LOVE' IN THE AMAZIGH FOLKTALES OF AIT SEGHROUSHEN OF MOROCCO ISSLI AND TISSLIT AND THE JINN WHO FELL IN LOVE WITH THE GIRL AS CASE STUDY
DOI: 10.15468/dl.9ktxem
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Crenicichla jegui",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 6 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016302-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18636038
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  $[MW_{AC,grid}/MW_{DC,array}]$, 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 1.21  $[MW_{DC,array}/MW_{AC,nameplate}]$.

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 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: 10.5281/zenodo.18421976
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  $[MW_{AC,grid}/MW_{DC,array}]$, 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 1.21  $[MW_{DC,array}/MW_{AC,nameplate}]$.

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 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: 10.13140/rg.2.2.30780.50562
title: Asesoría de Estadística Descriptiva e Inferencial con SPSS
DOI: 10.15468/dl.ygk5wv
title: Occurrence Download

description: A dataset containing 2454930 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Chamaecrista rotundifolia, Chenopodium carnosulum, Chenopodium detestans, Chenopodium giganteum, Chenopodium hircinum, Chenopodium pratericola, Chenopodium trigonon, Chimaphila maculata, Chimonobambusa marmorea, Chloris pycnothrix, Chlorophytum capense, Choisya ternata, Chromolaena odorata, Chrysanthemum indicum, Chrysanthemum morifolium, Chrysocoma cernua, Chrysophyllum cainito, Chrysopogon aciculatus, Cicerbita macrophylla, Cineraria lyratiformis, Cinnamomum verum, Cirsium helenioides, Cistus lasianthus, Citrus aurantium, Citrus maxima, Citrus medica, Citrus trifoliata, Cladium mariscus, Clarkia pulchella, Claytonia sibirica, Clematis flammula, Clematis montana, Clematis tangutica, Clematis terniflora, Cleome gynandra, Cleome houtteana, Cleome trachysperma, Cleome viscosa, Cleretum bellidiforme, Clerodendrum chinense, Clerodendrum poggei, Clerodendrum thomsoniae, Clinopodium vulgare, Clitoria ternatea, Cobaea scandens, Coccinia grandis, Cochlearia officinalis, Cochliasanthus caracalla, Cocos nucifera, Codiaeum variegatum, Coffea arabica, Coincya monensis, Coincya tournefortii, Coix lacryma-jobi, Coleostephus myconis, Coleus amboinicus, Collomia linearis, Colocasia esculenta, Colutea orientalis, Commelina africana, Commelina chamissonis, Commelina communis, Commelina latifolia, Commelina tuberosa, Convallaria majalis, Convolvulus althaeoides, Convolvulus lineatus, Coprosma baueri, Coprosma repens, Coprosma robusta, Corchorus olitorius, Corchorus trilocularis, Cordia myxa, Cordyline australis, Cordyline stricta, Corema album, Coreopsis basalis, Coreopsis grandiflora, Coreopsis lanceolata, Corispermum declinatum, Corispermum marschallii, Corispermum pallasii, Cornus capitata, Cornus kousa, Cornus sericea, Cortaderia jubata, Corylus avellana, Corylus maxima, Corynephorus divaricatus, Cota austriaca, Cotoneaster coriaceus, Cotoneaster dammeri, Cotoneaster franchetii, Cotoneaster frigidus, Cotoneaster glaucophyllus, Cotoneaster horizontalis, Cotoneaster integrifolius)",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 2454930 records from 5167 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016257-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/cc5yjl6
title: CCDC 177338: Experimental Crystal Structure Determination

description: Related Article: Chen-Hsiung Hung, Chen-Kuo Ou, Gene-Hsiang Lee, Shie-Ming Peng|2001|Inorg.Chem.|40|6845|doi:10.1021/ic010705h
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.