cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/7tnwkmnyyk
title: Tweets of Nepali Political Parties leaders

description: To use this dataset on your research paper use the following reference.

@artical{s13102024ijcatr13101005,
Title = "Comparing Political Inclination Classification on Twitter Posts using Naive Bayes, SVM, and XGBoost", 
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "13",
Issue ="10",
Pages ="62 - 65",
Year = "2024",
Authors ="Shashank Shree Neupane, Atish Shakya, Bishan Rokka, Sagar Acharya"}
The details of the article is:

International Journal of Computer Applications Technology and Research Volume 13–Issue 10, 62 – 65, 2024, ISSN:-2319–8656 DOI:10.7753/IJCATR1310.1005

The link to article: https://ijcat.com/archieve/volume13/issue10/ijcatr13101005

The dataset contains the twitter post of nepali political leader who are on political parties. The dataset can be used to know the inclination of people towards a political party with their post on the social media such as X (formerly twitter).
DOI: 10.17632/7tnwkmnyyk.1
title: Tweets of Nepali Political Parties leaders

description: To use this dataset on your research paper use the following reference.

@artical{s13102024ijcatr13101005,
Title = "Comparing Political Inclination Classification on Twitter Posts using Naive Bayes, SVM, and XGBoost", 
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "13",
Issue ="10",
Pages ="62 - 65",
Year = "2024",
Authors ="Shashank Shree Neupane, Atish Shakya, Bishan Rokka, Sagar Acharya"}
The details of the article is:

International Journal of Computer Applications Technology and Research Volume 13–Issue 10, 62 – 65, 2024, ISSN:-2319–8656 DOI:10.7753/IJCATR1310.1005

The link to article: https://ijcat.com/archieve/volume13/issue10/ijcatr13101005

The dataset contains the twitter post of nepali political leader who are on political parties. The dataset can be used to know the inclination of people towards a political party with their post on the social media such as X (formerly twitter).
DOI: 10.17632/wdy3xgv4xt
title: Social Security

description: Forecasting Social Security Dynamics in Oman with Temporal Fusion Transformers
DOI: 10.17632/wdy3xgv4xt.1
title: Social Security

description: Forecasting Social Security Dynamics in Oman with Temporal Fusion Transformers
DOI: 10.34740/kaggle/dsv/13862203
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.383d45
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Hong Kong, China, Chinese Taipei)",
 "TaxonKey is Euproctis pseudoconspersa"
 ]
}

 The dataset includes 26 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007354-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/4y33k5c7bc.2
title: Psoriasis and PM

description: Supplementary Figure 1. Flow chart.
Supplementary Table 1. Definition of comorbidities 
Supplementary Table 2. Baseline characteristics of study participants in psoriasis incidence
Supplementary Table 3. Associations between PM2.5 long-term exposure with 15μg/m3 as cut off and incidence of psoriasis 
Supplementary Table 4. Associations between PM10 long-term exposure with 50μg/m3 as cut off and incidence of psoriasis
DOI: 10.17632/2np9vw5v7w
title: Statewide California focal mechanism catalog and stress model (1981-2021)

description: Statewide California focal mechanism catalog and stress model (1981-2021) in pickle format, including:
1. Focal mechanism catalog (focmec_ca_final.pickle);
2. 2D stress model (stress_CA_2D.pickle);
3. 3D stress model (stress_CA_3D.pickle).
Please use pickle package in python to open the datasets.
Example:
import pickle
with open('stress_CA_2D.pickle', 'rb') as file: 
 stress_CA_2D = pickle.load(file)
 print(stress_CA_2D.keys())

Here are the descriptions of the keys in each dataset
***** 1. Focal mechanism catalog: *****
num (total number of events); 
nc_sc (from NCSN or SCSN); ev_bl (earthquake or blast)
year; month; day; hour; minute; second; 
time_year (time in year); time_utc (time in UTC format); 
evid; lat; lon; dep; mag; 
lat_reloc (latitude after relocation); lon_reloc (longitude after relocation); dep_reloc (depth after relocation); 
strike_orig (strike angle from original catalog); dip_orig (dip angle from original catalog); rake_orig (rake angle from original catalog); 
strike; dip; rake; 
uncer (uncertainty of nodel plane); npol (number of polarities); pol_misfit (polarity misfit); nspr (number of S/P amplitude ratio; spr_misfit (average log10(S/P) amplitude ratio misfit); prob (probability of solution close to real solution); azi_gap (maximum azimuth gap in station coverage); dip_gap (maximum take-off angle gap in station coverage); stdr (station distribution ratio); npamp (number of P-wave amplitude); pamp_misfit (log10(P-wave amplitude) misfit; nsamp (number of S-wave amplitude); samp_misfit (log10(S-wave amplitude) misfit; 
ftype (faulting type from -1 to 1); p_azi (P-axis azimuth); p_dip (P-axis plunge angle); t_azi (T-axis azimuth); t_dip (T-xis plunge angle); 

***** 2. 2D stress model *****
***** 3. 3D stress model *****
lat; lon; dep (only for 3D)
s1_azi (azimuth of sigma1); s1_dip (plunge angle of sigma1); s2_azi (azimuth of sigma1); s2_dip (plunge angle of sigma1); s3_azi (azimuth of sigma1); s3_dip (plunge angle of sigma1); 
s11 (sigma11); s12 (sigma12); s13 (sigma13); s21 (sigma21); s22 (sigma22); s23 (sigma23); s31 (sigma31); s32 (sigma32); s33 (sigma33); 
SHmax (maximum horizontal stress azimuth); R (R value); friction (frictional coefficient); A (faulting style from 0 to 3); ftype (faulting style from -1 to 1); mslip_dev (mean slip deviation);
s1_error (uncertainty of sigma1); s2_error (uncertainty of sigma2); s3_error (uncertainty of sigma3); 
R_error (R value uncertainty); A_error (A value uncertainty)
DOI: 10.17632/2np9vw5v7w.2
title: Statewide California focal mechanism catalog and stress model (1981-2021)

description: Statewide California focal mechanism catalog and stress model (1981-2021) in pickle format, including:
1. Focal mechanism catalog (focmec_ca_final.pickle);
2. 2D stress model (stress_CA_2D.pickle);
3. 3D stress model (stress_CA_3D.pickle).
Please use pickle package in python to open the datasets.
Example:
import pickle
with open('stress_CA_2D.pickle', 'rb') as file: 
 stress_CA_2D = pickle.load(file)
 print(stress_CA_2D.keys())

Here are the descriptions of the keys in each dataset
***** 1. Focal mechanism catalog: *****
num (total number of events); 
nc_sc (from NCSN or SCSN); ev_bl (earthquake or blast)
year; month; day; hour; minute; second; 
time_year (time in year); time_utc (time in UTC format); 
evid; lat; lon; dep; mag; 
lat_reloc (latitude after relocation); lon_reloc (longitude after relocation); dep_reloc (depth after relocation); 
strike_orig (strike angle from original catalog); dip_orig (dip angle from original catalog); rake_orig (rake angle from original catalog); 
strike; dip; rake; 
uncer (uncertainty of nodel plane); npol (number of polarities); pol_misfit (polarity misfit); nspr (number of S/P amplitude ratio; spr_misfit (average log10(S/P) amplitude ratio misfit); prob (probability of solution close to real solution); azi_gap (maximum azimuth gap in station coverage); dip_gap (maximum take-off angle gap in station coverage); stdr (station distribution ratio); npamp (number of P-wave amplitude); pamp_misfit (log10(P-wave amplitude) misfit; nsamp (number of S-wave amplitude); samp_misfit (log10(S-wave amplitude) misfit; 
ftype (faulting type from -1 to 1); p_azi (P-axis azimuth); p_dip (P-axis plunge angle); t_azi (T-axis azimuth); t_dip (T-xis plunge angle); 

***** 2. 2D stress model *****
***** 3. 3D stress model *****
lat; lon; dep (only for 3D)
s1_azi (azimuth of sigma1); s1_dip (plunge angle of sigma1); s2_azi (azimuth of sigma1); s2_dip (plunge angle of sigma1); s3_azi (azimuth of sigma1); s3_dip (plunge angle of sigma1); 
s11 (sigma11); s12 (sigma12); s13 (sigma13); s21 (sigma21); s22 (sigma22); s23 (sigma23); s31 (sigma31); s32 (sigma32); s33 (sigma33); 
SHmax (maximum horizontal stress azimuth); R (R value); friction (frictional coefficient); A (faulting style from 0 to 3); ftype (faulting style from -1 to 1); mslip_dev (mean slip deviation);
s1_error (uncertainty of sigma1); s2_error (uncertainty of sigma2); s3_error (uncertainty of sigma3); 
R_error (R value uncertainty); A_error (A value uncertainty)
DOI: 10.17632/2ws6xbb7kd
title: Morphology of Alluvial Fan Longitudinal Profiles in the Parlung Tsangpo Basin

description: The longitudinal profile (LP) of an alluvial fan is a fundamental morphological feature that records formation processes and distinguishes fan types. Here, we quantitatively analyze LP morphology using data from 18 alluvial fans in the Parlung Tsangpo Basin (PTB) in southeast Tibet—an alpine valley basin hosting fluvial, colluvial, and debris-flow fans (FF, CF, and DF) shaped by distinct dominant processes. The data include the geomorphological characteristics of alluvial fans (location, area, length, mean slope, etc.), longitudinal profile concavity, local slope variability parameters, and corresponding channel longitudinal profile characteristics.This work aims to clarify morphological differences in the LPs of distinct alluvial fan types and provide data support for fan type identification.
DOI: 10.17632/2ws6xbb7kd.1
title: Morphology of Alluvial Fan Longitudinal Profiles in the Parlung Tsangpo Basin

description: The longitudinal profile (LP) of an alluvial fan is a fundamental morphological feature that records formation processes and distinguishes fan types. Here, we quantitatively analyze LP morphology using data from 18 alluvial fans in the Parlung Tsangpo Basin (PTB) in southeast Tibet—an alpine valley basin hosting fluvial, colluvial, and debris-flow fans (FF, CF, and DF) shaped by distinct dominant processes. The data include the geomorphological characteristics of alluvial fans (location, area, length, mean slope, etc.), longitudinal profile concavity, local slope variability parameters, and corresponding channel longitudinal profile characteristics.This work aims to clarify morphological differences in the LPs of distinct alluvial fan types and provide data support for fan type identification.
DOI: 10.17632/zhcw2n8ntr
title: Affective benefits of cheerleading, spinning and karate: a comparative field study

description: This study investigated how affective states change before, immediately after, and 60 minutes following participation in cheerleading, spinning, and karate, representing interactive, co-active, and hybrid exercise modalities. Fifty-nine athletes completed validated measures of affective valence, arousal, and positive and negative affect at all three time points during their regular training sessions. Using linear mixed-effects models, the analysis revealed consistent increases in positive affect, feeling state, and arousal immediately after exercise, alongside decreases in negative affect, regardless of sport type. One hour later, most affective responses returned to baseline levels, although feeling state remained slightly elevated. No significant differences emerged across the three modalities, indicating that the short-term emotional benefits of exercise are robust and largely independent of social interaction structure. Overall, the results suggest that common psychophysiological processes, rather than modality-specific features, primarily drive acute improvements in affect during and after exercise.
DOI: 10.17632/zhcw2n8ntr.1
title: Affective benefits of cheerleading, spinning and karate: a comparative field study

description: This study investigated how affective states change before, immediately after, and 60 minutes following participation in cheerleading, spinning, and karate, representing interactive, co-active, and hybrid exercise modalities. Fifty-nine athletes completed validated measures of affective valence, arousal, and positive and negative affect at all three time points during their regular training sessions. Using linear mixed-effects models, the analysis revealed consistent increases in positive affect, feeling state, and arousal immediately after exercise, alongside decreases in negative affect, regardless of sport type. One hour later, most affective responses returned to baseline levels, although feeling state remained slightly elevated. No significant differences emerged across the three modalities, indicating that the short-term emotional benefits of exercise are robust and largely independent of social interaction structure. Overall, the results suggest that common psychophysiological processes, rather than modality-specific features, primarily drive acute improvements in affect during and after exercise.
DOI: 10.17632/k5pd4n3bj8
title: Dataset_Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment

description: Title: Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment
1. Raw data (including wind speed and air density) from six typical sites
2. DataExtraction_QC.py
3. Stations_List.csv
DOI: 10.15468/dl.9wcydb
title: Occurrence Download

description: A dataset containing 908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Attalea allenii",
 "HasCoordinate is true"
 ]
}

 The dataset includes 908 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007355-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/k5pd4n3bj8.1
title: Dataset_Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment

description: Title: Nonparametric copula modelling of the joint probability density function of air density and wind speed for wind resource assessment
1. Raw data (including wind speed and air density) from six typical sites
2. DataExtraction_QC.py
3. Stations_List.csv
DOI: 10.17632/2zshchrhjh.1
title: Data and Code for SVEMnet Publication

description: Data and Code for SVEMnet Publication
DOI: 10.17632/ngh9cf4dww.1
title: Flame speed Hydrogen Ammonia

description: Flame Speed calculations done using Chemkin
DOI: 10.17632/wfvdw3cd7x
title: Parameter file for "Genotyping errors can affect indirect predictions of young selection candidates: a simulation study"

description: QMSim parameter file used to simulate the dataset for the paper "Genotyping errors can affect indirect predictions of young selection candidates: a simulation study"
DOI: 10.17632/wfvdw3cd7x.1
title: Parameter file for "Genotyping errors can affect indirect predictions of young selection candidates: a simulation study"

description: QMSim parameter file used to simulate the dataset for the paper "Genotyping errors can affect indirect predictions of young selection candidates: a simulation study"
DOI: 10.17632/ds3582v368.2
title: Background Values of Trace Metals in the Soil of the Katangan Copperbelt, DR Congo

description: This dataset compiles measurements of trace metals and major elements in soils collected across the Katangan Copperbelt in the Democratic Republic of Congo. The samples were taken from two soil layers (0-25 cm and 25-50 cm) and systematically classified by lithostratigraphic unit. For each sampling point, the dataset includes geographic coordinates, field observations, and concentrations of a broad suite of elements such as As, Co, Cu, Mn, Ni, Pb, Zn. 
Beyond raw concentrations, the dataset provides an interactive map that brings together all results. Users can select the trace metal of interest (e.g. Cu, Co) and the soil horizon (topsoil: 0–25 cm; subsoil: 25–50 cm), access summary statistics (50th and 90th percentiles, mean, standard deviation, coefficient of variation) and display spatial distribution maps for the KCB. The interface allows users to query a point by GPS coordinates and view the PGB values and other statistics of the lithostratigraphic unit in which that point lies. It also supports map export for direct inclusion in reports. Available in French, English and German, this interactive map improves accessibility and uptake by stakeholders.
DOI: 10.17632/kc6zvzv6y7
title: Model Parameters for Probabilistic Urban Temperature and Building Energy Forecasting Models

description: This dataset provides model parameters for the probabilistic short-term urban temperature forecasting model (PSTM) and the building energy load forecasting model (PSLFM) used in Choi et al. (2025, under review at Energy and Buildings). These parameters enable replication of model outputs and support uncertainty-aware analyses. Test code and initialization details will be available upon publication and upon request in the project repository.

- Probabilistic Urban Temperature Forecasting Model (PSTM): This folder contains the model parameters for the probabilistic short-term urban temperature forecasting model (PSTM).
- Probabilistic Load Forecasting Model (PSLFM): This folder contains the model parameters for the probabilistic building energy load forecasting model (PSLFM).
DOI: 10.17632/kc6zvzv6y7.1
title: Model Parameters for Probabilistic Urban Temperature and Building Energy Forecasting Models

description: This dataset provides model parameters for the probabilistic short-term urban temperature forecasting model (PSTM) and the building energy load forecasting model (PSLFM) used in Choi et al. (2025, under review at Energy and Buildings). These parameters enable replication of model outputs and support uncertainty-aware analyses. Test code and initialization details will be available upon publication and upon request in the project repository.

- Probabilistic Urban Temperature Forecasting Model (PSTM): This folder contains the model parameters for the probabilistic short-term urban temperature forecasting model (PSTM).
- Probabilistic Load Forecasting Model (PSLFM): This folder contains the model parameters for the probabilistic building energy load forecasting model (PSLFM).
DOI: 10.15468/dl.u7wsfy
title: Occurrence Download

description: A dataset containing 2862 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Raphia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2862 records from 151 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007357-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pfeeux
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.69985 24.859786, 89.622461 24.854209, 89.546997 24.837616, 89.475336 24.810421, 89.409256 24.7733, 89.350393 24.727174, 89.3002 24.673189, 89.25990899999999 24.612683, 89.230502 24.547154, 89.21268999999999 24.478222, 89.206892 24.407588, 89.213232 24.336992, 89.231533 24.268172, 89.26132699999999 24.202817, 89.301867 24.142531, 89.352146 24.08879, 89.410923 24.042908, 89.476754 24.006006, 89.548028 23.978986, 89.623002 23.962506, 89.69985 23.956967, 89.776698 23.962506, 89.85167199999999 23.978986, 89.922946 24.006006, 89.988777 24.042908, 90.04755400000001 24.08879, 90.09783299999999 24.142531, 90.138373 24.202817, 90.168167 24.268172, 90.186468 24.336992, 90.192808 24.407588, 90.18701 24.478222, 90.16919799999999 24.547154, 90.139791 24.612683, 90.09950000000001 24.673189, 90.049307 24.727174, 89.990444 24.7733, 89.924364 24.810421, 89.85270300000001 24.837616, 89.77723899999999 24.854209, 89.69985 24.859786))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005986-260120142942310/datasets/export for details.
DOI: 10.15468/dl.qm9j7t
title: Occurrence Download

description: A dataset containing 1272598 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Asteraceae",
 "Continent is one of (Africa, South America)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1272598 records from 1424 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005934-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31130602
title: Table of material examined for a study of four new species of minute Australian Punctidae.

description: A table of material examined. for a study of four new species of minute Australian Punctidae.
DOI: 10.6084/m9.figshare.31130602.v1
title: Table of material examined for a study of four new species of minute Australian Punctidae.

description: A table of material examined. for a study of four new species of minute Australian Punctidae.
DOI: 10.15468/dl.wt3h8q
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.69985 24.906783, 89.62243100000001 24.901206, 89.54694000000001 24.884614, 89.475251 24.857419, 89.40914600000001 24.820297, 89.350262 24.774171, 89.30005 24.720186, 89.259744 24.65968, 89.230327 24.594152, 89.212509 24.52522, 89.206709 24.454586, 89.213052 24.383991, 89.23136 24.315171, 89.261166 24.249817, 89.301721 24.189532, 89.352019 24.135791, 89.41081800000001 24.08991, 89.47667300000001 24.053009, 89.547972 24.025988, 89.622974 24.009508, 89.69985 24.00397, 89.776726 24.009508, 89.85172799999999 24.025988, 89.923027 24.053009, 89.988882 24.08991, 90.047681 24.135791, 90.097979 24.189532, 90.13853400000001 24.249817, 90.16834 24.315171, 90.18664800000001 24.383991, 90.19299100000001 24.454586, 90.187191 24.52522, 90.16937299999999 24.594152, 90.139956 24.65968, 90.09965 24.720186, 90.04943799999999 24.774171, 89.990554 24.820297, 89.924449 24.857419, 89.85276 24.884614, 89.777269 24.901206, 89.69985 24.906783))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005988-260120142942310/datasets/export for details.