cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p1gv2ygj
title: Data on behavior of adult horses and burros between 2016 and 2020, in Utah and Arizona, USA

description: We investigated the behavior of feral horses (Equus caballus) at Conger Herd Management Area (HMA), UT, and of burros (Equus asinus) at Lake Pleasant HMA, AZ, and Sinbad HMA, UT between 2016 and 2020 (Sinbad HMA 2016 to 2019; Conger and Lake Pleasant HMAs 2017 to 2020). We collected data on the time budget and social behavior of adults (2 years old and older) between April and September annually in Utah, and year-round in Arizona. We also collected data on group composition year-round at Conger, Lake Pleasant, and Sinbad HMAs with the addition of Frisco HMA (horses; UT) in order to derive social network metrics.

This dataset includes three .csv files:
Horse_burro_all_occurrence_data_2016_2020.csv - frequency per hour of all occurrence behaviors expressed by individual horses (Conger HMA, UT) and burros (Lake Pleasant HMA, AZ, and Sinbad HMA, UT).
Horse_burro_time_budget_data_2016_2020.csv - frequency per hour of all maintenance behaviors of individual horses (Conger HMA, UT) and burros (Lake Pleasant HMA, AZ, and Sinbad HMA, UT).
Horse_burro_network_metrics_2016_2020.csv - social network metrics of horse (Conger and Frisco HMAs, UT) and burro (Lake Pleasant HMA, AZ, and Sinbad HMA, UT) populations per season.
DOI: 10.15468/dl.chzedq
title: Occurrence Download

description: A dataset containing 2668 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Occurrence evidence, Specimen)",
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Agrilus planipennis Fairmaire, 1888"
 ]
}

 The dataset includes 2668 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022693-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/8940718
title: USGS Streamgages (All 50 States)
DOI: 10.25345/c5qv3cf81
title: MassIVE MSV000095321 - The discovery and structural basis of two distinct state-dependent inhibitors of BamA Mass Spectrometry Data
title: Mass spec raw data files for "The discovery and structural basis of two distinct state-dependent inhibitors of BamA".
DOI: 10.15468/dl.827xc6
title: Occurrence Download

description: A dataset containing 294 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Conepatus mesoleucus (Lichtenstein, 1832)"
 ]
}

 The dataset includes 294 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022695-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.njkygd
title: Occurrence Download

description: A dataset listing the 691362 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is NMNH Extant Specimen Records (USNM, US)"
 ]
}

 The dataset's 691362 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022691-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7693657
title: Model setup code and input for internal tide-eddy simulation

description: The dataset includes the setup code and input files for simulations of internal tide-eddy interactions using MITgcm. Due to the large size of the model output data, it is not included but can be made available upon request at yangwangow@gmail.com. If you have any questions about using or testing these files, please feel free to reach out. Cheers!
DOI: 10.5281/zenodo.7693658
title: Model setup code and input for internal tide-eddy simulation

description: The dataset includes the setup code and input files for simulations of internal tide-eddy interactions using MITgcm. Due to the large size of the model output data, it is not included but can be made available upon request at yangwangow@gmail.com. If you have any questions about using or testing these files, please feel free to reach out. Cheers!
DOI: 10.5281/zenodo.12735167
title: Dataset behavior matrix and survivor of termites and termitophiles
DOI: 10.5281/zenodo.12735168
title: Dataset behavior matrix and survivor of termites and termitophiles
DOI: 10.15468/dl.vwzvx3
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Conepatus semistriatus (Boddaert, 1785)"
 ]
}

 The dataset includes 154 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022696-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12734879
title: Accompanying Files for "M/K-Dwarf Exoplanets With Similar Sizes and Instellations to Earth Typically Follow Near-Circular Orbits"

description: === gammaposteriors.tar.gz ===

Each file (in the tarball) is named after the corresponding planetary object. The file contains samples drawn from the a-posteriori distribution of rho_{*,transit}/rho_{*,independent}. This ratio is referred to as gamma in the paper.

=== stellarposteriors.tar.gz ===

Each file contains the joint posterior distribution for the fundamental stellar parameters. The columns are:

Mass (Solar masses), Radius (Solar radii), Density (kg/m^3)

=== transittimes.tar.gz ===

A pair of files are produced for each KOI/TOI, named appropriately. The pdf figure is a summary figure showing the TTV data with errors (black), along with the best fitting sinusoidal model in brown. We also report the FAP (false alarm probability) of that signal, the associated TTV period and amplitude as annotations on the figure. We also provide the actual transit times for each epoch, in the files *times.dat, using the columns...

Epoch, Transit Time (BJD_UTC - 2,457,000), Error, TTV (mins), Error (mins)

=== lightcurves.tar.gz ===

Each file contains a detrended light curve, detrended using the method marginalisation process described in the paper. The columns are:

Time (BJD_UTC - 2,400,000*)Relative IntensityUncertainty

For the TOIs, we include a fourth column which is the CROWDSAP value.

* For TOIs, this becomes 2,457,000

=== hbms -> *.tar.gz ===

Each tarball contains samples drawn from the a-posteriori joint distribution of the Hierarchical Bayesian Model (HBM) fits. Each tarball is a distinct model, and thus HBM:

exp.tar.gz - exponential distributionbeta.tar.gz - Beta distributionrayleigh.tar.gz - Rayleigh distributiontremaine.tar.gz - Tremaine distributionexp_without4087.tar.gz - exponential distribution excluding KOI-4087.01beta_without4087.tar.gz - Beta distribution excluding KOI-4087.01exp_without4087_fake_0p0.tar.gz - exponential distribution excluding KOI-4087.01, but applied to fake data where e=0.0

There are two types of files in each container.

popchain_*.dat contains posterior samples of the population/hyper-parameters.hkplate_*.dat contains posterior samples of the population/hyper-parameters.

There are 20 walkers for each file type, labelled 1-20 at the suffix of the file. The columns in each file are organised as follows...

popchain_exp*.datmu_e, var_e, loglikepopchain_rayl*.datmu_e, var_e, loglikepopchain_trem*.datmu_e, var_e, loglikepopchain_beta*.datmu_e, var_e, alpha, beta, loglike

hkplatechain_*.dath'_1, k'_1, h'_2, k'_2,...
DOI: 10.5281/zenodo.12734880
title: Accompanying Files for "M/K-Dwarf Exoplanets With Similar Sizes and Instellations to Earth Typically Follow Near-Circular Orbits"

description: === gammaposteriors.tar.gz ===

Each file (in the tarball) is named after the corresponding planetary object. The file contains samples drawn from the a-posteriori distribution of rho_{*,transit}/rho_{*,independent}. This ratio is referred to as gamma in the paper.

=== stellarposteriors.tar.gz ===

Each file contains the joint posterior distribution for the fundamental stellar parameters. The columns are:

Mass (Solar masses), Radius (Solar radii), Density (kg/m^3)

=== transittimes.tar.gz ===

A pair of files are produced for each KOI/TOI, named appropriately. The pdf figure is a summary figure showing the TTV data with errors (black), along with the best fitting sinusoidal model in brown. We also report the FAP (false alarm probability) of that signal, the associated TTV period and amplitude as annotations on the figure. We also provide the actual transit times for each epoch, in the files *times.dat, using the columns...

Epoch, Transit Time (BJD_UTC - 2,457,000), Error, TTV (mins), Error (mins)

=== lightcurves.tar.gz ===

Each file contains a detrended light curve, detrended using the method marginalisation process described in the paper. The columns are:

Time (BJD_UTC - 2,400,000*)Relative IntensityUncertainty

For the TOIs, we include a fourth column which is the CROWDSAP value.

* For TOIs, this becomes 2,457,000

=== hbms -> *.tar.gz ===

Each tarball contains samples drawn from the a-posteriori joint distribution of the Hierarchical Bayesian Model (HBM) fits. Each tarball is a distinct model, and thus HBM:

exp.tar.gz - exponential distributionbeta.tar.gz - Beta distributionrayleigh.tar.gz - Rayleigh distributiontremaine.tar.gz - Tremaine distributionexp_without4087.tar.gz - exponential distribution excluding KOI-4087.01beta_without4087.tar.gz - Beta distribution excluding KOI-4087.01exp_without4087_fake_0p0.tar.gz - exponential distribution excluding KOI-4087.01, but applied to fake data where e=0.0

There are two types of files in each container.

popchain_*.dat contains posterior samples of the population/hyper-parameters.hkplate_*.dat contains posterior samples of the population/hyper-parameters.

There are 20 walkers for each file type, labelled 1-20 at the suffix of the file. The columns in each file are organised as follows...

popchain_exp*.datmu_e, var_e, loglikepopchain_rayl*.datmu_e, var_e, loglikepopchain_trem*.datmu_e, var_e, loglikepopchain_beta*.datmu_e, var_e, alpha, beta, loglike

hkplatechain_*.dath'_1, k'_1, h'_2, k'_2,...
DOI: 10.6084/m9.figshare.26280601
title: Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist

description: This sheet is the PRISMA-ScR for the study titled "The application of exponential random graph models to online learning networks: a scoping review."
DOI: 10.6084/m9.figshare.26280601.v1
title: Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist

description: This sheet is the PRISMA-ScR for the study titled "The application of exponential random graph models to online learning networks: a scoping review."
DOI: 10.15468/dl.jcksbm
title: Occurrence Download

description: A dataset containing 159 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.6_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Bison bison (Linnaeus, 1758)",
 "Year 2000-2024"
 ]
}

 The dataset includes 159 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022698-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pmqs8h
title: Occurrence Download

description: A dataset containing 454 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Puma concolor (Linnaeus, 1771)",
 "Year 2019-2024"
 ]
}

 The dataset includes 454 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022699-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4qu8u4
title: Occurrence Download

description: A dataset containing 433 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Eira barbara (Linnaeus, 1758)"
 ]
}

 The dataset includes 433 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022701-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7930626
title: Feature selection in an interactive search-based PLA design approach

description: The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an interactive optimization problem with many conflicting factors. Incorporate Decision Makers’ (DM) preferences during the search process may help the algorithms to find more adequate solutions for their profiles. Interactive approaches allow the DM to evaluate solutions, guiding the optimization according to their preferences. However, this brings up human fatigue problems caused by the excessive amount of interactions and solutions to evaluate. A common strategy to prevent this problem is limiting the number of interactions and solutions evaluated by the DM. Machine Learning (ML) models were also used to learn how to evaluate solutions according to the DM profile and replace them after some interactions. Feature selection performs an essential task as non-relevant and/or redundant features used to train the ML model can reduce the accuracy and comprehensibility of the hypotheses induced by ML algorithms. This work aims to select features of a ML model used to prevent human fatigue in an interactive search-based PLA design approach. We applied four selectors and through results we were able to reduce 30% of features, obtaining an accuracy of 99%.
DOI: 10.5281/zenodo.7930627
title: Feature selection in an interactive search-based PLA design approach

description: The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an interactive optimization problem with many conflicting factors. Incorporate Decision Makers’ (DM) preferences during the search process may help the algorithms to find more adequate solutions for their profiles. Interactive approaches allow the DM to evaluate solutions, guiding the optimization according to their preferences. However, this brings up human fatigue problems caused by the excessive amount of interactions and solutions to evaluate. A common strategy to prevent this problem is limiting the number of interactions and solutions evaluated by the DM. Machine Learning (ML) models were also used to learn how to evaluate solutions according to the DM profile and replace them after some interactions. Feature selection performs an essential task as non-relevant and/or redundant features used to train the ML model can reduce the accuracy and comprehensibility of the hypotheses induced by ML algorithms. This work aims to select features of a ML model used to prevent human fatigue in an interactive search-based PLA design approach. We applied four selectors and through results we were able to reduce 30% of features, obtaining an accuracy of 99%.
DOI: 10.5281/zenodo.7942374
title: Feature selection in an interactive search-based PLA design approach

description: The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an interactive optimization problem with many conflicting factors. Incorporate Decision Makers’ (DM) preferences during the search process may help the algorithms to find more adequate solutions for their profiles. Interactive approaches allow the DM to evaluate solutions, guiding the optimization according to their preferences. However, this brings up human fatigue problems caused by the excessive amount of interactions and solutions to evaluate. A common strategy to prevent this problem is limiting the number of interactions and solutions evaluated by the DM. Machine Learning (ML) models were also used to learn how to evaluate solutions according to the DM profile and replace them after some interactions. Feature selection performs an essential task as non-relevant and/or redundant features used to train the ML model can reduce the accuracy and comprehensibility of the hypotheses induced by ML algorithms. This work aims to select features of a ML model used to prevent human fatigue in an interactive search-based PLA design approach. We applied four selectors and through results we were able to reduce 30% of features, obtaining an accuracy of 99%.
DOI: 10.5281/zenodo.10962037
title: Toward a Better Understanding of Curve Number and Initial Abstraction Values from a Global Watershed Dataset Perspective

description: The code within this repository is founded on the analysis of the Natural Resources Conservation Service Curve Number (NRCS-CN) method and its parameters. The analyses make use of the [CARAVAN dataset and your extensios] (https://zenodo.org/records/6578598). The separation of streamflow into baseflow and runoff is based on the methodology established by Xie et al. (2020) (https://doi.org/10.1016/j.jhydrol.2020.124628).

Contained within this repository are 5 Jupyter Notebooks, each focused on the application of the NRCS-CN method using distinct data analysis approaches:



Principal Components Analysis

Random Forest

NRCS-NEH

NRCS-ASYMPTOTIC (Natural)

NRCS-LEAST-SQUARES


Additionally, the results of the metrics from applying these methods can be found in the files Metrics_runoff_G1 and Metrics_runoff_G2. The folder Times_series contains the time series of simulated runoff using the NRCS methods.
DOI: 10.15468/dl.j3hqyr
title: Occurrence Download

description: A dataset containing 6445 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ceroxylon quindiuense (H.Karst.) H.Wendl."
 ]
}

 The dataset includes 6445 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022703-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7un3yn
title: Occurrence Download

description: A dataset containing 1273 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Plethodon cheoah Highton & Peabody, 2000)"
 ]
}

 The dataset includes 1273 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022704-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5883/bold:aep1975
title: Overview of Barcode Index Number - BOLD:AEP1975

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.15468/dl.mph5s8
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Enhydra lutris (Linnaeus, 1758)"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022705-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5883/bold:aea1645
title: Overview of Barcode Index Number - BOLD:AEA1645

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.15468/dl.532xmg
title: Occurrence Download

description: A dataset containing 1321 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Plethodon shenandoah Highton & Worthington, 1967)"
 ]
}

 The dataset includes 1321 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022706-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jsnh23
title: Occurrence Download

description: A dataset containing 403728 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Podarcis Wagler, 1830",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 403728 records from 4050 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022687-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z2avtn
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acianthera compressicaulis (Dod) Pridgeon & M.W.Chase"
 ]
}

 The dataset includes 6 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0075612-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fveion
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Encholirium brachypodum L.B.Sm. & Read"
 ]
}

 The dataset includes 44 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022130-151016162008034/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ahzy56
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lessingianthus pubescens M.B.Angulo & Dematt."
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0175057-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rhw95t
title: Occurrence Download

description: A dataset containing 972 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Charinidae"
 ]
}

 The dataset includes 972 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064782-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s3phfa
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galeandra bicarinata G.A.Romero & P.M.Br."
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0075617-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.