cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.yw9988
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Typha grossheimii Pobed."
 ]
}

 The dataset includes 12 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024995-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-05.2802412/2345673
title: Data for EMSL Project 51536 from May 2023
DOI: 10.15468/dl.3tvbbf
title: Occurrence Download

description: A dataset containing 620 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Smilax tamnoides L.",
 "BasisOfRecord is one of (Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (United States of America, Canada)"
 ]
}

 The dataset includes 620 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0272442-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.af5ur7
title: Occurrence Download

description: A dataset containing 3183 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Typha ×glauca Godr."
 ]
}

 The dataset includes 3183 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024994-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mustkz
title: Occurrence Download

description: A dataset containing 108219 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "Geometry POLYGON((-73.53571 6.93294,-73.16854 6.93294,-73.16854 7.08937,-73.53571 7.08937,-73.53571 6.93294))",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 108219 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000074-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yfkpp9
title: Occurrence Download

description: A dataset containing 254463 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pipistrellus pygmaeus (Leach, 1825)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2013",
 "Year is less than or equal to 2018",
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 254463 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000076-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.58132/okmyf4
title: Raw XRD data from: Fe-nodules from the southern Baltic Sea. Morphology, mineralogy and geochemistry

description: Raw XRD data from research paper "Fe-nodules from the southern Baltic Sea: morphology, mineralogy and geochemistry". These are raw *.xrdml files which may be opened by notepad (in Windows OS) or Profex software (open-source).For the analysis of the samples, X-ray diffraction in the Bragg-Brentano configuration was employed using an X'Pert PRO MPD diffractometer (PANalytical B.V., Netherlands) equipped with a cobalt anode tube and an iron filter. The samples were dried, crushed, and ground into powder (90% of particles ≤ 80 μm) using an agate mortar and pestle. The powdered samples were pressed into thin pellets using a back-loading method. Diffractograms were recorded over the angular range of 4° to 78° (2θ) using Co Kα radiation, with a voltage of 40 kV and a current of 30 mA, at a measurement step size of 0.026°/min and a step time of 2 seconds. The total analysis duration was 4 hours.
DOI: 10.15468/dl.fvkcf9
title: Occurrence Download

description: A dataset containing 7508 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Kenya Wildlife Service, Laikipia Samburu Aerial Census, 2008"
 ]
}

 The dataset includes 7508 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000094-250121130708018/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.28045834
title: Distribution-on-Scalar Single-Index Quantile Regression Model for Handling Tumor Heterogeneity

description: This article develops a distribution-on-scalar single-index quantile regression modeling framework to investigate the relationship between cancer imaging responses and scalar covariates of interest while tackling tumor heterogeneity. Conventional association analysis methods typically assume that the imaging responses are well-aligned after some preprocessing steps. However, this assumption is often violated in practice due to imaging heterogeneity. Although some distribution-based approaches are developed to deal with this heterogeneity, major challenges have been posted due to the nonlinear subspace formed by the distributional responses, the unknown nonlinear association structure, and the lack of statistical inference. Our method can successfully address all the challenges. We establish both estimation and inference procedures for the unknown functions in our model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed method is assessed by using both Monte Carlo simulations and a real data example on brain cancer images from TCIA-GBM collection.
DOI: 10.6084/m9.figshare.28045834.v1
title: Distribution-on-scalar Single-index Quantile Regression Model for Handling Tumor Heterogeneity

description: This paper develops a distribution-on-scalar single-index quantile regression modeling framework to investigate the relationship between cancer imaging responses and scalar covariates of interest while tackling tumor heterogeneity. Conventional association analysis methods typically assume that the imaging responses are well-aligned after some preprocessing steps. However, this assumption is often violated in practice due to imaging heterogeneity. Although some distribution-based approaches are developed to deal with this heterogeneity, major challenges have been posted due to the nonlinear subspace formed by the distributional responses, the unknown nonlinear association structure, and the lack of statistical inference. Our method can successfully address all the challenges. We establish both estimation and inference procedures for the unknown functions in our model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed method is assessed by using both Monte Carlo simulations and a real data example on brain cancer images from TCIA-GBM collection.
DOI: 10.6084/m9.figshare.28045834.v2
title: Distribution-on-Scalar Single-Index Quantile Regression Model for Handling Tumor Heterogeneity

description: This article develops a distribution-on-scalar single-index quantile regression modeling framework to investigate the relationship between cancer imaging responses and scalar covariates of interest while tackling tumor heterogeneity. Conventional association analysis methods typically assume that the imaging responses are well-aligned after some preprocessing steps. However, this assumption is often violated in practice due to imaging heterogeneity. Although some distribution-based approaches are developed to deal with this heterogeneity, major challenges have been posted due to the nonlinear subspace formed by the distributional responses, the unknown nonlinear association structure, and the lack of statistical inference. Our method can successfully address all the challenges. We establish both estimation and inference procedures for the unknown functions in our model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed method is assessed by using both Monte Carlo simulations and a real data example on brain cancer images from TCIA-GBM collection.
DOI: 10.6084/m9.figshare.28045727
title: Detecting Influential Observations in Single-Index Fréchet Regression

description: Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook’s distance extending the original Cook’s distances to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook’s distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analysis of distributions of COVID-19 transmission in the State of Texas and the analysis of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.
DOI: 10.6084/m9.figshare.28045727.v2
title: Detecting Influential Observations in Single-Index Fréchet Regression

description: Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook’s distance extending the original Cook’s distances to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook’s distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analysis of distributions of COVID-19 transmission in the State of Texas and the analysis of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.
DOI: 10.15468/dl.rpnyby
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plecotus macrobullaris Kuzyakin, 1965",
 "HasCoordinate is true",
 "Year is greater than or equal to 2013",
 "Year is less than or equal to 2018",
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000095-250121130708018/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.28045727.v1
title: Detecting influential observations in single-index Fréchet regression

description: Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook’s distance extending the original Cook’s distances of Cook (1977) to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook’s distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analysis of distributions of COVID-19 transmission in the State of Texas and the analysis of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.
DOI: 10.15468/dl.r29r2j
title: Occurrence Download

description: A dataset containing 89804 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Zenaida macroura (Linnaeus, 1758)"
 ]
}

 The dataset includes 89804 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000093-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14010304
title: Training Set and Test Set for Fast Atmospheric Radiative Transfer Inversion - Clear sky

description: This dataset is organized into two ZIP folders: a Training Set and a Test Set.

1. Training Set

The Training Set contains two subfolders:



Input Files: This folder includes all input variables describing the atmospheric scenarios necessary for simulations.

Output Files: This folder contains the corresponding simulated spectra generated by the forward model.


2. Test Set

The Test Set consists of three subfolders:



Input Files: Similar to the training set, this folder contains the input variables for the atmospheric scenarios.

Output Files: This folder holds the simulated spectra for the test cases.

A Priori Files: This folder contains the a priori data generated by perturbing true profiles according to a precision matrix SaS_aSa, constructed based on error estimates from the UK Met Office for the routine assimilation of IASI products into their operational Numerical Weather Prediction (NWP) system.


Data Generation Process

The definition of our datasets requires input variables that describe atmospheric scenarios and output variables representing the corresponding FORUM simulated spectra. The atmospheric state was constructed using data from the ECMWF (European Center for Medium-range Weather Forecast) Re-Analysis ERA5 database. Concentrations of additional atmospheric gases, including CO2, CO, CH4, NO2, HNO3, and SO2, were obtained from the CAMS (Copernicus Atmosphere Monitoring Service) global Greenhouse Gas reanalysis (EGG4). The remaining atmospheric gases were sourced from the Initial Guess 2 (IG2) database.

Surface emissivity profiles were derived from the Huang database, utilizing a tailored procedure to associate the most suitable profile based on the time and geolocation. 

For spectral simulation, the radiative transfer problem was addressed using the CLouds and Atmospheric Inversion Module (CLAIM), an advanced version of the inversion code previously used, based on the LBLRTM (Line By Line Radiative Transfer Model) forward model. The inversion process employed by CLAIM integrates the Optimal Estimation (OE) approach, the Gauss-Newton minimization method, the Levenberg-Marquardt technique, and a final Tikhonov regularization step using the Iterative Variable Strength (IVS) method.

Instrument characteristics for the FORUM were taken from one of the two instrument concepts available during the phase A/B1 of the FORUM E2E project.
DOI: 10.5281/zenodo.14010305
title: Training Set and Test Set for Fast Atmospheric Radiative Transfer Inversion - Clear sky

description: This dataset is organized into two ZIP folders: a Training Set and a Test Set.

1. Training Set

The Training Set contains two subfolders:



Input Files: This folder includes all input variables describing the atmospheric scenarios necessary for simulations.

Output Files: This folder contains the corresponding simulated spectra generated by the forward model.


2. Test Set

The Test Set consists of three subfolders:



Input Files: Similar to the training set, this folder contains the input variables for the atmospheric scenarios.

Output Files: This folder holds the simulated spectra for the test cases.

A Priori Files: This folder contains the a priori data generated by perturbing true profiles according to a precision matrix SaS_aSa, constructed based on error estimates from the UK Met Office for the routine assimilation of IASI products into their operational Numerical Weather Prediction (NWP) system.


Data Generation Process

The definition of our datasets requires input variables that describe atmospheric scenarios and output variables representing the corresponding FORUM simulated spectra. The atmospheric state was constructed using data from the ECMWF (European Center for Medium-range Weather Forecast) Re-Analysis ERA5 database. Concentrations of additional atmospheric gases, including CO2, CO, CH4, NO2, HNO3, and SO2, were obtained from the CAMS (Copernicus Atmosphere Monitoring Service) global Greenhouse Gas reanalysis (EGG4). The remaining atmospheric gases were sourced from the Initial Guess 2 (IG2) database.

Surface emissivity profiles were derived from the Huang database, utilizing a tailored procedure to associate the most suitable profile based on the time and geolocation. 

For spectral simulation, the radiative transfer problem was addressed using the CLouds and Atmospheric Inversion Module (CLAIM), an advanced version of the inversion code previously used, based on the LBLRTM (Line By Line Radiative Transfer Model) forward model. The inversion process employed by CLAIM integrates the Optimal Estimation (OE) approach, the Gauss-Newton minimization method, the Levenberg-Marquardt technique, and a final Tikhonov regularization step using the Iterative Variable Strength (IVS) method.

Instrument characteristics for the FORUM were taken from one of the two instrument concepts available during the phase A/B1 of the FORUM E2E project.
DOI: 10.17909/aa43-mt38
title: The Panchromatic Hubble Andromeda Southern Treasury (PHAST)

description: The Panchromatic Hubble Andromeda Southern Treasury (PHAST) is a large 195-orbit Hubble Space Telescope program designed to map roughly 0.45 square degrees of the southern half of M31’s star-forming disk at optical and near-ultraviolet (NUV) wavelengths. The PHAST survey extends the northern coverage of the Panchromatic Hubble Andromeda Treasury (PHAT) down to the southern half of M31, covering out to R~13 kpc along the southern major axis and, in total, roughly 2/3 of M31’s star-forming disk. The PHAST survey covers key diagnostic features that are sensitive to M31’s complex merger history, which were previously unconstrained by the northern disk observations alone. PHAST creates a legacy map with stellar photometry of over 90 million resolved stars using HST’s resolution and sensitivity, enabling a wide range of scientific endeavors.
DOI: 10.15468/dl.j2mk9m
title: Occurrence Download

description: A dataset containing 1978 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypsugo savii (Bonaparte, 1837)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2013",
 "Year is less than or equal to 2018",
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 1978 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000097-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14193758
title: Multimodal mismatch responses in mouse auditory cortex

description: All raw data and Matlab code necessary to produce the figures of https://elifesciences.org/reviewed-preprints/95398
DOI: 10.7910/dvn/bqe66o
title: MADRS_MaddenDissertation_Chapter4_SupCourtLinguisticResults

description: This workbook contains the results (in 3 separate tabbed spreadsheets) of linguistic variable software measurements for selected superior court decisions that form part of a study described in Chapter 4 of Mike Madden's doctoral dissertation.
DOI: 10.58053/mapbiomas/pxwizf
title: Coverage and transitions statistics by land categories and protected areas - Biosphere Reserves - MapBiomas Brasil Collection 9

description: Area data (ha) of land cover class for Biosphere Reserves from 1985 to 2023, based on MapBiomas Brazil Collection 9
DOI: 10.15468/dl.qn2h5b
title: Occurrence Download

description: A dataset containing 4380 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Dendrortyx macroura (Jardine & Selby, 1828)"
 ]
}

 The dataset includes 4380 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000098-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3gr5g5
title: Occurrence Download

description: A dataset containing 43541 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Dumetella carolinensis (Linnaeus, 1766)"
 ]
}

 The dataset includes 43541 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000096-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nd7shw
title: Occurrence Download

description: A dataset containing 1254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Felis silvestris Schreber, 1777",
 "HasCoordinate is true",
 "Year is greater than or equal to 2013",
 "Year is less than or equal to 2018",
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 1254 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000100-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cbw8zp
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Sweden",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bubo bubo (Linnaeus, 1758)",
 "Year 2024-2025"
 ]
}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000101-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14243470
title: Smart Home environment data across 4 European countries

description: This dataset contains anonymised Smart Home environment data that was collected during a project over 359 days (16.05.2023-08.05.2024). The dataset contains information about temperature (°C), humidity (%), ambient light (lux), CO2 (ppm), VOC (ppm), sound pressure (db) in a frequency of 2 to 5 minutes in addition to event based data from PIR-Sensors and door contact sensors. Additionally, time and location information for each data point is available in the form of a time stamp, the user ID, the room and the country. The dataset was collected in 4 different European countries from a total of 62 users at their residential (not every sensor type was installed at every installation). The target group for the field trials were elderly people 65+.
DOI: 10.5281/zenodo.14243471
title: Smart Home environment data across 4 European countries

description: This dataset contains anonymised Smart Home environment data that was collected during a project over 359 days (16.05.2023-08.05.2024). The dataset contains information about temperature (°C), humidity (%), ambient light (lux), CO2 (ppm), VOC (ppm), sound pressure (db) in a frequency of 2 to 5 minutes in addition to event based data from PIR-Sensors and door contact sensors. Additionally, time and location information for each data point is available in the form of a time stamp, the user ID, the room and the country. The dataset was collected in 4 different European countries from a total of 62 users at their residential (not every sensor type was installed at every installation). The target group for the field trials were elderly people 65+.
DOI: 10.6084/m9.figshare.27652623.v26
title: toy-datasets

description: Toy datasets of different genome data and file types.