cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.937546
title: Sun Photometer Data (AOD) 2016 from Ny-Ålesund, Svalbard (AWIPEV)

description: Aerosol optical depth (AOD) is measured by a sun photometer, type SP1a by Dr. Schulz & Partner GmbH in 17 wavelengths between λ = 369nm to 1023nm with a field of view of 1° × 1° and a time resolution of 1 minute. In winter 2012/13 a new sun photometer was installed and just 10 of 17 wavelengths remained in the same wavelength range. With the nine out of ten wavelengths optical parameters like the AOD are computed. The one, which is devoted to water vapor is omitted. The instrument is calibrated regularly in pristine conditions at Izaña, Tenerife, via Langley method. A cloud screening based on short scale fluctuations of the AOD is used. The uncertainty for the AOD is generally said to be around 0.01. However, this is the maximum error of the instrument because the fluctuations are much smaller by comparing data minute by minute under low or constant aerosol conditions.The number of individual measurements differs between a few hundreds, especially in March and September, to up to 12,000 in early summer. No trend in each month can be seen comparing the amount of cloud-free measurements over the years. Only an annual cycle due to polar day and night is included in the data. Due to the instrument data is only available in clear sky conditions. In this regard the data should represent the real aerosol conditions. Only aerosols that are advected and processed within clouds or hygroscopic growth cannot be measured by this instrument.In this data set AOD, Angstrom-Exponent and modified Angstrom-Exponent (Graßl, Ritter 2019, Remote Sensing, https://doi.org/10.3390/rs11111362) are given for the sun photometer at AWIPEV for the time 8. March 2016 until 30. September 2016. The variables are in netcdf format for each measurement day. The used instrument for the measurement period was SP1A31.
description: Each measurement day contains in netcdf format the following variables:Time (minutes after midnight UTC)wavelengths (nm lambda)AODModified Angstrom parameters (3 values!)traditional Angstrom parameters (2 values)
DOI: 10.15468/dl.zawvb4
title: Occurrence Download

description: A dataset containing 1589 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-4.17656 64.85789,-19.86286 61.13856,-15.04902 32.85496,0.03975 36.4721,9.66374 38.44757,13.21172 35.88336,28.06171 33.10222,43.52345 33.114,42.62344 70.01577,24.62343 73.77774,-4.17656 64.85789))",
 "HasGeospatialIssue is false",
 "TaxonKey is Liriodendron tulipifera L."
 ]
}

 The dataset includes 1589 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0307795-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.937596
title: Sun Photometer Data (AOD) 2017 from Ny-Ålesund, Svalbard (AWIPEV)

description: Aerosol optical depth (AOD) is measured by a sun photometer, type SP1a by Dr. Schulz & Partner GmbH in 17 wavelengths between λ = 369nm to 1023nm with a field of view of 1° × 1° and a time resolution of 1 minute. In winter 2012/13 a new sun photometer was installed and just 10 of 17 wavelengths remained in the same wavelength range. With the nine out of ten wavelengths optical parameters like the AOD are computed. The one, which is devoted to water vapor is omitted. The instrument is calibrated regularly in pristine conditions at Izaña, Tenerife, via Langley method. A cloud screening based on short scale fluctuations of the AOD is used. The uncertainty for the AOD is generally said to be around 0.01. However, this is the maximum error of the instrument because the fluctuations are much smaller by comparing data minute by minute under low or constant aerosol conditions.The number of individual measurements differs between a few hundreds, especially in March and September, to up to 12,000 in early summer. No trend in each month can be seen comparing the amount of cloud-free measurements over the years. Only an annual cycle due to polar day and night is included in the data. Due to the instrument data is only available in clear sky conditions. In this regard the data should represent the real aerosol conditions. Only aerosols that are advected and processed within clouds or hygroscopic growth cannot be measured by this instrument.In this data set AOD, Angstrom-Exponent and modified Angstrom-Exponent (Graßl, Ritter 2019, Remote Sensing, https://doi.org/10.3390/rs11111362) are given for the sun photometer at AWIPEV for the time 18. March 2017 until 30. September 2017. The variables are in netcdf format for each measurement day. The used instrument for the measurement period was SP1A31.
description: Each measurement day contains in netcdf format the following variables:Time (minutes after midnight UTC)wavelengths (nm lambda)AODModified Angstrom parameters (3 values!)traditional Angstrom parameters (2 values)
DOI: 10.15468/dl.bj5xjg
title: Occurrence Download

description: A dataset containing 7878965 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Occurrence evidence, Living Specimen, Specimen, Material citation, Material sample, Human Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 7878965 records from 5416 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015348-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qlju0q
title: Occurrence Download

description: A dataset containing 9942 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Solea solea (Linnaeus, 1758)"
 ]
}

 The dataset includes 9942 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003405-150922153815467/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hdqpwu
title: Occurrence Download

description: A dataset containing 3792 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eriophorum gracile W.D.J.Koch",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3792 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036861-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nszkae
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Goodyera wuana Tang & F.T.Wang"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008534-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vve65d
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hypericum prostratum Cuatrec."
 ]
}

 The dataset includes 79 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003406-150410134957023/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4osv7h
title: Occurrence Download

description: A dataset containing 781 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Epipactis gigantea Dougl. ex Hook.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 781 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036863-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.0zrzkv
title: Occurrence Download

description: A dataset containing 78 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Colombia",
 "Country is Costa Rica"
 ]
 },
 "HasCoordinate is true",
 "TaxonKey is Begonia conchifolia A.Dietr.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 78 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003406-170627171947987/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5tfnea
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leucopogon altissimus Hislop"
 ]
}

 The dataset includes 32 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0169416-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bvj21s
title: Occurrence Download

description: A dataset containing 5299 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Opegrapha atra Pers.",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 5299 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036865-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pepgsr
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus monticola Douglas",
 "HasCoordinate is true",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 47 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001378-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ticgax
title: Occurrence Download

description: A dataset containing 16753 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Maianthemum bifolium (L.) F.W.Schmidt",
 "Country is Sweden",
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Observation",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Material sample"
 ]
 },
 "Year 1990-2016"
 ]
}

 The dataset includes 16753 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003407-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yabtbr
title: Occurrence Download

description: A dataset containing 9950736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Hong Kong, Macao)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9950736 records from 2756 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001379-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17808225
title: Accompanying data for "A Multilevel statistical process control chart framework for personalized patient monitoring: A simulation study using patients historical data"

description: Statistical Process Control (SPC) charts offer a potentially effective means of continuously monitoring patient time-series data to enable the early detection ofclinical abnormalities. A significant challenge in implementing SPC methods, such as the Exponentially Weighted Moving Average (EWMA), is obtaining sufficient phase-I data from individual patients. This data is crucial for accurately estimating control limits using the expected mean and standard deviation duringstable periods. In contrast to other fields where ample historical data is available, patient monitoring systems often lack enough observations to effectively set these limits. To address this issue, we propose a multilevel SPC approach that uses historical data from stable subjects. By fitting a multilevel model with arandom intercept, our method enables comparison of an individual’s longitudinal measurements against cross-sectional data from other stable patients. Themodel recursively estimates the expected mean within a calibration window, applying distinct control limit coefficients compared to phase-II monitoring period. Person-specific deviations, computed as residuals between actual and estimated values are then monitored via an EWMA control chart. Simulation studies usingsynthetic data modeled on left ventricular assist device (LVAD) patients were conducted to evaluate this approach. The model was evaluated across multiplesimulation scenarios, varying parameters such as sampling frequency, shift size and type, to assess its robustness under different operating conditions. Resultsshowed that longer calibration periods enhanced alignment with patient data, thereby increasing the true-positive rate and reducing false alarms. However, if the calibration window becomes excessively long, it may lead to overfitting, which could delay detection or result in an increased false-positive rate. Our multilevel SPC framework demonstrates potential to improve early detection of mean shifts in patient monitoring data, offering a refined approach to personalized healthcaremonitoring and data-driven clinical decision-making.

This repository contains the simulated LVAD data used in our study on multilevel statistical process control for personalized patient monitoring. The simulations were generated using the CPAR (probability autoregressive) model across all parameter combinations. Data with shifts stored in data_with_shifts.zip file.

Using the CPAR framework, we generated 25,600 distinct simulation scenarios (5 × 4 × 4 × 2 × 2 × 2 × 2 × 5 × 4 combinations of settings basedon the simulation nstudy). For each scenario, a cohort of 500 unique patients was created: 250 patients for monitoringand 250 patients from a stable cohort, randomly sampled to assess the impact of the simulation parameters. Each patient trajectory was generated uniquely for that specific scenario based on the CPAR model. Code can be accessed via HERE.
DOI: 10.5281/zenodo.17808226
title: Accompanying data for "A Multilevel statistical process control chart framework for personalized patient monitoring: A simulation study using patients historical data"

description: Statistical Process Control (SPC) charts offer a potentially effective means of continuously monitoring patient time-series data to enable the early detection ofclinical abnormalities. A significant challenge in implementing SPC methods, such as the Exponentially Weighted Moving Average (EWMA), is obtaining sufficient phase-I data from individual patients. This data is crucial for accurately estimating control limits using the expected mean and standard deviation duringstable periods. In contrast to other fields where ample historical data is available, patient monitoring systems often lack enough observations to effectively set these limits. To address this issue, we propose a multilevel SPC approach that uses historical data from stable subjects. By fitting a multilevel model with arandom intercept, our method enables comparison of an individual’s longitudinal measurements against cross-sectional data from other stable patients. Themodel recursively estimates the expected mean within a calibration window, applying distinct control limit coefficients compared to phase-II monitoring period. Person-specific deviations, computed as residuals between actual and estimated values are then monitored via an EWMA control chart. Simulation studies usingsynthetic data modeled on left ventricular assist device (LVAD) patients were conducted to evaluate this approach. The model was evaluated across multiplesimulation scenarios, varying parameters such as sampling frequency, shift size and type, to assess its robustness under different operating conditions. Resultsshowed that longer calibration periods enhanced alignment with patient data, thereby increasing the true-positive rate and reducing false alarms. However, if the calibration window becomes excessively long, it may lead to overfitting, which could delay detection or result in an increased false-positive rate. Our multilevel SPC framework demonstrates potential to improve early detection of mean shifts in patient monitoring data, offering a refined approach to personalized healthcaremonitoring and data-driven clinical decision-making.

This repository contains the simulated LVAD data used in our study on multilevel statistical process control for personalized patient monitoring. The simulations were generated using the CPAR (probability autoregressive) model across all parameter combinations. Data with shifts stored in data_with_shifts.zip file.

Using the CPAR framework, we generated 25,600 distinct simulation scenarios (5 × 4 × 4 × 2 × 2 × 2 × 2 × 5 × 4 combinations of settings basedon the simulation nstudy). For each scenario, a cohort of 500 unique patients was created: 250 patients for monitoringand 250 patients from a stable cohort, randomly sampled to assess the impact of the simulation parameters. Each patient trajectory was generated uniquely for that specific scenario based on the CPAR model. Code can be accessed via HERE.
DOI: 10.15468/dl.zuamq5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((101.603 0.987184, 101.532727 0.981616, 101.464184 0.96505, 101.399061 0.937895, 101.338961 0.900818, 101.285364 0.854732, 101.23959 0.800775, 101.202765 0.740272, 101.175797 0.674716, 101.159348 0.60572, 101.153823 0.534983, 101.159358 0.464247, 101.175816 0.395254, 101.202792 0.329701, 101.239621 0.269204, 101.285397 0.215251, 101.338992 0.169171, 101.399088 0.132098, 101.464204 0.104946, 101.532737 0.088382, 101.603 0.082815, 101.673263 0.088382, 101.741796 0.104946, 101.806912 0.132098, 101.867008 0.169171, 101.920603 0.215251, 101.966379 0.269204, 102.003208 0.329701, 102.030184 0.395254, 102.046642 0.464247, 102.052177 0.534983, 102.046652 0.60572, 102.030203 0.674716, 102.003235 0.740272, 101.96641 0.800775, 101.920636 0.854732, 101.867039 0.900818, 101.806939 0.937895, 101.741816 0.96505, 101.673273 0.981616, 101.603 0.987184))"},{"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/0040537-251120083545085/datasets/export for details.
DOI: 10.17026/ar/k9hzqj
title: PAN-00153237 - Early Medieval rectangular mount (belt)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00153237&lt;/p&gt;
description: Modified: 2025-10-27T13:50:41+01:00
DOI: 10.15468/dl.282z33
title: Occurrence Download

description: A dataset containing 391 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hibiscus sterculiifolius",
 "Continent is Africa"
 ]
}

 The dataset includes 391 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040539-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8sfg5c
title: Occurrence Download

description: A dataset containing 3901875 species occurrences available in GBIF matching the query:
{
 "and" : [
 "KingdomKey is Plantae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1984",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "GadmGid is BRA",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 500",
 "CoordinateUncertaintyInMeters is null"
 ]
 },
 "DistanceFromCentroidInMeters is greater than or equal to 1000"
 ]
}

 The dataset includes 3901875 records from 855 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040497-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.72jr9u
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Limnodriloides appendiculatus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 48 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040541-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17865719
title: Dataset and Code for: Tuning of Language Models in Eastern European Languages on Twitter/X

description: This dataset contains the data and experimental code used in the study:

Filip, T., Pavlíček, M., & Sosík, P. (2024)."Tuning of language models in Eastern European languages on Twitter/X."In Proceedings of the Workshop on Artificial Intelligence and Language Technologies (Vol. 4092). CEUR-WS.

The dataset includes:– text data from multiple Eastern European V4 languages collected from Twitter/X,– preprocessing and cleaning scripts,– experimental code used for training and evaluation,– evaluation outputs (metrics, tables, plots),– all resources required to reproduce the results presented in the article.

This dataset serves as supplementary material to the publication referenced above.

Funding acknowledgment:This work has been produced with the financial support of the European Union under the "Biography of Fake News with a Touch of AI: Dangerous Phenomenon through the Prism of Modern Human Sciences" project no. CZ.02.01.01/00/23_025/0008724 via the Operational Programme Jan Ámos Komenský (OP JAK).
DOI: 10.5281/zenodo.17865720
title: Dataset and Code for: Tuning of Language Models in Eastern European Languages on Twitter/X

description: This dataset contains the data and experimental code used in the study:

Filip, T., Pavlíček, M., & Sosík, P. (2024)."Tuning of language models in Eastern European languages on Twitter/X."In Proceedings of the Workshop on Artificial Intelligence and Language Technologies (Vol. 4092). CEUR-WS.

The dataset includes:– text data from multiple Eastern European V4 languages collected from Twitter/X,– preprocessing and cleaning scripts,– experimental code used for training and evaluation,– evaluation outputs (metrics, tables, plots),– all resources required to reproduce the results presented in the article.

This dataset serves as supplementary material to the publication referenced above.

Funding acknowledgment:This work has been produced with the financial support of the European Union under the "Biography of Fake News with a Touch of AI: Dangerous Phenomenon through the Prism of Modern Human Sciences" project no. CZ.02.01.01/00/23_025/0008724 via the Operational Programme Jan Ámos Komenský (OP JAK).