cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31040867.v1
title: A Comparative Study of Parametric Dynamic Mode Decomposition Algorithms on Thermal-Hydraulics Case Studies

description: In recent years, algorithms aiming to learn models from available data have become quite popular due to two factors: (1) the significant developments in artificial intelligence techniques and (2) the availability of large amounts of data. Nevertheless, this topic has already been addressed by methodologies belonging to the reduced order modelling framework, of which perhaps the most famous equation-free technique is dynamic mode decomposition. This algorithm aims to learn the best linear model that represents the physical phenomena described by a time series data set. Its output is a best-fit linear operator of the underlying dynamical system that can be used, in principle, to advance the system itself in time even beyond the training time interval. However, in its standard formulation, this technique cannot deal with parametric time series, meaning that a different linear model has to be derived for each configuration. Research on this is ongoing, and some versions of a parametric dynamic mode decomposition already exist. This work contributes to this research field by comparing the different algorithms presently deployed and assessing their advantages and shortcomings compared to each other. To this aim, three different thermal-hydraulic problems are considered: two benchmark flow-over cylinder test cases at diverse Reynolds numbers, and the DYNASTY experimental facility operating at Politecnico di Milano, which studies the natural circulation established by internally heated fluids for Generation-IV nuclear applications simulated using the RELAP5 nodal solver. As a key result, this paper highlights the main advantages and disadvantages of the available parametric dynamic mode decomposition methods, concluding that the choice of the algorithm version strongly depends on the problem under consideration and on the user’s priority (i.e. accuracy or computational speed of the online phase). Additionally, this paper shows that an interpretable linear model can be learned from parametric data sets governed by nonlinear models that can be used for parameter interpolation, and most importantly, for state prediction in time.
DOI: 10.15156/bio/sh0190320.10fu
title: SH0190320.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0190321.10fu
title: SH0190321.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0190322.10fu
title: SH0190322.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0190323.10fu
title: SH0190323.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.26704296
title: Additional file 21 of The transcriptional response in mosquitoes distinguishes between fungi and bacteria but not Gram types

description: Additional file 21.
DOI: 10.6084/m9.figshare.26704296.v1
title: Additional file 21 of The transcriptional response in mosquitoes distinguishes between fungi and bacteria but not Gram types

description: Additional file 21.
DOI: 10.6084/m9.figshare.26704299
title: Additional file 22 of The transcriptional response in mosquitoes distinguishes between fungi and bacteria but not Gram types

description: Additional file 22.
DOI: 10.6084/m9.figshare.26704299.v1
title: Additional file 22 of The transcriptional response in mosquitoes distinguishes between fungi and bacteria but not Gram types

description: Additional file 22.
DOI: 10.6084/m9.figshare.26704302
title: Additional file 23 of The transcriptional response in mosquitoes distinguishes between fungi and bacteria but not Gram types

description: Additional file 23.
DOI: 10.6084/m9.figshare.26704302.v1
title: Additional file 23 of The transcriptional response in mosquitoes distinguishes between fungi and bacteria but not Gram types

description: Additional file 23.
DOI: 10.15468/dl.8yc958
title: Occurrence Download

description: A dataset containing 557 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Russian Federation, Japan, China)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Selaginella selaginoides (L.) Schrank & C.F.P.Mart.",
 "Year 1900-2024"
 ]
}

 The dataset includes 557 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067865-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h6m4w6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Didymosphenia geminatum (Lyngbye) M.Schmidt, 1899)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1900",
 "BasisOfRecord is one of (Specimen)",
 "Country is one of (New Zealand)",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than or equal to 20000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067866-240626123714530/datasets/export for details.
DOI: 10.15468/dl.8x9vet
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.688405 -32.896996, 148.688405 -32.899241, 148.695143 -32.899241, 148.695143 -32.896996, 148.688405 -32.896996))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067867-240626123714530/datasets/export for details.
DOI: 10.6084/m9.figshare.26575460.v1
title: Additional file 1 of Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning

description: Additional file 1: Table S1. Clinical diagnosis frequencies for sequenced RCHSD cases broken down by positive/negative molecular diagnosis status. Table S2. Primary clinical diagnoses for sequenced Utah NeoSeq cases broken down by positive/negative molecular diagnosis status. Table S3. Individual MPSE scores for RCHSD cohort patients. Table S4. Individual MPSE scores for Utah NeoSeq cohort patients.
DOI: 10.6084/m9.figshare.26575460
title: Additional file 1 of Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning

description: Additional file 1: Table S1. Clinical diagnosis frequencies for sequenced RCHSD cases broken down by positive/negative molecular diagnosis status. Table S2. Primary clinical diagnoses for sequenced Utah NeoSeq cases broken down by positive/negative molecular diagnosis status. Table S3. Individual MPSE scores for RCHSD cohort patients. Table S4. Individual MPSE scores for Utah NeoSeq cohort patients.
DOI: 10.6084/m9.figshare.26615635.v1
title: Additional file 2 of Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes

description: Additional file 2: Data S1. Excel file providing the complete RNA-seq and Gene ontology analysis results. Data is separated into sheets by sex (Purple for Female analyses; Green for Male analyses).
DOI: 10.6084/m9.figshare.26615641.v1
title: Additional file 4 of Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes

description: Additional file 4: Data S3. List of miRNAs, associated SNP/variant ID and phenotype.
DOI: 10.6084/m9.figshare.26615635
title: Additional file 2 of Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes

description: Additional file 2: Data S1. Excel file providing the complete RNA-seq and Gene ontology analysis results. Data is separated into sheets by sex (Purple for Female analyses; Green for Male analyses).
DOI: 10.6084/m9.figshare.26615638
title: Additional file 3 of Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes

description: Additional file 3: Data S2. Excel file providing the complete small RNA-seq and Gene ontology analysis results. Data is separated into sheets by sex (Purple for Female analyses; Green for Male analyses).
DOI: 10.6084/m9.figshare.26615641
title: Additional file 4 of Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes

description: Additional file 4: Data S3. List of miRNAs, associated SNP/variant ID and phenotype.
DOI: 10.6084/m9.figshare.26615638.v1
title: Additional file 3 of Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes

description: Additional file 3: Data S2. Excel file providing the complete small RNA-seq and Gene ontology analysis results. Data is separated into sheets by sex (Purple for Female analyses; Green for Male analyses).
DOI: 10.6084/m9.figshare.26575493
title: Additional file 2 of NOMe-HiC: joint profiling of genetic variant, DNA methylation, chromatin accessibility, and 3D genome in the same DNA molecule

description: Additional file 2: Table S1. The list of public data used in the study. Table S2. Sequencing summary statistics in NOMe-HiC (DNA). Table S3. Sequencing summary statistics in NOMe-HiC (RNA). Table S4. Three groups of long-range allele specific GpC methyltransferase footprint regions. Table S5. LD blocks overlapped with the SNP anchors in group 3 Allele-specific GpC methyltransferase footprint.
DOI: 10.6084/m9.figshare.26575493.v1
title: Additional file 2 of NOMe-HiC: joint profiling of genetic variant, DNA methylation, chromatin accessibility, and 3D genome in the same DNA molecule

description: Additional file 2: Table S1. The list of public data used in the study. Table S2. Sequencing summary statistics in NOMe-HiC (DNA). Table S3. Sequencing summary statistics in NOMe-HiC (RNA). Table S4. Three groups of long-range allele specific GpC methyltransferase footprint regions. Table S5. LD blocks overlapped with the SNP anchors in group 3 Allele-specific GpC methyltransferase footprint.
DOI: 10.15468/dl.nmqcgq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Duttaphrynus melanostictus (Schneider, 1799))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1900",
 "BasisOfRecord is one of (Specimen)",
 "Country is one of (Madagascar)",
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than or equal to 20000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067868-240626123714530/datasets/export for details.
DOI: 10.15468/dl.7suzhn
title: Occurrence Download

description: A dataset containing 39172666 species occurrences available in GBIF matching the query:
(Query is too complex. Can be viewed on the landing page)

 The dataset includes 39172666 records from 14886 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067858-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m2vbyz
title: Occurrence Download

description: A dataset containing 859 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Korea, Republic of",
 "TaxonKey is Calliphora lata Coquillett, 1898"
 ]
}

 The dataset includes 859 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022165-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pysb2h
title: Occurrence Download

description: A dataset listing the 301 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is CHN.10_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (May, June, January, February, March, April, July, August, September, October, November, December)",
 "Year 2016-2016"
 ]
}

 The dataset's 301 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022164-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uqqas8
title: Occurrence Download

description: A dataset containing 795 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "PublishingOrg is 396d5f30-dea9-11db-8ab4-b8a03c50a862",
 "TaxonKey is Bombus pomorum (Panzer, 1805)"
 ]
}

 The dataset includes 795 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022166-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/veyp-sk69
title: Multi-spectral iPPG Differential Features and Multi-region of Interest iPPG Differential Features

description: We extract three channel iPPG signals that are obtained from the Red, Green and Blue lights, respectively, from the same one Region of Interest (ROI), analyzed the differences among these three iPPGs in terms of its relationship with BP levels. And we extract iPPGs from two different ROIs, compared and analyzed the differences of iPPGs of each wavelength from the two ROIs.
DOI: 10.5281/zenodo.14842722
title: Evaluation of background ionizing radiation dose variation in Targoviste town area (Romania), 2024

description: Measurements of background ionizing radiation dose rate, performed in Târgoviște town area and surroundings in 2024 (Romania), by the student Dicu Andrei Cătălin under supervision of Prof. Dr. Adrian Iftime as part of a diploma thesis project (C.Davila University of Medicine and Pharmacy). 

A graphical overview of the values is included (targoviste_doserate_values_2024.png). 

Fields description (file targoviste_dose_rate_2024.csv):

Location_ID: unique identification key for each location; Latitude, Longitude: geographical coordinates of the location;Date, Time: date and time of the measurement;Location_Type: local environment conditions, “Buildings” or “Vegetation”;uSvh_average: Average ionizing radiation dose rate at the location, expressed in microSievert/hour;uSvh_SD: Standard devition for the averaged value (uSVh_average);CPS_average: Average of Counts Per Second events recorded at the location by the recording device; CPS_SD: Standard deviation of the averaged value (CPS_average); NA: not available data

CPS_1, CPS_2, etc: individual measurements done at the location (Counts per second)uSv_1, uSV_2, etc: individual measurements done at the location (microSievert/hour)

NOTE: the calculation of microSievert/h from CPS was done internally by the measurement device (GammaScount Standard handheld radiodozimeter); the device was calibrated by the producer. 

Please cite as: DOI: 10.5281/zenodo.14842723
DOI: 10.5281/zenodo.14842723
title: Evaluation of background ionizing radiation dose variation in Targoviste town area (Romania), 2024

description: Measurements of background ionizing radiation dose rate, performed in Târgoviște town area and surroundings in 2024 (Romania), by the student Dicu Andrei Cătălin under supervision of Prof. Dr. Adrian Iftime as part of a diploma thesis project (C.Davila University of Medicine and Pharmacy). 

A graphical overview of the values is included (targoviste_doserate_values_2024.png). 

Fields description (file targoviste_dose_rate_2024.csv):

Location_ID: unique identification key for each location; Latitude, Longitude: geographical coordinates of the location;Date, Time: date and time of the measurement;Location_Type: local environment conditions, “Buildings” or “Vegetation”;uSvh_average: Average ionizing radiation dose rate at the location, expressed in microSievert/hour;uSvh_SD: Standard devition for the averaged value (uSVh_average);CPS_average: Average of Counts Per Second events recorded at the location by the recording device; CPS_SD: Standard deviation of the averaged value (CPS_average); NA: not available data

CPS_1, CPS_2, etc: individual measurements done at the location (Counts per second)uSv_1, uSV_2, etc: individual measurements done at the location (microSievert/hour)

NOTE: the calculation of microSievert/h from CPS was done internally by the measurement device (GammaScount Standard handheld radiodozimeter); the device was calibrated by the producer. 

Please cite as: DOI: 10.5281/zenodo.14842723
DOI: 10.15468/dl.mrd9ff
title: Occurrence Download

description: A dataset containing 9463 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is CHN.10_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (May, June, January, February, March, April, July, August, September, October, November, December)",
 "Year 2017-2017"
 ]
}

 The dataset includes 9463 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022168-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.29ba36
title: Occurrence Download

description: A dataset containing 795 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "PublishingOrg is 396d5f30-dea9-11db-8ab4-b8a03c50a862",
 "TaxonKey is Bombus pomorum (Panzer, 1805)"
 ]
}

 The dataset includes 795 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022169-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6pyrvk
title: Occurrence Download

description: A dataset containing 142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Austria",
 "TaxonKey is Dendrobaena octaedra (Savigny, 1826)"
 ]
}

 The dataset includes 142 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022170-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14534771
title: TEM raw data vor 1Q(0.5)-8L-10T(0.5) and 1Q(0.5)-5L-10T(0.5)
DOI: 10.5281/zenodo.14534772
title: TEM raw data vor 1Q(0.5)-8L-10T(0.5) and 1Q(0.5)-5L-10T(0.5)
DOI: 10.15468/dl.pv5apj
title: Occurrence Download

description: A dataset containing 9463 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is CHN.10_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (May, June, January, February, March, April, July, August, September, October, November, December)",
 "Year 2017-2017"
 ]
}

 The dataset includes 9463 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022171-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.