cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13379317
title: ELITE SLWR: global 1km daily SLUR at SIN Grid   (2005_001-059)

description: The Essential thermal Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

 

This dataset is ELITE daily surface longwave upward radiation (SLUR) product at 1km spatial resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2022 (continuously updated). This dataset was generated from the ELITE surface longwave upward radiation (SLUR) product using the algorithms developed by Zeng et al. (2021).

 

This is the ELITE Daily SLUR product in 2005 (DOY: 001-059). Please click here to download the ELITE Daily SLUR product in 2005 (DOY: 060-120).

Dataset Characteristics:



Spatial Coverage: Global

Temporal Coverage: 2005-01-01 ~ 2005-02-28

Spatial Resolution: 1 km

Temporal Resolution: Daily

Data Format: h5

Projection: Sinusoidal

Scale: 0.05

Offset: -1000

Fill value: 999


 

Citation (Please cite these papers when using the data):



Zeng, Q., & Cheng, J. (2021). Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11), 1674-1704.
DOI: 10.5281/zenodo.13379318
title: ELITE SLWR: global 1km daily SLUR at SIN Grid   (2005_001-059)

description: The Essential thermal Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

 

This dataset is ELITE daily surface longwave upward radiation (SLUR) product at 1km spatial resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2022 (continuously updated). This dataset was generated from the ELITE surface longwave upward radiation (SLUR) product using the algorithms developed by Zeng et al. (2021).

 

This is the ELITE Daily SLUR product in 2005 (DOY: 001-059). Please click here to download the ELITE Daily SLUR product in 2005 (DOY: 060-120).

Dataset Characteristics:



Spatial Coverage: Global

Temporal Coverage: 2005-01-01 ~ 2005-02-28

Spatial Resolution: 1 km

Temporal Resolution: Daily

Data Format: h5

Projection: Sinusoidal

Scale: 0.05

Offset: -1000

Fill value: 999


 

Citation (Please cite these papers when using the data):



Zeng, Q., & Cheng, J. (2021). Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. International Journal of Digital Earth, 14(11), 1674-1704.
DOI: 10.15468/dl.9a95ap
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psephellus pecho (Albov) Wagenitz"
 ]
}

 The dataset includes 12 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107491-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13464620
title: Supplementary material and dataset for article "Structure-Function Relationship of Iron Oxide Nanoflowers: Optimal Sizes for Magnetic Hyperthermia Depending on Alternating Magnetic Field Conditions"

description: The supporting information file contains additional measurements compared to the main manuscript of the related article: TEM size histograms, SAED and XRD patterns, absorption and fluorescence spectra, DC magnetization curves, AC hysteresis loops, HR-TEM, their FTT patterns and inverse FFT images after applying a mask in the reciprocal space, and various other plots of this multi-parametric study on the structure-properties relations of magnetic iron oxide nanoflowers. Whenever needed for comparison to other experimental results or theoretical fitting, the raw data of all DC magnetization curves, AC hysteresis loops, ZFC-FC magnetization curves vs. temperature (and their derivatives on temperature) are made freely available in this dataset.
DOI: 10.5281/zenodo.7596847
title: Supplementary material and dataset for article "Structure-Function Relationship of Iron Oxide Nanoflowers: Optimal Sizes for Magnetic Hyperthermia Depending on Alternating Magnetic Field Conditions"

description: The supporting information file contains additional measurements compared to the main manuscript of the related article: TEM size histograms, SAED and XRD patterns, absorption and fluorescence spectra, DC magnetization curves, AC hysteresis loops, HR-TEM, their FTT patterns and inverse FFT images after applying a mask in the reciprocal space, and various other plots of this multi-parametric study on the structure-properties relations of magnetic iron oxide nanoflowers. Whenever needed for comparison to other experimental results or theoretical fitting, the raw data of all DC magnetization curves, AC hysteresis loops, ZFC-FC magnetization curves vs. temperature (and their derivatives on temperature) are made freely available in this dataset.
DOI: 10.5281/zenodo.7596848
title: Supplementary material and dataset for article "Optimal sizes of iron oxide nanoflowers for magnetic hyperthermia depend on the alternating magnetic field conditions"

description: The supporting information file contains additional measurements compared to the main manuscript of the related article: TEM size histograms, SAED and XRD patterns, absorption and fluorescence spectra, DC magnetization curves, AC hysteresis loops, HR-TEM, their FTT patterns and inverse FFT images after applying a mask in the reciprocal space, and various other plots of this multi-parametric study on the structure-properties relations of magnetic iron oxide nanoflowers. Whenever needed for comparison to other experimental results or theoretical fitting, the raw data of all DC magnetization curves, AC hysteresis loops, ZFC-FC magnetization curves vs. temperature (and their derivatives on temperature) are made freely available in this dataset.
description: {"references": ["M. Bejko et al. (2023). Data associated to preprint manuscript available at https://dx.doi.org/10.26434/chemrxiv-2023-0pw9d"]}
DOI: 10.5281/zenodo.13343339
title: GO Enrichment Analysis on Single-Cell RNA-Seq Data
DOI: 10.5281/zenodo.13343340
title: Markers
DOI: 10.5281/zenodo.13461890
title: GO Enrichment Analysis on Single-Cell RNA-Seq Data
DOI: 10.15468/dl.hsgb8q
title: Occurrence Download

description: A dataset containing 69695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Avena sativa L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 69695 records from 915 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107490-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48677/1229e034-97ca-4cef-9703-33f67bf6c2db
title: Kartographisches Modell 1:250 000 Raster UTM32 Stichtag 15.07.2024

description: Das Kartographische Modell 1:250 000 - Raster (KM250-R) ist das vom Datenbestand des KM250-V gerasterte Abbild des gesamten österreichischen Staatsgebietes im Maßstab 1:250 000. Es liegt blattschnittfrei und georeferenziert vor. Die Aktualisierung erfolgt auf Basis des KM250-V laufend nach für diesen Maßstab relevanten Veränderungsinformationen.

Die Kartographischen Modelle - Raster (KM-R) werden durch folgende Themen strukturiert:
• Aufdruck
• Gewässer
• Höhenschichtlinien
• Relief
• Situation
• Wald

Stichtag = Erstellungsdatum:
Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.5281/zenodo.13465503
title: Using the IBM analog in-memory hardware acceleration kit for neural network training and inference - Supplementary Material

description: Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this Tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. Finally, we show examples of how users can expand and customize AIHWKit for their own needs. This Tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial.
DOI: 10.5281/zenodo.13465504
title: Using the IBM analog in-memory hardware acceleration kit for neural network training and inference - Supplementary Material

description: Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this Tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, a platform that provides the benefits of using the AIHWKit simulation in a fully managed cloud setting along with physical AIMC hardware access, freely available at https://aihw-composer.draco.res.ibm.com. Finally, we show examples of how users can expand and customize AIHWKit for their own needs. This Tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial.
DOI: 10.48677/a350f1f1-48e2-4798-a6ac-6cac12a32906
title: Kartographisches Modell 1:500 000 Raster Stichtag 01.01.2023

description: Das Kartographische Modell 1:500 000 - Raster (KM500-R) ist die digitale Karte des gesamten österreichischen Staatsgebietes im Maßstab 1:500 000. Das KM500-R liegt in einer Datei georeferenziert, im Koordinatensystem ETRS 89 und in der Lambert`schen Projektion vor.

Die Kartographischen Modelle - Raster (KM-R) werden durch folgende Themen strukturiert:
 • Aufdruck
 • Gewässer
 • Höhenschichtlinien
 • Relief
 • Situation
 • Wald

Stichtag = Erstellungsdatum:
Die Daten sind zu diesem Tag der aktuellste Datenbestand des BEV.
Dies ist nicht ident mit dem Aufnahme- bzw. Vermessungsdatum.
DOI: 10.15468/dl.nxtnrw
title: Occurrence Download

description: A dataset containing 3288 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Avena strigosa Schreb.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3288 records from 196 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107493-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hg6553
title: Occurrence Download

description: A dataset containing 2634 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Averrhoa carambola L.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2634 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107494-240626123714530/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.26869456
title: Barriers to the uptake of digital mental health interventions in low resource settings: perceptions of young people in Uganda

description: <b>Introduction:</b> One of the benefits of digital mental health is enhanced access to healthcare, particularly in low and middle-income countries where government investment in mental healthcare is low. However, uptake among young people is low. This study aimed to explore young people’s perceptions of the barriers to the uptake of digital mental health interventions in low-resource settings.<b>Methods:</b> A qualitative descriptive approach was used to collect data. Six face-to-face focus group discussions were conducted with 50 purposively selected young people from nine universities in Uganda. A thematic approach was used to interpret the results.<b>Results:</b> Most respondents had a positive attitude towards digital mental health and some of them had used it before. There was a preference for digital interventions that are reliable and have an option for human interaction. The perceived barriers to young people’s uptake of digital mental health included the fear of stigma, affordability, inequitable access, privacy and confidentiality concerns, and app-related challenges. Access and use of digital mental health can be enhanced through public engagement, creating awareness, enhanced training, and access to affordable DMH interventions.<b>Conclusion:</b> Digital mental health was deemed important in extending much-needed healthcare to young people, particularly in health systems where traditional mental health services are not readily available. However, several factors hinder equitable access to digital mental health in low-resource settings. There is a need for long-term investment in digital health technologies aimed at tackling structural barriers that generate health disparities.
DOI: 10.5281/zenodo.13464872
title: Molecular modeling of luciferyl adenylate deprotonation in the active site of Photinus pyralis luciferase

description: Supporting material for the paper
DOI: 10.5281/zenodo.13464873
title: Molecular modeling of luciferyl adenylate deprotonation in the active site of Photinus pyralis luciferase

description: Supporting material for the paper
DOI: 10.15468/dl.wek27y
title: Occurrence Download

description: A dataset containing 488 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Avicennia alba Blume)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 488 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107495-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p7jqc9
title: Occurrence Download

description: A dataset containing 288861 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Botaurus stellaris (Linnaeus, 1758)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 288861 records from 471 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0107492-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh462149.07fu
title: SH462149.07FU

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/sh462150.07fu
title: SH462150.07FU

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/sh1830215.08fu
title: SH1830215.08FU

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/sh1830216.08fu
title: SH1830216.08FU

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/sh462151.07fu
title: SH462151.07FU

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/sh462152.07fu
title: SH462152.07FU

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.