cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.9jg8ms
title: Occurrence Download

description: A dataset containing 174 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Botryllus schlosseri (Pallas, 1766)",
 "Country is Canada",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 174 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012376-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10450451
title: Supplementary dataset for a research paper "A Farewell to a Dam: ecological impacts of the Kakhovka Reservoir ravaging by warfare in Ukraine"
DOI: 10.5281/zenodo.10450452
title: Supplementary dataset for a research paper "A Farewell to Dams: using rivers as arms may cause continental-scale environmental damages"
DOI: 10.5281/zenodo.14135939
title: Supplementary dataset for a research paper "A Farewell to a Dam: ecological impacts of the Kakhovka Reservoir ravaging by warfare in Ukraine"
DOI: 10.6084/m9.figshare.27692304.v1
title: <b>Delphi Technique in simulating innovative learning practices : Moderating the role of IT artefacts </b>

description: The responses from Round 1 were aggregated and analysed. All the experts were requested to answer the questionnaire, which was arranged on a 7-point Likert scale.The consensus level was recorded as (i) high if the quartile deviation was lower or equal to 1; (ii) medium if the quartile deviation was between 1 and 2; and (iii) no consensus if the quartile deviation was above 2. On the other hand, the importance level was considered very high when the median value was above 6 and low when the median value was 5 or below. In this research, items categorised as highly important levels and with significant consensus levels were utilised to develop innovative learning practices assessment criteria.
DOI: 10.5281/zenodo.12065571
title: The EVI data for each solar farm
DOI: 10.5281/zenodo.14058564
title: The EVI data for each solar farm
DOI: 10.5281/zenodo.12065570
title: The EVI data for each solar farm
DOI: 10.5281/zenodo.14138846
title: The EVI data for each solar farm
DOI: 10.5281/zenodo.14139322
title: MIGNEX policy database

description: Dataset of migration-relevant policies. See documentation for details.
DOI: 10.5281/zenodo.14139323
title: MIGNEX policy database

description: Dataset of migration-relevant policies. See documentation for details.
DOI: 10.5281/zenodo.14103976
title: An Empirical Comparative Study of Convolutional Neural Network and Support Vector Machine in Digital Signature for Digital Document Authentication

description: Dataset dan figure of the research
DOI: 10.5281/zenodo.14103977
title: An Empirical Comparative Study of Convolutional Neural Network and Support Vector Machine in Digital Signature for Digital Document Authentication

description: Dataset dan figure of the research
DOI: 10.5281/zenodo.14136489
title: Cetaceans sighting positions from 2009 to 2021 (animal conservation)
title: Cetaceans 2009_2021

description: Sighting position of the Striped dolphin (Stenella coeruleoalba), Common bottlenose dolphin (Tursiops truncatus) and Fin whale (Balaenoptera physalus) in the 2009 - 2021 period collected buy aerial line transect distance sampling. These data coupled with other from public database are linked with the Copernicus in order to fit ensemble SDMs 16 and predict their current and future potential distribution at the Mediterranean subregional scale.
DOI: 10.5281/zenodo.14136490
title: Cetaceans sighting positions from 2009 to 2021 (animal conservation)
title: Cetaceans 2009_2021

description: Sighting position of the Striped dolphin (Stenella coeruleoalba), Common bottlenose dolphin (Tursiops truncatus) and Fin whale (Balaenoptera physalus) in the 2009 - 2021 period collected buy aerial line transect distance sampling. These data coupled with other from public database are linked with the Copernicus in order to fit ensemble SDMs 16 and predict their current and future potential distribution at the Mediterranean subregional scale.
DOI: 10.48323/dbptf-zav57
title: MS_Exploris_2024_September

description: Exploris 480 + Vanquish Flex
MHO03CC_Tuftsin
MHO03CD_Imatinib
DOI: 10.5281/zenodo.10955732
title: Dataset for: A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces

description: This dataset is part of "A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces" available online.

The source code for testing the dataset is available on GitHub.

The dataset is organized as follows:



\training: contains three subsets, i.e., train, valid, and test.    Each subset consists of two .npy files:



_set: it contains the side-channel traces that are preprocessed accordingly.

 _labels: itcontains the target labels for training the CNN, labeling each data as cipher start, cipher rest, or noise.



\inference: contains two files as a demo of the inference pipeline.    One file is the is the side-channel trace containing an undefined number of AES encryptions. The other file is a list of plaintexts matching the AES encryptions to test a CPA attack.


Cite:



@INPROCEEDINGS{10546758,
   author={Chiari, Giuseppe and Galli, Davide and Lattari, Francesco and Matteucci, Matteo and Zoni, Davide},
   booktitle={2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)}, 
   title={A Deep- Learning Technique to Locate Cryptographic Operations in Side-Channel Traces}, 
   year={2024},
   pages={1-6},
   doi={10.23919/DATE58400.2024.10546758}}


This repository is protected by copyright and licensed under the Creative Commons Attribution 4.0 International license.

© 2024 hardware-fab
DOI: 10.5281/zenodo.10955733
title: Dataset for: A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces

description: This dataset is part of "A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces" available online.

The source code for testing the dataset is available on GitHub.

The dataset is organized as follows:



\training: contains three subsets, i.e., train, valid, and test.    Each subset consists of two .npy files:



_set: it contains the side-channel traces that are preprocessed accordingly.

 _labels: itcontains the target labels for training the CNN, labeling each data as cipher start, cipher rest, or noise.



\inference: contains two files as a demo of the inference pipeline.    One file is the is the side-channel trace containing an undefined number of AES encryptions. The other file is a list of plaintexts matching the AES encryptions to test a CPA attack.


Cite:



@INPROCEEDINGS{10546758,
   author={Chiari, Giuseppe and Galli, Davide and Lattari, Francesco and Matteucci, Matteo and Zoni, Davide},
   booktitle={2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)}, 
   title={A Deep- Learning Technique to Locate Cryptographic Operations in Side-Channel Traces}, 
   year={2024},
   pages={1-6},
   doi={10.23919/DATE58400.2024.10546758}}


This repository is protected by copyright and licensed under the Creative Commons Attribution 4.0 International license.

© 2024 hardware-fab
DOI: 10.15468/dl.5a75rc
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ambrosia pannosa W.W.Payne, Ambrosia pannosa W. W. Payne, Ambrosia pannosa W.W.Payne, Ambrosia pannosa W.W.Payne, Ambrosia pannosa W.W.Payne, 1966, Ambrosia pannosa W.W.Payne, Ambrosia pannosa W.W.Payne, 1966)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012377-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5284/1120955
title: Data from a Geophysical Survey South of Spencers Wood, Berkshire, December 2023

description: This collection comprises geophysics and GIS data from an archaeological geophysical survey carried out by MOLA Northampton on land south of Spencers Wood, Berkshire during December 2023.
DOI: 10.15468/dl.fq25nr
title: Occurrence Download

description: A dataset containing 133 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Branchiomma bairdi (McIntosh, 1885)",
 "Country is Mexico",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 133 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012379-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.crcadr
title: Occurrence Download

description: A dataset containing 236528 species occurrences available in GBIF matching the query:
{
 "GadmGid" : [
 "is CHN.15_1"
 ]
}

 The dataset includes 236528 records from 203 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012344-241107131044228/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.27692304
title: <b>Delphi Technique in simulating innovative learning practices : Moderating the role of IT artefacts </b>

description: The responses from Round 1 were aggregated and analysed. All the experts were requested to answer the questionnaire, which was arranged on a 7-point Likert scale.The consensus level was recorded as (i) high if the quartile deviation was lower or equal to 1; (ii) medium if the quartile deviation was between 1 and 2; and (iii) no consensus if the quartile deviation was above 2. On the other hand, the importance level was considered very high when the median value was above 6 and low when the median value was 5 or below. In this research, items categorised as highly important levels and with significant consensus levels were utilised to develop innovative learning practices assessment criteria.
DOI: 10.6084/m9.figshare.27692304.v3
title: <b>Delphi Technique in simulating innovative learning practices : Moderating the role of IT artefacts </b>

description: The responses from Round 1 were aggregated and analysed. All the experts were requested to answer the questionnaire, which was arranged on a 7-point Likert scale.The consensus level was recorded as (i) high if the quartile deviation was lower or equal to 1; (ii) medium if the quartile deviation was between 1 and 2; and (iii) no consensus if the quartile deviation was above 2. On the other hand, the importance level was considered very high when the median value was above 6 and low when the median value was 5 or below. In this research, items categorised as highly important levels and with significant consensus levels were utilised to develop innovative learning practices assessment criteria.
DOI: 10.6084/m9.figshare.27692304.v2
title: <b>Delphi Technique in simulating innovative learning practices : Moderating the role of IT artefacts </b>

description: The responses from Round 1 were aggregated and analysed. All the experts were requested to answer the questionnaire, which was arranged on a 7-point Likert scale.The consensus level was recorded as (i) high if the quartile deviation was lower or equal to 1; (ii) medium if the quartile deviation was between 1 and 2; and (iii) no consensus if the quartile deviation was above 2. On the other hand, the importance level was considered very high when the median value was above 6 and low when the median value was 5 or below. In this research, items categorised as highly important levels and with significant consensus levels were utilised to develop innovative learning practices assessment criteria.
DOI: 10.15468/dl.gen5zy
title: Occurrence Download

description: A dataset containing 941630 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Parus major Linnaeus, 1758"
 ]
}

 The dataset includes 941630 records from 189 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012318-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ubj23j
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ambrosia pannosa W.W.Payne, Ambrosia pannosa W. W. Payne, Ambrosia pannosa W.W.Payne, Ambrosia pannosa W.W.Payne, Ambrosia pannosa W.W.Payne, 1966, Ambrosia pannosa W.W.Payne, Ambrosia pannosa W.W.Payne, 1966)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012382-241107131044228/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.27692457
title: AM fungal hyphosphere bacterial 16S rRNA sequence data

description: The sequence data of bacteria colonized on AM fungal hyphae
DOI: 10.15468/vwfd9z
title: Données naturalistes opportunistes issues de la plateforme INaturalist-Données naturalistes opportunistes "yann-64"

description: Données naturalistes opportunistes saisies sur iNaturalist à partir du compte "yann-64"
DOI: 10.13140/rg.2.2.12994.30404
title: 5Feb2021 Atlantic Follow-up on Rayleigh-Taylor instabilities in Atmospheric Circulation. Diego Fdez-Sevilla PhD.png
DOI: 10.13140/rg.2.2.15274.95689
title: Scopus Journals List (October 2020)
DOI: 10.13140/rg.2.2.35827.04647
title: Writing Foundations to Assist the Scholarly Student
DOI: 10.15156/bio/sh0184477.10fu
title: SH0184477.10FU

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/sh0184478.10fu
title: SH0184478.10FU

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/sh0184479.10fu
title: SH0184479.10FU

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/sh0184480.10fu
title: SH0184480.10FU

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.30385891
title: Supporting data for "Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes"

description: This repository includes the raw STEM image datasets, the trained machine learning models, 3D animations of defect visualizations, and the molecular dynamics simulation input and output files for the publication "Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes" by Grace Guinan et al.
DOI: 10.6084/m9.figshare.30385891.v2
title: Supporting data for "Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes"

description: This repository includes the raw STEM image datasets, the trained machine learning models, 3D animations of defect visualizations, and the molecular dynamics simulation input and output files for the publication "Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes" by Grace Guinan et al.
DOI: 10.6084/m9.figshare.30385891.v1
title: Supporting data for "Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes"

description: This repository includes the raw STEM image datasets, the trained machine learning models, 3D animations of defect visualizations, and the molecular dynamics simulation input and output files for the publication "Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes" by Grace Guinan et al.
DOI: 10.15156/bio/sh0184481.10fu
title: SH0184481.10FU

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/sh0184482.10fu
title: SH0184482.10FU

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/sh0184483.10fu
title: SH0184483.10FU

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.