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

description: A dataset containing 342 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polytaenia DC.",
 "BasisOfRecord is one of (Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Continent is North America"
 ]
}

 The dataset includes 342 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0159481-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jhb09k
title: Occurrence Download

description: A dataset containing 134601834 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Albania",
 "Country is Germany",
 "Country is Andorra",
 "Country is Austria",
 "Country is Belarus",
 "Country is Belgium",
 "Country is France",
 "Country is Estonia",
 "Country is Finland",
 "Country is Greece",
 "Country is Hungary",
 "Country is Italy",
 "Country is Iceland",
 "Country is Ireland",
 "Country is Luxembourg",
 "Country is North Macedonia",
 "Country is Malta",
 "Country is Moldova, Republic of",
 "Country is Monaco",
 "Country is Montenegro",
 "Country is Norway",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "Country is Spain",
 "Country is Sweden",
 "Country is Russian Federation",
 "Country is Saint Martin (French part)",
 "Country is Serbia",
 "Country is Slovakia",
 "Country is Slovenia",
 "Country is Switzerland",
 "Country is Bosnia and Herzegovina",
 "Country is Bulgaria",
 "Country is Cyprus",
 "Country is Croatia",
 "Country is Denmark",
 "Country is Netherlands",
 "Country is Liechtenstein",
 "Country is Latvia",
 "Country is Lithuania",
 "Country is Poland",
 "Country is Portugal",
 "Country is Czechia",
 "Country is Romania",
 "Country is Ukraine",
 "Country is Kosovo"
 ]
 },
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 134601834 records from 2125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031619-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gmtj7tjthx.1
title: Dataset for water and nitrogen deficit stress detection in soilless tomato crops based on spectral indices

description: The dataset for water and nitrogen deficit stress detection in soilless tomato crops based on spectral indices focuses on analysing data from four experiments to detect water and nitrogen deficits automatically using a classification tree methodology. The sheet "schedule" provides the scheduling information for the nitrogen and water treatments applied to the plants to create deficit conditions. It also includes a timetable and planning details, likely outlining the experimental procedures and timelines.The file integrates key aspects of the research process, from data collection to operational planning.
A sheet named "Data 4Exp." contains the main experimental results, organised into groups based on periods. A total of 186 data samples were randomly split into 80% for training and validation and 20% for testing, as presented in "Data 4Exp. test" sheet. The full description of the methodology followed is presented in the manuscript:
Elvanidi, A., Katsoulas, N., & Kittas, C. (2018). Automation for Water and Nitrogen Deficit Stress Detection in Soilless Tomato Crops Based on Spectral Indices. Horticulturae, 4(4), 47. https://doi.org/10.3390/horticulturae4040047
DOI: 10.17632/gmtj7tjthx.2
title: Dataset for water and nitrogen deficit stress detection in soilless tomato crops based on spectral indices

description: The dataset for water and nitrogen deficit stress detection in soilless tomato crops based on spectral indices focuses on analysing data from four experiments to detect water and nitrogen deficits automatically using a classification tree methodology. The sheet "schedule" provides the scheduling information for the nitrogen and water treatments applied to the plants to create deficit conditions. It also includes a timetable and planning details, likely outlining the experimental procedures and timelines.The file integrates key aspects of the research process, from data collection to operational planning.
A sheet named "Data 4Exp." contains the main experimental results, organised into groups based on periods. A total of 186 data samples were randomly split into 80% for training and validation and 20% for testing, as presented in "Data 4Exp. test" sheet. The full description of the methodology followed is presented in the manuscript:
Elvanidi, A., Katsoulas, N., & Kittas, C. (2018). Automation for Water and Nitrogen Deficit Stress Detection in Soilless Tomato Crops Based on Spectral Indices. Horticulturae, 4(4), 47. https://doi.org/10.3390/horticulturae4040047
DOI: 10.5281/zenodo.14343608
title: AMS_AGY_v2_focus_vs_temperature

description: Zenodo hosts the archival version of this document; for convenient viewing, please visit https://github.com/amsikking/AMS_AGY_v2_focus_vs_temperature

Abstract

Data showing how the focal plane of an AMS-AGY v2 objective moves axially with temperature.
description: v1.0.0: Pre-release to get DOI.

v1.0.1: 1st release with embedded DOI.
DOI: 10.5281/zenodo.14343609
title: AMS_AGY_v2_focus_vs_temperature

description: Zenodo hosts the archival version of this document; for convenient viewing, please visit https://github.com/amsikking/AMS_AGY_v2_focus_vs_temperature

Abstract

Data showing how the focal plane of an AMS-AGY v2 objective moves axially with temperature.
description: v1.0.0: Pre-release to get DOI.
DOI: 10.5281/zenodo.14343658
title: AMS_AGY_v2_focus_vs_temperature

description: Zenodo hosts the archival version of this document; for convenient viewing, please visit https://github.com/amsikking/AMS_AGY_v2_focus_vs_temperature

Abstract

Data showing how the focal plane of an AMS-AGY v2 objective moves axially with temperature.
description: v1.0.0: Pre-release to get DOI.

v1.0.1: 1st release with embedded DOI.
DOI: 10.15468/dl.tma8ny
title: Occurrence Download

description: A dataset containing 52808 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Cacicus melanicterus (Bonaparte, 1825)"
 ]
}

 The dataset includes 52808 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027241-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10964647
title: Farm-Flow | AG-IoT Security: Intrusion Detection in Smart Agriculture Dataset

description: Introduction:

The "Farm-Flow" dataset was created to emulate real-world Agricultural Internet of Things (AG-IoT) systems, encompassing network attacks and data collection. Following comprehensive cleaning and processing, the "Farm-Flow" dataset comprises 532 MB of data with 1,310,000 instances, structured around "flows," which represent consecutive series of packets transmitted from a single source to a specific destination. The dataset demonstrates an intrusion detection accuracy of 92.67% and is intended to enhance the security of AG-IoT systems, safeguarding information such as crop health, weather patterns, and soil conditions

Captures:

The captures comprises three months of network traffic: August, September, and October of 2022. Each month is divided into folders, which categorize the network traffic. These folders contain numerous .pcap files, which have been divided into 5-second intervals. This segmentation is necessary because, as previously mentioned, flows aggregate packets, resulting in only one row of flow data for ongoing connections. To address this, a script was developed to segment the .pcap files into 5-second increments. This approach allows for the generation of multiple rows of flow connections, thereby providing more quantity of data for model training.

Dataset:

The dataset comprises 532 MB of data, encompassing 1,310,000 instances. These instances have been classified into eight distinct attack types and one category for normal traffic. The identified attacks include Arp Spoofing, BotNet DDoS, HTTP Flood, ICMP Flood, MQTT Flood, Port Scanning, TCP Flood, and UDP Flood. Among the data set, there are 27,458 instances of normal traffic and 1,282,429 instances of aggregated attack traffic.

Zip Folder:

The zip folder is structured into two main directories: Captures and Dataset. The Captures directory is organized by the month of capture and further categorized by network traffic type. The Datasets directory includes the Farm-Flow Dataset, alongside four additional datasets that have undergone pre-processing: the training and testing datasets for binary classification, and the training and testing datasets for multiclass classification. Additionally, there are further datasets categorized by month and type of network traffic.

 Article Information:

The work involved in developing the Farm-Flow dataset is described in the following paper. Please cite the paper and the dataset when using the Farm-Flow dataset.



Rafael Ferreira, Ivo Bispo, Carlos Rabadão, Leonel Santos, and Rogério Luís de C. Costa (2025). Farm-flow dataset: Intrusion detection in smart agriculture based on network flows, Computers and Electrical Engineering, Volume 121, 109892, DOI:  10.1016/j.compeleceng.2024.109892
DOI: 10.5281/zenodo.10964648
title: Farm-Flow | AG-IoT Security: Intrusion Detection in Smart Agriculture Dataset

description: Introduction:

The "Farm-Flow" dataset was created to emulate real-world Agricultural Internet of Things (AG-IoT) systems, encompassing network attacks and data collection. Following comprehensive cleaning and processing, the "Farm-Flow" dataset comprises 532 MB of data with 1,310,000 instances, structured around "flows," which represent consecutive series of packets transmitted from a single source to a specific destination. The dataset demonstrates an intrusion detection accuracy of 92.67% and is intended to enhance the security of AG-IoT systems, safeguarding information such as crop health, weather patterns, and soil conditions

Captures:

The captures comprises three months of network traffic: August, September, and October of 2022. Each month is divided into folders, which categorize the network traffic. These folders contain numerous .pcap files, which have been divided into 5-second intervals. This segmentation is necessary because, as previously mentioned, flows aggregate packets, resulting in only one row of flow data for ongoing connections. To address this, a script was developed to segment the .pcap files into 5-second increments. This approach allows for the generation of multiple rows of flow connections, thereby providing more quantity of data for model training.

Dataset:

The dataset comprises 532 MB of data, encompassing 1,310,000 instances. These instances have been classified into eight distinct attack types and one category for normal traffic. The identified attacks include Arp Spoofing, BotNet DDoS, HTTP Flood, ICMP Flood, MQTT Flood, Port Scanning, TCP Flood, and UDP Flood. Among the data set, there are 27,458 instances of normal traffic and 1,282,429 instances of aggregated attack traffic.

Zip Folder:

The zip folder is structured into two main directories: Captures and Dataset. The Captures directory is organized by the month of capture and further categorized by network traffic type. The Datasets directory includes the Farm-Flow Dataset, alongside four additional datasets that have undergone pre-processing: the training and testing datasets for binary classification, and the training and testing datasets for multiclass classification. Additionally, there are further datasets categorized by month and type of network traffic.

 Article Information:

The work involved in developing the Farm-Flow dataset is described in the following paper. Please cite the paper and the dataset when using the Farm-Flow dataset.



Rafael Ferreira, Ivo Bispo, Carlos Rabadão, Leonel Santos, and Rogério Luís de C. Costa (2025). Farm-flow dataset: Intrusion detection in smart agriculture based on network flows, Computers and Electrical Engineering, Volume 121, 109892, DOI:  10.1016/j.compeleceng.2024.109892
DOI: 10.6084/m9.figshare.27895527
title: Vector sequences

description: The sequences provided are of the combinatorial vector and the dox inducible vector.
DOI: 10.5281/zenodo.14238547
title: Enhanced Alzheimer's Brain Scan Dataset: Normal and Synthesized

description: Enhanced Alzheimer's Brain Scan Dataset: Normal and Synthesized












Unveiling the Invisible: A Novel Approach to Alzheimer's Image Analysis

Dataset Overview

This unique dataset comprises two distinct sets of brain scan images related to Alzheimer's disease:



Normal Dataset: 2000 carefully selected images for each stage of Alzheimer's progression.

Synthesized Dataset: Corresponding enhanced images using our innovative pseudo-RGB transformation technique.


Total Images: 16,000 (8,000 normal + 8,000 synthesized)

Why This Dataset Matters



Enhanced Feature Visibility: The synthesized images amplify subtle brain structures that may be indicative of Alzheimer's progression.

Multi-Perspective Analysis: By providing both normal and synthesized versions, this dataset enables researchers to compare and leverage both representations.

Balanced Categories: With equal representation across Alzheimer's stages, this dataset supports unbiased model training.

Novel Research Opportunities: The unique pseudo-RGB transformation opens doors for innovative approaches in medical image analysis.


Potential Applications



Training more accurate deep learning models for Alzheimer's detection

Comparative studies between traditional and enhanced image analysis techniques

Exploration of feature importance in Alzheimer's diagnosis

Development of new visualization tools for medical professionals
DOI: 10.6084/m9.figshare.27895485
title: Uveal Melanoma Cell line Analysis: SNVs, Indels and other variant calls. Circos plots of each genome.

description: Genomic DNA was extracted from cell lines using the Gentra Puregene Core Kit A (Qiagen) and library construction was performed using the NEBNext NEB Ultra II custom kit (New England Biolabs) according to the manufacturer’s instructions. Genomes were sequenced such that on average 79% of the genome of each cell line was covered by &gt;30x coverage, with the remaining 20% covered by more than 10x sequence depth. Sequencing was performed by Illumina Hi Seq X Paired End Sequencing. SAMtools v1.10 was used to convert BAM files to FASTQ files. QC was performed using FastQC v0.11.8. Reads were aligned to GRCh38 using BWA mem v0.7.17. GATK v4.4.0.0 was used for germline short variant calling (SNVs, indels) with the HaplotypeCaller because these cell lines are clonal, not tumors. Germline variants were removed with bcftools using gnomAD data excluding all variants with an allele frequency &gt;0.01. Identification of structural variants was performed using GRIDSS. Circos plots were generated in R v4.1.1 with StructuralVariantAnnotation v1.10.1, VariantAnnotation v1.40.0 and circlize v0.4.15 packages. Copy number calling was performed using CNVKit Version 0.9.8.
DOI: 10.6084/m9.figshare.27895485.v1
title: Uveal Melanoma Cell line Analysis: SNVs, Indels and other variant calls. Circos plots of each genome.

description: Genomic DNA was extracted from cell lines using the Gentra Puregene Core Kit A (Qiagen) and library construction was performed using the NEBNext NEB Ultra II custom kit (New England Biolabs) according to the manufacturer’s instructions. Genomes were sequenced such that on average 79% of the genome of each cell line was covered by &gt;30x coverage, with the remaining 20% covered by more than 10x sequence depth. Sequencing was performed by Illumina Hi Seq X Paired End Sequencing. SAMtools v1.10 was used to convert BAM files to FASTQ files. QC was performed using FastQC v0.11.8. Reads were aligned to GRCh38 using BWA mem v0.7.17. GATK v4.4.0.0 was used for germline short variant calling (SNVs, indels) with the HaplotypeCaller because these cell lines are clonal, not tumors. Germline variants were removed with bcftools using gnomAD data excluding all variants with an allele frequency &gt;0.01. Identification of structural variants was performed using GRIDSS. Circos plots were generated in R v4.1.1 with StructuralVariantAnnotation v1.10.1, VariantAnnotation v1.40.0 and circlize v0.4.15 packages. Copy number calling was performed using CNVKit Version 0.9.8.
DOI: 10.6084/m9.figshare.27895482
title: CNVKit calls on the uveal melanoma cell lines

description: Copy number calling was performed using CNVKit Version 0.9.8 on each of the uveal melanoma cell lines: 92.1Mel202Mel270Mel285MP38MP41MP46OMM1OMM2.3OMM2.5
DOI: 10.7266/tx087c8w
title: Vegetation thresholds for sediment transport at Cubit's Gap from 2018-03-07 to 2019-08-21
DOI: 10.15468/dl.y3hjn2
title: Occurrence Download

description: A dataset containing 3904 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material citation, Human Observation)",
 "Geometry POLYGON((-16.19959 34.73745,46.85064 38.63072,48.01947 57.78245,48.01947 79.25519,36.33116 80.86568,12.95454 81.91429,-28.5913 65.78573,-23.94441 50.55115,-16.19959 34.73745))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Geodetic datum invalid, Geodetic datum assumed WGS84, Coordinate rounded, Coordinate reprojected, Continent derived from coordinates)",
 "TaxonKey is Sphagnum lindbergii Schimp."
 ]
}

 The dataset includes 3904 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027243-241126133413365/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.27895479
title: Uveal Melanoma Cell Line Genome and RNA-Seq QC and Genome Analysis Methods

description: This folder contains QC for the genome sequences of the uveal melanoma cell lines and also the QC on each line.
DOI: 10.6084/m9.figshare.27895479.v1
title: Uveal Melanoma Cell Line Genome and RNA-Seq QC and Genome Analysis Methods

description: This folder contains QC for the genome sequences of the uveal melanoma cell lines and also the QC on each line.
DOI: 10.15468/dl.9rpphk
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-140.49413 60.79829,-136.68664 59.11972,-123.79032 61.45334,-138.24239 66.16152,-140.49413 60.79829))",
 "OccurrenceStatus is Present",
 "TaxonKey is Agrypnia improba (Hagen, 1873)"
 ]
}

 The dataset includes 71 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027244-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j9js9k
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-88.50113 47.05885,-88.75234 46.30322,-88.56876 45.62493,-87.39004 45.56408,-86.75237 45.90128,-86.70406 46.84781,-88.50113 47.05885))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Canis lupus Linnaeus, 1758"
 ]
}

 The dataset includes 70 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027245-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ycgfhf2fwb.1
title: Questionnaire raw data Rudi.csv

description: This research data were collected through 134 questionnaires from 67 hotels, each with 2 respondents (supervisor and staff). The data was analyzed with the aim of helping to analyze the organizational data to provide an overview of the conditions of the organization in each hotel. Therefore, merging the data from supervisors and staff can help simplify the analysis. 
The research variable are:
X: organizational learning 
Y1: innovation Capability
Y2: Business Resilience
DOI: 10.17632/wfty5v2f4w.1
title: flooddata

description: geospatial data
DOI: 10.6084/m9.figshare.25343671.v2
title: Dataset for: Evidence of sensory error threshold in triggering locomotor adaptations in humans

description: <b>Task:</b> 23 healthy adults (25.0 ± 6.2 y.o., 12 males, 11 females) with no known neurological disease or persistent musculoskeletal injury were divided into two groups: (i) Group 1 (N=11) to perform the walking task with only a kinematic constraint, and (ii) Group 2 (N=12) to perform the walking task with both a kinematic constraint and asymmetric limb loading. The participants walked on the instrumented treadmill with ground reaction force sensors (Bertec, Columbus, OH) with each session consisting of three trial types: control, adaptation, and washout. The control trials documented baseline gait pattern for five minutes of walking at 1 m/s. During the asymmetric trials, participants walked with the device on the right leg constraining the stride length of the left limb. Participants were instructed to not touch the device with the left leg while walking for ten minutes. Specifically for Group 2, participants were explicitly instructed to unload their constrained leg, and verbal feedback was given during the task execution when the unloading was not evident from the real-time vertical component of ground reaction force. The washout period allowed the observation of aftereffects after the removal of the constraints.<b>Dataset: </b>The dataset consists of two analyses: (i) comparing the cumulative sum of ground reaction forces (GRFs) for each leg during the stance phase between Groups 1 and 2, and (ii) comparing the asymmetry index between Groups 1 and 2. For the first analysis, the data can be found in the file <i>nTable_CS.csv</i>, see below for more details. For the second analysis, the data can be found in the file <i>nTable_AI.csv</i>, see below for more details. To calculate the asymmetry index (AI), use the following equation: AI = (nDS_Left_Leading_s - nDS_Left_Trailing_s) / (nDS_Left_Leading_s + nDS_Left_Trailing_s).<br><i>nTable_CS.csv</i>Columns:idGroup -- group identifier (either 1 or 2)sSession -- session identifier (S + #)sCondition -- condition identifier (either control, asymL (adaptation), or washout)sLeg -- leg identifier (either L (left) or R (right)nCumulSum -- cumulative sum of GRFs during the stance phase, normalized to the participant's weight and the number of steps in each condition<br><i>nTable_AI.csv</i>Columns:idGroup -- group identifier (either 1 or 2)sSession -- session identifier (S + #)sCondition -- condition identifier (either Control, Adapt (i.e., adaptation), or Post (i.e., washout))nDS_Left_Leading_s -- left leading double stance (i.e., the phase of the gait cycle where both leg are in contact with the ground and the left leg is in the front) durations in secondsnDS_Left_Trailing_s -- left trailing double stance (i.e., the phase of the gait cycle where both leg are in contact with the ground and the left leg is in the back) durations in seconds<br>
DOI: 10.15468/dl.5a3fsx
title: Occurrence Download

description: A dataset containing 191 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Atelopus mindoensis Peters, 1973"
 ]
}

 The dataset includes 191 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027250-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.