cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/j64btzdsdy.2
title: A dataset of labelled device Wi-Fi probe requests for MAC address de-randomization - 2021

description: A Wi-Fi client device can perform a passive scan to detect wireless networks within its radio range, looking for Beacon Frames, i.e., packets issued by the Access Points (APs) to signal their presence. Alternatively, the client can speed up this process by actively searching for a network connection; to this, it transmits Probe Requests messages periodically, which are management frames of the IEEE standard 802.11. The process by which these messages are captured is called sniffing. Sniffing can be performed via a Wi-Fi interface set in monitor mode and tuned to the same channel (or an adjacent channel) where the transmission occurred. Management messages are not encrypted, so they can be used to implement device counting algorithms based on MAC addresses analysis. However, major operating system producers, in order to avoid tracking the device owners, developed functionalities for MAC address randomisation. Devices that change their physical address periodically and randomly, challenge counting algorithms that must then perform an additional address de-randomization, i.e., cluster the probe requests according to the source device by analysing appropriate message features. To the best of our knowledge, our dataset is the only one available with labelled (indication of the emitting source) Wi-Fi probe requests. To obtain the labels, the data has been collected either in an isolated environment (the anechoic chamber of our department) or in a "noisy" environment (a chamber without particular shielding, but with no other sources of probe requests within a radius of two meters). The first type of data is published after removing only packets originating from the Raspberry Pi embedded interface MAC address; the second type of data has been additionally filtered to simulate the anechoic chamber environment. Each capture file has a duration of 20 minutes and considers three non-overlapping channels (channels 1, 6, and 11) simultaneously. The dataset contains Probe Requests from 22 different devices, each observed separately in 6 different modes, including settings based on display status, Wi-Fi connection, and power saving. We collected 315 non-empty files in total. Captures that were empty after filtering were removed. The device used for the capture is a Raspberry Pi with three Wi-Fi dongle interfaces installed, each used to collect data from a channel. The main characteristic of the dataset is its subdivision by device, which enables a more accurate behavior analysis of individual devices in different modes. Moreover, it is possible to use the labelled data to train Machine Learning algorithms or to verify the correct functioning of algorithms that have as their objective the counting of devices through probe request analysis in the presence of random MAC addresses.

Note: In version 2, all device directories have been moved inside the folder "Individual devices" and renamed. Moreover, we added the link to a new database published in 2024.
DOI: 10.15468/dl.wrue7r
title: Occurrence Download

description: A dataset containing 628 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Austropuccinia psidii",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 628 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040033-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qcxkd9
title: Occurrence Download

description: A dataset containing 78 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Ficus ovata",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 78 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040034-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/g7nfwm5c83
title: original date

description: Original Date
DOI: 10.17632/g7nfwm5c83.1
title: original date

description: Original Date
DOI: 10.15468/dl.5x7e4g
title: Occurrence Download

description: A dataset containing 366 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helianthus tuberosus",
 "Country is Luxembourg",
 "HasCoordinate is true"
 ]
}

 The dataset includes 366 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040037-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kvj6vrd6rf
title: Individual vibrato parameters from high choral singers blending to a pre-recorded singer

description: This data set contains the descriptive statistics (mean and standard deviation) values of Length, Cycles, Rate, Extent, Jitter and Shimmer - which are the vibrato parameters measured from 23 choral singers under different singing conditions: Solo Singing and Blending Singing (Unaltered, Low Rate, High Rate, Low Extent, and High Extent).
DOI: 10.15468/dl.tmtxvd
title: Occurrence Download

description: A dataset containing 32565 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Antirrhinum majus"
 ]
}

 The dataset includes 32565 records from 581 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040032-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kvj6vrd6rf.1
title: Individual vibrato parameters from high choral singers blending to a pre-recorded singer

description: This data set contains the descriptive statistics (mean and standard deviation) values of Length, Cycles, Rate, Extent, Jitter and Shimmer - which are the vibrato parameters measured from 23 choral singers under different singing conditions: Solo Singing and Blending Singing (Unaltered, Low Rate, High Rate, Low Extent, and High Extent).
DOI: 10.25345/c5br8mv3b
title: MassIVE MSV000098633 - Lysosomal reduced thiols are essential for mouse embryonic development
title: Chemoproteomic profiling of Mfsd12 wild-type and Mfsd12 heterozygous knockout embryos harvested at E12.0-12.0.
DOI: 10.15468/dl.323ne4
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cyathea cooperi",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040038-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57761/gj4y-6664
title: Item Response Warehouse

description: itemresponsewarehouse@stanford.edu
description: The Item Response Warehouse (IRW) is a collection of open, harmonized item response datasets.

- Open: The data are licensed for reformatting and public reuse. Their provenance is documented and code used to transform original data to meet the IRW data standard is available.
  
- Harmonized: The data are formatted to a common standard. The goal of the IRW is to make large quantities of data available for standardized analysis given the common formatting. 
  
 - Item Response: The IRW is comprised of cross-classified item responses. The key element required of data for inclusion in the IRW is that the data need to report individual responses that are cross-classified by the focus of measurement (typically a person) and the measurement tool (typically an item).
  
These datasets in IRW span a wide range of measures; examples include measures of ability in education settings, measures of partisanship based on voting records, and personality measures based on survey questions.  Responses can be scored in a variety of ways (dichotomous, polytomous, continuous) and may contain additional data (e.g., response time).
description: Tables have been harmonized as per details given [here](<https://datapages.github.io/irw/standard.html>).


description: Please find information about data licenses and citation info [here](<https://datapages.github.io/irw/docs.html>).
DOI: 10.15468/dl.xzbpq7
title: Occurrence Download

description: A dataset containing 12596 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Echinacea purpurea"
 ]
}

 The dataset includes 12596 records from 284 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040036-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57783/28qg-7q09
title: IPUMS-1880
DOI: 10.57783/zgf8-rm56
title: IPUMS-1880
DOI: 10.57783/fjk2-z694
title: IPUMS-1940
DOI: 10.57761/yr14-t248
title: Item Response Warehouse 2
DOI: 10.15468/dl.brhwhh
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Notarius bonillai)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 44 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040039-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wpcyvu
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-73.02118 42.79696,-72.33222 42.80655,-71.60892 44.95788,-72.84926 45.01305,-73.50835 44.99219,-73.34042 42.8046,-73.02118 42.79696))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Clemmys guttata"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040040-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/yr7jxm7fzv
title: gesture recognition

description: A sign language gesture sensor data, including sEMG and IMU.
DOI: 10.17632/yr7jxm7fzv.1
title: gesture recognition

description: A sign language gesture sensor data, including sEMG and IMU.
DOI: 10.5281/zenodo.15513852
title: ERA Ontology

description: This is an ontology defined by the European Union Agency for Railways (ERA) to describe the concepts and relationships related to the European railway infrastructure and the vehicles authorized to operate over it.
DOI: 10.15468/dl.fsju6y
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helianthus tuberosus",
 "Country is Montenegro",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040041-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8pnxdfv7w7
title: TBI-6000 : A Labeled Brain CT Dataset for 6 different types of Haemorrhage Classification

description: TBI-6000 is a curated dataset consisting of 6,000 unique and preprocessed brain CT images, specifically developed to support machine learning research in the automated classification of traumatic brain injury (TBI) and intracranial haemorrhage. The dataset is systematically organized into six balanced subdirectories, each representing a distinct diagnostic category: Epidural Haemorrhage, Subdural Haemorrhage, Subarachnoid Haemorrhage, Intraventricular Haemorrhage, Intraparenchymal Haemorrhage, and No Haemorrhage (Control Class). Each folder contains exactly 1,000 labeled CT slices, and all images are unique within and across folders, ensuring a balanced and non-redundant distribution that enhances the reliability and fairness of model training and evaluation. To maintain data integrity and facilitate seamless integration into AI pipelines, all images have been renamed using a standardized filename convention that encodes each image’s diagnostic class as a prefix (e.g., epidural_, no_hemorrhage_) followed by a unique numeric identifier. This consistent naming scheme promotes reproducibility, simplifies data parsing, and supports efficient downstream processing. TBI-6000 serves as a high-quality resource for the development and benchmarking of deep learning models targeting intracranial haemorrhage detection and classification in the context of traumatic brain injuries.
DOI: 10.17632/8pnxdfv7w7.2
title: TBI-6000 : A Labeled Brain CT Dataset for 6 different types of Haemorrhage Classification

description: TBI-6000 is a curated dataset consisting of 6,000 unique and preprocessed brain CT images, specifically developed to support machine learning research in the automated classification of traumatic brain injury (TBI) and intracranial haemorrhage. The dataset is systematically organized into six balanced subdirectories, each representing a distinct diagnostic category: Epidural Haemorrhage, Subdural Haemorrhage, Subarachnoid Haemorrhage, Intraventricular Haemorrhage, Intraparenchymal Haemorrhage, and No Haemorrhage (Control Class). Each folder contains exactly 1,000 labeled CT slices, and all images are unique within and across folders, ensuring a balanced and non-redundant distribution that enhances the reliability and fairness of model training and evaluation. To maintain data integrity and facilitate seamless integration into AI pipelines, all images have been renamed using a standardized filename convention that encodes each image’s diagnostic class as a prefix (e.g., epidural_, no_hemorrhage_) followed by a unique numeric identifier. This consistent naming scheme promotes reproducibility, simplifies data parsing, and supports efficient downstream processing. TBI-6000 serves as a high-quality resource for the development and benchmarking of deep learning models targeting intracranial haemorrhage detection and classification in the context of traumatic brain injuries.
DOI: 10.15468/dl.fand3c
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sinogastromyzon nanpanjiangensis)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040042-250717081556266/datasets/export for details.
DOI: 10.15468/dl.326sz3
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tagetes erecta",
 "Country is Albania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040043-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gszh55gkc2
title: ER-YOLO

description: In response to the challenges faced by existing object detection algorithms in the application to armored vehicle pose detection, including the inability to effectively utilize edge information and the difficulty in processing multi-scale feature inputs, this paper presents a novel Edge-Refined YOLO (ER-YOLO). To address the issues above, this paper introduces the ER-YOLO framework. First, we designed the Multi-Scale Edge Info Generator (MSEIG) and Conv Edge Fusion (CEF) modules. The MSEIG extracts multi-scale edge feature information from images at shallow layers of the backbone network. Then the CEF module fuses the edge information with deep feature information. Next, we designed the Efficient Parallel Deep Convolution (EPD Conv) to reconstruct the backbone network. This is achieved by decomposing large-kernel depth convolutions into four parallel branches, thereby expanding the receptive field of the network and optimizing the feature extraction capability for multi-scale inputs while maintaining detection performance. Finally, we proposed the Adaptive Region-Edge Awareness Model (AREAM) attention module for the lower layers of the backbone network. This module dynamically filters image regions based on edge and complementary information transmitted from the previous layers, achieving a content-aware sparse attention mechanism.
DOI: 10.17632/gszh55gkc2.1
title: ER-YOLO

description: In response to the challenges faced by existing object detection algorithms in the application to armored vehicle pose detection, including the inability to effectively utilize edge information and the difficulty in processing multi-scale feature inputs, this paper presents a novel Edge-Refined YOLO (ER-YOLO). To address the issues above, this paper introduces the ER-YOLO framework. First, we designed the Multi-Scale Edge Info Generator (MSEIG) and Conv Edge Fusion (CEF) modules. The MSEIG extracts multi-scale edge feature information from images at shallow layers of the backbone network. Then the CEF module fuses the edge information with deep feature information. Next, we designed the Efficient Parallel Deep Convolution (EPD Conv) to reconstruct the backbone network. This is achieved by decomposing large-kernel depth convolutions into four parallel branches, thereby expanding the receptive field of the network and optimizing the feature extraction capability for multi-scale inputs while maintaining detection performance. Finally, we proposed the Adaptive Region-Edge Awareness Model (AREAM) attention module for the lower layers of the backbone network. This module dynamically filters image regions based on edge and complementary information transmitted from the previous layers, achieving a content-aware sparse attention mechanism.
DOI: 10.15468/dl.5ztush
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tagetes erecta",
 "Country is Armenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 13 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040044-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/c4468s4j4h
title: Data from: Increased vigilance to neighbor duets broadcast from unexpected territorial boundaries in rufous horneros

description: Data and R codes for reproducing the statistical analyses from the article:
Amorim, P. S., & Diniz, P. (2025). Increased vigilance to neighbor duets broadcast from unexpected territorial boundaries in rufous horneros. Behavioural Processes, 105237. https://doi.org/10.1016/j.beproc.2025.105237
DOI: 10.17632/c4468s4j4h.1
title: Data from: Increased vigilance to neighbor duets broadcast from unexpected territorial boundaries in rufous horneros

description: Data and R codes for reproducing the statistical analyses from the article:
Amorim, P. S., & Diniz, P. (2025). Increased vigilance to neighbor duets broadcast from unexpected territorial boundaries in rufous horneros. Behavioural Processes, 105237. https://doi.org/10.1016/j.beproc.2025.105237
DOI: 10.15468/dl.hsyheh
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Labeo altivelis)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 45 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040045-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7nzfkw23tf
title: Low melting temperature bismuth-doped tin phosphate glass

description: Data from "Low melting temperature bismuth-doped tin phosphate glass"
Abstract: We fabricated Bi-doped tin phosphate glasses using SnO, phosphoric acid, and Bi2O3 at melting temperatures between 550 °C and 1100 °C. In undoped glasses, the Tauc bandgap increases with temperature, attributed to more bridging oxygens in PO4 units. Doping with 0.2 mol% Bi2O3 introduces absorption bands at 360 nm and 540 nm and darkens the glass due to phase separation and SnO/SnO2 crystallite formation. Under green excitation, a broad NIR photoluminescence band centered at 1180 nm with dual lifetime components (~10 µs and ~100 µs) is observed. The 550 °C glass sets a new low-temperature threshold for Bi activation in oxide glasses.
DOI: 10.17632/7nzfkw23tf.1
title: Low melting temperature bismuth-doped tin phosphate glass

description: Data from "Low melting temperature bismuth-doped tin phosphate glass"
Abstract: We fabricated Bi-doped tin phosphate glasses using SnO, phosphoric acid, and Bi2O3 at melting temperatures between 550 °C and 1100 °C. In undoped glasses, the Tauc bandgap increases with temperature, attributed to more bridging oxygens in PO4 units. Doping with 0.2 mol% Bi2O3 introduces absorption bands at 360 nm and 540 nm and darkens the glass due to phase separation and SnO/SnO2 crystallite formation. Under green excitation, a broad NIR photoluminescence band centered at 1180 nm with dual lifetime components (~10 µs and ~100 µs) is observed. The 550 °C glass sets a new low-temperature threshold for Bi activation in oxide glasses.
DOI: 10.15468/dl.527yhv
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Ficus pachyclada",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 118 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040046-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.