cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.q2djvr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((31.8862 -21.3285,30.5701 -20.3186,30.5701 -24.0715,35.5909 -24.0715,35.6007 -24.0157,35.6007 -23.8125,35.4969 -23.7035,35.6007 -23.165,35.6007 -22.9702,35.227 -22.2482,34.6862 -21.6944,34.013 -21.3095,33.5 -21.1593,32.9675 -21.1092,32.4352 -21.1612,31.8862 -21.3285))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045441-250525065834625/datasets/export for details.
DOI: 10.15468/dl.vs7vyx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.5808 -24.13,35.5909 -24.0715,30.5701 -24.0715,30.5701 -28.5786,32.4847 -28.5786,32.55 -28.5222,33.0346 -26.8668,32.9741 -26.7838,33.0415 -26.059,32.9304 -26.0011,32.7994 -26.1281,32.6834 -26.0015,32.813 -25.8728,32.8513 -25.7166,33.3048 -25.3731,35.1489 -24.6699,35.5808 -24.13))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045440-250525065834625/datasets/export for details.
DOI: 10.15468/dl.9emf7j
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.6.4_1",
 "TaxonKey is Suncus murinus (Linnaeus, 1766)"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045442-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vv99kg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((31.8862 -21.3285,30.5701 -20.3186,30.5701 -24.0715,35.5909 -24.0715,35.6007 -24.0157,35.6007 -23.8125,35.4969 -23.7035,35.6007 -23.165,35.6007 -22.9702,35.227 -22.2482,34.6862 -21.6944,34.013 -21.3095,33.5 -21.1593,32.9675 -21.1092,32.4352 -21.1612,31.8862 -21.3285))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045443-250525065834625/datasets/export for details.
DOI: 10.15468/dl.kccsey
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1586 -33.5889,28.2048 -32.9007,25.5395 -32.9007,25.5395 -34.1098,25.6468 -34.1222,25.7787 -34.0734,25.7098 -33.9109,25.8639 -33.797,26.4806 -33.8473,27.1586 -33.5889))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045444-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15661392
title: logistics anomaly detection dataset

description: This dataset captures real-time operational, environmental, and behavioral data from a fleet of connected vehicles and UAVs involved in logistics, supply chain, and autonomous delivery services. Designed to support advanced research in anomaly detection, sensor fault prediction, and driver behavior analysis, the dataset reflects realistic conditions observed in decentralized and distributed logistics environments.

🧾 Data Overview

The dataset contains over 60,000 timestamped records collected from multiple edge nodes (vehicles, drones, and IoT-enabled transport units). Each record encapsulates multi-dimensional readings ranging from vehicular dynamics and cargo conditions to network latency and access control status. These data points enable the modeling of context-aware anomalies in logistics systems.

⚙️ Feature Groups

The dataset includes more than 55 features, which can be grouped into the following categories:

1. Vehicle and Driving Dynamics





Vehicle_ID, Speed, RPM, Gear_Position, Brake_Status, Steering_Angle, Odometer




Accel_X, Accel_Y, Accel_Z, Harsh_Braking, Harsh_Acceleration, Drowsy, Seat_Belt



2. Geospatial and Route Metrics





Latitude, Longitude, Altitude, Route_Deviation, Distance_Traveled, ETA, Arrival_Delay



3. Environmental Conditions





Ambient_Temperature, Humidity, Road_Type, Traffic_Density, Weather_Condition



4. Cargo Telemetry





Cargo_ID, Cargo_Type, Cargo_Weight, Cargo_Temp, Shock_Events, Vibration_Level, Seal_Status



5. Driver and Identity Attributes





Driver_ID, Phone_Use, Access_Code_Used, Access_Time, Access_Location, Auth_Status, Unauthorized_Access



6. Sensor and Communication Data





Sensor_ID, Sensor_Type, Sensor_Status, Latency, Packet_Loss, System_Uptime, Firmware_Version



7. Maintenance Records





Last_Maintenance, Next_Maintenance, Maintenance_Type, Component_Replaced, Breakdown_History, Repair_Duration



8. Target Label





Anomaly_Status: Binary flag (0 = normal, 1 = anomaly), indicating whether an anomaly was detected based on combined system-level criteria (e.g., unexpected shocks, route deviations, latency spikes, unauthorized access, etc.).
DOI: 10.5281/zenodo.15661393
title: logistics anomaly detection dataset

description: This dataset captures real-time operational, environmental, and behavioral data from a fleet of connected vehicles and UAVs involved in logistics, supply chain, and autonomous delivery services. Designed to support advanced research in anomaly detection, sensor fault prediction, and driver behavior analysis, the dataset reflects realistic conditions observed in decentralized and distributed logistics environments.

🧾 Data Overview

The dataset contains over 60,000 timestamped records collected from multiple edge nodes (vehicles, drones, and IoT-enabled transport units). Each record encapsulates multi-dimensional readings ranging from vehicular dynamics and cargo conditions to network latency and access control status. These data points enable the modeling of context-aware anomalies in logistics systems.

⚙️ Feature Groups

The dataset includes more than 55 features, which can be grouped into the following categories:

1. Vehicle and Driving Dynamics





Vehicle_ID, Speed, RPM, Gear_Position, Brake_Status, Steering_Angle, Odometer




Accel_X, Accel_Y, Accel_Z, Harsh_Braking, Harsh_Acceleration, Drowsy, Seat_Belt



2. Geospatial and Route Metrics





Latitude, Longitude, Altitude, Route_Deviation, Distance_Traveled, ETA, Arrival_Delay



3. Environmental Conditions





Ambient_Temperature, Humidity, Road_Type, Traffic_Density, Weather_Condition



4. Cargo Telemetry





Cargo_ID, Cargo_Type, Cargo_Weight, Cargo_Temp, Shock_Events, Vibration_Level, Seal_Status



5. Driver and Identity Attributes





Driver_ID, Phone_Use, Access_Code_Used, Access_Time, Access_Location, Auth_Status, Unauthorized_Access



6. Sensor and Communication Data





Sensor_ID, Sensor_Type, Sensor_Status, Latency, Packet_Loss, System_Uptime, Firmware_Version



7. Maintenance Records





Last_Maintenance, Next_Maintenance, Maintenance_Type, Component_Replaced, Breakdown_History, Repair_Duration



8. Target Label





Anomaly_Status: Binary flag (0 = normal, 1 = anomaly), indicating whether an anomaly was detected based on combined system-level criteria (e.g., unexpected shocks, route deviations, latency spikes, unauthorized access, etc.).
DOI: 10.6084/m9.figshare.27367188
title: Transcriptome sequencing of Pseudogymnoascus destructans treated with ketone compounds

description: Compared with the control group, gene expression of <i>Pseudogymnoascus destructans</i> changed after treatment with 2-nonanone and 2-undecanone, respectively. Each group had four biological replicates.
DOI: 10.6084/m9.figshare.27367188.v2
title: Transcriptome sequencing of Pseudogymnoascus destructans treated with ketone compounds

description: Compared with the control group, gene expression of <i>Pseudogymnoascus destructans</i> changed after treatment with 2-nonanone and 2-undecanone, respectively. Each group had four biological replicates.
DOI: 10.6084/m9.figshare.27367188.v1
title: 酮体处理的 Pd 的转录组测序

description: 与对照组相比，分别用 Pd 处理 2-壬酮和 2-十一酮后，Pd 的基因表达发生改变。每组 4 次生物学重复。
DOI: 10.21227/16xb-3y81
title: "PII Sharing Mobile Apps Dataset (Synthetic) for Categories"

description: "Each data set is derived in .csv format, consisting of 50 user records with 22 attributes in a structured dataset of dimensions 22X50. There are total of 4 categories (Dating, Communication, Shopping and Games) of such, each category having 10 sub-datasets."
DOI: 10.15468/dl.5neqmr
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((18.2339 -32.5266,18.1411 -32.657,18.1007 -32.7021,18.0224 -32.6366,17.8923 -32.6419,17.754 -32.8255,17.7723 -32.9007,20.509 -32.9007,20.509 -28.5786,16.2782 -28.5786,16.4358 -28.7202,16.726 -29.1235,17.1957 -30.3752,17.6655 -31.1743,18.1322 -31.7679,18.2495 -32.2638,18.2339 -32.5266))"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045446-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j6tfe6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1586 -33.5889,28.2048 -32.9007,25.5395 -32.9007,25.5395 -34.1098,25.6468 -34.1222,25.7787 -34.0734,25.7098 -33.9109,25.8639 -33.797,26.4806 -33.8473,27.1586 -33.5889))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045445-250525065834625/datasets/export for details.
DOI: 10.15468/dl.pawt48
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045448-250525065834625/datasets/export for details.
DOI: 10.15468/dl.afnfb9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((18.2339 -32.5266,18.1411 -32.657,18.1007 -32.7021,18.0224 -32.6366,17.8923 -32.6419,17.754 -32.8255,17.7723 -32.9007,20.509 -32.9007,20.509 -28.5786,16.2782 -28.5786,16.4358 -28.7202,16.726 -29.1235,17.1957 -30.3752,17.6655 -31.1743,18.1322 -31.7679,18.2495 -32.2638,18.2339 -32.5266))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045447-250525065834625/datasets/export for details.
DOI: 10.15468/dl.wejepb
title: Occurrence Download

description: A dataset containing 23814 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhamnus lycioides L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 23814 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045449-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t8ghxp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.4784 -23.4046,15.4933 -24.0715,20.509 -24.0715,20.509 -24.0455,20.0952 -23.5072,19.9968 -23.017,19.7843 -22.5405,19.1988 -21.8785,18.5972 -21.5447,17.8859 -21.3993,17.1626 -21.4805,16.5332 -21.7596,16.1257 -22.0882,15.8234 -22.4662,15.606 -22.8993,15.4784 -23.4046))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045451-250525065834625/datasets/export for details.
DOI: 10.15468/dl.5thavx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippodonta hungarica (Grunow) Lange-Bertalot, Metzeltin & Witkowski",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045450-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.29320312
title: Scoping review of communication impairment management in adults with brain tumours

description: There are limited clinical pathways on the management of communication impairment in people with primary brain tumour. This scoping review aimed to collate the evidence on management of communication impairment for people with primary brain tumours and determine if there is sufficient evidence to develop a clinical pathway. A systematic search strategy (September 2023) used Medline, CINAHL, PsychInfo, and The Cochrane Library. Eligibility criteria required the publication to be primary evidence, any study design, published in a peer reviewed journal in English, and investigating speech-language pathologist led assessment, rehabilitation, or education of communication impairment (aphasia, cognitive-communication impairment, dysarthria, apraxia of speech). A yield of 1500 articles were inputted into Research Screener software for title and abstract screening. Fourteen full-text articles assessed met eligibility criteria. Data were extracted on pre-, intra-, and post-operative assessment, rehabilitation, and education. Assessment of aphasia was most frequently investigated. Some common formal assessments were reported not sensitive enough to detect aphasia in this population. Limited information was reported on intra-operative assessment, post-operative assessment, and rehabilitation. No studies focussed on education. Evidence on communication management for people with primary brain tumours is limited and more research is needed to inform a clinical pathway.
DOI: 10.6084/m9.figshare.29320312.v1
title: Scoping review of communication impairment management in adults with brain tumours

description: There are limited clinical pathways on the management of communication impairment in people with primary brain tumour. This scoping review aimed to collate the evidence on management of communication impairment for people with primary brain tumours and determine if there is sufficient evidence to develop a clinical pathway. A systematic search strategy (September 2023) used Medline, CINAHL, PsychInfo, and The Cochrane Library. Eligibility criteria required the publication to be primary evidence, any study design, published in a peer reviewed journal in English, and investigating speech-language pathologist led assessment, rehabilitation, or education of communication impairment (aphasia, cognitive-communication impairment, dysarthria, apraxia of speech). A yield of 1500 articles were inputted into Research Screener software for title and abstract screening. Fourteen full-text articles assessed met eligibility criteria. Data were extracted on pre-, intra-, and post-operative assessment, rehabilitation, and education. Assessment of aphasia was most frequently investigated. Some common formal assessments were reported not sensitive enough to detect aphasia in this population. Limited information was reported on intra-operative assessment, post-operative assessment, and rehabilitation. No studies focussed on education. Evidence on communication management for people with primary brain tumours is limited and more research is needed to inform a clinical pathway.
DOI: 10.6084/m9.figshare.29318729
title: Data related to RScripts available at github repositoryhttps://github.com/SDmetagenomics/Kust_Cyano_2023

description: Data related to RScripts available at github repository https://github.com/SDmetagenomics/Kust_Cyano_2023
DOI: 10.6084/m9.figshare.29318729.v1
title: Data related to RScripts available at github repositoryhttps://github.com/SDmetagenomics/Kust_Cyano_2023

description: Data related to RScripts available at github repository https://github.com/SDmetagenomics/Kust_Cyano_2023
DOI: 10.15468/dl.kvsfm8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elegia grandispicata H.P.Linder",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -28.5786,20.509 -28.5786,20.509 -32.9007,25.5395 -32.9007,25.5395 -28.5786))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045453-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.28444583
title: SP-LCC — a dataset on the structure and properties of lignin-carbohydrate complexes

description: Database of structure and properties of 95 lignin-carbohydrate complex samples. The data is structured in the following way:

· Sample ID, biorefinery processing conditions, yield, lignin moieties identified from 2D HSQC NMR, and physiochemical properties for all samples are available in the CSV file named “SP-LCC_table.csv”. The file is structured as described in Table 1.

· Intensities of the NMR spectra are contained in the archive “NMR_spectra.zip”, which contains one file for the intensities per sample named “NMRspectrum_{sampleID}.csv”. The corresponding chemical shifts, at which these intensities were measured, are contained in the same archive NMR_spectra.zip in two files per sample, for the x-axis (F1 C axis) “xarr_{sampleID}.csv” and the y-axis (F2 H axis) “yarr_{sampleID}.csv”, where {sampleID} refers to the sample ID of the respective sample. The files are structured as described in Table 2.

· The CSV files containing the NMR data were generated from Bruker files, which we make available, too, in the “NMR_spectra_Bruker.zip” archive. In the archive, every subfolder is named by the sample ID of the respective sample.

· For full transparency, we also include python scripts to parse and plot the NMR spectra both from the Bruker files, with “parse_and_plot_Bruker_NMR_spectrum.py”, and from the .csv files with “plot_NMR_spectrum.py”. The dependencies for their use are described in the file “readme.txt”.

· Chromatograms are contained in the archives “RI_Chromatogram.zip” and “LS_Chromatogram.zip” with one file per sample named “RI_Chromatogram_{sampleID}.csv” the refractive index and “LS_Chromatogram_{sampleID}.csv” for light scattering chromatograms. For parallel measurements, “_2” has been added to the file name. The files are structured as described in Table 2. Additional details of the chromatogram data are described in section 2 of the Supplementary information.

· Thermograms are contained in the archives “TD_Thermogram.zip” and “Tg_Thermogram.zip” with one file per sample named “Tg_Thermogram_{sampleID}.csv” for thermograms obtained from DSC analyses and “TD_Thermogram_{sampleID}.csv” obtained from TGA analyses. For parallel measurements, “_2” has been added to the file name. The files are structured as described in Table 2. Additional details of the thermogram data are described in section 2 of the Supplementary information.