cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.d94uas
title: Occurrence Download

description: A dataset containing 436 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Puerto Rico",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhizophora mangle"
 ]
}

 The dataset includes 436 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034678-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fnxf6u
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.1188 23.39913, -107.9688 23.39913, -107.9688 23.54913, -108.1188 23.54913, -108.1188 23.39913))"
 ]
}

 The dataset includes 26 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034679-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rya8y6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heterotilapia buttikoferi",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034680-251025141854904/datasets/export for details.
DOI: 10.34740/kaggle/dsv/13738196
title: Egypt Gold Prices | Daily Updated
DOI: 10.15468/dl.p8k7ey
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-107.9688 23.39913, -107.8188 23.39913, -107.8188 23.54913, -107.9688 23.54913, -107.9688 23.39913))"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034681-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d68g47
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heterotis niloticus",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034682-251025141854904/datasets/export for details.
DOI: 10.15468/dl.725rhv
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-107.8188 23.39913, -107.6688 23.39913, -107.6688 23.54913, -107.8188 23.54913, -107.8188 23.39913))"
 ]
}

 The dataset includes 25 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034683-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hwv3j8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hiodon alosoides",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034684-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17614066
title: Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2018-04-27

description: This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2018-04-27. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/dl.gunqwk
title: Occurrence Download

description: A dataset containing 2851966707 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 2851966707 records from 57586 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034194-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9vtwdz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Senna siamea",
 "Country is Fiji",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024874-250811113504898/datasets/export for details.
DOI: 10.13140/rg.2.2.21030.38720
title: Stata module to unify unit root tests
DOI: 10.15468/dl.md5b47
title: Occurrence Download

description: A dataset containing 895301 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anthriscus sylvestris"
 ]
}

 The dataset includes 895301 records from 2670 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009351-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mytgqj
title: Occurrence Download

description: A dataset containing 22186 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-750.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Thuja plicata Donn ex D.Don, Thuja plicata Lamb.)"
 ]
}

 The dataset includes 22186 records from 133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001665-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15000525
title: Source Data for "Identification of Leukemia-Enriched Signature Through the Development of a Comprehensive Pediatric Single-Cell Atlas"

description: Source data files for each main and supplementary figure in the manuscript "Identification of Leukemia-Enriched Signature Through the Development of a Comprehensive Pediatric Single-Cell Atlas". Files are in comma-delimited format, saved in individual ".txt" files.
DOI: 10.5281/zenodo.15000526
title: Source Data for "Identification of Leukemia-Enriched Signature Through the Development of a Comprehensive Pediatric Single-Cell Atlas"

description: Source data files for each main and supplementary figure in the manuscript "Identification of Leukemia-Enriched Signature Through the Development of a Comprehensive Pediatric Single-Cell Atlas". Files are in comma-delimited format, saved in individual ".txt" files.
DOI: 10.15468/dl.9mfndt
title: Occurrence Download

description: A dataset containing 6430 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((72.52768 18.92563,73.22624 18.92563,73.22624 19.20505,72.52768 19.20505,72.52768 18.92563))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 1976-2025"
 ]
}

 The dataset includes 6430 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001664-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.88bnb3
title: Occurrence Download

description: A dataset containing 3732 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((72.61438 18.99953,73.09740 18.99953,73.09740 19.19274,72.61438 19.19274,72.61438 18.99953))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 1976-2025"
 ]
}

 The dataset includes 3732 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001666-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ssfnhsg23g
title: Data for: Bone-preserving hybrid onlay-Grammont implantation improves humeral stem stability in reverse shoulder arthroplasty: An explorative time-elapsed µCT-based cadaveric study

description: This dataset comprises micro–computed tomography (μCT) images of intact proximal humeral bones from two cadaveric specimens (large and small) analysed in our accompanying exploratory study on humeral stem stability in reverse shoulder arthroplasty. For each specimen, μCT images of both the left and right humeri are provided.
DOI: 10.17632/ssfnhsg23g.1
title: Data for: Bone-preserving hybrid onlay-Grammont implantation improves humeral stem stability in reverse shoulder arthroplasty: An explorative time-elapsed µCT-based cadaveric study

description: This dataset comprises micro–computed tomography (μCT) images of intact proximal humeral bones from two cadaveric specimens (large and small) analysed in our accompanying exploratory study on humeral stem stability in reverse shoulder arthroplasty. For each specimen, μCT images of both the left and right humeri are provided.
DOI: 10.17632/mtmdm78gws
title: Authors' Perception of Study Quality

description: This study surveyed authors' perceptions of study quality factors. A researcher-made questionnaire (Appendix A) was developed and data were collected (see Appendix B. Data Collection Procedure) to determine the factors using factor analysis. Then, a path analysis (Appendix D) was run. The raw data file is StudyQuality.sav and a figure was created to show the distribution (R code is presented in Appendix C) and QualityResearch.csv was used to create the figure.
DOI: 10.5281/zenodo.18432312
title: Evolutionary Game Theory Reveals Modular Specialization as a Universal Solution to Neural Trade-offs

description: What this is

This dataset accompanies the CRN manuscript and provides reproducible artifacts for the evolutionary game-theory (EGT) analysis showing that modular specialization emerges as an evolutionarily stable strategy (ESS) under physical trade-offs.

Core claim (summary):



A physical trade-off exists: noise optimal for transport (higher κ) conflicts with memory stability (lower κ).

Modular specialization yields a fitness advantage (typ. +6% to +31%) and takes over under replicator dynamics (example: 99.86% within 71 generations).

Robustness is tested across parameter grids and disappears when the trade-off is removed (causal control).
DOI: 10.17632/mtmdm78gws.1
title: Authors' Perception of Study Quality

description: This study surveyed authors' perceptions of study quality factors. A researcher-made questionnaire (Appendix A) was developed and data were collected (see Appendix B. Data Collection Procedure) to determine the factors using factor analysis. Then, a path analysis (Appendix D) was run. The raw data file is StudyQuality.sav and a figure was created to show the distribution (R code is presented in Appendix C) and QualityResearch.csv was used to create the figure.
DOI: 10.15468/dl.6pcwet
title: Occurrence Download

description: A dataset containing 141712 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycopodium",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 141712 records from 1181 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002100-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18379850
title: Evolutionary Game Theory Reveals Modular Specialization as a Universal Solution to Neural Trade-offs

description: What this is

This dataset accompanies the CRN manuscript and provides reproducible artifacts for the evolutionary game-theory (EGT) analysis showing that modular specialization emerges as an evolutionarily stable strategy (ESS) under physical trade-offs.

Core claim (summary):



A physical trade-off exists: noise optimal for transport (higher κ) conflicts with memory stability (lower κ).

Modular specialization yields a fitness advantage (typ. +6% to +31%) and takes over under replicator dynamics (example: 99.86% within 71 generations).

Robustness is tested across parameter grids and disappears when the trade-off is removed (causal control).
DOI: 10.17632/853sxvpx79
title: Processed_ADFA_LD dataset

description: The Processed_ADFA_LD dataset is a preprocessed version of the ADFA Linux Dataset (ADFA-LD), which was originally developed by the Australian Defence Force Academy (ADFA) for research in host-based intrusion detection systems (HIDS). The dataset focuses on detecting malicious behavior at the operating system level by analyzing system call traces generated by Linux processes.

Original ADFA-LD Dataset Overview

The original ADFA-LD dataset contains system call sequences collected from a Linux environment under two main conditions:

Normal behavior: Legitimate system activities generated by standard user operations.

Attack behavior: System call traces produced during various cyber-attacks, including privilege escalation, denial-of-service, and remote exploits.

Unlike older datasets (e.g., KDD Cup 99), ADFA-LD was designed to reflect modern Linux systems and more realistic attack scenarios.

Processing and Transformation

The Processed_ADFA_LD dataset refers to the cleaned and transformed version of the original dataset to make it suitable for machine learning and deep learning models. Common preprocessing steps include:

System call encoding (e.g., integer mapping or frequency-based encoding)

Sequence normalization or padding to handle variable-length traces

Feature extraction, such as:

n-grams of system calls

statistical features (frequency, entropy, transition probabilities)

Labeling, typically:

0 → Normal behavior

1 → Attack behavior

Train-test splitting for supervised learning experiments

Data Characteristics

Data type: Sequential / time-series data

Features: Encoded system call sequences or derived statistical features

Labels: Binary (Normal vs. Attack) or multiclass (depending on processing)

Domain: Cybersecurity, Host-Based Intrusion Detection

Operating System: Linux

Applications

The Processed_ADFA_LD dataset is widely used for:

Intrusion detection system (IDS) evaluation

Anomaly detection research

Benchmarking machine learning and deep learning models such as:

LSTM / GRU

CNN-based sequence models

Autoencoders

Traditional classifiers (SVM, Random Forest, k-NN)

Advantages

Reflects realistic modern attack behavior

Avoids outdated network-focused features

Well-suited for sequence-based learning models

Limitations

Host-specific (Linux-only)

Limited attack diversity compared to large-scale enterprise datasets

Requires careful preprocessing due to variable-length sequences
DOI: 10.17632/853sxvpx79.1
title: Processed_ADFA_LD dataset

description: The Processed_ADFA_LD dataset is a preprocessed version of the ADFA Linux Dataset (ADFA-LD), which was originally developed by the Australian Defence Force Academy (ADFA) for research in host-based intrusion detection systems (HIDS). The dataset focuses on detecting malicious behavior at the operating system level by analyzing system call traces generated by Linux processes.

Original ADFA-LD Dataset Overview

The original ADFA-LD dataset contains system call sequences collected from a Linux environment under two main conditions:

Normal behavior: Legitimate system activities generated by standard user operations.

Attack behavior: System call traces produced during various cyber-attacks, including privilege escalation, denial-of-service, and remote exploits.

Unlike older datasets (e.g., KDD Cup 99), ADFA-LD was designed to reflect modern Linux systems and more realistic attack scenarios.

Processing and Transformation

The Processed_ADFA_LD dataset refers to the cleaned and transformed version of the original dataset to make it suitable for machine learning and deep learning models. Common preprocessing steps include:

System call encoding (e.g., integer mapping or frequency-based encoding)

Sequence normalization or padding to handle variable-length traces

Feature extraction, such as:

n-grams of system calls

statistical features (frequency, entropy, transition probabilities)

Labeling, typically:

0 → Normal behavior

1 → Attack behavior

Train-test splitting for supervised learning experiments

Data Characteristics

Data type: Sequential / time-series data

Features: Encoded system call sequences or derived statistical features

Labels: Binary (Normal vs. Attack) or multiclass (depending on processing)

Domain: Cybersecurity, Host-Based Intrusion Detection

Operating System: Linux

Applications

The Processed_ADFA_LD dataset is widely used for:

Intrusion detection system (IDS) evaluation

Anomaly detection research

Benchmarking machine learning and deep learning models such as:

LSTM / GRU

CNN-based sequence models

Autoencoders

Traditional classifiers (SVM, Random Forest, k-NN)

Advantages

Reflects realistic modern attack behavior

Avoids outdated network-focused features

Well-suited for sequence-based learning models

Limitations

Host-specific (Linux-only)

Limited attack diversity compared to large-scale enterprise datasets

Requires careful preprocessing due to variable-length sequences
DOI: 10.17632/nx5p49dfng
title: Chewing gum improve early quality of recovery after thyroid surgery in females

description: This data was about our study: The effects of chewing gum on early quality of recovery after thyroid surgery in female patients.
DOI: 10.17632/nx5p49dfng.1
title: Chewing gum improve early quality of recovery after thyroid surgery in females

description: This data was about our study: The effects of chewing gum on early quality of recovery after thyroid surgery in female patients.
DOI: 10.17632/97yd3b5f5h
title: American Sign Language Image Dataset (figerspelling original + keypoints + evaluation)

description: The dataset consists of four folders: (1) Dataset Original, (2) Dataset Keypoints, (3) Dataset Black Background, and (4) Controlled Evaluation Dataset. Each folder contains image data, dimension 192 x 192, for each alphabet letter (static only) in American Sign Language (ASL). 
Under CC BY 4.0 license, researchers can re-use this dataset for ASL fingerspelling recognition and translation, as long as you give appropriate credit by citing the dataset.
DOI: 10.17632/97yd3b5f5h.1
title: American Sign Language Image Dataset (figerspelling original + keypoints + evaluation)

description: The dataset consists of four folders: (1) Dataset Original, (2) Dataset Keypoints, (3) Dataset Black Background, and (4) Controlled Evaluation Dataset. Each folder contains image data, dimension 192 x 192, for each alphabet letter (static only) in American Sign Language (ASL). 
Under CC BY 4.0 license, researchers can re-use this dataset for ASL fingerspelling recognition and translation, as long as you give appropriate credit by citing the dataset.
DOI: 10.17632/wbzpks38nv.1
title: 钙氮复合胁迫及不同氮形态配比对香椿幼苗氮代谢与生理生态响应机制的影响
DOI: 10.17632/hkkcrsbjbw.1
title: Ship Data

description: Data for Machine Learning in Ship Route Optimisation