cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17362820
title: TABLE 1 in A new genus and six new species of endemic landhopper in the family Arcitalitridae (Amphipoda: Talitroidea) from New Zealand

description: TABLE 1. Koekotroides gen. nov. specimens (numbers correspond to specimen identifiers) used for genetic analyses, their collection locality and GenBank accession number.
TaxonSite name (Crosby area)1GenBank Accessions 16SKoekotroides pekehau sp. nov. 1Tāputaputa (A) (Northland)PV056482Koekotroides pekehau sp. nov. 2Tāputaputa (A) (Northland)PV056481Koekotroides pekehau sp. nov. 3Kohuroa (A) (Northland)PV056483Koekotroides pekehau sp. nov. 4Unuwhao (A) (Northland)PQ154943Koekotroides pekehau sp. nov. 5Unuwhao (A) (Northland)PV056484Koekotroides pekehau sp. nov. 6Unuwhao (A) (Northland)PV056485Koekotroides taapinea sp. nov.North Cape (Northland)PQ154916Koekotroides tewhaarua sp. nov.Great Island (Three Kings Islands)PQ154915Koekotroides moowhitihauuru sp. nov. 1Bushy Park (Whanganui)PV056487Koekotroides moowhitihauuru sp. nov. 2Bushy Park (Whanganui)PQ154914Koekotroides moowhitihauuru sp. nov. 3Kepa Bush (Auckland)PV056486Koekotroides moowhitihauuru sp. nov. 4Kepa Bush (Auckland)PV056488Koekotroides ngaokiroa sp. nov. 1Te Huka (B) (Northland)PV056489Koekotroides ngaokiroa sp. nov. 2Kohuroa (A) (Northland)PQ154944Koekotroides ngaokiroa sp. nov. 3Kohuroa (A) (Northland)PV056490Koekotroides ngaokiroa sp. nov. 4Kohuroa (A) (Northland)PV056491Koekotroides ngaokiroa sp. nov. 5Tāputaputa (B) (Northland)PV056492Koekotroides ngaokiroa sp. nov. 6Darkies Ridge (Northland)PV056496Koekotroides ngaokiroa sp. nov. 7Shenstone Block (Northland)PV056494Koekotroides ngaokiroa sp. nov. 8Shenstone Block (Northland)PV056493Koekotroides ngaokiroa sp. nov. 9Radar Bush (Northland)PV056495
1 Area location names are based on the boundary definitions of Crosby et al. (1998).
description: Published as part of J. - P, Olivier, Myers, Alan A. & Shepherd, Lara D., 2025, A new genus and six new species of endemic landhopper in the family Arcitalitridae (Amphipoda: Talitroidea) from New Zealand, pp. 279 in Zootaxa 5691 (2) on page 279, DOI: 10.11646/zootaxa.5691.2.5, http://zenodo.org/record/17320820
DOI: 10.15468/dl.rtfsgw
title: Occurrence Download

description: A dataset containing 41282 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nyctalus lasiopterus"
 ]
}

 The dataset includes 41282 records from 410 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024780-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.68cv64
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-3.09501 42.64195,-3.13956 42.3277,-3.12186 42.12214,-3.00943 41.98461,-2.68367 41.9321,-2.249 41.97528,-1.77504 41.80485,-1.72414 42.11293,-1.67063 42.21312,-1.90675 42.44491,-2.96958 42.70374,-3.09501 42.64195))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lucanus cervus"
 ]
}

 The dataset includes 56 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024784-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cyp4re
title: Occurrence Download

description: A dataset containing 393 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-95.45492 -59.48701,-43.17738 -59.48701,-43.17738 -9.53088,-95.45492 -9.53088,-95.45492 -59.48701))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carcharodon carcharias"
 ]
}

 The dataset includes 393 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024785-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uuqkbz
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nyctalus maximus"
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024786-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w4n5tp
title: Occurrence Download

description: A dataset containing 52489 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Malawi",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 52489 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024762-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17523204
title: Data for "Machine learning-accelerated evolutionary Monte Carlo for rapid phase exploration of compositionally complex materials in reactive environments"

description: The HEA and HEO data are stored in folder `Co0.25Mo0.45Fe0.1Ni0.1Cu0.1` and `Co0.2Ni0.2Mg0.2Zn0.2Mn0.2Al2O4`, respectively. In each folder, there are 3 sub-folders: `models` contains the DFT dataset (in `.xyz` format) for training the MACE MLIP, and the trained MACE MLIP `.model` file; `bulk` contains Python scripts for generating initial population, running bulk GE-EMC simulation and performing MBAR analysis; `111` or `311` contains Python scripts for generating initial population, running surface GC-EMC simulation, performing MBAR analysis and plotting surface phase diagrams.
DOI: 10.15468/dl.9uhc7k
title: Occurrence Download

description: A dataset containing 144856 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ghana",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 144856 records from 130 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024764-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21951/rithyms_v1/2587036
title: Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States

description: This dataset provides inputs, evaluation results, and trained weights from a large-sample Long Short-Term Memory (LSTM) model designed to predict daily stream temperatures across unregulated river reaches in the conterminous United States (CONUS). It includes dynamic meteorological and hydrologic forcings, static physiographic attributes, and model outputs from cross-validation experiments spanning 300 basins. It supports reproducible modeling, direct application for new basins, and provides data suitable for integration with reservoir and river simulations under current and future climates. It contains two .zip files described below · RQ-AI_runs.zip: Model outputs from 10-fold cross-validation experiments, including observed and predicted daily stream temperatures, along with test performance metrics for water years 2017–2019. Two versions are included: 1. Model trained and validated using subbasin-area weighted dynamic features. 2. Model trained and validated using whole-basin area weighted dynamic features. · RQ-AI_inputs.zip: Collection of all formatted dynamic and static predictor datasets (meteorological, hydrologic, and physiographic features) used in model training and analysis. Detailed instructions and data structure is held at the following GitLab repository: https://code.ornl.gov/tempwise/training.
DOI: 10.34810/data2333
title: PApple_RGB-D-Size dataset

description: Fuji apple dataset for on-tree apple detection and measurment containing 3925 images annotated with modal and amodal segmentation masks and fruit diameter ground truth. This dataset was used to train the deep learning architecture published at https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing. The resulting trained model is available by downloading in the file: model_000999.pth
DOI: 10.3886/e239939
title: India ERP Software Market: Industry Outlook and Growth Forecast

description: The India ERP Software Market was valued at USD 1.68 billion in 2024, with
a projected increase to USD 2.13 billion by 2025. The market is expected to
continue its rapid expansion, reaching USD 5.21 billion by 2030, with a CAGR of
19.56% from 2025 to 2030. Growth is driven by the booming healthcare sector,
government digitalization initiatives, and increasing adoption across
industries such as manufacturing, BFSI, and logistics. Challenges such as high
implementation costs, regulatory complexities, and a shortage of skilled
professionals persist.<br><br>For more Information visit: https://www.nextmsc.com/report/india-erp-software-market-ic3611
DOI: 10.5281/zenodo.17570809
title: Ultrasonic Sensor Data for Human or Nonhuman Classification

description: Title:Ultrasonic Sensor Data for Human or Nonhuman Classification

Author:Budi SetiadiPoliteknik Negeri Bandung, IndonesiaEmail: [budi.setiadi@polban.ac.id]

Year: 2025

Description:The dataset consists of distance measurements from 11 ultrasonic sensors (HC-SR04) arranged vertically on a wall with 10 cm spacing and positioned between 80 cm and 180 cm above the floor. Each sensor measures the reflected distance of passing objects to recognize their vertical distance patterns.The dataset includes two object categories:1.    Human – visually impaired individuals.2.    Nonhuman – inanimate objects such as chairs or other static items.

Keywords:Ultrasonic Sensor Array, MLP, Classification.

File consist of three:- MLP_Ultrasonic_Classification_Human-Nonhuman_Training1400.csv- MLP_Ultrasonic_Classification_Human-Nonhuman_Testing600.csv- README_MLP_Ultrasonic_Classification_Human-Nonhuman_Dataset_2025.txt 

Data Acquisition ProcessThe data were collected directly at SLBN-A Citeureup, Cimahi City, using an ultrasonic sensor array connected to a microcontroller through multiplexers (CD74HC4067). A total of 10 human subjects participated in repeated testing sessions to produce 2,000 ultrasonic sensor readings representing both human and nonhuman reflection patterns.Each measurement contains 11 distance values (in centimeters) corresponding to sensors 1–11, and one label indicating the object class (“human” or “nonhuman”). All data were stored in .csv format.

Dataset PurposeThe dataset aims to:•    Train and evaluate Multi Layer Perceptron (MLP) for object classification based on ultrasonic distance patterns.•    Provide representative data reflecting vertical ultrasonic reflection patterns between humans and nonhuman objects.•    Support the development of adaptive navigation assistance systems for visually impaired individuals.

Data StructureColumn    Description    Data TypeSensor₁ … Sensor₁₁    Distance values from each of the 11 ultrasonic sensors (in cm)    Numeric (float/int)Label    Object class (“Human” / “Nonhuman”)    Categorical (binary)

The dataset was divided into two subsets:•    Training set (70%) → 1,400 samples.•    Testing set (30%)  600 samplesusing a stratified sampling method to maintain class balance

License:Creative Commons Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/

Suggested Citation:Budi Setiadi (2025). Ultrasonic Sensor Data for Human or Nonhuman Classification. Zenodo. DOI: [10.5281/zenodo.17570045]

Summary•    Total samples: 2,000•    Number of features: 11 input sensors + 1 output label•    Acquisition site: SLBN-A Citeureup, Cimahi City, Indonesia•    File format: .csv•    Purpose: Training and testing of a MLP classification model for adaptive object detection and navigation aid for visually impaired individuals.
DOI: 10.5281/zenodo.17570810
title: Ultrasonic Sensor Data for Human or Nonhuman Classification

description: Title:Ultrasonic Sensor Data for Human or Nonhuman Classification

Author:Budi SetiadiPoliteknik Negeri Bandung, IndonesiaEmail: [budi.setiadi@polban.ac.id]

Year: 2025

Description:The dataset consists of distance measurements from 11 ultrasonic sensors (HC-SR04) arranged vertically on a wall with 10 cm spacing and positioned between 80 cm and 180 cm above the floor. Each sensor measures the reflected distance of passing objects to recognize their vertical distance patterns.The dataset includes two object categories:1.    Human – visually impaired individuals.2.    Nonhuman – inanimate objects such as chairs or other static items.

Keywords:Ultrasonic Sensor Array, MLP, Classification.

File consist of three:- MLP_Ultrasonic_Classification_Human-Nonhuman_Training1400.csv- MLP_Ultrasonic_Classification_Human-Nonhuman_Testing600.csv- README_MLP_Ultrasonic_Classification_Human-Nonhuman_Dataset_2025.txt 

Data Acquisition ProcessThe data were collected directly at SLBN-A Citeureup, Cimahi City, using an ultrasonic sensor array connected to a microcontroller through multiplexers (CD74HC4067). A total of 10 human subjects participated in repeated testing sessions to produce 2,000 ultrasonic sensor readings representing both human and nonhuman reflection patterns.Each measurement contains 11 distance values (in centimeters) corresponding to sensors 1–11, and one label indicating the object class (“human” or “nonhuman”). All data were stored in .csv format.

Dataset PurposeThe dataset aims to:•    Train and evaluate Multi Layer Perceptron (MLP) for object classification based on ultrasonic distance patterns.•    Provide representative data reflecting vertical ultrasonic reflection patterns between humans and nonhuman objects.•    Support the development of adaptive navigation assistance systems for visually impaired individuals.

Data StructureColumn    Description    Data TypeSensor₁ … Sensor₁₁    Distance values from each of the 11 ultrasonic sensors (in cm)    Numeric (float/int)Label    Object class (“Human” / “Nonhuman”)    Categorical (binary)

The dataset was divided into two subsets:•    Training set (70%) → 1,400 samples.•    Testing set (30%)  600 samplesusing a stratified sampling method to maintain class balance

License:Creative Commons Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/

Suggested Citation:Budi Setiadi (2025). Ultrasonic Sensor Data for Human or Nonhuman Classification. Zenodo. DOI: [10.5281/zenodo.17570045]

Summary•    Total samples: 2,000•    Number of features: 11 input sensors + 1 output label•    Acquisition site: SLBN-A Citeureup, Cimahi City, Indonesia•    File format: .csv•    Purpose: Training and testing of a MLP classification model for adaptive object detection and navigation aid for visually impaired individuals.
DOI: 10.34740/kaggle/ds/8681816
title: Close-View-Flood-Dataset (CVFD)
DOI: 10.5517/ccdc.csd.cc2py8wt
title: CCDC 2500797: Experimental Crystal Structure Determination

description: Related Article: Eiko Mieda|2025|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2py9cb
title: CCDC 2500812: Experimental Crystal Structure Determination

description: Related Article: Sergey G. Krivokolysko, Bogdan S. Krivokolysko, Andrei V. Churakov|2025|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15472/kkadom
title: Colección Microbiológica de la Universidad de Nariño

description: La colección de microorganismos de la Universidad de Nariño busca mantener, preservar y suministrar diferentes clases de especímenes derivados principalmente de trabajos de investigación y algunos microorganismos control (ATCC). Apoyando e incentivando la investigación en el departamento de Nariño garantizando la conservación y viabilidad de los ejemplares durante periodos prolongados de tiempo, evitando la pérdida de especímenes y garantizando su disponibilidad para futuras investigaciones.Hasta el momento la colección cuenta con 197 registros de cultivos microbianos colectados en los años 2012 y 2021 pertenecientes a 15 géneros de bacterias, hongos, virus y protistas.
DOI: 10.5517/ccdc.csd.cc2pyc56
title: CCDC 2500868: Experimental Crystal Structure Determination

description: Related Article: Michael A. Carroll, Shno M. Omar, Paul G. Waddell|2025|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.2jp3b8
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-119.65749 -59.48701,-45.11358 -59.48701,-45.11358 -12.38193,-119.65749 -12.38193,-119.65749 -59.48701))",
 "HasGeospatialIssue is false",
 "TaxonKey is Cephaloscyllium ventriosum"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024791-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2fwa6x
title: Occurrence Download

description: A dataset containing 18 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-163.2221 -59.48701,-48.98599 -59.48701,-48.98599 -17.98521,-163.2221 -17.98521,-163.2221 -59.48701))",
 "HasGeospatialIssue is false",
 "TaxonKey is Etmopterus lucifer"
 ]
}

 The dataset includes 18 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024794-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9pbdev
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Rudbeckia mohrii"
 ]
}

 The dataset includes 54 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024793-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data2336
title: MTLS_point_cloud_generation

description: Matlab implementation to generate 3D point clouds from data acquired with VLP-16 and GNSS GPS1200+. This project is a Matlab implementation to generate 3D point clouds from data acquired with a mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor Velodyne VLP-16 (Velodyne LIDAR Inc., San Jose, CA, USA) and a GNSS position sensor GPS1200+ (Leica Geosystems AG, Heerbrugg, Swizeland). This implementation was used to generate the point clouds provided in LFuji-air dataset, which contains 3D LiDAR data of 11Fuji apple trees with the corresponding fruit position annotations.
description: This software is stored and maintained in the following github repository: https://github.com/GRAP-UdL-AT/MTLS_point_cloud_generation
description: Matlab, 2019b
description: Veloview, 3.5.0
DOI: 10.15468/dl.9y5cnk
title: Occurrence Download

description: A dataset containing 5930 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((108.87175 -4.58581,118.84708 -4.58581,119.14171 0.30898,119.35689 1.10073,119.56353 1.85363,118.84708 7.99182,108.87175 7.99182,108.57068 2.06869,108.87175 -4.58581))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 5930 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024792-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v4raxd
title: Occurrence Download

description: A dataset containing 1239899 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Year 2005-2021"
 ]
}

 The dataset includes 1239899 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024779-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xwx7se
title: Occurrence Download

description: A dataset containing 9233 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acanalonia conica"
 ]
}

 The dataset includes 9233 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024795-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data2326
title: RGBD_sensors_evaluation_in_Orchards

description: Matlab implementation to evaluate RGB-D sensor performance in orchard environments. This project is a matlab implementation to evaluate RGB-D sensor performances by analysing RGB-D data acquired in different orchard conditions. The code follows the assessment methodology presented in [1], and it was used to evaluate the performance of Microsoft Kinect v2 by using the KEvOr dataset.
description: This software is stored and maintained in the following github repository: https://github.com/GRAP-UdL-AT/RGBD_sensors_evaluation_in_Orchards

Computer Vision System Toolbox
Statistics and Machine Learning Toolbox
description: MATLAB, R2020a
DOI: 10.15468/dl.be83t5
title: Occurrence Download

description: A dataset containing 50708 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Coeloglossum viride",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Occurrence evidence)",
 "OccurrenceStatus is Present",
 "HasCoordinate is true"
 ]
}

 The dataset includes 50708 records from 388 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024750-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4hubyz
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aster hayatae",
 "HasCoordinate is TRUE",
 "Country is Korea, Republic of"
 ]
}

 The dataset includes 63 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078549-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.