cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/cdzx7kxczn
title: Innovative Intellectual Property Rights in the Marine Seed Industry

description: Our research employs the Group Eigenvalue Method (GEM) to identify key indicators, aiming to enhance the accuracy of value assessment. The principal advantage of GEM lies in its ability to effectively overcome the inconsistency issues common in the judgement matrices of the Analytic Hierarchy Process (AHP). Moreover, it offers simplicity in data processing (as used in method analysis). This method is applicable not only to quantitative issues but also to the analysis of qualitative problems.
For the research problem addressed in this paper, staff from companies and research institutions in the marine seed industry technology innovation field were selected as experts to score the preliminary screening indicators. The ten invited experts hail from prestigious institutions including Shandong Marine Limited Company, Shandong New Sunshine Seed Industry Science and Technology Limited Company, Tongji University, Xiamen University, the First Institute of Natural Resources Ministry, and the Chinese Academy of Sciences' Institute of Oceanography. The expert consultation process was conducted in two phases: the first round involved distributing questionnaires to the experts who assessed each indicator based on their knowledge and experience to collect baseline data. The scoring was designed according to the Likert 7-point scale method, as shown in Table 2. The second round involved detailed processing of the collected data; through further consultation with the experts, we discussed the threshold setting standards for each sub-indicator system and sought expert advice on retaining, eliminating, or merging indicators. 
 Although the GEM has been applied to critically screen the indicators for the valuation of intellectual property in marine seed industry innovation, the selected indicators may still exhibit some degree of correlation. According to the principle of diversity in the design of the assessment indicator system, excessive correlation among indicators can reduce the precision and practicality of the evaluation results. Therefore, maintaining an appropriate level of independence among indicators is crucial for constructing a scientifically sound and rational assessment system. By employing Pearson correlation analysis, it is possible to effectively identify highly correlated indicators, which often provide overlapping information. Consequently, merging or eliminating these highly correlated indicators from the assessment system is an essential step in ensuring the effectiveness and accuracy of the indicator system. Through this approach, we ensure the independence and scientific integrity of the assessment indicator system, enhancing the reliability and utility of the overall evaluation model.
DOI: 10.5281/zenodo.17194430
title: AML raw datasets to preprocess
DOI: 10.17632/cdzx7kxczn.4
title: Innovative Intellectual Property Rights in the Marine Seed Industry

description: Our research employs the Group Eigenvalue Method (GEM) to identify key indicators, aiming to enhance the accuracy of value assessment. The principal advantage of GEM lies in its ability to effectively overcome the inconsistency issues common in the judgement matrices of the Analytic Hierarchy Process (AHP). Moreover, it offers simplicity in data processing (as used in method analysis). This method is applicable not only to quantitative issues but also to the analysis of qualitative problems.
For the research problem addressed in this paper, staff from companies and research institutions in the marine seed industry technology innovation field were selected as experts to score the preliminary screening indicators. The ten invited experts hail from prestigious institutions including Shandong Marine Limited Company, Shandong New Sunshine Seed Industry Science and Technology Limited Company, Tongji University, Xiamen University, the First Institute of Natural Resources Ministry, and the Chinese Academy of Sciences' Institute of Oceanography. The expert consultation process was conducted in two phases: the first round involved distributing questionnaires to the experts who assessed each indicator based on their knowledge and experience to collect baseline data. The scoring was designed according to the Likert 7-point scale method, as shown in Table 2. The second round involved detailed processing of the collected data; through further consultation with the experts, we discussed the threshold setting standards for each sub-indicator system and sought expert advice on retaining, eliminating, or merging indicators. 
 Although the GEM has been applied to critically screen the indicators for the valuation of intellectual property in marine seed industry innovation, the selected indicators may still exhibit some degree of correlation. According to the principle of diversity in the design of the assessment indicator system, excessive correlation among indicators can reduce the precision and practicality of the evaluation results. Therefore, maintaining an appropriate level of independence among indicators is crucial for constructing a scientifically sound and rational assessment system. By employing Pearson correlation analysis, it is possible to effectively identify highly correlated indicators, which often provide overlapping information. Consequently, merging or eliminating these highly correlated indicators from the assessment system is an essential step in ensuring the effectiveness and accuracy of the indicator system. Through this approach, we ensure the independence and scientific integrity of the assessment indicator system, enhancing the reliability and utility of the overall evaluation model.
DOI: 10.5281/zenodo.17202693
title: trivago-clicks

description: This is a hypergraph, where nodes are accommodations (mostly hotels), and hyperedges are sets of accommodations for which a user performed the "click-out" action during the same browsing session, which means the user was forwarded to a partner site. A few hyperedges are repeated. The dataset was derived from the ACM RecSys Challenge 2019. Each node is labeled with the country where the accommodation is located.

Find the dataset details in AHORN.
DOI: 10.5281/zenodo.17202694
title: trivago-clicks

description: This is a hypergraph, where nodes are accommodations (mostly hotels), and hyperedges are sets of accommodations for which a user performed the "click-out" action during the same browsing session, which means the user was forwarded to a partner site. A few hyperedges are repeated. The dataset was derived from the ACM RecSys Challenge 2019. Each node is labeled with the country where the accommodation is located.

Find the dataset details in AHORN.
DOI: 10.17632/kcym3ptsyw
title: Stroke Patients’ Balance Ability Test Dataset Utilizing Array Pressure Acquisition System

description: The dataset includes raw pressure data and heatmap images, which support quantitative analysis and visualization of pressure patterns. Raw pressure data are stored in .csv files, which include pressure data presented in hexadecimal format, whereas the heatmap data are saved as .png files. All data are arranged into corresponding subdirectories based on five experimental movements: sit-to-stand, in-place stepping, static balance with eyes open, static balance with eyes closed, and stepping on a soft mat with eyes closed.
Each participant's data is stored separately in a folder. For example, the folder "3_RawData" contains the data for Participant 3. Inside the "3_RawData" folder, there are two subfolders: "3_PressureData" and "3_HeatmapData," which hold the raw pressure data and the generated heatmap data, respectively. Each subdirectory contains multiple .csv files and .png files that represent specific experimental records.
Relevant participant information is included in the "Participant_Information.csv" file, including age, height, weight, gender, health status (whether they are stroke patients), and balance ability levels. All participants' private information related to the test has been fully de-identified, and the recorded dataset in this study has obtained the participants' informed consent and authorization for publication.
Each pressure data frame includes a frame header, frame type, length, packet type, data field, and checksum. The frame header data type is uint16_t, with a specific value of 0xA55A. The frame type data type is uint8_t, with a particular value of 0x01. The length of the data type is uint16_t, with a particular value equal to the size of the packet minus 2. The packet type data type is uint8_t, with a particular value of 0x01.
Researchers can process raw pressure data to generate the heatmaps they need. For the plantar pressure sensor data, after removing the frame header, frame type, length, packet type, and checksum from a raw pressure data frame. Tthe remaining data is the pressure data in the data field. This pressure data from one data field is then converted into a 32×64 data matrix for analysis.For the flexible seat cushion pressure sensor data, similarly, after removing the frame header, frame type, length, packet type, and checksum from a frame of raw pressure data, the remaining data is the pressure data in the data field. This pressure data from one data field is adjusted into a 16×16 data matrix, which can then be analysed.
DOI: 10.17632/kcym3ptsyw.1
title: Stroke Patients’ Balance Ability Test Dataset Utilizing Array Pressure Acquisition System

description: The dataset includes raw pressure data and heatmap images, which support quantitative analysis and visualization of pressure patterns. Raw pressure data are stored in .csv files, which include pressure data presented in hexadecimal format, whereas the heatmap data are saved as .png files. All data are arranged into corresponding subdirectories based on five experimental movements: sit-to-stand, in-place stepping, static balance with eyes open, static balance with eyes closed, and stepping on a soft mat with eyes closed.
Each participant's data is stored separately in a folder. For example, the folder "3_RawData" contains the data for Participant 3. Inside the "3_RawData" folder, there are two subfolders: "3_PressureData" and "3_HeatmapData," which hold the raw pressure data and the generated heatmap data, respectively. Each subdirectory contains multiple .csv files and .png files that represent specific experimental records.
Relevant participant information is included in the "Participant_Information.csv" file, including age, height, weight, gender, health status (whether they are stroke patients), and balance ability levels. All participants' private information related to the test has been fully de-identified, and the recorded dataset in this study has obtained the participants' informed consent and authorization for publication.
Each pressure data frame includes a frame header, frame type, length, packet type, data field, and checksum. The frame header data type is uint16_t, with a specific value of 0xA55A. The frame type data type is uint8_t, with a particular value of 0x01. The length of the data type is uint16_t, with a particular value equal to the size of the packet minus 2. The packet type data type is uint8_t, with a particular value of 0x01.
Researchers can process raw pressure data to generate the heatmaps they need. For the plantar pressure sensor data, after removing the frame header, frame type, length, packet type, and checksum from a raw pressure data frame. Tthe remaining data is the pressure data in the data field. This pressure data from one data field is then converted into a 32×64 data matrix for analysis.For the flexible seat cushion pressure sensor data, similarly, after removing the frame header, frame type, length, packet type, and checksum from a frame of raw pressure data, the remaining data is the pressure data in the data field. This pressure data from one data field is adjusted into a 16×16 data matrix, which can then be analysed.
DOI: 10.15468/dl.hygmss
title: Occurrence Download

description: A dataset containing 73611 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Terrapene carolina"
 ]
}

 The dataset includes 73611 records from 145 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016123-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8wjk3422dn
title: Primer registro oficial de Spilogale angustifrons en tierras altas de la Cordillera Volcánica Central, Costa Rica

description: Evidencia del primer registro oficial de Spilogale angustifrons en el Parque Nacional Volcán Irazú, ubicado en la Cordillera Volcánica Central, Costa Rica.
DOI: 10.17632/8wjk3422dn.1
title: Primer registro oficial de Spilogale angustifrons en tierras altas de la Cordillera Volcánica Central, Costa Rica

description: Evidencia del primer registro oficial de Spilogale angustifrons en el Parque Nacional Volcán Irazú, ubicado en la Cordillera Volcánica Central, Costa Rica.
DOI: 10.17026/ar/xut1yi
title: PAN-00173265 - devotional pendant Modern period

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00173265&lt;/p&gt;
description: Modified: 2025-04-26T11:21:55+02:00
DOI: 10.17026/ar/esdz3p
title: PAN-00105606 - coin/coin-related, Septimius Severus (193-211), denarius

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00105606&lt;/p&gt;
description: Modified: 2025-05-12T13:03:18+02:00
DOI: 10.17026/ar/zet8nk
title: PAN-00175275 - seal lead (pinch lead)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00175275&lt;/p&gt;
description: Modified: 2025-06-23T11:55:00+02:00
DOI: 10.5519/qd.jmrx4m22
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (31 records)
DOI: 10.15468/dl.q2ynae
title: Occurrence Download

description: A dataset containing 4612 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hoplobatrachus rugulosus",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4612 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016184-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tvunv8
title: Occurrence Download

description: A dataset containing 232 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hillebrandia sandwicensis"
 ]
}

 The dataset includes 232 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016185-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/l6q1iw
title: PAN-00167727 - finger ring with equal decoration elements all around

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00167727&lt;/p&gt;
description: Modified: 2025-04-30T13:48:53+02:00
DOI: 10.17026/ar/cxyzqz
title: PAN-00171075 - Roman gem (unspecified jewellery)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00171075&lt;/p&gt;
description: Modified: 2025-05-13T12:08:25+02:00
DOI: 10.15468/dl.v983zp
title: Occurrence Download

description: A dataset containing 48744 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-6.38311 37.03815,-6.49295 36.67282,-6.07956 36.14049,-5.55987 35.92081,-4.78034 36.44513,-5.54806 37.69845,-6.38311 37.03815))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 48744 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016188-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/4idxlx
title: PAN-00006189 - coin/coin-related, Constantinus I (306-337), voor Constantius II, nummus

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00006189&lt;/p&gt;
description: Modified: 2025-05-20T09:13:36+02:00
DOI: 10.17026/ar/pozhqb
title: PAN-00104979 - coin/coin-related, Constantinus I (306-337), nummus

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00104979&lt;/p&gt;
description: Modified: 2025-05-12T07:27:34+02:00
DOI: 10.17026/ar/whfgyg
title: PAN-00112420 - coin/coin-related, Marcus Aurelius (161-180), dupondius

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00112420&lt;/p&gt;
description: Modified: 2025-07-08T08:27:45+02:00
DOI: 10.15468/dl.9ghzqg
title: Occurrence Download

description: A dataset containing 80330 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Chelydra serpentina"
 ]
}

 The dataset includes 80330 records from 212 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016126-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pcvrh3z9nh.1
title: Data Liderazgo Transformaciona Digital Verde

description: Se realizo una revisión bibliográfica identificando las variables latentes, incluyendo más de 100 artículos que utilizan palabras clave como «liderazgo transformacional digital verde» y «desempeño en las cadenas de suministros sustentables». Sobre esta base, se elabora un cuestionario que consta de dos secciones. La primera consiste en una introducción para recabar información demográfica sobre el encuestado y la empresa, como el puesto de trabajo, los años en el puesto, el sexo, el sector industrial al que pertenece y el número de empleados. La segunda sección investiga todos los constructos y las variables observadas de cada uno. El cuestionario debe responderse en una escala tipo Likert de cinco puntos para indicar el nivel de acuerdo de los involucrados en cada ítem dentro de la empresa. También se establecen los modelos propuestos en esta investigación y se proporciona una conceptualización de cada uno.
DOI: 10.17632/vsh529wvx6
title: Reimbursement Variations for General Dermatology Service by Commercial Insurers

description: Supplemental Figure 1. State-level median negotiated rate for general dermatology services in the US, as of July 2024
DOI: 10.17632/vsh529wvx6.1
title: Reimbursement Variations for General Dermatology Service by Commercial Insurers

description: Supplemental Figure 1. State-level median negotiated rate for general dermatology services in the US, as of July 2024
DOI: 10.17026/ar/tswalu
title: PAN-00010625 - cast tubular lead net sinker

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00010625&lt;/p&gt;
description: Modified: 2025-07-10T14:36:14+02:00
DOI: 10.17632/96pzsbdtyg
title: Temperature Playground

description: The design files include the full schematic, PCB layout, fabrication outputs, bill of materials, and optional 3D model for the Temperature Playground hardware. These files ensure reproducibility, allow direct editing in EasyEDA or compatible EDA tools, and provide everything needed for both educational use and hardware replication.

Schematic (.json) – Editable circuit schematic created in EasyEDA, containing all components, interconnections, and designators.

PCB Layout (.json) – Editable PCB design file for the two-layer FR-4 board (approx. 8 cm × 5 cm), including routing, footprints, and silkscreen layers.

Gerber Files (.zip) – Standard manufacturing output with copper, drill, and silkscreen layers required for PCB fabrication.

Bill of Materials (.csv) – Full component list with designators, values, package types, and sourcing references.
DOI: 10.17632/96pzsbdtyg.1
title: Temperature Playground

description: The design files include the full schematic, PCB layout, fabrication outputs, bill of materials, and optional 3D model for the Temperature Playground hardware. These files ensure reproducibility, allow direct editing in EasyEDA or compatible EDA tools, and provide everything needed for both educational use and hardware replication.

Schematic (.json) – Editable circuit schematic created in EasyEDA, containing all components, interconnections, and designators.

PCB Layout (.json) – Editable PCB design file for the two-layer FR-4 board (approx. 8 cm × 5 cm), including routing, footprints, and silkscreen layers.

Gerber Files (.zip) – Standard manufacturing output with copper, drill, and silkscreen layers required for PCB fabrication.

Bill of Materials (.csv) – Full component list with designators, values, package types, and sourcing references.
DOI: 10.17632/h42dy8j4h6
title: RPLIDARC1 Emulator

description: This project implements a RPLiDAR C1 sensor emulator using an Arduino Uno board. The emulator generates data that replicates the basic operation of a LiDAR sensor, allowing software testing, serial communication, and visualization without the need for the physical device.

The system is designed to:

Transmit emulated distance data through the Arduino Uno’s serial port, following a format similar to that used by the real LiDAR C1.
Generate points with random distances and an incrementing angle, simulating the characteristic scanning behavior of a LiDAR.
Configure basic parameters* (such as range, update frequency, and values generated either randomly or predefined) to reproduce different test scenarios.
Facilitate integration and debugging in robotics projects, autonomous vehicles, or 2D/3D mapping systems, where communication and data processing need to be validated before having the real hardware.

This emulator is especially useful for developers, students, and researchers who require a fast and cost-effective validation environment, ensuring compatibility with applications that typically rely on the LiDAR C1.
DOI: 10.17632/h42dy8j4h6.1
title: RPLIDARC1 Emulator

description: This project implements a RPLiDAR C1 sensor emulator using an Arduino Uno board. The emulator generates data that replicates the basic operation of a LiDAR sensor, allowing software testing, serial communication, and visualization without the need for the physical device.

The system is designed to:

Transmit emulated distance data through the Arduino Uno’s serial port, following a format similar to that used by the real LiDAR C1.
Generate points with random distances and an incrementing angle, simulating the characteristic scanning behavior of a LiDAR.
Configure basic parameters* (such as range, update frequency, and values generated either randomly or predefined) to reproduce different test scenarios.
Facilitate integration and debugging in robotics projects, autonomous vehicles, or 2D/3D mapping systems, where communication and data processing need to be validated before having the real hardware.

This emulator is especially useful for developers, students, and researchers who require a fast and cost-effective validation environment, ensuring compatibility with applications that typically rely on the LiDAR C1.
DOI: 10.17632/c8h8sjf2hd
title: Oncom Fermentation

description: This dataset presents experimental results from an IoT-based monitoring system for oncom fermentation using Raspberry Pi 4B, DHT22 sensor, and Firebase integration. It includes temperature, humidity, and image data, along with validation results, QoS measurements, and mobile application testing outcomes. The dataset supports reproducibility and can be used as a reference for studies in food fermentation monitoring, IoT applications, and environmental control systems.