cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ry7phxn4js
title: Large Language Models in Materials Science: Assessing RAG Evaluation Frameworks through graphene synthesis

description: Retrieval-Augmented Generation (RAG) systems increasingly support scientific research, yet evaluating their performance in specialized domains remains challenging due to the technical complexity and precision requirements of scientific knowledge. This study presents the first systematic analysis of automated evaluation frameworks for scientific RAG systems, using graphene synthesis in materials science as a representative case study. We develop a comprehensive evaluation protocol comparing four assessment approaches: RAGAS (an automated RAG evaluation framework), BERTScore, LLM-as-a-Judge, and expert human evaluation across 20 domain-specific questions. Our analysis of automated evaluators reveals that BERTScore lacks the interpretability and score sensitivity required to distinguish meaningful performance difference, while LLM-as-a-Judge failed to capture retrieval augmentation benefits. In contrast, RAGAS successfully captured relative performance improvements from retrieval augmentation, identifying performance gains in RAG-augmented systems (0.52-point improvement for Gemini, 1.03-point for Qwen on a 10-point scale), and demonstrating particular sensitivity to retrieval benefits in smaller, open-source models. However, it still exhibits fundamental limitations in absolute score interpretation for scientific content. These findings establish methodological guidelines for scientific RAG evaluation and highlight critical considerations for researchers deploying AI systems in specialized domains.
DOI: 10.17632/ry7phxn4js.3
title: Large Language Models in Materials Science: Assessing RAG Evaluation Frameworks through graphene synthesis

description: Retrieval-Augmented Generation (RAG) systems increasingly support scientific research, yet evaluating their performance in specialized domains remains challenging due to the technical complexity and precision requirements of scientific knowledge. This study presents the first systematic analysis of automated evaluation frameworks for scientific RAG systems, using graphene synthesis in materials science as a representative case study. We develop a comprehensive evaluation protocol comparing four assessment approaches: RAGAS (an automated RAG evaluation framework), BERTScore, LLM-as-a-Judge, and expert human evaluation across 20 domain-specific questions. Our analysis of automated evaluators reveals that BERTScore lacks the interpretability and score sensitivity required to distinguish meaningful performance difference, while LLM-as-a-Judge failed to capture retrieval augmentation benefits. In contrast, RAGAS successfully captured relative performance improvements from retrieval augmentation, identifying performance gains in RAG-augmented systems (0.52-point improvement for Gemini, 1.03-point for Qwen on a 10-point scale), and demonstrating particular sensitivity to retrieval benefits in smaller, open-source models. However, it still exhibits fundamental limitations in absolute score interpretation for scientific content. These findings establish methodological guidelines for scientific RAG evaluation and highlight critical considerations for researchers deploying AI systems in specialized domains.
DOI: 10.17632/jrgj465xhr
title: CRAMP &SS WB data

description: original data for CRAMP&SS project
DOI: 10.17632/jrgj465xhr.1
title: CRAMP &SS WB data

description: original data for CRAMP&SS project
DOI: 10.17632/3m5hdc73f2
title: EV Driver Behaviors

description: • Data including route choice behavior reflecting individual preferences for infrastructural and psychological factors under long-distance EV driving conditions
• Modeling heterogeneous route choice behavior of EV drivers considering route-specific attributes and attitudinal segmentation
• Providing practical insights for EV infrastructure planning and policy by identifying key influencing factors
DOI: 10.17632/3m5hdc73f2.2
title: EV Driver Behaviors

description: • Data including route choice behavior reflecting individual preferences for infrastructural and psychological factors under long-distance EV driving conditions
• Modeling heterogeneous route choice behavior of EV drivers considering route-specific attributes and attitudinal segmentation
• Providing practical insights for EV infrastructure planning and policy by identifying key influencing factors
DOI: 10.17632/44bb9wz59m.2
title: Wet yet reduced: The conundrum of magmatic water and fO2 in porphyry Sn-(W) system

description: Data for: Wet yet reduced: The conundrum of magmatic water and fO2 in porphyry Sn-(W) system
DOI: 10.15468/dl.uuymdd
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyphessobrycon wosiackii",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 6 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005129-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2wjs6mbt48
title: Natural Language Industrial Control

description: Intelligent Industrial Automation Platform: Integration of Edge AI, IoT, and Cloud Supervision
Abstract
This project represents a modern approach to industrial automation, bridging the gap between traditional PLC (Programmable Logic Controller) systems and advanced AI/IoT technologies. It consists of a distributed architecture featuring an intelligent edge node (Raspberry Pi), a central coordination server, and a high-performance React-based HMI (Human-Machine Interface). The system allows for real-time control, data logging, and novel 'Natural Language Control' capabilities, enabling users to interact with industrial hardware using conversational AI

System Architecture
The project is divided into three main components:

1. Intelligent Edge Controller (ServidorPlc)
Hosted on a Raspberry Pi, this component acts as the "Soft PLC".

Technology: Node.js, Express, Socket.io, Node-OPCUA.
Functionality:
Hardware Interface: Direct actuation (PWM, Digital I/O) and sensing (ADC communication).
Control Loops: Implements PI/PID algorithms and automatic system identification (ARX models).
AI Integration: Processes natural language prompts (via OpenAI/Local LLMs) to generate control commands dynamically.
Connectivity: MQTT and WebSockets for real-time telemetry.
2. Supervisory Interface / HMI (Plc)
A web-based SCADA/HMI application for monitoring and control.

Technology: React, Vite, CoreUI, Redux, Vega-Lite.
Features:
Data Visualization: Real-time charts (Vega-Lite) for sensor data (temperature, level, flow).
Command Center: Interface to send voice/text commands to the PLC.
Dashboard: CoreUI-based administrative panels for user management and configuration.
3. Backend & Data Layer (ServidorPc)
(Contextual) A central server or database handler for long-term storage and aggregation.

Technology: Node.js, PostgreSQL.
Functionality:
Data persistence for historical analysis.
Centralized authentication and user management.
Key Features
Automatic Model Identification: The system can automatically perturb the plant, record response data, and fit ARX (AutoRegressive with Exogenous input) models (Order 1-3) to mathematically represent the system dynamics.
Generative AI Control: Users can issue commands like "Maintain level at 50% indefinitely" or "Identification test with steps of 5 seconds," which are parsed into executable machine code.
Remote Telemetry: Real-time streaming of process variables (Setpoints, Process Values, Control Outputs).
Extensibility: Modular Node.js architecture allows easy addition of new protocols (Modbus, OPC UA) or AI models.
DOI: 10.17632/2wjs6mbt48.3
title: Natural Language Industrial Control

description: Intelligent Industrial Automation Platform: Integration of Edge AI, IoT, and Cloud Supervision
Abstract
This project represents a modern approach to industrial automation, bridging the gap between traditional PLC (Programmable Logic Controller) systems and advanced AI/IoT technologies. It consists of a distributed architecture featuring an intelligent edge node (Raspberry Pi), a central coordination server, and a high-performance React-based HMI (Human-Machine Interface). The system allows for real-time control, data logging, and novel 'Natural Language Control' capabilities, enabling users to interact with industrial hardware using conversational AI

System Architecture
The project is divided into three main components:

1. Intelligent Edge Controller (ServidorPlc)
Hosted on a Raspberry Pi, this component acts as the "Soft PLC".

Technology: Node.js, Express, Socket.io, Node-OPCUA.
Functionality:
Hardware Interface: Direct actuation (PWM, Digital I/O) and sensing (ADC communication).
Control Loops: Implements PI/PID algorithms and automatic system identification (ARX models).
AI Integration: Processes natural language prompts (via OpenAI/Local LLMs) to generate control commands dynamically.
Connectivity: MQTT and WebSockets for real-time telemetry.
2. Supervisory Interface / HMI (Plc)
A web-based SCADA/HMI application for monitoring and control.

Technology: React, Vite, CoreUI, Redux, Vega-Lite.
Features:
Data Visualization: Real-time charts (Vega-Lite) for sensor data (temperature, level, flow).
Command Center: Interface to send voice/text commands to the PLC.
Dashboard: CoreUI-based administrative panels for user management and configuration.
3. Backend & Data Layer (ServidorPc)
(Contextual) A central server or database handler for long-term storage and aggregation.

Technology: Node.js, PostgreSQL.
Functionality:
Data persistence for historical analysis.
Centralized authentication and user management.
Key Features
Automatic Model Identification: The system can automatically perturb the plant, record response data, and fit ARX (AutoRegressive with Exogenous input) models (Order 1-3) to mathematically represent the system dynamics.
Generative AI Control: Users can issue commands like "Maintain level at 50% indefinitely" or "Identification test with steps of 5 seconds," which are parsed into executable machine code.
Remote Telemetry: Real-time streaming of process variables (Setpoints, Process Values, Control Outputs).
Extensibility: Modular Node.js architecture allows easy addition of new protocols (Modbus, OPC UA) or AI models.
DOI: 10.15468/dl.8mq6ep
title: Occurrence Download

description: A dataset containing 321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Adenanthera microsperma",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 321 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005130-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/n4fz5vj56d
title: Banks level data Vietnam 2018 - 2024

description: Data from commercial banks in Vietnam from 2018 to 2024
DOI: 10.15468/dl.8z8ypu
title: Occurrence Download

description: A dataset containing 1267 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phyllostachys edulis"
 ]
}

 The dataset includes 1267 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005131-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0169290.10fu
title: SH0169290.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169291.10fu
title: SH0169291.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169292.10fu
title: SH0169292.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169293.10fu
title: SH0169293.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.31037029
title: ANSIEDAD MATEMÁTICA Y FACTORES DEMOGRÁFICOS

description: This dataset originates from a cross-sectional quantitative study conducted with 2,508 high school students from 13 public urban institutions in Imbabura, Ecuador. The objective was to examine whether mathematics anxiety varies across gender, age, and ethnicity. The Mathematics Attitude Scale (MAS) was administered to measure levels of anxiety toward mathematical tasks. The analyses revealed statistically significant differences: female students presented higher mathematics anxiety than male students; older students (18–19 years) showed elevated anxiety compared to younger peers (14–15 years); and ethnic group comparisons indicated higher levels among Afro-descendant students relative to Mestizo and White students. Overall, the dataset supports research examining socio-demographic disparities in mathematics anxiety and highlights the relevance of culturally and developmentally informed educational interventions.
DOI: 10.6084/m9.figshare.31037029.v1
title: ANSIEDAD MATEMÁTICA Y FACTORES DEMOGRÁFICOS

description: This dataset originates from a cross-sectional quantitative study conducted with 2,508 high school students from 13 public urban institutions in Imbabura, Ecuador. The objective was to examine whether mathematics anxiety varies across gender, age, and ethnicity. The Mathematics Attitude Scale (MAS) was administered to measure levels of anxiety toward mathematical tasks. The analyses revealed statistically significant differences: female students presented higher mathematics anxiety than male students; older students (18–19 years) showed elevated anxiety compared to younger peers (14–15 years); and ethnic group comparisons indicated higher levels among Afro-descendant students relative to Mestizo and White students. Overall, the dataset supports research examining socio-demographic disparities in mathematics anxiety and highlights the relevance of culturally and developmentally informed educational interventions.
DOI: 10.15156/bio/sh0169294.10fu
title: SH0169294.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169295.10fu
title: SH0169295.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169296.10fu
title: SH0169296.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169297.10fu
title: SH0169297.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0169298.10fu
title: SH0169298.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.