cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17035/cardiff.27328110.v1
title: Dynamic Containment Service from Industrial Demand Response Resources Coordinated with Energy Storage Systems

description: Industrial Demand Response Resources (DRRs) are widely used in the frequency response service market. A Virtual Energy Storage System (VESS) model is developed to enable industrial DRRs to participate in the Dynamic Cointainment (DC) service by coordinating with an Energy Storage System (ESS). The power and energy capacity of the ESS are determined by considering its complementary characteristics with industrial DRRs, enabling the VESS to successfully provide the DC service as a whole under the proposed control strategy. Meanwhile, the operational baseline of the ESS is updated based on the “state of energy” management rules for energy-limited units as defined in the DC service.“Numerical results and figures.xlsx” provides the numerical results of Fig. 6 - Fig. 11 of the paper. It contains seven sheets, providing the data behind Fig. 6 - Fig. 11 of the paper.In the “Fig. 6” sheet, the x-axis describes the time variable (unit: h), and the y-axis describes grid frequency variation (unit: Hz), delivery ratios of DC high service, related power curves (unit: MW), and state of charge (SOC) changing process of energy storage system-1 in scenario A.In the “Fig. 7” sheet, the x-axis describes the time variable (unit: h), and the y-axis describes delivery power curves of the virtual energy storage systems (unit: MW), steam power generation (unit: MW), and energy storage system-2 in scenario A.In the “Fig. 8” sheet, the x-axis describes the time variable (unit: h), and the y-axis describes grid frequency variation (unit: Hz), delivery ratios of DC high service, related power curves (unit: MW), and SOC changing process of energy storage system-1 in scenario B.In the “Fig. 9” sheet, the x-axis describes the time variable (unit: h), and the y-axis describes delivery power curves of the virtual energy storage systems (unit: MW), steam power generation (unit: MW), and energy storage system-2 in scenario B.In the “Fig. 10” sheet, the x-axis describes the time variable (unit: h), and the y-axis describes grid frequency variation (unit: Hz), delivery ratios of DC high service, related power curves (unit: MW), and SOC changing process of energy storage system-1 in scenario C.In the “Fig. 11” sheet, the x-axis describes the time variable (unit: h), and the y-axis describes delivery power curves of the virtual energy storage systems (unit: MW), steam power generation (unit: MW), and energy storage system-2 in scenario C. Moreover, the y-axis also describes recovery power curve (unit: MW), actual power curve (unit: MW), and SOC changing process of ESS-2.<br><br><br>
DOI: 10.5281/zenodo.11154528
title: Underwater images collected by  Scuba diving in Hermitage, Réunion - 2021-03-17

description: This dataset was collected by  Scuba diving in Hermitage, Réunion - 2021-03-17. 

            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.

            

            
 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!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.15183486
title: Data of the Reconstruction of martensite variant microstructures in grains of deformed NiTi shape memory alloy by TEM

description: Martensite variant microstructure analysis in nanograins of plastically deformed NiTi shape memry wire with TEM based methods. Data acquistion and processing is provided in the Experimental part in the publication.
DOI: 10.5281/zenodo.15183487
title: Data of the Reconstruction of martensite variant microstructures in grains of deformed NiTi shape memory alloy by TEM

description: Martensite variant microstructure analysis in nanograins of plastically deformed NiTi shape memry wire with TEM based methods. Data acquistion and processing is provided in the Experimental part in the publication.
DOI: 10.15468/dl.5fzd8p
title: Occurrence Download

description: A dataset containing 1314 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is MIR-PIB Bacteria and Flagellates Biodiversity Database"
 ]
}

 The dataset includes 1314 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017496-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11154639
title: Underwater images collected by  Scuba diving in Hermitage, Réunion - 2021-03-17

description: This dataset was collected by  Scuba diving in Hermitage, Réunion - 2021-03-17. 

            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.

            

            
 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!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.15468/dl.36k7mu
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biodiversity around home: A case study for monitoring urban biodiversity at Barrackpore town, North 24 Parganas, West Bengal"
 ]
}

 The dataset includes 240 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017494-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sqy79z
title: Occurrence Download

description: A dataset containing 202 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Macquarie University Biodiversity and Conservation Student Observations"
 ]
}

 The dataset includes 202 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017495-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14212112
title: amphiesma_stolatum_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14212113
title: amphiesma_stolatum_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14212102
title: bio files
DOI: 10.5281/zenodo.14212103
title: bio files
DOI: 10.5281/zenodo.14212125
title: ahaetulla_nasuta_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14212126
title: ahaetulla_nasuta_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14212135
title: acanthocercus_atricollis_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14212136
title: acanthocercus_atricollis_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14212078
title: Supplementary Material - An overview of limnological studies in Brazilian fish farms

description: Este repositório contém uma planilha Excel com dados complementares para o artigo "Uma visão geral dos estudos limnológicos nas pisciculturas brasileiras". A planilha inclui informações sobre a qualidade da água e parâmetros limnológicos observados em pisciculturas no Brasil, conforme discutido no artigo. Os dados abrangem variáveis como temperatura, pH, oxigênio dissolvido, entre outras, coletadas em diversas pisciculturas espalhadas pelo país.
DOI: 10.5281/zenodo.14212079
title: Supplementary Material - An overview of limnological studies in Brazilian fish farms

description: Este repositório contém uma planilha Excel com dados complementares para o artigo "Uma visão geral dos estudos limnológicos nas pisciculturas brasileiras". A planilha inclui informações sobre a qualidade da água e parâmetros limnológicos observados em pisciculturas no Brasil, conforme discutido no artigo. Os dados abrangem variáveis como temperatura, pH, oxigênio dissolvido, entre outras, coletadas em diversas pisciculturas espalhadas pelo país.
DOI: 10.5281/zenodo.14212143
title: acanthocercus_annectans_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.14212144
title: acanthocercus_annectans_zanfona_1.fasta.gz
DOI: 10.25345/c5k64b517
title: MassIVE MSV000096511 - GNPS - Ethyl acetate Fraction K. cistoidea
title: Bioactive Fraction against bacterial from ethanolic extract from K. cistoidea
DOI: 10.15468/dl.c36y3j
title: Occurrence Download

description: A dataset containing 31154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Cambodia",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 31154 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031350-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z7mybk
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (iNaturalist Research-grade Observations, Observation.org, Nature data from around the World)",
 "OccurrenceStatus is Present",
 "TaxonKey is Centris niveofasciata Friese, 1900"
 ]
}

 The dataset includes 39 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031351-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xfh8zz
title: Occurrence Download

description: A dataset containing 5770 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Procyon cancrivorus G"
 ]
}

 The dataset includes 5770 records from 250 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031352-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0206736.10fu
title: SH0206736.10FU

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/sh0206737.10fu
title: SH0206737.10FU

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/sh0206738.10fu
title: SH0206738.10FU

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/sh0206739.10fu
title: SH0206739.10FU

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/sh0206740.10fu
title: SH0206740.10FU

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/sh0206741.10fu
title: SH0206741.10FU

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/sh0206742.10fu
title: SH0206742.10FU

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/sh0206743.10fu
title: SH0206743.10FU

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.