cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/ilshjphotbmrgu-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C20H17N3/c1-2-12-21-17(5-1)15-8-10-16(11-9-15)20(18-6-3-13-22-18)19-7-4-14-23-19/h1-14,20,22-23H

description: dataset for infrared absorption spectroscopy (IR)
DOI: 10.14272/ilshjphotbmrgu-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C20H17N3/c1-2-12-21-17(5-1)15-8-10-16(11-9-15)20(18-6-3-13-22-18)19-7-4-14-23-19/h1-14,20,22-23H

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.14272/ilshjphotbmrgu-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C20H17N3/c1-2-12-21-17(5-1)15-8-10-16(11-9-15)20(18-6-3-13-22-18)19-7-4-14-23-19/h1-14,20,22-23H

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/ilshjphotbmrgu-uhfffaoysa-n/chmo0000604
title: heteronuclear single quantum coherence (HSQC) ()
title: InChI=1S/C20H17N3/c1-2-12-21-17(5-1)15-8-10-16(11-9-15)20(18-6-3-13-22-18)19-7-4-14-23-19/h1-14,20,22-23H

description: dataset for heteronuclear single quantum coherence (HSQC)
DOI: 10.60656/eb7939d0e8414644
title: Custom collection of classification data from Palaiseau between 1 Jan and 31 Dec 2024
DOI: 10.14272/ilshjphotbmrgu-uhfffaoysa-n.1
title: 2-(4-(di(1H-pyrrol-2-yl)methyl)phenyl)pyridine
title: InChI=1S/C20H17N3/c1-2-12-21-17(5-1)15-8-10-16(11-9-15)20(18-6-3-13-22-18)19-7-4-14-23-19/h1-14,20,22-23H

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C20H17N3/c1-2-12-21-17(5-1)15-8-10-16(11-9-15)20(18-6-3-13-22-18)19-7-4-14-23-19/h1-14,20,22-23H, and canonical SMILES descriptor[cheminf_000007]: c1ccc(nc1)c1ccc(cc1)C(c1[nH]ccc1)c1[nH]ccc1, and by the IUPAC name[cheminf_000107]: .
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-50030

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 129.0 (°C)
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000604 | heteronuclear single quantum coherence (HSQC)
 
 CHMO:0000470 | mass spectrometry (MS)
 
 CHMO:0000292 | ultraviolet-visible spectrophotometry (UV-VIS)
 
 CHMO:0000630 | infrared absorption spectroscopy (IR)
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/bdtmrqvywgtpfo-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (1,2-bis[4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl]ethane-1,2-dione)
title: InChI=1S/C26H32B2O6/c1-23(2)24(3,4)32-27(31-23)19-13-9-17(10-14-19)21(29)22(30)18-11-15-20(16-12-18)28-33-25(5,6)26(7,8)34-28/h9-16H,1-8H3

description: dataset for mass spectrometry (MS)
DOI: 10.14272/bdtmrqvywgtpfo-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (1,2-bis[4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl]ethane-1,2-dione)
title: InChI=1S/C26H32B2O6/c1-23(2)24(3,4)32-27(31-23)19-13-9-17(10-14-19)21(29)22(30)18-11-15-20(16-12-18)28-33-25(5,6)26(7,8)34-28/h9-16H,1-8H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR)
DOI: 10.14272/bdtmrqvywgtpfo-uhfffaoysa-n/chmo0000594
title: 11B nuclear magnetic resonance spectroscopy (11B NMR) (1,2-bis[4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl]ethane-1,2-dione)
title: InChI=1S/C26H32B2O6/c1-23(2)24(3,4)32-27(31-23)19-13-9-17(10-14-19)21(29)22(30)18-11-15-20(16-12-18)28-33-25(5,6)26(7,8)34-28/h9-16H,1-8H3

description: dataset for 11B nuclear magnetic resonance spectroscopy (11B NMR)
DOI: 10.14272/bdtmrqvywgtpfo-uhfffaoysa-n/chmo0000604
title: heteronuclear single quantum coherence (HSQC) (1,2-bis[4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl]ethane-1,2-dione)
title: InChI=1S/C26H32B2O6/c1-23(2)24(3,4)32-27(31-23)19-13-9-17(10-14-19)21(29)22(30)18-11-15-20(16-12-18)28-33-25(5,6)26(7,8)34-28/h9-16H,1-8H3

description: dataset for heteronuclear single quantum coherence (HSQC)
DOI: 10.14272/bdtmrqvywgtpfo-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (1,2-bis[4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl]ethane-1,2-dione)
title: InChI=1S/C26H32B2O6/c1-23(2)24(3,4)32-27(31-23)19-13-9-17(10-14-19)21(29)22(30)18-11-15-20(16-12-18)28-33-25(5,6)26(7,8)34-28/h9-16H,1-8H3

description: dataset for infrared absorption spectroscopy (IR)
DOI: 10.5281/zenodo.11242793
title: Water eDNA metabarcoding: mifish primer dataset

description: In June 2019, the Thünen Institute conducted the WH429 research cruise with the aim of assessing marine biodiversity through environmental DNA (eDNA) analysis. During the expedition, eDNA samples were collected from various marine locations and subsequently processed for metabarcoding using the MiFish primers, which target a specific region of the mitochondrial 12S rRNA gene in fish. The resulting eDNA sequences were generated on the Illumina MiSeq platform, producing high-quality fastq files. This dataset provides critical metadata, including sequencing reads, quality scores, which are essential for studying the spatial and temporal patterns of fish diversity. The comprehensive eDNA dataset serves as a valuable resource for ecological and conservation research, enabling the identification of fish species and the monitoring of marine ecosystems with high resolution and accuracy.
DOI: 10.5281/zenodo.11242794
title: Water eDNA metabarcoding: mifish primer dataset

description: In June 2019, the Thünen Institute conducted the WH429 research cruise with the aim of assessing marine biodiversity through environmental DNA (eDNA) analysis. During the expedition, eDNA samples were collected from various marine locations and subsequently processed for metabarcoding using the MiFish primers, which target a specific region of the mitochondrial 12S rRNA gene in fish. The resulting eDNA sequences were generated on the Illumina MiSeq platform, producing high-quality fastq files. This dataset provides critical metadata, including sequencing reads, quality scores, which are essential for studying the spatial and temporal patterns of fish diversity. The comprehensive eDNA dataset serves as a valuable resource for ecological and conservation research, enabling the identification of fish species and the monitoring of marine ecosystems with high resolution and accuracy.
DOI: 10.14272/bdtmrqvywgtpfo-uhfffaoysa-n.1
title: 1,2-bis[4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl]ethane-1,2-dione
title: InChI=1S/C26H32B2O6/c1-23(2)24(3,4)32-27(31-23)19-13-9-17(10-14-19)21(29)22(30)18-11-15-20(16-12-18)28-33-25(5,6)26(7,8)34-28/h9-16H,1-8H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367].
 The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]:
 InChI descriptor[cheminf_000113]: InChI=1S/C26H32B2O6/c1-23(2)24(3,4)32-27(31-23)19-13-9-17(10-14-19)21(29)22(30)18-11-15-20(16-12-18)28-33-25(5,6)26(7,8)34-28/h9-16H,1-8H3, and canonical SMILES descriptor[cheminf_000007]: O=C(C(=O)c1ccc(cc1)B1OC(C(O1)(C)C)(C)C)c1ccc(cc1)B1OC(C(O1)(C)C)(C)C, and by the IUPAC name[cheminf_000107]: 1,2-bis[4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl]ethane-1,2-dione.
 
 The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]:
 
 The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-50049

 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]:
 Melting point descriptor[CHEMINF_000256]: 221.0 (°C)
 Boiling point descriptor[CHEMINF_000257]: 
 Refractive index descriptor[CHEMINF_000253]:

 The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]:
 
 CHMO:0000604 | heteronuclear single quantum coherence (HSQC)
 
 CHMO:0000594 | 11B nuclear magnetic resonance spectroscopy (11B NMR)
 
 CHMO:0000470 | mass spectrometry (MS)
 
 CHMO:0000630 | infrared absorption spectroscopy (IR)
 
 CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR)
 
 CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR)
 
 The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: 

 Used ontologies:
 CHEBI - Chemical Entities of Biological Interest
 CHEMINF - chemical information ontology (information entities about chemical entities)
 CHMO - Chemical Methods Ontology
 OBI - Ontology for Biomedical Investigations
DOI: 10.14272/bdtmrqvywgtpfo-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (1,2-bis[4-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)phenyl]ethane-1,2-dione)
title: InChI=1S/C26H32B2O6/c1-23(2)24(3,4)32-27(31-23)19-13-9-17(10-14-19)21(29)22(30)18-11-15-20(16-12-18)28-33-25(5,6)26(7,8)34-28/h9-16H,1-8H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.5281/zenodo.14711105
title: Time Series Validation dataset for "Shoreline time series analysis through the Isoradiometric Method: bridging Landscape Evolution and coastal management"

description: This repository contains the dataset used for the validation performed in the related submitted article "Shoreline time series analysis through the Isoradiometric Method: bridging Landscape Evolution and coastal management" Authors: F. Caldareri, N. Parrino, L. Balsamo, G. Dardanelli, S. Todaro, A. Sulli, A. Maltese

The linear vector data within the repository are distinguished based on the method used to obtain the shoreline position. 'D' stands for shoreline obtained through manual digitization performed on Google Earth satellite images; 'IM' stands for shoreline obtained through the Isoradiometric Method (Caldareri et. al., 2024, DOI: https://doi.org/10.1016/j.rse.2024.114286) application on Planetscope and Landsat satellite products.

Statistical parameters shown in the tables of the related article were obtained by calculating them on the distances between the vertices extracted on the corresponding shorelines analyzed in pairs.
DOI: 10.5281/zenodo.14711106
title: Time Series Validation dataset for "Shoreline time series analysis through the Isoradiometric Method: bridging Landscape Evolution and coastal management"

description: This repository contains the dataset used for the validation performed in the related submitted article "Shoreline time series analysis through the Isoradiometric Method: bridging Landscape Evolution and coastal management" Authors: F. Caldareri, N. Parrino, L. Balsamo, G. Dardanelli, S. Todaro, A. Sulli, A. Maltese

The linear vector data within the repository are distinguished based on the method used to obtain the shoreline position. 'D' stands for shoreline obtained through manual digitization performed on Google Earth satellite images; 'IM' stands for shoreline obtained through the Isoradiometric Method (Caldareri et. al., 2024, DOI: https://doi.org/10.1016/j.rse.2024.114286) application on Planetscope and Landsat satellite products.

Statistical parameters shown in the tables of the related article were obtained by calculating them on the distances between the vertices extracted on the corresponding shorelines analyzed in pairs.
DOI: 10.15468/dl.svxpgd
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Material sample",
 "DatasetKey is International Barcode of Life project (iBOL)",
 "TaxonKey is Apohyale stebbingi (Chevreux, 1888)"
 ]
}

 The dataset includes 43 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000018-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25403/upresearchdata.28246676
title: Enhancing the power conversion efficiency of organic solar cells : bacterial cellulose

description: The dataset underpinned a dissertation titled "Enhancing the conductivity of bacterial cellulose/polyvinyl alcohol composite for the development of flexible transparent electrodes". The advancement of sustainable and renewable materials in the photovoltaics industry has led to the exploration of new substrates for organic solar cells (OSCs). Bacterial cellulose (BC), generated by aerobic bacteria, has emerged as a promising option due to its unique properties compared to plant cellulose. BC membrane has a higher molecular weight, is transparent, is 99% hydrated, has high cellulose crystallinity (60–90%), high mechanical strength, and full biocompatibility. Presently, conventional conductive metal oxide substrates like fluorinedoped tin oxide (FTO) and indium tin oxide (ITO) dominate the market. However, these ma-terials have drawbacks, including the scarce availability of indium, high costs associated with necessary deposition processes, and the brittle nature of their ceramic structure. These disad-vantages restrict their application in flexible solar cells. Therefore, in this study, we developed transparent, conductive, and flexible films composed of BC and silver nanowires (AgNWs) as an alternative to traditional glass substrates. The shape, microstructure, and arrangement of cellulose fibrils were observed using SEM and TEM. X-ray diffraction (XRD) was used to examine the crystalline structure of the BC. Fourier transform infrared spectroscopy (FTIR) was used to identify functional groups and chemical bonds within the BC; results illustrated the formation of an H-bond between a hydroxyl group and an amine group of the BC.
DOI: 10.5281/zenodo.14674179
title: CRscores
DOI: 10.5281/zenodo.14674180
title: CognitiveReserveScores
DOI: 10.5281/zenodo.14699151
title: CRscores
DOI: 10.20350/digitalcsic/17017
title: Code and data for the stochastic skeleton model of the Madden-Julian oscilation with time-dependent observation-based forcing

description: [Description of methods used for collection/generation of data] Described in the file README.pdf
description: [Methods for processing the data] Described in the file README.pdf
description: The Madden-Julian oscillation (MJO) is a planetary-scale wave envelope of smaller-scale convective processes, slowly propagating eastward at the equator, going around the Earth in 30 to 60 days. The convection and circulation anomalies associated with the MJO affect weather and climate in many parts of the globe. This set of files contain a Julia implementation of the MJO skeleton model, a minimal nonlinear oscillator model of thie MJO. Previous studies primarily relied on mathematically simple and time-independent forcings in this model. Here, observation-based, time-dependent forcing functions are used to capture more realistic variability. The code presented here reads-in the needed reanalysis data, runs the skeleton model on them, and produces figures from the output data.
description: README.pdf forcing-profiles.ipynb Stochastic-skeleton-model-main.ipynb Stochastic-skeleton-model-plots.ipynb other folders and files used by these scripts, and described in the README.pdf
DOI: 10.20350/digitalcsic/17018
title: Supplementary material : Fast and green methodology based on miniaturised liquid–liquid extraction with a hydrophobic natural deep eutectic solvent of opium alkaloids from poppy seed beverages
DOI: 10.15454/htrlxe/buv3s0
title: read-me meteo Hauts Anjou_ENG.pdf

description: :unav
DOI: 10.15454/htrlxe/ccwqb2
title: weather_data_hourly_2020_champigne.tab

description: :unav
DOI: 10.15454/htrlxe/vktyre
title: Caracteristiques_techniques iMetos_3.3.pdf

description: :unav
DOI: 10.15454/htrlxe/wgrtiu
title: Technical_specifications_iMetos_3.3.pdf

description: :unav
DOI: 10.15454/htrlxe/xpl1ls
title: read-me meteo Hauts Anjou_FR.pdf

description: :unav
DOI: 10.15454/htrlxe/yoicb6
title: PET_daily.tab

description: :unav
DOI: 10.15454/htrlxe/2cyu2g
title: weather_data_hourly_2021_champigne.tab

description: :unav
DOI: 10.20350/digitalcsic/17019
title: Supplementary material : Development of a luminescence-based method for measuring West Nile Virus MTase activity and its application to screen for antivirals
DOI: 10.57745/5jq0pk
title: read-me meteo Hauts Anjou_ENG.pdf

description: :unav
DOI: 10.57745/7mg5qg
title: weather_data_hourly_2022_champigne.tab

description: :unav
DOI: 10.57745/opwy4o
title: PET_daily.tab

description: :unav
DOI: 10.57745/osfwlp
title: PET_daily.tab

description: :unav
DOI: 10.57745/r6xiqt
title: weather_data_hourly_2024_champigne.tab

description: :unav
DOI: 10.57745/sjibn4
title: read-me meteo Hauts Anjou_FR.pdf

description: :unav
DOI: 10.57745/w4prsd
title: weather_data_hourly_2023_champigne.tab

description: :unav
DOI: 10.15468/dl.nr6wfw
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hieracium asturicum Pau"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000019-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13026/2kfz-4798
title: MIMIC-IV-Ext-DiReCT

description: Large language models (LLMs) have recently demonstrated remarkable
capabilities across a broad spectrum of tasks and applications, including the
medical field. Models like GPT-4 excel in medical question answering but
encounter challenges in interpretability when managing complex tasks in real
clinical settings. To address this, we introduce the Diagnostic Reasoning
Dataset for Clinical Notes (DiReCT), designed to evaluate the reasoning
ability and interpretability of LLMs compared to human doctors. The dataset
comprises 511 clinical notes (sourced from MIMIC-IV), each meticulously
annotated by physicians, detailing the diagnostic reasoning process from
initial observations to the final diagnosis.
DOI: 10.13026/yf96-kc87
title: MIMIC-IV-Ext-DiReCT

description: Large language models (LLMs) have recently demonstrated remarkable
capabilities across a broad spectrum of tasks and applications, including the
medical field. Models like GPT-4 excel in medical question answering but
encounter challenges in interpretability when managing complex tasks in real
clinical settings. To address this, we introduce the Diagnostic Reasoning
Dataset for Clinical Notes (DiReCT), designed to evaluate the reasoning
ability and interpretability of LLMs compared to human doctors. The dataset
comprises 511 clinical notes (sourced from MIMIC-IV), each meticulously
annotated by physicians, detailing the diagnostic reasoning process from
initial observations to the final diagnosis.
DOI: 10.5281/zenodo.14701640
title: Dataset of A Multi-Drone System Proof of Concept for Forestry Applications

description: The dataset presented in this study originates from a multi-drone proof of concept designed for efficient forest mapping and autonomous operation, conducted within the framework of the OPENSWARM EU Project. It comprises data collected from field experiments where multiple drones collaboratively navigated and mapped a forest environment. The dataset includes sensor data from state-of-the-art open-source SLAM frameworks, such as LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) and Distributed Collaborative LiDAR SLAM (DCL-SLAM). These frameworks were integrated within the MRS UAV System and Swarm Formation packages, utilizing Robot Operating System (ROS) middleware. The recorded data consists of LiDAR point clouds, IMU readings, GPS trajectories, and system logs, capturing the drones' performance in complex, real-world forestry conditions. Additionally, flight control parameters optimized using an auto-tuning particle swarm optimization method are included to support reproducibility and further analysis. This dataset aims to facilitate research in autonomous multi-drone systems for forestry applications, offering valuable insights for scalable and sustainable forest management solutions.
description: @Article{drones9020080,

AUTHOR = {Araújo, André G. and Pizzino, Carlos A. P. and Couceiro, Micael S. and Rocha, Rui P.},

TITLE = {A Multi-Drone System Proof of Concept for Forestry Applications},

JOURNAL = {Drones},

VOLUME = {9},

YEAR = {2025},

NUMBER = {2},

ARTICLE-NUMBER = {80},

URL = {https://www.mdpi.com/2504-446X/9/2/80},

ISSN = {2504-446X},

DOI = {10.3390/drones9020080}

}
DOI: 10.5281/zenodo.14701641
title: Dataset of A Multi-Drone System Proof of Concept for Forestry Applications

description: The dataset presented in this study originates from a multi-drone proof of concept designed for efficient forest mapping and autonomous operation, conducted within the framework of the OPENSWARM EU Project. It comprises data collected from field experiments where multiple drones collaboratively navigated and mapped a forest environment. The dataset includes sensor data from state-of-the-art open-source SLAM frameworks, such as LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) and Distributed Collaborative LiDAR SLAM (DCL-SLAM). These frameworks were integrated within the MRS UAV System and Swarm Formation packages, utilizing Robot Operating System (ROS) middleware. The recorded data consists of LiDAR point clouds, IMU readings, GPS trajectories, and system logs, capturing the drones' performance in complex, real-world forestry conditions. Additionally, flight control parameters optimized using an auto-tuning particle swarm optimization method are included to support reproducibility and further analysis. This dataset aims to facilitate research in autonomous multi-drone systems for forestry applications, offering valuable insights for scalable and sustainable forest management solutions.
description: @Article{drones9020080,

AUTHOR = {Araújo, André G. and Pizzino, Carlos A. P. and Couceiro, Micael S. and Rocha, Rui P.},

TITLE = {A Multi-Drone System Proof of Concept for Forestry Applications},

JOURNAL = {Drones},

VOLUME = {9},

YEAR = {2025},

NUMBER = {2},

ARTICLE-NUMBER = {80},

URL = {https://www.mdpi.com/2504-446X/9/2/80},

ISSN = {2504-446X},

DOI = {10.3390/drones9020080}

}
DOI: 10.14278/rodare.3394
title: Data publication: Matlab scripts for PSD measurments and rate spectra analysis

description: The open datasets provide the Matlab scripts for the calculation of Power Spectra Density and Rate Spectra in the manuscirpt 'How crystal surface reactivity controls the evolution of surface microtopography during dissolution'.
DOI: 10.14278/rodare.3393
title: Data publication: Matlab scripts for PSD measurments and rate spectra analysis

description: The open datasets provide the Matlab scripts for the calculation of Power Spectra Density and Rate Spectra in the manuscirpt 'How crystal surface reactivity controls the evolution of surface microtopography during dissolution'.