cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24996464.v1
title: Amac Morphology Raw Data

description: This dataset was used for the analyses reported in "Trait variation in patchy landscapes: morphology of spotted salamanders (Ambystoma maculatum) varies more within ponds than between ponds". Data were collected from photo images in 2016 by E. Green and D. Daversa. The data reports morphological traits of spotted salamanders collected in Eastern Missouri. Pond characteristics are also included in the data set. Specifically, the dataframe contains the following columns: ID: Unique identifier for individualsdate: date of capture yearday: day of the year that individual was capturedspecies: the Genus and species of the individual (all are A. maculatum)side_image: A unique ID for photo image taken of the side of the individual (we also took ventral photos that were not used in the analysis)stage: larval developmental stage as described by Harrison (1969). See main text for citation. pond: name of the pond where the individual was capturesite: name of the conservation property where the pond of capture was located dipnets: number of dipnets made at the pond where individual was captureddensity: estimated salamander density in ponds according to dipnet surveysdensity_key: a categorical variable describing the density of salamanders at the pond, relative to other survey pondsspecies_richness: Number of amphibian species captured during dipnet surveypredator_sum: the total number of predator individuals found at the pondpredator_richness: The number of predator species found at the pondpredator_density: estimated number of predators in the pond of capturepred_key: A categorical variable describing the density of predators in the pond of capture, relative to other survey pondsorigin: The age of the pond of capture (new vs. old)head_length: linear length of head (mm)head_height: Linear height of head (mm)tail_height: linear height of tail (mm)total length: linear body length, including head and tail (mm)mass: the weight of the individual (g)whole_shape: morphometric shape of individual, including all body segmentsbody_shape: morphometric shape of the body, excluding head and tailtail_shape: morphometric shape of the tailhead_shape: morphometric shape of the head
DOI: 10.6084/m9.figshare.24790842
title: Verifying Opacity of Discrete-Timed Automata Artifact

description: Opacity is a powerful confidentiality property that holds if a system cannot leak secret information through observable behavior. In recent years, time has become an increasingly popular attack vector. The notion of opacity has therefore been extended to timed automata (TA). However, the verification of opacity of TA has been proven to be undecidable for the commonly used dense time model. To make the problem decidable, state of the art approaches consider weaker notions of opacity or heavily restrict the class of considered TA, resulting in unrealistic threat models.<br>We address the problem of verifying opacity of TA without restrictions. For this purpose, we consider a discrete time setting. We present a novel algorithm to transform TA to equivalent finite automata (FA) and then use known methods to verify opacity of the resulting FA. To improve the efficiency of our algorithm, we use a novel time abstraction that significantly reduces the state space of the resulting FA, improving the scalability of our approach. We validate our method using randomized systems, as well as four case studies from the literature showing that our approach is applicable in practice.<br><br>We provide a VM with all software pre-installed to run our evaluation.<br>The code is also provided in our gitlab repository: https://gitlab.com/julianklein/opacity-verification-of-discrete-timed-automataThe VM has commit b3b18a0f8bb071a6c0e0b29be5c11e5a1caa4c84 (on main branch) installed.
DOI: 10.6084/m9.figshare.24790842.v3
title: Verifying Opacity of Discrete-Timed Automata Artifact

description: Opacity is a powerful confidentiality property that holds if a system cannot leak secret information through observable behavior. In recent years, time has become an increasingly popular attack vector. The notion of opacity has therefore been extended to timed automata (TA). However, the verification of opacity of TA has been proven to be undecidable for the commonly used dense time model. To make the problem decidable, state of the art approaches consider weaker notions of opacity or heavily restrict the class of considered TA, resulting in unrealistic threat models.<br>We address the problem of verifying opacity of TA without restrictions. For this purpose, we consider a discrete time setting. We present a novel algorithm to transform TA to equivalent finite automata (FA) and then use known methods to verify opacity of the resulting FA. To improve the efficiency of our algorithm, we use a novel time abstraction that significantly reduces the state space of the resulting FA, improving the scalability of our approach. We validate our method using randomized systems, as well as four case studies from the literature showing that our approach is applicable in practice.<br><br>We provide a VM with all software pre-installed to run our evaluation.
DOI: 10.5281/zenodo.10511429
title: Respuesta por acceso a información pública de actos administrativos revocados en 2022- APN Argentina

description: Procesamiento de respuesta a pedidos de acceso a información pública a 18 ministerios de la Administración Pública Nacional Argentina, en relación a actos administrativos revocados por razones de ilegitimidad durante el año 2022.
DOI: 10.5281/zenodo.10511430
title: Respuesta por acceso a información pública de actos administrativos revocados en 2022- APN Argentina

description: Procesamiento de respuesta a pedidos de acceso a información pública a 18 ministerios de la Administración Pública Nacional Argentina, en relación a actos administrativos revocados por razones de ilegitimidad durante el año 2022.
DOI: 10.6084/m9.figshare.24994742
title: Geodiversity in the Amazon Drainage Basin

description: This dataset contains all maps used in the analysis of geodiversity in the Amazon drainage basin, for the article "Geodiversity in the Amazon Drainage Basin" published in Philosophical Transactions A (https://doi.org/10.1098/rsta.2023.0065). The database consists of the following:A map of the geodiversity index A map of the geological diversity index and it's source dataA map of the geomorphological diversity index and it's source dataA map of the hydrological diversity indexA map of the main rivers and river colours in the Amazon drainage basinA map of the soil diversity index and it's source dataA map with the major sedimentary basins, the Andean orogeny and cratons in the Amazon drainage basinA map with the areas and locations mentioned in the literature reviewAll the geodiversity and geodiversity component index maps were retrieved from an open access global geodiversity dataset by Polman et al. (https://doi.org/10.21942/uva.23496923). The base layers were also retrieved from open access databases:The lithological base layer was retrieved from Hartmann and Moosdorf (2012), http://doi.wiley.com/10.1029/2012GC004370The geomorphological base layer was based of a DEM created by Yamazaki et al. (2017), http://doi.wiley.com/10.1002/2017GL072874The river water colour map was retrieved from Venticinque et al (2016), doi: 10.5194/essd-8-651-2016The soil diversity map was retrieved from Hengl et al. (2017), doi: 10.1371/journal.pone.0169748Metadata of how maps were produced is supplied per map, layer and feature set in the database. There, you'll find references to additional works that were used to draw the extent of sedimentary basins, structural arches, cratons, and catchments as portrayed in the maps.
DOI: 10.34740/kaggle/dsv/7402743
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.34740/kaggle/dsv/7402746
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.5281/zenodo.10362368
title: Sample MD trajectory

description: MD trajectory used for training graph convolutional neural networks as coarse grained force fields as described in the following publications:



E. Ricci, G. Giannakopoulos, V. Karkaletsis, D. N. Theodorou, N. Vergadou. 2022. "Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls". In Proceedings of 12th Conference on Artificial Intelligence (SETN). ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3549737.3549793. Open access https://zenodo.org/record/7078577

Gerakinis, D.-P., Ricci, E., Giannakopoulos, G., Karkaletsis, V., Theodorou, D. N., & Vergadou, N. (2024). Machine Learning-Based Coarse Grained Interaction Potentials for Molecular Systems. Zenodo. https://doi.org/10.5281/zenodo.10501037


The compressed archive contains input and output files for a NVT molecular dynamics simulation of a system containing 500 molecules of liquid benzene at 300 K, performed using LAMMPS.

The reference code used to train the model, with detailed usage instructions, is available at: https://github.com/ml-multimem/schnetpack-for-bulk-systems
DOI: 10.5281/zenodo.10362367
title: Sample MD trajectory

description: MD trajectory used for training graph convolutional neural networks as coarse grained force fields as described in the following publications:



E. Ricci, G. Giannakopoulos, V. Karkaletsis, D. N. Theodorou, N. Vergadou. 2022. "Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls". In Proceedings of 12th Conference on Artificial Intelligence (SETN). ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3549737.3549793. Open access https://zenodo.org/record/7078577

Gerakinis, D.-P., Ricci, E., Giannakopoulos, G., Karkaletsis, V., Theodorou, D. N., & Vergadou, N. (2024). Machine Learning-Based Coarse Grained Interaction Potentials for Molecular Systems. Zenodo. https://doi.org/10.5281/zenodo.10501037


The compressed archive contains input and output files for a NVT molecular dynamics simulation of a system containing 500 molecules of liquid benzene at 300 K, performed using LAMMPS.

The reference code used to train the model, with detailed usage instructions, is available at: https://github.com/ml-multimem/schnetpack-for-bulk-systems
DOI: 10.6084/m9.figshare.24996404
title: <b>Supplementary Files: Seagrass-mediated rhizosphere redox gradients are linked with ammonium accumulation driven by diazotrophs</b>

description: Seagrasses can enhance nutrient mobilization in their rhizosphere via complex interactions with sediment redox conditions and microbial populations. Yet, limited knowledge exists on how seagrass-derived rhizosphere dynamics affect nitrogen cycling. Using optode and gel27 sampler based chemical imaging, we show that radial O<sub>2</sub> loss (ROL) from rhizomes and roots leads to formation of redox gradients around below-ground tissues of seagrass (Zostera marina), which are co-localised with regions of high ammonium concentrations in the rhizosphere. Combining such chemical imaging with fine-scale sampling for microbial community and gene expression analyses indicated that multiple biogeochemical pathways and microbial players can lead to high ammonium concentration within the oxidized regions of the seagrass rhizosphere. Symbiotic N<sub>2</sub>-fixing bacteria (Bradyrhizobium) were particularly abundant and expressed the diazotroph functional marker gene nifH in Z. marina rhizosphere areas with high ammonium concentrations. Such association between Z. marina and Bradyrhizobium can facilitate ammonium mobilization, the preferred nitrogen source for seagrasses, enhancing seagrass productivity within nitrogen-limited environments. ROL also caused strong gradients of sulphide at anoxic/oxic interfaces in rhizosphere areas where we found enhanced <i>nifH</i> transcription by sulphate-reducing bacteria. Furthermore, we found a high abundance of methylotrophic and sulphide-oxidizing bacteria in rhizosphere areas, where O<sub>2</sub> was released from seagrass rhizomes and roots, and these bacteria could play a beneficial role for the plants in terms of their methane and sulphide oxidation, as well as their formation of growth factors and phytohormones. ROL from below-ground tissues of seagrass thus seems crucial for ammonium production in the rhizosphere via stimulation of multiple diazotrophic associations.
DOI: 10.6084/m9.figshare.24996404.v1
title: <b>Supplementary Files: Seagrass-mediated rhizosphere redox gradients are linked with ammonium accumulation driven by diazotrophs</b>

description: Seagrasses can enhance nutrient mobilization in their rhizosphere via complex interactions with sediment redox conditions and microbial populations. Yet, limited knowledge exists on how seagrass-derived rhizosphere dynamics affect nitrogen cycling. Using optode and gel27 sampler based chemical imaging, we show that radial O<sub>2</sub> loss (ROL) from rhizomes and roots leads to formation of redox gradients around below-ground tissues of seagrass (Zostera marina), which are co-localised with regions of high ammonium concentrations in the rhizosphere. Combining such chemical imaging with fine-scale sampling for microbial community and gene expression analyses indicated that multiple biogeochemical pathways and microbial players can lead to high ammonium concentration within the oxidized regions of the seagrass rhizosphere. Symbiotic N<sub>2</sub>-fixing bacteria (Bradyrhizobium) were particularly abundant and expressed the diazotroph functional marker gene nifH in Z. marina rhizosphere areas with high ammonium concentrations. Such association between Z. marina and Bradyrhizobium can facilitate ammonium mobilization, the preferred nitrogen source for seagrasses, enhancing seagrass productivity within nitrogen-limited environments. ROL also caused strong gradients of sulphide at anoxic/oxic interfaces in rhizosphere areas where we found enhanced <i>nifH</i> transcription by sulphate-reducing bacteria. Furthermore, we found a high abundance of methylotrophic and sulphide-oxidizing bacteria in rhizosphere areas, where O<sub>2</sub> was released from seagrass rhizomes and roots, and these bacteria could play a beneficial role for the plants in terms of their methane and sulphide oxidation, as well as their formation of growth factors and phytohormones. ROL from below-ground tissues of seagrass thus seems crucial for ammonium production in the rhizosphere via stimulation of multiple diazotrophic associations.
DOI: 10.5281/zenodo.10511259
title: Thorsten-Voice Dataset 2023.09 Hessisch

description: The goal of project "Thorsten-Voice" is to provide voice datasets and TTS models for free and high quality german artificial voice.

This dataset "Thorsten-Voice dataset 2023.09 Hessisch" is based on normal german textual input but pronounced in a german regional dialect called "Hessisch". This special dialect is spoken mostly in the southern region of the german state "Hessen". It is recorded by Thorsten Müller, audio optimized by Dominik Kreutz and licenced under CC0 to provide it for anybody without any financial or licence struggle.



"I contribute my personal voice as a person believing in a world where all people are equal. No matter of gender, sexual orientation, religion, skin color and geocoordinates of birth location. A global world where everybody is warmly welcome on any place on this planet and open and free knowledge and education is available to everyone." (Thorsten Müller)


 

Dataset details:



ljspeech file and directory structure

2.108 recorded phrases (wav files)

samplerate 22.050Hz

mono

normalized to -24dB

no silence at beginning/ending


See more details on my Github page or Thorsten-Voice project website.
DOI: 10.35078/6ufhml/2vwtgt
title: Pedra Preta A.jpg

description: :unav
DOI: 10.35078/6ufhml/gocqpt
title: Adesílio Soares C.jpg

description: :unav
DOI: 10.35078/krrwu6/6badoh
title: São José da Piedade A.jpg

description: :unav
DOI: 10.35078/krrwu6/a8gl4u
title: Córrego dos Índios A.jpg

description: :unav
DOI: 10.35078/6ufhml/sebpkt
title: Dimas Ferraz B.jpg

description: :unav
DOI: 10.35078/krrwu6/ojr1eu
title: Ponte do Jacaré A.jpg

description: :unav
DOI: 10.35078/krrwu6/tyfiwd
title: Cercalá C.jpg

description: :unav
DOI: 10.35078/6ufhml/kilfbp
title: Ponte do Soberbo B.jpg

description: :unav
DOI: 10.35078/krrwu6/ucqzcd
title: Ponte Preta A.jpg

description: :unav
DOI: 10.35078/6ufhml/nui9b3
title: Taboão B.jpg

description: :unav
DOI: 10.35078/krrwu6/luhtpu
title: Cachoeira Alta A.jpg

description: :unav
DOI: 10.35078/6ufhml/s2m3yt
title: Ariny Lima A.jpg

description: :unav
DOI: 10.35078/krrwu6/ociccm
title: Bela Vista B.jpg

description: :unav
DOI: 10.35078/6ufhml/9h7uog
title: Volta Fria B.jpg

description: :unav
DOI: 10.35078/krrwu6/rpaemw
title: Petróleo A.jpg

description: :unav
DOI: 10.35078/6ufhml/ectwxa
title: José Joaquim B.jpg

description: :unav
DOI: 10.35078/6ufhml/5wayzf
title: São Bento A.jpg

description: :unav
DOI: 10.35078/krrwu6/m0ezh6
title: Bom Destino B.jpg

description: :unav
DOI: 10.35078/krrwu6/xbujdz
title: Córrego dos Frades Sítio D.jpg

description: :unav
DOI: 10.35078/6ufhml/i65gdw
title: Pedreira A.jpg

description: :unav
DOI: 10.35078/krrwu6/wdkazk
title: José Santana Sítio B.jpg

description: :unav
DOI: 10.35078/6ufhml/keuswa
title: Lobo B.jpg

description: :unav
DOI: 10.35078/krrwu6/fotc9y
title: Paciência B.jpg

description: :unav
DOI: 10.35078/6ufhml/rjcgyd
title: Adolfo Brum A.jpg

description: :unav
DOI: 10.35078/krrwu6/ytx4ar
title: Ivan Luiz Pinto Moreira A.jpg

description: :unav
DOI: 10.35078/6ufhml/gehrjf
title: Lobo C.jpg

description: :unav
DOI: 10.35078/krrwu6/alm7vf
title: Boa Vista II B.jpg

description: :unav
DOI: 10.35078/6ufhml/xbftnx
title: Vituruna A.jpg

description: :unav
DOI: 10.35078/krrwu6/ousync
title: Varinhas B.jpg

description: :unav
DOI: 10.35078/6ufhml/8w4fxu
title: Viçoso D.jpg

description: :unav
DOI: 10.35078/krrwu6/xyejyd
title: Lambe Beiço Sítio B.jpg

description: :unav
DOI: 10.35078/krrwu6/f1oih7
title: Cachoeira Alta B.jpg

description: :unav
DOI: 10.35078/6ufhml/souou0
title: Vista Alegre D.jpg

description: :unav
DOI: 10.35078/krrwu6/iask6j
title: Pouso Alto D.jpg

description: :unav
DOI: 10.35078/6ufhml/ml26xa
title: Valadão B.jpg

description: :unav
DOI: 10.35078/6ufhml/dx06wt
title: Colônia B.jpg

description: :unav
DOI: 10.35078/krrwu6/ttxfxq
title: Alsácia Lourena A.jpg

description: :unav
DOI: 10.35078/6ufhml/y1iedj
title: Ariny Lima C.jpg

description: :unav
DOI: 10.35078/krrwu6/znosis
title: Oreto Pereira B.jpg

description: :unav
DOI: 10.35078/6ufhml/rbuaoe
title: João S. Félix A.jpg

description: :unav
DOI: 10.35078/krrwu6/wocrmn
title: José Santana Sítio A.jpg

description: :unav
DOI: 10.35078/krrwu6/hzmkwr
title: Pouso Alto A.jpg

description: :unav
DOI: 10.35078/6ufhml/ls7297
title: Floresta B.jpg

description: :unav
DOI: 10.35078/6ufhml/1mpjdl
title: Zito Soares Vila B.jpg

description: :unav
DOI: 10.35078/krrwu6/c6fuzl
title: Bom Destino A.jpg

description: :unav
DOI: 10.35078/krrwu6/kdfbhp
title: Lagoa A.jpg

description: :unav
DOI: 10.35078/6ufhml/atznoi
title: João Ilário Sítio A.jpg

description: :unav
DOI: 10.35078/6ufhml/abcmm0
title: Vituruna C.jpg

description: :unav
DOI: 10.35078/krrwu6/ry5jlt
title: Alegria A.jpg

description: :unav
DOI: 10.35078/6ufhml/y8r5ho
title: Ariny Lima B.jpg

description: :unav
DOI: 10.35078/krrwu6/ihq1kl
title: Ponte Preta B.jpg

description: :unav
DOI: 10.35078/6ufhml/etq0ca
title: Aguiar B.jpg

description: :unav
DOI: 10.35078/krrwu6/v7jmpg
title: Paciência A.jpg

description: :unav
DOI: 10.35078/krrwu6/yquwdy
title: Bonfim A.jpg

description: :unav
DOI: 10.35078/6ufhml/5ewan7
title: Quilombo II B.jpg

description: :unav
DOI: 10.35078/krrwu6/tfjgmb
title: Córrego dos Frades Sítio C.jpg

description: :unav
DOI: 10.35078/6ufhml/zyszgr
title: Mutuca A.jpg

description: :unav
DOI: 10.35078/krrwu6/ubojbk
title: Matinha A.jpg

description: :unav
DOI: 10.35078/6ufhml/0lgavl
title: Orives A.jpg

description: :unav
DOI: 10.35078/krrwu6/iozwzj
title: Cercalá D.jpg

description: :unav
DOI: 10.35078/6ufhml/iqm5x7
title: José Barcelos B.jpg

description: :unav
DOI: 10.35078/krrwu6/cpl2fa
title: Vista Alegre II A.jpg

description: :unav
DOI: 10.35078/6ufhml/rn7g3m
title: Lobo D.jpg

description: :unav
DOI: 10.35078/krrwu6/b0l8ls
title: Córrego dos Frades Sítio A.jpg

description: :unav
DOI: 10.35078/6ufhml/b0mvrm
title: Penenên A.jpg

description: :unav
DOI: 10.35078/krrwu6/rj6fkh
title: Vitória B.jpg

description: :unav
DOI: 10.35078/6ufhml/zdrqw6
title: Cachoeira Alta B.jpg

description: :unav
DOI: 10.35078/krrwu6/3igvkz
title: Osvaldo Lana B.jpg

description: :unav
DOI: 10.35078/6ufhml/cpoqoq
title: José Gino B.jpg

description: :unav
DOI: 10.35078/krrwu6/wz1lxp
title: Ponte Queimada A.jpg

description: :unav
DOI: 10.35078/6ufhml/amascj
title: Viçoso C.jpg

description: :unav
DOI: 10.35078/krrwu6/mjrqns
title: Córrego dos Maia A.jpg

description: :unav
DOI: 10.35078/6ufhml/49gqvr
title: Cabeceira da Boa Viagem A.jpg

description: :unav
DOI: 10.35078/krrwu6/oayxjb
title: Bandeiras B.jpg

description: :unav
DOI: 10.35078/6ufhml/0dlvze
title: Paulo Vieira A.jpg

description: :unav
DOI: 10.35078/krrwu6/dvzx3a
title: Pouso Alto C.jpg

description: :unav
DOI: 10.35078/6ufhml/gfqpla
title: José Joaquim A.jpg

description: :unav
DOI: 10.35078/krrwu6/sck5fz
title: Pouso Alto B.jpg

description: :unav
DOI: 10.35078/6ufhml/vkgp9x
title: Santa Rita A.jpg

description: :unav
DOI: 10.35078/krrwu6/zdhbky
title: Sapecado B.jpg

description: :unav
DOI: 10.35078/6ufhml/m4ynuc
title: Empreitada A.jpg

description: :unav
DOI: 10.35078/krrwu6/zhw2n4
title: Petróleo C.jpg

description: :unav
DOI: 10.35078/krrwu6/mvabtt
title: José Flaviano Baião B.jpg

description: :unav
DOI: 10.35078/6ufhml/ovuiaw
title: Tocaia A.jpg

description: :unav
DOI: 10.35078/krrwu6/nh9kpu
title: Cabeceira do Sapecado Sítio B.jpg

description: :unav
DOI: 10.35078/6ufhml/pafveq
title: Lavra B.jpg

description: :unav
DOI: 10.35078/krrwu6/vnnw58
title: Vai Volta Sítio B.jpg

description: :unav
DOI: 10.35078/6ufhml/8fjvvr
title: Viçoso B.jpg

description: :unav
DOI: 10.35078/krrwu6/jjdqst
title: Vista Alegre I B.jpg

description: :unav
DOI: 10.35078/6ufhml/wiwmt2
title: Ana de Souza B.jpg

description: :unav
DOI: 10.35078/krrwu6/e4yt4a
title: Córrego dos Frades Sítio B.jpg

description: :unav
DOI: 10.35078/6ufhml/uxdeqi
title: São Bento B.jpg

description: :unav
DOI: 10.35078/krrwu6/vbqsxk
title: Bela Vista A.jpg

description: :unav
DOI: 10.35078/krrwu6/frjojz
title: Córrego Grande B.jpg

description: :unav
DOI: 10.35078/6ufhml/ianzws
title: Onça C.jpg

description: :unav
DOI: 10.35078/krrwu6/nsgvzl
title: Santo Antônio do Grama Cid. B.jpg

description: :unav
DOI: 10.35078/6ufhml/94ntcg
title: Recreio D.jpg

description: :unav
DOI: 10.35078/krrwu6/p3fftk
title: Valão B.jpg

description: :unav
DOI: 10.35078/6ufhml/pbhb2o
title: Taboão A.jpg

description: :unav
DOI: 10.35078/krrwu6/8khbgk
title: Córrego Santa Cruz A.jpg

description: :unav
DOI: 10.35078/krrwu6/setkwz
title: Petróleo D.jpg

description: :unav
DOI: 10.35078/6ufhml/iioc9j
title: Limeira A.jpg

description: :unav
DOI: 10.35078/krrwu6/3p38iw
title: Canta Galo A.jpg

description: :unav
DOI: 10.35078/6ufhml/x9uvmo
title: Pedra Sítio D.jpg

description: :unav
DOI: 10.35078/krrwu6/rflr2b
title: Córrego dos Índios B.jpg

description: :unav
DOI: 10.35078/6ufhml/jhgwkx
title: Esmeraldas A.jpg

description: :unav
DOI: 10.35078/krrwu6/w6qyyw
title: Cabeceira dos Frades Sítio C.jpg

description: :unav
DOI: 10.35078/6ufhml/fcv9ba
title: Dimas Ferraz A.jpg

description: :unav
DOI: 10.35078/krrwu6/lobxpi
title: Córrego dos Frades A.jpg

description: :unav
DOI: 10.35078/6ufhml/htu6mk
title: Cutia B.jpg

description: :unav
DOI: 10.35078/krrwu6/ajl7xt
title: Estrela do Sul B.jpg

description: :unav
DOI: 10.35078/krrwu6/uvst1w
title: São Domingos A.jpg

description: :unav
DOI: 10.35078/6ufhml/5tmmfs
title: Luiz Pizani B.jpg

description: :unav
DOI: 10.35078/krrwu6/byavpt
title: Lambe Beiço Sítio A.jpg

description: :unav
DOI: 10.35078/6ufhml/zgp1y6
title: José C. Lúcio B.jpg

description: :unav
DOI: 10.35078/krrwu6/g90ngv
title: Boa Vista I B.jpg

description: :unav