cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15123882
title: Geographic variation of leaf form among indigenous woody angiosperms in New Zealand

description: Correlations of non-monocot woody angiosperm leaf traits to macroclimate are often used to reconstruct terrestrial paleoclimate under the assumption that macroclimate correlates with leaf phenotype are globally uniform, regardless of evolutionary history. Here, we evaluate if global trends in leaf trait variation with macroclimate are observed in the predominantly evergreen indigenous flora of New Zealand. A dataset of 557 indigenous woody dicot species and over 100,000 occurrences was employed to investigate community-level relationships of four leaf characters (leaf pubescence, margin teeth, area and length-to-width ratio) with geographic variation in temperature, precipitation, water deficit and solar radiation. Leaf area and the frequency of toothed leaves decline at higher latitudes in New Zealand. Variation in leaf pubescence and leaf teeth is associated primarily with measures of water availability, such as annual rainfall and annual water deficit; whereas leaf size is associated primarily with temperature. Variation in leaf length-to-width ratio was weakly correlated to climate parameters. The New Zealand relationship of leaf area with temperature aligns with global patterns, highlighting the importance of small leaves in limiting night-time chilling. The global negative correlation of leaf teeth with temperature is apparent in New Zealand trees and vines, but not in shrubs or all woody dicots combined. However, the primary correlate of leaf teeth in New Zealand is water availability, showing that the response of this trait to macroclimate is not globally uniform. The high occurrence of pubescent leaves in low rainfall and drought-prone environments in New Zealand suggests that the trait is associated with water retention in drier climates.
DOI: 10.6084/m9.figshare.15123882.v1
title: Geographic variation of leaf form among indigenous woody angiosperms in New Zealand

description: Correlations of non-monocot woody angiosperm leaf traits to macroclimate are often used to reconstruct terrestrial paleoclimate under the assumption that macroclimate correlates with leaf phenotype are globally uniform, regardless of evolutionary history. Here, we evaluate if global trends in leaf trait variation with macroclimate are observed in the predominantly evergreen indigenous flora of New Zealand. A dataset of 557 indigenous woody dicot species and over 100,000 occurrences was employed to investigate community-level relationships of four leaf characters (leaf pubescence, margin teeth, area and length-to-width ratio) with geographic variation in temperature, precipitation, water deficit and solar radiation. Leaf area and the frequency of toothed leaves decline at higher latitudes in New Zealand. Variation in leaf pubescence and leaf teeth is associated primarily with measures of water availability, such as annual rainfall and annual water deficit; whereas leaf size is associated primarily with temperature. Variation in leaf length-to-width ratio was weakly correlated to climate parameters. The New Zealand relationship of leaf area with temperature aligns with global patterns, highlighting the importance of small leaves in limiting night-time chilling. The global negative correlation of leaf teeth with temperature is apparent in New Zealand trees and vines, but not in shrubs or all woody dicots combined. However, the primary correlate of leaf teeth in New Zealand is water availability, showing that the response of this trait to macroclimate is not globally uniform. The high occurrence of pubescent leaves in low rainfall and drought-prone environments in New Zealand suggests that the trait is associated with water retention in drier climates.
DOI: 10.21227/j35y-wz56
title: Dataset of SSIOE (Self-Supervised Indoor Occupancy Estimation for Intelligent Building Management)

description: Our data set has 5136 records collected in 214 days. The sampling rate of the sensors is 1 hour. Each record includes the number of vehicles entering and leaving the parking lot in an hour, the CO2 concentration of every building floor at the recording time, and the power consumption of each floor in an hour.
DOI: 10.5281/zenodo.6529810
title: Sistema automatizado para determinar el Riesgo Cardiovascular Global.

description: <strong>El presente sistema automatizado facilita el cálculo del riesgo cardiovascular global y facilita guardar los datos de su investigación. Se recomienda utilizar la Versión 3.0, ya que se han rectificado errores de las dos versiones anteriores. Las tablas se utilizan a partir de los 30 años de edad y están destinadas a utilizar en Cuba con la selección de la Provincia, el municipio y el área de salud. </strong> <strong>Artículo Publicado en: </strong> http://www.revinformatica.sld.cu/index.php/rcim/article/view/406 <strong>RESUMEN </strong> <strong>Introducción: </strong>El riesgo cardiovascular global es la probabilidad que tiene un individuo de contraer una enfermedad cardiovascular en un periodo de tiempo, basado en el número de factores de riesgo presentes en el mismo individuo o teniendo en cuenta la magnitud de cada uno de ellos. Las técnicas automatizadas en el procesamiento de la información logran una mayor eficacia en el trabajo. <strong>Objetivos: </strong>Diseñar un sistema automatizado para determinar el riesgo cardiovascular global<strong>. </strong> <strong>Métodos: </strong>Se realizó una investigación de innovación tecnológica, consistente en el diseño de un sistema automatizado para determinar el Riesgo Cardiovascular Global<strong>.</strong> Se realizó además un análisis de las herramientas computacionales disponibles para la realización del sistema y determinar cuál o cuáles eran las más apropiadas de acuerdo al objetivo del sistema y a las características del personal encargado de su implementación y uso. Se utilizaron las herramientas para tabulación de información y fórmulas que ofrece el programa Excel del paquete Microsoft Office 2013. Para el diseño se utilizó una interface muy simple, de fácil manejo por el posible usuario. <strong>Resultados:</strong> Se propone el presente sistema cuya característica principal es la simplicidad de su uso. El mismo determina el Riesgo Cardiovascular Global de forma automatizada<strong>.</strong> Según las tablas de riesgo de Framingham por categorías (Wilson), en su versión de 1998 y las tablas de predicción del riesgo cardiovascular global de la OMS. Para la región AMR A del 2008. <strong>Conclusiones: </strong>Se realizó el diseño del sistema automatizado para facilitar la determinación del Riesgo Cardiovascular Global.
description: http://www.revinformatica.sld.cu/index.php/rcim/article/view/406
DOI: 10.5281/zenodo.6029629
title: Sistema automatizado para determinar el Riesgo Cardiovascular Global.

description: <strong>El presente sistema automatizado facilita el cálculo del riesgo cardiovascular global y facilita guardar los datos de su investigación. Se recomienda utilizar la Versión 3.0, ya que se han rectificado errores de las dos versiones anteriores. Las tablas se utilizan a partir de los 30 años de edad y están destinadas a utilizar en Cuba con la selección de la Provincia, el municipio y el área de salud. </strong> <strong>Artículo Publicado en: </strong> http://www.revinformatica.sld.cu/index.php/rcim/article/view/406 <strong>RESUMEN </strong> <strong>Introducción: </strong>El riesgo cardiovascular global es la probabilidad que tiene un individuo de contraer una enfermedad cardiovascular en un periodo de tiempo, basado en el número de factores de riesgo presentes en el mismo individuo o teniendo en cuenta la magnitud de cada uno de ellos. Las técnicas automatizadas en el procesamiento de la información logran una mayor eficacia en el trabajo. <strong>Objetivos: </strong>Diseñar un sistema automatizado para determinar el riesgo cardiovascular global<strong>. </strong> <strong>Métodos: </strong>Se realizó una investigación de innovación tecnológica, consistente en el diseño de un sistema automatizado para determinar el Riesgo Cardiovascular Global<strong>.</strong> Se realizó además un análisis de las herramientas computacionales disponibles para la realización del sistema y determinar cuál o cuáles eran las más apropiadas de acuerdo al objetivo del sistema y a las características del personal encargado de su implementación y uso. Se utilizaron las herramientas para tabulación de información y fórmulas que ofrece el programa Excel del paquete Microsoft Office 2013. Para el diseño se utilizó una interface muy simple, de fácil manejo por el posible usuario. <strong>Resultados:</strong> Se propone el presente sistema cuya característica principal es la simplicidad de su uso. El mismo determina el Riesgo Cardiovascular Global de forma automatizada<strong>.</strong> Según las tablas de riesgo de Framingham por categorías (Wilson), en su versión de 1998 y las tablas de predicción del riesgo cardiovascular global de la OMS. Para la región AMR A del 2008. <strong>Conclusiones: </strong>Se realizó el diseño del sistema automatizado para facilitar la determinación del Riesgo Cardiovascular Global.
description: http://www.revinformatica.sld.cu/index.php/rcim/article/view/406
DOI: 10.25919/rcst-9d44
title: Isotopic data from Nova-Bollinger Ni-Cu-Co deposit

description: S, Nd and Sr isotope data on samples from the Nova-Bollinger Ni-Cu-Co sulfide deposit, Fraser Zone, WA, with additional trace element and isotopic data from other intrusions and metasedimentary rocks in the Fraser Zone.
DOI: 10.5281/zenodo.6501705
title: GRIST-VR data

description: Model output data (mean precipitation and the diurnal cycle of precipitation) for the manuscript entitled "Improved precipitation simulation over East Asia with a revised double-plume convective parameterization for variable-resolution modeling".
DOI: 10.5281/zenodo.6501704
title: GRIST-VR data

description: Model output data (mean precipitation and the diurnal cycle of precipitation) for the manuscript entitled "Improved precipitation simulation over East Asia with a revised double-plume convective parameterization for variable-resolution modeling".
DOI: 10.34740/kaggle/dsv/3602264
title: BBC News
DOI: 10.6084/m9.figshare.19730053
title: neraca 2021_merged.pdf

description: laporan keuangan tahun 2021
DOI: 10.6084/m9.figshare.19730053.v1
title: neraca 2021_merged.pdf

description: laporan keuangan tahun 2021
DOI: 10.5517/ccdc.csd.cc1z3fm4
title: CCDC 1820556: Experimental Crystal Structure Determination

description: Related Article: Santu Sadhukhan, Beeraiah Baire|2022|J.Org.Chem.|87|5530|doi:10.1021/acs.joc.1c02780
DOI: 10.5517/ccdc.csd.cc27ggls
title: CCDC 2069485: Experimental Crystal Structure Determination

description: Related Article: Hemant K. Sharma, Alejandro J. Metta-Magaña, Keith H. Pannell|2022|Organometallics|41|977|doi:10.1021/acs.organomet.2c00031
DOI: 10.5517/ccdc.csd.cc27ggmt
title: CCDC 2069486: Experimental Crystal Structure Determination

description: Related Article: Hemant K. Sharma, Alejandro J. Metta-Magaña, Keith H. Pannell|2022|Organometallics|41|977|doi:10.1021/acs.organomet.2c00031
DOI: 10.5517/ccdc.csd.cc27ggnv
title: CCDC 2069487: Experimental Crystal Structure Determination

description: Related Article: Hemant K. Sharma, Alejandro J. Metta-Magaña, Keith H. Pannell|2022|Organometallics|41|977|doi:10.1021/acs.organomet.2c00031
DOI: 10.5517/ccdc.csd.cc27ggpw
title: CCDC 2069488: Experimental Crystal Structure Determination

description: Related Article: Hemant K. Sharma, Alejandro J. Metta-Magaña, Keith H. Pannell|2022|Organometallics|41|977|doi:10.1021/acs.organomet.2c00031
DOI: 10.5517/ccdc.csd.cc28vrsp
title: CCDC 2111093: Experimental Crystal Structure Determination

description: Related Article: Takuya Murai, Yongning Xing, Mayu Kurokawa, Toshifumi Kuribayashi, Masanori Nikaido, Elghareeb E. Elboray, Shohei Hamada, Yusuke Kobayashi, Takahiro Sasamori, Takeo Kawabata, Takumi Furuta|2022|J.Org.Chem.|87|5510|doi:10.1021/acs.joc.1c02769
DOI: 10.5517/ccdc.csd.cc28z2v6
title: CCDC 2114288: Experimental Crystal Structure Determination

description: Related Article: Takumi Mizumachi, Minami Sato, Masashi Kaneko, Tomoyuki Takeyama, Satoru Tsushima, Koichiro Takao|2022|Inorg.Chem.|61|6175|doi:10.1021/acs.inorgchem.2c00306
DOI: 10.5517/ccdc.csd.cc28z2w7
title: CCDC 2114289: Experimental Crystal Structure Determination

description: Related Article: Takumi Mizumachi, Minami Sato, Masashi Kaneko, Tomoyuki Takeyama, Satoru Tsushima, Koichiro Takao|2022|Inorg.Chem.|61|6175|doi:10.1021/acs.inorgchem.2c00306
DOI: 10.5517/ccdc.csd.cc28vq1x
title: CCDC 2111039: Experimental Crystal Structure Determination

description: Related Article: Takuya Murai, Yongning Xing, Mayu Kurokawa, Toshifumi Kuribayashi, Masanori Nikaido, Elghareeb E. Elboray, Shohei Hamada, Yusuke Kobayashi, Takahiro Sasamori, Takeo Kawabata, Takumi Furuta|2022|J.Org.Chem.|87|5510|doi:10.1021/acs.joc.1c02769
DOI: 10.5517/ccdc.csd.cc29wtxy
title: CCDC 2141911: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wtyz
title: CCDC 2141912: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wtz0
title: CCDC 2141913: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv02
title: CCDC 2141914: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv13
title: CCDC 2141915: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv24
title: CCDC 2141916: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv35
title: CCDC 2141917: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv46
title: CCDC 2141918: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv57
title: CCDC 2141919: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv68
title: CCDC 2141920: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv79
title: CCDC 2141921: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv8b
title: CCDC 2141922: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wv9c
title: CCDC 2141923: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29wvbd
title: CCDC 2141924: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5517/ccdc.csd.cc29ydrf
title: CCDC 2143425: Experimental Crystal Structure Determination

description: Related Article: Magdalena Dolna, Micha Nowacki, Oksana Danylyuk, Artur Brotons-Rufes, Albert Poater, Micha Michalak|2022|J.Org.Chem.|87|6115|doi:10.1021/acs.joc.2c00380
DOI: 10.5517/ccdc.csd.cc29ydvj
title: CCDC 2143428: Experimental Crystal Structure Determination

description: Related Article: Magdalena Dolna, Micha Nowacki, Oksana Danylyuk, Artur Brotons-Rufes, Albert Poater, Micha Michalak|2022|J.Org.Chem.|87|6115|doi:10.1021/acs.joc.2c00380
DOI: 10.5517/ccdc.csd.cc2bckw5
title: CCDC 2156077: Experimental Crystal Structure Determination

description: Related Article: Lars Hirneise, Dennis A. Buschmann, Cäcilia Maichle-Mössmer, Reiner Anwander|2022|Organometallics|41|962|doi:10.1021/acs.organomet.2c00029
DOI: 10.5281/zenodo.6528380
title: Models and simulation results of "A relatively dry mantle transition zone revealed by geomagnetic diurnal variations"

description: Data and models used in Zhang et al. 2022: A relatively dry mantle transition zone revealed by geomagnetic diurnal variations. See ReadMe for more detailed description of file contents. Files includes: 1. 3D Conductivity models (ascii) and corresponding perturbed magnetic field on the surface (Matalab Mat format): Global*Model, Secondary*Hs_1cpd.mat 2. inhomogeneous surface conductance layer for thin-sheet modeling (Matlab Mat format): SurfaceCond1degx1deg.mat 3. 1D inversion profiles to generate Figure 1 (Matalab Mat format) depth.mat, MultiMode*.mat 4. PCA used for the data analysis (Matalab Mat format). Data_1-4cpd_Mode_1-5.mat 5. Source estimates results used for the preferred conductivity model (Matalab Mat format). SphereCoeff_PerferedSource.mat
DOI: 10.5281/zenodo.6526015
title: Models and simulation results of "A relatively dry mantle transition zone revealed by geomagnetic diurnal variations"

description: Data and models used in Zhang et al. 2022: A relatively dry mantle transition zone revealed by geomagnetic diurnal variations. See ReadMe for more detailed description of file contents. Files includes: 1. 3D Conductivity models (ascii) and corresponding perturbed magnetic field on the surface (Matalab Mat format): Global*Model, Secondary*Hs_1cpd.mat 2. inhomogeneous surface conductance layer for thin-sheet modeling (Matlab Mat format): SurfaceCond1degx1deg.mat 3. 1D inversion profiles to generate Figure 1 (Matalab Mat format) depth.mat, MultiMode*.mat 4. PCA used for the data analysis (Matalab Mat format). Data_1-4cpd_Mode_1-5.mat 5. Source estimates results used for the preferred conductivity model (Matalab Mat format). SphereCoeff_PerferedSource.mat
DOI: 10.5281/zenodo.6519307
title: OmniFold Weights | CMS 2011A Open Data | Jet Primary Dataset | pT 375-700 GeV

description: Unfolding weights corresponding to a selection of jets from the Jet Primary Dataset of the CMS 2011A Open Data in MOD HDF5 format and associated simulated datasets. The unfolding is performed in a high-dimensional manner using the OmniFold method, which can unfold all observables simultaneously. Particle Flow Networks are used in Step 1 and Step 2 of the OmniFold method to process the full phase space information. The datasets and neural networks were accessed/built via the EnergyFlow Python package. An upcoming version of the package will contain an example/demo demonstrating how to use these weights. The phase space selections for the data, sim, and gen datasets (using the terminology of the OmniFold paper) are: data: \(p_T^{\rm jet}\in [375, 700]\) GeV, \(|\eta^{\rm jet}|&lt;2.4\), jet quality \(\ge\) 2 sim: \(p_{T,\text{corr}}^{\rm jet} \in [375, 700]\) GeV, \(|\eta^{\rm jet}| &lt; 2.4\), gen jet matched ('gen_jet_pts != -1' in EnergyFlow), jets from the 170 and 1800 MC datasets are excluded gen: Matched to sim jet The omnifold_weights.npz file contains two arrays, 'wssim' corresopnding to the Step 1 weights \(\omega_n\), and 'wsgen' corresponding to the Step 2 weights \(\nu_n\), for iteration \(n\). The shape of each of these arrays is (6, 16489054), with the first axis being the iteration axis and the second axis being the event axis. There are 5 iterations, but 6 sets of weights in each array, with the 0th entry being the starting weights.
DOI: 10.5281/zenodo.6519306
title: OmniFold Weights | CMS 2011A Open Data | Jet Primary Dataset | pT 375-700 GeV

description: Unfolding weights corresponding to a selection of jets from the Jet Primary Dataset of the CMS 2011A Open Data in MOD HDF5 format and associated simulated datasets. The unfolding is performed in a high-dimensional manner using the OmniFold method, which can unfold all observables simultaneously. Particle Flow Networks are used in Step 1 and Step 2 of the OmniFold method to process the full phase space information. The datasets and neural networks were accessed/built via the EnergyFlow Python package. An upcoming version of the package will contain an example/demo demonstrating how to use these weights. The phase space selections for the data, sim, and gen datasets (using the terminology of the OmniFold paper) are: data: \(p_T^{\rm jet}\in [375, 700]\) GeV, \(|\eta^{\rm jet}|&lt;2.4\), jet quality \(\ge\) 2 sim: \(p_{T,\text{corr}}^{\rm jet} \in [375, 700]\) GeV, \(|\eta^{\rm jet}| &lt; 2.4\), gen jet matched ('gen_jet_pts != -1' in EnergyFlow), jets from the 170 and 1800 MC datasets are excluded gen: Matched to sim jet The omnifold_weights.npz file contains two arrays, 'wssim' corresopnding to the Step 1 weights \(\omega_n\), and 'wsgen' corresponding to the Step 2 weights \(\nu_n\), for iteration \(n\). The shape of each of these arrays is (6, 16489054), with the first axis being the iteration axis and the second axis being the event axis. There are 5 iterations, but 6 sets of weights in each array, with the 0th entry being the starting weights.
DOI: 10.6084/m9.figshare.19730076
title: Additional file 1 of A putatively new family of alphaproteobacterial chloromethane degraders from a deciduous forest soil revealed by stable isotope probing and metagenomics

description: Additional file 1. Table S2: Metagenomes used for read-recruitment against ‘Mineral soil bin 2’ MAG and the closely related OTU from Chaignaud et al. 2018.
DOI: 10.6084/m9.figshare.19730076.v1
title: Additional file 1 of A putatively new family of alphaproteobacterial chloromethane degraders from a deciduous forest soil revealed by stable isotope probing and metagenomics

description: Additional file 1. Table S2: Metagenomes used for read-recruitment against ‘Mineral soil bin 2’ MAG and the closely related OTU from Chaignaud et al. 2018.
DOI: 10.5281/zenodo.6529837
title: Data for paper "Graph Deep Learning Model for Mapping Mineral Prospectivity"

description: Four prospecting information, namely, NE- and NW- trending faults, Agno Batholithic pluton margins, and porphyry intrusive contacts for mineral prospectivity mapping in Baguio district, Philippines.
DOI: 10.5281/zenodo.6529836
title: Data for paper "Graph Deep Learning Model for Mapping Mineral Prospectivity"

description: Four prospecting information, namely, NE- and NW- trending faults, Agno Batholithic pluton margins, and porphyry intrusive contacts for mineral prospectivity mapping in Baguio district, Philippines.