cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34848/fk2/xvrma2/uu8xyg
title: guatemala.svg
DOI: 10.34848/fk2/xvrma2/01wfsc
title: peru.svg
DOI: 10.34848/fk2/xvrma2/zgtg1a
title: puerto rico.svg
DOI: 10.34848/fk2/xvrma2/hh1b8k
title: panama.svg
DOI: 10.34848/fk2/xvrma2/ouhcsl
title: ecuador.svg
DOI: 10.34848/fk2/xvrma2/ugjsbf
title: costa rica.svg
DOI: 10.34848/fk2/2kvda7/koqmm1
title: republica dominicana.tab
DOI: 10.34848/fk2/2kvda7/d6qbmo
title: costa rica.tab
DOI: 10.34848/fk2/2kvda7/3bbogs
title: chile.tab
DOI: 10.34848/fk2/2kvda7/ydinid
title: paraguay.tab
DOI: 10.34848/fk2/2kvda7/pe33fz
title: panama.tab
DOI: 10.34848/fk2/2kvda7/iindgp
title: latam.tab
DOI: 10.34848/fk2/2kvda7/p55nil
title: uruguay.tab
DOI: 10.34848/fk2/2kvda7/spjo3f
title: puerto rico.tab
DOI: 10.34848/fk2/2kvda7/svvkj8
title: venezuela.tab
DOI: 10.34848/fk2/2kvda7/hkdu5s
title: el salvador.tab
DOI: 10.34848/fk2/2kvda7/c7t7hr
title: argentina.tab
DOI: 10.34848/fk2/2kvda7/w2fr6m
title: guatemala.tab
DOI: 10.34848/fk2/2kvda7/gt1bvf
title: bolivia.tab
DOI: 10.34848/fk2/2kvda7/xupij9
title: ecuador.tab
DOI: 10.34848/fk2/2kvda7/gebkbs
title: peru.tab
DOI: 10.34848/fk2/2kvda7/qpvvgs
title: cuba.csv
DOI: 10.34848/fk2/2kvda7/ithsua
title: honduras.tab
DOI: 10.34848/fk2/2kvda7/68hbxy
title: nicaragua.tab
DOI: 10.34848/fk2/cjiiup/8rwqg0
title: cuba.svg
DOI: 10.34848/fk2/cjiiup/u84daz
title: ecuador.svg
DOI: 10.34848/fk2/cjiiup/rclr3b
title: guatemala.svg
DOI: 10.34848/fk2/cjiiup/i95hsa
title: honduras.svg
DOI: 10.34848/fk2/cjiiup/honcjn
title: mexico.svg
DOI: 10.34848/fk2/cjiiup/kwbl6f
title: nicaragua.svg
DOI: 10.34848/fk2/cjiiup/lmxi31
title: panama.svg
DOI: 10.34848/fk2/cjiiup/9zwfyy
title: paraguay.svg
DOI: 10.34848/fk2/cjiiup/zaxwxw
title: peru.svg
DOI: 10.34848/fk2/cjiiup/xnkmna
title: puerto rico.svg
DOI: 10.34848/fk2/cjiiup/ssuhlc
title: republica dominicana.svg
DOI: 10.34848/fk2/cjiiup/zpmtjx
title: uruguay.svg
DOI: 10.34848/fk2/cjiiup/zowune
title: venezuela.svg
DOI: 10.34848/fk2/cjiiup/bfgked
title: argentina.svg
DOI: 10.34848/fk2/cjiiup/ititqm
title: bolivia.svg
DOI: 10.34848/fk2/cjiiup/k5nukz
title: brasil.svg
DOI: 10.34848/fk2/cjiiup/efsalz
title: chile.svg
DOI: 10.34848/fk2/cjiiup/ws2xnb
title: colombia.svg
DOI: 10.34848/fk2/cjiiup/h2wybo
title: costa rica.svg
DOI: 10.34848/fk2/9tarp2/xpl3qx
title: chile.gml
DOI: 10.34848/fk2/9tarp2/mr8zai
title: el salvador.gml
DOI: 10.34848/fk2/9tarp2/aupenm
title: brasil.gml
DOI: 10.34848/fk2/9tarp2/jitcsf
title: peru.gml
DOI: 10.34848/fk2/9tarp2/ejamjt
title: nicaragua.gml
DOI: 10.34848/fk2/9tarp2/ugujra
title: bolivia.gml
DOI: 10.34848/fk2/9tarp2/l25bfx
title: costa rica.gml
DOI: 10.34848/fk2/9tarp2/ogbh23
title: uruguay.gml
DOI: 10.34848/fk2/9tarp2/e3aynf
title: colombia.gml
DOI: 10.34848/fk2/9tarp2/qhr4qi
title: guatemala.gml
DOI: 10.34848/fk2/9tarp2/5ccovf
title: honduras.gml
DOI: 10.34848/fk2/9tarp2/puanzl
title: ecuador.gml
DOI: 10.34848/fk2/9tarp2/rowxpq
title: puerto rico.gml
DOI: 10.34848/fk2/9tarp2/v3yk9v
title: venezuela.gml
DOI: 10.34848/fk2/9tarp2/daptdh
title: republica dominicana.gml
DOI: 10.34848/fk2/9tarp2/fl0b1w
title: argentina.gml
DOI: 10.34848/fk2/9tarp2/wsjvql
title: panama.gml
DOI: 10.34848/fk2/9tarp2/aet83g
title: paraguay.gml
DOI: 10.34848/fk2/9tarp2/vsow5s
title: cuba.gml
DOI: 10.34848/fk2/9tarp2/nqwtrx
title: mexico.gml
DOI: 10.34848/fk2/9tarp2/d65kxu
title: latam.gml
DOI: 10.34848/fk2/x7ttuo/ch4xli
title: argentina.tab
DOI: 10.34848/fk2/x7ttuo/zi6eff
title: ecuador.tab
DOI: 10.34848/fk2/x7ttuo/qvavuo
title: brasil.tab
DOI: 10.34848/fk2/x7ttuo/vj1ic0
title: honduras.tab
DOI: 10.34848/fk2/x7ttuo/3bgpxr
title: colombia.tab
DOI: 10.34848/fk2/x7ttuo/k3oxnx
title: guatemala.tab
DOI: 10.34848/fk2/x7ttuo/h1ylgu
title: costa rica.tab
DOI: 10.34848/fk2/x7ttuo/klyxey
title: peru.tab
DOI: 10.34848/fk2/x7ttuo/k611x2
title: bolivia.tab
DOI: 10.34848/fk2/x7ttuo/ruzjy2
title: puerto rico.tab
DOI: 10.34848/fk2/x7ttuo/pxk0pr
title: mexico.tab
DOI: 10.34848/fk2/x7ttuo/npyzyd
title: paraguay.tab
DOI: 10.34848/fk2/x7ttuo/tii0do
title: venezuela.tab
DOI: 10.34848/fk2/x7ttuo/n34mlu
title: chile.tab
DOI: 10.34848/fk2/x7ttuo/pdr0qn
title: nicaragua.tab
DOI: 10.34848/fk2/x7ttuo/em7m37
title: uruguay.tab
DOI: 10.34848/fk2/x7ttuo/t46qkp
title: republica dominicana.tab
DOI: 10.34848/fk2/x7ttuo/sfxfcf
title: panama.tab
DOI: 10.34848/fk2/x7ttuo/xxeqyt
title: latam.tab
DOI: 10.34848/fk2/x7ttuo/gspocp
title: cuba.tab
DOI: 10.5281/zenodo.3894774
title: Data: Search and Rescue with Airborne Optical Sectioning

description: This dataset supports the finding of our study "Search and Rescue with Airborne Optical Sectioning". Abstract: We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification. Here, we employed image integration by Airborne Optical Sectioning (AOS)---a synthetic aperture imaging technique that uses camera drones to capture unstructured thermal light fields---to achieve this with a recall of 93%. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with use of AOS integral images. Our findings lay the foundation for effective future search and rescue technologies that can be applied in combination with autonomous or manned aircraft.<br>
DOI: 10.5281/zenodo.3891406
title: The role of shear fabric in controlling breakdown processes during laboratory slow slip events

description: Understanding the physical mechanisms at the origin of slow slip events has been proven a very challenging task. In particular little is known on the role of fault heterogeneity during slow slip. In this study we provide evidence that fault fabric controls slip velocity time histories during slow slip events generated in the laboratory under the specific boundary condition of imposing a stiffness ratio K=k/k<sub>c</sub>~1. We have performed experiments using a double-direct biaxial shear apparatus and two different fault gouges, homogeneous quartz powder and heterogeneous anhydrite/dolomite mixture. We measure fine details of fault slip to resolve the slip velocity time history and volumetric deformation that, coupled with an analysis of the resulting microstructure, allow us to infer the mechanical processes at play. Our results show that slow slip events can be generated for both fault gouges when k~k<sub>c</sub> with similar values of breakdown work. The shear fabric exerts a strong influence during the co-seismic breakdown stage of instabilities. In quartz, where most of the slip occurs on a localized (1 µm) slipping surface, the peak slip velocity is attained near the final stage of friction breakdown and therefore a relevant amount of the mechanical work is absorbed during slip acceleration to peak slip velocity. In anhydrite/dolomite mixture, the peak slip velocity is suddenly reached after a relatively small drop in friction, accompanied by fault dilation, implying that most of the mechanical work is absorbed during slip deceleration. For anhydrite/dolomite mixture these results are likely related to heterogeneous slip distribution along the observed foliation. Taken together these observations suggest that the mechanics of slow slip events depends on shear zone fabric. The slip velocity function contains the necessary dynamic information to characterize the evolution of shear stress. The data are uploaded are structured as follow: 1) a .txt file of the datafile that is recorded from the machine (raw data) 2) a reduction file (_r) that is used to elaborate the raw data 3) a file in .txt format containing the elaborated data (data_rp) The data are analyzed using rawPy that can be found at https://github.com/marcoscuderi/rawPy For any additional information please do not hesitate to contact the corresponding author Marco Maria Scuderi at marco.scuderi@uniroma1.it.
DOI: 10.5281/zenodo.3898725
title: The role of shear fabric in controlling breakdown processes during laboratory slow slip events

description: Understanding the physical mechanisms at the origin of slow slip events has been proven a very challenging task. In particular little is known on the role of fault heterogeneity during slow slip. In this study we provide evidence that fault fabric controls slip velocity time histories during slow slip events generated in the laboratory under the specific boundary condition of imposing a stiffness ratio K=k/k<sub>c</sub>~1. We have performed experiments using a double-direct biaxial shear apparatus and two different fault gouges, homogeneous quartz powder and heterogeneous anhydrite/dolomite mixture. We measure fine details of fault slip to resolve the slip velocity time history and volumetric deformation that, coupled with an analysis of the resulting microstructure, allow us to infer the mechanical processes at play. Our results show that slow slip events can be generated for both fault gouges when k~k<sub>c</sub> with similar values of breakdown work. The shear fabric exerts a strong influence during the co-seismic breakdown stage of instabilities. In quartz, where most of the slip occurs on a localized (1 µm) slipping surface, the peak slip velocity is attained near the final stage of friction breakdown and therefore a relevant amount of the mechanical work is absorbed during slip acceleration to peak slip velocity. In anhydrite/dolomite mixture, the peak slip velocity is suddenly reached after a relatively small drop in friction, accompanied by fault dilation, implying that most of the mechanical work is absorbed during slip deceleration. For anhydrite/dolomite mixture these results are likely related to heterogeneous slip distribution along the observed foliation. Taken together these observations suggest that the mechanics of slow slip events depends on shear zone fabric. The slip velocity function contains the necessary dynamic information to characterize the evolution of shear stress. The data are uploaded are structured as follow: 1) a .txt file of the datafile that is recorded from the machine (raw data) 2) a reduction file (_r) that is used to elaborate the raw data 3) a file in .txt format containing the elaborated data (data_rp) The data are analyzed using rawPy that can be found at https://github.com/marcoscuderi/rawPy For any additional information please do not hesitate to contact the corresponding author Marco Maria Scuderi at marco.scuderi@uniroma1.it.
DOI: 10.5281/zenodo.3891405
title: The role of shear fabric in controlling breakdown processes during laboratory slow slip events

description: Understanding the physical mechanisms at the origin of slow slip events has been proven a very challenging task. In particular little is known on the role of fault heterogeneity during slow slip. In this study we provide evidence that fault fabric controls slip velocity time histories during slow slip events generated in the laboratory under the specific boundary condition of imposing a stiffness ratio K=k/k<sub>c</sub>~1. We have performed experiments using a double-direct biaxial shear apparatus and two different fault gouges, homogeneous quartz powder and heterogeneous anhydrite/dolomite mixture. We measure fine details of fault slip to resolve the slip velocity time history and volumetric deformation that, coupled with an analysis of the resulting microstructure, allow us to infer the mechanical processes at play. Our results show that slow slip events can be generated for both fault gouges when k~k<sub>c</sub> with similar values of breakdown work. The shear fabric exerts a strong influence during the co-seismic breakdown stage of instabilities. In quartz, where most of the slip occurs on a localized (1 µm) slipping surface, the peak slip velocity is attained near the final stage of friction breakdown and therefore a relevant amount of the mechanical work is absorbed during slip acceleration to peak slip velocity. In anhydrite/dolomite mixture, the peak slip velocity is suddenly reached after a relatively small drop in friction, accompanied by fault dilation, implying that most of the mechanical work is absorbed during slip deceleration. For anhydrite/dolomite mixture these results are likely related to heterogeneous slip distribution along the observed foliation. Taken together these observations suggest that the mechanics of slow slip events depends on shear zone fabric. The slip velocity function contains the necessary dynamic information to characterize the evolution of shear stress. The data are uploaded are structured as follow: 1) a .txt file of the datafile that is recorded from the machine (raw data) 2) a reduction file (_r) that is used to elaborate the raw data 3) a file in .txt format containing the elaborated data (data_rp) The data are analyzed using rawPy that can be found at https://github.com/marcoscuderi/rawPy For any additional information please do not hesitate to contact the corresponding author Marco Maria Scuderi at marco.scuderi@uniroma1.it.
DOI: 10.25386/genetics.12473447.v1
title: Supplemental Material for Maharjan, McKowen, and Hart, 2020

description: Supplemental File 1: Insulator (dimers), promoter (monomers) and primer sequences
DOI: 10.25386/genetics.12473447
title: Supplemental Material for Maharjan, McKowen, and Hart, 2020

description: Supplemental File 1: Insulator (dimers), promoter (monomers) and primer sequences
DOI: 10.18430/m36xdh
title: X-ray diffraction data for the crystal Structure of NendoU (Uridylate-specific endoribonuclease, nsp15) from Betacoronavirus SARS-CoV-2 (6XDH)
DOI: 10.5281/zenodo.3898749
title: NPM Package Information from Libraries.io

description: This zip file contains a JSON file of package and repository information for 12 million NPM packages, sourced from libaries.io (10.5281/zenodo.3626071, https://libraries.io/data, January 12, 2020). The file is 2.39GB uncompressed. The file was generated from the 'Projects with related Repository fields' csv file, filtering for NPM packages only. The original files contains inconsistent columns between the first row, containing labels, and the subsequent rows, containing data, however the additional, unlabelled column is empty for all NPM packages so it has been ignored, and the subsequent rows shifted up into their correct positions. This dataset has not modified otherwise. Includes data from Libraries.io.
DOI: 10.5281/zenodo.3898748
title: NPM Package Information from Libraries.io

description: This zip file contains a JSON file of package and repository information for 12 million NPM packages, sourced from libaries.io (10.5281/zenodo.3626071, https://libraries.io/data, January 12, 2020). The file is 2.39GB uncompressed. The file was generated from the 'Projects with related Repository fields' csv file, filtering for NPM packages only. The original files contains inconsistent columns between the first row, containing labels, and the subsequent rows, containing data, however the additional, unlabelled column is empty for all NPM packages so it has been ignored, and the subsequent rows shifted up into their correct positions. This dataset has not modified otherwise. Includes data from Libraries.io.
DOI: 10.25959/5eea32c374d89
title: Data from: Antarctic Ice Shelves Lose Most of Their Mass From Shallow Depths
DOI: 10.5281/zenodo.3891912
title: Supporting Data for Sagoo et al. GRL: Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks

description: This dataset includes CESM climatologies relevant for Sagoo et al.: “Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks” in Geophysical Research Letters. Data is included for high (HI) and low (LO) obliquity experiments with the default model (OOTB) and with supercooled liquid fraction constrained to satellite observations (SLF1 and SLF2), as described in the manuscript.
DOI: 10.5281/zenodo.3891911
title: Supporting Data for Sagoo et al. GRL: Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks

description: This dataset includes CESM climatologies relevant for Sagoo et al.: “Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks” in Geophysical Research Letters. Data is included for high (HI) and low (LO) obliquity experiments with the default model (OOTB) and with supercooled liquid fraction constrained to satellite observations (SLF1 and SLF2), as described in the manuscript.
DOI: 10.5281/zenodo.3753084
title: Image analysis data for the study of the reactivity of the phases in Nd-Fe-B magnets etched with HCl-saturated Cyphos IL 101

description: Scanning electronic microscopy (SEM), Energy dispersive X-rays Spectroscopy (EDS) and image analysis have been used as techniques to analyse the results of etching experiments carried on NdFeB permanent magnets by using the ionic liquid Cyphos IL 101 saturated with HCl. Image analysis is for the first time reported in the literature as a technique for corrosion studies. Operational conditions of the analysis equipment were the following: - The samples were analyzed via electron microscopy and image analysis. Scanning electron microscope (SEM) pictures and energy dispersed spectra (EDS) were collected with a JEOL JSM 5800 microscope, operating at 20 kV. The polished samples were made conductive by spraying a carbon layer on them using a Balzer SCD 050 sputter coater. - The EDS analysis were collected as average on 5 points per each SEM picture - A commercial software, ImageJ®, was used for the image analysis. Two data were analysed: the Feret diameter, d<sub>F</sub>, and the percentage of etched area, %area. The SEM images were converted to 8-bit grayscale, from 0 to 255 number of grey ranges. Simple linear scaling was applied
description: {"references": ["T. Ferreira, W. Rasband, ImageJ User Guide User Guide ImageJ, Image J User Guid. 1.46r (2012). doi:10.1038/nmeth.2019."]}
DOI: 10.5281/zenodo.3753083
title: Image analysis data for the study of the reactivity of the phases in Nd-Fe-B magnets etched with HCl-saturated Cyphos IL 101

description: Scanning electronic microscopy (SEM), Energy dispersive X-rays Spectroscopy (EDS) and image analysis have been used as techniques to analyse the results of etching experiments carried on NdFeB permanent magnets by using the ionic liquid Cyphos IL 101 saturated with HCl. Image analysis is for the first time reported in the literature as a technique for corrosion studies. Operational conditions of the analysis equipment were the following: - The samples were analyzed via electron microscopy and image analysis. Scanning electron microscope (SEM) pictures and energy dispersed spectra (EDS) were collected with a JEOL JSM 5800 microscope, operating at 20 kV. The polished samples were made conductive by spraying a carbon layer on them using a Balzer SCD 050 sputter coater. - The EDS analysis were collected as average on 5 points per each SEM picture - A commercial software, ImageJ®, was used for the image analysis. Two data were analysed: the Feret diameter, d<sub>F</sub>, and the percentage of etched area, %area. The SEM images were converted to 8-bit grayscale, from 0 to 255 number of grey ranges. Simple linear scaling was applied
description: {"references": ["T. Ferreira, W. Rasband, ImageJ User Guide User Guide ImageJ, Image J User Guid. 1.46r (2012). doi:10.1038/nmeth.2019."]}
DOI: 10.17632/3dgyytjbp2.1
title: Rim Paris meteorite

description: Research Data associated to the publication
DOI: 10.17632/rw4jh5w4kj.1
title: Data for: The Feasibility of a 100% Renewable Electricity Mix Before 2040 in China

description: China 2040 demand and supply prediction and Renewable Energy/Total
DOI: 10.17632/jdws46cmp3.1
title: Supplementary Online Content. Reducing Confounding Factors’ Impact on Skin Cancer Classification via Image Segmentation.

description: Supplementary Table 1. Train and test set class distribution for each dataset source. Supplementary Methods. Segmentation Model and Classifier Development. Supplementary Table 2. Overview of the secondary outcome metrics for each type of ensemble across the holdout, external, and overall test set. Supplementary Table 3. Overview of the secondary outcome metrics for each type of ensemble across the external test set’s three individual components.
DOI: 10.17632/jzc4mzr6hb.1
title: Data for geometry-based greenness index comparisons in lettuce biophysical estimations

description: The lettuce images were captured daily using consumer-grade Vivo17 Pro smartphone camera for full six-week crop life cycle. Four lettuces are both cultivated in indoor and outdoor aquaponic setup. The distance of camera to lettuce canopy is 10 inches. The presence of artificial and natural photosynthetic light is present on the images. The raw images have corresponding vegetation channel for triangular greenness index (TGI) and artificial bee colony-optimized visible band oblique dipyramid greenness index (vODGIabc). The vODGIabc channel must exhibit more enhanced vegetation pixels than TGI transformed channel. The data shows that using swarm intelligence based optimization and the introduction of saturation and value component rectification increases the sensitivity of vODGIabc to green color pixels resulting to subtracted non-vegetation pixels in the image even with the presence of shadow and bright light sources. TGI and vODGIabc images are discriminated against the ground truth images annotated using superpixel and watershed transformation. To further the analysis, each ground truth image is accompanied with actual measurement of biophysical signatures of lettuce. The use of computational intelligence for lettuce fresh weight, height, number of spanning leaves, leaf area index and growth stage estimation is of significant consideration. Matlab codes for optimization, and machine learning and deep transfer network estimations are also included.
DOI: 10.17632/jzc4mzr6hb.2
title: Data for geometry-based greenness index comparisons in lettuce biophysical estimations

description: The lettuce images were captured daily using consumer-grade Vivo17 Pro smartphone camera for full six-week crop life cycle. Four lettuces are both cultivated in indoor and outdoor aquaponic setup. The distance of camera to lettuce canopy is 10 inches. The presence of artificial and natural photosynthetic light is present on the images. The raw images have corresponding vegetation channel for triangular greenness index (TGI) and artificial bee colony-optimized visible band oblique dipyramid greenness index (vODGIabc). The vODGIabc channel must exhibit more enhanced vegetation pixels than TGI transformed channel. The data shows that using swarm intelligence based optimization and the introduction of saturation and value component rectification increases the sensitivity of vODGIabc to green color pixels resulting to subtracted non-vegetation pixels in the image even with the presence of shadow and bright light sources. TGI and vODGIabc images are discriminated against the ground truth images annotated using superpixel and watershed transformation. To further the analysis, each ground truth image is accompanied with actual measurement of biophysical signatures of lettuce. The use of computational intelligence for lettuce fresh weight, height, number of spanning leaves, leaf area index and growth stage estimation is of significant consideration. Matlab codes for optimization, and machine learning and deep transfer network estimations are also included.
DOI: 10.17632/jx7jgrp932.1
title: A framework for assessing, comparing and predicting the performance of autonomous RFID-based inventory robots for retail: validation dataset

description: The validation data used for assessing the framework presented in the article: "A framework for assessing, comparing and predicting the performance of autonomous RFID-based inventory robots for retail"
DOI: 10.17632/2szmr4tg3z.1
title: Drinking Water Supply Technologies

description: Table of technology options for Drinking Water Supply (DWS), classified by Capacity Requirement Level (CRL). It is a metric that defines the level a community must have to operate sustainably the DWS technology option. The DWS options are defined by five unit processes: Source Device, Treatment, Storage, and Distribution. The inventory of the DWS options was conducted based on the type of unit processes, and their classification was done using a machine learning algorithm.