cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5701201.v1
title: Additional file 1: of Accuracy of acute burns diagnosis made using smartphones and tablets: a questionnaire-based study among medical experts

description: Raw data for Table S4 and S6. The file contains raw data for Tables S4 and S6 with assessments of TBSA and depth for each image and participant. Participant 1 is the bedside diagnosis which is used as the gold standard for all analyses. Participant 1 was not used for reliability analyses. (XLSX 42Â kb)
DOI: 10.6084/m9.figshare.5701147
title: Dataset for: Magnetic drug targeting simulations in blood flows with fluid-structure interaction

description: We present fluid-structure interaction (FSI) simulations of magnetic drug targeting (MDT) in blood flows. In this procedure, a drug is attached to ferromagnetic particles in order to externally direct it to a specific target after it is injected inside the body. The goal is to minimize the healthy tissue affected by the treatment and to maximize the number of particles that reach the target location. MDT has been studied both experimentally and theoretically by several authors. In recent years, computational fluid dynamics (CFD) simulations of MDT in blood flows have been carried out to obtain further insight on the combination of parameters that provide the best capture efficiency. However, to this day, no computational study addressed MDT in an FSI setting. With this paper, we aim to fill this gap and investigate the impact of the solid deformation on the capture efficiency.
DOI: 10.6084/m9.figshare.5701147.v1
title: Dataset for: Magnetic drug targeting simulations in blood flows with fluid-structure interaction

description: We present fluid-structure interaction (FSI) simulations of magnetic drug targeting (MDT) in blood flows. In this procedure, a drug is attached to ferromagnetic particles in order to externally direct it to a specific target after it is injected inside the body. The goal is to minimize the healthy tissue affected by the treatment and to maximize the number of particles that reach the target location. MDT has been studied both experimentally and theoretically by several authors. In recent years, computational fluid dynamics (CFD) simulations of MDT in blood flows have been carried out to obtain further insight on the combination of parameters that provide the best capture efficiency. However, to this day, no computational study addressed MDT in an FSI setting. With this paper, we aim to fill this gap and investigate the impact of the solid deformation on the capture efficiency.
DOI: 10.6084/m9.figshare.5701090
title: MEMB-Qiime-files.tar.gz

description: These data contain primer constructs, mapping files and final QIIME1.9 outputs used to conduct the investigation in the manuscript titled "Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats" (Schuelke, Pereira, Hardy and Bik, 2018. Molecular Ecology)
DOI: 10.17608/k6.auckland.5701087
title: Measurements of ex vivo human corneal endothelium using Voronoi segmentation.

description: This dataset of size and shape measurements of 354,998 human corneal endothelial cells, from 2000 routine assessment images, from a sequential series of 678 corneas from 351 donors, was collected with consent by the New Zealand National Eye Bank over several years, after organ-culture storage but prior to release for transplantation. The measurement methodology using Voronoi segmentation is detailed in:<br>Brookes NH. Morphometry of organ-cultured corneal endothelium using Voronoi segmentation. Cell Tiss Bank. 2017; 18: 167-183. https://doi.org/10.1007/s10561-017-9622-9 PMID: 28374155.<br>Dataset structure:<br>1. Dataset ID2. Image Number3. Image - Mean Cell Area (µm^2)4. Image - Standard Error of the Mean (SEM) Cell Area (µm^2)5. Image - Total Cell Area (µm^2)6. Image - Cell Area Coefficient of Variation (CV)7. Image - Mean Cell Density (cells/mm^2)8. Image - SEM Cell Density (cells/mm^2)9. Image - 4-Sides Cell Count10. Image - 5-Sides Cell Count11. Image - 6-Sides Cell Count12. Image - 7-Sides Cell Count13. Image - 8-Sides Cell Count14. Image - 9-Sides Cell Count15. Image - 4-Sides Cell Area (µm^2)16. Image - 5-Sides Cell Area (µm^2)17. Image - 6-Sides Cell Area (µm^2)18. Image - 7-Sides Cell Area (µm^2)19. Image - 8-Sides Cell Area (µm^2)20. Image - 9-Sides Cell Area (µm^2)21. Image - 4-Sides % Area22. Image - 5-Sides % Area23. Image - 6-Sides % Area24. Image - 7-Sides % Area25. Image - 8-Sides % Area26. Image - 9-Sides % Area27. Image - Total Cells28. Image - Mean Hexagonal Cell Regularity29. Image - SEM Hexagonal Cell Regularity30. Image - Number of Pleomorphs31. Image - Mean Cell Sides32. Image - SEM Cell Sides33. Image - Cell Sides CV34. Image - Mean Shape Index35. Image - SEM Shape Index36. Cell Number37. Cell - Cell Area (µm^2)38. Cell - Number of Sides39. Cell - Hexagonal Cell Regularity40. Cell - Shape Index<br>
DOI: 10.17608/k6.auckland.5701087.v1
title: Measurements of ex vivo human corneal endothelium using Voronoi segmentation.

description: This dataset of size and shape measurements of 354,998 human corneal endothelial cells, from 2000 routine assessment images, from a sequential series of 678 corneas from 351 donors, was collected with consent by the New Zealand National Eye Bank over several years, after organ-culture storage but prior to release for transplantation. The measurement methodology using Voronoi segmentation is detailed in:<br>Brookes NH. Morphometry of organ-cultured corneal endothelium using Voronoi segmentation. Cell Tiss Bank. 2017; 18: 167-183. https://doi.org/10.1007/s10561-017-9622-9 PMID: 28374155.<br>Dataset structure:<br>1. Dataset ID2. Image Number3. Image - Mean Cell Area (µm^2)4. Image - Standard Error of the Mean (SEM) Cell Area (µm^2)5. Image - Total Cell Area (µm^2)6. Image - Cell Area Coefficient of Variation (CV)7. Image - Mean Cell Density (cells/mm^2)8. Image - SEM Cell Density (cells/mm^2)9. Image - 4-Sides Cell Count10. Image - 5-Sides Cell Count11. Image - 6-Sides Cell Count12. Image - 7-Sides Cell Count13. Image - 8-Sides Cell Count14. Image - 9-Sides Cell Count15. Image - 4-Sides Cell Area (µm^2)16. Image - 5-Sides Cell Area (µm^2)17. Image - 6-Sides Cell Area (µm^2)18. Image - 7-Sides Cell Area (µm^2)19. Image - 8-Sides Cell Area (µm^2)20. Image - 9-Sides Cell Area (µm^2)21. Image - 4-Sides % Area22. Image - 5-Sides % Area23. Image - 6-Sides % Area24. Image - 7-Sides % Area25. Image - 8-Sides % Area26. Image - 9-Sides % Area27. Image - Total Cells28. Image - Mean Hexagonal Cell Regularity29. Image - SEM Hexagonal Cell Regularity30. Image - Number of Pleomorphs31. Image - Mean Cell Sides32. Image - SEM Cell Sides33. Image - Cell Sides CV34. Image - Mean Shape Index35. Image - SEM Shape Index36. Cell Number37. Cell - Cell Area (µm^2)38. Cell - Number of Sides39. Cell - Hexagonal Cell Regularity40. Cell - Shape Index<br>
DOI: 10.6084/m9.figshare.5701054
title: Human Stories Work for evaluating the DSA Framework

description: The folders contain the data collection from human participants who created social media stories so that the automated stories generated by the DSA could be evaluated.
DOI: 10.6084/m9.figshare.5701051
title: Story Contents Uploaded to Storify, produced by the Dark and Stormy Archives Framework

description: The folders in this tarball contain the HTML, XML, and JSON Storify files corresponding to stories produced as part of AlNoamany's work on using Storytelling for summarizing Archive-It collections.
DOI: 10.6084/m9.figshare.5701036
title: sp_setores_censitarios.zip

description: Shapefile com os setores censitários do estado de São Paulo.
DOI: 10.6084/m9.figshare.5701051.v1
title: Story Contents Uploaded to Storify, produced by the Dark and Stormy Archives Framework

description: The folders in this tarball contain the HTML, XML, and JSON Storify files corresponding to stories produced as part of AlNoamany's work on using Storytelling for summarizing Archive-It collections.
DOI: 10.6084/m9.figshare.5701036.v1
title: sp_setores_censitarios.zip

description: Shapefile com os setores censitários do estado de São Paulo.
DOI: 10.6084/m9.figshare.5701024
title: Simulation with interior gravity

description: Simulation with interior gravity
DOI: 10.6084/m9.figshare.5701024.v1
title: Simulation with interior gravity

description: Simulation with interior gravity
DOI: 10.6084/m9.figshare.5701006
title: LAYER_MORADORES_DE_RUA_2014.zip

description: Shapefile contendo o mapeamento dos moradores de rua de São Paulo.
DOI: 10.6084/m9.figshare.5701000
title: LAYER_FAVELAS_2015.zip

description: Shapefile do mapeamento das favelas da cidade de São Paulo
DOI: 10.6084/m9.figshare.5700991
title: SAFT-MATLAB

description: This program iterates binary interaction coefficients for binary mixtures. It determines the AAD\% of thermodynamic properties of the mixtures with user-supplied experimental data (or data taken from DECHEMA).<br>Key scripts: <br>kijIter - Iterate kij values<br>gammaIter - Iterate gammaij values<br>VLEpure - Solves VLE for pure compounds - use to check validity of SAFT parameters<br><br>
DOI: 10.6084/m9.figshare.5700958
title: Transcriptional effects of a positive feedback circuit in Drosophila melanogaster

description: This is a sequence of a Drosophila transformation plasmid used in construction of tTAV-modified strains of D. melanogaster. The construct is based on the LA513 sequence described in the US patent 20070056051 A1 and it was built by R. Guy Reeves.
DOI: 10.6084/m9.figshare.5700958.v1
title: Transcriptional effects of a positive feedback circuit in Drosophila melanogaster

description: This is a sequence of a Drosophila transformation plasmid used in construction of tTAV-modified strains of D. melanogaster. The construct is based on the LA513 sequence described in the US patent 20070056051 A1 and it was built by R. Guy Reeves.
DOI: 10.6084/m9.figshare.5700865
title: SP_Exceto_a_Capital_20150527.zip

description: Datasets do IBGE para os setores censitários do estado de São Paulo, exceto a capital
DOI: 10.6084/m9.figshare.5700865.v1
title: SP_Exceto_a_Capital_20150527.zip

description: Datasets do IBGE para os setores censitários do estado de São Paulo, exceto a capital
DOI: 10.6084/m9.figshare.5700850
title: 0.1 offset explosion in a 1.5 polytrope

description: 0.1 offset explosion in a 1.5 polytrope
DOI: 10.6084/m9.figshare.5700850.v1
title: 0.1 offset explosion in a 1.5 polytrope

description: 0.1 offset explosion in a 1.5 polytrope
DOI: 10.6084/m9.figshare.5700832.v1
title: Signed networks from sociology and political science, biology, international relations, finance, and computational chemistry

description: This dataset contains a wide range of signed networks from different disciplines. For more information about the data, one may refer to the paper below:<br>Balance and Frustration in Signed Networks under Different Contexts (2018) ArXiv Preprint
DOI: 10.6084/m9.figshare.5700832.v2
title: Signed networks from sociology and political science, systems biology, international relations, finance, and computational chemistry

description: This dataset contains a wide range of signed networks from different disciplines. For more information about the data, one may refer to the paper below:<br>Samin Aref "Balance and Frustration in Signed Networks under Different Contexts" (2017) ArXiv Preprint
DOI: 10.6084/m9.figshare.5700832
title: Signed networks from sociology and political science, systems biology, international relations, finance, and computational chemistry

description: This dataset contains a wide range of signed networks from different disciplines. For more information about the data, one may refer to the paper below:<br>Aref, S., and Wilson, M. C. Balance and frustration in signed networks. Journal of Complex Networks (2019). doi: 10.1093/comnet/cny015.
DOI: 10.6084/m9.figshare.5700766
title: Centred explosion 1.5 polytrope

description: Centred explosion 1.5 polytrope
DOI: 10.6084/m9.figshare.5700766.v1
title: Centred explosion 1.5 polytrope

description: Centred explosion 1.5 polytrope
DOI: 10.6084/m9.figshare.5700763
title: TEM and array tomography images from Source data and images for "Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites" by Bloss, Cembrowski, Karsh, Colonell, Fetter, and Spruston (2018).

description: These are the raw images from Bloss et al. (2018), titled "Single excitatory axons from clustered synapses onto CA1 pyramidal cell dendrites"<br><br>Files include TEM micrographs, stacks of TEM micrographs, projections from array tomography volumes, and confocal laser microscopy images.
DOI: 10.6084/m9.figshare.5700757
title: SP Capital.zip

description: Dataset do IBGE para São Paulo - Capital
DOI: 10.6084/m9.figshare.5700721
title: Scripts for mutation rate calculation

description: Scripts used for the paper Cladogenesis and Genomic Streamlining in Extracellular Endosymbionts of Tropical Stink Bugs, doi: 10.193/gbe/evy033
DOI: 10.6084/m9.figshare.5700757.v1
title: SP Capital.zip

description: Dataset do IBGE para São Paulo - Capital
DOI: 10.6084/m9.figshare.5700721.v1
title: Scripts for mutation rate calculation

description: Scripts used for the paper Cladogenesis and Genomic Streamlining in Extracellular Endosymbionts of Tropical Stink Bugs, doi: 10.193/gbe/evy033
DOI: 10.6084/m9.figshare.5700718
title: Alignments

description: Alignments used for genomic analysis from Cladogenesis and Genomic Streamlining in Extracellular Endoymbionts of Tropical Stink Bugs.
DOI: 10.6084/m9.figshare.5700718.v1
title: Alignments

description: Alignments used for genomic analysis from Cladogenesis and Genomic Streamlining in Extracellular Endoymbionts of Tropical Stink Bugs.
DOI: 10.6084/m9.figshare.5700715
title: A quantitative analysis on the effects of critical factors limiting the effectiveness of species conservation in future-time

description: Climatic suitability data, dispersal parameterization, cost layers and planning-design models used upon the analysis performed under the study published as Alagador & Cerdeira (2018). A quantitative analysis on the effects of critical factors limiting the effectiveness of species conservation in future-time. Ecology & Evolution.<br>
DOI: 10.6084/m9.figshare.5700712
title: SP_sc_clusters_05.csv

description: O cluster e a renda média de todos os setores censitários de São Paulo
DOI: 10.6084/m9.figshare.5698861
title: Offset explosion 0.8 in a uniform density star

description: Offset explosion 0.8 in a uniform density star
DOI: 10.6084/m9.figshare.5698861.v1
title: Offset explosion 0.8 in a uniform density star

description: Offset explosion 0.8 in a uniform density star
DOI: 10.6084/m9.figshare.5698192.v1
title: Weekly Large Wildfire Probability in Western US Forests andWoodlands, 2005-2017

description: This dataset is a weekly time-series of images from 2005-2017 that depict the probability of large fire across western US forests and woodlands. Specifically, the images depict the probability that an area on the landscape will burn in a large (i.e., &gt; 405 ha) fire following an ignition event, on the given date. Each product in the dataset is a three-band GeoTIFF image (250-m resolution) in the WGS84 geographic coordinate reference system (EPSG:4326). Names of each product correspond to the image prediction date. Band 1 values are the output of a Random Forest classification algorithm, trained on 10 independent, random samples of small and large wildfires that occurred from 2005-2014, and represent the mean predicted probability of an individual pixel burning in a large fire. Band 1 values range from 0-100 (probability scaled by 100). Band 2 values represent the standard deviation of predicted probability of an individual pixel burning in a large fire, and values also range from 0-100 (standard deviation scaled by 100). Band 3 values indicate the quality of MODIS predictor variables. Multiple MODIS products were used as predictor variables to describe the vegetation and land surface immediately preceding a fire event. Only good quality pixels were retained for model training, but all pixels were retained when creating spatial predictions. Therefore, Band 3 indicates if one of these MODIS predictors had unreliable quality, where0 = All MODIS pixels were processed and good quality and 1 = At least one MODIS pixel was not processed or had bad quality. No Data values in each image are set to 255. <br>
DOI: 10.6084/m9.figshare.5698192.v2
title: Weekly Large Wildfire Probability in Western US Forests andWoodlands, 2005-2017

description: This dataset is a weekly time-series of images from 2005-2017 that depict the probability of large fire across western US forests and woodlands. Specifically, the images depict the probability that an area on the landscape will burn in a large (i.e., &gt; 405 ha) fire following an ignition event, on the given date. Each product in the dataset is a three-band GeoTIFF image (250-m resolution) in the WGS84 geographic coordinate reference system (EPSG:4326). Names of each product correspond to the image prediction date. Band 1 values are the output of a Random Forest classification algorithm, trained on 10 independent, random samples of small and large wildfires that occurred from 2005-2014, and represent the mean predicted probability of an individual pixel burning in a large fire. Band 1 values range from 0-100 (probability scaled by 100). Band 2 values represent the standard deviation of predicted probability of an individual pixel burning in a large fire, and values also range from 0-100 (standard deviation scaled by 100). Band 3 values indicate the quality of MODIS predictor variables. Multiple MODIS products were used as predictor variables to describe the vegetation and land surface immediately preceding a fire event. Only good quality pixels were retained for model training, but all pixels were retained when creating spatial predictions. Therefore, Band 3 indicates if one of these MODIS predictors had unreliable quality, where0 = All MODIS pixels were processed and good quality and 1 = At least one MODIS pixel was not processed or had bad quality. No Data values in each image are set to 255. <br>
DOI: 10.6084/m9.figshare.5698192.v3
title: Weekly Large Wildfire Probability in Western US Forests andWoodlands, 2005-2017

description: This dataset is a weekly time-series of images from 2005-2017 that depict the probability of large fire across western US forests and woodlands. Specifically, the images depict the probability that an area on the landscape will burn in a large (i.e., &gt; 405 ha) fire following an ignition event, on the given date. Each product in the dataset is a three-band GeoTIFF image (250-m resolution) in the WGS84 geographic coordinate reference system (EPSG:4326). Names of each product correspond to the image prediction date. Band 1 values are the output of a Random Forest classification algorithm, trained on 10 independent, random samples of small and large wildfires that occurred from 2005-2014, and represent the mean predicted probability of an individual pixel burning in a large fire. Band 1 values range from 0-100 (probability scaled by 100). Band 2 values represent the standard deviation of predicted probability of an individual pixel burning in a large fire, and values also range from 0-100 (standard deviation scaled by 100). Band 3 values indicate the quality of MODIS predictor variables. Multiple MODIS products were used as predictor variables to describe the vegetation and land surface immediately preceding a fire event. Only good quality pixels were retained for model training, but all pixels were retained when creating spatial predictions. Therefore, Band 3 indicates if one of these MODIS predictors had unreliable quality, where0 = All MODIS pixels were processed and good quality and 1 = At least one MODIS pixel was not processed or had bad quality. No Data values in each image are set to 255. <br>
DOI: 10.6084/m9.figshare.5698183
title: Offset explosion 0.6 uniform density star

description: Offset explosion 0.6 uniform density star
DOI: 10.6084/m9.figshare.5698183.v1
title: Offset explosion 0.6 uniform density star

description: Offset explosion 0.6 uniform density star
DOI: 10.6084/m9.figshare.5698153
title: Offset explosion 0.4 in a uniform density star

description: Offset explosion 0.4 in a uniform density star
DOI: 10.6084/m9.figshare.5698153.v1
title: Offset explosion 0.4 in a uniform density star

description: Offset explosion 0.4 in a uniform density star
DOI: 10.6084/m9.figshare.5698135
title: Offset explosion 0.2 in a uniform density star

description: Offset explosion 0.2 in a uniform density star
DOI: 10.6084/m9.figshare.5698135.v1
title: Offset explosion 0.2 in a uniform density star

description: Offset explosion 0.2 in a uniform density star
DOI: 10.6084/m9.figshare.5698096
title: Raw data for the paper Differences in cognitive functions between cytomegalovirus-infected and cytomegalovirus-free university students: a case control study

description: Raw data for the paper Differences in cognitive functions between cytomegalovirus-infected and cytomegalovirus-free university students: a case control study
DOI: 10.6084/m9.figshare.5698096.v1
title: Raw data for the paper Differences in cognitive functions between cytomegalovirus-infected and cytomegalovirus-free university students: a case control study

description: Raw data for the paper Differences in cognitive functions between cytomegalovirus-infected and cytomegalovirus-free university students: a case control study
DOI: 10.6084/m9.figshare.5698090
title: Central explosion in a uniform density star

description: Central explosion in a uniform density star
DOI: 10.6084/m9.figshare.5698090.v1
title: Central explosion in a uniform density star

description: Central explosion in a uniform density star
DOI: 10.6084/m9.figshare.5698069
title: X-ray tomography of termite nests from Senegal and Guinea

description: In this study, two different African <i>Trinervitermes geminatus</i> termite nests were excavated, one from Nguekokh, Senegal (during early October 2010) and the other from Kankan, Guinea (during early July 2007), which are about 1100 km apart. Both nests were in Savanna type vegetation, with higher tree density in Guinea. Both nests were exposed to the sun. The excavated nests were first imaged with a medical X-ray CT scanner at a pixel-resolution of 0.3-0.6 mm, which allowed us to investigate the complete structure of the nests in three dimensions non-destructively.<br><br>We then drilled multiple samples from different parts of the inner and outer walls of the nests, and scanned them non-destructively with a high-resolution X-ray micro-tomography scanner at a voxel size of 5 µm. All the X-ray micro-tomographic images in these datasets are from the outer walls, except the sample 'Senegal_termite_nest_Xray_micro_CT_sample_3' which was taken from the internal walls of the Senegal nest.<br><br>The details of the image properties and the usage notes are provided in the 'Read_me_files_Termite_nest' file.<br><br>The display pic shows the Guinea nest imaged with a medical X-ray CT scanner (left). The outer solid wall of the nest was imaged with a high-resolution X-ray micro-tomography scanner (right).<br><br><br>