cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.10266554
title: Metadata record for: A manually denoised audio-visual movie watching fMRI dataset for the studyforrest project

description: This dataset contains key characteristics about the data described in the Data Descriptor A manually denoised audio-visual movie watching fMRI dataset for the studyforrest project. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.<br>
DOI: 10.6084/m9.figshare.10266539
title: Mid-Atlantic USA wild bee abundance per transect, records with sampling info

description: Abundance of wild bees (superfamily Apoidea, clade Anthophila) per sampling transect in Maryland, Delaware, and Washington DC USA collected between 2002 and 2016 by the Native Bee Inventory and Monitoring Lab, United State Geological Survey. Includes transect-level sampling method and wild bee abundance adjusted by sampling effort.
DOI: 10.6084/m9.figshare.10266521
title: Towards a synthesis of Caribbean Biogeography for Terrestrial Arthropods

description: These are the IPs and ultimate OPs of the analyses we discuss in the paper. There are several other analyses that aren't included, as well as .p files, .t files or BEAST 2 TREES files...they were too large. If you need those or anything else not listed here, please ask. Details of all analyses can be found in the SI of the paper. Sorry it's as a zip but it was the only way to UL it maintaining the organization.
DOI: 10.6084/m9.figshare.10266536
title: Mid-Atlantic USA wild bee occurrences, records with sampling info

description: Geo-located occurrences of wild bees (superfamily Apoidea, clade Anthophila) in Maryland, Delaware, and Washington DC USA collected between 2002 and 2016 by the Native Bee Inventory and Monitoring Lab, United State Geological Survey. Dataset includes information on sampling method and effort, and is filtered to occurrences with these data.
DOI: 10.6084/m9.figshare.10266539.v1
title: Mid-Atlantic USA wild bee abundance per transect, records with sampling info

description: Abundance of wild bees (superfamily Apoidea, clade Anthophila) per sampling transect in Maryland, Delaware, and Washington DC USA collected between 2002 and 2016 by the Native Bee Inventory and Monitoring Lab, United State Geological Survey. Includes transect-level sampling method and wild bee abundance adjusted by sampling effort.
DOI: 10.6084/m9.figshare.10266464
title: Yield/Production data from Ecopath models of Lake Victoria for the Period 1971-2014

description: This datasets contains key Ecopath input parameters compiled from 8 models published on Lake Victoria between 1971 and 2014
DOI: 10.6084/m9.figshare.10266521.v1
title: Towards a synthesis of Caribbean Biogeography for Terrestrial Arthropods

description: These are the IPs and ultimate OPs of the analyses we discuss in the paper. There are several other analyses that aren't included, as well as .p files, .t files or BEAST 2 TREES files...they were too large. If you need those or anything else not listed here, please ask. Details of all analyses can be found in the SI of the paper. Sorry it's as a zip but it was the only way to UL it maintaining the organization.
DOI: 10.6084/m9.figshare.10266536.v1
title: Mid-Atlantic USA wild bee occurrences, records with sampling info

description: Geo-located occurrences of wild bees (superfamily Apoidea, clade Anthophila) in Maryland, Delaware, and Washington DC USA collected between 2002 and 2016 by the Native Bee Inventory and Monitoring Lab, United State Geological Survey. Dataset includes information on sampling method and effort, and is filtered to occurrences with these data.
DOI: 10.6084/m9.figshare.10266554.v1
title: Metadata record for: A manually denoised audio-visual movie watching fMRI dataset for the studyforrest project

description: This dataset contains key characteristics about the data described in the Data Descriptor A manually denoised audio-visual movie watching fMRI dataset for the studyforrest project. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.10266458
title: Mid-Atlantic USA wild bee occurrences, all records

description: Geo-located occurrences of wild bees (superfamily Apoidea, clade Anthophila) in Maryland, Delaware, and Washington DC USA collected between 2002 and 2016 by the Native Bee Inventory and Monitoring Lab, United State Geological Survey.
DOI: 10.6084/m9.figshare.10266458.v1
title: Mid-Atlantic USA wild bee occurrences, all records

description: Geo-located occurrences of wild bees (superfamily Apoidea, clade Anthophila) in Maryland, Delaware, and Washington DC USA collected between 2002 and 2016 by the Native Bee Inventory and Monitoring Lab, United State Geological Survey.
DOI: 10.6084/m9.figshare.10266206
title: Samaria Ancient Hebrew dataset

description: Reconstructions of Samaria ostraca characters
DOI: 10.6084/m9.figshare.10266299
title: 2019 Nature PhD Students Survey Data

description: Anonymised raw data, questionnaire and presentation of data from the 2019 Nature PhD Students survey.
DOI: 10.6084/m9.figshare.10266314
title: Acanthoscurria geniculata transcriptome assembled from mixed embryonic tissue

description: Transcriptome assembly of mixed embryonic Acanthoscurria geniculata<br><br>As described in <br>Embryonic development and secondary body axis induction in the Brazilian white knee tarantula Acanthoscurria geniculata, C. L. Koch, 1841 (Araneae; Mygalomorphae; Theraphosidae). <br><br>By Matthias Pechmann<br>
DOI: 10.6084/m9.figshare.10266194
title: Superresolved HYDI dataset

description: Super-resolved HYDI diffusion MRI dataset.<br>Nahla M H Elsaid, Pierrick Coupé, Yu-Chien Wu. Super-Resolution Hybrid Diffusion Imaging (SR-HYDI). ISMRM 2019. May 11-16, Montréal, Canada.<br>
DOI: 10.6084/m9.figshare.10265978
title: Supplementary Material for: Prediction of Circulating Adipokine Levels Based on Body Fat Compartments and Adipose Tissue Gene Expression

description: <b><i>Background:</i></b> Adipokines are hormones secreted from adipose tissue (AT), and a number of them have been established as risk factors for chronic diseases. However, it is not clear whether and to what extent adiposity, gene expression, and other factors determine their circulating levels. <b><i>Objectives:</i></b> To assess to what extent adiposity, as measured by the amount of subcutaneous AT (SAT) and visceral AT (VAT) using magnetic resonance imaging, and gene expression levels in SAT determine plasma concentrations of the adipokines adiponectin, leptin, soluble leptin receptor, resistin, interleukin 6, and fatty acid-binding protein 4 (FABP4). <b><i>Methods:</i></b> We performed a cross-sectional analysis of 156 participants from the EPIC Potsdam cohort study and analyzed multiple regression models and partial correlation coefficients. <b><i>Results:</i></b> For leptin and FABP4 concentrations, 81 and 45% variance were explained by SAT mass, VAT mass, and gene expression in SAT in multivariable regression models. For the remaining adipokines, AT mass and gene expression explained &lt;16% variance of plasma concentrations. Gene expression in SAT was a less important predictor compared to AT mass. SAT mass was a better predictor than VAT mass for leptin (partial correlation <i>r</i> = 0.81, 95% confidence interval 0.75–0.86, vs. <i>r</i> = 0.58, 95% confidence interval 0.46–0.67), while differences between AT compartments were small for the other adipokines. <b><i>Conclusions:</i></b> While plasma levels of leptin and FABP4 can be explained in a large and medium part by the amount of AT and SAT gene expression, surprisingly, these predictors explained only little variance for all other investigated adipokines.
DOI: 10.6084/m9.figshare.10265993
title: Dataset: Decoding of the other´s focus of attention by a temporal cortex module

description: The dataset contains electrophysilogical data recorded from the two monkeys used in "Decoding of the other's focus of attention by a temporal cortex module" by Hamidreza Ramezanpour and Peter Thier. <br>For more information please contact thier@uni-tuebinegn.de or hamidreza.ramezanpour@uni-tuebingen.de
DOI: 10.6084/m9.figshare.10265978.v1
title: Supplementary Material for: Prediction of Circulating Adipokine Levels Based on Body Fat Compartments and Adipose Tissue Gene Expression

description: <b><i>Background:</i></b> Adipokines are hormones secreted from adipose tissue (AT), and a number of them have been established as risk factors for chronic diseases. However, it is not clear whether and to what extent adiposity, gene expression, and other factors determine their circulating levels. <b><i>Objectives:</i></b> To assess to what extent adiposity, as measured by the amount of subcutaneous AT (SAT) and visceral AT (VAT) using magnetic resonance imaging, and gene expression levels in SAT determine plasma concentrations of the adipokines adiponectin, leptin, soluble leptin receptor, resistin, interleukin 6, and fatty acid-binding protein 4 (FABP4). <b><i>Methods:</i></b> We performed a cross-sectional analysis of 156 participants from the EPIC Potsdam cohort study and analyzed multiple regression models and partial correlation coefficients. <b><i>Results:</i></b> For leptin and FABP4 concentrations, 81 and 45% variance were explained by SAT mass, VAT mass, and gene expression in SAT in multivariable regression models. For the remaining adipokines, AT mass and gene expression explained &lt;16% variance of plasma concentrations. Gene expression in SAT was a less important predictor compared to AT mass. SAT mass was a better predictor than VAT mass for leptin (partial correlation <i>r</i> = 0.81, 95% confidence interval 0.75–0.86, vs. <i>r</i> = 0.58, 95% confidence interval 0.46–0.67), while differences between AT compartments were small for the other adipokines. <b><i>Conclusions:</i></b> While plasma levels of leptin and FABP4 can be explained in a large and medium part by the amount of AT and SAT gene expression, surprisingly, these predictors explained only little variance for all other investigated adipokines.
DOI: 10.6084/m9.figshare.10265855
title: Probability that a five residue sequence occurs in a protein by chance

description: Probability analysis for the likelihood of a five amino-acid motif occurring in two previously uncharacterised proteins by chance. The dataset presents analyses of several assumed examples.
DOI: 10.6084/m9.figshare.10265978.v3
title: Supplementary Material for: Prediction of Circulating Adipokine Levels Based on Body Fat Compartments and Adipose Tissue Gene Expression

description: <b><i>Background:</i></b> Adipokines are hormones secreted from adipose tissue (AT), and a number of them have been established as risk factors for chronic diseases. However, it is not clear whether and to what extent adiposity, gene expression, and other factors determine their circulating levels. <b><i>Objectives:</i></b> To assess to what extent adiposity, as measured by the amount of subcutaneous AT (SAT) and visceral AT (VAT) using magnetic resonance imaging, and gene expression levels in SAT determine plasma concentrations of the adipokines adiponectin, leptin, soluble leptin receptor, resistin, interleukin 6, and fatty acid-binding protein 4 (FABP4). <b><i>Methods:</i></b> We performed a cross-sectional analysis of 156 participants from the EPIC Potsdam cohort study and analyzed multiple regression models and partial correlation coefficients. <b><i>Results:</i></b> For leptin and FABP4 concentrations, 81 and 45% variance were explained by SAT mass, VAT mass, and gene expression in SAT in multivariable regression models. For the remaining adipokines, AT mass and gene expression explained &lt;16% variance of plasma concentrations. Gene expression in SAT was a less important predictor compared to AT mass. SAT mass was a better predictor than VAT mass for leptin (partial correlation <i>r</i> = 0.81, 95% confidence interval 0.75–0.86, vs. <i>r</i> = 0.58, 95% confidence interval 0.46–0.67), while differences between AT compartments were small for the other adipokines. <b><i>Conclusions:</i></b> While plasma levels of leptin and FABP4 can be explained in a large and medium part by the amount of AT and SAT gene expression, surprisingly, these predictors explained only little variance for all other investigated adipokines.
DOI: 10.6084/m9.figshare.10265978.v2
title: Supplementary Material for: Prediction of Circulating Adipokine Levels Based on Body Fat Compartments and Adipose Tissue Gene Expression

description: <b><i>Background:</i></b> Adipokines are hormones secreted from adipose tissue (AT), and a number of them have been established as risk factors for chronic diseases. However, it is not clear whether and to what extent adiposity, gene expression, and other factors determine their circulating levels. <b><i>Objectives:</i></b> To assess to what extent adiposity, as measured by the amount of subcutaneous AT (SAT) and visceral AT (VAT) using magnetic resonance imaging, and gene expression levels in SAT determine plasma concentrations of the adipokines adiponectin, leptin, soluble leptin receptor, resistin, interleukin 6, and fatty acid-binding protein 4 (FABP4). <b><i>Methods:</i></b> We performed a cross-sectional analysis of 156 participants from the EPIC Potsdam cohort study and analyzed multiple regression models and partial correlation coefficients. <b><i>Results:</i></b> For leptin and FABP4 concentrations, 81 and 45% variance were explained by SAT mass, VAT mass, and gene expression in SAT in multivariable regression models. For the remaining adipokines, AT mass and gene expression explained &lt;16% variance of plasma concentrations. Gene expression in SAT was a less important predictor compared to AT mass. SAT mass was a better predictor than VAT mass for leptin (partial correlation <i>r</i> = 0.81, 95% confidence interval 0.75–0.86, vs. <i>r</i> = 0.58, 95% confidence interval 0.46–0.67), while differences between AT compartments were small for the other adipokines. <b><i>Conclusions:</i></b> While plasma levels of leptin and FABP4 can be explained in a large and medium part by the amount of AT and SAT gene expression, surprisingly, these predictors explained only little variance for all other investigated adipokines.
DOI: 10.6084/m9.figshare.10265630
title: Urban populations of Aedes aegypti from central Argentina: dispersal patterns assesed by Bayesian and multivariate methods

description: <i>Aedes aegypti</i>, the main vector of dengue and other arboviruses, was declared eradicated from Argentina in 1964; however, in 1987 it was detected again and nowadays it occurs in most of the country territory. To understand the transmission of vector-borne diseases, knowledge of the dispersal of vector populations is essential to evaluate the risk of pathogen transmission. We conducted a micro-geographic population genetic analysis of <i>Ae. aegypti</i> in 20 neighborhoods from Córdoba, the second largest city in Argentina, using 10 microsatellite loci. High genetic differentiation and the absence of an isolation by distance pattern was found using Weir and Cockerham’s θ. Bayesian and multivariate clustering analyses showed that all studied sites included individuals with high membership coefficients (Q) in their populations and others admixed or with membership in another cluster. Individuals with high Q in clusters different from the population in which they were collected strongly suggests that passive transport is important to shape Ae. aegypti dispersal pattern in Córdoba city. Knowing the genetic structure of <i>Ae. aegypti </i>populations and their dispersal patterns would contribute to the implementation of vector control program.<br>
DOI: 10.6084/m9.figshare.10265825
title: Pharmacokinetic modeling of 68Ga-PSMA-11 in primary prostate cancer patients

description: 15 patients underwent a 60-minute dynamic <sup>68</sup>Ga-PSMA-11 PET scan (patient 101P had its scan interrupted at 40 min) in list-mode with the field of view over the pelvic area. PET data were reconstructed with a 3D-Ordered-Subsets Expectation Maximization (OSEM) algorithm (3 iteration, 21 subsets, matrix 256x256, 4 mm Gaussian filter) and corrected for decay, scatter and attenuation using Dixon-based MR sequences. The list mode data were reconstructed into 28 frames (10 x 30 s, 5 x 60 s, 5 x 120 s, and 8 x 300 s).<br>Volumes of interest (VOIs) were outlined on the PET images to derive time-activity curves (TACs) in unit Bq/mL. Tumor lesion TACs were derived from VOIs using isocontour threshold of 40% of maximum SUV on late PET images (last 15 min of scan) with the location confirmed on the T2-weighted MR images. Spherical VOIs of 1 mL were outlined on normal prostate and gluteus muscle to derive normal tissue TACs.<br>Arterial blood activity was measured by continuous blood sampling from the radial artery during the first 10 min using an automatic blood sampling device with 1 s temporal resolution. Manual arterial blood samples at 6 time points (approximately 3, 7, 15, 25, 40 and 60 min post-injection). The manual blood samples were immediately put on ice and centrifuged to separate plasma. Whole blood and plasma-activities were measured in a gamma counter. The arterial input function was generated from the blood sampler curve corrected for decay, background and dispersion (15 s), merged with the decay-corrected manual whole blood samples to get a 60-min AIF. This whole-blood AIF was converted into a plasma AIF using the average plasma-to-blood activity ratios from the manual samples for each subject.<br><br>IDIF were generated by extracting the median PET activity within a vessel mask at each timeframe. The vessel mask was defined by segmentation of both external iliac arteries clearly visible on Dixon MR registered to PET. The IDIF were corrected for spillover of PET activity from surrounding tissue to the arteries and partial volume errors using the approach described in Croteau et al 2010. The IDIF were converted to plasma curves using the average plasma-to-blood ratio determined from the arterial blood sampling.<br><br>Included in the data set:- 1 excel file with patient data- 15 text files with blood data xxx_Blood: c_blo: whole-blood TAC, c_inp: plasma TAC (arterial input function)- 12 text files with image-derived blood data xxx_IDIF (patients 101, 152 and 207 excluded): c_blo: whole-blood IDIF TAC, c_inp: plasma IDIF TAC - 14 text files with tissue TACs xxx_TAC (subject 222 did not present any PSMA PET uptake and was excluded from lesion-based analysis): midtime and frame duration in min, uptake data in Bq/mL.
DOI: 10.6084/m9.figshare.10265825.v2
title: Pharmacokinetic modeling of 68Ga-PSMA-11 in primary prostate cancer patients

description: 15 patients underwent a 60-minute dynamic <sup>68</sup>Ga-PSMA-11 PET scan (patient 101P had its scan interrupted at 40 min) in list-mode with the field of view over the pelvic area. PET data were reconstructed with a 3D-Ordered-Subsets Expectation Maximization (OSEM) algorithm (3 iteration, 21 subsets, matrix 256x256, 4 mm Gaussian filter) and corrected for decay, scatter and attenuation using Dixon-based MR sequences. The list mode data were reconstructed into 28 frames (10 x 30 s, 5 x 60 s, 5 x 120 s, and 8 x 300 s).<br>Volumes of interest (VOIs) were outlined on the PET images to derive time-activity curves (TACs) in unit Bq/mL. Tumor lesion TACs were derived from VOIs using isocontour threshold of 40% of maximum SUV on late PET images (last 15 min of scan) with the location confirmed on the T2-weighted MR images. Spherical VOIs of 1 mL were outlined on normal prostate and gluteus muscle to derive normal tissue TACs.<br>Arterial blood activity was measured by continuous blood sampling from the radial artery during the first 10 min using an automatic blood sampling device with 1 s temporal resolution. Manual arterial blood samples at 6 time points (approximately 3, 7, 15, 25, 40 and 60 min post-injection). The manual blood samples were immediately put on ice and centrifuged to separate plasma. Whole blood and plasma-activities were measured in a gamma counter. The arterial input function was generated from the blood sampler curve corrected for decay, background and dispersion (15 s), merged with the decay-corrected manual whole blood samples to get a 60-min AIF. This whole-blood AIF was converted into a plasma AIF using the average plasma-to-blood activity ratios from the manual samples for each subject.<br><br>IDIF were generated by extracting the median PET activity within a vessel mask at each timeframe. The vessel mask was defined by segmentation of both external iliac arteries clearly visible on Dixon MR registered to PET. The IDIF were corrected for spillover of PET activity from surrounding tissue to the arteries and partial volume errors using the approach described in Croteau et al 2010. The IDIF were converted to plasma curves using the average plasma-to-blood ratio determined from the arterial blood sampling.<br><br>Included in the data set:- 1 excel file with patient data- 15 text files with blood data xxx_Blood: c_blo: whole-blood TAC, c_inp: plasma TAC (arterial input function)- 12 text files with image-derived blood data xxx_IDIF (patients 101, 152 and 207 excluded): c_blo: whole-blood IDIF TAC, c_inp: plasma IDIF TAC - 14 text files with tissue TACs xxx_TAC (subject 222 did not present any PSMA PET uptake and was excluded from lesion-based analysis): midtime and frame duration in min, uptake data in Bq/mL.
DOI: 10.6084/m9.figshare.10265630.v1
title: Urban populations of Aedes aegypti from central Argentina: micro-geographic genetic differentiation and estimation of effective dispersal by spatial autocorrelation

description: <i>Aedes aegypti,</i> the main vector of dengue and other arboviruses, was declared eradicated from Argentina in 1964; however, in 1987 it was detected again and nowadays it occurs in most of the country territory. To understand the transmission of vector-borne diseases, knowledge of the dispersal of vector populations is essential to evaluate the risk of pathogen transmission. We conducted a micro-geographic population genetic analysis of Ae. aegypti in 20 neighborhoods from Córdoba, the second largest city in Argentina, using 10 microsatellite loci. A marked genetic differentiation between population pairs was revealed using Weir and Cockerham’s θ and Bayesian approaches. The absence of an isolation by distance pattern might be due to the introduction of different genetic stocks through passive transport . Finally, spatial autocorrelation was significantly high for individuals separated by distances of up to 200 m, indicating that the effective dispersal would not exceed this range. The high levels of polymorphism and the genetic structure observed among populations in the city would result from the short range of movement of this vector and a continuous inflow of migrants from neighboring countries. These observations highlight the importance of knowing the genetic structure of <i>Ae. aegypti</i> populations and their dispersal patterns in order to implement vector control programs.<br>