cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6998477
title: AB amyloid_human liver protocols and data.zip

description: <br> <br><b>Impaired Hepatic Amyloid-Beta Degradation in Alzheimer’s Disease. </b> <br>Chera L. Maarouf<sup>1</sup>, Jessica E. Walker<sup>1</sup>, Lucia I. Sue<sup>1</sup>, Brittany N. Dugger<sup>2</sup>, Thomas G. Beach<sup>1</sup> and<sup> </sup>Geidy E. Serrano<sup>1</sup> <br> <br>Banner Sun Health Research Institute, Sun City, AZ, 85351Department of Pathology and Laboratory Medicine, University of California Davis School of Medicine Sacramento, CA 95817 <br> <br> <br>Corresponding author: Geidy E. Serrano, PhD <br> Banner Sun Health Research Institute<br> 10515 W Santa Fe Drive, Bldg B, 3rd Fl<br> Sun City, AZ 85351<br> Ph: 623-832-5608 <br> Fax: 623-832-5681<br> email: Geidy.Serrano@bannerhealth.com <br> <br>Extensive research strongly suggests that amyloid beta (Aβ) aggregates in the brain have a central role in Alzheimer’s disease (AD) pathogenesis. Pathological Aβ deposition is likely due to an altered balance between overproduction and elimination. Rodent studies have suggested that the liver has a major role in Aβ degradation. It is possible alterations of liver function could affect brain Aβ levels through changes in blood Aβ concentration. In this study, we hypothesized hepatic Aβ degradation to be impaired in AD subjects. To test our hypothesis, an Aβ degradation assay was developed using synthetic fluorescein-labeled Aβ40 and Aβ42 spiked into human liver homogenates. Aβ degradation rates were lower in AD-derived homogenates as compared with those from non-demented (ND) control subjects, even after accounting for such covariates as age, sex, and APOE genotype. The protein expression of potential Aβ-degrading enzymes were also examined. Neprilysin levels were not different in AD liver samples, while cathepsin D and insulin-degrading enzyme were significantly altered in AD subjects. The results support the possibility that impaired hepatic Aβ degradation could be a factor contributing to increased brain Aβ accumulation and AD. <br><br><br>
DOI: 10.6084/m9.figshare.6998477.v1
title: AB amyloid_human liver protocols and data.zip

description: <br> <br><b>Impaired Hepatic Amyloid-Beta Degradation in Alzheimer’s Disease. </b> <br>Chera L. Maarouf<sup>1</sup>, Jessica E. Walker<sup>1</sup>, Lucia I. Sue<sup>1</sup>, Brittany N. Dugger<sup>2</sup>, Thomas G. Beach<sup>1</sup> and<sup> </sup>Geidy E. Serrano<sup>1</sup> <br> <br>Banner Sun Health Research Institute, Sun City, AZ, 85351Department of Pathology and Laboratory Medicine, University of California Davis School of Medicine Sacramento, CA 95817 <br> <br> <br>Corresponding author: Geidy E. Serrano, PhD <br> Banner Sun Health Research Institute<br> 10515 W Santa Fe Drive, Bldg B, 3rd Fl<br> Sun City, AZ 85351<br> Ph: 623-832-5608 <br> Fax: 623-832-5681<br> email: Geidy.Serrano@bannerhealth.com <br> <br>Extensive research strongly suggests that amyloid beta (Aβ) aggregates in the brain have a central role in Alzheimer’s disease (AD) pathogenesis. Pathological Aβ deposition is likely due to an altered balance between overproduction and elimination. Rodent studies have suggested that the liver has a major role in Aβ degradation. It is possible alterations of liver function could affect brain Aβ levels through changes in blood Aβ concentration. In this study, we hypothesized hepatic Aβ degradation to be impaired in AD subjects. To test our hypothesis, an Aβ degradation assay was developed using synthetic fluorescein-labeled Aβ40 and Aβ42 spiked into human liver homogenates. Aβ degradation rates were lower in AD-derived homogenates as compared with those from non-demented (ND) control subjects, even after accounting for such covariates as age, sex, and APOE genotype. The protein expression of potential Aβ-degrading enzymes were also examined. Neprilysin levels were not different in AD liver samples, while cathepsin D and insulin-degrading enzyme were significantly altered in AD subjects. The results support the possibility that impaired hepatic Aβ degradation could be a factor contributing to increased brain Aβ accumulation and AD. <br><br><br>
DOI: 10.6084/m9.figshare.6998456
title: General Data - Penile Cancer Project

description: <pre>Data of patients with penile cancer - Laboratory of Immunofluorescence and Electronic Microscopy, Presidente Dutra University Hospital, São Luís, Maranhão, Brazil</pre>
DOI: 10.6084/m9.figshare.6998420
title: Model Results for Bramson et al. 2019 JGR-Planets

description: All thermal model outputs, inputs, code, as well as trough observations that went into the paper "A Migration Model for the Polar Spiral Troughs of Mars"<br>Citation:Bramson, A. M., S. Byrne, J. Bapst, I.B. Smith, and T. McClintock (2019), A Migration Model for the Polar Spiral Troughs of Mars. <em>Journal of Geophysical Research: Planets</em>, 124, doi:10.1029/2018JE005806.<br>
DOI: 10.6084/m9.figshare.6998420.v1
title: Model Results for Bramson et al. 2019 JGR-Planets

description: All thermal model outputs, inputs, code, as well as trough observations that went into the paper "A Migration Model for the Polar Spiral Troughs of Mars"<br>Citation:Bramson, A. M., S. Byrne, J. Bapst, I.B. Smith, and T. McClintock (2019), A Migration Model for the Polar Spiral Troughs of Mars. <em>Journal of Geophysical Research: Planets</em>, 124, doi:10.1029/2018JE005806.<br>
DOI: 10.18462/iir.rankine.2020.1141
title: Diffusion absorption refrigeration cycle analysis with low GWP refrigerants.
DOI: 10.7284/101283
title: Multibeam Echosounder data collected during research cruise KM0204 using a Kongsberg EM120 instrument system onboard the platform RV Kilo Moana
DOI: 10.6084/m9.figshare.6998456.v3
title: General Data - Penile Cancer Project

description: <pre>Data of patients with penile cancer - Laboratory of Immunofluorescence and Electronic Microscopy, Presidente Dutra University Hospital, São Luís, Maranhão, Brazil</pre>
DOI: 10.6084/m9.figshare.6998081
title: 3D models

description: 3D models of ophiuroid arm range of motion in Autodesk Maya. Numbers in title refer to figures in the paper "Integrating morphology and <i>in vivo</i> skeletal mobility with digital models to infer function in brittle star arms" in the Journal of Anatomy.
DOI: 10.6084/m9.figshare.6998081.v1
title: 3D models

description: 3D models of ophiuroid arm range of motion in Autodesk Maya. Numbers in title refer to figures in the paper "Integrating morphology and <i>in vivo</i> skeletal mobility with digital models to infer function in brittle star arms" in the Journal of Anatomy.
DOI: 10.6084/m9.figshare.6998057
title: PhenoPredict Model Construction

description: Code for constructing phenoclimate models used in PhenoPredict 1.0<br>
DOI: 10.6084/m9.figshare.6998057.v1
title: PhenoPredict Model Construction

description: Code for constructing phenoclimate models used in PhenoPredict 1.0<br>
DOI: 10.6084/m9.figshare.6998009
title: TreeBASEdmp-2018-08-22

description: TreeBASE (http://www.treebase.org) schema and data ingestible by PostgreSQL
DOI: 10.6084/m9.figshare.6998009.v1
title: TreeBASEdmp-2018-08-22

description: TreeBASE (http://www.treebase.org) schema and data ingestible by PostgreSQL
DOI: 10.6084/m9.figshare.6997253
title: Pipeline for 16S rRNA processing using DADA2

description: Here you will find all of the code and explanations to completely reproduce our DADA2 pipeline for processing raw, pair-end 16S rRNA Illumina amplicon data. <br> The pipeline consists of two (2) parts of the workflow. <br> 1. The first part will process the data from different runs separately. Samples were sequenced across three runs, each of which was processed separately. This part consists of steps to filter and trim, learn errors, dereplicate, infer amplicon sequence variants (ASV), and merge paired end reads. We also include an optional step for identifying chimeras and assigning taxonomy for each run individually, however we actually did this on the combined data set in the next part. <br> 2. In the second part we merge the outputs from the first part, identify chimeras, and assign taxonomy. <br> Also included is the HTML file for the workflow. This can be viewed in any web browser.<br>
DOI: 10.6084/m9.figshare.6995039
title: Origin and hidden diversity within the poorly known Galápagos snake radiation (Serpentes: Dipsadidae)

description: Galápagos snakes are among the least studied terrestrial vertebrates of the Archipelago. Here, we provide a phylogenetic analysis and a time calibrated tree for the group, based on a sampling of the major populations known to occur in the Archipelago. Our study revealed the presence of two previously unknown species from Santiago and Rábida Islands, and one from Tortuga, Isabela, and Fernandina. We also recognize six additional species of <i>Pseudalsophis</i> in the Galápagos Archipelago (<i>Pseudalsophis biserialis</i> from San Cristobal, Floreana and adjacent islets; <i>Pseudalsophis hoodensis</i> from Española and adjacent islets; <i>Pseudalsophis dorsalis</i> from Santa Cruz, Baltra, Santa Fé, and adjacent islets; <i>Pseudalsophis occidentalis</i> from Fernandina, Isabela, and Tortuga; <i>Pseudalsophis slevini</i> from Pinzon, and <i>Pseudalsophis steindachneri</i> from Baltra, Santa Cruz and adjacent islets). Our time calibrated tree suggests that the genus <i>Pseudalsophis</i> colonized the Galápagos Archipelago through a single event of oceanic dispersion from the coast of South America that occurred at approximately between 6.9 Ma and 4.4 Ma, near the Miocene/Pliocene boundary.www.zoobank.org/urn:lsid:zoobank.org:pub:2679FD19-01E5-48FE-A0DA-A88FF145DE56
DOI: 10.6084/m9.figshare.6997253.v1
title: Pipeline for 16S rRNA processing using DADA2

description: Here you will find all of the code and explanations to completely reproduce our DADA2 pipeline for processing raw, pair-end 16S rRNA Illumina amplicon data. <br> The pipeline consists of two (2) parts of the workflow. <br> 1. The first part will process the data from different runs separately. Samples were sequenced across three runs, each of which was processed separately. This part consists of steps to filter and trim, learn errors, dereplicate, infer amplicon sequence variants (ASV), and merge paired end reads. We also include an optional step for identifying chimeras and assigning taxonomy for each run individually, however we actually did this on the combined data set in the next part. <br> 2. In the second part we merge the outputs from the first part, identify chimeras, and assign taxonomy. <br> Also included is the HTML file for the workflow. This can be viewed in any web browser.<br>
DOI: 10.6084/m9.figshare.6995039.v1
title: Origin and hidden diversity within the poorly known Galápagos snake radiation (Serpentes: Dipsadidae)

description: Galápagos snakes are among the least studied terrestrial vertebrates of the Archipelago. Here, we provide a phylogenetic analysis and a time calibrated tree for the group, based on a sampling of the major populations known to occur in the Archipelago. Our study revealed the presence of two previously unknown species from Santiago and Rábida Islands, and one from Tortuga, Isabela, and Fernandina. We also recognize six additional species of <i>Pseudalsophis</i> in the Galápagos Archipelago (<i>Pseudalsophis biserialis</i> from San Cristobal, Floreana and adjacent islets; <i>Pseudalsophis hoodensis</i> from Española and adjacent islets; <i>Pseudalsophis dorsalis</i> from Santa Cruz, Baltra, Santa Fé, and adjacent islets; <i>Pseudalsophis occidentalis</i> from Fernandina, Isabela, and Tortuga; <i>Pseudalsophis slevini</i> from Pinzon, and <i>Pseudalsophis steindachneri</i> from Baltra, Santa Cruz and adjacent islets). Our time calibrated tree suggests that the genus <i>Pseudalsophis</i> colonized the Galápagos Archipelago through a single event of oceanic dispersion from the coast of South America that occurred at approximately between 6.9 Ma and 4.4 Ma, near the Miocene/Pliocene boundary.www.zoobank.org/urn:lsid:zoobank.org:pub:2679FD19-01E5-48FE-A0DA-A88FF145DE56
DOI: 10.6084/m9.figshare.6997253.v2
title: Pipeline for 16S rRNA processing using DADA2

description: Here you will find all of the code and explanations to completely reproduce our DADA2 pipeline for processing raw, pair-end 16S rRNA Illumina amplicon data. <br> The pipeline consists of two (2) parts of the workflow. <br> 1. The first part will process the data from different runs separately. Samples were sequenced across three runs, each of which was processed separately. This part consists of steps to filter and trim, learn errors, dereplicate, infer amplicon sequence variants (ASV), and merge paired end reads. We also include an optional step for identifying chimeras and assigning taxonomy for each run individually, however we actually did this on the combined data set in the next part. <br> 2. In the second part we merge the outputs from the first part, identify chimeras, and assign taxonomy. <br> Also included is the HTML file for the workflow. This can be viewed in any web browser.<br>
DOI: 10.6084/m9.figshare.6995012
title: VIC simulation in the Colorado River Basin over 1915-2016

description: Monthly data is archived. Two sub-directories: "ori" contains baseline simulation and "detrend" is for the T-detrend simulation.<br>Daily data is available here: http://ucla.box.com/v/crb-runoff-paper
DOI: 10.6084/m9.figshare.6995012.v1
title: VIC simulation in the Colorado River Basin over 1915-2016

description: Monthly data is archived. Two sub-directories: "ori" contains baseline simulation and "detrend" is for the T-detrend simulation.<br>Daily data is available here: http://ucla.box.com/v/crb-runoff-paper
DOI: 10.6084/m9.figshare.6997253.v3
title: Pipeline for 16S rRNA processing using DADA2

description: Here you will find all of the code and explanations to completely reproduce our DADA2 pipeline for processing raw, pair-end 16S rRNA Illumina amplicon data. <br> The pipeline consists of two (2) parts of the workflow. <br> 1. The first part will process the data from different runs separately. Samples were sequenced across three runs, each of which was processed separately. This part consists of steps to filter and trim, learn errors, dereplicate, infer amplicon sequence variants (ASV), and merge paired end reads. We also include an optional step for identifying chimeras and assigning taxonomy for each run individually, however we actually did this on the combined data set in the next part. <br> 2. In the second part we merge the outputs from the first part, identify chimeras, and assign taxonomy. <br> Also included is the HTML file for the workflow. This can be viewed in any web browser.<br>
DOI: 10.6084/m9.figshare.6995009
title: CBP mouse clinical chemistry prism histo quantification.pzfx

description: CPB Mouse clinical chemistry and histology quantification
DOI: 10.6084/m9.figshare.6995009.v1
title: CBP mouse clinical chemistry prism histo quantification.pzfx

description: CPB Mouse clinical chemistry and histology quantification
DOI: 10.6084/m9.figshare.6997253.v4
title: Pipeline for 16S rRNA processing using DADA2

description: Here you will find all of the code and explanations to completely reproduce our DADA2 pipeline for processing raw, pair-end 16S rRNA Illumina amplicon data. <br> The pipeline consists of two (2) parts of the workflow. <br> 1. The first part will process the data from different runs separately. Samples were sequenced across three runs, each of which was processed separately. This part consists of steps to filter and trim, learn errors, dereplicate, infer amplicon sequence variants (ASV), and merge paired end reads. We also include an optional step for identifying chimeras and assigning taxonomy for each run individually, however we actually did this on the combined data set in the next part. <br> 2. In the second part we merge the outputs from the first part, identify chimeras, and assign taxonomy. <br> Also included is the HTML file for the workflow. This can be viewed in any web browser.<br>
DOI: 10.6084/m9.figshare.6994994.v1
title: Earth Analytics Bootcamp | Final Project Teaching Subset

description: This is a teaching data subset that contains data for fire occurrence in California between 1992 and 2015. Data included:<br><br>Source: U.S. Forest Service<br>
DOI: 10.6084/m9.figshare.6994994.v2
title: Earth Analytics Bootcamp | Final Project Teaching Subset

description: This is a teaching data subset that contains data for fire occurrence in California between 1992 and 2015. Data included:<br><br>Source: U.S. Forest Service<br>
DOI: 10.6084/m9.figshare.6994994.v3
title: Earth Analytics Bootcamp | Final Project Teaching Subset

description: This is a teaching data subset that contains data for fire occurrence in California between 1992 and 2015. Data included:<br><br>Source: U.S. Forest Service<br>
DOI: 10.6084/m9.figshare.6994994.v4
title: Earth Analytics Bootcamp | Final Project Teaching Subset

description: This is a teaching data subset that contains data for fire occurrence in California between 1992 and 2015. Data included:<br><br>Source: U.S. Forest Service<br>
DOI: 10.7284/101286
title: Multibeam Echosounder data collected during research cruise KM0205 using a Kongsberg EM1002 instrument system onboard the platform RV Kilo Moana
DOI: 10.6084/m9.figshare.6994994.v5
title: Earth Analytics Bootcamp | Final Project Teaching Subset

description: This is a teaching data subset that contains data for fire occurrence in California between 1992 and 2015. Data included:<br><br>Source: U.S. Forest Service<br><br>This teaching data subset also contains data on total monthly precipitation (inches) in 1992 to 2018 at Los Angeles International Airport (LAX). <br>Source: National Weather Service
DOI: 10.6084/m9.figshare.6994994.v6
title: Earth Analytics Bootcamp | Final Project Teaching Subset

description: This is a teaching data subset that contains data for fire occurrence in California between 1992 and 2015. Data included:<br><br>Source: U.S. Forest Service<br><br>This teaching data subset also contains data on total monthly precipitation (inches) in 1992 to 2018 at Los Angeles International Airport (LAX). <br>Source: National Weather Service
DOI: 10.6084/m9.figshare.6994946
title: Relative Power EEG and Bayley Scales Infant data

description: Spreadsheet with resting state relative power electroencephalography data and Bayley Scales of Infant Development Scales (version 3) scores for each infant participant at each assessment. Source data for manuscript "Relationships between variance in electroencephalography relative power and developmental status in infants with typical development and at risk for developmental disability: An observational study" by the same authors. Please note, a version 2 spreadsheet was uploaded on February 23, 2020. This fixed a transposed data error for electrode values at visit 1 in the original spreadsheet. This error occurred when we compiled variables to create a single spreadsheet for figshare, it did not affect our analyses. We would also like to note that for prediction of Bayley Raw Fine Motor scores at visit 3 with SD RP from visit 1 analyses included only infants with EEG data at both visits 1 and 3, N=22.
DOI: 10.6084/m9.figshare.6994946.v1
title: Relative Power EEG and Bayley Scales Infant data

description: Spreadsheet with resting state relative power electroencephalography data and Bayley Scales of Infant Development Scales (version 3) scores for each infant participant at each assessment. Source data for manuscript "Relationships between variance in electroencephalography relative power and developmental status in infants with typical development and at risk for developmental disability: An observational study" by the same authors.
DOI: 10.6084/m9.figshare.6994874
title: Antibiotic prescribing trends in England - Data sets

description: Data sets for our paper: "Time Trends and Geographical Variation in Prescribing of Antibiotics in England 1998-2017"Curtis et al, published in J. Antimicrobial Chemotherapy, 2018 (doi:10.103/jac/dky377)<br>These datasets accompany the following scripts:<br>- Long term trends analysis:https://gist.github.com/HelenCEBM/76a5f9a607d905c21ac98cbcf11671fc<br><br>- Practice level analysis and regression:https://gist.github.com/HelenCEBM/d6c59c8fb6ed6bf0761e0d023ef4fd6d<br>Note CCG memberships are updated for 2018 in the practice_level data file.
DOI: 10.6084/m9.figshare.6994874.v1
title: Antibiotic prescribing trends in England - Data sets

description: Data sets for our paper: "Time Trends and Geographical Variation in Prescribing of Antibiotics in England 1998-2017"Curtis et al, published in J. Antimicrobial Chemotherapy, 2018 (doi:10.103/jac/dky377)<br>These datasets accompany the following scripts:<br>- Long term trends analysis:https://gist.github.com/HelenCEBM/76a5f9a607d905c21ac98cbcf11671fc<br><br>- Practice level analysis and regression:https://gist.github.com/HelenCEBM/d6c59c8fb6ed6bf0761e0d023ef4fd6d<br>Note CCG memberships are updated for 2018 in the practice_level data file.
DOI: 10.6084/m9.figshare.6994859
title: Index_DTMs

description: The file contains a list of all the image pairs used to produce DTMs and their corresponding file names.
DOI: 10.6084/m9.figshare.6994787
title: DEM_B18_016528_B19_016884

description: CTX DTM produced from image pair B18_016528_1713_XN_08S077W and B19_016884_1713_XN_08S077W.
DOI: 10.6084/m9.figshare.6994787.v1
title: DEM_B18_016528_B19_016884

description: CTX DTM produced from image pair B18_016528_1713_XN_08S077W and B19_016884_1713_XN_08S077W.
DOI: 10.6084/m9.figshare.6994784
title: DEM_G20_025877_P22_009803

description: CTX DTM produced from image pair G20_025877_1699_XN_10S077W and P22_009803_1699_XN_10S077W.
DOI: 10.6084/m9.figshare.6994784.v1
title: DEM_G20_025877_P22_009803

description: CTX DTM produced from image pair G20_025877_1699_XN_10S077W and P22_009803_1699_XN_10S077W.
DOI: 10.6084/m9.figshare.6994778
title: DEM_B09_012994_B19_017240

description: CTX DTM produced from image pair B09_012994_1700_XN_10S075W and B19_017240_1701_XN_09S075W.
DOI: 10.6084/m9.figshare.6994772
title: DEM_B21_017807_G02_018875

description: CTX DTM made from image pair B21_017807_1701_XN_09S076W and G02_018875_1701_XN_09S076W.
DOI: 10.6084/m9.figshare.6994778.v1
title: DEM_B09_012994_B19_017240

description: CTX DTM produced from image pair B09_012994_1700_XN_10S075W and B19_017240_1701_XN_09S075W.
DOI: 10.6084/m9.figshare.6994772.v1
title: DEM_B21_017807_G02_018875

description: CTX DTM made from image pair B21_017807_1701_XN_09S076W and G02_018875_1701_XN_09S076W.
DOI: 10.6084/m9.figshare.6994757
title: DEM_P20_008735_P20_009025

description: CTX DTM made from image pair P20_008735_1702_XI_09S076W and P20_009025_1704_XI_09S076W.
DOI: 10.6084/m9.figshare.6994763
title: DEM_B07_012216_B01_009948

description: CTX DTM made from image pair B07_012216_1704_XN_09S077W and B01_009948_1704_XI_09S077W.
DOI: 10.6084/m9.figshare.6994748
title: DEM_G18_025178_B10_013561

description: CTX DTM made from image pair G18_025178_1704_XN_09S076W and G18_025244_1704_XN_09S076W.
DOI: 10.6084/m9.figshare.6994757.v1
title: DEM_P20_008735_P20_009025

description: CTX DTM made from image pair P20_008735_1702_XI_09S076W and P20_009025_1704_XI_09S076W.
DOI: 10.6084/m9.figshare.6994763.v1
title: DEM_B07_012216_B01_009948

description: CTX DTM made from image pair B07_012216_1704_XN_09S077W and B01_009948_1704_XI_09S077W.
DOI: 10.6084/m9.figshare.6994736
title: DEM_B11_013561_B10_013983

description: CTX DTM produced from image pair B11_013983_1704_XN_09S076W and B10_013561_1704_XN_09S076W.<br><br>
DOI: 10.6084/m9.figshare.6994736.v1
title: DEM_B11_013561_B10_013983

description: CTX DTM produced from image pair B11_013983_1704_XN_09S076W and B10_013561_1704_XN_09S076W.<br><br>
DOI: 10.6084/m9.figshare.6994748.v2
title: DEM_G18_025178_B10_013561

description: CTX DTM made from image pair G18_025178_1704_XN_09S076W and G18_025244_1704_XN_09S076W.
DOI: 10.6084/m9.figshare.6994748.v1
title: Untitled Item

description: CTX DTM made from image pair G18_025178_1704_XN_09S076W and G18_025244_1704_XN_09S076W.
DOI: 10.6084/m9.figshare.6994703
title: DEM_007443_007733

description: HiRISE DEM produced from image pair PSP_007443_1700 and PSP_007733_1705.