cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e189841v1-137740
title: Data and Code for: Does Childhood Immunization Rebound After Extreme Shocks? Evidence from Floods and Strikes in Pakistan

description: We investigate the effect of two shocks on childhood immunization - strikes and floods - using data on the universe of vaccinated children in 7 districts in Sindh, Pakistan. We show that both flood and strike impacted areas have not caught up on vaccinations that would have occurred in the absence of such shocks, even after extensive outreach campaigns.<br>
description: Vaccinated children enrolled in Sindh Electronic Immunization Registry (SEIR)<br>.
DOI: 10.6084/m9.figshare.22728950
title: The Index of Mainstreaming in Labour Market Support.docx

description: The Policy Index of Mainstreaming in Labour Market Support (the MLMS) is a policy index based on data from the Eurostat Labour Market Policy (LMP) database provided by the European Commission’s Directorate General for Employment, Social Affairs and Inclusion; the MIPEX; and administrative sources in each EU country. The MLMS index was developed by Dr. Salta Zhumatova, Postdoctoral Research Fellow at Simon Fraser University (Canada). There are two versions of the MLMS Index. One is an extended dataset that covers the data from 2006 till 2016 across 25 EU/EEA countries. It is available from the author upon reasonable request. The data presented here are the most recent available data from 2016.
DOI: 10.6084/m9.figshare.22728950.v1
title: The Index of Mainstreaming in Labour Market Support.docx

description: The Policy Index of Mainstreaming in Labour Market Support (the MLMS) is a policy index based on data from the Eurostat Labour Market Policy (LMP) database provided by the European Commission’s Directorate General for Employment, Social Affairs and Inclusion; the MIPEX; and administrative sources in each EU country. The MLMS index was developed by Dr. Salta Zhumatova, Postdoctoral Research Fellow at Simon Fraser University (Canada). There are two versions of the MLMS Index. One is an extended dataset that covers the data from 2006 till 2016 across 25 EU/EEA countries. It is available from the author upon reasonable request. The data presented here are the most recent available data from 2016.
DOI: 10.24431/rw1k7dk
title: Identification of ambiguous baleen whale calls using nonlinear signal processing

description: This dataset is composed of 5 CSV files that summarize baleen whale call detections from acoustic data collected in the Bering, Chukchi, and Western Beaufort Seas by th Alaska Fisheries Scientific Center and funded by BOEM. The full acoustic dataset is available from the National Centers for Environmental Information (NCEI) here: http://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.nodc:0143303
DOI: 10.34740/kaggle/dsv/5578209
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.15784/601684
title: Biogeochemical measurements of water tracks and adjacent dry soils from the McMurdo Dry Valleys

description: This dataset contains soil properties data from the McMurdo Dry Valleys, focused on the properties of water tracks and adjacent dry soils. The dataset is surface and near-surface soil sample data, including sample location, water content by weight, organic matter content by weight, soluble salt extract composition by ion, and cation exchange extract concentrations from the soils.
DOI: 10.25914/akcy-n669
title: Magnetotelluric surveys of the AusLAMP Musgraves Province within the Northern Territory, 2016 to 2018 (Level 1 concatenated, resampled, rotated MTH5 time series)
DOI: 10.6084/m9.figshare.22580473.v4
title: Global Urban Network SVI Data

description: This data set contains segmentation masks for city-scale street view imagery. We employ Meta's Mask2Former approach (Cheng, 2022) for image segmentation. <br> We obtain SVI from Mapillary which is the world's largest platform for free and open street view imagery (SVI). Till date, Mapillary's coverage has penetrated most global cities around the world. Compared to other proprietary options such as Google Street View, Mapillary images are hosted on a CC-BY-SA 4.0 licence, which allows users to freely share, use, and adapt images. <br> Images are subsampled proportionately based on geographic tiling of each city's administrative boundary and filtered to remove non-frontal facing and non-daylight images. <br> <br>
DOI: 10.5281/zenodo.7884470
title: Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

description: This data is in accordance with the submitted article to <strong>The Journal of Chemical Theory and Computation. </strong>This article is entitled, “<strong>Data Driven Discovery of MOFs for Hydrogen Gas Adsorption</strong>” by Samrendra K. Singh, Abhishek T. Sose, Fangxi Wang, Karteek K. Bejagam, and Sanket A. Deshmukh; to be published in the special issue on “Machine Learning for Molecular Simulation” in Journal of Chemical Theory and Computation.
DOI: 10.5281/zenodo.7884469
title: Data Driven Discovery of MOFs for Hydrogen Gas Adsorption

description: This data is in accordance with the submitted article to <strong>The Journal of Chemical Theory and Computation. </strong>This article is entitled, “<strong>Data Driven Discovery of MOFs for Hydrogen Gas Adsorption</strong>” by Samrendra K. Singh, Abhishek T. Sose, Fangxi Wang, Karteek K. Bejagam, and Sanket A. Deshmukh; to be published in the special issue on “Machine Learning for Molecular Simulation” in Journal of Chemical Theory and Computation.
DOI: 10.5281/zenodo.7884681
title: Assemblies for "Linear time complexity de novo long read genome assembly with GoldRush"

description: GoldRush is a <em>de novo</em> genome assembly algorithm with linear time complexity in the number of input long sequencing reads. We tested GoldRush on Oxford Nanopore Technologies datasets with different base error profiles describing the genomes of three human cell lines (NA24385, HG01243 and HG02055), Oryza sativa (rice), and Solanum lycopersicum (tomato). Here, we provide the assemblies for the GoldRush, Flye, Redbean and Shasta assemblies of these long read datasets.
description: {"references": ["Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nature Biotechnology 37, 540\u2013546 (2019).", "Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nature Methods 17, 155\u2013158 (2020).", "Shafin, K. et al. Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes. Nature Biotechnology 38, 1044\u20131053 (2020)."]}
DOI: 10.5281/zenodo.7884680
title: Assemblies for "Linear time complexity de novo long read genome assembly with GoldRush"

description: GoldRush is a <em>de novo</em> genome assembly algorithm with linear time complexity in the number of input long sequencing reads. We tested GoldRush on Oxford Nanopore Technologies datasets with different base error profiles describing the genomes of three human cell lines (NA24385, HG01243 and HG02055), Oryza sativa (rice), and Solanum lycopersicum (tomato). Here, we provide the assemblies for the GoldRush, Flye, Redbean and Shasta assemblies of these long read datasets.
description: {"references": ["Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nature Biotechnology 37, 540\u2013546 (2019).", "Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nature Methods 17, 155\u2013158 (2020).", "Shafin, K. et al. Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes. Nature Biotechnology 38, 1044\u20131053 (2020)."]}
DOI: 10.5281/zenodo.7882390
title: Preliminary assemblies of the three-generational pedigree of African-American ancestry

description: Monika Cechova<sup>1</sup>, Sergey Koren<sup>2</sup>, Julian K. Lucas<sup>1</sup>, Rebecca Serra Mari<sup>3</sup>, Mobin Asri<sup>1</sup>, David Porubsky<sup>4</sup>, Jordan M. Eizenga<sup>1</sup>, Brandy McNulty<sup>1</sup>, Andrey Bzikadze<sup>5</sup>, Christopher Markovic<sup>6</sup>, Tamara Potapova<sup>7</sup>, Jennifer L. Gerton<sup>7</sup>, Pavel A. Pevzner<sup>8</sup>, Evan E. Eichler<sup>4</sup>, Benedict Paten<sup>1</sup>, Adam M. Phillippy<sup>2</sup>, Ting Wang<sup>9</sup>, Nathan O. Stitziel<sup>10</sup> , Robert S. Fulton<sup>6</sup>, Tobias Marschall<sup>3</sup>, Karen H. Miga<sup>1</sup> <sup>1</sup> UC Santa Cruz Genomics Institute, University of California, Santa Cruz, 1156 High St, Santa Cruz, CA, USA <sup>2</sup> Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA <sup>3</sup> Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany <sup>4</sup> Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA <sup>5</sup> Graduate Program in Bioinformatics and Systems Biology, University of California San Diego, San Diego, CA, USA <sup>6</sup> McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA <sup>7</sup> Stowers Institute for Medical Research, Kansas City, MO, USA <sup>8</sup> Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA <sup>9 </sup>Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA <sup>10</sup> Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
description: verkko 1.1, HiFi + UL + trio (PAN027) /Hi-C (PAN010, PAN011, PAN028)
DOI: 10.5281/zenodo.7882389
title: Preliminary assemblies of the three-generational pedigree of African-American ancestry

description: Monika Cechova<sup>1</sup>, Sergey Koren<sup>2</sup>, Julian K. Lucas<sup>1</sup>, Rebecca Serra Mari<sup>3</sup>, Mobin Asri<sup>1</sup>, David Porubsky<sup>4</sup>, Jordan M. Eizenga<sup>1</sup>, Brandy McNulty<sup>1</sup>, Andrey Bzikadze<sup>5</sup>, Christopher Markovic<sup>6</sup>, Tamara Potapova<sup>7</sup>, Jennifer L. Gerton<sup>7</sup>, Pavel A. Pevzner<sup>8</sup>, Evan E. Eichler<sup>4</sup>, Benedict Paten<sup>1</sup>, Adam M. Phillippy<sup>2</sup>, Ting Wang<sup>9</sup>, Nathan O. Stitziel<sup>10</sup> , Robert S. Fulton<sup>6</sup>, Tobias Marschall<sup>3</sup>, Karen H. Miga<sup>1</sup> <sup>1</sup> UC Santa Cruz Genomics Institute, University of California, Santa Cruz, 1156 High St, Santa Cruz, CA, USA <sup>2</sup> Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA <sup>3</sup> Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany <sup>4</sup> Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA <sup>5</sup> Graduate Program in Bioinformatics and Systems Biology, University of California San Diego, San Diego, CA, USA <sup>6</sup> McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA <sup>7</sup> Stowers Institute for Medical Research, Kansas City, MO, USA <sup>8</sup> Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA <sup>9 </sup>Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA <sup>10</sup> Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
description: verkko 1.1, HiFi + UL + trio (PAN027) /Hi-C (PAN010, PAN011, PAN028)
DOI: 10.5281/zenodo.7884729
title: Supplemenjc.2023-00169 OSM

description: <strong>Supplementary Online Material</strong> - Supplementary table 1-5 - Supplementary Figure
DOI: 10.5281/zenodo.7884728
title: Supplemenjc.2023-00169 OSM

description: <strong>Supplementary Online Material</strong> - Supplementary table 1-5 - Supplementary Figure
DOI: 10.6084/m9.figshare.22116776
title: Supplemtental Tables_YAP promotes AP-1 expression in tubular epithelial cells in the kidney.pdf

description: AP-1 manuscript to AJP-Renal
DOI: 10.6084/m9.figshare.22116776.v1
title: Supplemtental Tables_YAP promotes AP-1 expression in tubular epithelial cells in the kidney.pdf

description: AP-1 manuscript to AJP-Renal
DOI: 10.11579/1970325
title: Climate model synthetic satellite brightness temperature data
DOI: 10.25582/data.2019-12.1907632/1971751
title: Data for EMSL Project 51155 from December 2019
DOI: 10.25582/data.2020-05.1993989/1971752
title: Data for EMSL Project 51155 from May 2020
DOI: 10.25582/data.2020-05.1994001/1971753
title: Data for EMSL Project 51155 from May 2020
DOI: 10.25582/data.2020-05.1994011/1971754
title: Data for EMSL Project 51155 from May 2020
DOI: 10.25582/data.2020-05.1994012/1971755
title: Data for EMSL Project 51155 from May 2020
DOI: 10.25582/data.2020-05.1994015/1971756
title: Data for EMSL Project 51155 from May 2020
DOI: 10.25582/data.2020-05.1994017/1971757
title: Data for EMSL Project 51155 from May 2020
DOI: 10.25582/data.2020-05.1994029/1971758
title: Data for EMSL Project 51155 from May 2020
DOI: 10.25582/data.2020-05.1994030/1971759
title: Data for EMSL Project 51155 from May 2020
DOI: 10.25582/data.2020-07.2014673/1971760
title: Data for EMSL Project 51155 from July 2020
DOI: 10.25582/data.2020-07.2021187/1971761
title: Data for EMSL Project 51155 from July 2020
DOI: 10.25582/data.2020-07.2021188/1971762
title: Data for EMSL Project 51155 from July 2020
DOI: 10.25584/1971764
title: PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection

description: The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host Responses to Understand Severe Human Virus Infections" program project was a highly integrated and comprehensive systems biology research core, funded by the National Institute of Health (U19AI106772) from 2013-06-01 to 2018-05-31, investigating the complex host response to category A, B, and C priority pathogen infections. Resulting project deliverables include an extensive comprehensive collections of linked primary and secondary transformation viral experimental infection data. Here we provide a never before released comprehensive infectious disease collection of primary and secondary transformation multi-Omics data profiling a series of priority pathogen primary experimental studies for enhanced open access to viral Omics lifecycle datasets and project metadata. Using a highly integrated and multidisciplinary approach, linked primary data and metadata supporting secondary normalization datasets, provide critical information necessary for research reproducibility and long-term preservation. Enabling on-demand data access for research community consumption and developer reuse, serves to support new mechanistic insights and discoveries into host-pathogen interactions for aiding future biohazard data preparedness efforts in emergency response to global health crises involving viral infection.
DOI: 10.5439/1971765
title: twrcamtarmac (a1)

description: twrcamtarmac
DOI: 10.5439/1971773
title: aafnavdsm10hz (a1)

description: This umbrella contains data from all instrumentation associated with the aircraft’s navigational location, motion, and attitude data. Navigational instrumentation on the Gulfstream-159 includes navigational data recorded from the AIMMS-20 (for other AIMMS produced data see the instrument category MET-AIR), CMigits global positioning system (GPS) & Inertial Navigational System (INS) Tactical System, Differential Survey Module (DSM), and VectorNav VN-200 INS. For a higher-order product that contains navigational data integrated with meteorological data and sampling masks/flags to aid analysis please see the categorical value added product NAVMET-AIR.
DOI: 10.5439/1971774
title: aafnavcmig (a1)

description: This umbrella contains data from all instrumentation associated with the aircraft’s navigational location, motion, and attitude data. Navigational instrumentation on the Gulfstream-159 includes navigational data recorded from the AIMMS-20 (for other AIMMS produced data see the instrument category MET-AIR), CMigits global positioning system (GPS) & Inertial Navigational System (INS) Tactical System, Differential Survey Module (DSM), and VectorNav VN-200 INS. For a higher-order product that contains navigational data integrated with meteorological data and sampling masks/flags to aid analysis please see the categorical value added product NAVMET-AIR.
DOI: 10.17608/k6.auckland.14460054
title: Identifying Refactoring Opportunities for Large Packages by Analyzing Maintainability Characteristics in Java OSS

description: This dataset is provided as a supplementary material for the paper (entitled: Identifying Refactoring Opportunities for Large Packages by Analyzing Maintainability Characteristics in Java OSS). A README file is included with the description of the dataset and Python script used for performing the empirical analysis.
DOI: 10.17608/k6.auckland.14460054.v1
title: Identifying Refactoring Opportunities for Large Packages by Analyzing Maintainability Characteristics in Java OSS

description: This dataset is provided as a supplementary material for the paper (entitled: Identifying Refactoring Opportunities for Large Packages by Analyzing Maintainability Characteristics in Java OSS). A README file is included with the description of the dataset and Python script used for performing the empirical analysis.
DOI: 10.5281/zenodo.7883341
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2013

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500 m spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500 m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2013. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2012</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2014</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2013 Spatial Resolution: 500 m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7883340
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2013

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500 m spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500 m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2013. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2012</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2014</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2013 Spatial Resolution: 500 m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7789384
title: Estimating the probability of public companies to suffer financial distress in emerging markets

description: Dataset for estimating the probability of public companies to suffer financial distress in emerging markets
DOI: 10.5281/zenodo.7789385
title: Estimating the probability of public companies to suffer financial distress in emerging markets

description: Dataset for estimating the probability of public companies to suffer financial distress in emerging markets
DOI: 10.5281/zenodo.7884841
title: Estimating the probability of public companies to suffer financial distress in emerging markets

description: Dataset for estimating the probability of public companies to suffer financial distress in emerging markets
DOI: 10.5281/zenodo.7884844
title: COVID vaccine segmentation data

description: COVID vaccine segmentation data from Kenya, Côte d'Ivoire, Burkina Faso and Pakistan
DOI: 10.5281/zenodo.7884843
title: COVID vaccine segmentation data

description: COVID vaccine segmentation data from Kenya, Côte d'Ivoire, Burkina Faso and Pakistan
DOI: 10.6084/m9.figshare.22729175
title: Additional file 2 of Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles

description: Additional file 2: Data S1. Differentially expressed genes in the different human islet cell clusters from the scRNA-seq studies.
DOI: 10.6084/m9.figshare.22729175.v1
title: Additional file 2 of Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles

description: Additional file 2: Data S1. Differentially expressed genes in the different human islet cell clusters from the scRNA-seq studies.
DOI: 10.6084/m9.figshare.22729178
title: Additional file 3 of Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles

description: Additional file 3: Data S2. Differentially expressed genes in the different human islet cell clusters from the snRNA-seq studies in vitro.
DOI: 10.6084/m9.figshare.22729178.v1
title: Additional file 3 of Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles

description: Additional file 3: Data S2. Differentially expressed genes in the different human islet cell clusters from the snRNA-seq studies in vitro.