cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5t14ts9d
title: MassIVE MSV000089237 - Genentech IBD Fecal Proteomics
title: Comparison of the human fecal proteomes from healthy control (HC), ulcerative colitis (UC), and Crohn's disease (CD) stool. Two cohorts: Exploratory with N=5 samples from each group (HC, UC, CD) and Confirmation with N=20 samples from each group (HC, UC, CD).
DOI: 10.6073/pasta/07733008f80be25c397c879b7d71d55c
title: Dissolved inorganic carbon (DIC) concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2022, ongoing)

description: The McMurdo Long Term Ecological Research (LTER) project monitors patterns of inorganic material transport in perennial ice-capped lakes. This data set addresses this core area of research and quantifies dissolved inorganic carbon concentrations at specific depths in McMurdo Dry Valley lakes. Dissolved inorganic carbon is also necessary for the computation of primary productivity.
DOI: 10.6073/pasta/c9190d7080b86f4ccc6380600502684f
title: Hydrogen ion concentrations (pH) in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2022, ongoing)

description: As part of the McMurdo Long Term Ecological Research (LTER) project in the Dry Valleys of Antarctica, hydrogen ion concentrations were monitored in various lakes of the region. An Orion portable pH meter was used to record hydrogen ion concentrations at depth specific intervals in perennial ice-covered lakes.
DOI: 10.5281/zenodo.5765545
title: ChinaHighPM10: Big Data Seamless 10 km Ground-level PM10 Dataset for China (Closed)

description: ChinaHighPM<sub>10</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level ground-level PM<sub>10</sub> products in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.34 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>10</sub> dataset is available at: http://doi.org/10.5281/zenodo.3752465</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290", "Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 269, 112775. https://doi.org/10.1016/j.rse.2021.112775"]}
DOI: 10.5281/zenodo.4641550
title: ChinaHighPM10: Big Data Seamless 10 km Ground-level PM10 Dataset for China (Closed)

description: ChinaHighPM<sub>10</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level ground-level PM<sub>10</sub> products in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.34 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>10</sub> dataset is available at: http://doi.org/10.5281/zenodo.3752465</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290", "Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 269, 112775. https://doi.org/10.1016/j.rse.2021.112775"]}
DOI: 10.17632/y5npsm3gkj.1
title: TimberSeg 1.0

description: The TimberSeg 1.0 dataset is composed of 220 images showing wood logs in various environments and conditions in Canada. The images are densely annotated with segmentation masks for each log instance, as well as the corresponding bounding box and class label. This dataset aim towards enabling autonomous forestry forwarders, therefore it contains nearly 2500 instances of wood logs from an operators' point-of-view. Images were taken in the forest, near the roadside, in lumberyards and above timber-filled trailers. The logs were annotated considering a grasping perspective, meaning that only the logs above the piles and accessible are segmented.
DOI: 10.5281/zenodo.5090006
title: ChinaHighPM10: MODIS/Terra+Aqua 1 km Ground-level PM10 Dataset for China (Closed)

description: ChinaHighPM<sub>10</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the MODIS/Terra+Aqua daily 1 km (D1K) ground-level PM<sub>10</sub> dataset in Eastern China from 2013 to 2020, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.28 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>10</sub> dataset is available at: http://doi.org/10.5281/zenodo.3752465</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290"]}
DOI: 10.5281/zenodo.5090005
title: ChinaHighPM10: MODIS/Terra+Aqua 1 km Ground-level PM10 Dataset for China (Closed)

description: ChinaHighPM<sub>10</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the MODIS/Terra+Aqua daily 1 km (D1K) ground-level PM<sub>10</sub> dataset in Eastern China from 2013 to 2020, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 24.28 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM<sub>10</sub> dataset is available at: http://doi.org/10.5281/zenodo.3752465</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290"]}
DOI: 10.3886/e167382
title: Socialist growth revisited: Insights from Yugoslavia

description: This file contains data that can be used to replicate the figures and tables reported in the main text of "Socialist growth revisited: Insights from Yugoslavia".
DOI: 10.3886/e167382v1
title: Socialist growth revisited: Insights from Yugoslavia

description: This file contains data that can be used to replicate the figures and tables reported in the main text of "Socialist growth revisited: Insights from Yugoslavia".
DOI: 10.5281/zenodo.6444046
title: Large Scale Volcanism and the heat-death of terrestrial worlds

description: The files herein are used to generate the main and SI figures in the GRL paper: Title: Large Scale Volcanism and the heat-death of terrestrial worlds<br> Authors: M.J. Way, Richard E. Ernst, Jeffrey D. Scargle The data are derived from an Excel formatted table: WAY2021-TableS1.xlsx
DOI: 10.5281/zenodo.4964815
title: Large Scale Volcanism and the heat-death of terrestrial worlds

description: The files herein are used to generate the main and SI figures in the GRL paper: Title: Large Scale Volcanism and the heat-death of terrestrial worlds<br> Authors: M.J. Way, Richard E. Ernst, Jeffrey D. Scargle The data are derived from an Excel formatted table: WAY2021-TableS1.xlsx
DOI: 10.6084/m9.figshare.19518982
title: Supplementary Material

description: Supplementary Material
DOI: 10.6084/m9.figshare.19518982.v4
title: Supplementary Material

description: Supplementary Material
DOI: 10.6084/m9.figshare.19518982.v3
title: Supplementary Material - Part I

description: Supplementary Material
DOI: 10.6084/m9.figshare.19579405.v1
title: supplementay_table_1

description: List of primers and sequences used in the pilot MPRA experiment
DOI: 10.5281/zenodo.6027024
title: Convolutional neural network and data used for applied soundscape classification with Soundscapes 2 Landscapes (S2L)

description: This repository documents the ABGQI-CNN manuscript (DOI: https://doi.org/10.1016/j.ecolind.2022.108831). It contains supplementary materials, data used to train a soundscape classification convolutional neural network (CNN), and data to generate manuscript results. The accompanying code can be found at https://doi.org/10.5281/zenodo.6038459. Files include: <strong>ABGQI-CNN.tar: </strong>saved CNN model weights for the 5-class soundscape classifier using a MobileNetV2 architecture pre-trained with bird vocalization data. <strong>ABGQI_mel_spectrograms.tar</strong>: spectrograms used for fine-tuning the pre-trained CNN, above, with training, validation, and testing data splits. <strong>freesound_licensing.csv</strong>: file names and license information related to Freesound auxiliary files. <strong>RavenLite_Training_Data_Collection.pdf</strong>: a manual for RavenLite ROI annotation. <strong>S2L_site_geog-env_data.csv</strong>: environmental and geographic data (sans GPS) related to site locations in S2L project 2017-2020. <strong>site_avg_ABGQIU_fscore_075_daytime.csv</strong>: the average site rate of soundscape components for 5 a.m. to 8 p.m. <strong>site_by_hour_ABGQIU_fscore_075.csv</strong>: the average hourly site rate of soundscape components <strong>site_classifications_beta075.tar</strong>: a directory containing a CSV for every site with threshold optimized classifications for each 2-s Mel spectrogram <strong>site_prediction_probabilies.tar</strong>: a directory containing a CSV for every site with ABGQI-CNN probabilities for each 2-s Mel spectrogram <strong>Supplementary_Materials.pdf</strong>: includes additional material and analyses related to the accompanying manuscript. Contact Colin Quinn at cq73@nau.edu for questions related to this repository or if you have an interest in the original wav recordings. Please be aware that underlying software, specifically for the CNN implementation, may not continue stability as python libraries are updated.
DOI: 10.5281/zenodo.6027023
title: Convolutional neural network and data used for applied soundscape classification with Soundscapes 2 Landscapes (S2L)

description: This repository documents the ABGQI-CNN manuscript (DOI: https://doi.org/10.1016/j.ecolind.2022.108831). It contains supplementary materials, data used to train a soundscape classification convolutional neural network (CNN), and data to generate manuscript results. The accompanying code can be found at https://doi.org/10.5281/zenodo.6038459. Files include: <strong>ABGQI-CNN.tar: </strong>saved CNN model weights for the 5-class soundscape classifier using a MobileNetV2 architecture pre-trained with bird vocalization data. <strong>ABGQI_mel_spectrograms.tar</strong>: spectrograms used for fine-tuning the pre-trained CNN, above, with training, validation, and testing data splits. <strong>freesound_licensing.csv</strong>: file names and license information related to Freesound auxiliary files. <strong>RavenLite_Training_Data_Collection.pdf</strong>: a manual for RavenLite ROI annotation. <strong>S2L_site_geog-env_data.csv</strong>: environmental and geographic data (sans GPS) related to site locations in S2L project 2017-2020. <strong>site_avg_ABGQIU_fscore_075_daytime.csv</strong>: the average site rate of soundscape components for 5 a.m. to 8 p.m. <strong>site_by_hour_ABGQIU_fscore_075.csv</strong>: the average hourly site rate of soundscape components <strong>site_classifications_beta075.tar</strong>: a directory containing a CSV for every site with threshold optimized classifications for each 2-s Mel spectrogram <strong>site_prediction_probabilies.tar</strong>: a directory containing a CSV for every site with ABGQI-CNN probabilities for each 2-s Mel spectrogram <strong>Supplementary_Materials.pdf</strong>: includes additional material and analyses related to the accompanying manuscript. Contact Colin Quinn at cq73@nau.edu for questions related to this repository or if you have an interest in the original wav recordings. Please be aware that underlying software, specifically for the CNN implementation, may not continue stability as python libraries are updated.
DOI: 10.5066/p9iryfmb
title: Selected Chemical and Physical Properties and Inorganic Constituents and Time-Series Nitrate in Samples from selected wells and/or springs, Petaluma Valley watershed, Sonoma County, California, 1959-2015.

description: This data release contains appendix C1 and appendix C2 for the USGS Scientific Investigations Report (SIR) titled Hydrologic and Geochemical Characterization of the Petaluma River Watershed, Sonoma County, California. Appendix C1 contains selected chemical and physical properties and inorganic constituents in samples from selected springs, and wells, Petaluma Valley watershed, Sonoma County, California, 1959-2015. Samples are presented from 66 groundwater wells, 3 springs, and 2 surface-water sites. Data samples were collected by the U.S. Geological Survey (USGS), California Department of Water Resources (CDWR), California Department of Health Services (CDPH), and the City of Petaluma as part of the study.
DOI: 10.6084/m9.figshare.19579525
title: Chapter3_SuppTable_PrimerEfficiencies

description: Supplementary data associated to Chapter 3 of PhD thesis. <br> File contains the raw qPCR data used to evaluate the binding efficiencies of the primers for the pilot MPRA
DOI: 10.6084/m9.figshare.19579525.v2
title: Chapter3_SuppTable_PrimerEfficiencies

description: Supplementary data associated to Chapter 3 of PhD thesis. <br> File contains the raw qPCR data used to evaluate the binding efficiencies of the primers for the pilot MPRA
DOI: 10.6084/m9.figshare.19579525.v1
title: Chapter3_SuppTable_2

description: Supplementary data associated to Chapter 3 of PhD thesis. <br> File contains the raw qPCR data used to evaluate the binding efficiencies of the primers for the pilot MPRA
DOI: 10.6084/m9.figshare.19579405
title: Chapter3_SuppTable_PilotMPRA

description: Supplementary file associated to chapter 3 of PhD thesis. <br> File contains the list of primers and sequences used in the pilot MPRA experiment
DOI: 10.6084/m9.figshare.19579405.v3
title: Chapter3_SuppTable_PilotMPRA

description: Supplementary file associated to chapter 3 of PhD thesis. <br> File contains the list of primers and sequences used in the pilot MPRA experiment
DOI: 10.6084/m9.figshare.19579405.v2
title: Chapter3_SuppTable_1

description: Supplementary file associated to chapter 3 of PhD thesis. <br> File contains the list of primers and sequences used in the pilot MPRA experiment
DOI: 10.25345/c5p844052
title: MassIVE MSV000089238 - Zinc is a master-regulator of sperm function associated with binding, motility, and metabolic modulation during porcine sperm capacitation
title: The objectives of this study were therefore i) to selectively isolate and identify zinc-binding and interacting proteins from porcine spermatozoa, and with the help of bioinformatics to determine how these may be involved in porcine sperm physiology, and ii) to identify zincoproteins that change during capacitation and thus may be regulated by Zn2+ during this event.
DOI: 10.17632/4vsf5hgy6x.5
title: ENDOCOVID observational study

description: Data sharing of the ENDOCOVID observational study published under the name of "Oxidative stress-induced endothelial dysfunction and decreased vascular nitric oxide in COVID-19 patients" in EBioMedicine
DOI: 10.5281/zenodo.6450384
title: Effects of air pollution exposure on inflammatory and endurance performance in recreationally trained cyclists adapted to traffic-related air pollution

description: Supplemental Table 1. Baseline values for blood parameters.
description: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Coordination of Superior Level Staff Improvement - Academic Excellence Program (CAPES-PROEX). André C. Silveira was supported by a Sao Paulo Research Foundation scholarship (FAPESP – Grant: 2018/21600-0). Luisa Giles was supported by Research Incentive Grant (no grant number). Romulo Bertuzzi was supported by a grant from the National Council for Scientific and Technological Development (CNPq 301330/2019-0).
DOI: 10.5281/zenodo.6362652
title: Effects of air pollution exposure on inflammatory and endurance performance in recreationally trained cyclists adapted to traffic-related air pollution

description: Supplemental Table 1. Baseline values for blood parameters.
description: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Coordination of Superior Level Staff Improvement - Academic Excellence Program (CAPES-PROEX). André C. Silveira was supported by a Sao Paulo Research Foundation scholarship (FAPESP – Grant: 2018/21600-0). Luisa Giles was supported by Research Incentive Grant (no grant number). Romulo Bertuzzi was supported by a grant from the National Council for Scientific and Technological Development (CNPq 301330/2019-0).
DOI: 10.5281/zenodo.6426023
title: Sub-hourly water temperature data collected by UNBC's northern hydrometeorology group (NHG) across the Nechako Watershed, 2019-2021

description: This is a dataset of water temperature time series collected at 15 minute intervals at 24 sites across the Nechako Watershed. Data are provided in comma-delimited format with one file for each site. Each file name identifies the site location. The first column contains the date/time (UTC) in the format MM/DD/YEAR HH:MM. The second column contains the corresponding water temperature in degrees Celsius to two decimals. The third column contains a recommended flag for data quality, with "P" representing a "pass", "F" denoting a "fail", and "B" denoting "backwater conditions" with the likely presence of ice and below freezing temperatures. Additional issues encountered at specific sites are identified in the attached "read me" file along with basic metadata (site coordinates, elevation and period of record). A map of the sites is also attached to depict their locations across the Nechako Watershed.
description: Data collection at most sites is ongoing and time series will be periodically updated as more recent data become available.
DOI: 10.5281/zenodo.5504624
title: Avian auditory hair cell regeneration is accompanied by JAK/STAT-dependent expression of immune-related genes in supporting cells

description: Normalized log count matrix and single cell experiment container
DOI: 10.5281/zenodo.5504623
title: Avian auditory hair cell regeneration is accompanied by JAK/STAT-dependent expression of immune-related genes in supporting cells

description: Normalized log count matrix and single cell experiment container
DOI: 10.6084/m9.figshare.19401128.v2
title: High activity and high functional connectivity are mutually exclusive in resting state zebrafish and human brains

description: Zarei, M., Xie, D., Jiang, F. <em>et al.</em> High activity and high functional connectivity are mutually exclusive in resting state zebrafish and human brains. <em>BMC Biol</em> <strong>20,</strong> 84 (2022). https://doi.org/10.1186/s12915-022-01286-3 <br>
DOI: 10.6084/m9.figshare.19579264.v1
title: MMus GRCM38 bismark reference index

description: Made as described here: https://hackmd.io/@astrobiomike/generating-bs-seq-output-test-data
DOI: 10.6084/m9.figshare.19579264
title: MMus GRCM38 bismark reference index

description: Made as described here: https://hackmd.io/@astrobiomike/generating-bs-seq-output-test-data
DOI: 10.6084/m9.figshare.19579810
title: Element and Li-O isotope data of minerals from Eppawala carbonatites, Sri Lanka

description: Table 1 Elemental (major, wt.%; trace, ppm) and Li-O isotopic (‰) compositions of olivine in the Eppawala carbonatite (EP1 and EP1A) and marble (MTL2a) samples. Table 2 Elemental (major, wt.%; trace, ppm) and Li-O isotopic (‰) compositions of clinopyroxene in the Eppawala carbonatites Table 3 Elemental (major, wt.%; trace, ppm) and Li-O isotopic (‰) compositions of dolomite and calcite in the Eppawala carbonatites Table 4 Element partitioning coefficients of olivine and clinopyroxene with carbonates and carbonatite melts estimated in this study and literature
DOI: 10.6084/m9.figshare.14589165.v5
title: SCS

description: Dynamical impact of the Mekong River plume in the South China Sea model configurations, figures and codes.